llm-applications

rerank-0.5.json
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{
2
    "config": {
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        "experiment_name": "rerank-0.5",
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        "chunk_size": 700,
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        "chunk_overlap": 50,
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        "num_chunks": 30,
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        "embedding_model_name": "thenlper/gte-large",
8
        "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1",
9
        "temperature": 0.0,
10
        "max_context_length": 32768,
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        "system_content": "Answer the query using the context provided. Be succinct.",
12
        "assistant_content": "",
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        "docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",
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        "experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",
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        "references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",
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        "num_samples": 177
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    },
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    "results": [
19
        {
20
            "question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",
21
            "sources": [
22
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
23
                "https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",
24
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",
25
                "https://docs.ray.io/en/master/data/key-concepts.html#transforming-data",
26
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",
27
                "https://docs.ray.io/en/master/data/working-with-tensors.html#transforming-tensor-data",
28
                "https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.MultiHotEncoder.transform_batch.html#ray-data-preprocessors-multihotencoder-transform-batch",
29
                "https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.transform_batch.html#ray-data-preprocessor-preprocessor-transform-batch",
30
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
31
                "https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.OneHotEncoder.transform_batch.html#ray-data-preprocessors-onehotencoder-transform-batch",
32
                "https://docs.ray.io/en/master/data/working-with-images.html#transforming-images",
33
                "https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.OrdinalEncoder.transform_batch.html#ray-data-preprocessors-ordinalencoder-transform-batch",
34
                "https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.SimpleImputer.transform_batch.html#ray-data-preprocessors-simpleimputer-transform-batch",
35
                "https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.LabelEncoder.transform_batch.html#ray-data-preprocessors-labelencoder-transform-batch"
36
            ],
37
            "document_ids": [
38
                7081,
39
                6790,
40
                6786,
41
                6733,
42
                8046,
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                6777,
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                7421,
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                7241,
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                7085,
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                7553,
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                6724,
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                7476,
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                7136,
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                7135
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            ],
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            "answer": " When using `map_batches` in Ray Data, you can specify the batch format as a parameter. By default, batches are represented as dicts of NumPy ndarrays. However, you can return either NumPy or pandas formats from your function.\n\nHere's an example of increasing brightness using NumPy:\n\n```python\nfrom typing import Dict\nimport numpy as np\nimport ray\n\ndef increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n    batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n    return batch\n\nds = (\n    ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n    .map_batches(increase_brightness, batch_format=\"numpy\")\n)\n```\n\nAnd here's an example using pandas:\n\n```python\nimport pandas as pd\n\ndef increase_brightness(batch: Dict[str, pd.Series]) -> Dict[str, pd.Series]:\n    batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n    return batch\n\nds = (\n    ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n    .map_batches(increase_brightness, batch_format=\"pandas\")\n)\n```\n\nMake sure to return the desired format from your function.",
54
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
55
        },
56
        {
57
            "question": "How does autoscaling work in a Ray Serve application?",
58
            "sources": [
59
                "https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",
60
                "https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",
61
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",
62
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",
63
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
64
                "https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#dynamic-request-batching",
65
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
66
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
67
                "https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",
68
                "https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",
69
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
70
                "https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",
71
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",
72
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling"
73
            ],
74
            "document_ids": [
75
                13471,
76
                13471,
77
                13448,
78
                13447,
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                13449,
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                13693,
81
                13598,
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                13623,
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                13618,
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                13619,
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                6363,
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                5559,
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                5969,
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                5953
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            ],
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            "answer": " Ray Serve's autoscaling feature automatically adjusts the number of replicas in a deployment based on its load. The Ray Serve autoscaler works on top of the Ray Autoscaler, requesting the start of a number of replica actors based on request demand. If there aren't enough available CPUs to place these actors, the Ray Autoscaler will request more Ray nodes from the cloud provider.\n\nThe autoscaling algorithm takes into consideration user-specified parameters such as `min_replicas` (the minimum number of replicas for the deployment) when deciding the target number of replicas for your deployment.\n\nWhen Ray Serve scales down, it tries to terminate replica actors in a way that results in the most nodes having no Ray actors or tasks running on them, allowing the Ray autoscaler to remove those nodes.\n\nThe latency constraint is 100ms, and you can have at most 10 requests ongoing per replica to ensure the last requests finish within the latency constraint. You should benchmark your application code and set this number based on end-to-end latency objectives.",
91
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
92
        },
93
        {
94
            "question": "how do I get the address of a ray node",
95
            "sources": [
96
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
97
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",
98
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
99
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",
100
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",
101
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
102
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
103
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.nodes.html#ray-nodes",
104
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",
105
                "https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",
106
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",
107
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
108
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",
109
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs"
110
            ],
111
            "document_ids": [
112
                739,
113
                6411,
114
                4817,
115
                754,
116
                4446,
117
                6552,
118
                493,
119
                4769,
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                6502,
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                5587,
122
                4448,
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                4824,
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                4187,
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                695
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            ],
127
            "answer": " To get the address of a Ray node, you can use the `ray.nodes()` function, which returns information about the nodes in the Ray cluster. The node's address can be found in the `NodeManagerAddress` field of the returned dictionary.\n\nHere is an example:\n```\nimport ray\nray.init()\n\nnodes = ray.nodes()\nnode_address = nodes[0]['NodeManagerAddress']\nprint(node_address)\n```\nThis will print the address of the first node in the Ray cluster.\n\nAlternatively, if you have the node ID, you can use the `ray.util.state.get_node` function to get the node information and retrieve the `NodeManagerAddress` field from the returned dictionary.\n\nFor example:\n```\nfrom ray.util.state import get_node\n\nnode_id = \"2691a0c1aed6f45e262b2372baf58871734332d7\"\nnode = get_node(node_id)\nnode_address = node.NodeManagerAddress\nprint(node_address)\n```\nThis will print the address of the node with the given node ID.",
128
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
129
        },
130
        {
131
            "question": "Does Ray support NCCL?",
132
            "sources": [
133
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
134
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#starting-a-connection-on-older-ray-versions",
135
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",
136
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
137
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray",
138
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",
139
                "https://docs.ray.io/en/master/installation.html#installing-ray",
140
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
141
                "https://docs.ray.io/en/master/ray-overview/index.html#overview",
142
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
143
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
144
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
145
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
146
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#llms-and-gen-ai"
147
            ],
148
            "document_ids": [
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                4662,
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                6628,
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                14391,
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                4811,
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                14537,
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                284,
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                59,
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                6835,
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                364,
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                13974,
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                6208,
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                370,
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                5656,
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                139
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            ],
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            "answer": " Yes, Ray supports NCCL through its collective communication library, which uses NCCL and GLOO as optional high-performance communication backends. NCCL is used for efficient out-of-band communication between Ray actor and task processes, and it operates on both distributed CPUs and GPUs.",
165
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
166
        },
167
        {
168
            "question": "Is Ray integrated with DeepSpeed?",
169
            "sources": [
170
                "https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",
171
                "https://docs.ray.io/en/master/rllib/rllib-dev.html#contributing-algorithms",
172
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.deepspeed_engine.html#ray-train-lightning-raydeepspeedstrategy-deepspeed-engine",
173
                "https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",
174
                "https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",
175
                "https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",
176
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.strategy_name.html#ray-train-lightning-raydeepspeedstrategy-strategy-name",
177
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#fine-tune-vicuna-13b-with-lightning-and-deepspeed",
178
                "https://docs.ray.io/en/master/train/deepspeed.html#training-with-deepspeed",
179
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",
180
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.html#ray-train-lightning-raydeepspeedstrategy",
181
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#fine-tuning-the-model-with-ray-train-a-name-train-a",
182
                "https://docs.ray.io/en/master/workflows/basics.html#ray-integration",
183
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.DEEPSPEED_ENV_VAR.html#ray-train-lightning-raydeepspeedstrategy-deepspeed-env-var"
184
            ],
185
            "document_ids": [
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                8232,
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                10595,
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                8996,
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                8331,
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                8249,
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                8881,
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                8578,
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                8207,
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                9393,
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                8784,
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                10313,
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                9685,
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                8633
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            ],
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            "answer": " Yes, Ray is integrated with DeepSpeed through the Ray DeepSpeedStrategy. This integration allows you to use DeepSpeed with Ray Train for distributed training. You can configure DeepSpeed using the Accelerator object in your training function and set the strategy name to 'deepspeed'. The integration supports DeepSpeed's FSDP and MegatronLM, and it automatically sets certain Ray AIR configs based on the Accelerate configuration.",
202
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
203
        },
204
        {
205
            "question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",
206
            "sources": [
207
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",
208
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",
209
                "https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example",
210
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",
211
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",
212
                "https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",
213
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",
214
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#running-a-task",
215
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",
216
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",
217
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle",
218
                "https://docs.ray.io/en/master/ray-core/tasks.html#tasks",
219
                "https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",
220
                "https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups"
221
            ],
222
            "document_ids": [
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                4600,
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                4600,
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                5197,
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                4611,
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                4599,
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                5248,
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                4598,
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                4485,
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                4603,
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                4601,
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                14277,
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                4360,
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                13643,
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                4636
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            ],
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            "answer": " If you use AsyncIO's `await` keyword to wait for a Ray future, like `await x.remote()`, it will allow you to use Ray futures in existing concurrent applications that use AsyncIO. However, it's important to note that Ray does not support `async/await` syntax for remote tasks directly. Instead, you can wrap the async function with a regular function and use `asyncio.run()` to execute it. Also, you can convert a Ray ObjectRef to an AsyncIO Future using `asyncio.wrap_future()` function.\n\nHere is an example of how you can use `await` with Ray futures:\n\n```python\nimport ray\nimport asyncio\n\n@ray.remote\ndef some_task():\n    return 1\n\nasync def await_obj_ref():\n    result = await some_task.remote()\n    print(result)\n\nasyncio.run(await_obj_ref())\n```\n\nKeep in mind that this will only work for Ray futures and not for remote tasks that use `async/await` syntax.",
239
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
240
        },
241
        {
242
            "question": "How would you compare Spark, Ray, Dask?",
243
            "sources": [
244
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",
245
                "https://docs.ray.io/en/master/tune/examples/tune-sklearn.html#walkthrough",
246
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",
247
                "https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",
248
                "https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",
249
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",
250
                "https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",
251
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",
252
                "https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",
253
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",
254
                "https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure",
255
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",
256
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
257
                "https://docs.ray.io/en/master/data/api/api.html#ray-data-api"
258
            ],
259
            "document_ids": [
260
                14465,
261
                2640,
262
                14479,
263
                6847,
264
                14647,
265
                14502,
266
                6846,
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                14467,
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                6809,
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                14469,
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                9809,
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                6752,
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                13620,
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            ],
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            "answer": " Spark, Ray, and Dask are all distributed computing systems, but they have different focuses and use cases.\n\nApache Spark is a general-purpose cluster-computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general computation graphs. It's well-suited for ETL, machine learning, and graph processing tasks.\n\nRay is a distributed execution engine that focuses on scaling and simplifying machine learning and AI applications. It's designed to be general-purpose and supports a wide range of use cases, including machine learning, reinforcement learning, and large-scale data processing. Ray's unique feature is its ability to seamlessly mix and execute tasks written in different programming languages, such as Python, Java, and C++.\n\nDask is a flexible parallel computing library for analytic computing, built with the Python ecosystem in mind. It's designed to integrate with Python libraries like Pandas and NumPy, making it easy to scale data science workloads. Dask is often used for distributed data processing, machine learning, and scientific computing tasks.\n\nWhen comparing Spark, Ray, and Dask, it's essential to consider the specific requirements of your use case. Spark is a good choice for general-purpose data processing, while Ray excels at scaling machine learning and AI applications. Dask, on the other hand, is an excellent option for scaling Python-based data science workloads.\n\nIn the context of the provided text, Dask can be used on Ray to leverage Ray's fast, fault-tolerant distributed task execution system while using Dask's familiar NumPy and Pandas APIs. This combination allows you to build data analyses using Dask's collections and execute them on a Ray cluster.",
276
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
277
        },
278
        {
279
            "question": "why would ray overload a node w/ more task that the resources allow ?",
280
            "sources": [
281
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",
282
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",
283
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
284
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",
285
                "https://docs.ray.io/en/master/ray-core/tasks.html#scheduling",
286
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",
287
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#locality-aware-scheduling",
288
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",
289
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
290
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case",
291
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks",
292
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
293
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#custom-resources",
294
                "https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#pattern-using-ray-wait-to-limit-the-number-of-pending-tasks"
295
            ],
296
            "document_ids": [
297
                5231,
298
                5230,
299
                5112,
300
                5104,
301
                4376,
302
                5230,
303
                5152,
304
                6504,
305
                5115,
306
                5232,
307
                4199,
308
                6364,
309
                5106,
310
                5246
311
            ],
312
            "answer": " Ray's scheduler limits the number of concurrently running tasks based on the resources required by each task. If a node is overloaded with more tasks than its resources allow, it could be due to incorrect resource specifications for the tasks. To prevent this, you can reduce the number of tasks per node by increasing the resources requested by each task, or by specifying a custom resource and limiting the number of tasks that can access it. However, it's important to note that resource requirements only impose logical limits and it's the user's responsibility to ensure tasks use no more resources than specified.",
313
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
314
        },
315
        {
316
            "question": "when should I use Ray Client?",
317
            "sources": [
318
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
319
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",
320
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",
321
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",
322
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",
323
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#which-crd-should-you-choose",
324
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",
325
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice",
326
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",
327
                "https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",
328
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",
329
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet_bulk.html#ray-data-read-parquet-bulk",
330
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
331
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api"
332
            ],
333
            "document_ids": [
334
                6615,
335
                6611,
336
                6608,
337
                6612,
338
                6613,
339
                5649,
340
                5785,
341
                5739,
342
                6637,
343
                5636,
344
                6610,
345
                7828,
346
                4811,
347
                13974
348
            ],
349
            "answer": " You should use Ray Client when you want to connect an interactive Python shell to a remote cluster for development purposes. However, it's important to note that Ray Client has limitations and may not work as expected for ML workloads like Ray Tune or Ray Train. For those cases, the Ray Jobs API is recommended. Additionally, Ray Client requires a stable connection to the remote cluster and will terminate the workload if the connection is lost for more than 30 seconds.",
350
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
351
        },
352
        {
353
            "question": "how to scatter actors across the cluster?",
354
            "sources": [
355
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",
356
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.__init__.html#ray-train-lightning-raydeepspeedstrategy-init",
357
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",
358
                "https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",
359
                "https://docs.ray.io/en/master/serve/architecture.html#high-level-view",
360
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",
361
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
362
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#default",
363
                "https://docs.ray.io/en/master/ray-core/api/utility.html#utility",
364
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
365
                "https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",
366
                "https://docs.ray.io/en/master/data/api/doc/ray.data.ActorPoolStrategy.html#ray-data-actorpoolstrategy",
367
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
368
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources"
369
            ],
370
            "document_ids": [
371
                5145,
372
                9056,
373
                1027,
374
                5195,
375
                13466,
376
                5391,
377
                6787,
378
                5143,
379
                4677,
380
                8119,
381
                659,
382
                7733,
383
                6949,
384
                5065
385
            ],
386
            "answer": " To scatter actors across the cluster in Ray, you can use the `scheduling_strategy` parameter when creating the actor and set it to `\"SPREAD\"`. Here's an example:\n\n```python\n@ray.remote(scheduling_strategy=\"SPREAD\")\nclass SpreadActor:\n    pass\n\n# Spread actors across the cluster.\nactors = [SpreadActor.remote() for _ in range(10)]\n```\n\nThis will tell Ray to spread the actors across the available nodes in the cluster. You can also specify other resources such as `num_cpus` or `num_gpus` if needed.\n\nNote that actors do not necessarily have to live on the same node as your main trainable. You can control the placement of actors using the `strategy` parameter or placement groups. Refer to the documentation on placement groups for more information.",
387
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
388
        },
389
        {
390
            "question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",
391
            "sources": [
392
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",
393
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
394
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
395
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",
396
                "https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",
397
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",
398
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",
399
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#connect-to-the-cluster",
400
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
401
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
402
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
403
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",
404
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",
405
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting"
406
            ],
407
            "document_ids": [
408
                5681,
409
                1005,
410
                4815,
411
                6610,
412
                14678,
413
                4266,
414
                6619,
415
                6526,
416
                13718,
417
                6551,
418
                4817,
419
                4264,
420
                6620,
421
                6505
422
            ],
423
            "answer": " The connection refused error you're encountering when running `ray debug` on your remote Ray cluster is likely due to the Dashboard, Dashboard Agent, or GCS not being fully operational yet. These components may take a few seconds to start up after the head Pod is ready. If the issue persists after waiting for 1 minute, it's possible that the dashboard or dashboard agent may have failed to start.\n\nYou can try waiting for a while and then running the command again. If the problem persists, you might want to check the status of the dashboard and dashboard agent to see if they are running correctly.",
424
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
425
        },
426
        {
427
            "question": "How does Ray AIR set up the model to communicate gradient updates across machines?",
428
            "sources": [
429
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",
430
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",
431
                "https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#asynchronous-parameter-server-training",
432
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",
433
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",
434
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",
435
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",
436
                "https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",
437
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",
438
                "https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#defining-the-parameter-server",
439
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary",
440
                "https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#synchronous-parameter-server-training",
441
                "https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",
442
                "https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#fine-tuning-dreambooth-with-ray-train"
443
            ],
444
            "document_ids": [
445
                9845,
446
                9845,
447
                5338,
448
                9820,
449
                156,
450
                374,
451
                337,
452
                183,
453
                10019,
454
                5329,
455
                9867,
456
                5334,
457
                9887,
458
                10147
459
            ],
460
            "answer": " Ray AIR sets up model communication for gradient updates across machines using a data-parallel training approach. It does this by sharding the training data and setting up a parameter server to communicate with worker nodes. The workers asynchronously compute gradients given their current weights and send these gradients to the parameter server. The parameter server then applies the new gradients and sends a copy of the current weights back to the worker. The worker updates its weights and repeats the process. This is facilitated by importing Ray Train and Ray AIR Session, and using the `ParameterServer` and `DataWorker` classes in Ray.",
461
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
462
        },
463
        {
464
            "question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",
465
            "sources": [
466
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
467
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
468
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
469
                "https://docs.ray.io/en/master/serve/index.html#why-choose-serve",
470
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
471
                "https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",
472
                "https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",
473
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",
474
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",
475
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice",
476
                "https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",
477
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments",
478
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
479
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"
480
            ],
481
            "document_ids": [
482
                13621,
483
                1005,
484
                13974,
485
                13614,
486
                13597,
487
                13420,
488
                10282,
489
                144,
490
                5785,
491
                5739,
492
                13618,
493
                13727,
494
                13596,
495
                13623
496
            ],
497
            "answer": " Ray Serve is a scalable model serving library that allows you to serve models built with various frameworks and libraries, and is not tied to a specific cloud provider or machine learning library. It is framework-agnostic and focuses on model composition, providing a general-purpose scalable serving layer. Unlike Modal or Seldon, Ray Serve does not have built-in functionality for managing the lifecycle of your models or visualizing their performance. It is primarily focused on model serving and providing the primitives for you to build your own ML platform on top. If you have your own infrastructure and want to avoid being locked into a specific solution, Ray Serve is a good choice. If you are looking for a full-fledged ML platform with more features for managing and visualizing models, you may want to consider Modal or Seldon.",
498
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
499
        },
500
        {
501
            "question": "How do I deploy an LLM workload on top of Ray Serve?",
502
            "sources": [
503
                "https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",
504
                "https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",
505
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
506
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/lsf.html#deploying-on-lsf",
507
                "https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",
508
                "https://docs.ray.io/en/master/serve/api/index.html#serve",
509
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
510
                "https://docs.ray.io/en/master/serve/key-concepts.html#deployment",
511
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",
512
                "https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",
513
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
514
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
515
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",
516
                "https://docs.ray.io/en/master/serve/deploy-many-models/index.html#deploy-many-models"
517
            ],
518
            "document_ids": [
519
                13618,
520
                13618,
521
                13677,
522
                6382,
523
                5636,
524
                13983,
525
                13598,
526
                13416,
527
                13647,
528
                10282,
529
                13621,
530
                13622,
531
                144,
532
                13676
533
            ],
534
            "answer": " To deploy a large language model (LLM) workload on top of Ray Serve, follow these steps:\n\n1. Write your LLM application as a single Python module, including prompt preprocessing, vector database lookups, LLM API calls, and response validation. Ray Serve supports any arbitrary Python code, enabling rapid development and easy testing.\n2. Use the `@serve.deployment` decorator to define a deployment for your Python class or function. This will create a deployment that contains the business logic or ML model to handle incoming requests.\n3. Deploy your Ray Serve LLM application to production using the Ray Serve CLI. You can deploy to a local, single-node cluster or a remote, multi-node cluster started with the Ray Cluster Launcher.\n4. Ray Serve allows for independent autoscaling of each application step, efficiently accommodating user traffic without wasting resources.\n5. Utilize Ray Serve features for batching and integration to improve the performance of your LLM applications.\n\nRefer to the provided context for more information on deploying Ray Serve config files, updating applications, and deploying to remote clusters.",
535
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
536
        },
537
        {
538
            "question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",
539
            "sources": [
540
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
541
                "https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",
542
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",
543
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
544
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
545
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
546
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
547
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
548
                "https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",
549
                "https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",
550
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",
551
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
552
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
553
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#how-do-i-configure-the-memory-monitor"
554
            ],
555
            "document_ids": [
556
                4878,
557
                14606,
558
                14121,
559
                14137,
560
                14299,
561
                14183,
562
                14221,
563
                14062,
564
                10280,
565
                10260,
566
                855,
567
                10373,
568
                10380,
569
                5121
570
            ],
571
            "answer": " The memory requirement for setting `model_id` to \u201cEleutherAI/gpt-j-6B\u201d would depend on the specifications of your hardware. The example provided requires at least one GPU with 16 or more GBs of memory. The context also shows memory usage in bytes, but it doesn\u2019t specify which part of the memory should be set for the model. It is recommended to have some overhead for the system to function optimally.",
572
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
573
        },
574
        {
575
            "question": "How do I log the results from multiple distributed workers into a single tensorboard?",
576
            "sources": [
577
                "https://docs.ray.io/en/master/train/examples/tf/tensorflow_mnist_example.html#running-distributed-training-of-a-tensorflow-model-on-mnist-with-ray-train",
578
                "https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#monitoring-and-logging-metrics",
579
                "https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",
580
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",
581
                "https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#example-logging-to-mlflow-and-tensorboard",
582
                "https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#quickstart",
583
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",
584
                "https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",
585
                "https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-tune-results-on-a-single-node",
586
                "https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-concurrent-tune-trials",
587
                "https://docs.ray.io/en/master/rllib/key-concepts.html#key-subconcepts",
588
                "https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#aggregating-results",
589
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",
590
                "https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowTrainer.html#ray-train-tensorflow-tensorflowtrainer"
591
            ],
592
            "document_ids": [
593
                9544,
594
                9260,
595
                10927,
596
                2354,
597
                9144,
598
                8286,
599
                231,
600
                2411,
601
                2383,
602
                2380,
603
                10730,
604
                8301,
605
                9532,
606
                8541
607
            ],
608
            "answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use TensorFlow's built-in MultiWorkerMirroredStrategy for training your model. The results from all workers will be automatically aggregated in TensorBoard. Here's an example:\n\n1. Define your training function using the `MultiWorkerMirroredStrategy`.\n2. Run the training function with the `tf_config` environment variable set to the JSON string of your distributed training configuration.\n3. Start TensorBoard on the remote head machine, pointing it to the log directory.\n\nHere's a simplified example based on the context:\n\n```python\nimport json\nimport os\nimport tensorflow as tf\n\ntf_config = json.loads(os.environ[\"TF_CONFIG\"])\nnum_workers = len(tf_config[\"cluster\"][\"worker\"])\n\nstrategy = tf.distribute.MultiWorkerMirroredStrategy()\n\n# ... define your model, dataset, and training loop here ...\n\n# Run the training function with the distributed strategy\nwith strategy.scope():\n    train_func()\n```\n\nTo start TensorBoard:\n\n```bash\nray exec tune-default.yaml 'tensorboard --logdir=<LOG_DIR> --port 6006' --port-forward 6006\n```\n\nReplace `<LOG_DIR>` with the path to your TensorBoard log directory.\n\nKeep in mind that TensorBoard might not display results from all workers in real-time, but it will aggregate the results once the training is complete.",
609
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
610
        },
611
        {
612
            "question": "how do you config SyncConfig for a Ray AIR job?",
613
            "sources": [
614
                "https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration",
615
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary",
616
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
617
                "https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",
618
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningConfigBuilder.checkpointing.html#ray-train-lightning-lightningconfigbuilder-checkpointing",
619
                "https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",
620
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
621
                "https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_timeout.html#ray-train-syncconfig-sync-timeout",
622
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
623
                "https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.checkpoint_config.html#ray-train-runconfig-checkpoint-config",
624
                "https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.syncer.html#ray-train-syncconfig-syncer",
625
                "https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.html#ray-train-runconfig",
626
                "https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",
627
                "https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_on_checkpoint.html#ray-train-syncconfig-sync-on-checkpoint"
628
            ],
629
            "document_ids": [
630
                1138,
631
                9867,
632
                10466,
633
                8441,
634
                8371,
635
                1443,
636
                10464,
637
                1852,
638
                1782,
639
                9098,
640
                1678,
641
                8430,
642
                8881,
643
                1550
644
            ],
645
            "answer": " To configure `SyncConfig` for a Ray AIR job, you can use the `ray.train.SyncConfig` class which provides various options for syncing files and checkpoints to the storage path specified in `RunConfig`.\n\nHere's an example of how to use `SyncConfig`:\n```python\nfrom ray import train\n\nsync_config = train.SyncConfig(\n    sync_period=300,  # sync every 300 seconds\n    sync_timeout=1800,  # timeout after 1800 seconds\n    sync_artifacts=False,  # don't sync artifacts\n    sync_artifacts_on_checkpoint=True,  # sync artifacts on checkpoint\n    sync_on_checkpoint=False  # don't sync on checkpoint\n)\n\nrun_config = train.RunConfig(\n    sync_config=sync_config,\n    # other config options\n)\n```\nNote that syncing checkpoints and other artifacts from worker nodes to the head node is no longer supported in Ray AIR. This means that the checkpoints and artifacts saved by trials scheduled on worker nodes will not be accessible during the run or after the run.\n\nAdditionally, the `syncer` argument is deprecated and will not take effect in resumed runs.",
646
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
647
        },
648
        {
649
            "question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",
650
            "sources": [
651
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",
652
                "https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",
653
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
654
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#job-profiling",
655
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",
656
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
657
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
658
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
659
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/observability.html#method-3-check-logs-of-ray-pods",
660
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",
661
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",
662
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
663
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",
664
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions"
665
            ],
666
            "document_ids": [
667
                5764,
668
                2309,
669
                444,
670
                422,
671
                415,
672
                13523,
673
                6631,
674
                6157,
675
                5855,
676
                5665,
677
                6181,
678
                445,
679
                6530,
680
                4658
681
            ],
682
            "answer": " To quickly narrow down the root cause of a failed Ray job, you can check the output of the Ray job using the command `kubectl logs -l=job-name=rayjob-sample`. Additionally, you can access the Ray logs in the cluster, which are organized by node and log file name. The logs view provides search functionality to help you find specific log messages. Ray job logs can be found at `/tmp/ray/session_latest/logs` on the head node. If the Ray job is submitted by the Job API, the job logs are available from the Dashboard. You can also use the `ray job logs` command to follow the job's logs.\n\nIn the logs, look for Ray exceptions such as `ray.exceptions.LocalRayletDiedError`, `ray.exceptions.WorkerCrashedError`, `ray.exceptions.TaskPlacementGroupRemoved`, `ray.exceptions.ActorPlacementGroupRemoved`, `ray.exceptions.ObjectStoreFullError`, `ray.exceptions.OutOfDiskError`, and `ray.exceptions.ObjectLostError(...)` which may indicate the root cause of the job failure.",
683
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
684
        },
685
        {
686
            "question": "How do I specify how many GPUs a serve deployment needs?",
687
            "sources": [
688
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",
689
                "https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",
690
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",
691
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#resource-management-cpus-gpus",
692
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",
693
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments",
694
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",
695
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
696
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
697
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",
698
                "https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",
699
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",
700
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#usage-example",
701
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#resources"
702
            ],
703
            "document_ids": [
704
                13458,
705
                13575,
706
                13456,
707
                13778,
708
                13776,
709
                13728,
710
                14121,
711
                14093,
712
                1006,
713
                11062,
714
                10283,
715
                11063,
716
                6420,
717
                5927
718
            ],
719
            "answer": " To specify how many GPUs a Serve deployment needs, you can use the `ray_actor_options` parameter in the `@serve.deployment` decorator and set the `\"num_gpus\"` key to the desired number of GPUs. For example:\n```\n@serve.deployment(ray_actor_options={\"num_gpus\": 1})\ndef func(*args):\n    return do_something_with_my_gpu()\n```\nThis will allocate one GPU to each replica of the deployment. You can also specify a fractional number of GPUs to pack multiple replicas together on a machine with fewer GPUs than replicas.",
720
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
721
        },
722
        {
723
            "question": "One of my worker nodes keeps dying on using TensorflowTrainer with around 1500 workers, I observe SIGTERM has been received to the died node's raylet. How can I debug this?",
724
            "sources": [
725
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
726
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
727
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
728
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
729
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
730
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
731
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
732
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
733
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
734
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",
735
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",
736
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",
737
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
738
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed"
739
            ],
740
            "document_ids": [
741
                5129,
742
                1006,
743
                9493,
744
                10333,
745
                557,
746
                555,
747
                10339,
748
                9483,
749
                10456,
750
                9326,
751
                9325,
752
                9405,
753
                10443,
754
                10350
755
            ],
756
            "answer": " The worker node is dying due to memory pressure (OOM), which means the node ran out of memory. To debug this issue, you can:\n\n1. Check the logs of the raylet process on the affected node using the command: `ray logs raylet.out -ip 172.31.183.172` (replace the IP address with the one relevant to your case). This will provide more information about the workers killed due to memory pressure.\n2. Refer to the Ray documentation on how to address the out-of-memory issue: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>.\n3. Monitor the memory usage on the node to ensure it doesn't exceed the available memory. You can do this by using `ray logs raylet.out -ip <node_ip>` and checking the memory usage percentage.\n4. Consider adjusting the memory configuration for Ray and TensorFlowTrainer to allocate more memory to the workers.\n5. Review your TensorFlowTrainer code to ensure it's memory-efficient and doesn't cause excessive memory usage. This may involve optimizing your model architecture, batch size, or other training parameters.",
757
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
758
        },
759
        {
760
            "question": "what are the possible reasons for nodes dying in a cluster?",
761
            "sources": [
762
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
763
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",
764
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",
765
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",
766
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",
767
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
768
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
769
                "https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",
770
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
771
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",
772
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
773
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#cluster-setting",
774
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
775
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/aws-eks-gpu-cluster.html#step-3-verify-the-node-groups"
776
            ],
777
            "document_ids": [
778
                13898,
779
                11075,
780
                4522,
781
                4519,
782
                4524,
783
                9524,
784
                554,
785
                5639,
786
                5129,
787
                4790,
788
                13889,
789
                9337,
790
                467,
791
                5897
792
            ],
793
            "answer": " There are several possible reasons for nodes dying in a cluster:\n\n1. Head node failure: When the head node fails, the entire Ray cluster fails. This can be caused by killing the head pod or the head node.\n2. Raylet failure: When a raylet process fails, the corresponding node is marked as dead and treated as a new raylet/node to the Ray cluster.\n3. Worker node failure: A worker node failure can be simulated in a Kubernetes cluster by looking at the nodes and pods running in the cluster and killing one of the pods.\n4. Memory pressure: Workers can be killed due to memory pressure, causing the node to run low on memory.\n5. Overloading the head node: Overloading the head node with too many simultaneous connections can cause the worker nodes to fail to connect to the cluster.\n6. Cluster termination: The cluster can be terminated due to user action, causing all nodes to die.\n\nTo tolerate head node failures, it is necessary to make the GCS fault-tolerant so that when a new head node is started, all the cluster-level data is still available. Node fault tolerance can be improved by setting placement strategies that pack bundles into as few nodes as possible or spread them across distinct nodes as evenly as possible.",
794
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
795
        },
796
        {
797
            "question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",
798
            "sources": [
799
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
800
                "https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",
801
                "https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",
802
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
803
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
804
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
805
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",
806
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#upscaling-and-downscaling-speed",
807
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",
808
                "https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",
809
                "https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",
810
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-autoscaling",
811
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",
812
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters"
813
            ],
814
            "document_ids": [
815
                6363,
816
                6715,
817
                6715,
818
                6365,
819
                5953,
820
                6337,
821
                6457,
822
                5965,
823
                5973,
824
                5559,
825
                5600,
826
                6018,
827
                6170,
828
                5964
829
            ],
830
            "answer": " To programmatically set the ray remote cluster to a target size immediately without scaling up through the autoscaler, you can use the `ray.autoscaler.sdk.request_resources()` function within a Ray program. This function commands the autoscaler to scale the cluster up to a desired size, bypassing normal upscaling speed constraints.\n\nHere is an example of how to use this function:\n\n```python\nimport ray\nray.init()\n\n# Request resources to set the cluster size to 10 nodes\nray.autoscaler.sdk.request_resources(num_cpus=10 * <number of CPUs per node>)\n```\n\nNote that this will immediately request the resources to set the cluster size to the target value, but it will not guarantee that the autoscaler will be able to fulfill the request immediately. The autoscaler will still need to add or remove nodes from the cluster as necessary to meet the requested resources.\n\nAdditionally, keep in mind that this method of setting the cluster size bypasses the normal upscaling speed constraints, so it should be used with caution. If you are new to Ray, it is recommended to start with non-autoscaling clusters.\n\nFor more information, you can refer to the Ray documentation on configuring autoscaling and the `ray.autoscaler.sdk.request_resources()` function.",
831
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
832
        },
833
        {
834
            "question": "how do you disable async iter_batches with Ray Dataset?",
835
            "sources": [
836
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
837
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",
838
                "https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",
839
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.streaming_split.html#ray-data-dataset-streaming-split",
840
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",
841
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches",
842
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",
843
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",
844
                "https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",
845
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#pytorch-dataloader",
846
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.default_batch_format.html#ray-data-dataset-default-batch-format",
847
                "https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches",
848
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",
849
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing"
850
            ],
851
            "document_ids": [
852
                7081,
853
                7379,
854
                7188,
855
                7414,
856
                7380,
857
                7811,
858
                7328,
859
                7545,
860
                7189,
861
                6990,
862
                7791,
863
                6861,
864
                7543,
865
                8046
866
            ],
867
            "answer": " To disable async iter\\_batches with Ray Dataset, you can use the `map_batches()` method instead. The `iter_batches()` method returns an asynchronous iterator, while `map_batches()` applies a function to each batch synchronously. Here's an example:\n\n```python\ndef process_batch(batch):\n    # Your batch processing code here\n    ...\n\nprocessed_ds = ds.map_batches(process_batch)\n```\n\nThis will process the batches synchronously, disabling the asynchronous behavior of `iter_batches()`.",
868
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
869
        },
870
        {
871
            "question": "what is the different between a batch and a block, for ray datasets?",
872
            "sources": [
873
                "https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",
874
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
875
                "https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.batch_to_block.html#ray-data-block-blockaccessor-batch-to-block",
876
                "https://docs.ray.io/en/master/data/data-internals.html#execution-memory",
877
                "https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-data",
878
                "https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches",
879
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.take_batch.html#ray-data-dataset-take-batch",
880
                "https://docs.ray.io/en/master/data/examples/batch_training.html#contents",
881
                "https://docs.ray.io/en/master/data/examples/batch_training.html#transforming-a-dataset-in-parallel-using-custom-functions-a-class-anchor-id-transform-ds-a",
882
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",
883
                "https://docs.ray.io/en/master/data/working-with-tensors.html#batches-of-variable-shape-tensors",
884
                "https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",
885
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
886
                "https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.to_batch_format.html#ray-data-block-blockaccessor-to-batch-format"
887
            ],
888
            "document_ids": [
889
                6872,
890
                7081,
891
                7150,
892
                6895,
893
                6859,
894
                6902,
895
                7825,
896
                7861,
897
                7875,
898
                7735,
899
                6775,
900
                6846,
901
                7988,
902
                7384
903
            ],
904
            "answer": " A batch and a block in Ray Datasets refer to different levels of data organization. A block is a disjoint subset of rows in a dataset, and Ray Data loads and transforms these blocks in parallel. On the other hand, a batch contains data from multiple rows, and it can be created from user-facing data formats like PyArrow tables, Pandas DataFrames, or dictionaries of NumPy ndarrays. Batches can have a fixed size, and they can be iterated over for processing. Blocks are an internal concept for parallel data processing, while batches are a more user-facing way to handle data in chunks.",
905
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
906
        },
907
        {
908
            "question": "How to setup the development environments for ray project?",
909
            "sources": [
910
                "https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-from-source",
911
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#environment-dependencies",
912
                "https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",
913
                "https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",
914
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#development-workflow",
915
                "https://docs.ray.io/en/master/serve/api/index.html#serve-run",
916
                "https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-on-linux-macos-full",
917
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",
918
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher",
919
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",
920
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
921
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",
922
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#environment-dependencies",
923
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"
924
            ],
925
            "document_ids": [
926
                877,
927
                4247,
928
                910,
929
                10168,
930
                13709,
931
                13990,
932
                889,
933
                4251,
934
                4250,
935
                6614,
936
                4811,
937
                9342,
938
                4247,
939
                6334
940
            ],
941
            "answer": " To set up the development environment for the Ray project, follow these steps:\n\n1. Clone the Ray repository:\n   ```\n   git clone https://github.com/ray-project/ray.git\n   ```\n2. Go to the example directory and install dependencies:\n   ```\n   cd doc/source/templates/05_dreambooth_finetuning\n   pip install -Ur dreambooth/requirements.txt\n   ```\n3. Prepare some directories and environment variables.\n4. Build Ray from source by following the instructions for your operating system (Linux, MacOS, or Windows) found in the provided context.\n\nFor dynamic development and experimentation, Ray recommends using runtime environments to install dependencies on the fly. This can be done using the `--runtime-env` or `--runtime-env-json` options with `ray.init()`. For production usage or non-changing environments, install dependencies into a container image and specify the image using the Cluster Launcher.",
942
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
943
        },
944
        {
945
            "question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",
946
            "sources": [
947
                "https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#rewriting-trajectories",
948
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.__init__.html#ray-rllib-evaluation-rollout-worker-rolloutworker-init",
949
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",
950
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",
951
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.set_weights.html#ray-rllib-evaluation-rollout-worker-rolloutworker-set-weights",
952
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.sample.html#ray-rllib-evaluation-rollout-worker-rolloutworker-sample",
953
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.lock.html#ray-rllib-evaluation-rollout-worker-rolloutworker-lock",
954
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.foreach_worker_async.html#ray-rllib-evaluation-worker-set-workerset-foreach-worker-async",
955
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.SyncSampler.html#ray-rllib-evaluation-sampler-syncsampler",
956
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.sample_with_count.html#ray-rllib-evaluation-rollout-worker-rolloutworker-sample-with-count",
957
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.unlock.html#ray-rllib-evaluation-rollout-worker-rolloutworker-unlock",
958
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.AsyncSampler.html#ray-rllib-evaluation-sampler-asyncsampler",
959
                "https://docs.ray.io/en/master/rllib/rllib-training.html#accessing-policy-state",
960
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers"
961
            ],
962
            "document_ids": [
963
                10845,
964
                13196,
965
                12708,
966
                12707,
967
                12965,
968
                12739,
969
                11794,
970
                13127,
971
                12797,
972
                12242,
973
                13022,
974
                12378,
975
                10940,
976
                10991
977
            ],
978
            "answer": " To debug why Ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`, you can try the following steps:\n\n1. Check if the `RolloutWorker` is being correctly initialized with the required parameters such as `env_creator`, `validate_env`, `config`, `worker_index`, `num_workers`, `recreated_worker`, `log_dir`, `spaces`, `default_policy_class`, and `dataset_shards`.\n2. Use the `lock()` and `unlock()` methods of the `RolloutWorker` class to ensure that the worker is properly locked and unlocked during the execution of the sample API.\n3. If you are using `SyncSampler` or `AsyncSampler`, make sure that the required parameters such as `worker`, `env`, `clip_rewards`, `rollout_fragment_length`, `count_steps_by`, `callbacks`, `multiple_episodes_in_batch`, `normalize_actions`, `clip_actions`, `observation_fn`, `sample_collector_class`, `render`, `policies`, `policy_mapping_fn`, `preprocessors`, `obs_filters`, `tf_sess`, `horizon`, and `soft_horizon` are correctly passed.\n4. Check if the `WorkerSet.foreach_worker_async()` method is being used correctly to call the given function asynchronously with each worker as the argument.\n5. Make sure that the `get_weights()` and `set_weights()` methods are being used correctly to get and set the weights of the worker.\n6. If you have configured `num_rollout_workers` > 0, note that updates here may not be immediately reflected in your rollout workers.\n7. You can also use the `ray.timeline()` function to profile and debug the Ray application and identify any performance bottlenecks or deadlocks.\n\nBy following these steps, you can debug why Ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`.",
979
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
980
        },
981
        {
982
            "question": "how do I join two ray datasets?",
983
            "sources": [
984
                "https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",
985
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
986
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
987
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",
988
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",
989
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",
990
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",
991
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#load-and-preprocess-datasets",
992
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-ml-libraries",
993
                "https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",
994
                "https://docs.ray.io/en/master/data/preprocessors.html#overview",
995
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",
996
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",
997
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray"
998
            ],
999
            "document_ids": [
1000
                8006,
1001
                1006,
1002
                6835,
1003
                9180,
1004
                9188,
1005
                7255,
1006
                7289,
1007
                9348,
1008
                6756,
1009
                6809,
1010
                6917,
1011
                7254,
1012
                10013,
1013
                143
1014
            ],
1015
            "answer": " To join two Ray Datasets, you can use the `union()` method, which materializes and concatenates the datasets across rows. Here's an example:\n\n```python\ndataset1 = ray.data.range(5)\ndataset2 = ray.data.range(5)\nmerged_dataset = dataset1.union(dataset2)\n```\n\nThe `union()` method preserves the order of the blocks in the datasets and the relative ordering between the datasets passed in the argument list. However, keep in mind that unioned datasets aren't lineage-serializable, meaning they can't be used as a tunable hyperparameter in Ray Tune.",
1016
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1017
        },
1018
        {
1019
            "question": "Is there a way to retrieve an object ref from its id?",
1020
            "sources": [
1021
                "https://docs.ray.io/en/master/ray-core/objects.html#objects",
1022
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",
1023
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",
1024
                "https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",
1025
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait",
1026
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",
1027
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",
1028
                "https://docs.ray.io/en/master/ray-core/api/core.html#objects",
1029
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",
1030
                "https://docs.ray.io/en/master/ray-core/objects.html#objects",
1031
                "https://docs.ray.io/en/master/ray-core/objects.html#closure-capture-of-objects",
1032
                "https://docs.ray.io/en/master/ray-core/patterns/return-ray-put.html#anti-pattern-returning-ray-put-objectrefs-from-a-task-harms-performance-and-fault-tolerance",
1033
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",
1034
                "https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor"
1035
            ],
1036
            "document_ids": [
1037
                4413,
1038
                5432,
1039
                5426,
1040
                4982,
1041
                4750,
1042
                4885,
1043
                14041,
1044
                4651,
1045
                5436,
1046
                4412,
1047
                4439,
1048
                5173,
1049
                4884,
1050
                4984
1051
            ],
1052
            "answer": " Yes, you can retrieve an object by its object reference (ObjectRef) in Ray. Objects in Ray's object store can be referred to by ObjectRefs, which are unique identifiers for those objects. To get the object from an ObjectRef, you can use the `ray.get()` function, passing the ObjectRef as an argument. This function will block until the object is available in the local object store.\n\nIn the provided context, `retrieve_refs` and `follow_up_refs` are lists of ObjectRefs that can be used with `ray.get()` to retrieve the objects.\n\nExample:\n\n```python\n@ray.remote\ndef retrieve_task(item, db_object_ref):\n    # ... retrieve data using the item and db_object_ref ...\n    return data\n\n# Create object references using ray.put() or remote function calls\ndb_object_ref = ray.put(database)\nretrieve_refs = [retrieve_task.remote(item, db_object_ref) for item in [0, 2, 4, 6]]\n\n# Retrieve objects using ray.get()\nretrieved_objects = [ray.get(ref) for ref in retrieve_refs]\n```",
1053
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1054
        },
1055
        {
1056
            "question": "how to create model Checkpoint from the model in memory?",
1057
            "sources": [
1058
                "https://docs.ray.io/en/master/tune/examples/includes/pbt_convnet_function_example.html#pbt-convnet-example",
1059
                "https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",
1060
                "https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",
1061
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#check-the-training-results-and-checkpoints",
1062
                "https://docs.ray.io/en/master/tune/examples/tune-pytorch-cifar.html#the-train-function",
1063
                "https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",
1064
                "https://docs.ray.io/en/master/tune/examples/includes/xgboost_dynamic_resources_example.html#xgboost-dynamic-resources-example",
1065
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#enabling-checkpointing-to-retrieve-the-model",
1066
                "https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example",
1067
                "https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint",
1068
                "https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#class-api-checkpointing",
1069
                "https://docs.ray.io/en/master/tune/examples/includes/pbt_memnn_example.html#memory-nn-example",
1070
                "https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#class-api-checkpointing",
1071
                "https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model"
1072
            ],
1073
            "document_ids": [
1074
                4104,
1075
                8798,
1076
                9037,
1077
                9331,
1078
                3858,
1079
                8798,
1080
                3964,
1081
                9857,
1082
                4073,
1083
                9066,
1084
                2220,
1085
                4027,
1086
                2219,
1087
                9036
1088
            ],
1089
            "answer": " To create a model checkpoint from a model in memory in TensorFlow, you can use the `TensorflowCheckpoint.from_model` class method. Here's an example:\n```python\nimport tensorflow as tf\nfrom ray.train.tensorflow import TensorflowCheckpoint\n\n# Create a Keras model\nmodel = tf.keras.applications.resnet.ResNet101()\n\n# Create a checkpoint from the model\ncheckpoint = TensorflowCheckpoint.from_model(model)\n```\nAlternatively, you can also write the checkpoint data to a directory using the `to_directory` method of the `Checkpoint` class:\n```python\ncheckpoint = tf.train.Checkpoint(model=model)\ncheckpoint.save(\"/path/to/checkpoint_directory\")\n```\nTo restore the model from the checkpoint, you can use the `from_directory` method of the `Checkpoint` class:\n```python\ncheckpoint = tf.train.Checkpoint.from_directory(\"/path/to/checkpoint_directory\")\ncheckpoint.restore()\n```\nNote that the exact method to use may depend on the specific requirements of your use case.",
1090
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1091
        },
1092
        {
1093
            "question": "what is Deployment in Ray Serve?",
1094
            "sources": [
1095
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
1096
                "https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",
1097
                "https://docs.ray.io/en/master/serve/key-concepts.html#deployment",
1098
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment",
1099
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
1100
                "https://docs.ray.io/en/master/serve/tutorials/java.html#converting-to-a-ray-serve-deployment",
1101
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
1102
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",
1103
                "https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",
1104
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
1105
                "https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",
1106
                "https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",
1107
                "https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",
1108
                "https://docs.ray.io/en/master/serve/api/index.html#put-api-serve-deployments"
1109
            ],
1110
            "document_ids": [
1111
                14255,
1112
                13797,
1113
                13416,
1114
                14150,
1115
                13679,
1116
                14560,
1117
                13677,
1118
                13776,
1119
                13987,
1120
                13974,
1121
                13797,
1122
                14561,
1123
                14598,
1124
                14000
1125
            ],
1126
            "answer": " In Ray Serve, a deployment is a central concept that contains business logic or an ML model to handle incoming requests. It can be scaled up to run across a Ray cluster, with multiple replicas (individual copies of the class or function) started in separate Ray Actors (processes). The number of replicas can be adjusted to match the incoming request load. Deployments are created using the `@serve.deployment` decorator on a Python class or function. The `Deployment` class, created by the decorator, can be used to configure and deploy the deployment to a Ray Serve instance. The new deployment API in Ray Serve 2.0 is recommended, although the 1.x API is still available.",
1127
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1128
        },
1129
        {
1130
            "question": "What is user config in Ray Serve? how do I use it?",
1131
            "sources": [
1132
                "https://docs.ray.io/en/master/serve/api/index.html#serve-config",
1133
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
1134
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",
1135
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
1136
                "https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#updating-user-config",
1137
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",
1138
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-4-prepare-your-ray-serve-python-scripts-and-run-the-ray-serve-application",
1139
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#dynamically-changing-parameters-without-restarting-your-replicas-user-config",
1140
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#adding-deleting-and-updating-applications",
1141
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
1142
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
1143
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
1144
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",
1145
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"
1146
            ],
1147
            "document_ids": [
1148
                13986,
1149
                1005,
1150
                13831,
1151
                13974,
1152
                13687,
1153
                13551,
1154
                5983,
1155
                13565,
1156
                13661,
1157
                6615,
1158
                13678,
1159
                14288,
1160
                13776,
1161
                13852
1162
            ],
1163
            "answer": " User config in Ray Serve refers to the configuration that can be set for each deployment in a Ray Serve application. It can be used to update the deployments with lightweight changes, such as changing the number of replicas or updating the route prefix.\n\nTo use user config, you can set the `user_config` parameter when creating a deployment, either in the decorator or the Serve config file. The `reconfigure()` method can be used to update the user\\_config of a deployment.\n\nFor example, if you have a deployment named \"Model\" with a `user_config` parameter of `{\"threshold\": 1.5}`, you can update the threshold by updating the `user_config` in the Serve config file and reapplying it to the Ray cluster.\n\nYou can use the `serve config` command to see the running app's config and check the current user config of a deployment.\n\nYou can also use the `serve reconfigure` command to update the user config of a deployment without reapplying the entire Serve config file.\n\nFor more information, you can refer to the \"Updating User Config\" and \"Configure Ray Serve deployments\" sections in the provided context.",
1164
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1165
        },
1166
        {
1167
            "question": "What is the difference between PACK and SPREAD strategy?",
1168
            "sources": [
1169
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",
1170
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",
1171
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",
1172
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",
1173
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",
1174
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",
1175
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",
1176
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",
1177
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",
1178
                "https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.placement_strategy.html#ray-train-scalingconfig-placement-strategy",
1179
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#placement-groups",
1180
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",
1181
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
1182
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema"
1183
            ],
1184
            "document_ids": [
1185
                1872,
1186
                5078,
1187
                12053,
1188
                11075,
1189
                5075,
1190
                5078,
1191
                5077,
1192
                4789,
1193
                5145,
1194
                8773,
1195
                4202,
1196
                5076,
1197
                14193,
1198
                14072
1199
            ],
1200
            "answer": " The PACK strategy places bundles onto as few nodes as possible, while the SPREAD strategy places bundles across distinct nodes as evenly as possible. PACK tries to minimize the number of nodes used, while SPREAD tries to maximize the distribution of bundles across nodes.",
1201
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1202
        },
1203
        {
1204
            "question": "What\u2019s the best way to run ray across multiple machines?",
1205
            "sources": [
1206
                "https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",
1207
                "https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",
1208
                "https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",
1209
                "https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",
1210
                "https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",
1211
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
1212
                "https://docs.ray.io/en/master/serve/index.html#why-choose-serve",
1213
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",
1214
                "https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",
1215
                "https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure",
1216
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters",
1217
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
1218
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
1219
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray"
1220
            ],
1221
            "document_ids": [
1222
                5636,
1223
                1237,
1224
                5600,
1225
                4644,
1226
                14710,
1227
                372,
1228
                13615,
1229
                4186,
1230
                5634,
1231
                9809,
1232
                4455,
1233
                13598,
1234
                4207,
1235
                4185
1236
            ],
1237
            "answer": " The best way to run Ray across multiple machines is to deploy a Ray cluster. A Ray cluster consists of a head node and worker nodes connected together. You can deploy a fixed-size Ray cluster or one that autoscales based on the resources requested by applications running on the cluster. While it is possible to run multiple jobs from different users simultaneously in a Ray cluster, it is not recommended for production due to the lack of strong resource isolation and priority support.",
1238
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1239
        },
1240
        {
1241
            "question": "how do I specify ScalingConfig for a Tuner run?",
1242
            "sources": [
1243
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",
1244
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-configure-search-spaces",
1245
                "https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",
1246
                "https://docs.ray.io/en/master/tune/key-concepts.html#tune-trials",
1247
                "https://docs.ray.io/en/master/tune/examples/pbt_ppo_example.html#example",
1248
                "https://docs.ray.io/en/master/tune/examples/nevergrad_example.html#optional-passing-the-hyper-parameter-space-into-the-search-algorithm",
1249
                "https://docs.ray.io/en/master/tune/examples/flaml_example.html#incorporating-a-time-budget-to-the-experiment",
1250
                "https://docs.ray.io/en/master/tune/tutorials/tune-search-spaces.html#working-with-tune-search-spaces",
1251
                "https://docs.ray.io/en/master/tune/examples/optuna_example.html#providing-an-initial-set-of-hyperparameters",
1252
                "https://docs.ray.io/en/master/tune/examples/bayesopt_example.html#running-tune-experiments-with-bayesopt",
1253
                "https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",
1254
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#putting-it-together",
1255
                "https://docs.ray.io/en/master/tune/api/suggestion.html#saving-and-restoring-tune-search-algorithms",
1256
                "https://docs.ray.io/en/master/tune/api/suggestion.html#tune-search-algorithms-tune-search"
1257
            ],
1258
            "document_ids": [
1259
                1826,
1260
                1063,
1261
                1303,
1262
                967,
1263
                2741,
1264
                2569,
1265
                2860,
1266
                2312,
1267
                3140,
1268
                3454,
1269
                2400,
1270
                2969,
1271
                1279,
1272
                1276
1273
            ],
1274
            "answer": " To specify the `ScalingConfig` for a Tuner run, you can include the `resources_per_trial` in the `tune.with_resources()` function when defining the `tuner`. Here's an example:\n\n```python\nfrom ray import tune\n\n# Define your trainable function here\ndef train_fn(config):\n    # Your training code here\n    pass\n\n# Define the resources required for each trial\nresources_per_trial = {\"cpu\": 1, \"gpu\": 0.5}\n\n# Create the tuner with specified resources\ntuner = tune.Tuner(\n    tune.with_resources(train_fn, resources=resources_per_trial),\n    tune_config=tune.TuneConfig(\n        metric=\"loss\",\n        mode=\"min\",\n        num_samples=100,\n    ),\n    param_space={\"param\": tune.uniform(0, 1)},\n)\n\n# Run the tuner\nresults = tuner.fit()\n```\n\nIn this example, the `resources_per_trial` dictionary specifies the CPU and GPU resources for each trial. You can adjust the values according to your needs.",
1275
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1276
        },
1277
        {
1278
            "question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",
1279
            "sources": [
1280
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",
1281
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#gpu-profiling",
1282
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_numpy_refs.html#ray-data-dataset-to-numpy-refs",
1283
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
1284
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",
1285
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",
1286
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1287
                "https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",
1288
                "https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy.html#ray-data-from-numpy",
1289
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only",
1290
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",
1291
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.write_numpy.html#ray-data-dataset-write-numpy",
1292
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",
1293
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map"
1294
            ],
1295
            "document_ids": [
1296
                4942,
1297
                645,
1298
                7644,
1299
                4537,
1300
                4945,
1301
                4225,
1302
                7085,
1303
                7102,
1304
                7467,
1305
                4944,
1306
                7495,
1307
                7247,
1308
                9180,
1309
                7637
1310
            ],
1311
            "answer": " To utilize the zero-copy feature Ray provides for numpy arrays, you should use the Pickle protocol 5 for serialization and store numpy arrays as read-only objects in the object store. When working with Ray workers on the same node, you can read the numpy array in the object store without copying. Each numpy array object in the worker process will hold a pointer to the relevant array in shared memory. However, if you need to write to the read-only object, you must first copy it into the local process memory.\n\nTo convert a Ray Dataset into a distributed set of NumPy ndarrays using zero-copy access, you can use the `to_numpy_refs()` function. This function returns a list of Ray object references to the numpy ndarrays.\n\nHere's an example:\n\n```python\nimport ray\n\n# Create a Ray Dataset\nds = ray.data.range(10, parallelism=2)\n\n# Convert the Dataset into a distributed set of NumPy ndarrays\nrefs = ds.to_numpy_refs()\n\n# Get the actual NumPy arrays from the object references\nnumpy_arrays = [ray.get(ref) for ref in refs]\n```\n\nKeep in mind that this method does not induce a copy of the data, but instead provides zero-copy access to the underlying data.",
1312
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1313
        },
1314
        {
1315
            "question": "if there are O(millions) of keys that all have state, is it ok to spin up 1=1 actors? Or would it be advised to create \u2018key pools\u2019 where an actor can hold 1=many keys?",
1316
            "sources": [
1317
                "https://docs.ray.io/en/master/data/examples/random-access.html#architecture",
1318
                "https://docs.ray.io/en/master/ray-more-libs/modin/index.html#machine-learning",
1319
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
1320
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
1321
                "https://docs.ray.io/en/master/data/examples/random-access.html#performance",
1322
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",
1323
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",
1324
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",
1325
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_random_access_dataset.html#ray-data-dataset-to-random-access-dataset",
1326
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",
1327
                "https://docs.ray.io/en/master/data/api/doc/ray.data.ActorPoolStrategy.html#ray-data-actorpoolstrategy",
1328
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",
1329
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
1330
                "https://docs.ray.io/en/master/data/batch_inference.html#handling-cpu-out-of-memory-failures"
1331
            ],
1332
            "document_ids": [
1333
                8000,
1334
                14718,
1335
                8119,
1336
                4878,
1337
                8002,
1338
                8146,
1339
                5231,
1340
                8101,
1341
                7308,
1342
                641,
1343
                7732,
1344
                5441,
1345
                5191,
1346
                6961
1347
            ],
1348
            "answer": " It is not advised to spin up 1=1 actors for each of O(millions) of keys, as it can lead to resource allocation and management issues. Instead, it is recommended to create 'key pools' where an actor can hold many keys. This approach can help manage resources more efficiently and improve performance. This is similar to how Modin uses Ray Actors for machine learning support, where one actor can hold and aggregate data for multiple partitions on a node.",
1349
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1350
        },
1351
        {
1352
            "question": "How to find the best checkpoint from the trial directory?",
1353
            "sources": [
1354
                "https://docs.ray.io/en/master/train/api/doc/ray.train.Result.html#ray-train-result",
1355
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",
1356
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",
1357
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",
1358
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",
1359
                "https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",
1360
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",
1361
                "https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",
1362
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_trial_checkpoints_paths.html#ray-tune-experimentanalysis-get-trial-checkpoints-paths",
1363
                "https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",
1364
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis",
1365
                "https://docs.ray.io/en/master/train/user-guides/results.html#other-checkpoints",
1366
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",
1367
                "https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-python-api"
1368
            ],
1369
            "document_ids": [
1370
                8414,
1371
                1431,
1372
                1431,
1373
                2188,
1374
                1645,
1375
                3759,
1376
                1430,
1377
                11460,
1378
                1692,
1379
                3760,
1380
                1474,
1381
                9248,
1382
                2348,
1383
                10935
1384
            ],
1385
            "answer": " To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` function, which is part of the Ray Tune library. This function allows you to specify a metric and a mode (either \"min\" or \"max\") to determine the best checkpoint. Any checkpoints without an associated metric value will be filtered out.\n\nHere is an example of how to use this function:\n```\nfrom ray.tune.experiment import ExperimentAnalysis\n\nanalysis = ExperimentAnalysis.from_directory(path)\nbest_checkpoint = analysis.get_best_checkpoint(metric=\"your_metric\", mode=\"max\")\n```\nIn this example, `path` is the path to the trial directory. `metric` is the key for checkpoints to order on, and `mode` is one of [\"min\", \"max\"]. The function will return the best checkpoint based on the specified metric and mode.\n\nNote: This API is currently in alpha and may change before becoming stable.",
1386
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1387
        },
1388
        {
1389
            "question": "what are the advantage and disadvantage of using singleton Actor ?",
1390
            "sources": [
1391
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
1392
                "https://docs.ray.io/en/master/tune/faq.html#what-are-hyperparameters",
1393
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
1394
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
1395
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
1396
                "https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#default-concurrency-group",
1397
                "https://docs.ray.io/en/master/ray-core/patterns/index.html#design-patterns-anti-patterns",
1398
                "https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",
1399
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
1400
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1401
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
1402
                "https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",
1403
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
1404
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks"
1405
            ],
1406
            "document_ids": [
1407
                5192,
1408
                1007,
1409
                14708,
1410
                4505,
1411
                11597,
1412
                4641,
1413
                5244,
1414
                4638,
1415
                4630,
1416
                4624,
1417
                5066,
1418
                4468,
1419
                8108,
1420
                5231
1421
            ],
1422
            "answer": " Actor is a stateful object that can manage its own state, which can be an advantage when you need to maintain state between method invocations. However, if you don't need the stateful part of an actor, you're mostly better off using tasks.\n\nOne disadvantage of using a singleton actor is that it can become a bottleneck if many tasks or actors are trying to access it simultaneously. This can be mitigated by increasing the resources requested by the actor or limiting the number of concurrently running tasks and actors on each node.\n\nAnother potential issue is that using class variables to manage state between instances of the same class is not supported in actors, as each actor instance is instantiated in its own process and has its own copy of the class variables. Instead, actors should manage their state using instance variables.\n\nFinally, it's important to note that if you don't need the stateful part of an actor, you're mostly better off using tasks. Tasks are lightweight and can be used to parallelize function calls without the overhead of creating and managing stateful actors.",
1423
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1424
        },
1425
        {
1426
            "question": "what are the advantages of using a named actor?",
1427
            "sources": [
1428
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1429
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
1430
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1431
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
1432
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1433
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
1434
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
1435
                "https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",
1436
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",
1437
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
1438
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
1439
                "https://docs.ray.io/en/master/ray-core/actors.html#more-about-ray-actors",
1440
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
1441
                "https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors"
1442
            ],
1443
            "document_ids": [
1444
                4623,
1445
                4629,
1446
                4625,
1447
                4871,
1448
                4624,
1449
                4880,
1450
                4505,
1451
                4451,
1452
                4200,
1453
                4503,
1454
                4650,
1455
                4507,
1456
                4631,
1457
                4521
1458
            ],
1459
            "answer": " The advantages of using a named actor are:\n\n1. Retrieval of the actor from any job in the Ray cluster, which is useful if you cannot directly pass the actor handle to the task that needs it or if you are trying to access an actor launched by another driver.\n2. Get-Or-Create option, which allows creating an actor only if it doesn\u2019t exist, and if it does, a handle to the actor will be returned, and the arguments will be ignored.\n3. Specifying a namespace for a named actor while creating it, which allows the actor to belong to the specified namespace, no matter what namespace of the current job is.\n4. Decoupling actor lifetimes from the job, allowing an actor to persist even after the driver process of the job exits, which is useful for long-running services.",
1460
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1461
        },
1462
        {
1463
            "question": "How do I read a text file stored on S3 using Ray Data?",
1464
            "sources": [
1465
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",
1466
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",
1467
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",
1468
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",
1469
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",
1470
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
1471
                "https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",
1472
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
1473
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",
1474
                "https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",
1475
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
1476
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_json.html#ray-data-read-json",
1477
                "https://docs.ray.io/en/master/data/loading-data.html#reading-files-from-cloud-storage",
1478
                "https://docs.ray.io/en/master/data/working-with-images.html#saving-images"
1479
            ],
1480
            "document_ids": [
1481
                7565,
1482
                7565,
1483
                7783,
1484
                7496,
1485
                7261,
1486
                6994,
1487
                6779,
1488
                6996,
1489
                7217,
1490
                7909,
1491
                6995,
1492
                7317,
1493
                6743,
1494
                6729
1495
            ],
1496
            "answer": " To read a text file stored on S3 using Ray Data, you can use the `read_text` function and provide the S3 path to the file as an argument. Here's an example:\n\n```python\nimport ray\n\n# S3 path to the text file\ns3_path = \"s3://anonymous@ray-example-data/this.txt\"\n\n# Read the text file from S3\nds = ray.data.read_text(s3_path)\n\n# Print the schema of the DataFrame\nprint(ds.schema())\n```\n\nThis will create a Ray DataFrame `ds` with a single column named \"text\" containing the contents of the text file.",
1497
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1498
        },
1499
        {
1500
            "question": "how do I get the IP of the head node for my Ray cluster?",
1501
            "sources": [
1502
                "https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",
1503
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",
1504
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",
1505
                "https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",
1506
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",
1507
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",
1508
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats",
1509
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",
1510
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
1511
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",
1512
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",
1513
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
1514
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
1515
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port"
1516
            ],
1517
            "document_ids": [
1518
                5631,
1519
                6411,
1520
                6501,
1521
                5587,
1522
                6502,
1523
                6409,
1524
                6416,
1525
                6616,
1526
                6552,
1527
                6619,
1528
                6708,
1529
                6353,
1530
                6561,
1531
                5528
1532
            ],
1533
            "answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command. It is used as follows:\n\n```\nray get_head_ip [OPTIONS] CLUSTER_CONFIG_FILE\n```\n\nHere, `CLUSTER_CONFIG_FILE` is a required argument that specifies the configuration file for the Ray cluster. The `--cluster-name` option can be used to override the configured cluster name.\n\nAlternatively, if you have access to the head node, you can use the following Python script to get the IP of the head node:\n\n```python\nimport ray\nray.init()\nprint(ray.nodes()[0]['node_ip_address'])\n```\n\nThis script uses the `ray.nodes()` function to get information about the nodes in the Ray cluster, and then prints the IP address of the first node in the list, which should be the head node.",
1534
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1535
        },
1536
        {
1537
            "question": "How to write a map function that returns a list of object for `map_batches`?",
1538
            "sources": [
1539
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1540
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.write_sql.html#ray-data-dataset-write-sql",
1541
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1542
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",
1543
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1544
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
1545
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",
1546
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1547
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",
1548
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
1549
                "https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",
1550
                "https://docs.ray.io/en/master/data/examples/batch_training.html#define-training-functions",
1551
                "https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_torch_batches.html#ray-data-dataiterator-iter-torch-batches",
1552
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"
1553
            ],
1554
            "document_ids": [
1555
                7082,
1556
                7662,
1557
                7091,
1558
                7640,
1559
                7081,
1560
                7800,
1561
                7959,
1562
                7086,
1563
                7738,
1564
                8070,
1565
                10266,
1566
                7888,
1567
                7679,
1568
                7085
1569
            ],
1570
            "answer": " To write a map function that returns a list of objects for `map_batches`, you can define a function that takes a batch of data as input and yields the desired objects in a list. Here's an example using a Python function:\n```python\nfrom typing import Dict, Iterator\nimport numpy as np\n\ndef map_fn_with_large_output(batch: Dict[str, np.ndarray]) -> Iterator[Dict[str, np.ndarray]]:\n    for i in range(3):\n        yield {\"large_output_\" + str(i): np.ones((100, 1000))}\n```\nThis function takes a batch of data as a dictionary with numpy arrays, and yields three dictionaries with a large numpy array in each one.\n\nThen, you can use `map_batches` to apply this function to your dataset:\n```python\nds = (\n    ray.data.from_items([1])\n    .map_batches(map_fn_with_large_output)\n)\n```\nNote that if your function returns large objects, you should yield outputs in chunks and use the `Iterator` type hint for the function return type.",
1571
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1572
        },
1573
        {
1574
            "question": "How do I set a maximum episode length when training with Rllib?",
1575
            "sources": [
1576
                "https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",
1577
                "https://docs.ray.io/en/master/rllib/rllib-replay-buffers.html#advanced-usage",
1578
                "https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",
1579
                "https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",
1580
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.reporting.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-reporting",
1581
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",
1582
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#evolution-strategies-es",
1583
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.train_buffered.html#ray-rllib-algorithms-algorithm-algorithm-train-buffered",
1584
                "https://docs.ray.io/en/master/rllib/rllib-offline.html#getting-started",
1585
                "https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",
1586
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.reporting.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-reporting",
1587
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",
1588
                "https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",
1589
                "https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data"
1590
            ],
1591
            "document_ids": [
1592
                10821,
1593
                10724,
1594
                10510,
1595
                11458,
1596
                13177,
1597
                11048,
1598
                11925,
1599
                12332,
1600
                10594,
1601
                10823,
1602
                13176,
1603
                11047,
1604
                10926,
1605
                10514
1606
            ],
1607
            "answer": " In RLlib, you can't set a maximum episode length directly during training. However, you can control the number of episodes used for training by setting the `evaluation_duration` and `evaluation_duration_unit` parameters in the configuration.\n\nIf you want to limit the length of individual episodes, you can do so within your environment's implementation by limiting the number of steps taken in each episode. Here's an example of how to limit the episode length to 100 steps in a custom OpenAI Gym environment:\n\n```python\nclass MyEnv(gym.Env):\n    def __init__(self, ...):\n        ...\n        self.max_episode_steps = 100\n\n    def step(self, action):\n        ...\n        if self.current_step < self.max_episode_steps:\n            self.current_step += 1\n        else:\n            self.current_step = 0\n            done = True\n        return obs, reward, done, truncated, info\n```\n\nIn this example, the `step()` method checks if the current step count is less than the maximum episode length. If it is, the environment continues to step through the episode. Once the maximum episode length is reached, the environment resets and the episode ends.",
1608
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1609
        },
1610
        {
1611
            "question": "how do I make a Ray Tune trial retry on failures?",
1612
            "sources": [
1613
                "https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",
1614
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
1615
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
1616
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",
1617
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_error.html#ray-tune-callback-on-trial-error",
1618
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_recover.html#ray-tune-callback-on-trial-recover",
1619
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",
1620
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.MedianStoppingRule.html#ray-tune-schedulers-medianstoppingrule",
1621
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.LoggerCallback.on_trial_recover.html#ray-tune-logger-loggercallback-on-trial-recover",
1622
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.HyperBandScheduler.html#ray-tune-schedulers-hyperbandscheduler",
1623
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.JsonLoggerCallback.on_trial_recover.html#ray-tune-logger-jsonloggercallback-on-trial-recover",
1624
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.on_trial_error.html#ray-tune-schedulers-trialscheduler-on-trial-error",
1625
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_save.html#ray-tune-callback-on-trial-save",
1626
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.aim.AimLoggerCallback.on_trial_recover.html#ray-tune-logger-aim-aimloggercallback-on-trial-recover"
1627
            ],
1628
            "document_ids": [
1629
                2326,
1630
                1006,
1631
                1114,
1632
                2266,
1633
                1980,
1634
                2152,
1635
                1926,
1636
                1728,
1637
                1966,
1638
                1974,
1639
                2104,
1640
                1749,
1641
                1385,
1642
                1762
1643
            ],
1644
            "answer": " To make a Ray Tune trial retry on failures, you can use the `FailureConfig` to configure failure handling of individual trials. Here is an example:\n\n```python\nfrom ray import tune\n\n# Define the FailureConfig\nfail_config = tune.FailureConfig(max_retries=3)\n\n# Create the tuner with the FailureConfig\ntuner = tune.Tuner(train_model, param_space=trial_space, failure_config=fail_config)\n\n# Run the tuner\nresults = tuner.fit()\n```\n\nIn this example, the `max_retries` parameter is set to 3, which means that if a trial fails, it will be retried up to 3 times before being marked as failed. You can adjust the number of retries according to your needs.\n\nAdditionally, you can use the `ray.tune.Callback.on_trial_error` and `ray.tune.Callback.on_trial_recover` callbacks to customize the behavior when a trial errors or is recovered. These callbacks are called after a trial instance failed (errored) but can be used to implement custom behavior before the trial is retried.",
1645
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1646
        },
1647
        {
1648
            "question": "For the supervised actor pattern, can we keep the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor, to allow the worker actor to remain even on Supervisor / Driver failure?",
1649
            "sources": [
1650
                "https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",
1651
                "https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",
1652
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
1653
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
1654
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
1655
                "https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",
1656
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#how-to-write-fault-tolerant-ray-applications",
1657
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
1658
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#advanced-detached-placement-group",
1659
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
1660
                "https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",
1661
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
1662
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#worker-node-failure",
1663
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"
1664
            ],
1665
            "document_ids": [
1666
                5208,
1667
                5208,
1668
                4561,
1669
                4575,
1670
                4631,
1671
                5195,
1672
                4351,
1673
                5191,
1674
                5095,
1675
                5192,
1676
                4451,
1677
                4878,
1678
                4520,
1679
                4623
1680
            ],
1681
            "answer": " No, keeping the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor does not prevent the worker actor from being terminated on Supervisor/Driver failure. This is because of actor reference counting - if the supervisor dies, the worker actors are automatically terminated. This pattern is simplified in the driver, as it manages only a few supervisors and does not deal with failures from worker actors directly.",
1682
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1683
        },
1684
        {
1685
            "question": "How do I read a large text file in S3 with Ray?",
1686
            "sources": [
1687
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",
1688
                "https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",
1689
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",
1690
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",
1691
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",
1692
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
1693
                "https://docs.ray.io/en/master/data/loading-data.html#handling-compressed-files",
1694
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
1695
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",
1696
                "https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",
1697
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
1698
                "https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",
1699
                "https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",
1700
                "https://docs.ray.io/en/master/data/key-concepts.html#loading-data"
1701
            ],
1702
            "document_ids": [
1703
                7565,
1704
                6993,
1705
                7783,
1706
                7496,
1707
                7261,
1708
                6994,
1709
                6747,
1710
                6996,
1711
                7217,
1712
                6993,
1713
                6995,
1714
                7909,
1715
                6779,
1716
                6732
1717
            ],
1718
            "answer": " To read a large text file in S3 with Ray, you can use the `read_text` function and provide the S3 path to the text file as an argument. Here's an example:\n```python\nimport ray\n\nds = ray.data.read_text(\"s3://your-bucket-name/path/to/your/text-file.txt\")\n```\nThis will create a Ray Dataset `ds` that contains the text data from the file. If the text file is compressed (e.g., with gzip), you can specify the compression type in `arrow_open_stream_args`:\n```python\nds = ray.data.read_text(\"s3://your-bucket-name/path/to/your/compressed-text-file.txt.gz\",\n                        arrow_open_stream_args={\"compression\": \"gzip\"})\n```\nIf you need to read multiple text files from a directory in S3, you can use the `read_text_files` function instead:\n```python\nds = ray.data.read_text_files(\"s3://your-bucket-name/path/to/your/text-files/\")\n```\nThis will create a Ray Dataset `ds` that contains the text data from all the text files in the specified directory.",
1719
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1720
        },
1721
        {
1722
            "question": "how do I get a ray dataset from pandas",
1723
            "sources": [
1724
                "https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-pandas",
1725
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",
1726
                "https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",
1727
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",
1728
                "https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",
1729
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
1730
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_pandas.html#ray-data-dataset-to-pandas",
1731
                "https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-rows",
1732
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-datasets",
1733
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",
1734
                "https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",
1735
                "https://docs.ray.io/en/master/data/api/from_other_data_libs.html#for-pandas-users",
1736
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",
1737
                "https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches"
1738
            ],
1739
            "document_ids": [
1740
                6806,
1741
                6749,
1742
                6901,
1743
                6752,
1744
                7818,
1745
                7363,
1746
                7302,
1747
                6860,
1748
                6984,
1749
                7216,
1750
                7865,
1751
                7022,
1752
                6750,
1753
                6902
1754
            ],
1755
            "answer": " To get a Ray dataset from a pandas DataFrame, you can use the `ray.data.from_pandas()` function. Here's an example:\n\n```python\nimport pandas as pd\nimport ray\n\n# Create a pandas DataFrame\ndf = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\n\n# Convert the DataFrame to a Ray dataset\nds = ray.data.from_pandas(df)\n```\n\nThis will create a Ray dataset `ds` from the pandas DataFrame `df`. Note that if your DataFrame is too large to fit in memory, you may need to use a distributed dataframe library like Dask or Spark instead.",
1756
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1757
        },
1758
        {
1759
            "question": "can you give me an example of using `ray.data.map` ?",
1760
            "sources": [
1761
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
1762
                "https://docs.ray.io/en/master/rllib/rllib-examples.html#custom-and-complex-models",
1763
                "https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",
1764
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",
1765
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1766
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
1767
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",
1768
                "https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",
1769
                "https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",
1770
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",
1771
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",
1772
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",
1773
                "https://docs.ray.io/en/master/data/api/from_other_data_libs.html#api-guide-for-users-from-other-data-libraries",
1774
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-transforms"
1775
            ],
1776
            "document_ids": [
1777
                7796,
1778
                10636,
1779
                7274,
1780
                9180,
1781
                7081,
1782
                6835,
1783
                8046,
1784
                8006,
1785
                6773,
1786
                7971,
1787
                6749,
1788
                6782,
1789
                7020,
1790
                6976
1791
            ],
1792
            "answer": " Sure, here's an example of using `ray.data.Dataset.map`:\n\nSuppose we have a Ray Dataset `ds` with the following schema:\n```python\nDataset(num_blocks=..., num_rows=..., schema={column1: int32, column2: string})\n```\nWe can use `map` to apply a function to each row of the dataset and create a new column with the result:\n```python\ndef process_row(row: Dict[str, Any]) -> Dict[str, Any]:\n    row[\"new_column\"] = row[\"column1\"] * 2\n",
1793
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1794
        },
1795
        {
1796
            "question": "can you give me an example of using `ray.data.map` , with a callable class as input?",
1797
            "sources": [
1798
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
1799
                "https://docs.ray.io/en/master/rllib/rllib-examples.html#custom-and-complex-models",
1800
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",
1801
                "https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",
1802
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",
1803
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",
1804
                "https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",
1805
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",
1806
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",
1807
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",
1808
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",
1809
                "https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",
1810
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",
1811
                "https://docs.ray.io/en/master/data/working-with-text.html#transforming-text"
1812
            ],
1813
            "document_ids": [
1814
                7796,
1815
                10636,
1816
                250,
1817
                7274,
1818
                7636,
1819
                6749,
1820
                7628,
1821
                247,
1822
                10014,
1823
                10303,
1824
                6782,
1825
                7102,
1826
                9180,
1827
                6997
1828
            ],
1829
            "answer": " Sure, here's an example of using `ray.data.map` with a callable class as input:\n```python\nimport ray\n\n@ray.remote\nclass MyCallableClass:\n    def __call__(self, row):\n        return {**row, \"new_col\": \"new_value\"}\n\nds = ray.data.range(10)\n\n# Create an instance of MyCallableClass\nmy_callable = MyCallableClass.remote()\n\n# Use the map method with the callable class\nds_with_new_col = ds.map(my_callable)\n\n# Print the resulting dataset\nprint(ds_with_new_col.take(5))\n```\nIn this example, we define a remote class `MyCallableClass` that implements the `__call__` method. We then create an instance of this class and use it as the input to the `map` method of a `ray.data.Dataset`. The `map` method applies the callable to each row of the dataset and returns a new dataset with the transformed rows.\n\nNote that we use the `remote` decorator to create a remote version of the class that can be executed in parallel by Ray.",
1830
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1831
        },
1832
        {
1833
            "question": "How to set memory limit for each trial in Ray Tuner?",
1834
            "sources": [
1835
                "https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",
1836
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
1837
                "https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",
1838
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",
1839
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",
1840
                "https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-trials-individually-with-a-timeout",
1841
                "https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria",
1842
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",
1843
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",
1844
                "https://docs.ray.io/en/master/tune/examples/pbt_guide.html#replaying-a-pbt-run",
1845
                "https://docs.ray.io/en/master/tune/faq.html#how-can-i-use-tune-with-kubernetes",
1846
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",
1847
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",
1848
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.max_concurrent_trials.html#ray-tune-tuneconfig-max-concurrent-trials"
1849
            ],
1850
            "document_ids": [
1851
                2325,
1852
                1114,
1853
                2406,
1854
                2266,
1855
                2001,
1856
                2204,
1857
                2198,
1858
                1025,
1859
                2003,
1860
                3167,
1861
                1062,
1862
                2966,
1863
                1926,
1864
                1939
1865
            ],
1866
            "answer": " To set the memory limit for each trial in Ray Tuner, you can use the `with_resources` function to customize the resources allocated per trial. However, Ray Tuner does not directly support setting a memory limit for each trial. Instead, you can control the parallelism by setting `max_concurrent_trials` in `TuneConfig`. The number of trials running concurrently will be determined by how many trials can fit in the cluster at once, considering the resources required by each trial.\n\nHere's an example of how to set 4 GB of memory for each trial:\n\n```python\nimport os\nfrom ray import tune\n\n# Set memory limit to 4 GB for each trial\nmemory_limit = 4 * 1024 ** 3  # 4 GB in bytes\n\ntrain_model = tune.with_resources(train_model, {\"memory\": memory_limit})\n\ntuner = tune.Tuner(\n    train_model,\n    tune_config=tune.TuneConfig(\n        max_concurrent_trials=os.cpu_count()  # Adjust this value based on your system\n    ),\n    run_config=train.RunConfig(storage_path=\"s3://your-s3-bucket/durable-trial/\")\n)\n\ntuner.fit()\n```\n\nPlease note that it is the user's responsibility to ensure that the model fits in the allocated memory. Ray does not automatically handle sharing memory between trials.",
1867
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1868
        },
1869
        {
1870
            "question": "how do I get the actor id of an actor",
1871
            "sources": [
1872
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_actor_id.html#ray-runtime-context-runtimecontext-get-actor-id",
1873
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",
1874
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1875
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
1876
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
1877
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
1878
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
1879
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.current_actor.html#ray-runtime-context-runtimecontext-current-actor",
1880
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
1881
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
1882
                "https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",
1883
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",
1884
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
1885
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors"
1886
            ],
1887
            "document_ids": [
1888
                4787,
1889
                4931,
1890
                4623,
1891
                4629,
1892
                4871,
1893
                4616,
1894
                5071,
1895
                4874,
1896
                4622,
1897
                4621,
1898
                4241,
1899
                5117,
1900
                4472,
1901
                4200
1902
            ],
1903
            "answer": " To get the actor ID of the current actor, you can use the `RuntimeContext.get_actor_id()` or `RuntimeContext.actor_id` property in Ray. These methods return the current actor ID in hex format if there is an actor ID; otherwise, they return None. Note that you should not use these methods in a driver process.\n\nIf you have created a named actor, you can also retrieve the actor using `ray.get_actor(name)` function, which returns an `ActorHandle` to the actor with the given name.\n\nHere's an example of creating a named actor and retrieving it later:\n```python\nimport ray\n\n@ray.remote\nclass Counter:\n    pass\n\n# Create an actor with a name\ncounter = Counter.options(name=\"some_name\").remote()\n\n# Retrieve the actor later somewhere\ncounter = ray.get_actor(\"some_name\")\n```\nTo check the state of actors, you can use the `ray list actors` command from the Ray State API. This command shows the state of all actors in the cluster, including their IDs, class names, job IDs, and death causes.",
1904
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1905
        },
1906
        {
1907
            "question": "can ray.init() can check if ray is all-ready initiated ?",
1908
            "sources": [
1909
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",
1910
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobType.SUBMISSION.html#ray-job-submission-jobtype-submission",
1911
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
1912
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",
1913
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",
1914
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",
1915
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",
1916
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#starting-ray",
1917
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",
1918
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
1919
                "https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",
1920
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
1921
                "https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.__init__.html#ray-data-preprocessor-preprocessor-init",
1922
                "https://docs.ray.io/en/master/ray-core/configure.html#java-applications"
1923
            ],
1924
            "document_ids": [
1925
                4785,
1926
                6647,
1927
                4811,
1928
                4194,
1929
                4190,
1930
                4188,
1931
                4189,
1932
                5386,
1933
                4186,
1934
                4204,
1935
                5399,
1936
                4576,
1937
                7833,
1938
                4404
1939
            ],
1940
            "answer": " No, `ray.init()` is not used to check if Ray is already initiated. Instead, you should use `ray.is_initialized()` to check if Ray has been initialized in the current process. This function returns `True` if `ray.init()` has been called and `False` otherwise.",
1941
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1942
        },
1943
        {
1944
            "question": "What does the `compute=actor`  argument do within `ray.data.map_batches` ?",
1945
            "sources": [
1946
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
1947
                "https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",
1948
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
1949
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
1950
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
1951
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",
1952
                "https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",
1953
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
1954
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
1955
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
1956
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
1957
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
1958
                "https://docs.ray.io/en/master/data/batch_inference.html#using-models-from-ray-train",
1959
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#verify-and-save-results"
1960
            ],
1961
            "document_ids": [
1962
                6787,
1963
                6885,
1964
                8108,
1965
                8070,
1966
                7988,
1967
                6981,
1968
                6999,
1969
                6952,
1970
                6941,
1971
                6954,
1972
                6944,
1973
                8119,
1974
                6966,
1975
                8073
1976
            ],
1977
            "answer": " The `compute=actor` argument within `ray.data.map_batches` is used to specify that the function provided to `map_batches` should be run as long-running Ray actors, utilizing the ActorPoolStrategy. This allows for parallel processing of data partitions by different actors.",
1978
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1979
        },
1980
        {
1981
            "question": "how do I use wandb logger with accelerateTrainer?",
1982
            "sources": [
1983
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",
1984
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
1985
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",
1986
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",
1987
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",
1988
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",
1989
                "https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",
1990
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",
1991
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",
1992
                "https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",
1993
                "https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",
1994
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#tune-wandb-api-reference",
1995
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",
1996
                "https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries"
1997
            ],
1998
            "document_ids": [
1999
                8470,
2000
                1006,
2001
                2432,
2002
                2463,
2003
                9530,
2004
                2431,
2005
                1132,
2006
                2466,
2007
                9529,
2008
                9136,
2009
                9137,
2010
                2462,
2011
                2434,
2012
                9138
2013
            ],
2014
            "answer": " To use WandB logger with AccelerateTrainer in PyTorch Lightning, you can follow these steps:\n\n1. Install WandB: `pip install wandb`\n2. Import the necessary libraries:\n```python\nimport os\nimport pytorch_lightning as pl\nfrom pytorch_lightning.loggers import WandbLogger\n```\n3. Set the WANDB\\_API\\_KEY environment variable on each worker:\n```python\nWANDB_API_KEY = os.environ[\"WANDB_API_KEY\"]\nos.environ[\"WANDB_API_KEY\"] = WANDB_API_KEY\n```\n4. Initialize the WandB logger:\n```python\nwandb_logger = WandBLogger(name=\"your_experiment_name\", project=\"your_project_name\")\n```\n5. Initialize the AccelerateTrainer with the WandB logger:\n```python\ntrainer = pl.Trainers.accelerate.AccelerateTrainer(logger=wandb_logger, **accelerate_args)\n```\nReplace `accelerate_args` with the appropriate arguments for AccelerateTrainer.\n\nNow you can train your model using the `trainer.fit()` method. The training progress and metrics will be logged to WandB.",
2015
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2016
        },
2017
        {
2018
            "question": "What will be implicitly put into object store?",
2019
            "sources": [
2020
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",
2021
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#move-the-data-loader-to-the-training-function",
2022
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#plasma-store",
2023
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",
2024
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
2025
                "https://docs.ray.io/en/master/data/data-internals.html#object-store-memory",
2026
                "https://docs.ray.io/en/master/ray-core/objects.html#objects",
2027
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
2028
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",
2029
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
2030
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
2031
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
2032
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
2033
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"
2034
            ],
2035
            "document_ids": [
2036
                5013,
2037
                9858,
2038
                4941,
2039
                5014,
2040
                14748,
2041
                6896,
2042
                4414,
2043
                14094,
2044
                14122,
2045
                14138,
2046
                14063,
2047
                14222,
2048
                14184,
2049
                14300
2050
            ],
2051
            "answer": " Implicitly, objects created in the object store via `ray.put` and returned values from remote functions will be put into the object store memory. When the object store fills up, objects will be spilled to disk. Objects are reference counted and evicted when they fall out of scope. Each node has its own object store, and data is not automatically broadcasted to other nodes. Objects in the object store can be efficiently shared among many actors and tasks.",
2052
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2053
        },
2054
        {
2055
            "question": "How do I kill or cancel a ray task that I already started?",
2056
            "sources": [
2057
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",
2058
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
2059
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
2060
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
2061
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
2062
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",
2063
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
2064
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",
2065
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
2066
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
2067
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
2068
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
2069
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
2070
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"
2071
            ],
2072
            "document_ids": [
2073
                4550,
2074
                1005,
2075
                4375,
2076
                4894,
2077
                4494,
2078
                14702,
2079
                11591,
2080
                14703,
2081
                4497,
2082
                11592,
2083
                4896,
2084
                11590,
2085
                4492,
2086
                14701
2087
            ],
2088
            "answer": " To kill or cancel a Ray task that you have already started, you can call `ray.cancel()` on the Object Ref returned by the task. This will send a KeyboardInterrupt to the task's worker if it is currently in mid-execution. If you want to force-exit the worker, you can pass `force=True` to `ray.cancel()`. Note that Ray will not automatically retry tasks that have been cancelled.\n\nHere is an example of how to cancel a Ray task:\n\n```\n@ray.remote\ndef blocking_operation():\n    time.sleep(10e6)\n\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n\ntry:\n    ray.get(obj_ref)\nexcept ray.exceptions.TaskCancelledError:\n    print(\"Object reference was cancelled.\")\n```\n\nThis cancellation approach aligns with the standards presented in asyncio task cancellation. Note that asyncio.Task won\u2019t be interrupted in the middle of execution if you don\u2019t await within the async function. Ray attempts to cancel Tasks on a best-effort basis, meaning cancellation isn\u2019t always guaranteed. You can check if a Task was successfully cancelled using `ray.get(actor_task_ref)`.\n\nFor Actor Tasks, if the specified Task is pending execution, it is cancelled and not executed. If the Task is currently executing, the behavior depends on the execution model of an Actor. If it is a regular Actor or a threaded Actor, the execution isn\u2019t cancelled. Actor Tasks cannot be interrupted because Actors have states. If it is an async Actor, Ray cancels a asyncio.Task. The semantic of cancellation is equivalent to asyncio\u2019s cancellation. If the Task has finished, nothing happens. Only `force=False` is allowed for an Actor Task. Otherwise, it raises ValueError. Use `ray.kill(actor)` instead to kill an Actor.\n\nTo cancel an Actor Task, you can also call `ray.cancel()` on the returned Object Ref.\n\n```\nimport ray\nimport asyncio\nimport time\n\n@ray.remote\nclass Actor:\n    async def f(self):\n        try:\n            await asyncio.sleep(5)\n        except asyncio.CancelledError:\n            print(\"Actor task canceled.\")\n\nactor = Actor.remote()\nref = actor.f.remote()\n\n# Wait until task is scheduled.\ntime.sleep(1)\nray.cancel(ref)\n```",
2089
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2090
        },
2091
        {
2092
            "question": "how to send  extra arguments in dataset.map_batches function?",
2093
            "sources": [
2094
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
2095
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
2096
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",
2097
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
2098
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
2099
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",
2100
                "https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",
2101
                "https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches",
2102
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
2103
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",
2104
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
2105
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#pytorch-dataloader",
2106
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",
2107
                "https://docs.ray.io/en/master/data/examples/batch_training.html#define-training-functions"
2108
            ],
2109
            "document_ids": [
2110
                7082,
2111
                7081,
2112
                7959,
2113
                8070,
2114
                7988,
2115
                7640,
2116
                6956,
2117
                6861,
2118
                7796,
2119
                7738,
2120
                7091,
2121
                6990,
2122
                7379,
2123
                7888
2124
            ],
2125
            "answer": " To send extra arguments in the `dataset.map_batches` function, you can use the `fn_kwargs` parameter. This parameter allows you to pass a dictionary of keyword arguments that will be used when calling the `fn` function for each batch. Here's an example:\n```python\nextra_arg1 = \"value1\"\nextra_arg2 = 42\n\nds.map_batches(my_function, fn_kwargs={\"arg1\": extra_arg1, \"arg2\": extra_arg2})\n```\nIn this example, `my_function` is the function that will be called for each batch, and `extra_arg1` and `extra_arg2` are the extra arguments you want to pass. The `fn_kwargs` parameter is a dictionary that maps the argument names (`\"arg1\"` and `\"arg2\"`) to the argument values (`extra_arg1` and `extra_arg2`). When `my_function` is called for each batch, it will receive these extra arguments in its `**kwargs` parameter.",
2126
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2127
        },
2128
        {
2129
            "question": "where does ray GCS store the history of jobs run on a kuberay cluster? What type of database and format does it use for this?",
2130
            "sources": [
2131
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance",
2132
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",
2133
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",
2134
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",
2135
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/experimental.html#gcs-fault-tolerance",
2136
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",
2137
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
2138
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",
2139
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",
2140
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
2141
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#about-kuberay",
2142
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-2-deploy-a-ray-cluster-on-kubernetes-with-the-kuberay-operator",
2143
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",
2144
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#accessing-the-cluster-with-kubectl-exec"
2145
            ],
2146
            "document_ids": [
2147
                5830,
2148
                5754,
2149
                5642,
2150
                5647,
2151
                5916,
2152
                5763,
2153
                6149,
2154
                5860,
2155
                5658,
2156
                5656,
2157
                5660,
2158
                6162,
2159
                5754,
2160
                5833
2161
            ],
2162
            "answer": " Ray GCS, which stands for Global Control Store, stores the history of jobs run on a Kuberay cluster in an internal key-value store by default. This metadata can be written to an external Redis database for fault tolerance and high availability. The exact format of the data stored in Redis is not specified in the provided context. To use this feature, the environment variable RAY\\_REDIS\\_ADDRESS and the --redis-password need to be passed in the Ray head node section of the Kubernetes deployment config file. Additionally, KubeRay offers a RayJob custom resource definition that automatically creates a RayCluster and submits a job when the cluster is ready, with the option to automatically delete the RayCluster once the job finishes.",
2163
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2164
        },
2165
        {
2166
            "question": "How to resolve ValueError: The actor ImplicitFunc is too large?",
2167
            "sources": [
2168
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
2169
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",
2170
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#move-the-data-loader-to-the-training-function",
2171
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#verify-and-save-results",
2172
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
2173
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
2174
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler",
2175
                "https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",
2176
                "https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",
2177
                "https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",
2178
                "https://docs.ray.io/en/master/data/batch_inference.html#handling-cpu-out-of-memory-failures",
2179
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
2180
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
2181
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options"
2182
            ],
2183
            "document_ids": [
2184
                6150,
2185
                5118,
2186
                9858,
2187
                7992,
2188
                8119,
2189
                8108,
2190
                1895,
2191
                10267,
2192
                9908,
2193
                5465,
2194
                6961,
2195
                476,
2196
                6954,
2197
                4878
2198
            ],
2199
            "answer": " The ValueError \"The actor ImplicitFunc is too large\" is likely caused by a large model or data object being implicitly captured in the actor's scope. To resolve this issue, you can:\n\n1. Move the data loader to the training function and use `ray.put()` to put large objects in the Ray object store.\n2. Reduce the batch size to ensure full parallelization across the actor pool.\n3. Consider using a smaller model or breaking down the data into smaller chunks to reduce the size of the actor.\n\nAdditionally, you can enable and configure the memory monitor to detect and resolve memory issues. The memory monitor can help prevent Out-Of-Memory errors by providing information on memory usage and identifying any processes that are consuming a large amount of heap space.",
2200
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2201
        },
2202
        {
2203
            "question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",
2204
            "sources": [
2205
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",
2206
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
2207
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",
2208
                "https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#distributed-training",
2209
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
2210
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
2211
                "https://docs.ray.io/en/master/train/huggingface-accelerate.html#training-with-huggingface-accelerate",
2212
                "https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others",
2213
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",
2214
                "https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#training",
2215
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",
2216
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#define-the-training-loop",
2217
                "https://docs.ray.io/en/master/train/getting-started-pytorch.html#getting-started-with-pytorch",
2218
                "https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#how-it-works"
2219
            ],
2220
            "document_ids": [
2221
                9845,
2222
                1006,
2223
                226,
2224
                10155,
2225
                370,
2226
                6940,
2227
                8247,
2228
                14313,
2229
                228,
2230
                5515,
2231
                9820,
2232
                10196,
2233
                8312,
2234
                10150
2235
            ],
2236
            "answer": " To distribute training for your custom neural net written using Keras in Databricks, you can use Ray Train and Ray AIR. Here are the steps:\n\n1. Import Ray Train and Ray AIR Session:\n```python\nimport ray.train as train\n```\n2. Convert your training function to a distributed multi-worker training function using `ray.train.torch.prepare_model` and `ray.train.torch.prepare_data_loader` utility functions. This will automatically wrap the model with DistributedDataParallel and place it on the right device, and add DistributedSampler to the DataLoaders.\n```python\nfrom ray import train\n\ndef train_func_distributed():\n    num_epochs = 3\n    batch_size = 64\n\n    dataset = get_dataset()\n    dataloader = DataLoader(dataset, batch_size=batch_size)\n    dataloader = train.torch.prepare_data_loader(dataloader)\n\n    model = NeuralNetwork()\n    model = train.torch.prepare_model(model)\n```\n3. Load the data shard for each worker with `session.get_dataset_shard(\"train\")` and iterate over the dataset with `train_dataset.iter_torch_batches()`.\n4. Set up the model to communicate gradient updates across machines and report the results back to Ray Train.\n\nHere is an example of how to set up Multi-worker training with Keras using Ray Train:\n\n1. Install Ray Train and Tensorflow packages:\n```python\npip install -U \"ray[train]\" tensorflow\n```\n2. Set up your dataset and model.\n3. Use `TorchTrainer` to distribute the training:\n```python\nfrom ray.train.torch import TorchTrainer\nfrom ray.train import ScalingConfig\n\ntrainer = TorchTrainer(\n    train_func_distributed,\n    scaling_config=ScalingConfig(num_workers=4, use_gpu=use_gpu)\n)\n\nresults = trainer.fit()\n```\n\nNote: You can find more information on how to use Ray Train and Ray AIR in the Ray documentation.",
2237
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2238
        },
2239
        {
2240
            "question": "how to use ray.put and ray,get?",
2241
            "sources": [
2242
                "https://docs.ray.io/en/master/ray-core/api/core.html#objects",
2243
                "https://docs.ray.io/en/master/tune/examples/hpo-frameworks.html#tune-hyperparameter-optimization-framework-examples",
2244
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",
2245
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
2246
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
2247
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",
2248
                "https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",
2249
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
2250
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",
2251
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
2252
                "https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",
2253
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
2254
                "https://docs.ray.io/en/master/ray-core/objects.html#objects",
2255
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"
2256
            ],
2257
            "document_ids": [
2258
                4651,
2259
                2429,
2260
                5443,
2261
                4849,
2262
                4205,
2263
                4884,
2264
                5219,
2265
                4209,
2266
                4775,
2267
                4811,
2268
                13646,
2269
                5380,
2270
                4413,
2271
                6615
2272
            ],
2273
            "answer": " To use `ray.put` and `ray.get`, you need to understand that these functions are used to store and retrieve objects in Ray's object store. `ray.put(value)` is used to store an object in the object store and returns an object reference (`ObjectRef`). This reference can then be used to retrieve the object using `ray.get(object_ref)`.\n\nHere's a simple example:\n\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Put an object in the object store\nobj_ref = ray.put(\"Hello, Ray!\")\n\n# Get the object from the object store\nobj = ray.get(obj_ref)\nprint(obj)  # Output: 'Hello, Ray!'\n```\n\nIn this example, we first initialize Ray using `ray.init()`. Then, we store a string \"Hello, Ray!\" in the object store using `ray.put()`, which returns an object reference. Finally, we retrieve the object from the object store using `ray.get()` and print it.\n\nNote that `ray.get()` is a blocking operation, meaning that it will wait until the object is available in the object store before returning. If you want to wait for the object to be available without retrieving it, you can use `ray.wait()`.\n\nAlso, keep in mind that unnecessary use of `ray.get()` can harm performance, as it requires transferring objects between nodes. It's best to wait as long as possible before calling `ray.get()` or design your program to avoid having to call `ray.get()` at all.",
2274
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2275
        },
2276
        {
2277
            "question": "how do I use Ray Data to pre process many files?",
2278
            "sources": [
2279
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",
2280
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",
2281
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",
2282
                "https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",
2283
                "https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors",
2284
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data",
2285
                "https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",
2286
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
2287
                "https://docs.ray.io/en/master/train/user-guides.html#ray-train-user-guides",
2288
                "https://docs.ray.io/en/master/data/user-guide.html#user-guides",
2289
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-preprocess-data-for-training",
2290
                "https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.__init__.html#ray-data-preprocessor-preprocessor-init",
2291
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-data",
2292
                "https://docs.ray.io/en/master/data/working-with-text.html#working-with-text"
2293
            ],
2294
            "document_ids": [
2295
                9178,
2296
                8010,
2297
                9222,
2298
                6849,
2299
                6914,
2300
                9212,
2301
                6838,
2302
                6835,
2303
                8110,
2304
                6911,
2305
                8155,
2306
                7833,
2307
                9189,
2308
                6993
2309
            ],
2310
            "answer": " To preprocess many files using Ray Data, you can follow these steps:\n\n1. Load the data using Ray Data's `read_files` method, which supports reading data from various file formats.\n2. Preprocess the data using Ray Data's preprocessors, which are a higher-level API for transforming tabular and structured data. These preprocessors can be applied to the dataset before passing it into a Trainer.\n3. If the preprocessed dataset is small enough to fit in Ray's object store memory, materialize the preprocessed dataset in Ray's object store by calling `materialize()` on the preprocessed dataset. This will compute the entire preprocessed dataset and pin it in the Ray object store memory, avoiding re-running preprocessing operations when iterating over the dataset repeatedly.\n\nHere's an example of how to use Ray Data to preprocess a tabular dataset:\n```python\nimport ray\nfrom ray.data.preprocessors import MinMaxScaler\nfrom ray.train.xgboost import XGBoostTrainer\nfrom ray.train import ScalingConfig\n\n# Load data from files\ndata = ray.data.read_files(\"my_data_files/*.csv\")\n\n# Preprocess the data\nscaler = MinMaxScaler()\ndata = data.map_batches(scaler, batch_format=\"pandas\")\n\n# Materialize the preprocessed dataset in Ray's object store (if it fits in memory)\nif data.count() < 0.3 * ray.cluster_resources()[\"memory_total_mb\"]:\n    data = data.materialize()\n\n# Train a model using the preprocessed dataset\ntrainer = XGBoostTrainer(...)\nscaling_config = ScalingConfig(num_workers=...)\nray.train.run(trainer, config=scaling_config, dataset=data)\n```\nNote that if the preprocessed dataset is too large to fit into Ray's object store memory, it's not recommended to materialize it, as it will decrease performance due to data spillage to and from disk. In this case, you can consider using Ray's streaming Dataset primitive to stream the data from CPU preprocessing tasks to GPU inferencing or training tasks, allowing you to utilize both sets of resources concurrently.",
2311
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2312
        },
2313
        {
2314
            "question": "can\u2019t pickle SSLContext objects",
2315
            "sources": [
2316
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",
2317
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",
2318
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",
2319
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",
2320
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",
2321
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#known-issues",
2322
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#tls-verification",
2323
                "https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#checkpoints-are-py-version-specific-but-can-be-converted-to-be-version-independent",
2324
                "https://docs.ray.io/en/master/serve/tutorials/java.html#calling-ray-serve-deployment-with-http",
2325
                "https://docs.ray.io/en/master/ray-core/configure.html#step-3-set-the-environment-variables-for-both-ray-head-and-worker-to-enable-tls",
2326
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",
2327
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installed-python-dependencies",
2328
                "https://docs.ray.io/en/master/ray-overview/installation.html#installed-python-dependencies",
2329
                "https://docs.ray.io/en/master/installation.html#installed-python-dependencies"
2330
            ],
2331
            "document_ids": [
2332
                4953,
2333
                4939,
2334
                4952,
2335
                4939,
2336
                4957,
2337
                4958,
2338
                6592,
2339
                10551,
2340
                14578,
2341
                4400,
2342
                4260,
2343
                14645,
2344
                335,
2345
                110
2346
            ],
2347
            "answer": " The error message indicates that `SSLContext` objects cannot be pickled, which is causing the serialization to fail. This is a known issue with certain versions of Python's pickle module. To resolve this issue, you can try upgrading to a later version of Python (3.8.2rc1 or 3.9.0 alpha 4 or later) where this issue has been fixed. Alternatively, you can convert the pickle checkpoint to a msgpack one using an RLlib utility function. Additionally, you can enable TLS by setting the appropriate environment variables for both Ray head and worker.",
2348
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2349
        },
2350
        {
2351
            "question": "How do I install CRDs in Kuberay?",
2352
            "sources": [
2353
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",
2354
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",
2355
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",
2356
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",
2357
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-2-create-a-raycluster-cr",
2358
                "https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-3-install-a-kuberay-operator",
2359
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-3-install-a-kuberay-operator",
2360
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-2-install-the-kuberay-operator",
2361
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-1-install-a-kuberay-cluster",
2362
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-1-install-the-kuberay-operator",
2363
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/kubeflow.html#step-3-install-kuberay-operator",
2364
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-2-install-the-kuberay-operator",
2365
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-2-install-the-kuberay-operator",
2366
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-2-install-the-kuberay-operator"
2367
            ],
2368
            "document_ids": [
2369
                5720,
2370
                6103,
2371
                5817,
2372
                5647,
2373
                5981,
2374
                5774,
2375
                6057,
2376
                6044,
2377
                5980,
2378
                6143,
2379
                6092,
2380
                5741,
2381
                5759,
2382
                5787
2383
            ],
2384
            "answer": " To install CRDs in Kuberay, follow these steps:\n\n1. Add the Kuberay Helm repository:\nhelm repo add kuberay https://ray-project.github.io/kuberay-helm/\n\n2. Install the Kuberay operator and CRDs (v0.6.0 in this example):\nhelm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n\nThe CRDs will be installed as part of the Kuberay operator deployment.",
2385
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2386
        },
2387
        {
2388
            "question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",
2389
            "sources": [
2390
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
2391
                "https://docs.ray.io/en/master/data/batch_inference.html#end-to-end-offline-batch-inference",
2392
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
2393
                "https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",
2394
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",
2395
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",
2396
                "https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",
2397
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",
2398
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
2399
                "https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",
2400
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
2401
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",
2402
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
2403
                "https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format"
2404
            ],
2405
            "document_ids": [
2406
                8107,
2407
                6934,
2408
                6788,
2409
                6955,
2410
                7955,
2411
                7380,
2412
                6998,
2413
                7328,
2414
                7985,
2415
                7632,
2416
                6789,
2417
                8043,
2418
                7082,
2419
                6790
2420
            ],
2421
            "answer": " The function for Ray data batch inference has to be named as `__call__()` because it is a method of a class that is intended to be used like a function, specifically as a callable object in the `map_batches()` function of Ray Data. This method is used to perform computations on each batch of data.",
2422
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2423
        },
2424
        {
2425
            "question": "How to disconnnect ray client?",
2426
            "sources": [
2427
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
2428
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",
2429
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",
2430
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",
2431
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",
2432
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",
2433
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
2434
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
2435
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",
2436
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
2437
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options",
2438
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
2439
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.shutdown.html#ray-shutdown",
2440
                "https://docs.ray.io/en/master/cluster/usage-stats.html#requesting-removal-of-collected-data"
2441
            ],
2442
            "document_ids": [
2443
                6615,
2444
                161,
2445
                6626,
2446
                6624,
2447
                14507,
2448
                6608,
2449
                4613,
2450
                4894,
2451
                6612,
2452
                4375,
2453
                14500,
2454
                4618,
2455
                4827,
2456
                5569
2457
            ],
2458
            "answer": " To disconnect a Ray client, you can call the `disconnect()` method on the client object. If the client disconnects unexpectedly, it will attempt to reconnect to the server for a certain period before dropping all references. You can increase this time by setting the environment variable `RAY_CLIENT_RECONNECT_GRACE_PERIOD`.\n\nHere is an example of how to use the Ray client and disconnect it:\n\n```python\nfrom ray import client, put, get\n\n# Connect to the Ray cluster\nray_client = client.RayClient()\n\n# Put some objects into the object store\nobj1 = put(\"obj1\")\nobj2 = put(\"obj2\")\n\n# Get the objects from the object store\nassert get(obj2) == \"obj2\"\ntry:\n    get(obj1)  # Cross-cluster ops not allowed.\nexcept:\n    print(\"Failed to get object which doesn't belong to this cluster\")\nassert \"obj\" == get(obj)\n\n# Disconnect the client\nray_client.disconnect()\n```\n\nNote that the client won't be disconnected automatically, so you need to call `disconnect()` explicitly to close the connection. Also, object references can only be used by the client from which it was obtained.",
2459
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2460
        },
2461
        {
2462
            "question": "how to submit job with python with local files?",
2463
            "sources": [
2464
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
2465
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",
2466
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
2467
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",
2468
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",
2469
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",
2470
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",
2471
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#jobsubmissionclient",
2472
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",
2473
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#dependency-management",
2474
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",
2475
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-2-submit-a-ray-job-to-the-raycluster-via-ray-job-submission-sdk",
2476
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job",
2477
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm-launch.html#slurm-launch-py"
2478
            ],
2479
            "document_ids": [
2480
                6550,
2481
                6595,
2482
                6553,
2483
                6578,
2484
                6579,
2485
                6575,
2486
                6595,
2487
                6540,
2488
                6581,
2489
                6585,
2490
                6567,
2491
                5732,
2492
                6693,
2493
                6429
2494
            ],
2495
            "answer": " To submit a job with Python while including local files, you can use the Ray Jobs Python SDK's `JobSubmissionClient`. Here's an example of how to do this:\n\n1. First, create a script that you want to run in the job, let's call it `script.py`:\n\n   ```python\n   import ray\n\n   @ray.remote\n   def hello_world():\n       return \"hello world\"\n\n   ray.init()\n   print(ray.get(hello_world.remote()))\n   ```\n\n2. Next, submit the job using `JobSubmissionClient` and specifying the path to the local directory that contains the script in the `runtime_env` parameter:\n\n   ```python\n   from ray.job_submission import JobSubmissionClient\n\n   client = JobSubmissionClient(\"http://127.0.0.1:8265\")\n   job_id = client.submit_job(\n       entrypoint=\"python script.py\",\n       runtime_env={\"working_dir\": \"./\"}\n   )\n   print(job_id)\n   ```\n\nReplace `\"http://127.0.0.1:8265\"` with the address of your Ray cluster head node if you're using a remote cluster.\n\nThis will submit the `script.py` script as a job to the Ray cluster and print the job ID. Make sure that `script.py` is located in the same directory as the script submitting the job. If it's located in a different directory, adjust the `working_dir` value accordingly.",
2496
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2497
        },
2498
        {
2499
            "question": "How do I do inference from a model trained by Ray tune.fit()?",
2500
            "sources": [
2501
                "https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",
2502
                "https://docs.ray.io/en/master/tune/examples/includes/tune_basic_example.html#tune-basic-example",
2503
                "https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",
2504
                "https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#configuring-the-search-space-and-starting-ray-tune",
2505
                "https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",
2506
                "https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve",
2507
                "https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#analyzing-tune-experiment-results",
2508
                "https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-out-of-ray-tune",
2509
                "https://docs.ray.io/en/master/tune/examples/bayesopt_example.html#running-tune-experiments-with-bayesopt",
2510
                "https://docs.ray.io/en/master/tune/api/suggestion.html#tune-search-algorithms-tune-search",
2511
                "https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-in-and-out-of-tune",
2512
                "https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",
2513
                "https://docs.ray.io/en/master/tune/api/api.html#ray-tune-api",
2514
                "https://docs.ray.io/en/master/tune/examples/tune-sklearn.html#overview"
2515
            ],
2516
            "document_ids": [
2517
                986,
2518
                4111,
2519
                1080,
2520
                2767,
2521
                1090,
2522
                2747,
2523
                3670,
2524
                2290,
2525
                3448,
2526
                1275,
2527
                2274,
2528
                1079,
2529
                1195,
2530
                2636
2531
            ],
2532
            "answer": " To do inference from a model trained by Ray tune.fit(), you can use the trained model as a trainable in a new Ray Tune experiment and use the `tune.report()` function to get data out of the Trainable worker. Here is an example:\n\n```\nfrom ray import train, tune\n\ndef inference_fn(step, model):\n    # Use the model to make predictions\n    predictions = model.predict(data)\n    # Report the predictions back to Tune\n    tune.report(predictions=predictions)\n\ntune.run(inference_fn, config={\"model\": model})\n```\n\nIn this example, `model` is the trained model that you want to use for inference. You can pass it as a configuration parameter to the `tune.run()` function. Then, in the `inference_fn` function, you can use the model to make predictions and report the predictions back to Tune using the `tune.report()` function.\n\nKeep in mind that this is just a simple example, and you may need to modify it to fit the specific needs of your use case. For more information, you can refer to the Ray Tune documentation and examples.",
2533
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2534
        },
2535
        {
2536
            "question": "is there a way to load and run inference without using pytorch or tensorflow directly?",
2537
            "sources": [
2538
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",
2539
                "https://docs.ray.io/en/master/cluster/usage-stats.html#how-to-disable-it",
2540
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-2-inference-on-a-single-batch",
2541
                "https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",
2542
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",
2543
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",
2544
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",
2545
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",
2546
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
2547
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",
2548
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
2549
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
2550
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
2551
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data"
2552
            ],
2553
            "document_ids": [
2554
                6978,
2555
                5565,
2556
                7976,
2557
                6725,
2558
                8010,
2559
                6980,
2560
                6979,
2561
                8043,
2562
                6947,
2563
                7970,
2564
                6941,
2565
                6940,
2566
                8107,
2567
                7955
2568
            ],
2569
            "answer": " Yes, it is possible to load and run inference without using PyTorch or TensorFlow directly by using a pre-trained model and preprocessing the data before passing it to the model. The example provided demonstrates how to do this using Ray Datasets and a PyTorch model for image classification. The data is preprocessed using the `transform_image` function, and then the pre-trained model is used to make predictions on the preprocessed data. This approach allows you to perform inference without having to write the code for defining and training the model directly in PyTorch or TensorFlow.",
2570
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2571
        },
2572
        {
2573
            "question": "what does ray do",
2574
            "sources": [
2575
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
2576
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
2577
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
2578
                "https://docs.ray.io/en/master/data/data.html#learn-more",
2579
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
2580
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
2581
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
2582
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
2583
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
2584
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
2585
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
2586
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
2587
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
2588
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary"
2589
            ],
2590
            "document_ids": [
2591
                4811,
2592
                1005,
2593
                5380,
2594
                6910,
2595
                6208,
2596
                14255,
2597
                6015,
2598
                5656,
2599
                5556,
2600
                4662,
2601
                6835,
2602
                6615,
2603
                13974,
2604
                5443
2605
            ],
2606
            "answer": " Ray is a framework for building and running distributed applications. It allows you to parallelize and distribute compute-heavy workloads across multiple machines. The provided context mentions several Ray functions and methods, including:\n\n1. `ray.init()`: Initializes a new Ray cluster or connects to an existing one.\n2. `.remote()`: Runs actor methods or tasks on your Ray cluster and is used to instantiate actors.\n3. `ray.put()`: Puts values into Ray's object store.\n4. `ray.get()`: Gets values from the object store.\n5. `ray.wait()`: Returns two lists of object references, one with finished tasks and one with unfinished tasks.\n\nThese functions and methods enable you to manage and distribute data and tasks in a Ray cluster.",
2607
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2608
        },
2609
        {
2610
            "question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",
2611
            "sources": [
2612
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",
2613
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",
2614
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",
2615
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",
2616
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",
2617
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",
2618
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",
2619
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",
2620
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",
2621
                "https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus",
2622
                "https://docs.ray.io/en/master/data/performance-tips.html#configuring-resources-and-locality",
2623
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",
2624
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
2625
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources"
2626
            ],
2627
            "document_ids": [
2628
                13457,
2629
                5001,
2630
                1025,
2631
                4999,
2632
                11062,
2633
                2966,
2634
                11068,
2635
                12049,
2636
                5116,
2637
                2730,
2638
                6829,
2639
                6590,
2640
                6145,
2641
                1024
2642
            ],
2643
            "answer": " If you specify a fractional GPU in the resource spec, you can still use more than that fraction of GPU. However, doing so may impact the performance of other tasks or actors sharing the same GPU. It's the user's responsibility to ensure that multiple trials can share GPUs and there is enough memory to do so. Ray does not automatically handle this for you.",
2644
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2645
        },
2646
        {
2647
            "question": "how to pickle a variable defined in actor\u2019s init method",
2648
            "sources": [
2649
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
2650
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",
2651
                "https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",
2652
                "https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",
2653
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
2654
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
2655
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
2656
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
2657
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
2658
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",
2659
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",
2660
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
2661
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
2662
                "https://docs.ray.io/en/master/ray-core/actors.html#calling-the-actor"
2663
            ],
2664
            "document_ids": [
2665
                5192,
2666
                5194,
2667
                4435,
2668
                4241,
2669
                4469,
2670
                5191,
2671
                4630,
2672
                5066,
2673
                4878,
2674
                4260,
2675
                5193,
2676
                4629,
2677
                4472,
2678
                4477
2679
            ],
2680
            "answer": " To pickle a variable defined in an actor's `__init__` method, you can't directly pickle the class variable because each actor instance has its own copy of class variables. Instead, you can encapsulate the global state in an actor and pass the actor handle to other tasks and actors. Here's an example using the `GlobalVarActor`:\n\n```python\nimport ray\n\n@ray.remote\nclass GlobalVarActor:\n    def __init__(self):\n        self.global_var = 3\n\n    def set_global_var(self, var):\n        self.global_var = var\n\n    def get_global_var(self):\n        return self.global_var\n\nglobal_var_actor = GlobalVarActor.remote()\nray.get(global_var_actor.set_global_var.remote(4))\n\n# Now, you can pass the actor handle to other tasks or actors.\n```\n\nHowever, pickling the actor handle is not supported in Ray. You can pass the actor handle by value or by reference to other tasks or actors, but you cannot pickle it.",
2681
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2682
        },
2683
        {
2684
            "question": "how do I do an all_reduce operation among a list of actors",
2685
            "sources": [
2686
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",
2687
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
2688
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",
2689
                "https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",
2690
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
2691
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
2692
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#collective-communication",
2693
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayFSDPStrategy.reduce.html#ray-train-lightning-rayfsdpstrategy-reduce",
2694
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",
2695
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",
2696
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDDPStrategy.reduce.html#ray-train-lightning-rayddpstrategy-reduce",
2697
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.reduce.html#ray-train-lightning-raydeepspeedstrategy-reduce",
2698
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
2699
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#shuffling-and-reducing-data"
2700
            ],
2701
            "document_ids": [
2702
                788,
2703
                1006,
2704
                14443,
2705
                659,
2706
                6787,
2707
                673,
2708
                14415,
2709
                8854,
2710
                8101,
2711
                641,
2712
                8424,
2713
                9076,
2714
                6939,
2715
                5353
2716
            ],
2717
            "answer": " To perform an all\\_reduce operation among a list of actors in Ray, you can use the `ray.util.collective.allreduce` function. Here's an example of how you can use it:\n\nFirst, define a remote function that performs the allreduce operation on a tensor using a GPU:\n```python\n@ray.remote(num_gpus=1)\nclass Worker:\n    def __init__(self):\n        self.buffer = cupy.ones((10,), dtype=cupy.float32)\n\n    def compute(self):\n        col.allreduce(self.buffer, \"default\")\n        return self.buffer\n```\nThen, create multiple actors and invoke the `compute` function remotely:\n```python\nA = Worker.remote()\nB = Worker.remote()\n\nray.get([A.compute.remote(), B.compute.remote()])\n```\nThis will perform an allreduce operation on the `buffer` attribute of each actor. Note that you can use `col.allreduce` with any tensor that is on a GPU.\n\nTo perform an allreduce operation on a list of tensors, you can use the `ray.util.collective.allreduce` function directly:\n```python\ntensor_list = [tensor1, tensor2, tensor3]\ngroup_name = \"my_group\"\n\nray.util.collective.allreduce(tensor_list, group_name)\n```\nThis will perform an allreduce operation on each tensor in `tensor_list` and store the results in the same list. The `group_name` parameter specifies the collective group to perform the allreduce operation in. By default, all processes belong to the \"default\" group.",
2718
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2719
        },
2720
        {
2721
            "question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",
2722
            "sources": [
2723
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",
2724
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
2725
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",
2726
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#bundles",
2727
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",
2728
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",
2729
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",
2730
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",
2731
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",
2732
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",
2733
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
2734
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
2735
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.PlacementGroup.bundle_count.html#ray-util-placement-group-placementgroup-bundle-count",
2736
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources"
2737
            ],
2738
            "document_ids": [
2739
                4790,
2740
                5072,
2741
                5063,
2742
                5050,
2743
                5051,
2744
                12053,
2745
                11075,
2746
                5076,
2747
                5060,
2748
                4789,
2749
                5069,
2750
                14262,
2751
                4746,
2752
                5070
2753
            ],
2754
            "answer": " Specifying a bundle with `{\"CPU\":0}` in the PlacementGroup will reserve a bundle with 0 CPUs. However, this is not allowed as a bundle must be able to fit on a single node on the Ray cluster and contain at least one CPU. Therefore, creating a PlacementGroup with this bundle will fail.",
2755
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2756
        },
2757
        {
2758
            "question": "How to cancel job from UI?",
2759
            "sources": [
2760
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",
2761
                "https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",
2762
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
2763
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",
2764
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
2765
                "https://docs.ray.io/en/master/workflows/comparison.html#trip-booking",
2766
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",
2767
                "https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",
2768
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
2769
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",
2770
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
2771
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobStatus.STOPPED.html#ray-job-submission-jobstatus-stopped",
2772
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.stop_job.html#ray-job-submission-jobsubmissionclient-stop-job",
2773
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#cleaning-up"
2774
            ],
2775
            "document_ids": [
2776
                4550,
2777
                9791,
2778
                4375,
2779
                6559,
2780
                4894,
2781
                9757,
2782
                6710,
2783
                9791,
2784
                11590,
2785
                14701,
2786
                4492,
2787
                6690,
2788
                6686,
2789
                6452
2790
            ],
2791
            "answer": " To cancel a job from the UI, you can use the `ray job stop` command, providing the job ID as an argument. This will request the job to exit asynchronously. If you want to delete the job and all its associated data, you can use the `ray delete job` command instead.\n\nIn the context provided, there is no direct mention of canceling a job from the UI, but the `ray job stop` command can be used in a command-line interface, which is a common way to interact with a UI.",
2792
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2793
        },
2794
        {
2795
            "question": "how do I get my project files on the cluster when using Ray Serve? My workflow is to call `serve deploy config.yaml --address <remote dashboard agent address>`",
2796
            "sources": [
2797
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",
2798
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
2799
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
2800
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
2801
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
2802
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#development-workflow-with-serve-run",
2803
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
2804
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",
2805
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",
2806
                "https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",
2807
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup",
2808
                "https://docs.ray.io/en/master/serve/api/index.html#serve-config",
2809
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
2810
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files"
2811
            ],
2812
            "document_ids": [
2813
                13680,
2814
                1006,
2815
                6562,
2816
                13677,
2817
                6552,
2818
                13659,
2819
                13719,
2820
                6577,
2821
                13831,
2822
                13987,
2823
                6549,
2824
                13986,
2825
                13718,
2826
                4262
2827
            ],
2828
            "answer": " To get your project files on the cluster when using Ray Serve, you can use the `--working-dir` option in the `serve run` command to specify the local directory containing your project files. The directory and its contents will be uploaded to the cluster and imported by Ray Serve. Here's an example command:\n```bash\nserve run --address=ray://<head-node-ip-address>:10001 --working-dir=\"./project/src\" local_dev:graph\n",
2829
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2830
        },
2831
        {
2832
            "question": "how do i install ray nightly wheel",
2833
            "sources": [
2834
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",
2835
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
2836
                "https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",
2837
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
2838
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2839
                "https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",
2840
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",
2841
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",
2842
                "https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",
2843
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",
2844
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
2845
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2846
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2847
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions"
2848
            ],
2849
            "document_ids": [
2850
                14545,
2851
                63,
2852
                288,
2853
                63,
2854
                6335,
2855
                66,
2856
                291,
2857
                14551,
2858
                883,
2859
                5412,
2860
                271,
2861
                6351,
2862
                6317,
2863
                6103
2864
            ],
2865
            "answer": " To install the Ray nightly wheel, first cleanly remove any existing Ray installation using `pip uninstall -y ray`. Then, use the `pip install` command with the appropriate link to the nightly wheel. For example, for a Linux system with Python 3.9 and x86\\_64 architecture, you would use:\n```\npip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"\n```\nReplace `LINK_TO_WHEEL.whl` with the actual link to the nightly wheel for your system and Python version. You can find the appropriate link in the context provided, under the \"Daily Releases (Nightlies)\" section.",
2866
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2867
        },
2868
        {
2869
            "question": "how do i install the latest ray nightly wheel?",
2870
            "sources": [
2871
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",
2872
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
2873
                "https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",
2874
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
2875
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2876
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",
2877
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",
2878
                "https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",
2879
                "https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",
2880
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",
2881
                "https://docs.ray.io/en/master/installation.html#from-wheels",
2882
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",
2883
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#install-the-latest-ray-release",
2884
                "https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only"
2885
            ],
2886
            "document_ids": [
2887
                14545,
2888
                271,
2889
                288,
2890
                63,
2891
                6335,
2892
                291,
2893
                14551,
2894
                66,
2895
                286,
2896
                14541,
2897
                61,
2898
                5412,
2899
                5820,
2900
                883
2901
            ],
2902
            "answer": " To install the latest Ray nightly wheel, you can follow these steps:\n\n1. Clean removal of previous Ray install:\n```\npip uninstall -y ray\n```\n2. Install Ray with support for the dashboard and cluster launcher using the nightly wheel:\n```bash\npip install -U \"ray[default] @ LINK_TO_NIGHTLY_WHEEL.whl\"\n```\nReplace `LINK_TO_NIGHTLY_WHEEL.whl` with the actual link to the nightly wheel you want to install. You can find the appropriate link for your system and Python version in the context provided.\n\nNote: The provided context contains multiple links for different configurations. Choose the one that matches your system and Python version.",
2903
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2904
        },
2905
        {
2906
            "question": "how can I write unit tests for Ray code?",
2907
            "sources": [
2908
                "https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",
2909
                "https://docs.ray.io/en/master/tune/faq.html#how-can-i-develop-and-test-tune-locally",
2910
                "https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",
2911
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
2912
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",
2913
                "https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",
2914
                "https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",
2915
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
2916
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",
2917
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
2918
                "https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution",
2919
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
2920
                "https://docs.ray.io/en/master/ray-core/patterns/return-ray-put.html#code-example",
2921
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally"
2922
            ],
2923
            "document_ids": [
2924
                5397,
2925
                1048,
2926
                5395,
2927
                4811,
2928
                4190,
2929
                4338,
2930
                14561,
2931
                5380,
2932
                6621,
2933
                4204,
2934
                9644,
2935
                5757,
2936
                5179,
2937
                13435
2938
            ],
2939
            "answer": " To write unit tests for Ray code, you can follow these tips:\n\n1. Share the Ray cluster across tests if possible to avoid the overhead of creating a new cluster for each test. You can do this by calling `ray.init()` before running any tests and `ray.shutdown()` after all tests have finished.\n2. Fix the resource quantity with `ray.init(num_cpus=...)` to ensure that each test has consistent resources.\n3. Consider creating a mini-cluster with `ray.cluster_utils.Cluster` for more complex tests.\n4. Be careful when running tests in parallel to avoid race conditions.\n\nHere is an example of how to set up a basic test case for Ray:\n\n```python\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n    def setUp(self):\n        ray.init(num_cpus=4, num_gpus=0)\n\n    def tearDown(self):\n        ray.shutdown()\n\n    def test_my_ray_code(self):\n        # Test your Ray code here.\n        pass\n```\n\nYou can then run your tests using a test runner like `unittest.main()`.\n\nNote that if you are testing Tune specifically, you can follow the instructions in the Tune documentation for setting up and running tests.",
2940
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2941
        },
2942
        {
2943
            "question": "How I stop Ray from spamming lots of Info updates on stdout?",
2944
            "sources": [
2945
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",
2946
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
2947
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#disable-logging-to-the-driver",
2948
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#distributed-progress-bars-tqdm",
2949
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",
2950
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
2951
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",
2952
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",
2953
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
2954
                "https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",
2955
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",
2956
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",
2957
                "https://docs.ray.io/en/master/cluster/cli.html#ray-stop",
2958
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks"
2959
            ],
2960
            "document_ids": [
2961
                6558,
2962
                1006,
2963
                523,
2964
                529,
2965
                524,
2966
                4209,
2967
                519,
2968
                410,
2969
                5128,
2970
                13929,
2971
                5766,
2972
                525,
2973
                5613,
2974
                4550
2975
            ],
2976
            "answer": " To stop Ray from spamming lots of Info updates on stdout, you can disable logging to the driver by setting `log_to_driver=False` in `ray.init()`. This will prevent Task and Actor logs from being copied to the driver stdout. Additionally, you can set `RAY_TQDM_PATCH_PRINT=0` to disable the patching of the built-in print function, which may improve performance if there are more than a couple thousand updates per second.\n\nHere's an example of how to disable logging to the driver:\n\n```python\nimport ray\n\nray.init(log_to_driver=False)\n```\n\nAnd here's how to disable the patching of the built-in print function:\n\n```bash\nexport RAY_TQDM_PATCH_PRINT=0\n```",
2977
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2978
        },
2979
        {
2980
            "question": "how to deploy stable diffusion 2.1 with Ray Serve?",
2981
            "sources": [
2982
                "https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",
2983
                "https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#code-updates",
2984
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
2985
                "https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",
2986
                "https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",
2987
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
2988
                "https://docs.ray.io/en/master/serve/index.html#why-choose-serve",
2989
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
2990
                "https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",
2991
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#deploy-ray-serve-in-production",
2992
                "https://docs.ray.io/en/master/serve/key-concepts.html#deployment",
2993
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",
2994
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",
2995
                "https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air"
2996
            ],
2997
            "document_ids": [
2998
                14409,
2999
                13692,
3000
                13622,
3001
                14416,
3002
                13797,
3003
                13677,
3004
                13614,
3005
                13621,
3006
                13987,
3007
                13441,
3008
                13416,
3009
                13647,
3010
                6127,
3011
                10138
3012
            ],
3013
            "answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install the required packages:\n   ```\n   pip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers\n   ```\n\n2. Save the Stable Diffusion code in a file named `stable_diffusion.py`. The code should include the necessary imports, FastAPI app, and Ray Serve deployment configuration.\n\n3. Create a RayServe deployment configuration file, e.g., `stable_diffusion_config.yaml`, with the required information for the Stable Diffusion application.\n\n4. If deploying to a remote cluster, use the Ray Serve CLI to deploy the config file:\n   ```\n   ray stop\n   ray start --head\n   serve deploy stable_diffusion_config.yaml\n   ```\n\n5. If deploying to a local, single-node cluster, use the following command to start the RayServe application:\n   ```\n   serve run stable_diffusion:entrypoint --autoscaling config.autoscaling --address 0.0.0.0\n   ```\n\n6. To deploy in production on Kubernetes, use the KubeRay operator and copy the YAML file generated in the previous step directly into the Kubernetes configuration.\n\nRemember to avoid locking yourself into a solution tied to a specific framework, as Ray Serve is not tied to any specific machine learning library or framework.",
3014
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3015
        },
3016
        {
3017
            "question": "what is actor_handle?",
3018
            "sources": [
3019
                "https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",
3020
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
3021
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3022
                "https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",
3023
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",
3024
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
3025
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
3026
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
3027
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3028
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
3029
                "https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",
3030
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.current_actor.html#ray-runtime-context-runtimecontext-current-actor",
3031
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",
3032
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill"
3033
            ],
3034
            "document_ids": [
3035
                4487,
3036
                4860,
3037
                4615,
3038
                4489,
3039
                4200,
3040
                4623,
3041
                4650,
3042
                4871,
3043
                4467,
3044
                4878,
3045
                4435,
3046
                4874,
3047
                4579,
3048
                4859
3049
            ],
3050
            "answer": " In the given context, an 'actor\\_handle' is a reference to a Ray actor, which is a stateful, long-running object in a Ray cluster. This handle can be passed around and used to call methods on the actor, even from other tasks or actors. It can also be used to manage the actor's lifecycle, such as force-killing it or restarting it.",
3051
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3052
        },
3053
        {
3054
            "question": "how to kill a r detached actors?",
3055
            "sources": [
3056
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
3057
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
3058
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
3059
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",
3060
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3061
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3062
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",
3063
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
3064
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3065
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
3066
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3067
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
3068
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",
3069
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure"
3070
            ],
3071
            "document_ids": [
3072
                4633,
3073
                4578,
3074
                4859,
3075
                2157,
3076
                4615,
3077
                4613,
3078
                4579,
3079
                4848,
3080
                4620,
3081
                4561,
3082
                4618,
3083
                4631,
3084
                8106,
3085
                4577
3086
            ],
3087
            "answer": " To kill a detached actor in Ray, you can use the `ray.kill()` function, passing the actor handle as an argument. This will immediately terminate the actor, causing any current, pending, and future tasks to fail. Here's an example:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n    def ping(self):\n        return \"hello\"\n\ndetached_actor = Actor.options(name=\"actor\", lifetime=\"detached\").remote()\n\n# ... use the actor ...\n\nray.kill(detached_actor)\n```\nNote that this will not go through the normal teardown logic, so any exit handlers installed in the actor using `std::atexit` or `atexit` will not be called. Also, subsequent calls to get the actor handle via `ray.get_actor()` will fail.\n\nIf you want to terminate the actor gracefully, you can wait for any previously submitted tasks to finish executing and then exit the process gracefully with `sys.exit()`. However, this is not currently implemented in Ray for C++ actors.\n\nFor non-detached actors, Ray will automatically terminate them when all actor handles to them in Python are deleted. This will cause them to complete any outstanding work and then shut down. If you want to kill actors immediately, you can also call `ray.kill(actor)`.",
3088
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3089
        },
3090
        {
3091
            "question": "How to force upgrade the pip package in the runtime environment if an old version exists?",
3092
            "sources": [
3093
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
3094
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",
3095
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
3096
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
3097
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",
3098
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
3099
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",
3100
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
3101
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",
3102
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
3103
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
3104
                "https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",
3105
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",
3106
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris"
3107
            ],
3108
            "document_ids": [
3109
                4329,
3110
                4260,
3111
                4717,
3112
                4327,
3113
                4260,
3114
                4330,
3115
                4267,
3116
                4328,
3117
                4268,
3118
                4283,
3119
                4719,
3120
                10281,
3121
                4252,
3122
                4310
3123
            ],
3124
            "answer": " To force upgrade the pip package in the runtime environment if an old version exists, you can specify the pip version in the runtime environment using the \"pip\\_version\" parameter with the \"==\" operator and the desired pip version. For example:\n```python\nRuntimeEnv(\n    pip={\"packages\":[\"tensorflow\", \"requests\"], \"pip_check\": False,\n    \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"}\n)\n```\nThis will ensure that the specified pip version is installed in the runtime environment, even if an older version already exists. Note that this will not automatically upgrade existing installations of the package in the environment, but will install the specified version alongside any existing installations.",
3125
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3126
        },
3127
        {
3128
            "question": "How do I do global shuffle with Ray?",
3129
            "sources": [
3130
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",
3131
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
3132
                "https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",
3133
                "https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",
3134
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",
3135
                "https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",
3136
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",
3137
                "https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",
3138
                "https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",
3139
                "https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",
3140
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition",
3141
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",
3142
                "https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",
3143
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility"
3144
            ],
3145
            "document_ids": [
3146
                7395,
3147
                1006,
3148
                6795,
3149
                6827,
3150
                9209,
3151
                6826,
3152
                9206,
3153
                6876,
3154
                6864,
3155
                6825,
3156
                7352,
3157
                14502,
3158
                6824,
3159
                9211
3160
            ],
3161
            "answer": " To do a global shuffle of all rows in a Ray Dataset, you can use the `random_shuffle()` function. Here's an example:\n```python\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/sms_spam_collection_subset.txt\")\nds = ds.random_shuffle()\n```\nBy default, `random_shuffle()` performs a full global shuffle, which is more expensive but ensures that the row iteration order is fully decorrelated from the original dataset order.\n\nIf you want to avoid bottlenecks when shuffling large datasets, you can use the push-based shuffle by setting the `use_push_based_shuffle` flag in the `DataContext`:\n```python\nctx = ray.data.DataContext.get_current()\nctx.use_push_based_shuffle = True\n\nds = ray.data.range(1000).random_shuffle()\n```\nThis approach requires less communication and can be faster for large datasets. However, it's worth noting that push-based shuffle is still an experimental feature.",
3162
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3163
        },
3164
        {
3165
            "question": "How to find namespace of an Actor?",
3166
            "sources": [
3167
                "https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",
3168
                "https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",
3169
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
3170
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
3171
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
3172
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
3173
                "https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",
3174
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace",
3175
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
3176
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
3177
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
3178
                "https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",
3179
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
3180
                "https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors"
3181
            ],
3182
            "document_ids": [
3183
                4521,
3184
                4528,
3185
                4623,
3186
                4504,
3187
                4871,
3188
                4626,
3189
                4528,
3190
                4751,
3191
                4625,
3192
                4499,
3193
                4516,
3194
                4476,
3195
                4509,
3196
                4523
3197
            ],
3198
            "answer": " To find the namespace of an Actor in Ray, you can use the `ray.get_actor()` function, which returns an `ActorHandle` to the actor with the given name. The second argument to this function is the namespace of the actor. If you're not sure what namespace the actor is in, you can call `ray.global_state.namespaces()` to get a list of all namespaces in the cluster. Then, you can iterate over each namespace and try to get the actor handle using `ray.get_actor(name, namespace)` until you find the correct namespace where the actor exists.\n\nHere's an example in Python:\n```python\nimport ray\n\nnamespaces = ray.global_state.namespaces()\nfor namespace in namespaces:\n    try:\n        actor = ray.get_actor(\"my_actor\", namespace)\n        print(f\"Actor found in namespace: {namespace}\")\n        break\n    except ValueError:\n        continue\n```\nThis code will print the namespace of the actor with name \"my\\_actor\". If the actor does not exist, it will raise a `ValueError` exception.",
3199
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3200
        },
3201
        {
3202
            "question": "How does Ray work with async.io ?",
3203
            "sources": [
3204
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",
3205
                "https://docs.ray.io/en/master/ray-core/patterns/pipelining.html#code-example",
3206
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",
3207
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",
3208
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
3209
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",
3210
                "https://docs.ray.io/en/master/ray-core/tasks.html#tasks",
3211
                "https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",
3212
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",
3213
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
3214
                "https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution",
3215
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
3216
                "https://docs.ray.io/en/master/ray-core/patterns/concurrent-operations-async-actor.html#pattern-using-asyncio-to-run-actor-methods-concurrently",
3217
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api"
3218
            ],
3219
            "document_ids": [
3220
                4598,
3221
                5190,
3222
                4603,
3223
                4599,
3224
                4208,
3225
                5443,
3226
                4360,
3227
                4636,
3228
                4611,
3229
                4205,
3230
                9644,
3231
                4811,
3232
                5161,
3233
                13974
3234
            ],
3235
            "answer": " Ray can work with async.io as it natively integrates with it. You can use Ray alongside popular async frameworks like aiohttp, aioredis, etc. By defining async methods in Ray actors, Ray can automatically detect whether an actor supports async calls or not. However, Ray does not support asyncio for remote tasks. Instead, you can wrap the async function with a wrapper to run the task synchronously. The `.remote()` postfix is used to make remote function calls asynchronously, and `ray.get()` is used to get the actual results, which blocks until the results are available.",
3236
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3237
        },
3238
        {
3239
            "question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",
3240
            "sources": [
3241
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",
3242
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
3243
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
3244
                "https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",
3245
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",
3246
                "https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",
3247
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-too-many-objects.html#anti-pattern-fetching-too-many-objects-at-once-with-ray-get-causes-failure",
3248
                "https://docs.ray.io/en/master/ray-core/tasks.html#tasks",
3249
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#stepping-between-ray-tasks",
3250
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
3251
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",
3252
                "https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",
3253
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",
3254
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-too-many-objects.html#anti-pattern-fetching-too-many-objects-at-once-with-ray-get-causes-failure"
3255
            ],
3256
            "document_ids": [
3257
                5235,
3258
                1006,
3259
                4209,
3260
                4416,
3261
                5236,
3262
                5219,
3263
                5169,
3264
                4361,
3265
                581,
3266
                4214,
3267
                14421,
3268
                13646,
3269
                4884,
3270
                5168
3271
            ],
3272
            "answer": " To debug a hanging `ray.get()` call that you have reproduced locally, you should avoid calling `ray.get()` in a loop, as it is a blocking call that waits until the requested result is available. This can harm parallelism, as the loop will not continue to run until the call to `ray.get()` is resolved. Instead, you can separate the call to `ray.get()` from the call to the remote functions, so that all remote functions are spawned before waiting for the results and can run in parallel in the background. Additionally, you can pass a list of object references to `ray.get()` instead of calling it one by one to wait for all of the tasks to finish.",
3273
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3274
        },
3275
        {
3276
            "question": "can you show me an example of ray.actor.exit_actor()",
3277
            "sources": [
3278
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3279
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3280
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3281
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3282
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",
3283
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
3284
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3285
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
3286
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.AsyncioActorExit.html#ray-exceptions-asyncioactorexit",
3287
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",
3288
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.shutdown.html#ray-shutdown",
3289
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",
3290
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3291
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.pop_idle.html#ray-util-actorpool-pop-idle"
3292
            ],
3293
            "document_ids": [
3294
                4618,
3295
                4613,
3296
                4620,
3297
                4619,
3298
                4579,
3299
                4859,
3300
                4615,
3301
                4650,
3302
                4781,
3303
                4488,
3304
                4827,
3305
                4565,
3306
                4467,
3307
                4780
3308
            ],
3309
            "answer": " The `ray.actor.exit_actor()` function is used to manually terminate a Ray actor from within one of the actor methods. This will kill the actor process and release resources associated with the actor. However, it's generally not necessary as actors are automatically garbage collected. Here's an example of how to use it:\n```python\n@ray.remote\nclass Actor:\n    def exit(self):\n        ray.actor.exit_actor()\n\nactor = Actor.remote()\nactor.exit.remote()\n```\nNote that this method of termination waits until any previously submitted tasks finish executing and then exits the process gracefully.\n\nAlternatively, you can use `ray.kill(actor_handle)` to forcefully terminate an actor. This should be reserved for cases where an actor is unexpectedly hanging or leaking resources, and for detached actors, which must be manually destroyed. Here's an example:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n    pass\n\nactor_handle = Actor.remote()\n\nray.kill(actor_handle)\n```\nThis will not go through the normal Python sys.exit teardown logic, so any exit handlers installed in the actor using `atexit` will not be called.",
3310
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3311
        },
3312
        {
3313
            "question": "how to add log inside actor?",
3314
            "sources": [
3315
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",
3316
                "https://docs.ray.io/en/master/train/examples/horovod/horovod_example.html#horovod-distributed-training-example-with-pytorch-ray-train",
3317
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
3318
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",
3319
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
3320
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
3321
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
3322
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
3323
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
3324
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#actors-view",
3325
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",
3326
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
3327
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
3328
                "https://docs.ray.io/en/master/rllib/rllib-catalogs.html#write-a-catalog-from-scratch"
3329
            ],
3330
            "document_ids": [
3331
                414,
3332
                9568,
3333
                540,
3334
                520,
3335
                14324,
3336
                14202,
3337
                14286,
3338
                14295,
3339
                14106,
3340
                434,
3341
                682,
3342
                477,
3343
                476,
3344
                11539
3345
            ],
3346
            "answer": " To add logging inside an actor in Ray, you can define a log method within the actor class and use the logging library to add log messages. Here's an example:\n\n```python\nimport logging\n\n@ray.remote\nclass MyActor:\n    def __init__(self):\n        self.logger = logging.getLogger(__name__)\n\n    def log(self, msg):\n        self.logger.info(msg)\n\n    def some_method(self):\n        # some code here\n        self.log(\"Log message from inside the actor\")\n```\n\nThen, you can call the log method from a remote function:\n\n```python\nactor = MyActor.remote()\nray.get(actor.log.remote(\"A log message for an actor.\"))\n```\n\nThis will add log messages to the driver output by default. If you want to customize the logging prefixes for actor logs, you can define the `__repr__` method for the actor class to replace the actor name with a custom representation. For example:\n\n```python\nimport ray\n\n@ray.remote\nclass MyActor:\n    def __init__(self, index):\n        self.index = index\n\n    def __repr__(self):\n        return f\"MyActor(index={self.index})\"\n\n    def log(self, msg):\n        self.logger = logging.getLogger(__name__)\n        self.logger.info(msg)\n\n# Create 5 actors with custom indexes\nactors = [MyActor.remote(i) for i in range(5)]\n\n# Call the log method for each actor\nfor actor in actors:\n    ray.get(actor.log.remote(f\"Log message from actor {actor}\"))\n```\n\nThis will add log messages with custom prefixes to distinguish between log messages from different actors.",
3347
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3348
        },
3349
        {
3350
            "question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",
3351
            "sources": [
3352
                "https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",
3353
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",
3354
                "https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",
3355
                "https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",
3356
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
3357
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",
3358
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
3359
                "https://docs.ray.io/en/master/data/batch_inference.html#using-models-from-ray-train",
3360
                "https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",
3361
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
3362
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",
3363
                "https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",
3364
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#overview",
3365
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference"
3366
            ],
3367
            "document_ids": [
3368
                6999,
3369
                7970,
3370
                6998,
3371
                6843,
3372
                6938,
3373
                7970,
3374
                6936,
3375
                6962,
3376
                6845,
3377
                8107,
3378
                6981,
3379
                6842,
3380
                8077,
3381
                8111
3382
            ],
3383
            "answer": " Yes, I can help you write a script to perform batch inference with GPT-2 on text data from an S3 bucket using Ray Data. Here's a script based on the provided context and the TextClassifier class:\n\n```python\nimport ray\nfrom ray import data_sdk\n\n# Initialize Ray\nray.init()\n\n# Define the TextClassifier class\nclass TextClassifier:\n    def __init__(self):\n        from transformers import pipeline\n        self.model = pipeline(\"text-generation\", model=\"gpt2\")\n\n    def __call__(self, batch: data_sdk.Block):\n        inputs = batch[\"text\"].tolist()\n        outputs = [self.model(text, max_length=20, num_return_sequences=1) for text in inputs]\n        batch[\"output\"] = [sequences[\"generated_text\"] for sequences in outputs]\n        return batch\n\n# Read text data from the S3 bucket\nds = data_sdk.read_text(\"s3://your-s3-bucket/your-text-data.txt\")\n\n# Define the number of parallel actors for inference\nscale = data_sdk.ActorPoolStrategy(size=2)\n\n# Perform batch inference\npredictions = ds.map_batches(TextClassifier, compute=scale)\n\n# Show one prediction output\npredictions.show(limit=1)\n\n# Don't forget to shut down Ray when you're done\nray.shutdown()\n```\n\nReplace `\"s3://your-s3-bucket/your-text-data.txt\"` with the path to your text data in the S3 bucket. This script assumes that you have already set up Ray and have the necessary permissions to access the S3 bucket.\n\nKeep in mind that this script uses GPT-2 for text generation, not text classification. You may need to adjust the script based on your specific use case.",
3384
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3385
        },
3386
        {
3387
            "question": "How do I enable Ray debug logs?",
3388
            "sources": [
3389
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
3390
                "https://docs.ray.io/en/master/ray-observability/user-guides/index.html#user-guides",
3391
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",
3392
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#debugger-commands",
3393
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",
3394
                "https://docs.ray.io/en/master/ray-observability/index.html#monitoring-and-debugging",
3395
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#monitoring-with-the-cli-or-sdk",
3396
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#debugger",
3397
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
3398
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
3399
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#print",
3400
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",
3401
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#disable-logging-to-the-driver",
3402
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3"
3403
            ],
3404
            "document_ids": [
3405
                680,
3406
                546,
3407
                510,
3408
                579,
3409
                573,
3410
                450,
3411
                465,
3412
                405,
3413
                445,
3414
                444,
3415
                600,
3416
                530,
3417
                523,
3418
                533
3419
            ],
3420
            "answer": " To enable Ray debug logs, you can use Ray's built-in logger. When you import Ray, its logger is initialized with a default configuration. You can modify the logging level and other settings using the logging module in Python.\n\nHere's an example of how to modify the Ray logger to enable debug logs:\n```python\nimport logging\nimport ray\n\n# Get the Ray logger\nlogger = logging.getLogger(\"ray\")\n\n# Set the logging level to debug\nlogger.setLevel(logging.DEBUG)\n\n# Initialize Ray\nray.init()\n```\nThis will enable debug logs for Ray. Note that this will also enable debug logs for all Ray libraries, so you may see a lot of output. You can customize the logging configuration further by specifying the appropriate logger name for each Ray library.\n\nIf you want to disable logs being copied to the driver in large-scale runs, you can set `log_to_driver=False` in `ray.init()`.\n\nFor application logs, you can use Python loggers to format and structure your logs. You need to set up the logger for both driver and worker processes separately. For the worker processes, you can use `worker_process_setup_hook` to configure the Python logger for all worker processes.",
3421
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3422
        },
3423
        {
3424
            "question": "How do I list the current Ray actors from python?",
3425
            "sources": [
3426
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
3427
                "https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",
3428
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",
3429
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
3430
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
3431
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#ray-states",
3432
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
3433
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3434
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
3435
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
3436
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.html#ray-util-actorpool",
3437
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#getting-a-summary-of-states-of-entities-by-type",
3438
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",
3439
                "https://docs.ray.io/en/master/ray-observability/reference/api.html#list-apis"
3440
            ],
3441
            "document_ids": [
3442
                472,
3443
                4464,
3444
                788,
3445
                673,
3446
                475,
3447
                402,
3448
                11575,
3449
                4467,
3450
                14680,
3451
                4623,
3452
                4742,
3453
                480,
3454
                488,
3455
                660
3456
            ],
3457
            "answer": " To list the current Ray actors from Python, you can use the `list_actors` function from the `ray.util.state` module. Here is an example:\n\n```python\nimport ray\nfrom ray.util.state import list_actors\n\n# Start Ray\nray.init()\n\n# List actors\nactors = list_actors()\n\n# Print actor information\nfor actor in actors:\n    print(actor)\n```\n\nThis will print information about all the actors in the Ray cluster. If you want to filter the actors by state or other attributes, you can use the `filters` parameter of the `list_actors` function. For example, to list only the actors with state \"ALIVE\", you can use:\n\n```python\nactors = list_actors(filters=[(\"state\", \"=\", \"ALIVE\")])\n```\n\nNote that you need to have Ray installed and running to use this function.",
3458
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3459
        },
3460
        {
3461
            "question": "I want to kill the replica actor from Python. how do I do it?",
3462
            "sources": [
3463
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
3464
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
3465
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
3466
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
3467
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3468
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3469
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
3470
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3471
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",
3472
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",
3473
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3474
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
3475
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",
3476
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure"
3477
            ],
3478
            "document_ids": [
3479
                13912,
3480
                1006,
3481
                4859,
3482
                4848,
3483
                4620,
3484
                4613,
3485
                4860,
3486
                4618,
3487
                4579,
3488
                13916,
3489
                4615,
3490
                4577,
3491
                13915,
3492
                13905
3493
            ],
3494
            "answer": " To kill a replica actor from Python, you can use the `ray.kill()` function, passing in the actor handle as a parameter. Here is an example:\n```\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nThis will forcefully terminate the actor, interrupting any running tasks and causing them to fail immediately. If you want to let pending tasks finish before killing the actor, you can call `actor.__ray_terminate__.remote()` instead.\n\nNote that if you only have one reference to an actor handle, calling `del actor` may trigger actor deletion. However, if your program has multiple references to the same actor handle, actor termination will not occur until the reference count goes to 0.\n\nAlso, keep in mind that if the actor is a detached actor, subsequent calls to get its handle via `ray.get_actor()` will fail after it has been killed.",
3495
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3496
        },
3497
        {
3498
            "question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",
3499
            "sources": [
3500
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",
3501
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
3502
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#accelerator-types",
3503
                "https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",
3504
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",
3505
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",
3506
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",
3507
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-workload-scheduling",
3508
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",
3509
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",
3510
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
3511
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
3512
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",
3513
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements"
3514
            ],
3515
            "document_ids": [
3516
                4994,
3517
                1006,
3518
                5006,
3519
                4366,
3520
                6590,
3521
                4996,
3522
                4997,
3523
                6017,
3524
                4697,
3525
                4702,
3526
                5114,
3527
                14767,
3528
                14461,
3529
                5115
3530
            ],
3531
            "answer": " To specify that a task should run on a node with a V100 GPU, you can use the `accelerator_type` parameter in the `@ray.remote` decorator. Here's an example:\n```\n@ray.remote(accelerator_type=NVIDIA_TESLA_V100)\ndef train(data):\n    return \"This function was run on a node with a Tesla V100 GPU\"\n\nray.get(train.remote(1))\n```\nThe `NVIDIA_TESLA_V100` constant is defined in `ray.util.accelerators`.\n\nKeep in mind that this will only ensure that the task is scheduled to run on a node with a V100 GPU. It does not guarantee that the task will actually use the GPU. You will need to make sure that your code is written to use the GPU correctly.\n\nAlso, note that this feature is only available in Ray 1.11.0 or later. If you are using an earlier version of Ray, you will need to upgrade.",
3532
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3533
        },
3534
        {
3535
            "question": "How do I get started?",
3536
            "sources": [
3537
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",
3538
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
3539
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#getting-started",
3540
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-autoscaler-private-fake-multi-node-test-utils-dockercluster",
3541
                "https://docs.ray.io/en/master/serve/index.html#learn-more",
3542
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",
3543
                "https://docs.ray.io/en/master/tune/faq.html#how-can-i-get-started-contributing-to-tune",
3544
                "https://docs.ray.io/en/master/serve/advanced-guides/index.html#advanced-guides",
3545
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",
3546
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-1-create-a-kind-cluster",
3547
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#getting-started-with-kuberay",
3548
                "https://docs.ray.io/en/master/train/train.html#get-started",
3549
                "https://docs.ray.io/en/master/serve/key-concepts.html#whats-next",
3550
                "https://docs.ray.io/en/master/cluster/vms/index.html#learn-more"
3551
            ],
3552
            "document_ids": [
3553
                6482,
3554
                1006,
3555
                220,
3556
                864,
3557
                13625,
3558
                4483,
3559
                1049,
3560
                13827,
3561
                4185,
3562
                6043,
3563
                5646,
3564
                8244,
3565
                13428,
3566
                6210
3567
            ],
3568
            "answer": " To get started with Ray, you should install it via `pip install -U ray`. Then, you can import and initialize Ray in your code with `import ray` and `ray.init()`. If you're new to Ray Serve, the Quickstart guide is recommended. To contribute to Tune, you can look at issues labeled \"good first issue\" and \"help wanted\" on the Tune Github project board. To get started with distributed training using Ray Train, you can check out the guides for PyTorch, PyTorch Lightning, and Hugging Face Transformers. For more information on running Ray workloads on cloud VMs, you can refer to the Ray docs.",
3569
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3570
        },
3571
        {
3572
            "question": "How to specify python version in runtime_env?",
3573
            "sources": [
3574
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
3575
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",
3576
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
3577
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
3578
                "https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",
3579
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",
3580
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",
3581
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
3582
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
3583
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
3584
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",
3585
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.runtime_env.html#ray-job-submission-jobinfo-runtime-env",
3586
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.set_runtime_env.html#ray-job-config-jobconfig-set-runtime-env",
3587
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris"
3588
            ],
3589
            "document_ids": [
3590
                4330,
3591
                6565,
3592
                4717,
3593
                4329,
3594
                4661,
3595
                13856,
3596
                4253,
3597
                4327,
3598
                4283,
3599
                4712,
3600
                5679,
3601
                6665,
3602
                4826,
3603
                4310
3604
            ],
3605
            "answer": " To specify the Python version in the runtime environment, you can use the `conda` key in the `runtime_env` dictionary to create a Conda environment with a specific Python version. Here's an example:\n```python\nruntime_env = {\n    \"conda\": {\n        \"name\": \"my_env\",\n        \"channels\": [\"conda-forge\"],\n        \"dependencies\": [\n            \"python=3.8\",\n            \"numpy\",\n            \"pandas\",\n        ],\n    }\n}\n```\nIn this example, a Conda environment named `my_env` is created with Python 3.8, along with the `numpy` and `pandas` packages. Note that the `conda` key cannot be used with the `pip` key in the same `runtime_env` dictionary. If you need to install pip packages in the Conda environment, you can specify them in the `dependencies` list as shown above.",
3606
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3607
        },
3608
        {
3609
            "question": "how to create a Actor in a namespace?",
3610
            "sources": [
3611
                "https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",
3612
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",
3613
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
3614
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
3615
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
3616
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
3617
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
3618
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
3619
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
3620
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
3621
                "https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",
3622
                "https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",
3623
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
3624
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"
3625
            ],
3626
            "document_ids": [
3627
                4521,
3628
                5438,
3629
                4626,
3630
                4625,
3631
                4623,
3632
                4513,
3633
                4504,
3634
                4509,
3635
                4499,
3636
                4516,
3637
                4528,
3638
                4523,
3639
                4871,
3640
                4627
3641
            ],
3642
            "answer": " To create an actor in a specific namespace in Ray, you can use the `options` method on the actor class and set the `name` and `namespace` parameters. Here is an example in Python:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n    pass\n\n# Connect to the Ray cluster\nctx = ray.init(namespace=\"colors\")\n\n# Create an actor with a specified name and namespace\nActor.options(name=\"orange\", namespace=\"colors\", lifetime=\"detached\").remote()\n```\n\nIn this example, the actor `orange` is created in the `colors` namespace. Note that the current job's namespace does not affect the namespace of the created actor.\n\nYou can also retrieve the actor later using the `get_actor` function and specifying the namespace:\n\n```python\n# Get the actor handle\nactor_handle = ray.get_actor(\"orange\", namespace=\"colors\")\n```\n\nNamed actors are scoped by namespace, and if no namespace is assigned, they will be placed in an anonymous namespace by default.",
3643
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3644
        },
3645
        {
3646
            "question": "Can I specify multiple working directories?",
3647
            "sources": [
3648
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
3649
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
3650
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
3651
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",
3652
                "https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.PathPartitionParser.html#ray-data-datasource-pathpartitionparser",
3653
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
3654
                "https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",
3655
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
3656
                "https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",
3657
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",
3658
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",
3659
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",
3660
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
3661
                "https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.PathPartitionParser.html#ray-data-datasource-pathpartitionparser"
3662
            ],
3663
            "document_ids": [
3664
                1775,
3665
                1006,
3666
                4277,
3667
                4301,
3668
                7297,
3669
                4273,
3670
                10170,
3671
                1790,
3672
                10169,
3673
                1066,
3674
                4305,
3675
                6597,
3676
                6632,
3677
                7298
3678
            ],
3679
            "answer": " No, you cannot specify multiple working directories in Ray Tune. However, you can use relative paths for read-only purposes and make sure the directory is synced on all nodes if running on multiple machines. Additionally, you can use remote URIs to specify local paths or remote zip files as working directories. If you need to access files relative to the original working directory in your Tune training function, you can configure Tune not to change the working directory to the trial directory.",
3680
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3681
        },
3682
        {
3683
            "question": "what if I set num_cpus=0 for tasks",
3684
            "sources": [
3685
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",
3686
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
3687
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",
3688
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",
3689
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
3690
                "https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",
3691
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#code-example",
3692
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
3693
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
3694
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.entrypoint_num_cpus.html#ray-job-submission-jobinfo-entrypoint-num-cpus",
3695
                "https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.num_cpus_per_worker.html#ray-train-scalingconfig-num-cpus-per-worker",
3696
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
3697
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",
3698
                "https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application"
3699
            ],
3700
            "document_ids": [
3701
                11063,
3702
                1005,
3703
                623,
3704
                5936,
3705
                4850,
3706
                6716,
3707
                5234,
3708
                4853,
3709
                4877,
3710
                6703,
3711
                8998,
3712
                5068,
3713
                7639,
3714
                13575
3715
            ],
3716
            "answer": " If you set `num_cpus=0` for tasks in Ray, your policies will be built solely on the CPU, even if GPUs are available on the machine. However, it's important to note that if your tasks are very small or have variable durations, you might not see speedups due to Ray's task overhead and the need to batch tasks together for better performance.\n\nAdditionally, when specifying resources for Ray tasks, you can use the `num_cpus` field to reserve CPU cores for the task or actor. This value can be a float, allowing you to pack multiple replicas together on a machine with fewer CPUs than replicas. The same applies to `num_gpus`, which can also be specified as a float to pack multiple replicas together on a machine with fewer GPUs than replicas.\n\nKeep in mind that the values for `num_cpus` and `num_gpus` are optional, and you can omit them if needed.",
3717
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3718
        },
3719
        {
3720
            "question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",
3721
            "sources": [
3722
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
3723
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",
3724
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",
3725
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",
3726
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",
3727
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
3728
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",
3729
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",
3730
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#overview",
3731
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",
3732
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",
3733
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",
3734
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-status",
3735
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster"
3736
            ],
3737
            "document_ids": [
3738
                5953,
3739
                5818,
3740
                5752,
3741
                5969,
3742
                5963,
3743
                5656,
3744
                5642,
3745
                5742,
3746
                5956,
3747
                5958,
3748
                5975,
3749
                5788,
3750
                6184,
3751
                5973
3752
            ],
3753
            "answer": " Yes, it is possible to have Ray on K8s without using KubeRay and still have autoscaling enabled. Although KubeRay provides a built-in autoscaling feature, you can deploy a static Ray cluster to Kubernetes without using KubeRay. However, you will need to manually configure the autoscaling rules and ensure that the necessary Kubernetes admin permissions are available for deploying and managing the autoscaler.\n\nAdditionally, you can use the Kubernetes Cluster Autoscaler to complement the Ray Autoscaler, allowing the Ray Autoscaler to create and delete Ray pods while the Kubernetes Cluster Autoscaler provisions and cleans up Kubernetes nodes as needed. This can help ensure that Ray Autoscaler pod scaling events correspond roughly one-to-one with cluster autoscaler events.",
3754
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3755
        },
3756
        {
3757
            "question": "how to manually configure and manage Ray cluster on Kubernetes",
3758
            "sources": [
3759
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",
3760
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",
3761
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",
3762
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
3763
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",
3764
                "https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",
3765
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gke-gcs-bucket.html#create-a-minimal-raycluster-yaml-manifest",
3766
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
3767
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",
3768
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide",
3769
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-2-deploy-a-ray-cluster-on-kubernetes-with-the-kuberay-operator",
3770
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",
3771
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-2-deploy-a-ray-cluster-on-kubernetes-with-the-kuberay-operator",
3772
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#run-your-first-ray-application-on-kubernetes"
3773
            ],
3774
            "document_ids": [
3775
                5717,
3776
                5918,
3777
                5917,
3778
                5656,
3779
                5642,
3780
                5605,
3781
                6034,
3782
                5953,
3783
                5958,
3784
                5784,
3785
                6161,
3786
                6500,
3787
                6163,
3788
                5653
3789
            ],
3790
            "answer": " To manually configure and manage a Ray cluster on Kubernetes, follow these steps:\n\n1. Install `kubectl` and Helm.\n2. Prepare a Kubernetes cluster.\n3. Deploy a KubeRay operator.\n4. Create a `RayCluster` custom resource YAML file (`raycluster.yaml`) with the desired configuration.\n5. Apply the `RayCluster` custom resource to create the Ray cluster: `kubectl apply -f raycluster.yaml`\n6. To manage the Ray cluster, use the commands provided in the 'Cluster Management CLI' section of the Ray documentation.\n7. For autoscaling, follow the 'KubeRay Autoscaling' guide.\n8. To update the Ray cluster config, modify the `RayService` CR config and use `kubectl apply` to update the cluster.\n\nFor a minimal RayCluster YAML manifest, you can download it using `curl -LO <https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-cluster.gke-bucket.yaml>`.\n\nFor more information, refer to the 'RayCluster Quickstart' and 'RayCluster Configuration' guides.",
3791
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3792
        },
3793
        {
3794
            "question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",
3795
            "sources": [
3796
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
3797
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
3798
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",
3799
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",
3800
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",
3801
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options",
3802
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
3803
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.shutdown.html#ray-serve-shutdown",
3804
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",
3805
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",
3806
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job",
3807
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",
3808
                "https://docs.ray.io/en/master/cluster/key-concepts.html#worker-node",
3809
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors"
3810
            ],
3811
            "document_ids": [
3812
                5129,
3813
                4503,
3814
                4524,
3815
                6216,
3816
                4519,
3817
                14500,
3818
                6390,
3819
                14043,
3820
                14507,
3821
                13448,
3822
                6694,
3823
                4510,
3824
                5558,
3825
                555
3826
            ],
3827
            "answer": " Shutting down a raylet will not directly kill tasks and workers on that node. However, it may lead to their termination indirectly. When a raylet process fails, the corresponding node is marked as dead, which will kill currently working jobs on that node. Additionally, if the node's memory usage exceeds the threshold, Ray may kill the worker, as happened in the provided context. Nonetheless, the direct action of shutting down a raylet does not immediately result in task and worker termination.",
3828
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3829
        },
3830
        {
3831
            "question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",
3832
            "sources": [
3833
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",
3834
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
3835
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",
3836
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",
3837
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management",
3838
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#what-s-the-out-of-memory-error",
3839
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",
3840
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",
3841
                "https://docs.ray.io/en/master/serve/monitoring.html#profiling-memory",
3842
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",
3843
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3844
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#what-is-the-memory-monitor",
3845
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",
3846
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#failures-due-to-memory-issues"
3847
            ],
3848
            "document_ids": [
3849
                547,
3850
                1005,
3851
                548,
3852
                559,
3853
                5010,
3854
                549,
3855
                5118,
3856
                562,
3857
                13549,
3858
                5016,
3859
                557,
3860
                5119,
3861
                5024,
3862
                606
3863
            ],
3864
            "answer": " To debug out-of-memory issues in Ray, you should first familiarize yourself with the Ray Memory Management model. If you are experiencing out-of-memory problems on your cluster, you can view the per Task and Actor memory usage graph for more details. To locate the source of the memory leak, you can use the CLI command `htop` or view Find per Task and Actor Memory Usage. The memory usage from the per component graph uses RSS - SHR.\n\nRefer to the documentation on how to address the out of memory issue: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>. Ray memory monitor also periodically prints the aggregated out-of-memory killer summary to Ray drivers.\n\nYou can use the `ray memory` command to help track down what ObjectRef references are in scope and may be causing an ObjectStoreFullError. Running `ray memory` from the command line while a Ray application is running will give you a dump of all of the ObjectRef references that are currently held by the driver, actors, and tasks in the cluster.\n\nYou can also refer to the documentation on debugging memory issues and known problems: <https://docs.ray.io/en/latest/memory-debugging.html>",
3865
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3866
        },
3867
        {
3868
            "question": "How to use callback in Trainer?",
3869
            "sources": [
3870
                "https://docs.ray.io/en/master/train/examples/transformers/transformers_torch_trainer_basic.html#ray-train-basic-example-for-huggingface-transformers",
3871
                "https://docs.ray.io/en/master/tune/examples/hpo-frameworks.html#tune-hyperparameter-optimization-framework-examples",
3872
                "https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",
3873
                "https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.html#ray-train-huggingface-transformers-raytrainreportcallback",
3874
                "https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",
3875
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.html#ray-tune-callback",
3876
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_end.html#ray-train-lightning-raytrainreportcallback-on-init-end",
3877
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_start.html#ray-train-lightning-raytrainreportcallback-on-init-start",
3878
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
3879
                "https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune",
3880
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_batch_start.html#ray-train-lightning-raytrainreportcallback-on-batch-start",
3881
                "https://docs.ray.io/en/master/tune/api/callbacks.html#callback-initialization-and-setup",
3882
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_pretrain_routine_start.html#ray-train-lightning-raytrainreportcallback-on-pretrain-routine-start",
3883
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_batch_end.html#ray-train-lightning-raytrainreportcallback-on-batch-end"
3884
            ],
3885
            "document_ids": [
3886
                9553,
3887
                2429,
3888
                9165,
3889
                8635,
3890
                8206,
3891
                1405,
3892
                8531,
3893
                8760,
3894
                9934,
3895
                2390,
3896
                8999,
3897
                1204,
3898
                9115,
3899
                8356
3900
            ],
3901
            "answer": " To use a callback in the Trainer class from the Hugging Face Transformers library, you can add a callback object to the Trainer object using the `add_callback` method. For example:\n```\nfrom transformers.trainer_callback import TrainerCallback\n\nclass MyTrainReportCallback(TrainerCallback):\n    def __init__(self):\n        super().__init__()\n        self.metrics = {}\n\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=small_train_ds,\n    eval_dataset=small_eval_ds,\n    compute_metrics=compute_metrics,\n)\n\ntrainer.add_callback(MyTrainReportCallback())\n```\nThe `MyTrainReportCallback` class in this example is a custom callback that collects metrics and reports them when a checkpoint is saved. You can also use built-in callbacks, such as `RayTrainReportCallback`, by importing them and adding them to the Trainer object in the same way.\n\nYou can find more information about using callbacks with the Trainer class in the Hugging Face Transformers documentation.",
3902
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3903
        },
3904
        {
3905
            "question": "How to provide current working directory to ray?",
3906
            "sources": [
3907
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
3908
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",
3909
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
3910
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",
3911
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
3912
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
3913
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
3914
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
3915
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
3916
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",
3917
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
3918
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",
3919
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
3920
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli"
3921
            ],
3922
            "document_ids": [
3923
                6632,
3924
                1066,
3925
                6633,
3926
                4262,
3927
                1790,
3928
                511,
3929
                6562,
3930
                6551,
3931
                6552,
3932
                6596,
3933
                6555,
3934
                4264,
3935
                4385,
3936
                4662
3937
            ],
3938
            "answer": " To provide the current working directory to Ray, you can specify a runtime environment when calling `ray.init()`. Here's an example:\n```python\nimport os\n\n# Create a working directory and add a file\nos.makedirs(\"/tmp/runtime_env_working_dir\", exist_ok=True)\nwith open(\"/tmp/runtime_env_working_dir/hello.txt\", \"w\") as hello_file:\n    hello_file.write(\"Hello World!\")\n\n# Specify a runtime environment for the entire Ray job\nray.init(runtime_env={\"working_dir\": \"/tmp/runtime_env_working_dir\"})\n```\nThis will make the contents of the specified working directory available to the Ray job by downloading the directory to all nodes in your cluster. Note that relative paths in the remote tasks and actors in the code will work on your laptop and on the cluster without any code changes.",
3939
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3940
        },
3941
        {
3942
            "question": "how to create an actor instance with parameter?",
3943
            "sources": [
3944
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
3945
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",
3946
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
3947
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
3948
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
3949
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3950
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
3951
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",
3952
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
3953
                "https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",
3954
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cross_language.java_actor_class.html#ray-cross-language-java-actor-class",
3955
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
3956
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3957
                "https://docs.ray.io/en/master/rllib/actors.html#actors"
3958
            ],
3959
            "document_ids": [
3960
                4876,
3961
                5391,
3962
                5192,
3963
                4629,
3964
                14682,
3965
                4469,
3966
                11576,
3967
                5391,
3968
                4650,
3969
                4435,
3970
                4916,
3971
                14686,
3972
                4472,
3973
                11578
3974
            ],
3975
            "answer": " To create an actor instance with a parameter, you can call the `ActorClass.remote()` method with arguments to the constructor. This creates and runs the actor on a remote worker process, and the return value is an actor handle that can be used to call its methods.\n\nHere's an example of creating a progress actor with a parameter `TOTAL_NUM_SAMPLES`:\n```python\nNUM_SAMPLING_TASKS = 10\nNUM_SAMPLES_PER_TASK = 10_000_000\nTOTAL_NUM_SAMPLES = NUM_SAMPLING_TASKS * NUM_SAMPLES_PER_TASK\n\n# Create the progress actor.\nprogress_actor = ProgressActor.remote(TOTAL_NUM_SAMPLES)\n```\nIn this example, `ProgressActor` is a user-defined actor class, and `TOTAL_NUM_SAMPLES` is a parameter passed to the constructor of `ProgressActor`.",
3976
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3977
        },
3978
        {
3979
            "question": "how to push a custom module to ray which is using by Actor ?",
3980
            "sources": [
3981
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
3982
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",
3983
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",
3984
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.push.html#ray-util-actorpool-push",
3985
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#memory-profiling-ray-tasks-and-actors",
3986
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#custom-resources-accelerator-types-and-more",
3987
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
3988
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3989
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
3990
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
3991
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#defining-the-progress-actor",
3992
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",
3993
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
3994
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources"
3995
            ],
3996
            "document_ids": [
3997
                4650,
3998
                169,
3999
                5443,
4000
                4758,
4001
                568,
4002
                13459,
4003
                14680,
4004
                4467,
4005
                11575,
4006
                14219,
4007
                5387,
4008
                4488,
4009
                14707,
4010
                4503
4011
            ],
4012
            "answer": " To push a custom module to Ray which is used by an Actor, you can use the `ray.put()` function to put the module into Ray's object store, and then get it back using `ray.get()` when needed. Here is an example:\n\nFirst, put the custom module into the object store:\n```\nray.put(my_custom_module)\n```\nThen, in the Actor class, you can get the custom module using `ray.get()`:\n```\n@ray.remote\nclass MyActor:\n    def __init__(self):\n        self.custom_module = ray.get(my_custom_module_ref)\n\n    def do_something(self):\n        # Use the custom module\n        result = self.custom_module.some_function()\n        return result\n```\nHere, `my_custom_module_ref` is the object reference of the custom module that was put into the object store.\n\nAlternatively, you can also use the `@ray.remote` decorator to turn the custom module into a task, and then call it using the `.remote()` method. For example:\n```\n@ray.remote\ndef my_custom_module():\n    # Define the custom module here\n    def some_function():\n        # Implementation of the function\n        pass\n    return some_function\n\n@ray.remote\nclass MyActor:\n    def do_something(self):\n        # Call the custom module as a task\n        custom_module = ray.get(my_custom_module.remote())\n        result = custom_module().some_function()\n        return result\n```\nThis way, you can use the custom module in your Actor class by calling it as a task.\n\nNote that in both cases, the custom module will be transferred to the worker nodes that execute the Actor methods, so it will be available on those nodes.\n\nFor more information, you can refer to the Ray documentation on tasks and actors:\n\n* Tasks: <https://docs.ray.io/en/latest/tasks.html>\n* Actors: <https://docs.ray.io/en/latest/actors.html>",
4013
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4014
        },
4015
        {
4016
            "question": "how to print ray working directory?",
4017
            "sources": [
4018
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
4019
                "https://docs.ray.io/en/master/rllib/user-guides.html#rllib-feature-guides",
4020
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
4021
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
4022
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#submit-the-workload",
4023
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#submit-the-workload",
4024
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
4025
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-2-submit-a-ray-job-to-the-raycluster-via-ray-job-submission-sdk",
4026
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
4027
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",
4028
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
4029
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",
4030
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",
4031
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files"
4032
            ],
4033
            "document_ids": [
4034
                6632,
4035
                10537,
4036
                6551,
4037
                511,
4038
                6528,
4039
                6179,
4040
                6633,
4041
                5732,
4042
                6157,
4043
                6530,
4044
                5757,
4045
                519,
4046
                6181,
4047
                4264
4048
            ],
4049
            "answer": " To print the working directory in Ray, you can use the following code:\n```python\nimport ray\nprint(ray.global_state.worker.tmp_dir)\n```\nThis will print the temporary directory of the current Ray worker, which is equivalent to the working directory specified in the runtime environment.",
4050
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4051
        },
4052
        {
4053
            "question": "why I can not see log.info in ray log?",
4054
            "sources": [
4055
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
4056
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
4057
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
4058
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
4059
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
4060
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
4061
                "https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",
4062
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",
4063
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",
4064
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
4065
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",
4066
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",
4067
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
4068
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence"
4069
            ],
4070
            "document_ids": [
4071
                444,
4072
                1005,
4073
                680,
4074
                6631,
4075
                445,
4076
                511,
4077
                662,
4078
                530,
4079
                415,
4080
                493,
4081
                6360,
4082
                535,
4083
                477,
4084
                6359
4085
            ],
4086
            "answer": " Based on the context provided, it seems like you are trying to view `log.info` messages in the Ray dashboard's Logs view. However, you are unable to see them.\n\nThere are a few reasons why this might be the case:\n\n1. **Driver logs are not captured for Ray Jobs submitted via Jobs API:** If you are using the Ray Jobs API to submit your Ray application, then the Driver logs are only captured if you submit the job via the Jobs API. If you are running the Ray Driver on the Head node directly or connecting via Ray Client, then the Driver logs are not accessible from the Dashboard. In this case, you can view the Driver logs in your terminal or Jupyter Notebook output.\n2. **Logs are not being written to the default logging directory:** By default, Ray writes logs to files in the directory `/tmp/ray/session_*/logs` on each Ray node's file system. If your logs are not being written to this directory, then they will not be visible in the Ray dashboard's Logs view. You can check the logging directory on each Ray node to see if your logs are being written there.\n3. **Log rotation configuration:** Ray has a log rotation configuration that might be causing your logs to be rotated or deleted before you have a chance to view them in the Ray dashboard's Logs view. You can check the log rotation configuration to see if this is the case.\n\nIf none of these reasons seem to be the cause of the issue, then you can try using the Ray log APIs to retrieve your logs directly. The `ray.util.state.list_logs()` function can be used to list the log files available, and the `ray.util.state.get_log()` function can be used to retrieve a log file based on its name or some entities' IDs (such as the process ID, actor ID, or task ID).\n\nHere's an example of how to use the `get_log()` function to retrieve the logs for a specific actor:\n```python\nfrom ray.util.state import get_log\n\n# Get the actor ID\nactor_id = <ACTOR_ID>\n\n# Get the log lines for the actor\nfor line in get_log(actor_id=actor_id):\n    print(line)\n```\nI hope this helps! Let me know if you have any further questions.",
4087
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4088
        },
4089
        {
4090
            "question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",
4091
            "sources": [
4092
                "https://docs.ray.io/en/master/data/performance-tips.html#parquet-column-pruning",
4093
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
4094
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
4095
                "https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",
4096
                "https://docs.ray.io/en/master/data/user-guide.html#user-guides",
4097
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",
4098
                "https://docs.ray.io/en/master/data/data-internals.html#reading-files",
4099
                "https://docs.ray.io/en/master/data/examples/batch_training.html#transforming-a-dataset-in-parallel-using-custom-functions-a-class-anchor-id-transform-ds-a",
4100
                "https://docs.ray.io/en/master/data/examples/batch_training.html#filtering-a-dataset-on-read-a-class-anchor-id-filter-ds-a",
4101
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",
4102
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
4103
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition",
4104
                "https://docs.ray.io/en/master/data/data-internals.html#fault-tolerance",
4105
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text"
4106
            ],
4107
            "document_ids": [
4108
                6818,
4109
                7362,
4110
                6835,
4111
                7865,
4112
                6911,
4113
                7495,
4114
                6874,
4115
                7875,
4116
                7870,
4117
                7782,
4118
                7360,
4119
                7351,
4120
                6889,
4121
                7564
4122
            ],
4123
            "answer": " No, when using Ray Dataset to read a file such as a Parquet file, the order of the data is not guaranteed to be preserved. This is because Ray Dataset uses parallelism to read and process data, which can result in the data being read in an order different from how it is stored in the file. If you need to preserve the order of the data, you may need to sort or order it after reading it into a Ray Dataset.",
4124
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4125
        },
4126
        {
4127
            "question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",
4128
            "sources": [
4129
                "https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",
4130
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
4131
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
4132
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
4133
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",
4134
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",
4135
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
4136
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
4137
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",
4138
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
4139
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayTaskError.html#ray-exceptions-raytaskerror",
4140
                "https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",
4141
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
4142
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure"
4143
            ],
4144
            "document_ids": [
4145
                4377,
4146
                4542,
4147
                4542,
4148
                4535,
4149
                4527,
4150
                4348,
4151
                4546,
4152
                4657,
4153
                4564,
4154
                4658,
4155
                4779,
4156
                4674,
4157
                4660,
4158
                4563
4159
            ],
4160
            "answer": " The sentence \"Ray will not retry tasks upon exceptions thrown by application code\" means that if a task fails due to an exception or error in the application's code, Ray will not automatically retry the task by default. This behavior can be changed by setting the `retry_exceptions` argument to `True` in the `ray.remote()` or `.options()` functions, which will enable retries upon any exception or pass a list of retryable exceptions.",
4161
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4162
        },
4163
        {
4164
            "question": "how do I specify the log directory when starting Ray?",
4165
            "sources": [
4166
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
4167
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
4168
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",
4169
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",
4170
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
4171
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#mounting-the-ray-log-directory",
4172
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",
4173
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",
4174
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
4175
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",
4176
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
4177
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
4178
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-rotation",
4179
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging"
4180
            ],
4181
            "document_ids": [
4182
                511,
4183
                511,
4184
                6360,
4185
                5861,
4186
                680,
4187
                5871,
4188
                510,
4189
                5880,
4190
                4811,
4191
                530,
4192
                542,
4193
                4385,
4194
                543,
4195
                4384
4196
            ],
4197
            "answer": " To specify the log directory when starting Ray, you can use the `--temp-dir` or `--logs-dir` option with the `ray start` or `ray.init()` command. Here's an example:\n```css\nray start --temp-dir /path/to/temp/dir --logs-dir /path/to/log/dir\n```\nor\n```css\nray.init(temp_dir='/path/to/temp/dir', logs_dir='/path/to/log/dir')\n```\nThis will set the temporary directory and log directory to the specified paths. Note that the temporary directory is also the parent directory of the log directory by default.",
4198
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4199
        },
4200
        {
4201
            "question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",
4202
            "sources": [
4203
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4204
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
4205
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4206
                "https://docs.ray.io/en/master/cluster/cli.html#ray-up",
4207
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
4208
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
4209
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",
4210
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",
4211
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",
4212
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",
4213
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",
4214
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",
4215
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4216
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters"
4217
            ],
4218
            "document_ids": [
4219
                6337,
4220
                264,
4221
                6328,
4222
                5615,
4223
                6203,
4224
                6392,
4225
                6522,
4226
                851,
4227
                4193,
4228
                6501,
4229
                14483,
4230
                6411,
4231
                6353,
4232
                4455
4233
            ],
4234
            "answer": " To launch a Ray cluster with 10 nodes without setting the minimum worker count as 10, you can adjust the `max_workers` field in the cluster configuration. However, you cannot set `max_workers` to 10 in the provided configuration, as it has a maximum value of 2 for the default worker type. To increase the limit, you can define a new worker type with the desired resources and `max_workers` value.\n\nHere's an example of how to modify the configuration to support 10 worker nodes:\n\n```yaml\ncluster_name: my-ray-cluster\n\n# Define a new worker type with 4 CPUs and a max_workers value of 10\nray.worker.new_worker:\n  resources: {\"CPU\": 4}\n  min_workers: 0\n  max_workers: 10\n\n# Set the max_workers value for the default worker type to a higher value\nray.worker.default:\n  min_workers: 0\n  max_workers: 5\n  resources: {\"CPU\": 2}\n\n# Set the max_workers value for the cluster to 10\nmax_workers: 10\n\n# ... (other configurations like docker, provider, auth, etc.)\n```\n\nAfter updating the configuration, you can launch the Ray cluster using the `ray up` command:\n\n```bash\nray up -y my-ray-cluster.yaml\n```\n\nThis command will start a Ray cluster with a head node and up to 10 worker nodes, as defined in the new_worker configuration.",
4235
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4236
        },
4237
        {
4238
            "question": "how to use ray api to scale up a cluster",
4239
            "sources": [
4240
                "https://docs.ray.io/en/master/cluster/vms/index.html#overview",
4241
                "https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",
4242
                "https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",
4243
                "https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",
4244
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
4245
                "https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",
4246
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started",
4247
                "https://docs.ray.io/en/master/cluster/cli.html#ray-up",
4248
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#best-practices-for-deploying-large-clusters",
4249
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/gcp.html#start-ray-with-the-ray-cluster-launcher",
4250
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",
4251
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4252
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-status",
4253
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code"
4254
            ],
4255
            "document_ids": [
4256
                6209,
4257
                6715,
4258
                5604,
4259
                5600,
4260
                6363,
4261
                6715,
4262
                6189,
4263
                5615,
4264
                6371,
4265
                6514,
4266
                6457,
4267
                6337,
4268
                6532,
4269
                6619
4270
            ],
4271
            "answer": " To use the Ray API to scale up a cluster, you can use the `ray.autoscaler.sdk.request_resources()` function within a Ray program to command the autoscaler to scale the cluster up to a desired size. This will immediately attempt to scale to accommodate the requested resources, bypassing normal upscaling speed constraints.\n\nAdditionally, you can use the Ray Cluster Management API to launch and manage Ray clusters on cloud VMs, such as AWS, GCP, Azure, Aliyun, and vSphere. The Ray cluster consists of a head node and a collection of worker nodes, and you can configure autoscaling support to size the cluster according to the requirements of your Ray workload.\n\nTo start or update a Ray cluster, you can use the `ray up` command with the cluster configuration file. The configuration file specifies the cluster name, the maximum number of worker nodes, and the autoscaling settings.\n\nOnce the cluster is running, you can use the `ray exec` command to connect to the cluster and then use Ray like you normally would.",
4272
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4273
        },
4274
        {
4275
            "question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",
4276
            "sources": [
4277
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
4278
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
4279
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
4280
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",
4281
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",
4282
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents",
4283
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",
4284
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",
4285
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-2-check-ports",
4286
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",
4287
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#launching-a-cluster-ray-up",
4288
                "https://docs.ray.io/en/master/cluster/vms/index.html#overview",
4289
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",
4290
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"
4291
            ],
4292
            "document_ids": [
4293
                6199,
4294
                6203,
4295
                6203,
4296
                6458,
4297
                6470,
4298
                6519,
4299
                6467,
4300
                6104,
4301
                6617,
4302
                6471,
4303
                6212,
4304
                6209,
4305
                6457,
4306
                6344
4307
            ],
4308
            "answer": " To specify a subnet in the Ray cluster deployment file when using the Ray cloud launcher to start a cluster in AWS, you can include the `subnet_id` in the `provider` section of the configuration file. Here is an example:\n\n```\ncluster_name: my-cluster\n\nprovider:\n  type: aws\n  region: us-west-2\n  subnet_id: subnet-01234567890abcdef0\n```\n\nThis will ensure that the head node and worker nodes of the Ray cluster are launched in the specified subnet.\n\nYou can find more information about the Ray cluster launcher and the cluster configuration file in the Ray documentation: <https://docs.ray.io/en/latest/cluster/cloud.html>",
4309
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4310
        },
4311
        {
4312
            "question": "where I can find HTTP server error code log for Ray serve",
4313
            "sources": [
4314
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
4315
                "https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",
4316
                "https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",
4317
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
4318
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",
4319
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",
4320
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",
4321
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",
4322
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally",
4323
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4324
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4325
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",
4326
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",
4327
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions"
4328
            ],
4329
            "document_ids": [
4330
                13974,
4331
                13539,
4332
                13541,
4333
                6631,
4334
                5671,
4335
                5747,
4336
                4694,
4337
                5802,
4338
                13437,
4339
                13517,
4340
                13523,
4341
                6183,
4342
                6531,
4343
                14025
4344
            ],
4345
            "answer": " To find the HTTP server error code log for Ray Serve, you can check the logs at `/tmp/ray/session_latest/logs/serve/` on the head node. Specifically, you can look for non-200 HTTP responses in the `ray_serve_num_deployment_http_error_requests` metric or in the Ray Serve access logs. Additionally, you can use the Ray Dashboard to view the status of your Ray Serve applications and check for any errors.",
4346
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4347
        },
4348
        {
4349
            "question": "I am running ray cluster on amazon and I have troubles displaying the dashboard. When a I tunnel the dashboard port from the headnode to my machine, the dashboard opens, and then it disappears (internal refresh fails). Is it a known problem? What am I doing wrong?",
4350
            "sources": [
4351
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",
4352
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",
4353
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",
4354
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
4355
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
4356
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",
4357
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",
4358
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",
4359
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#dashboard-host",
4360
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",
4361
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",
4362
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",
4363
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",
4364
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers"
4365
            ],
4366
            "document_ids": [
4367
                5530,
4368
                5528,
4369
                5526,
4370
                6562,
4371
                6561,
4372
                6223,
4373
                5543,
4374
                5531,
4375
                5935,
4376
                5527,
4377
                6183,
4378
                6531,
4379
                5534,
4380
                5533
4381
            ],
4382
            "answer": " Yes, it seems like you are experiencing a known issue with accessing the Ray Dashboard from outside the head node. This is because the dashboard port may not be publicly exposed in remote Ray clusters.\n\nOne solution is to use port forwarding to access the dashboard from your local machine. You can do this by running the `ray dashboard` command followed by the configuration file you used to launch your cluster. For example:\n\n`ray dashboard cluster.yaml`\n\nThis will set up port forwarding automatically and make the remote dashboard viewable in your local browser at localhost:8265.\n\nIf you are using KubeRay, you can also access the dashboard from within the Kubernetes cluster at <http://<RayCluster name>-head-svc:8265>. To expose the dashboard outside the cluster, you can either set up ingress or use port-forwarding.\n\nIt is important to note that for security purposes, you should not expose the Ray Dashboard publicly without proper authentication in place.\n\nIf you continue to experience issues, you may want to check the Ray Dashboard configurations, which may differ depending on how you launch Ray clusters. You can also refer to the Ray Dashboard documentation for more information on configuring and managing the dashboard.",
4383
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4384
        },
4385
        {
4386
            "question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",
4387
            "sources": [
4388
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#disabling-the-dashboard",
4389
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",
4390
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4391
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
4392
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",
4393
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",
4394
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",
4395
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",
4396
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4397
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4398
                "https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",
4399
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
4400
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4401
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#max-workers"
4402
            ],
4403
            "document_ids": [
4404
                5538,
4405
                6292,
4406
                6345,
4407
                6392,
4408
                6522,
4409
                6227,
4410
                6366,
4411
                6367,
4412
                6337,
4413
                6328,
4414
                5557,
4415
                5555,
4416
                6311,
4417
                6241
4418
            ],
4419
            "answer": " In the Ray cluster launcher YAML, `max_workers` includes only worker nodes, not the head node. This is consistent across different types of Ray clusters, including single-node local clusters, VM clusters, and KubeRay. The default number of max workers for the head node is 0, which means it will not launch any worker processes by default. The number of max workers for other node types can be set in the cluster configuration file.",
4420
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4421
        },
4422
        {
4423
            "question": "How to update files in working directory ?",
4424
            "sources": [
4425
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
4426
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",
4427
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
4428
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
4429
                "https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",
4430
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",
4431
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",
4432
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",
4433
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#outdated-function-definitions",
4434
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#synchronizing-files-from-the-cluster-ray-rsync-up-down",
4435
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
4436
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#common-workflow-syncing-git-branches",
4437
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
4438
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"
4439
            ],
4440
            "document_ids": [
4441
                6632,
4442
                1066,
4443
                1775,
4444
                1790,
4445
                8894,
4446
                1068,
4447
                6214,
4448
                1066,
4449
                619,
4450
                6222,
4451
                4273,
4452
                6225,
4453
                269,
4454
                6349
4455
            ],
4456
            "answer": " To update files in the working directory, you can use the `ray rsync-up` command to upload files to the cluster head node. Here's the syntax:\n```bash\nray rsync_up <path_to_file_or_directory_on_local_machine> '<destination_path_on_cluster>'\n```\nFor example, if you want to upload a file named `my_script.py` from your local machine to the cluster, you can use the following command:\n```bash\nray rsync_up my_script.py '~/my_project'\n",
4457
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4458
        },
4459
        {
4460
            "question": "How I can update working directory file when ray allready initiated ?",
4461
            "sources": [
4462
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
4463
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",
4464
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
4465
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
4466
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
4467
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
4468
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
4469
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",
4470
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
4471
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
4472
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",
4473
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
4474
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",
4475
                "https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution"
4476
            ],
4477
            "document_ids": [
4478
                6632,
4479
                1066,
4480
                4385,
4481
                4811,
4482
                6555,
4483
                1790,
4484
                1775,
4485
                4263,
4486
                6633,
4487
                511,
4488
                6214,
4489
                6157,
4490
                4262,
4491
                9644
4492
            ],
4493
            "answer": " To update the working directory file when Ray is already initiated, you can use the `ray.put()` and `ray.get()` functions to store and retrieve data in the object store, which is shared across all nodes in the Ray cluster. This allows you to modify the data and have the changes reflected across all nodes.\n\nAlternatively, you can use the `ray.train.get_context().get_trial_dir()` function to get the trial directory, and then use relative paths to access and modify files within that directory.\n\nNote that if you want to use relative paths to access files in the original working directory, you can disable the behavior of changing the working directory by setting the `RAY_CHDIR_TO_TRIAL_DIR=0` environment variable. However, you should use relative paths for read-only purposes and ensure that the directory is synced on all nodes if running on multiple machines.\n\nHere is an example of how to use `ray.put()` and `ray.get()` to update a file in the working directory:\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Write some data to a file in the working directory\nwith open(\"my_file.txt\", \"w\") as f:\n    f.write(\"Hello, Ray!\")\n\n# Put the file in the object store\nray.put(\"my_file.txt\")\n\n# Modify the data in the file\ndata = ray.get(\"my_file.txt\")\nmodified_data = data.replace(\"Hello, Ray!\", \"Updated: Hello, Ray!\")\n\n# Write the modified data back to the file in the object store\nray.put(modified_data, \"my_file.txt\")\n\n# Get the updated file from the object store\nupdated_file = ray.get(\"my_file.txt\")\nprint(updated_file)\n```\nThis will print:\n```\nUpdated: Hello, Ray!\n```\nNote that this example assumes that the file `my_file.txt` exists in the working directory when the script is run. If the file does not exist, you will need to create it using `ray.put()` before modifying it.",
4494
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4495
        },
4496
        {
4497
            "question": "how can I force ray head node to use custom pem file to ssh worker node?",
4498
            "sources": [
4499
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4500
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4501
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4502
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4503
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",
4504
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4505
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",
4506
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4507
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4508
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",
4509
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#minimal-configuration",
4510
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#create-a-minimal-cluster-config-yaml-named-cloudwatch-basic-yaml-with-the-following-contents",
4511
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",
4512
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"
4513
            ],
4514
            "document_ids": [
4515
                6330,
4516
                6307,
4517
                6340,
4518
                6318,
4519
                6205,
4520
                6314,
4521
                6502,
4522
                6324,
4523
                6353,
4524
                6620,
4525
                6301,
4526
                6484,
4527
                6501,
4528
                6348
4529
            ],
4530
            "answer": " To force the Ray head node to use a custom PEM file for SSHing into the worker node, you can specify the path to your custom SSH private key in the `ssh_private_key` field of the `auth` section in the Ray configuration file. For example:\n\n```\nauth:\n  ssh_user: ubuntu\n  ssh_private_key: /path/to/your/custom/key.pem\n```\n\nMake sure that the specified key is added to the project-wide metadata and that the corresponding public key is mounted to the expected location on the worker nodes (as specified in the `file_mounts` section).\n\nAdditionally, you may need to update the `ssh_user` field to match the user that the custom key is associated with.\n\nOnce you have updated the configuration file, you can start the Ray cluster with the updated settings using the `ray start` command.",
4531
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4532
        },
4533
        {
4534
            "question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",
4535
            "sources": [
4536
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
4537
                "https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",
4538
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",
4539
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",
4540
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",
4541
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-operating-system-settings",
4542
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",
4543
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",
4544
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",
4545
                "https://docs.ray.io/en/master/ray-core/configure.html#head-node",
4546
                "https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",
4547
                "https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",
4548
                "https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",
4549
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance"
4550
            ],
4551
            "document_ids": [
4552
                14740,
4553
                937,
4554
                4508,
4555
                6375,
4556
                563,
4557
                4457,
4558
                13870,
4559
                4522,
4560
                5011,
4561
                4390,
4562
                940,
4563
                13928,
4564
                939,
4565
                5830
4566
            ],
4567
            "answer": " The GCS (Global Control Service) server is a centralized metadata server for a Ray cluster that runs on the Ray head node. It has functions like managing node membership and actor directory. It's possible that the GCS server is taking up so much memory on the head node due to the workload and size of the cluster. To alleviate this, you can configure the head node with sufficient memory and network bandwidth, and also consider tuning operating system settings to support a large number of connections. Additionally, you can use Jemalloc for memory profiling on the GCS server to identify any memory-related issues.\n\nTo set up Jemalloc for memory profiling on the GCS server, you can follow these steps:\n\n1. Install Jemalloc on the head node.\n2. Set the `RAY_JEMALLOC_LIB_PATH` environment variable to the path of the Jemalloc shared library.\n3. Set the `RAY_JEMALLOC_CONF` environment variable to the MALLOC\\_CONF of Jemalloc, with the desired configuration options.\n4. Set the `RAY_JEMALLOC_PROFILE` environment variable to the name of the component you want to profile, in this case, `gcs_server`.\n5. Start Ray with the above environment variables set.\n\nYou can refer to the provided context for more information on configuring the head node and setting up Jemalloc.",
4568
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4569
        },
4570
        {
4571
            "question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",
4572
            "sources": [
4573
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
4574
                "https://docs.ray.io/en/master/tune/faq.html#how-can-i-use-tune-with-kubernetes",
4575
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",
4576
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
4577
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
4578
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",
4579
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource",
4580
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
4581
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",
4582
                "https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",
4583
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
4584
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",
4585
                "https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#troubleshooting",
4586
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#solution-1-container-command-recommended"
4587
            ],
4588
            "document_ids": [
4589
                467,
4590
                1061,
4591
                852,
4592
                6147,
4593
                9510,
4594
                6215,
4595
                5724,
4596
                254,
4597
                10208,
4598
                9898,
4599
                466,
4600
                10218,
4601
                2428,
4602
                5847
4603
            ],
4604
            "answer": " If some nodes are pending for a long time when starting a cluster with `ray up`, you can debug this by checking the Ray cluster status with the `ray status` command. This command displays the node status, resource usage, and information about pending and failed nodes. Additionally, you can check the Ray logs with `kubectl logs ${RAYCLUSTER_HEAD_POD}` to see if there are any errors or warnings related to the pending nodes.\n\nIf the pending nodes are due to insufficient resources, you can try increasing the resources available to the Ray cluster or reducing the number of actors or Tune trials. If the pending nodes are due to a bad state of the Ray cluster, you can try restarting the cluster with `ray up --restart-only`.\n\nYou can also use the `wait_for_nodes` function to wait for all nodes to join the cluster before starting your program. This function checks the number of nodes in the cluster every second and waits until the expected number of nodes have joined. Here is an example usage of the `wait_for_nodes` function:\n```python\nimport time\nimport sys\n\ndef wait_for_nodes(expected):\n    # Wait for all nodes to join the cluster.\n    while True:\n        num_nodes = len(ray.nodes())\n        if num_nodes < expected:\n            print(\n                \"{} nodes have joined so far, waiting for {} more.\".format(\n                    num_nodes, expected - num_nodes\n                )\n            )\n            sys.stdout.flush()\n            time.sleep(1)\n        else:\n            break\n\ndef main():\n    wait_for_nodes(4)\n    # Your program code here.\n```\nThis function can be useful to ensure that all nodes have joined the cluster before starting your program.",
4605
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4606
        },
4607
        {
4608
            "question": "how to install Ray 2.5.1 from github or wheel?",
4609
            "sources": [
4610
                "https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",
4611
                "https://docs.ray.io/en/master/ray-contribute/writing-code-snippets.html#how-to-locally-test-examples",
4612
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",
4613
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
4614
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4615
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",
4616
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",
4617
                "https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",
4618
                "https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",
4619
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",
4620
                "https://docs.ray.io/en/master/installation.html#from-wheels",
4621
                "https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",
4622
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",
4623
                "https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only"
4624
            ],
4625
            "document_ids": [
4626
                288,
4627
                849,
4628
                14545,
4629
                63,
4630
                6335,
4631
                14551,
4632
                291,
4633
                66,
4634
                286,
4635
                14541,
4636
                61,
4637
                10168,
4638
                5412,
4639
                883
4640
            ],
4641
            "answer": " To install Ray 2.5.1 from a GitHub wheel, you need to find the correct wheel for your system and Python version on the Ray releases page on GitHub. However, it seems that Ray 2.5.1 is not available as a wheel for direct installation.\n\nAs an alternative, you can install Ray 2.5.1 from a specific commit by following these steps:\n\n1. Find the commit hash for Ray 2.5.1 by visiting the Ray GitHub repository (<https://github.com/ray-project/ray>) and using the GitHub interface to find the commit for Ray 2.5.1. The commit hash will look something like this: `a1b2c3d4e5f6g7h8i9j0k1l2`.\n2. Use the following command to install Ray 2.5.1, replacing `{COMMIT_HASH}` with the actual commit hash you found in step 1:\n\n```bash\npip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/{COMMIT_HASH}/ray-2.5.1-cp{PYTHON_VERSION}-cp{PYTHON_VERSION}-{OS_VERSION}.whl\n```\n\nReplace `{PYTHON_VERSION}` with your Python version (e.g., `38` for Python 3.8) and `{OS_VERSION}` with your operating system version (e.g., `manylinux2014_x86_64` for a 64-bit Linux system).\n\nNote that you may need to uninstall any existing Ray installation before installing the new version. You can do this with the following command:\n\n```bash\npip uninstall -y ray\n```\n\nAlternatively, you can install Ray 2.5.1 from source by following the instructions in the Ray documentation (<https://docs.ray.io/en/latest/installation.html#install-from-source>).",
4642
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4643
        },
4644
        {
4645
            "question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",
4646
            "sources": [
4647
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
4648
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
4649
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.__init__.html#ray-rllib-evaluation-worker-set-workerset-init",
4650
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
4651
                "https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",
4652
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
4653
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#worker-setup-commands",
4654
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
4655
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
4656
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
4657
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.__init__.html#ray-rllib-evaluation-rollout-worker-rolloutworker-init",
4658
                "https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",
4659
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",
4660
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"
4661
            ],
4662
            "document_ids": [
4663
                542,
4664
                1006,
4665
                12971,
4666
                4287,
4667
                10762,
4668
                4720,
4669
                6259,
4670
                6202,
4671
                4288,
4672
                4881,
4673
                13199,
4674
                10768,
4675
                9342,
4676
                4272
4677
            ],
4678
            "answer": " To use `worker_setup_hook` in a runtime environment to set up on worker node creation, you can specify it in the `runtime_env` configuration when initializing Ray as follows:\n```python\nimport ray\n\ndef logging_setup_func():\n    # setup logging here\n    pass\n\nray.init(runtime_env={\"worker_process_setup_hook\": logging_setup_func})\n```\nThe `worker_process_setup_hook` is an experimental feature that allows you to specify a setup hook that is called after workers start and before tasks and actors are scheduled. The value has to be a callable when passed to the Job, Task, or Actor. The callable is then exported and this value is converted to a string in the worker setup command.\n\nNote that this feature is experimental, and if you encounter any issues, you should raise them in the Ray GitHub repository.",
4679
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4680
        },
4681
        {
4682
            "question": "how to use Ray dataset on aws",
4683
            "sources": [
4684
                "https://docs.ray.io/en/master/data/key-concepts.html#loading-data",
4685
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#ingress",
4686
                "https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",
4687
                "https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",
4688
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",
4689
                "https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",
4690
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
4691
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
4692
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",
4693
                "https://docs.ray.io/en/master/data/key-concepts.html#datasets",
4694
                "https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",
4695
                "https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",
4696
                "https://docs.ray.io/en/master/ray-core/using-ray-with-jupyter.html#working-with-jupyter-notebooks-jupyterlab",
4697
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images"
4698
            ],
4699
            "document_ids": [
4700
                6732,
4701
                6100,
4702
                8006,
4703
                6839,
4704
                9188,
4705
                7865,
4706
                6935,
4707
                6835,
4708
                6467,
4709
                6731,
4710
                6809,
4711
                6779,
4712
                4419,
4713
                7362
4714
            ],
4715
            "answer": " To use Ray dataset on AWS, you can read data from an S3 bucket using the `ray.data.read_csv` function. Here's an example:\n```python\nimport ray\n\n# Replace `s3://my-bucket/path/to/data` with the location of your data in S3\nds = ray.data.read_csv(\"s3://my-bucket/path/to/data\")\n```\nThis will create a Ray Dataset that you can use for distributed data processing. Ray Data supports various data sources and formats, including S3.\n\nBefore using the dataset, you should install Ray Data using `pip install -U \"ray[data]\"`.\n\nAdditionally, you can use the Ray cluster launcher to start a Ray cluster on AWS and use the dataset in a distributed manner. The Ray cluster launcher can be used with the AWS Python SDK to start an AWS Ray cluster.",
4716
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4717
        },
4718
        {
4719
            "question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",
4720
            "sources": [
4721
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
4722
                "https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",
4723
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",
4724
                "https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",
4725
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",
4726
                "https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",
4727
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition",
4728
                "https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-data",
4729
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
4730
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
4731
                "https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",
4732
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",
4733
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",
4734
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#pytorch-dataloader"
4735
            ],
4736
            "document_ids": [
4737
                7081,
4738
                6864,
4739
                7395,
4740
                6795,
4741
                8046,
4742
                6876,
4743
                7352,
4744
                6859,
4745
                7796,
4746
                7085,
4747
                7188,
4748
                7379,
4749
                7328,
4750
                6990
4751
            ],
4752
            "answer": " To avoid shuffling of your dataset during `ray.data.map_batches()`, you can use the `local_shuffle_buffer_size` parameter in `ray.data.Dataset.iter_batches()`. This allows you to shuffle a subset of rows up to a provided buffer size during iteration, which is more performant than a full global shuffle.\n\nHere's an example:\n\n```python\nds = ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n\n# Perform map_batches on the dataset without shuffling\nds_mapped = ds.map_batches(my_mapping_function)\n\n# Iterate over batches with local shuffle buffer size\nfor batch in ds_mapped.iter_batches(local_shuffle_buffer_size=1024):\n    # Process batches here\n    pass\n```\n\nChoose the smallest value for `local_shuffle_buffer_size` that achieves sufficient randomness for your use case. Higher values result in more randomness but slower iteration.",
4753
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4754
        },
4755
        {
4756
            "question": "Is the order of the input data preserved after a map_batches operation?",
4757
            "sources": [
4758
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",
4759
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",
4760
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
4761
                "https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms",
4762
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
4763
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
4764
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",
4765
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
4766
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",
4767
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
4768
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
4769
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
4770
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
4771
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#transformations-with-torch-tensors"
4772
            ],
4773
            "document_ids": [
4774
                7640,
4775
                7289,
4776
                7081,
4777
                6813,
4778
                7091,
4779
                7800,
4780
                7738,
4781
                7085,
4782
                7737,
4783
                7088,
4784
                7988,
4785
                7082,
4786
                8070,
4787
                6974
4788
            ],
4789
            "answer": " Yes, the order of the input data is preserved after a `map_batches` operation in Ray Dataset. This is because `map_batches` applies a user-defined function to batches of data, and the relative ordering of the batches is maintained. This is similar to the `map` function in Python, which also preserves the order of the elements in the input iterable. Additionally, the documentation for `map_batches` mentions that \"the order of the blocks in the datasets is preserved\" in the context of the `union` operation, which further suggests that the order is preserved in `map_batches`.",
4790
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4791
        },
4792
        {
4793
            "question": "ray serve returns generic internal service error when there is an internal failure, how do I get it to emit more detailed errors or logs?",
4794
            "sources": [
4795
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",
4796
                "https://docs.ray.io/en/master/serve/tutorials/gradio-integration.html#scaling-your-gradio-app-with-ray-serve",
4797
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",
4798
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4799
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",
4800
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",
4801
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
4802
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
4803
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",
4804
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",
4805
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
4806
                "https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",
4807
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#rayservice-troubleshooting",
4808
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-2-check-rayservice-cr-status"
4809
            ],
4810
            "document_ids": [
4811
                4349,
4812
                14427,
4813
                4527,
4814
                13517,
4815
                4348,
4816
                5671,
4817
                4660,
4818
                13974,
4819
                595,
4820
                4694,
4821
                4658,
4822
                13469,
4823
                5661,
4824
                5664
4825
            ],
4826
            "answer": " To get more detailed errors or logs when Ray Serve returns a generic internal service error due to an internal failure, you can:\n\n1. Check the application-level failures by catching errors, retrying failed code, and handling misbehaving code using task and actor fault tolerance mechanisms provided by Ray. Ray surfaces application-level failures as Python-level exceptions.\n2. Leverage Ray logging to understand system-level behavior and to surface application-level details during runtime. Ray Serve uses Python's standard logging module with a logger named \"ray.serve\". By default, logs are emitted from actors both to stderr and on disk on each node at /tmp/ray/session\\_latest/logs/serve/. In development, logs are streamed to the driver Ray program, so it\u2019s convenient to keep the driver running while debugging.\n3. If you are using KubeRay, you can check the status and events of the RayService CR to see if there are any errors using the command: `kubectl describe rayservice $RAYSERVICE_NAME -n $YOUR_NAMESPACE`.\n\nRefer to the context for more information on fault tolerance, Ray logging, and RayService troubleshooting.",
4827
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4828
        },
4829
        {
4830
            "question": "how do i track an uncaught exception in ray serve",
4831
            "sources": [
4832
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
4833
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging",
4834
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",
4835
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",
4836
                "https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",
4837
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",
4838
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception",
4839
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
4840
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
4841
                "https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",
4842
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",
4843
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#ray-dashboard",
4844
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_placement_group.html#ray-util-state-get-placement-group",
4845
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect"
4846
            ],
4847
            "document_ids": [
4848
                13974,
4849
                585,
4850
                4694,
4851
                4527,
4852
                4674,
4853
                5747,
4854
                778,
4855
                4658,
4856
                4660,
4857
                4675,
4858
                696,
4859
                416,
4860
                701,
4861
                5671
4862
            ],
4863
            "answer": " To track an uncaught exception in a Ray Serve application, you can use post-mortem debugging. This involves automatically dropping into the debugger when an error occurs or an exception is thrown. Here's how you can set this up:\n\n1. Install the required dependencies:\n```\npip install \"ray[serve]\" scikit-learn\n```\n2. Create a file called `serve_debugging.py` and add the following code:\n```python\nimport time\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.ensemble import GradientBoostingClassifier\n\nimport ray\nfrom ray import serve\n\nserve.start()\n```\n3. In this example, an uncaught exception is not intentionally thrown. However, if you have a specific function where you expect an uncaught exception, you can use a Python `try`/`except` block to catch and handle the exception as needed.\n4. To verify the status of the Serve application, you can use the Ray dashboard. Forward the dashboard port to localhost and check the Serve page at <http://localhost:8265/#/serve>. This can help you observe the application's performance and troubleshoot issues.\n\nFor more information on Ray exceptions and error handling, refer to the Ray documentation on exceptions.",
4864
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4865
        },
4866
        {
4867
            "question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",
4868
            "sources": [
4869
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4870
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4871
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
4872
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
4873
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
4874
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4875
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",
4876
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
4877
                "https://docs.ray.io/en/master/serve/monitoring.html#json-logging-format",
4878
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
4879
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4880
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",
4881
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",
4882
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3"
4883
            ],
4884
            "document_ids": [
4885
                13517,
4886
                13517,
4887
                680,
4888
                445,
4889
                4323,
4890
                13524,
4891
                519,
4892
                444,
4893
                13525,
4894
                493,
4895
                13523,
4896
                415,
4897
                411,
4898
                533
4899
            ],
4900
            "answer": " To view logs for your Ray Serve endpoint in the Ray cluster, you can look in the following locations:\n\n1. In development, logs are streamed to the driver Ray program, so it's convenient to keep the driver running while debugging.\n2. By default, logs are emitted from actors and saved on disk on each node at `/tmp/ray/session_latest/logs/serve/`.\n3. You can also enable runtime environment debugging log streaming by setting the environment variable `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1` on each node before starting Ray.\n4. If you're using the Ray Job API, worker logs of Ray Jobs are always captured in the Ray logging directory, while driver logs are captured only for Ray Jobs submitted via Jobs API.\n\nTo access logs from Ray clusters, you can use the Ray Log CLI, which allows you to access the log from the cluster. Note that only the logs from alive nodes are available through this API.\n\nFor JSON-formatted logging in the Serve log file, set the environment variable `RAY_SERVE_ENABLE_JSON_LOGGING=1`. The logs will then have the following format:\n```json\n{\"levelname\": \"INFO\", \"asctime\": \"2023-07-17 10:34:25,425\", \"deployment\": \"default_api\", \"replica\": \"default_api#bFDOnw\", \"request_id\": \"OGIVJJJPRb\", \"route\": \"/app1\", \"application\": \"default\", \"message\": \"replica.py:664 - Started executing request OGIVJJJPRb\"}\n```\nLastly, Serve supports log rotation of these logs through setting the environment variables `RAY_ROTATION_MAX_BYTES` and `RAY_ROTATION_BACKUP_COUNT`.",
4901
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4902
        },
4903
        {
4904
            "question": "where can I see logs for a failed ray serve deployment",
4905
            "sources": [
4906
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4907
                "https://docs.ray.io/en/master/serve/monitoring.html#filtering-logs-with-loki",
4908
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#inspect-deeper",
4909
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",
4910
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4911
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",
4912
                "https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",
4913
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4914
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",
4915
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",
4916
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#whats-next",
4917
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",
4918
                "https://docs.ray.io/en/master/serve/monitoring.html#monitor-your-application",
4919
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status"
4920
            ],
4921
            "document_ids": [
4922
                13523,
4923
                13536,
4924
                13658,
4925
                13442,
4926
                13517,
4927
                13960,
4928
                13537,
4929
                13521,
4930
                13514,
4931
                13959,
4932
                13721,
4933
                13516,
4934
                13513,
4935
                13831
4936
            ],
4937
            "answer": " The logs for a failed Ray serve deployment can be found in the following locations:\n\n1. /tmp/ray/session\\_latest/logs/serve/ - This is the default location for Ray logs, including system-level logs from the Serve controller and proxy, as well as access logs and custom user logs produced from within deployment replicas.\n2. Kuberay operator logs - These logs contain information about the Serve application deployment event and Serve application health checks. You can access them by running `kubectl logs -f <kuberay-operator-pod-name>`.\n3. Controller log and deployment log - These logs contain information about specific deployment failure reasons and autoscaling events. They are located under /tmp/ray/session\\_latest/logs/serve/ in both the head node pod and worker node pod.\n\nTo filter the Ray logs for the ones relevant to your deployment, you can use the following LogQL query in Loki: {job=\"ray\"} |= \"Counter\".",
4938
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4939
        },
4940
        {
4941
            "question": "How to take a subset of a Ray Dataset?",
4942
            "sources": [
4943
                "https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",
4944
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
4945
                "https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",
4946
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-ml-libraries",
4947
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
4948
                "https://docs.ray.io/en/master/data/examples/batch_training.html#contents",
4949
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.take.html#ray-data-dataset-take",
4950
                "https://docs.ray.io/en/master/data/key-concepts.html#loading-data",
4951
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
4952
                "https://docs.ray.io/en/master/data/key-concepts.html#datasets",
4953
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",
4954
                "https://docs.ray.io/en/master/data/data.html#ray-data-scalable-datasets-for-ml",
4955
                "https://docs.ray.io/en/master/data/loading-data.html#creating-synthetic-data",
4956
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-datasets"
4957
            ],
4958
            "document_ids": [
4959
                8006,
4960
                6835,
4961
                6901,
4962
                6756,
4963
                7362,
4964
                7861,
4965
                7408,
4966
                6732,
4967
                6935,
4968
                6731,
4969
                6749,
4970
                6907,
4971
                6769,
4972
                6984
4973
            ],
4974
            "answer": " To take a subset of a Ray Dataset, you can use the `take()` method, which returns up to a specified number of rows as a list of dictionaries. Here's an example:\n\n```python\nimport ray\n\nds = ray.data.read_csv(\"s3://anonymous@air-example-data/iris.csv\")\nsubset = ds.take(10)  # Take the first 10 rows\nprint(subset)\n```\n\nThis will return the first 10 rows of the Dataset. Note that the `take()` method will move the specified number of rows to the caller's machine, so be cautious if dealing with large datasets to avoid OutOfMemory errors.",
4975
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4976
        },
4977
        {
4978
            "question": "How do I load all checkpoints from trials of a Tune experiment launched with `tune.run`? I ran my initial experiment with cloud checkpointing, so I\u2019d need to download all the checkpoints to analyze them.",
4979
            "sources": [
4980
                "https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",
4981
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
4982
                "https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing",
4983
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",
4984
                "https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints",
4985
                "https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-out-of-tune-using-checkpoints-other-artifacts",
4986
                "https://docs.ray.io/en/master/tune/tutorials/overview.html#tune-feature-guides",
4987
                "https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#tunecontroller",
4988
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",
4989
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",
4990
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",
4991
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",
4992
                "https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",
4993
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example"
4994
            ],
4995
            "document_ids": [
4996
                2226,
4997
                1006,
4998
                2217,
4999
                2253,
5000
                2212,
5001
                2299,
5002
                2270,
5003
                2243,
5004
                2255,
5005
                2348,
5006
                2350,
5007
                2347,
5008
                2310,
5009
                2439
5010
            ],
5011
            "answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `Tuner.restore()` function. This function allows you to restore the experiment from a local path or a cloud storage bucket. You can specify the `resume_errored` option to true if you want to resume an experiment that was interrupted due to an error.\n\nHere is an example of how to use `Tuner.restore()` to restore an experiment from a local path:\n```\ntuner = tune.Tuner.restore(\n    os.path.expanduser(\"~/ray_results/tune_fault_tolerance_guide\"),\n    trainable=trainable,\n    resume_errored=True,\n)\ntuner.fit()\n```\nAnd here is an example of how to restore an experiment from a cloud storage bucket:\n```\ntuner = tune.Tuner.restore(\n    \"s3://my-checkpoints-bucket/path/my-tune-exp\",\n    trainable=my_trainable,\n    resume_errored=True\n)\ntuner.fit()\n```\nYou can also specify the `resume_unfinished` or `restart_errored` options to control how the experiment is restored. For more details, please see the documentation of `Tuner.restore()`.\n\nOnce the experiment is restored, you can access the results and checkpoints by calling `Tuner.get_results()` to receive the `ResultGrid` object, and then proceeding as outlined in the Tune documentation.\n\nNote that if you ran your initial experiment with cloud checkpointing, you will need to download all the checkpoints to your local machine before you can analyze them. You can do this by copying the checkpoint files from the cloud storage bucket to your local machine using a tool like the `aws s3 cp` command or the `gsutil cp` command.",
5012
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5013
        },
5014
        {
5015
            "question": "How can I kill a \"detached\" Actor ?",
5016
            "sources": [
5017
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
5018
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
5019
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
5020
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
5021
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
5022
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",
5023
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
5024
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
5025
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
5026
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
5027
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
5028
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
5029
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
5030
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle"
5031
            ],
5032
            "document_ids": [
5033
                4633,
5034
                4859,
5035
                4631,
5036
                4615,
5037
                4613,
5038
                4579,
5039
                4620,
5040
                4848,
5041
                4561,
5042
                4618,
5043
                4614,
5044
                4577,
5045
                4575,
5046
                4616
5047
            ],
5048
            "answer": " To kill a \"detached\" Actor in the context of Ray, you can use the `ray.kill` function, passing the actor's handle as an argument. This will forcefully terminate the actor, interrupting any running tasks and not running atexit handlers. Here's an example:\n\n```python\nimport ray\n\ncounter = Counter.options(name=\"CounterActor\", lifetime=\"detached\").remote()\n\n# ... (use the actor)\n\nray.kill(counter)\n```\n\nKeep in mind that, after calling `ray.kill`, subsequent attempts to get the actor's handle via `ray.get_actor` will fail. Also, note that detached actors must be manually destroyed once they are no longer needed, as garbage collection hasn't been implemented yet for actors in C++.",
5049
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5050
        },
5051
        {
5052
            "question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",
5053
            "sources": [
5054
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",
5055
                "https://docs.ray.io/en/master/tune/faq.html#how-can-i-continue-training-a-completed-tune-experiment-for-longer-and-with-new-configurations-iterative-experimentation",
5056
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",
5057
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5058
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
5059
                "https://docs.ray.io/en/master/serve/api/index.html#serve-run",
5060
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
5061
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",
5062
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#set-up-ray-a-name-setup-a",
5063
                "https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.init_method.html#ray-train-torch-torchconfig-init-method",
5064
                "https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",
5065
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",
5066
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",
5067
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv"
5068
            ],
5069
            "document_ids": [
5070
                6614,
5071
                1073,
5072
                9343,
5073
                4811,
5074
                4285,
5075
                13990,
5076
                542,
5077
                4253,
5078
                10295,
5079
                9118,
5080
                13850,
5081
                13684,
5082
                6613,
5083
                4712
5084
            ],
5085
            "answer": " To set environment variables in `ray.init()`, you can include the `env_vars` option in the `runtime_env` argument. For example:\n\n```python\nray.init(\n    runtime_env={\n        \"working_dir\": \"files/my_project\",\n        \"pip\": [\"toolz\", \"requests\"],\n        \"env_vars\": {\"foo\": \"foo\"},\n    },\n)\n```\n\nThis will set the environment variable `foo` to the value `\"foo\"` in the Ray cluster. The `env_vars` option allows you to override existing environment variables or reference them using `${ENV_VAR}` to achieve appending behavior. If the environment variable doesn't exist, it becomes an empty string.\n\nIn your specific case, you can set the `foo` environment variable as follows:\n\n```python\nray.init(\n    runtime_env={\n        \"pip\": [\n            \"datasets==2.13.1\",\n            \"torch>=1.13.0\",\n            \"deepspeed==0.9.4\",\n            \"accelerate==0.20.3\",\n            \"transformers==4.30.2\",\n            \"pytorch_lightning==2.0.3\",\n        ],\n        \"env_vars\": {\"foo\": \"foo\"},\n    },\n)\n```",
5086
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5087
        },
5088
        {
5089
            "question": "What is the rest api for getting the head node id?",
5090
            "sources": [
5091
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",
5092
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",
5093
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.node_id.html#ray-runtime-context-runtimecontext-node-id",
5094
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",
5095
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_node_id.html#ray-runtime-context-runtimecontext-get-node-id",
5096
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",
5097
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5098
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
5099
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
5100
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails",
5101
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
5102
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",
5103
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",
5104
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions"
5105
            ],
5106
            "document_ids": [
5107
                721,
5108
                6407,
5109
                4731,
5110
                754,
5111
                4909,
5112
                4450,
5113
                14324,
5114
                14202,
5115
                492,
5116
                6657,
5117
                493,
5118
                6407,
5119
                720,
5120
                14022
5121
            ],
5122
            "answer": " To get the head node ID in Ray, you can use the `get_node_id()` function from `ray.runtime_context.RuntimeContext`. This function returns the node ID in hex format.\n\nHere is an example:\n```python\nimport ray\nfrom ray.runtime_context import RuntimeContext\n\nray.init()\nnode_id = RuntimeContext.get_node_id()\nprint(node_id)\n```\nIf you want to get more information about the node, such as the node IP address, you can use the `get_node()` function from `ray.util.state`. This function returns a `NodeState` object that contains information about the node.\n\nHere is an example:\n```python\nimport ray\nfrom ray.util.state import get_node\n\nray.init()\nnode = get_node(node_id)\nprint(node.node_ip)\n```\nNote that you can get the node ID by using `ray list nodes` command in the CLI.",
5123
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5124
        },
5125
        {
5126
            "question": "how to rerun a canceled ray task",
5127
            "sources": [
5128
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
5129
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",
5130
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
5131
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",
5132
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
5133
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
5134
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
5135
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
5136
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
5137
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
5138
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
5139
                "https://docs.ray.io/en/master/ray-core/api/core.html#tasks",
5140
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
5141
                "https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance"
5142
            ],
5143
            "document_ids": [
5144
                4897,
5145
                5427,
5146
                4375,
5147
                4550,
5148
                4494,
5149
                4497,
5150
                4894,
5151
                4542,
5152
                4492,
5153
                4895,
5154
                4535,
5155
                4649,
5156
                4896,
5157
                4377
5158
            ],
5159
            "answer": " To rerun a canceled Ray task, you can't directly reuse the same task reference. Instead, you should re-create the task and call `ray.get()` on its new object reference. Here's an example using the `retrieve_task` function from the context:\n\n```python\n# Create a new task reference\nnew_ref = retrieve_task.remote(item, db)\n\n# Call ray.get() on the new reference to execute the task\nresult = ray.get(new_ref)\n```\n\nKeep in mind that canceled tasks aren't retried by default. If you want to retry a task upon exceptions thrown by application code, you can set the `retry_exceptions` argument to `True` when defining the remote function:\n\n```python\n@ray.remote(retry_exceptions=True)\ndef retrieve_task(item, db):\n    time.sleep(item / 10.)\n    return item, db[item]\n```\n\nNow, if an exception occurs during the execution of the task, Ray will automatically retry the task until it succeeds or the maximum number of retries is exceeded.",
5160
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5161
        },
5162
        {
5163
            "question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",
5164
            "sources": [
5165
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",
5166
                "https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",
5167
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.sigopt.SigOptSearch.html#ray-tune-search-sigopt-sigoptsearch",
5168
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.hyperopt.HyperOptSearch.set_max_concurrency.html#ray-tune-search-hyperopt-hyperoptsearch-set-max-concurrency",
5169
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.zoopt.ZOOptSearch.set_max_concurrency.html#ray-tune-search-zoopt-zooptsearch-set-max-concurrency",
5170
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ConcurrencyLimiter.html#ray-tune-search-concurrencylimiter",
5171
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.html#ray-tune-search-optuna-optunasearch",
5172
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.skopt.SkOptSearch.set_max_concurrency.html#ray-tune-search-skopt-skoptsearch-set-max-concurrency",
5173
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.bayesopt.BayesOptSearch.set_max_concurrency.html#ray-tune-search-bayesopt-bayesoptsearch-set-max-concurrency",
5174
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.set_max_concurrency.html#ray-tune-search-optuna-optunasearch-set-max-concurrency",
5175
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ax.AxSearch.set_max_concurrency.html#ray-tune-search-ax-axsearch-set-max-concurrency",
5176
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.dragonfly.DragonflySearch.set_max_concurrency.html#ray-tune-search-dragonfly-dragonflysearch-set-max-concurrency",
5177
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.set_max_concurrency.html#ray-tune-search-searcher-set-max-concurrency",
5178
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.batch.html#ray-serve-batch"
5179
            ],
5180
            "document_ids": [
5181
                7738,
5182
                2406,
5183
                2072,
5184
                1654,
5185
                1340,
5186
                1464,
5187
                1867,
5188
                1547,
5189
                1597,
5190
                1610,
5191
                1543,
5192
                1434,
5193
                1712,
5194
                14087
5195
            ],
5196
            "answer": " To set the maximum number of parallel concurrent scheduled tasks in `map_batches`, you can use the `max_batch_size` parameter in `BatchedDeployment.bind()`. However, if you're looking to limit the concurrency of trials in Ray Tune, you can set `max_concurrent_trials` in `TuneConfig`. The actual parallelism will be determined by how many trials can fit in the cluster at once. Alternatively, you can use a `ConcurrencyLimiter` to manage the concurrency of a searcher object. The `set_max_concurrency` method can be used to set the maximum concurrent trials this searcher can run.",
5197
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5198
        },
5199
        {
5200
            "question": "How do I get the number of cpus from ray cluster?",
5201
            "sources": [
5202
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",
5203
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",
5204
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",
5205
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-6-profile-using-ray-dashboard",
5206
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",
5207
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",
5208
                "https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",
5209
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",
5210
                "https://docs.ray.io/en/master/data/performance-tips.html#tuning-read-parallelism",
5211
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-8-a-loop-of-restarting-the-raycluster-occurs-when-the-kubernetes-cluster-runs-out-of-resources-kuberay-v0-6-1-or-earlier",
5212
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.head_cpus.html#ray-tune-execution-placement-groups-placementgroupfactory-head-cpus",
5213
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",
5214
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5215
                "https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible"
5216
            ],
5217
            "document_ids": [
5218
                10003,
5219
                6411,
5220
                5936,
5221
                6049,
5222
                6504,
5223
                5413,
5224
                4380,
5225
                6013,
5226
                6815,
5227
                5693,
5228
                2112,
5229
                614,
5230
                4818,
5231
                5399
5232
            ],
5233
            "answer": " To get the number of CPUs in a Ray cluster, you can use the `ray.cluster_resources()` function in Python. This function returns a dictionary containing various resources in the cluster, including the number of CPUs, which can be accessed using the key 'CPU'. Here is an example:\n```python\nimport ray\nray.init()\nprint(ray.cluster_resources()['CPU'])\n```\nThis will print the total number of CPUs in the Ray cluster. Note that this function returns the total number of CPUs available to Ray, which may be different from the total number of physical CPUs in the cluster if some CPUs are not allocated to Ray.",
5234
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5235
        },
5236
        {
5237
            "question": "How to use the exclude option to the runtime_env",
5238
            "sources": [
5239
                "https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",
5240
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.set_runtime_env.html#ray-job-config-jobconfig-set-runtime-env",
5241
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
5242
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_runtime_env_string.html#ray-runtime-context-runtimecontext-get-runtime-env-string",
5243
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",
5244
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",
5245
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.runtime_env.html#ray-runtime-context-runtimecontext-runtime-env",
5246
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",
5247
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",
5248
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
5249
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
5250
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",
5251
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
5252
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments"
5253
            ],
5254
            "document_ids": [
5255
                4661,
5256
                4826,
5257
                4712,
5258
                4912,
5259
                4291,
5260
                4764,
5261
                4770,
5262
                4319,
5263
                4308,
5264
                4330,
5265
                4716,
5266
                4310,
5267
                4324,
5268
                4252
5269
            ],
5270
            "answer": " To use the `exclude` option with the runtime environment in Ray, you can use the `RuntimeEnvConfig` class and set the `exclude` parameter to a list of packages that you want to exclude. Here's an example:\n```\nfrom ray.runtime_env import RuntimeEnvConfig\n\nruntime_env = RuntimeEnv(\n    runtime_env_config=RuntimeEnvConfig(\n        exclude=[\"package1\", \"package2\"]\n    )\n)\n```\nYou can then use this runtime environment when creating a job or actor by passing it to the `set_runtime_env` method of the `JobConfig` class.\n\nNote that the `exclude` option is not directly mentioned in the provided context, but the `RuntimeEnvConfig` class has a `exclude` parameter that can be used to exclude packages from the runtime environment.",
5271
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5272
        },
5273
        {
5274
            "question": "show a map batch example with batch_format",
5275
            "sources": [
5276
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
5277
                "https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",
5278
                "https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",
5279
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",
5280
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
5281
                "https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",
5282
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
5283
                "https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.to_batch_format.html#ray-data-block-blockaccessor-to-batch-format",
5284
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.default_batch_format.html#ray-data-dataset-default-batch-format",
5285
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
5286
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
5287
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
5288
                "https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches",
5289
                "https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text"
5290
            ],
5291
            "document_ids": [
5292
                7081,
5293
                6727,
5294
                6956,
5295
                7640,
5296
                7091,
5297
                6790,
5298
                7088,
5299
                7384,
5300
                7791,
5301
                7988,
5302
                8070,
5303
                7082,
5304
                6903,
5305
                6999
5306
            ],
5307
            "answer": " Here's an example of using `map_batches` with `batch_format` in Ray Data:\n\n```python\nfrom typing import Dict\nimport numpy as np\nimport ray\n\nclass ImageClassifier:\n    def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n        inputs = torch.from_numpy(batch[\"image\"])\n        with torch.inference_mode():\n            outputs = self.model(inputs)\n        return {\"class\": outputs.argmax(dim=1)}\n\nds = (\n    ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n    .map_batches(ImageClassifier, batch_format=\"numpy\", compute=ray.data.ActorPoolStrategy(size=2), batch_size=4)\n)\n\npredictions = ds.map_batches(ImageClassifier, batch_format=\"numpy\", compute=ray.data.ActorPoolStrategy(size=2), batch_size=4)\npredictions.show(3)\n```\n\nIn this example, we read image data using `read_images` and apply an `ImageClassifier` model to the dataset using `map_batches`. The `batch_format` argument is set to \"numpy\" to specify that batches should be represented as dictionaries of NumPy arrays. The `compute` argument is set to `ray.data.ActorPoolStrategy(size=2)` to specify that the UDF should run as long-running Ray actors, with two parallel actors. The `batch_size` argument is set to 4 to indicate the number of images in each batch. The resulting predictions are displayed using the `show` method.",
5308
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5309
        },
5310
        {
5311
            "question": "how to find local ray address",
5312
            "sources": [
5313
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
5314
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
5315
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5316
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",
5317
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",
5318
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",
5319
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5320
                "https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",
5321
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
5322
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",
5323
                "https://docs.ray.io/en/master/ray-core/configure.html#driver-options",
5324
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
5325
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_placement_groups.html#ray-util-state-list-placement-groups",
5326
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_objects.html#ray-util-state-list-objects"
5327
            ],
5328
            "document_ids": [
5329
                6552,
5330
                6552,
5331
                4817,
5332
                6531,
5333
                6183,
5334
                4187,
5335
                4818,
5336
                13988,
5337
                13974,
5338
                5413,
5339
                4409,
5340
                739,
5341
                772,
5342
                757
5343
            ],
5344
            "answer": " To find the local Ray address, you can look for the address of the Ray dashboard, which is displayed in the output when you start a local Ray cluster. For example, if you start a local Ray cluster using the commands `import ray` and `ray.init()`, the output will include a message like `View the Ray dashboard at http://127.0.0.1:8265`. The address in this message is the address of the Ray cluster. Alternatively, you can find the Ray address by checking the environment variable `RAY_ADDRESS`, or by checking the address of the latest cluster started in the directory `/tmp/ray/ray_current_cluster`.\n\nHere is an example of how you can find the local Ray address using the Ray CLI:\n\n1. Open a terminal and navigate to the directory where you have Ray installed.\n2. Start a local Ray cluster by running the command `ray start`.\n3. Find the address of the Ray cluster by checking the output of the `ray start` command, or by checking the environment variable `RAY_ADDRESS`, or by checking the address of the latest cluster started in the directory `/tmp/ray/ray_current_cluster`.\n4. To tell the Ray Jobs CLI how to find your Ray Cluster, you can set the `RAY_ADDRESS` environment variable to the address of the Ray cluster, or you can pass the `--address` flag explicitly to each Ray Jobs CLI command.\n\nFor example, to set the `RAY_ADDRESS` environment variable to the address of the Ray cluster, you can run the following command:\n\n```\nexport RAY_ADDRESS=\"http://127.0.0.1:8265\"\n```\n\nOr, to pass the `--address` flag explicitly to a Ray Jobs CLI command, you can run the following command:\n\n```\nray jobs submit --address=http://127.0.0.1:8265 my_job.yaml\n```\n\nI hope this helps! Let me know if you have any other questions.",
5345
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5346
        },
5347
        {
5348
            "question": "How to start ray cluster on multiple node via CLI?",
5349
            "sources": [
5350
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
5351
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",
5352
                "https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",
5353
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",
5354
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",
5355
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",
5356
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",
5357
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",
5358
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",
5359
                "https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",
5360
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",
5361
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
5362
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",
5363
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"
5364
            ],
5365
            "document_ids": [
5366
                6203,
5367
                6616,
5368
                5605,
5369
                6501,
5370
                6500,
5371
                6510,
5372
                6622,
5373
                6206,
5374
                6509,
5375
                5600,
5376
                5528,
5377
                6337,
5378
                6502,
5379
                6353
5380
            ],
5381
            "answer": " To start a Ray cluster on multiple nodes via CLI, first create a cluster configuration file (e.g., `config.yaml`) that includes the head node and worker nodes' information. Then, use the `ray up -y config.yaml` command to start the cluster. Ensure that Ray is installed on all nodes and the Ray Cluster CLI tools are available.\n\nIf you need to set up the cluster manually, install Ray on each machine using `pip install -U \"ray[default]\"`, and then use the `ray up example-full.yaml`, `ray attach example-full.yaml`, and `ray down example-full.yaml` commands to create, connect to, and tear down the cluster.\n\nAfter starting the cluster, on each worker node, run `ray start --address=<head-node-address:port>` to connect the worker nodes to the head node. Replace `<head-node-address:port>` with the value printed by the command on the head node.",
5382
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5383
        },
5384
        {
5385
            "question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",
5386
            "sources": [
5387
                "https://docs.ray.io/en/master/tune/api/trainable.html#advanced-resource-allocation",
5388
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
5389
                "https://docs.ray.io/en/master/tune/faq.html#what-are-all-these-extra-outputs",
5390
                "https://docs.ray.io/en/master/tune/examples/tune-aim.html#logging-tune-hyperparameter-configurations-and-results-to-aim",
5391
                "https://docs.ray.io/en/master/tune/examples/pbt_guide.html#replaying-a-pbt-run",
5392
                "https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",
5393
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.fit.html#ray-tune-tuner-fit",
5394
                "https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#scalability-and-overhead-benchmarks-for-ray-tune",
5395
                "https://docs.ray.io/en/master/tune/examples/pbt_visualization/pbt_visualization.html#configure-pbt-and-tuner",
5396
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
5397
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",
5398
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",
5399
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",
5400
                "https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune"
5401
            ],
5402
            "document_ids": [
5403
                1159,
5404
                1113,
5405
                1022,
5406
                3774,
5407
                3167,
5408
                2396,
5409
                1608,
5410
                2376,
5411
                4153,
5412
                5068,
5413
                625,
5414
                10218,
5415
                9397,
5416
                6879
5417
            ],
5418
            "answer": " The Ray Tuner showing \"running\" but with almost 0% CPU usage could be due to the trainable function/class not utilizing the CPU resources fully. If your trainable function creates further Ray actors or tasks that consume CPU resources, you may need to add more bundles to the PlacementGroupFactory to reserve extra resource slots. Additionally, if the trainable class requires 1 GPU itself but launches other actors using more GPUs, you should use tune.with\\_resources to reserve the necessary GPU resources.\n\nFurthermore, if you have set the maximum number of concurrent trials in Tune, and the trainable function does not utilize the CPU resources fully, you may see low CPU usage. In this case, you can try increasing the number of concurrent trials or limiting the number of concurrent trials with the max\\_concurrent\\_trials Tune option to ensure there are enough free CPUs for Ray Data to run on.",
5419
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5420
        },
5421
        {
5422
            "question": "should the Ray head node and all workers have the same object store memory size allocated?",
5423
            "sources": [
5424
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
5425
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#head-node-commands",
5426
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",
5427
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",
5428
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",
5429
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",
5430
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",
5431
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",
5432
                "https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",
5433
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",
5434
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",
5435
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",
5436
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#head-node-commands",
5437
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts"
5438
            ],
5439
            "document_ids": [
5440
                6394,
5441
                6441,
5442
                5109,
5443
                563,
5444
                5013,
5445
                6437,
5446
                6446,
5447
                855,
5448
                5557,
5449
                14483,
5450
                560,
5451
                6375,
5452
                6441,
5453
                5014
5454
            ],
5455
            "answer": " Yes, it is recommended to have the same object store memory size allocated for both the Ray head node and worker nodes. This is because the head node has more memory-demanding system components and can easily have out-of-memory problems if the same number of tasks and actors are executed from a head node. By default, Ray reserves 30% of available memory for object store memory, but this can be controlled using the `--object-store-memory` option. It is important to allocate enough memory to the object store to avoid running out of memory during application execution.",
5456
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5457
        },
5458
        {
5459
            "question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",
5460
            "sources": [
5461
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",
5462
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#launching-an-on-premise-cluster",
5463
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5464
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",
5465
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5466
                "https://docs.ray.io/en/master/serve/api/index.html#serve-start",
5467
                "https://docs.ray.io/en/master/serve/architecture.html#how-does-serve-ensure-horizontal-scalability-and-availability",
5468
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",
5469
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5470
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#build-serve-config-files-for-production-deployment",
5471
                "https://docs.ray.io/en/master/serve/architecture.html#high-level-view",
5472
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.ProxyLocation.HeadOnly.html#ray-serve-config-proxylocation-headonly",
5473
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",
5474
                "https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout"
5475
            ],
5476
            "document_ids": [
5477
                14022,
5478
                6499,
5479
                14055,
5480
                14025,
5481
                14288,
5482
                13994,
5483
                13476,
5484
                13958,
5485
                14287,
5486
                13439,
5487
                13465,
5488
                14163,
5489
                14045,
5490
                13795
5491
            ],
5492
            "answer": " To specify whether to set up an HTTP proxy on each node or just the head node in Ray Serve, you can use the `http_options` field in the Serve config and set the `proxy-location` property. The options are \"Disabled\", \"HeadOnly\", and \"EveryNode\", with \"EveryNode\" being the default, which starts one HTTP server per node. The `num_cpus` field is deprecated and no longer used.",
5493
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5494
        },
5495
        {
5496
            "question": "Want to embed Grafana into the Ray Dashboard, given that I am using KubeRay\n\nGiven the context that Prometheus and Grafana are not running on my Head node, and that I am using KubeRay, how should I be setting the following variables?\n\u2022 `RAY_GRAFANA_HOST`\n\u2022 `RAY_PROMETHEUS_HOST`\nAnd is there a way to set them more intelligently, given that head node IP is changing every time we reconfigure our cluster?",
5497
            "sources": [
5498
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",
5499
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",
5500
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",
5501
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",
5502
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-grafana-host-location",
5503
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",
5504
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-11-embed-grafana-panels-in-ray-dashboard",
5505
                "https://docs.ray.io/en/master/cluster/metrics.html#simplist-setting-up-grafana-with-ray-provided-configurations",
5506
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",
5507
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",
5508
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",
5509
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-prometheus-host-location",
5510
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#dashboard-host",
5511
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster"
5512
            ],
5513
            "document_ids": [
5514
                5544,
5515
                5543,
5516
                6061,
5517
                5543,
5518
                5547,
5519
                5545,
5520
                6086,
5521
                5597,
5522
                6084,
5523
                6085,
5524
                6058,
5525
                5546,
5526
                5935,
5527
                6062
5528
            ],
5529
            "answer": " Given that Prometheus and Grafana are not running on your Head node and you are using KubeRay, you should set `RAY_PROMETHEUS_HOST` to the address that the head node can use to access Prometheus, and `RAY_GRAFANA_HOST` to the address that the head node can use to access Grafana.\n\nTo make these settings more intelligent and avoid hard-coding the IP address of the head node, which changes every time you reconfigure the cluster, you can use the Kubernetes service name and port to access Prometheus and Grafana. For example, if Prometheus and Grafana are running in the `monitoring` namespace, you can set `RAY_PROMETHEUS_HOST` to `http://prometheus-server.monitoring.svc.cluster.local:9090` and `RAY_GRAFANA_HOST` to `http://grafana.monitoring.svc.cluster.local:3000`.\n\nYou should also set `RAY_GRAFANA_IFRAME_HOST` to the same value as `RAY_GRAFANA_HOST` to ensure that your browser can fetch the Grafana panels from the Grafana server rather than from the head node.\n\nNote that these settings should be configured when you start the Ray clusters.",
5530
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5531
        },
5532
        {
5533
            "question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",
5534
            "sources": [
5535
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",
5536
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
5537
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",
5538
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",
5539
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#deploy-a-ray-cluster",
5540
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",
5541
                "https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#experiment-results-kuberay-v0-6-0",
5542
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",
5543
                "https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-1-create-a-new-kubernetes-cluster",
5544
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#worker-init-container",
5545
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-1-set-up-a-kubernetes-cluster-on-gcp",
5546
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
5547
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",
5548
                "https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling"
5549
            ],
5550
            "document_ids": [
5551
                5975,
5552
                5953,
5553
                5972,
5554
                6170,
5555
                6175,
5556
                5973,
5557
                5779,
5558
                5963,
5559
                5772,
5560
                5708,
5561
                6159,
5562
                5954,
5563
                5969,
5564
                5559
5565
            ],
5566
            "answer": " The GCS (Global Control Service) in the KubeRay operator determines which Kubernetes pod to kill when using KubeRay autoscaling based on the resource demands of the Ray workload. It reacts to task and actor resource requests, and not application metrics or physical resource utilization. The Ray autoscaler tries to increase the number of worker nodes when the resource demands exceed the current capacity and removes worker nodes from the cluster when they sit idle. It's important to note that the autoscaler considers logical resource requests for scaling, not physical machine utilization.",
5567
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5568
        },
5569
        {
5570
            "question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",
5571
            "sources": [
5572
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",
5573
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",
5574
                "https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",
5575
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",
5576
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",
5577
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5578
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",
5579
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",
5580
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5581
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",
5582
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5583
                "https://docs.ray.io/en/master/serve/api/index.html#serve-config",
5584
                "https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",
5585
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#build-serve-config-files-for-production-deployment"
5586
            ],
5587
            "document_ids": [
5588
                13854,
5589
                13832,
5590
                13795,
5591
                14020,
5592
                14044,
5593
                14288,
5594
                14045,
5595
                13852,
5596
                14055,
5597
                13716,
5598
                14081,
5599
                13986,
5600
                13988,
5601
                13440
5602
            ],
5603
            "answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you can include it in the `http_options` field as follows:\n```yaml\nhttp_options:\n  host: 0.0.0.0\n  port: 8000\n  request_timeout_s: <value_in_seconds>\n\napplications:\n- name: <application_name>\n  ...\n```\nReplace `<value_in_seconds>` with the number of seconds you want to set as the request timeout, and `<application_name>` with the name of your application.\n\nFor example:\n```yaml\nhttp_options:\n  host: 0.0.0.0\n  port: 8000\n  request_timeout_s: 10\n\napplications:\n- name: my_app\n  ...\n```\nThis will set the end-to-end timeout for HTTP requests to 10 seconds before terminating and retrying at another replica.",
5604
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5605
        },
5606
        {
5607
            "question": "How do I make the GPU available on my M1 laptop to ray?",
5608
            "sources": [
5609
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",
5610
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
5611
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",
5612
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5613
                "https://docs.ray.io/en/master/installation.html#m1-mac-apple-silicon-support",
5614
                "https://docs.ray.io/en/master/ray-overview/installation.html#m1-mac-apple-silicon-support",
5615
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#m1-mac-apple-silicon-support",
5616
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#requesting-gpus",
5617
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",
5618
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",
5619
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",
5620
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gcp-gke-gpu-cluster.html#step-2-create-a-gpu-node-pool",
5621
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#starting-ray-nodes-with-gpus",
5622
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.utils.wait_for_gpu.html#ray-tune-utils-wait-for-gpu"
5623
            ],
5624
            "document_ids": [
5625
                4997,
5626
                1006,
5627
                4995,
5628
                4818,
5629
                73,
5630
                298,
5631
                14571,
5632
                6020,
5633
                2966,
5634
                6012,
5635
                417,
5636
                5950,
5637
                4992,
5638
                1487
5639
            ],
5640
            "answer": " To make the GPU available on your M1 laptop for Ray, you need to install Ray on a machine running Apple Silicon (such as M1 macs) using miniforge. However, multi-node clusters are currently untested on M1 Macs. Here are the steps to install Ray on an M1 Mac:\n\n1. Install miniforge by running the following command in the terminal:\n```bash\nwget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh\nbash Miniforge3-MacOSX-arm64.sh\nrm Miniforge3-MacOSX-arm64.sh # Cleanup.\n```\n2. Ensure you\u2019re using the miniforge environment by running the following commands:\n```bash\nsource ~/.bash_profile\nconda activate\n```\n3. Install Ray as you normally would by running the following command:\n```\npip install ray\n```\n\nTo use the GPU with Ray, you can define a Ray remote function with `num_gpus=1` and use external libraries like TensorFlow to make use of the GPU. Here is an example:\n\n```python\n@ray.remote(num_gpus=1)\ndef use_gpu():\n    print(\"ray.get_gpu_ids(): {}\".format(ray.get_gpu_ids()))\n    print(\"CUDA_VISIBLE_DEVICES: {}\".format(os.environ[\"CUDA_VISIBLE_DEVICES\"]))\n```\n\nNote that if you are using fractional GPUs, it is your responsibility to make sure multiple trials can share GPUs and there is enough memory to do so. Ray does not automatically handle this for you. If you want to use multiple GPUs per trial, you should check out LightningTrainer.",
5641
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5642
        },
5643
        {
5644
            "question": "How can I add a timeout for the Ray job?",
5645
            "sources": [
5646
                "https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",
5647
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.GetTimeoutError.html#ray-exceptions-gettimeouterror",
5648
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",
5649
                "https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#helpful-ray-commands",
5650
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
5651
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-tasks",
5652
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
5653
                "https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",
5654
                "https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",
5655
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",
5656
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
5657
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
5658
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-stop",
5659
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs"
5660
            ],
5661
            "document_ids": [
5662
                4416,
5663
                4796,
5664
                4765,
5665
                5525,
5666
                4214,
5667
                5424,
5668
                4535,
5669
                13795,
5670
                13698,
5671
                6556,
5672
                5757,
5673
                6554,
5674
                6602,
5675
                6558
5676
            ],
5677
            "answer": " To add a timeout for a Ray job, you can use the `timeout` parameter in the `ray.get()` function. This function is used to retrieve the result of a remote function execution. By specifying a timeout, the function will raise a `GetTimeoutError` if the result is not available within the specified time. Here is an example:\n\n```python\nfrom ray.exceptions import GetTimeoutError\n\n@ray.remote\ndef long_running_function():\n    time.sleep(8)\n\nobj_ref = long_running_function.remote()\ntry:\n    ray.get(obj_ref, timeout=4)\nexcept GetTimeoutError:\n    print(\"`get` timed out.\")\n```\n\nIn this example, the `long_running_function` will sleep for 8 seconds. By calling `ray.get(obj_ref, timeout=4)`, we are asking Ray to retrieve the result of the function execution, but we only want to wait for 4 seconds. If the result is not available within 4 seconds, a `GetTimeoutError` will be raised.\n\nHowever, this method is only suitable for timeout management of a single task. If you want to set a timeout for the entire Ray job, you can consider using the `ray job stop` command to stop the job manually after a certain period of time. Here is an example:\n\n```bash\nray job submit my_job.py --no-wait\nsleep 60  # wait for 60 seconds\nray job stop my_job_id\n```\n\nIn this example, we submit a Ray job using the `ray job submit` command, but we use the `--no-wait` option to prevent Ray from waiting for the job to finish. Then, we use the `sleep` command to wait for 60 seconds, and finally, we use the `ray job stop` command to stop the job. You need to replace `my_job.py` and `my_job_id` with your actual job script and ID.",
5678
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5679
        },
5680
        {
5681
            "question": "how do I set custom /tmp directory for remote cluster?",
5682
            "sources": [
5683
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
5684
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
5685
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
5686
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",
5687
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",
5688
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
5689
                "https://docs.ray.io/en/master/cluster/cli.html#ray-submit",
5690
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.restore.html#ray-rllib-algorithms-algorithm-algorithm-restore",
5691
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.restore.html#ray-tune-trainable-restore",
5692
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",
5693
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
5694
                "https://docs.ray.io/en/master/train/user-guides/persistent-storage.html#persistent-storage",
5695
                "https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",
5696
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster"
5697
            ],
5698
            "document_ids": [
5699
                4273,
5700
                1005,
5701
                6561,
5702
                872,
5703
                13680,
5704
                13718,
5705
                5625,
5706
                12465,
5707
                2081,
5708
                6205,
5709
                13719,
5710
                9150,
5711
                9160,
5712
                6562
5713
            ],
5714
            "answer": " To set a custom /tmp directory for a remote cluster in Ray, you can use the `RAY_TEMPDIR` environment variable to define where the temporary directory for the cluster files should be created. This directory should be accessible by the host, the outer container, and the inner container.\n\nHere is an example of how to set the `RAY_TEMPDIR` environment variable for a remote cluster:\n\n```\nRAY_TEMPDIR=\"/ray-mount\"\n```\n\nYou can then use this custom /tmp directory in your Ray application by referring to the `RAY_TEMPDIR` environment variable.\n\nNote: This setting can only be set per-job, i.e., in `ray.init()`. It cannot be set per-task or per-actor.\n\nFor more information about using a remote cluster in Ray, you can refer to the Ray documentation.",
5715
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5716
        },
5717
        {
5718
            "question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",
5719
            "sources": [
5720
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",
5721
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",
5722
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
5723
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",
5724
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
5725
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
5726
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",
5727
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
5728
                "https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",
5729
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
5730
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
5731
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",
5732
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",
5733
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"
5734
            ],
5735
            "document_ids": [
5736
                4960,
5737
                4960,
5738
                4385,
5739
                4959,
5740
                511,
5741
                6632,
5742
                872,
5743
                6396,
5744
                9160,
5745
                1775,
5746
                4384,
5747
                4961,
5748
                6360,
5749
                4823
5750
            ],
5751
            "answer": " Yes, if you set the `object_spilling_config[\"params\"][\"directory_path\"]` configuration in `ray.init()`, Ray will use the specified directory for object spilling instead of the default `/tmp`.\n\nHere's an example of how to set a custom directory for object spilling:\n\n```python\nimport json\nimport ray\n\nray.init(\n    _system_config=json.dumps(\n        {\n            \"object_spilling_config\": json.dumps(\n                {\"type\": \"filesystem\", \"params\": {\"directory_path\": \"/my/custom/spill/directory\"}}\n            )\n        }\n    )\n)\n```\n\nThis will configure Ray to spill objects to the `/my/custom/spill/directory` directory instead of the default `/tmp`.",
5752
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5753
        },
5754
        {
5755
            "question": "can you give me an example for *`--runtime-env-json`*",
5756
            "sources": [
5757
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",
5758
                "https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",
5759
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",
5760
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",
5761
                "https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",
5762
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
5763
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
5764
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
5765
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
5766
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
5767
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
5768
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",
5769
                "https://docs.ray.io/en/master/serve/api/index.html#serve-run",
5770
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"
5771
            ],
5772
            "document_ids": [
5773
                4308,
5774
                11458,
5775
                4319,
5776
                4253,
5777
                4661,
5778
                13720,
5779
                4712,
5780
                4716,
5781
                14217,
5782
                4713,
5783
                14236,
5784
                4292,
5785
                13990,
5786
                13856
5787
            ],
5788
            "answer": " The `--runtime-env-json` option is used to specify a JSON-serialized runtime environment dictionary that will be passed to `ray.init()` as the default for deployments. Here's an example:\n\n```json\nserve run --address=ray://<head-node-ip-address>:10001 --runtime-env-json='{\"env_vars\": {\"MY_ENV_VAR\": \"my-value\"}, \"working_dir\": \"./project/src\", \"pip\": [\"requests\", \"chess\"]}' local_dev:graph\n```\n\nIn this example, the runtime environment includes environment variables, a working directory, and pip packages.",
5789
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5790
        },
5791
        {
5792
            "question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",
5793
            "sources": [
5794
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5795
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
5796
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
5797
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5798
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
5799
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5800
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",
5801
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5802
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
5803
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5804
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-autoscaler",
5805
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
5806
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#configuring-the-autoscaler-sidecar-container",
5807
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#programmatic-scaling"
5808
            ],
5809
            "document_ids": [
5810
                13449,
5811
                14141,
5812
                14187,
5813
                14066,
5814
                14225,
5815
                14303,
5816
                5963,
5817
                13450,
5818
                14141,
5819
                13451,
5820
                6377,
5821
                14098,
5822
                5967,
5823
                6370
5824
            ],
5825
            "answer": " The value of `maxConcurrentReplicas` is not specified in the provided context. It is a parameter for configuring the autoscaling of a deployment, and its value would depend on the specific requirements of the application. The context provides information on various parameters that can be used in autoscaling configurations, but it does not give a specific value for `maxConcurrentReplicas`.",
5826
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5827
        },
5828
        {
5829
            "question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",
5830
            "sources": [
5831
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5832
                "https://docs.ray.io/en/master/rllib/package_ref/learner.html#torchlearner-configurations",
5833
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5834
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5835
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
5836
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
5837
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",
5838
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5839
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5840
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
5841
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5842
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
5843
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.zoopt.ZOOptSearch.set_max_concurrency.html#ray-tune-search-zoopt-zooptsearch-set-max-concurrency",
5844
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"
5845
            ],
5846
            "document_ids": [
5847
                13451,
5848
                11140,
5849
                14303,
5850
                14066,
5851
                14225,
5852
                14187,
5853
                13561,
5854
                13450,
5855
                14302,
5856
                14186,
5857
                14065,
5858
                14224,
5859
                1340,
5860
                14128
5861
            ],
5862
            "answer": " The value of `max_concurrent_queries` is independent of `target_num_ongoing_requests_per_replica`. It should be set to the maximum number of pending queries that a single replica can handle. The default value is 1, and it can be updated dynamically in the user config.",
5863
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5864
        },
5865
        {
5866
            "question": "what is a `smoothing_factor`",
5867
            "sources": [
5868
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5869
                "https://docs.ray.io/en/master/tune/faq.html#what-are-hyperparameters",
5870
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5871
                "https://docs.ray.io/en/master/tune/examples/includes/pb2_example.html#pb2-example",
5872
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",
5873
                "https://docs.ray.io/en/master/rllib/package_ref/utils.html#tensorflow-utilities",
5874
                "https://docs.ray.io/en/master/rllib/package_ref/utils.html#numpy-utilities",
5875
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.numpy.fc.html#ray-rllib-utils-numpy-fc",
5876
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#twin-delayed-ddpg-td3",
5877
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg",
5878
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.AsyncSampler.html#ray-rllib-evaluation-sampler-asyncsampler",
5879
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.AsyncHyperBandScheduler.html#ray-tune-schedulers-asynchyperbandscheduler",
5880
                "https://docs.ray.io/en/master/data/examples/batch_training.html#run-batch-training-using-map-groups",
5881
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.exploration.gaussian_noise.GaussianNoise.html#ray-rllib-utils-exploration-gaussian-noise-gaussiannoise"
5882
            ],
5883
            "document_ids": [
5884
                13454,
5885
                1007,
5886
                13455,
5887
                3991,
5888
                1920,
5889
                11114,
5890
                11120,
5891
                13324,
5892
                11867,
5893
                11849,
5894
                12379,
5895
                1735,
5896
                7897,
5897
                12675
5898
            ],
5899
            "answer": " A `smoothing_factor` is a hyperparameter that acts as a \"gain\" factor to amplify the response of the autoscaling algorithm in a system. It is used to speed up or slow down each upscaling or downscaling decision. For example, a higher `smoothing_factor` can be used to scale up resources quickly during high traffic volume, while a lower `smoothing_factor` can be used to make the application less sensitive to drops in traffic and scale down more conservatively.",
5900
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5901
        },
5902
        {
5903
            "question": "What is the reason actors change their state to unhealthy?",
5904
            "sources": [
5905
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",
5906
                "https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",
5907
                "https://docs.ray.io/en/master/serve/api/index.html#get-api-serve-deployments-status",
5908
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5909
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
5910
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
5911
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
5912
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",
5913
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",
5914
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#other-possible-scenarios-that-trigger-a-new-raycluster-preparation",
5915
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
5916
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.probe_unhealthy_workers.html#ray-rllib-evaluation-worker-set-workerset-probe-unhealthy-workers",
5917
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#other-possible-scenarios-that-trigger-a-new-raycluster-preparation",
5918
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"
5919
            ],
5920
            "document_ids": [
5921
                13836,
5922
                656,
5923
                14006,
5924
                14294,
5925
                14708,
5926
                4505,
5927
                11597,
5928
                13935,
5929
                747,
5930
                5814,
5931
                14131,
5932
                12306,
5933
                5813,
5934
                14306
5935
            ],
5936
            "answer": " Actors can become unhealthy in Ray if there's an error in the deployment's constructor, a crashed replica, or a general system or machine error. This information is provided in the context of the Ray framework and its state management for actors. The query about actors changing their state to unhealthy due to a particular reason like bad inputs or actions is not mentioned in the provided context.",
5937
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5938
        },
5939
        {
5940
            "question": "How do I access logs for a dead node?",
5941
            "sources": [
5942
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
5943
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
5944
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
5945
                "https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",
5946
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
5947
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
5948
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
5949
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
5950
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
5951
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",
5952
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",
5953
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
5954
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
5955
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused"
5956
            ],
5957
            "document_ids": [
5958
                492,
5959
                1006,
5960
                493,
5961
                662,
5962
                739,
5963
                740,
5964
                512,
5965
                6631,
5966
                13894,
5967
                695,
5968
                681,
5969
                445,
5970
                680,
5971
                5682
5972
            ],
5973
            "answer": " Based on the context provided, you cannot access the logs from a dead node directly. However, you can fetch the logs of a particular entity (task, actor, etc.) using the State API. Also, you could get the node ID or node IP from `ray list nodes` and use that to list the logs from a head node.\n\nHere's an example:\n```python\nfrom ray.util.state import list_logs\nlist_logs(node_id=<NODE_ID>)\n```\nAlternatively, you can use the `ray logs` command with the `--node-id` flag to get a particular log file from a node.\n\nFor example:\n```bash\nray logs gcs_server.out --node-id <NODE_ID>\n```\nNote that these methods will only work for logs on alive nodes. If you need to access logs from a dead node, you may need to manually retrieve them from the node's filesystem or from any log storage that was configured for your Ray cluster.",
5974
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5975
        },
5976
        {
5977
            "question": "What are the reasons for a node to change it\u2019s status to dead?",
5978
            "sources": [
5979
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",
5980
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#configuring-iam-role-and-ec2-instance-profile",
5981
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
5982
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",
5983
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#rescheduling-bundles-on-a-dead-node",
5984
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
5985
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.PlacementGroupState.html#ray-util-state-common-placementgroupstate",
5986
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#nodeaffinityschedulingstrategy",
5987
                "https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#handling-failures-and-node-preemption",
5988
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#provider-cache-stopped-nodes",
5989
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",
5990
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
5991
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",
5992
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure"
5993
            ],
5994
            "document_ids": [
5995
                4524,
5996
                6475,
5997
                554,
5998
                13869,
5999
                5098,
6000
                13898,
6001
                724,
6002
                5147,
6003
                9228,
6004
                6283,
6005
                5976,
6006
                467,
6007
                4790,
6008
                13894
6009
            ],
6010
            "answer": " A node in a Ray cluster can change its status to dead due to several reasons:\n\n1. Raylet failure: When the raylet process fails, the corresponding node is marked as dead and treated as a new node in the Ray cluster.\n2. Task killed due to low memory: If a worker node is running low on memory, the tasks or actors running on it may be killed to free up resources.\n3. Head node failure: If the head node fails, the Ray cluster will attempt to restart the head pod on another running node and bring up a new healthy node to replace it.\n4. Node affinity scheduling strategy failure: If a task or actor is scheduled to run on a particular node specified by its node ID, and the node doesn't exist or doesn't have the required resources, the task or actor will fail with TaskUnschedulableError or ActorUnschedulableError.\n5. Provider cache stopped nodes: If the provider cache stopped nodes feature is enabled, nodes will be stopped when the cluster scales down. If a node is stopped, it will be treated as dead and replaced with a new healthy node.\n\nAdditionally, nodes can also be marked as dead due to Kubernetes node scaling events or if they are terminated instead of stopped when the cluster scales down.",
6011
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6012
        },
6013
        {
6014
            "question": "What are the reasons for spikes in node CPU utilization",
6015
            "sources": [
6016
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
6017
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#view-the-resource-utilization",
6018
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#adding-cpu-only-nodes-to-your-cluster",
6019
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
6020
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",
6021
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
6022
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",
6023
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",
6024
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
6025
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",
6026
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",
6027
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",
6028
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",
6029
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training"
6030
            ],
6031
            "document_ids": [
6032
                468,
6033
                442,
6034
                9227,
6035
                554,
6036
                6375,
6037
                6364,
6038
                3015,
6039
                3008,
6040
                555,
6041
                3002,
6042
                3037,
6043
                5118,
6044
                3029,
6045
                3022
6046
            ],
6047
            "answer": " Spikes in node CPU utilization can be caused by:\n\n1. Increased logical resource demands from tasks and actors.\n2. Physical resource bottlenecks during preprocessing compute time.\n3. System processes running on the head node, like GCS, when deploying a large cluster.\n4. Autoscaling node launch overheads and configuration issues.\n5. High memory usage, which can lead to the operating system killing worker or raylet processes.\n\nIn your provided context, there are no specific reasons mentioned for spikes in CPU utilization. However, you should monitor resource utilization and memory usage to ensure they're within acceptable thresholds.",
6048
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6049
        },
6050
        {
6051
            "question": "What AWS machine type is recommended to deploy a RayService on EKS?",
6052
            "sources": [
6053
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-aks-microsoft-azure",
6054
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
6055
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",
6056
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",
6057
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-eks-cluster-aws",
6058
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",
6059
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#managed-kubernetes-services",
6060
                "https://docs.ray.io/en/master/cluster/getting-started.html#where-can-i-deploy-ray-clusters",
6061
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#q-are-you-deploying-on-public-cloud-providers-e-g-aws-gcp-azure",
6062
                "https://docs.ray.io/en/master/cluster/getting-started.html#what-s-next",
6063
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#what-does-the-rayservice-provide",
6064
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#launching-ray-clusters-on-aws-gcp-azure-on-prem",
6065
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/aws-eks-gpu-cluster.html#step-1-create-a-kubernetes-cluster-on-amazon-eks",
6066
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#provider-region"
6067
            ],
6068
            "document_ids": [
6069
                5887,
6070
                5656,
6071
                6376,
6072
                5642,
6073
                5886,
6074
                6458,
6075
                5884,
6076
                5601,
6077
                5651,
6078
                5602,
6079
                5738,
6080
                6518,
6081
                5889,
6082
                6277
6083
            ],
6084
            "answer": " To deploy a RayService on EKS, it is recommended to use machines with networking characteristics at least as good as an r5dn.16xlarge on AWS EC2. However, the specific AWS machine type or instance size is not specified in the context. Therefore, it's up to the user to choose an appropriate instance type based on their workload requirements and budget. Additionally, the user should set the quantity of logical CPU resources to 0 on the head node to avoid scheduling additional tasks on it.",
6085
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6086
        },
6087
        {
6088
            "question": "Is there a way to configure the session name generated by ray?",
6089
            "sources": [
6090
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
6091
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
6092
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
6093
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
6094
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",
6095
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",
6096
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
6097
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
6098
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
6099
                "https://docs.ray.io/en/master/cluster/cli.html#ray-attach",
6100
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#developing-ray-serve-python-scripts-on-a-raycluster",
6101
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
6102
                "https://docs.ray.io/en/master/ray-core/configure.html#configuring-ray",
6103
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs"
6104
            ],
6105
            "document_ids": [
6106
                4384,
6107
                4384,
6108
                4811,
6109
                13974,
6110
                6613,
6111
                4514,
6112
                511,
6113
                4823,
6114
                4662,
6115
                5628,
6116
                5979,
6117
                6615,
6118
                4379,
6119
                6631
6120
            ],
6121
            "answer": " Yes, you can configure the session name generated by Ray by using the `namespace` argument in the `ray.init()` function. This argument allows you to set a custom namespace for the session. However, the session name will still include a timestamp and process ID by default. If you want to change the default format of the session name, you may need to modify the Ray source code or submit a feature request to the Ray team.",
6122
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6123
        },
6124
        {
6125
            "question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",
6126
            "sources": [
6127
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",
6128
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-2-submit-a-ray-job-to-the-raycluster-via-ray-job-submission-sdk",
6129
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#python-sdk-api-reference",
6130
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli",
6131
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",
6132
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",
6133
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.get_job_info.html#ray-job-submission-jobsubmissionclient-get-job-info",
6134
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/rest.html#ray-jobs-rest-api",
6135
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",
6136
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
6137
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.list_jobs.html#ray-job-submission-jobsubmissionclient-list-jobs",
6138
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
6139
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup",
6140
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-jobs"
6141
            ],
6142
            "document_ids": [
6143
                6575,
6144
                5729,
6145
                6539,
6146
                6547,
6147
                6640,
6148
                6637,
6149
                6671,
6150
                6571,
6151
                6578,
6152
                6551,
6153
                6677,
6154
                6562,
6155
                6549,
6156
                5560
6157
            ],
6158
            "answer": " Yes, you can use the Python SDK to interact with the Ray Dashboard and get information about a job. The `ray.job_submission.JobSubmissionClient.get_job_info` method can be used to get the latest status and other information associated with a job. However, getting a direct link to the Ray Dashboard for a specific job is not explicitly mentioned in the provided context. The SDK allows you to submit jobs, list jobs, and get job information, but it doesn't seem to provide a specific function to get a direct link to the Ray Dashboard for a particular job.",
6159
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6160
        },
6161
        {
6162
            "question": "What may possible cause the node where this task was running crashed unexpectedly. This can happen if: (1) the instance where the node was running failed, (2) raylet crashes unexpectedly (OOM, preempted node, etc).",
6163
            "sources": [
6164
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
6165
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
6166
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
6167
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
6168
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.LocalRayletDiedError.html#ray-exceptions-localrayletdiederror",
6169
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.TaskUnschedulableError.html#ray-exceptions-taskunschedulableerror",
6170
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
6171
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#understanding-objectlosterrors",
6172
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.WorkerCrashedError.html#ray-exceptions-workercrashederror",
6173
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",
6174
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
6175
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",
6176
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",
6177
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance"
6178
            ],
6179
            "document_ids": [
6180
                5129,
6181
                555,
6182
                4658,
6183
                554,
6184
                4858,
6185
                4830,
6186
                557,
6187
                4559,
6188
                4906,
6189
                4524,
6190
                555,
6191
                5118,
6192
                597,
6193
                4519
6194
            ],
6195
            "answer": " The node where the task was running crashed unexpectedly due to the instance where the node was running failing or the raylet crashing unexpectedly, which could have been caused by memory pressure (OOM) or other reasons. Specifically, in this case, the node ran low on memory and Ray had to kill the worker that was running the task because it was the most recently scheduled task.",
6196
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6197
        },
6198
        {
6199
            "question": "Do you know how to resolve (gcs_server) gcs_health_check_manager.cc:108: Health check failed for node? I observed that the node is still up and running.",
6200
            "sources": [
6201
                "https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",
6202
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",
6203
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",
6204
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",
6205
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",
6206
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",
6207
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",
6208
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",
6209
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",
6210
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
6211
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#getting-the-status-of-the-application",
6212
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
6213
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
6214
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-9-upgrade-from-ray-serves-single-application-api-to-its-multi-application-api-without-downtime"
6215
            ],
6216
            "document_ids": [
6217
                5638,
6218
                13553,
6219
                6505,
6220
                13870,
6221
                4508,
6222
                4522,
6223
                13871,
6224
                5709,
6225
                4515,
6226
                14740,
6227
                13947,
6228
                13889,
6229
                467,
6230
                5705
6231
            ],
6232
            "answer": " The error message \"Health check failed for node\" suggests there's an issue with the health check of the worker node in Ray, a distributed computing framework. The node is still running, which means the problem is not a worker node failure but rather a failure in the health check process.\n\nThe context provided offers some insights into troubleshooting Ray issues, particularly those related to connecting to the head node (GCS). The most common cause of such issues is the worker node's inability to access the IP address or port of the head node. To debug this, you can use the 'ip route get' command on the worker node, targeting the head node's IP address.\n\nAnother possible cause for health check failures is a mismatch in Ray versions between the worker node and the head node. Ensure both nodes are running the same Ray version.\n\nLastly, there could be firewall settings preventing access to the head node. You can use tools like 'nmap' or 'nc' to check whether each port can be reached from a node.\n\nIn summary, to resolve the \"Health check failed for node\" issue:\n\n1. Check if the worker node can access the head node's IP address and port.\n2. Ensure both the worker node and the head node are running the same Ray version.\n3. Verify there are no firewall settings preventing access to the head node.",
6233
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6234
        },
6235
        {
6236
            "question": "Do you need the DAGDriver to deploy a serve application using RayServe?",
6237
            "sources": [
6238
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",
6239
                "https://docs.ray.io/en/master/serve/production-guide/index.html#working-example-fruitstand-application",
6240
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-the-applications",
6241
                "https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph",
6242
                "https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",
6243
                "https://docs.ray.io/en/master/serve/tutorials/gradio-dag-visualization.html#requirement-driver",
6244
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",
6245
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",
6246
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",
6247
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",
6248
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",
6249
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.run.html#ray-serve-run",
6250
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
6251
                "https://docs.ray.io/en/master/serve/getting_started.html#getting-started"
6252
            ],
6253
            "document_ids": [
6254
                13741,
6255
                13971,
6256
                13655,
6257
                13426,
6258
                13502,
6259
                14524,
6260
                13859,
6261
                13652,
6262
                13650,
6263
                13647,
6264
                13744,
6265
                14238,
6266
                13621,
6267
                13567
6268
            ],
6269
            "answer": " No, you don't need the DAGDriver to deploy a RayServe application. The DAGDriver is used to route HTTP requests through your call graph and can be useful when working with deployment graphs, but it is not a requirement for deploying a RayServe application. You can deploy an application using the `serve deploy` command and the configuration file for your deployments, as shown in the context.",
6270
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6271
        },
6272
        {
6273
            "question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",
6274
            "sources": [
6275
                "https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",
6276
                "https://docs.ray.io/en/master/serve/api/index.html#serve-run",
6277
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.Application.html#ray-serve-application",
6278
                "https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#defining-an-application-builder",
6279
                "https://docs.ray.io/en/master/serve/tutorials/batch.html#define-the-deployment",
6280
                "https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",
6281
                "https://docs.ray.io/en/master/serve/index.html#quickstart",
6282
                "https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",
6283
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serves-recovery-procedures",
6284
                "https://docs.ray.io/en/master/serve/api/index.html#put-api-serve-applications",
6285
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
6286
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
6287
                "https://docs.ray.io/en/master/serve/api/index.html#serve-run",
6288
                "https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-ray-train-logger-callbacks"
6289
            ],
6290
            "document_ids": [
6291
                13844,
6292
                13990,
6293
                14037,
6294
                13813,
6295
                14586,
6296
                13846,
6297
                13599,
6298
                13850,
6299
                13883,
6300
                14011,
6301
                14219,
6302
                13719,
6303
                13991,
6304
                9141
6305
            ],
6306
            "answer": " To provide the import path for a simple RayServe deployment, you can use a local file path or a remote URI that hosts the code in remote storage. For example, if you have a Python file named \"my_app.py\" in your current working directory, the import path would be \"python_file:my_app\". If the code is hosted on GitHub, you can use a config file with a remote URI, such as \"import_path: python\\_file:my\\_app, runtime\\_env: working\\_dir: 'https://github.com/my\\_username/my\\_repo/archive/HEAD.zip'\".",
6307
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6308
        },
6309
        {
6310
            "question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",
6311
            "sources": [
6312
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",
6313
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",
6314
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",
6315
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
6316
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",
6317
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",
6318
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",
6319
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#about-kuberay",
6320
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-2-deploy-a-ray-cluster-on-kubernetes-with-the-kuberay-operator",
6321
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",
6322
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",
6323
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",
6324
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-is-a-rayjob",
6325
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource"
6326
            ],
6327
            "document_ids": [
6328
                5751,
6329
                5760,
6330
                5752,
6331
                6144,
6332
                5647,
6333
                5658,
6334
                5763,
6335
                5660,
6336
                6162,
6337
                5742,
6338
                5754,
6339
                5642,
6340
                5753,
6341
                5723
6342
            ],
6343
            "answer": " Yes, the context includes an example of using RayJob in Kuberay. Here are the steps to install a RayJob:\n\n1. Download the `ray_v1alpha1_rayjob.yaml` file using the following command:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml\n```\n2. Create a RayJob by applying the `ray_v1alpha1_rayjob.yaml` file using the following command:\n```\nkubectl apply -f ray_v1alpha1_rayjob.yaml\n```\n\nThis will create a RayCluster and submit a Ray job to the cluster. The Ray job is defined in the `entrypoint` field of the RayJob custom resource. The RayCluster will not be deleted after the job finishes.\n\nNote that RayJob support in KubeRay v0.x is in alpha. You need Ray 1.10 or higher and KubeRay v0.3.0+ (v0.6.0+ is recommended) to use RayJob.",
6344
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6345
        },
6346
        {
6347
            "question": "Does Ray metrics have to be exported via an actor?",
6348
            "sources": [
6349
                "https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",
6350
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",
6351
                "https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",
6352
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",
6353
                "https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",
6354
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
6355
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
6356
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
6357
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",
6358
                "https://docs.ray.io/en/master/cluster/metrics.html#scraping-metrics",
6359
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
6360
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",
6361
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",
6362
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors"
6363
            ],
6364
            "document_ids": [
6365
                503,
6366
                403,
6367
                656,
6368
                438,
6369
                5573,
6370
                11596,
6371
                14707,
6372
                4503,
6373
                403,
6374
                5581,
6375
                14723,
6376
                5443,
6377
                4200,
6378
                14680
6379
            ],
6380
            "answer": " No, Ray metrics do not have to be exported via an actor. While the provided example shows an actor exporting metrics, metrics can also be exported using the `ray.util.metrics` module without an actor. The metrics are then collected and exposed by Ray's metrics agent, which runs on each node and exports the metrics in a Prometheus format.",
6381
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6382
        },
6383
        {
6384
            "question": "how can I avoid objects not getting spilled?",
6385
            "sources": [
6386
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",
6387
                "https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",
6388
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",
6389
                "https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",
6390
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
6391
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",
6392
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",
6393
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#out-of-core-data-processing",
6394
                "https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",
6395
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",
6396
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#cluster-mode",
6397
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#stats",
6398
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",
6399
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node"
6400
            ],
6401
            "document_ids": [
6402
                6374,
6403
                1006,
6404
                4959,
6405
                4447,
6406
                14748,
6407
                4960,
6408
                4963,
6409
                14485,
6410
                1247,
6411
                4962,
6412
                4968,
6413
                4969,
6414
                4223,
6415
                4961
6416
            ],
6417
            "answer": " To avoid objects not getting spilled and to prevent performance problems, you can use NVMe SSDs or other high-performance storage for Ray object spilling. By default, Ray spills objects to its temporary directory in the local filesystem. You can configure the directory where objects are spilled to by using the `object_spilling_config` parameter in the `ray.init()` function. This is particularly useful when working with large datasets that may not fit entirely in memory.",
6418
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6419
        },
6420
        {
6421
            "question": "what\u2019s ray core",
6422
            "sources": [
6423
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
6424
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",
6425
                "https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",
6426
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
6427
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#want-to-learn-more",
6428
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#basic-examples",
6429
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#resources",
6430
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",
6431
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
6432
                "https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",
6433
                "https://docs.ray.io/en/master/ray-core/api/core.html#core-api",
6434
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner.LearnerSpec.html#ray-rllib-core-learner-learner-learnerspec",
6435
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
6436
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview"
6437
            ],
6438
            "document_ids": [
6439
                4662,
6440
                4481,
6441
                4673,
6442
                4811,
6443
                5444,
6444
                5383,
6445
                5102,
6446
                5414,
6447
                5380,
6448
                4644,
6449
                4648,
6450
                12726,
6451
                5656,
6452
                6835
6453
            ],
6454
            "answer": " Ray Core is a part of Ray, an open-source distributed computing framework. Ray Core provides primitives such as tasks, actors, and objects for building and scaling distributed applications. It has a cluster-agnostic CLI and a cluster-aware API for creating and managing Ray applications. Ray allows seamless scaling of applications from a laptop to a cluster without code change by using resources, which are key-value pairs that abstract away physical machines and let you express computation in terms of resources.",
6455
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6456
        },
6457
        {
6458
            "question": "Does ray support cron job",
6459
            "sources": [
6460
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",
6461
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",
6462
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
6463
                "https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",
6464
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",
6465
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
6466
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide",
6467
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",
6468
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",
6469
                "https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",
6470
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
6471
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",
6472
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",
6473
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice"
6474
            ],
6475
            "document_ids": [
6476
                5754,
6477
                6637,
6478
                6208,
6479
                5635,
6480
                6638,
6481
                5656,
6482
                5784,
6483
                6530,
6484
                6181,
6485
                5634,
6486
                5757,
6487
                6596,
6488
                6560,
6489
                5780
6490
            ],
6491
            "answer": " No, Ray does not directly support cron jobs. However, you can set up cron jobs on the client side to periodically submit Ray jobs using the Ray Jobs API or other job submission methods. Ray provides a stable PublicAPI for job submission and supports running multiple jobs simultaneously on the same Ray cluster. However, it's important to note that Ray does not provide strong resource isolation or support priorities for multi-tenant production environments.",
6492
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6493
        },
6494
        {
6495
            "question": "can you give me the dependencies list for api read_images?",
6496
            "sources": [
6497
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
6498
                "https://docs.ray.io/en/master/serve/deploy-many-models/model-multiplexing.html#model-multiplexing",
6499
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
6500
                "https://docs.ray.io/en/master/data/working-with-images.html#reading-images",
6501
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",
6502
                "https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",
6503
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",
6504
                "https://docs.ray.io/en/master/installation.html#docker-source-images",
6505
                "https://docs.ray.io/en/master/serve/tutorials/object-detection.html#serving-an-object-detection-model",
6506
                "https://docs.ray.io/en/master/serve/http-guide.html#list-of-built-in-adapters",
6507
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset",
6508
                "https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",
6509
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning",
6510
                "https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data"
6511
            ],
6512
            "document_ids": [
6513
                7361,
6514
                13666,
6515
                7360,
6516
                6720,
6517
                10191,
6518
                306,
6519
                14587,
6520
                81,
6521
                14472,
6522
                13511,
6523
                8041,
6524
                6779,
6525
                6011,
6526
                7909
6527
            ],
6528
            "answer": " The dependencies for the `api read_images` function include:\n\n* The `ray` library for data processing and parallelism\n* The `pyarrow` library for handling file system operations\n* Optionally, a specific file system such as `s3` for reading images from cloud storage\n* The `numpy` library for handling image data in the format of numpy arrays\n* The `PIL` library for opening and manipulating image files\n* The `requests` library for downloading image files from the web\n\nThese dependencies are used in the `read_images` function to load and process image data. The specific dependencies required will depend on the use case and the format of the input image data.",
6529
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6530
        },
6531
        {
6532
            "question": "how do I kill a specific serve replica",
6533
            "sources": [
6534
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
6535
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
6536
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
6537
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",
6538
                "https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-deployments",
6539
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
6540
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",
6541
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
6542
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",
6543
                "https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-applications",
6544
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#replica-health-checking",
6545
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",
6546
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",
6547
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"
6548
            ],
6549
            "document_ids": [
6550
                13912,
6551
                13912,
6552
                13911,
6553
                13717,
6554
                14007,
6555
                13908,
6556
                13448,
6557
                13892,
6558
                13915,
6559
                14015,
6560
                13865,
6561
                13916,
6562
                13905,
6563
                13901
6564
            ],
6565
            "answer": " To kill a specific serve replica, you can use the following steps:\n\n1. Get the handle to the replica using `ray.get_actor()` function, providing the NAME of the replica from the output of `ray list actors` command.\n2. Use `ray.kill()` function to kill the replica, providing the replica handle as the argument. You can also specify `no_restart=True` to prevent the replica from automatically restarting.\n\nHere is an example:\n```python\nimport ray\n\n# Get the handle to the replica\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\n\n# Kill the replica\nray.kill(replica_handle, no_restart=True)\n```\nNote that while the replica is restarted, the other replicas can continue processing requests. Eventually, the replica will restart and continue serving requests.",
6566
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6567
        }
6568
    ]
6569
}
6570
llm-applications

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