llm-applications

1
{
2
    "config": {
3
        "experiment_name": "chunk-size-500",
4
        "chunk_size": 500,
5
        "chunk_overlap": 50,
6
        "num_chunks": 5,
7
        "embedding_model_name": "thenlper/gte-base",
8
        "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1",
9
        "temperature": 0.0,
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        "max_context_length": 32768,
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        "system_content": "Answer the query using the context provided. Be succinct.",
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        "assistant_content": "",
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        "docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",
14
        "experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",
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        "references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",
16
        "num_samples": 177
17
    },
18
    "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/data-internals.html#execution-memory",
25
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",
26
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"
27
            ],
28
            "document_ids": [
29
                9358,
30
                9120,
31
                9380,
32
                9109,
33
                19788
34
            ],
35
            "answer": " When using `map_batches`, you can specify the batch format as a NumPy ndarray or pandas DataFrame. By default, Ray Data represents batches as dicts of NumPy ndarrays. If you're experiencing memory issues, make sure the size of your Ray Data batches is small enough to fit into memory. Here's an example of how to use `map_batches` with a NumPy-based batch format:\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\nIn this example, the `batch_format` is set to \"numpy\" in the `map_batches` function. This tells Ray Data to represent the batches as dicts of NumPy ndarrays.",
36
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
37
        },
38
        {
39
            "question": "How does autoscaling work in a Ray Serve application?",
40
            "sources": [
41
                "https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",
42
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",
43
                "https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",
44
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
45
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling"
46
            ],
47
            "document_ids": [
48
                18089,
49
                18057,
50
                7537,
51
                8677,
52
                8021
53
            ],
54
            "answer": " Ray Serve's autoscaling feature automatically adjusts the number of replicas in a deployment based on request demand. The Ray Serve Autoscaler, which is an application-level autoscaler, requests Ray to start a specific number of replica actors. If there are not enough available CPUs to place these actors, the Ray Autoscaler will request more Ray nodes from the underlying cloud provider. The Ray autoscaler runs on the head node and adjusts the number of worker nodes based on the resource demands of the Ray workload. It is possible to configure the Ray autoscaler using the Ray cluster launcher or on Kubernetes.",
55
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
56
        },
57
        {
58
            "question": "how do I get the address of a ray node",
59
            "sources": [
60
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",
61
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",
62
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",
63
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",
64
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes"
65
            ],
66
            "document_ids": [
67
                5714,
68
                8907,
69
                7330,
70
                9005,
71
                8529
72
            ],
73
            "answer": " To get the address of a Ray node, you can look at the output of the `ray.nodes()` function, which will show information about the nodes in the Ray cluster. The address of the node can be found under the 'NodeManagerAddress' field. For example, in the provided context, the address of the node is '192.168.1.82'.\n\nAlternatively, if you want to get the address of the Ray dashboard, you can look for the first line of the output, which should contain the address in the format of 'http://<head-node-ip>:<port>'. In the provided context, the address of the Ray dashboard is 'http://127.0.0.1:8265'.",
74
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
75
        },
76
        {
77
            "question": "Does Ray support NCCL?",
78
            "sources": [
79
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#docker-image",
80
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
81
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",
82
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",
83
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli"
84
            ],
85
            "document_ids": [
86
                8419,
87
                5954,
88
                19336,
89
                19338,
90
                5894
91
            ],
92
            "answer": " Yes, Ray supports NCCL (NVIDIA Collective Communications Library) through its collective communication library, which uses NCCL and GLOO as optional high-performance communication backends for 10x more efficient out-of-band communication between Ray actor and task processes on both distributed CPUs and GPUs. This feature is suitable for distributed ML programs on Ray. However, the base Ray image does not come with CUDA or ML dependencies by default. You would need to use the Ray-ml image or add the necessary dependencies to the base Ray image for NCCL to work.",
93
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
94
        },
95
        {
96
            "question": "Is Ray integrated with DeepSpeed?",
97
            "sources": [
98
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",
99
                "https://docs.ray.io/en/master/workflows/basics.html#ray-integration",
100
                "https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",
101
                "https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",
102
                "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"
103
            ],
104
            "document_ids": [
105
                12491,
106
                12758,
107
                11175,
108
                10819,
109
                14501
110
            ],
111
            "answer": " Yes, Ray is integrated with DeepSpeed through the Ray Train component. The Transformers library has integrated with DeepSpeed and Ray Train, allowing for easy configuration and use of DDP and DeepSpeed. You just need to specify the DeepSpeed configuration in the TrainingArguments object.",
112
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
113
        },
114
        {
115
            "question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",
116
            "sources": [
117
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",
118
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",
119
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",
120
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",
121
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors"
122
            ],
123
            "document_ids": [
124
                6159,
125
                6138,
126
                6140,
127
                7371,
128
                6133
129
            ],
130
            "answer": " If you use `asyncio`'s `await` to wait for a Ray future like `await x.remote()`, the code will fail because Ray doesn't support `asyncio` for remote tasks. Instead, you can wrap the async function with a synchronous wrapper and use `ray.remote` on the wrapper. Alternatively, you can translate Ray ObjectRefs to `asyncio.Futures` and await on them in existing concurrent applications. To convert a Ray ObjectRef to an `asyncio.Future`, you can use `asyncio.wrap_future(ref.future())`.",
131
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
132
        },
133
        {
134
            "question": "How would you compare Spark, Ray, Dask?",
135
            "sources": [
136
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
137
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",
138
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",
139
                "https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",
140
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler"
141
            ],
142
            "document_ids": [
143
                8760,
144
                19427,
145
                19446,
146
                9258,
147
                19448
148
            ],
149
            "answer": " Spark, Ray, and Dask are all distributed computing systems, but they have different focuses. Spark is a general-purpose cluster computing system with a focus on batch processing and machine learning. Ray is a high-performance distributed execution system with a focus on scaling machine learning and reinforcement learning workloads, particularly on GPUs. Dask is a parallel computing library for analytics and scientific computing workloads, providing NumPy and Pandas-like APIs for larger-than-memory data and multi-machine clusters. Dask can be used on Ray for a more user-friendly API while still benefiting from Ray's performance. Dask-on-Ray may not achieve the same performance as using Ray directly.",
150
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
151
        },
152
        {
153
            "question": "why would ray overload a node w/ more task that the resources allow ?",
154
            "sources": [
155
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",
156
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
157
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
158
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",
159
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources"
160
            ],
161
            "document_ids": [
162
                7067,
163
                6913,
164
                6908,
165
                6897,
166
                6904
167
            ],
168
            "answer": " Ray might overload a node with more tasks than its resources allow if the tasks or actors are not set to use their proportionate share of resources. This can cause issues like Out-Of-Memory (OOM). To prevent this, you can reduce the number of concurrently running tasks or actors on each node by increasing the resources requested by them. Ray ensures that the sum of the resource requirements of all concurrent tasks and actors on a given node does not exceed the node\u2019s total resources.\n\nTo specify a task or actor\u2019s logical resource requirements, you can use the `SetResource` function in Ray. By default, Ray tasks use 1 logical CPU resource and Ray actors use 1 logical CPU for scheduling. However, resource requirements do not impose limits on actual physical resource usage. You can manually override the default resource quantities when starting the Ray cluster using `ray.init()`.",
169
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
170
        },
171
        {
172
            "question": "when should I use Ray Client?",
173
            "sources": [
174
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
175
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",
176
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",
177
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
178
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client"
179
            ],
180
            "document_ids": [
181
                8825,
182
                8813,
183
                8819,
184
                19791,
185
                8806
186
            ],
187
            "answer": " You should use Ray Client when you want to connect an interactive Python shell to a remote Ray cluster for development purposes. However, be aware that it requires a stable connection to the remote cluster and may not work as expected for ML workloads like Ray Tune or Ray Train. For long-running workloads, consider using Ray Jobs API instead.",
188
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
189
        },
190
        {
191
            "question": "how to scatter actors across the cluster?",
192
            "sources": [
193
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",
194
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",
195
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",
196
                "https://docs.ray.io/en/master/data/batch_inference.html#handling-cpu-out-of-memory-failures",
197
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard"
198
            ],
199
            "document_ids": [
200
                10637,
201
                7367,
202
                6956,
203
                9222,
204
                18154
205
            ],
206
            "answer": " To scatter actors across the cluster in Ray, you can use the \"SPREAD\" scheduling strategy. This strategy will try to spread the actors among available nodes. Here's an example of how to use it:\n\n```python\n@ray.remote(scheduling_strategy=\"SPREAD\")\nclass SpreadActor:\n    pass\n\n# Spread actors across the cluster.\nactors = [SpreadActor.options(scheduling_strategy=\"SPREAD\").remote() for _ in range(10)]\n```\n\nThis will ensure that the actors are spread out evenly across the nodes in your cluster. You can adjust the number of actors and the resources they use based on the specific needs and capabilities of your cluster.",
207
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
208
        },
209
        {
210
            "question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",
211
            "sources": [
212
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",
213
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
214
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
215
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",
216
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up"
217
            ],
218
            "document_ids": [
219
                8814,
220
                18754,
221
                6436,
222
                7690,
223
                5604
224
            ],
225
            "answer": " The \"connection refused\" error you're encountering when running `ray debug` on your remote Ray cluster suggests that your client is not able to establish a connection with the Ray cluster. This could be due to several reasons, such as an incorrect cluster address or the Ray cluster not being ready yet.\n\nWhen connecting to a remote Ray cluster, you should use `ray.init(\"ray://<head_node_host>:10001\")` to initialize the Ray cluster, where `<head_node_host>` is the IP address or hostname of the head node of your Ray cluster. This will connect your shell to the cluster and allow you to perform interactive work.\n\nIf you're testing on a remote cluster, you should use `serve run --address \"ray://<head-node-ip-address>:10001\"` to specify the address of the Ray cluster to connect to.\n\nIt's also worth noting that if the Ray cluster is not yet ready, you may encounter a \"connection refused\" error. In this case, you should wait a few seconds for the Dashboard, Dashboard Agent, and GCS to start up after the head Pod is ready.\n\nIf you continue to encounter issues, you may want to check the environment variable `RAY_ADDRESS` to ensure that it is set correctly. If it is not defined, you can check the address of the latest cluster started in `/tmp/ray/ray_current_cluster`. If this is also empty, you may need to start a new local Ray instance.",
226
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
227
        },
228
        {
229
            "question": "How does Ray AIR set up the model to communicate gradient updates across machines?",
230
            "sources": [
231
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",
232
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary",
233
                "https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#updating-your-training-function",
234
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",
235
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a"
236
            ],
237
            "document_ids": [
238
                13170,
239
                13199,
240
                11060,
241
                13139,
242
                13428
243
            ],
244
            "answer": " To set up the model to communicate gradient updates across machines in Ray AIR, you should wrap your model in MultiWorkerMirroredStrategy, which enables synchronous distributed training. The model must be built and compiled within the scope of the strategy. Additionally, you should update your Dataset batch size to the global batch size. When using the Ray AIR Transformers Trainer, you need to create the Trainer inside a function (trainer\\_init\\_per\\_worker) and return it, ensuring that you initialize the model, metric, and tokenizer inside that function. The training will then proceed by the means of PyTorch DDP.",
245
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
246
        },
247
        {
248
            "question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",
249
            "sources": [
250
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
251
                "https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests",
252
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",
253
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
254
                "https://docs.ray.io/en/master/serve/index.html#why-choose-serve"
255
            ],
256
            "document_ids": [
257
                18927,
258
                18697,
259
                18579,
260
                18378,
261
                18337
262
            ],
263
            "answer": " Use Ray Serve instead of Modal or Seldon because it is framework-agnostic, supports model composition, and provides production-ready features like autoscaling and resource allocation. Unlike using containers directly, Ray Serve allows for multi-model composition via a programmable API, flexible scaling, and integration with business logic.",
264
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
265
        },
266
        {
267
            "question": "How do I deploy an LLM workload on top of Ray Serve?",
268
            "sources": [
269
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
270
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
271
                "https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",
272
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
273
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment"
274
            ],
275
            "document_ids": [
276
                19150,
277
                517,
278
                18360,
279
                18548,
280
                18364
281
            ],
282
            "answer": " To deploy an LLM (large language model) workload on top of Ray Serve, you can follow these steps:\n\n1. Write your LLM application as a single Python module, which includes all the necessary steps such as prompt preprocessing, vector database lookups, LLM API calls, and response validation.\n2. Deploy your Ray Serve application to a production Ray cluster using the Ray Serve CLI. You can create a config file that includes the necessary deployment and resource information.\n3. Use the `serve deploy` command and specify the config file path to deploy the application to a Ray cluster over HTTP. This can be a local, single-node cluster or a remote, multi-node cluster started with the Ray Cluster Launcher.\n4. Configure your deployments to scale out by increasing the number of deployment replicas and assigning replica resources such as CPUs and GPUs.\n\nYou can refer to the Ray Serve documentation for more information on configuring a deployment.",
283
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
284
        },
285
        {
286
            "question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",
287
            "sources": [
288
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
289
                "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",
290
                "https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-size",
291
                "https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#task-based-batch-prediction",
292
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference"
293
            ],
294
            "document_ids": [
295
                11105,
296
                14497,
297
                9125,
298
                7405,
299
                10803
300
            ],
301
            "answer": " Based on the context provided, the model you are using (EleutherAI/gpt-j-6B) is quite large and may not fit in GPU memory, especially if the GPU has less than 16 GB of GRAM. To load this model, the example uses DeepSpeed, a library that optimizes the training process and reduces GRAM usage.\n\nThe batch size you choose will also affect the memory usage. Increasing the batch size can improve performance, but if it's too large, you may run out of memory. If you encounter an out-of-memory error, you should decrease the batch size.\n\nThe default batch size depends on the resource type. If you're using CPUs, the default batch size is 4096, while for GPUs, you must specify an explicit batch size.\n\nIn your case, since you are using a large model and want to set the `model_id` to \"EleutherAI/gpt-j-6B\", you should ensure that you have a GPU with sufficient memory to load the model. If you're not sure, you can try using DeepSpeed to reduce the memory usage. Additionally, you should choose a batch size that fits in your GPU memory.\n\nUnfortunately, the context provided does not give a specific memory size recommendation for your use case. However, it suggests that you should choose a batch size that fits in your GPU memory, and that DeepSpeed can help reduce memory usage if necessary.",
302
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
303
        },
304
        {
305
            "question": "How do I log the results from multiple distributed workers into a single tensorboard?",
306
            "sources": [
307
                "https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",
308
                "https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",
309
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",
310
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",
311
                "https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-tune-results-on-a-single-node"
312
            ],
313
            "document_ids": [
314
                3188,
315
                14445,
316
                13065,
317
                3111,
318
                3148
319
            ],
320
            "answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use TensorBoard logger in your code which is automatically outputted by Tune during Tuner.fit(). You can specify the output directory of your results to visualize your experiment with TensorBoard. Also, make sure to install tensorboardX using pip install tensorboardX. Each worker will log to the same directory, allowing TensorBoard to aggregate and visualize the results from all workers. Note that loggers are disabled by default when benchmarking many Tune results on a single node, except when explicitly noted.",
321
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
322
        },
323
        {
324
            "question": "how do you config SyncConfig for a Ray AIR job?",
325
            "sources": [
326
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
327
                "https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",
328
                "https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",
329
                "https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration",
330
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed"
331
            ],
332
            "document_ids": [
333
                15131,
334
                11301,
335
                1924,
336
                1536,
337
                15125
338
            ],
339
            "answer": " To configure `SyncConfig` for a Ray AIR job, you can create a `RunConfig` object and set the `SyncConfig` as an attribute. Here's an example:\n```python\nfrom ray import train\n\nsync_config = train.SyncConfig(\n    sync_period=300,\n    sync_timeout=1800,\n    sync_artifacts=False,\n    sync_artifacts_on_checkpoint=True,\n    sync_on_checkpoint=False,\n)\n\nrun_config = train.RunConfig(sync_config=sync_config)\n```\nThis creates a `SyncConfig` object with the specified parameters and sets it as an attribute of a `RunConfig` object. Note that synchronization of checkpoints and other artifacts from worker nodes to the head node is no longer supported in Ray AIR, as of the warning message provided in the context.",
340
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
341
        },
342
        {
343
            "question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",
344
            "sources": [
345
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",
346
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",
347
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",
348
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
349
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence"
350
            ],
351
            "document_ids": [
352
                629,
353
                981,
354
                7782,
355
                605,
356
                8672
357
            ],
358
            "answer": " To quickly narrow down the root cause of a failed Ray job, follow these steps:\n\n1. Access the logs for the Ray job, including both worker logs and (if available) driver logs.\n2. Use the `ray logs` command to view the logs, specifying the filename or resource identifiers as needed. For example, `ray logs cluster raylet.out --tail 500` will show the last 500 lines of `raylet.out` on the head node.\n3. If the Ray Job was submitted via the Job API, check the driver logs in the Dashboard UI or by using the `ray job logs` CLI command or the `JobSubmissionClient.get_logs()` or `JobSubmissionClient.tail_job_logs()` methods. The driver log file will follow the format `job-driver-<job_submission_id>.log`.\n4. If you executed the driver directly on the head node or used the Ray Client, the driver logs will not be accessible from the Dashboard. In this case, view the logs in the terminal or Jupyter Notebook output.\n5. Check the output of the Ray job using the `kubectl logs -l=job-name=rayjob-sample` command.\n\nBy following these steps, you should be able to quickly identify the root cause of the failed Ray job.",
359
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
360
        },
361
        {
362
            "question": "How do I specify how many GPUs a serve deployment needs?",
363
            "sources": [
364
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",
365
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",
366
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",
367
                "https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",
368
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#resources"
369
            ],
370
            "document_ids": [
371
                18072,
372
                18068,
373
                18069,
374
                18260,
375
                7989
376
            ],
377
            "answer": " To specify the number of GPUs a Serve deployment needs, you can use the `ray_actor_options` argument in the `@serve.deployment` decorator and set the `num_gpus` parameter 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```\nIn this example, each replica of the deployment will be allocated 1 GPU. You can also specify a fraction of a GPU if you want to pack multiple replicas together on a machine with fewer GPUs than replicas. For example, `\"num_gpus\": 0.5` will allocate 0.5 GPUs to each replica.",
378
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
379
        },
380
        {
381
            "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?",
382
            "sources": [
383
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
384
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
385
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
386
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
387
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a"
388
            ],
389
            "document_ids": [
390
                774,
391
                6934,
392
                15003,
393
                14958,
394
                13481
395
            ],
396
            "answer": " Based on the information provided, it seems like the worker node is running out of memory and is being killed due to memory pressure (OOM). This is indicated by the messages in the raylet logs that mention \"Workers killed due to memory pressure (OOM)\". To debug this issue, you can:\n\n1. Check the memory usage of the worker nodes by using the `ray memory` command. This will show you the memory usage of each node in the cluster.\n2. Check the logs of the raylet for the specific node that is experiencing the issue. You can do this by running the command `ray logs raylet.out -ip <node_ip>`, where `<node_ip>` is the IP address of the node that is experiencing the issue. This will show you the logs for the raylet on that node, which may contain more information about why the worker is running out of memory.\n3. Consider reducing the batch size or the number of workers if the model being trained is too large for the available memory.\n4. You can also try to profile the memory usage of your TensorflowTrainer to identify which parts of the code are consuming the most memory. You can use a tool like `tensorflow-profiler` to do this.\n5. Make sure that the nodes in the cluster have enough memory to run the desired number of workers. You can add more memory to the nodes or reduce the number of workers per node to avoid running out of memory.",
397
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
398
        },
399
        {
400
            "question": "what are the possible reasons for nodes dying in a cluster?",
401
            "sources": [
402
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",
403
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
404
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",
405
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
406
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/aws-eks-gpu-cluster.html#create-a-gpu-node-group"
407
            ],
408
            "document_ids": [
409
                5857,
410
                18693,
411
                5860,
412
                18663,
413
                7948
414
            ],
415
            "answer": " Based on the provided context, there are two possible reasons for nodes dying in a Ray cluster:",
416
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
417
        },
418
        {
419
            "question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",
420
            "sources": [
421
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",
422
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
423
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
424
                "https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",
425
                "https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling"
426
            ],
427
            "document_ids": [
428
                18882,
429
                8677,
430
                8021,
431
                7596,
432
                7537
433
            ],
434
            "answer": " To programmatically set the target size of a Ray remote cluster immediately, you can use the `ray.autoscaler.sdk.request_resources()` function. This function allows you to request a specific amount of resources for your cluster, overriding the autoscaler's default behavior.\n\nNote that this method will not scale up the cluster through the autoscaler, but will instead set the target size of the cluster to the specified value. If the current cluster size is less than the target size, new nodes will be added to the cluster immediately. If the current cluster size is greater than the target size, idle nodes will not be removed from the cluster until their resources are no longer needed.",
435
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
436
        },
437
        {
438
            "question": "how do you disable async iter_batches with Ray Dataset?",
439
            "sources": [
440
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",
441
                "https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",
442
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",
443
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",
444
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches"
445
            ],
446
            "document_ids": [
447
                9862,
448
                9611,
449
                10074,
450
                10075,
451
                10438
452
            ],
453
            "answer": " To disable async iter\\_batches with Ray Dataset, you can set `prefetch_batches=0` in the `iter_batches()` method. This will execute the iteration synchronously without prefetching any batches in advance. Here is an example:\n```python\ndataset.iter_batches(prefetch_batches=0, batch_size=256, ...)\n```\nNote that setting `prefetch_batches=0` may reduce the performance of your data processing pipeline, as it disables the benefits of overlapping data processing with data loading.",
454
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
455
        },
456
        {
457
            "question": "what is the different between a batch and a block, for ray datasets?",
458
            "sources": [
459
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
460
                "https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",
461
                "https://docs.ray.io/en/master/data/data-internals.html#execution-memory",
462
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
463
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches"
464
            ],
465
            "document_ids": [
466
                19789,
467
                9322,
468
                9380,
469
                9358,
470
                9862
471
            ],
472
            "answer": " In the context of Ray Datasets, a batch and a block refer to different concepts. A batch is a user-facing object used for model training, where a fixed number of data points (defined by the batch size) are used to compute and apply one gradient update to the model weights. On the other hand, a block is an internal abstraction used by Ray Data. It is a processing unit of data, where each block contains a disjoint subset of rows. Ray Data partitions rows into distributed data blocks, allowing it to perform operations in parallel. A large block size can lead to potential out-of-memory situations, so it is essential to adjust the batch size and block size accordingly to avoid such issues.",
473
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
474
        },
475
        {
476
            "question": "How to setup the development environments for ray project?",
477
            "sources": [
478
                "https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",
479
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",
480
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",
481
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",
482
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#environment-dependencies"
483
            ],
484
            "document_ids": [
485
                1236,
486
                12490,
487
                5838,
488
                8690,
489
                5623
490
            ],
491
            "answer": " To set up the development environment for the Ray project, follow these steps:\n\n1. Checkout the Ray repository and build Ray from source.\n2. Use runtime environments to ensure Ray workers have access to necessary packages.\n3. You can prepare dependencies on the cluster in advance or use Ray's runtime environments to install them on the fly.\n\nMake sure you have a full installation of Ray using `pip install \"ray[default]\"`. This feature is available starting with Ray 1.4.0 and is currently supported on macOS and Linux, with beta support on Windows.",
492
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
493
        },
494
        {
495
            "question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",
496
            "sources": [
497
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",
498
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
499
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",
500
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",
501
                "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"
502
            ],
503
            "document_ids": [
504
                17068,
505
                19831,
506
                17069,
507
                927,
508
                17717
509
            ],
510
            "answer": " Based on the provided context, it seems like the deadlocking of Ray rollout workers might be due to a lack of parallelization. Ray cannot parallelize the example because only a single actor (Sleeper) has been initialized. Since each actor is a single, stateful worker, the entire code is submitted and run on a single worker for the whole time. To resolve this issue, you may need to initialize more actors or ensure that the code is designed to allow for parallel execution.",
511
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
512
        },
513
        {
514
            "question": "how do I join two ray datasets?",
515
            "sources": [
516
                "https://docs.ray.io/en/master/data/preprocessors.html#overview",
517
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#load-and-preprocess-datasets",
518
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",
519
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",
520
                "https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy.html#ray-data-from-numpy"
521
            ],
522
            "document_ids": [
523
                9117,
524
                12506,
525
                13421,
526
                13420,
527
                9976
528
            ],
529
            "answer": " To join two Ray datasets, you can use the `ray.data.join()` function. Here's an example of how to join `dataset1` and `dataset2` on an identical column named `id`:\n\nFirst, ensure that both datasets have the same schema:\n\n",
530
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
531
        },
532
        {
533
            "question": "Is there a way to retrieve an object ref from its id?",
534
            "sources": [
535
                "https://docs.ray.io/en/master/ray-core/objects.html#objects",
536
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",
537
                "https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",
538
                "https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",
539
                "https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive"
540
            ],
541
            "document_ids": [
542
                6455,
543
                6354,
544
                6732,
545
                6734,
546
                18459
547
            ],
548
            "answer": " Yes, you can retrieve an object from its reference in Ray by using the `ray.get(ref)` function. This will return the actual object that the reference points to. If you want to wait for the result to be available without retrieving it, you can use `ray.wait([ref])`.",
549
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
550
        },
551
        {
552
            "question": "how to create model Checkpoint from the model in memory?",
553
            "sources": [
554
                "https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",
555
                "https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example",
556
                "https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchTrainer.html#ray-train-torch-torchtrainer",
557
                "https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint",
558
                "https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.from_model.html#ray-train-torch-torchcheckpoint-from-model"
559
            ],
560
            "document_ids": [
561
                11850,
562
                5077,
563
                11366,
564
                12069,
565
                11661
566
            ],
567
            "answer": " To create a model checkpoint from a model in memory, you can use the `TorchCheckpoint.from_model()` function. Here's an example:\n```python\ncheckpoint = TorchCheckpoint.from_model(model)\n```\nThis creates a checkpoint object that stores the model. If you want to save the checkpoint to a file, you can use the `save()` method:\n```python\ncheckpoint.save(\"my_checkpoint.ckpt\")\n```\nTo load the checkpoint from a file, you can use the `load()` method:\n```python\ncheckpoint = TorchCheckpoint.load(\"my_checkpoint.ckpt\")\n```\nYou can then retrieve the model from the checkpoint using the `get_model()` method:\n```python\nmodel = checkpoint.get_model()\n```\nNote that if your model is wrapped in a `DistributedDataParallel` instance, you should unwrap it first before creating the checkpoint:\n```python\nbase_model = (model.module if isinstance(model, DistributedDataParallel) else model)\ncheckpoint = TorchCheckpoint.from_model(base_model)\n```\nAlternatively, you can save the model state dictionary to a file and create a checkpoint from it using the `from_state_dict()` method:\n```python\ntorch.save(model.state_dict(), \"model.pt\")\ncheckpoint = TorchCheckpoint.from_state_dict(torch.load(\"model.pt\"))\n```",
568
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
569
        },
570
        {
571
            "question": "what is Deployment in Ray Serve?",
572
            "sources": [
573
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
574
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
575
                "https://docs.ray.io/en/master/serve/key-concepts.html#deployment",
576
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",
577
                "https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air"
578
            ],
579
            "document_ids": [
580
                19150,
581
                19794,
582
                18017,
583
                18364,
584
                14381
585
            ],
586
            "answer": " A Deployment in Ray Serve is a group of actors that can handle traffic. It's the central concept in Ray Serve and represents a class or function with its own business logic or ML model to handle incoming requests. At runtime, a deployment consists of multiple replicas, which are individual copies of the class or function running in separate Ray actors (processes). The number of replicas can be scaled up or down to match the incoming request load.",
587
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
588
        },
589
        {
590
            "question": "What is user config in Ray Serve? how do I use it?",
591
            "sources": [
592
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
593
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",
594
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
595
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",
596
                "https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests"
597
            ],
598
            "document_ids": [
599
                8825,
600
                18833,
601
                18927,
602
                18364,
603
                18697
604
            ],
605
            "answer": " User config in Ray Serve refers to the configuration that Ray Serve uses to deploy an application. You can use it by defining a serve configuration (serveConfigV2) that represents how you want Ray Serve to deploy your application. This configuration can be printed using the `serve build` command and then copy-pasted directly into your Kubernetes config and RayService CR.\n\nHere's an example of how to use user config in Ray Serve:\n\n```python\nfrom ray import serve\n\n# Define your application\n@serve.deployment(name=\"my_deployment\")\ndef my_application_function():\n    # Your application code here\n    pass\n\n# Build the Ray Serve configuration\nserve_config = serve.config()\n\n# Modify the configuration as needed, for example:\n# serve_config.deployments[\"my_deployment\"].instances = 10\n# serve_config.deployments[\"my_deployment\"].resources.cpu = 1\n\n# Print the configuration\nprint(serve_config)\n```\n\nYou can then copy-paste the output of `print(serve_config)` into your Kubernetes config and RayService CR to deploy your application with the desired configuration.\n\nNote that you can also configure your deployments to scale out by increasing the number of deployment replicas and assign replica resources such as CPUs and GPUs. Refer to the Ray Serve documentation for more details.",
606
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
607
        },
608
        {
609
            "question": "What is the difference between PACK and SPREAD strategy?",
610
            "sources": [
611
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",
612
                "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",
613
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",
614
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",
615
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory"
616
            ],
617
            "document_ids": [
618
                14936,
619
                14457,
620
                6382,
621
                6956,
622
                2486
623
            ],
624
            "answer": " The \"PACK\" strategy aims to place bundles into as few nodes as possible, while the \"SPREAD\" strategy disperses bundles across distinct nodes as evenly as possible.",
625
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
626
        },
627
        {
628
            "question": "What\u2019s the best way to run ray across multiple machines?",
629
            "sources": [
630
                "https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",
631
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",
632
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",
633
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
634
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime"
635
            ],
636
            "document_ids": [
637
                7637,
638
                19338,
639
                4277,
640
                8350,
641
                5587
642
            ],
643
            "answer": " To run Ray across multiple machines, you should start a Ray cluster for each user to ensure workload isolation. This can be done by explicitly starting the Ray runtime on multiple servers, instead of relying on the implicit initialization via `ray.init()` which is suitable for single machine setups. Ray does not automatically handle the distribution of GPUs, so for using multiple GPUs per trial, you should consider using LightningTrainer or other relevant tools.",
644
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
645
        },
646
        {
647
            "question": "how do I specify ScalingConfig for a Tuner run?",
648
            "sources": [
649
                "https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",
650
                "https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",
651
                "https://docs.ray.io/en/master/tune/examples/tune-pytorch-lightning.html#training-with-gpus",
652
                "https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",
653
                "https://docs.ray.io/en/master/train/user-guides/using-gpus.html#configuring-scale-and-gpus"
654
            ],
655
            "document_ids": [
656
                1797,
657
                12143,
658
                4562,
659
                3165,
660
                12313
661
            ],
662
            "answer": " To specify the `ScalingConfig` for a Tuner run, you can include it in the `TuneConfig` object when creating a `Tuner` instance. Here's an example:\n```python\nfrom ray.tune import TuneConfig, ScalingConfig\n\nscaling_config = ScalingConfig(\n    num_workers=3, use_gpu=True, resources_per_worker={\"CPU\": 1, \"GPU\": 1}\n)\n\ntune_config = TuneConfig(\n    num_samples=10,\n    scaling_config=scaling_config\n)\n\ntuner = Tuner(trainable, tune_config=tune_config)\nresults = tuner.fit()\n```\nAlternatively, you can also use `tune.with_resources` to override the resources per trial:\n```python\ntrainable_with_resources = tune.with_resources(trainable, scaling_config)\n\ntune_config = TuneConfig(num_samples=10)\ntuner = Tuner(trainable_with_resources, tune_config=tune_config)\nresults = tuner.fit()\n```",
663
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
664
        },
665
        {
666
            "question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",
667
            "sources": [
668
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only",
669
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",
670
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
671
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",
672
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"
673
            ],
674
            "document_ids": [
675
                6680,
676
                6677,
677
                9368,
678
                5740,
679
                9364
680
            ],
681
            "answer": " To utilize the 'zero-copy' feature Ray provides for numpy arrays, you don't need to do anything special when calling `ray.get()`. Ray automatically optimizes numpy arrays using Pickle protocol 5 with out-of-band data. This allows for zero-copy reads of the arrays in the object store without copying, as long as you don't mutate the input. If the function `fn` in your Ray task mutates the input numpy array, you will need to manually copy it into the local process memory before mutating it using `arr = arr.copy()`. To improve performance and decrease memory utilization, you can set `zero_copy_batch=True` in the Ray function if it doesn't mutate its input.",
682
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
683
        },
684
        {
685
            "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?",
686
            "sources": [
687
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",
688
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_random_access_dataset.html#ray-data-dataset-to-random-access-dataset",
689
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",
690
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",
691
                "https://docs.ray.io/en/master/data/examples/random-access.html#architecture"
692
            ],
693
            "document_ids": [
694
                7367,
695
                9775,
696
                10636,
697
                1011,
698
                10837
699
            ],
700
            "answer": " It is not advised to spin up 1 actor for each key (millions) as it would be resource-intensive and inefficient. It's better to create 'key pools' where an actor can hold many keys, allowing for more efficient random lookups and resource management. This is because each random lookup costs network round-trip time and computation on both the client and server side.",
701
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
702
        },
703
        {
704
            "question": "How to find the best checkpoint from the trial directory?",
705
            "sources": [
706
                "https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",
707
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",
708
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",
709
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",
710
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.get_experiment_checkpoint_dir.html#ray-tune-experiment-get-experiment-checkpoint-dir"
711
            ],
712
            "document_ids": [
713
                4613,
714
                2195,
715
                1910,
716
                2884,
717
                2707
718
            ],
719
            "answer": " To find the best checkpoint in the trial directory, you can use the `best_checkpoint` attribute or the `get_best_checkpoint` method from the `ray.tune.ExperimentAnalysis` class. \n\nThe `best_checkpoint` attribute returns the checkpoint object of the best trial in the experiment. It identifies the best trial using the metric and mode parameters passed to the `tune.run()` function.\n\nThe `get_best_checkpoint` method allows you to specify the trial, metric, and mode parameters to identify the best trial and return its last checkpoint path. If you don't specify the trial parameter, it will load the latest trial automatically.\n\nHere's an example of how to use these methods:\n\n```python\nfrom ray import tune\n\n# Assuming you have an ExperimentAnalysis object called analysis\nbest_checkpoint = analysis.best_checkpoint\nbest_checkpoint_path = analysis.get_best_checkpoint(return_path=True)\n```\n\nIn the context provided, you can access the trial directory using `best_result.path` and then use `analysis.best_checkpoint` or `analysis.get_best_checkpoint` to find the best checkpoint.",
720
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
721
        },
722
        {
723
            "question": "what are the advantage and disadvantage of using singleton Actor ?",
724
            "sources": [
725
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
726
                "https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#how-to-improve-the-gpu-utilization-rate",
727
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
728
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
729
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources"
730
            ],
731
            "document_ids": [
732
                7017,
733
                7423,
734
                16305,
735
                5839,
736
                19760
737
            ],
738
            "answer": " Advantage of using a singleton actor:\n\n* Efficiently reuses model initialization for many tasks\n* Allows for flexible task scheduling\n* Useful when the stateful part of an actor is needed\n\nDisadvantage of using a singleton actor:\n\n* If the stateful part of an actor is not needed, tasks might be better off using simpler functions or tasks instead of a full actor.",
739
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
740
        },
741
        {
742
            "question": "what are the advantages of using a named actor?",
743
            "sources": [
744
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
745
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
746
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
747
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
748
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options"
749
            ],
750
            "document_ids": [
751
                6202,
752
                16305,
753
                5839,
754
                19760,
755
                6604
756
            ],
757
            "answer": " The advantages of using a named actor are:\n1. Actor can be given a unique name within their namespace, allowing it to be retrieved from any job in the Ray cluster.\n2. 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.\n3. Allows for concurrent execution of both actors and tasks, with flexible scheduling.\n4. If you don't need the stateful part of an actor, you're mostly better off using tasks.\n5. The unique name and namespace can be used to retrieve the actor via `ray.get_actor(name)` as long as the actor is still alive.",
758
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
759
        },
760
        {
761
            "question": "How do I read a text file stored on S3 using Ray Data?",
762
            "sources": [
763
                "https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",
764
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",
765
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",
766
                "https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",
767
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files"
768
            ],
769
            "document_ids": [
770
                10585,
771
                10104,
772
                12254,
773
                10518,
774
                9332
775
            ],
776
            "answer": " To read a text file stored on S3 using Ray Data, you can use the `read_text` function and specify the S3 path of the text file. Here's an example:\n```python\nimport ray\n\nds = ray.data.read_text(\"s3://your-bucket-name/your-text-file.txt\")\n```\nThis will create a Ray Dataset with each line of the text file as a row. If the S3 bucket is private, make sure to set up the necessary authentication credentials for Ray to access the bucket.",
777
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
778
        },
779
        {
780
            "question": "how do I get the IP of the head node for my Ray cluster?",
781
            "sources": [
782
                "https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",
783
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",
784
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",
785
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
786
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes"
787
            ],
788
            "document_ids": [
789
                7630,
790
                8723,
791
                8527,
792
                675,
793
                8529
794
            ],
795
            "answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command. You'll need to provide the path to your cluster configuration file as an argument for this command. The head node IP will be printed to the console upon execution.\n\nHere's the command:\n\n```bash\nray get_head_ip CLUSTER_CONFIG_FILE\n```\n\nReplace `CLUSTER_CONFIG_FILE` with the path to your Ray cluster configuration file.\n\nIf you don't have a cluster configuration file, you can start a local Ray cluster with the following command:\n\n```bash\nray start --head\n```\n\nAfter running this command, the head node IP will be printed in the output, which you can use for your Ray cluster.",
796
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
797
        },
798
        {
799
            "question": "How to write a map function that returns a list of object for `map_batches`?",
800
            "sources": [
801
                "https://docs.ray.io/en/master/data/api/doc/ray.data.range_tensor.html#ray-data-range-tensor",
802
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
803
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
804
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",
805
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map"
806
            ],
807
            "document_ids": [
808
                9666,
809
                9358,
810
                9390,
811
                10717,
812
                10422
813
            ],
814
            "answer": " To write a map function that returns a list of objects for `map_batches`, you can define a lambda function that processes each batch (a list of rows) and returns a new list of object. Here's an example:\n\n```python\nds.map_batches(lambda row: [{\"data\": d * 2} for d in row[\"data\"]]).take(2)\n```\n\nIn this example, the lambda function takes a batch (`row`) and applies a list comprehension to each element of the `data` array, creating a new list of objects with the `data` field doubled.\n\nNote that the size of the batches provided to the lambda function might be smaller than the specified batch size if batch size doesn't evenly divide the blocks sent to a given map task. Also, the lambda function can return multiple objects, which will be concatenated to form the output batch.",
815
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
816
        },
817
        {
818
            "question": "How do I set a maximum episode length when training with Rllib?",
819
            "sources": [
820
                "https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",
821
                "https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",
822
                "https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",
823
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.replay_buffers.replay_buffer.StorageUnit.EPISODES.html#ray-rllib-utils-replay-buffers-replay-buffer-storageunit-episodes",
824
                "https://docs.ray.io/en/master/rllib-env.html#external-agents-and-applications"
825
            ],
826
            "document_ids": [
827
                13945,
828
                15270,
829
                13948,
830
                17147,
831
                76
832
            ],
833
            "answer": " To set a maximum episode length when training with RLlib, you can use the `evaluation_duration` and `evaluation_duration_unit` configuration parameters. However, these settings control the duration of evaluation steps rather than training steps.\n\nIf you want to limit the maximum length of individual episodes during training, you can use the `sample_batch_size` parameter in the `rollouts` configuration section. This parameter controls the number of timesteps to sample in each rollout and can be used to limit the length of individual episodes.\n\nHere's an example configuration:\n```yaml\nrollouts:\n  sample_batch_size: 1000  # Limit the number of timesteps per rollout to 1000.\n```\nNote that if you set `evaluation_duration_unit` to \"episodes\" and `evaluation_duration` to a value that is not divisible by the number of evaluation workers, RLlib will run the remainder of episodes on the first n eval RolloutWorkers and leave the remaining workers idle for that time.",
834
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
835
        },
836
        {
837
            "question": "how do I make a Ray Tune trial retry on failures?",
838
            "sources": [
839
                "https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",
840
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",
841
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#summary",
842
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",
843
                "https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance"
844
            ],
845
            "document_ids": [
846
                3074,
847
                2989,
848
                2991,
849
                2988,
850
                6347
851
            ],
852
            "answer": " To make a Ray Tune trial retry on failures, you can use the `FailureConfig` class. Here's an example:\n\n```python\nfrom ray import tune\n\ntuner = tune.Tuner(\n    train_model,\n    param_space=trial_space,\n    run_config=train.RunConfig(\n        storage_path=os.path.expanduser(\"~/ray_results\"),\n        name=\"trial_fault_tolerance\",\n        failure_config=train.FailureConfig(max_failures=3),\n    ),\n)\n\ntuner.fit()\n```\n\nIn this example, when a trial encounters a runtime error, it will be rescheduled up to `max_failures=3` times.",
853
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
854
        },
855
        {
856
            "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?",
857
            "sources": [
858
                "https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",
859
                "https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",
860
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
861
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
862
                "https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides"
863
            ],
864
            "document_ids": [
865
                7036,
866
                7037,
867
                6051,
868
                5976,
869
                6533
870
            ],
871
            "answer": " No, you cannot 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. This is because worker actors are automatically terminated when the supervisor or driver dies due to actor reference counting. If the owner of an actor dies, the actor will also be terminated, with the exception of detached actors, which do not have an owner and are cleaned up when the Ray cluster is destroyed.",
872
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
873
        },
874
        {
875
            "question": "How do I read a large text file in S3 with Ray?",
876
            "sources": [
877
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",
878
                "https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",
879
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",
880
                "https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",
881
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files"
882
            ],
883
            "document_ids": [
884
                10104,
885
                10518,
886
                12254,
887
                10585,
888
                9332
889
            ],
890
            "answer": " To read a large text file in S3 with Ray, you can use the `ray.data.read_text()` function and provide the S3 path to the text file as an argument. Here's an example:\n```python\nds = ray.data.read_text(\"s3://your-bucket-name/your-text-file.txt\")\n```\nThis will create a Ray Dataset with each line of the text file as a row. If you want to load the data from a private bucket, you need to make sure that your Ray application has the necessary permissions to access the bucket.\n\nIn the example you provided, the `read_text()` function is used to read a text file from a public S3 bucket. The resulting Ray Dataset `ds` can then be used for further processing, such as mapping a function to each row to perform some operation on the text.",
891
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
892
        },
893
        {
894
            "question": "how do I get a ray dataset from pandas",
895
            "sources": [
896
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
897
                "https://docs.ray.io/en/master/ray-more-libs/mars-on-ray.html#getting-started",
898
                "https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",
899
                "https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.SimpleImputer.html#ray-data-preprocessors-simpleimputer",
900
                "https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas"
901
            ],
902
            "document_ids": [
903
                9163,
904
                19536,
905
                10447,
906
                10165,
907
                10448
908
            ],
909
            "answer": " To get a Ray dataset from a Pandas DataFrame, you can use the `ray.data.from_pandas()` function, which creates a Dataset from a pandas DataFrame or a list of pandas DataFrames. Here's an example:\n```python\nimport pandas as pd\nimport ray\n\ndf = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\nds = ray.data.from_pandas(df)\n```\nIf you have a list of Pandas DataFrames, you can pass it as an argument to `ray.data.from_pandas()` to create a Ray Dataset from multiple DataFrames. For example:\n```python\nds = ray.data.from_pandas([df, df])\n```\nYou can also convert a Ray Dataset to a Pandas DataFrame using the `to_pandas()` method of the Dataset object. For example:\n```python\ndf2 = ds.to_pandas()\n```",
910
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
911
        },
912
        {
913
            "question": "can you give me an example of using `ray.data.map` ?",
914
            "sources": [
915
                "https://docs.ray.io/en/master/data/api/from_other_data_libs.html#api-guide-for-users-from-other-data-libraries",
916
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",
917
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
918
                "https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",
919
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a"
920
            ],
921
            "document_ids": [
922
                9431,
923
                11012,
924
                9358,
925
                10924,
926
                14472
927
            ],
928
            "answer": " Sure, here's an example of using `ray.data.map`:\n```python\n# Assume we have a function to preprocess the data\ndef preprocess_data(batch):\n    # do something to the batch\n    return preprocessed_batch\n\n# Assume we have a Ray dataset\nds = ray.data.from_huggingface(current_dataset[\"train\"])\n\n# We can use `map` to apply the `preprocess_data` function to each batch in the dataset\nds_processed = ds.map(preprocess_data)\n```\nIn this example, `preprocess_data` is a function that takes a batch of data and returns the preprocessed batch. We then use `map` to apply this function to each batch in the Ray dataset `ds`. The resulting dataset `ds_processed` contains the preprocessed batches.\n\nKeep in mind that `map` is lazy, meaning that it won't actually execute the function until you start consuming the results. This allows Ray to optimize the execution of the function across the resources in your Ray cluster.",
929
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
930
        },
931
        {
932
            "question": "can you give me an example of using `ray.data.map` , with a callable class as input?",
933
            "sources": [
934
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
935
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#walkthrough",
936
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#transformations-with-torch-tensors",
937
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",
938
                "https://docs.ray.io/en/master/ray-core/cross-language.html#cross-language-data-serialization"
939
            ],
940
            "document_ids": [
941
                10416,
942
                7130,
943
                9261,
944
                11012,
945
                6199
946
            ],
947
            "answer": " Sure, here's an example of using `ray.data.map` with a callable class as input:\n```python\nimport ray\nfrom typing import Dict\n",
948
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
949
        },
950
        {
951
            "question": "How to set memory limit for each trial in Ray Tuner?",
952
            "sources": [
953
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
954
                "https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",
955
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#summary",
956
                "https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",
957
                "https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria"
958
            ],
959
            "document_ids": [
960
                6937,
961
                3073,
962
                2991,
963
                3165,
964
                2895
965
            ],
966
            "answer": " To set the memory limit for each trial in Ray Tuner, you can use the `tune.with_resources` function to specify the resources for each trial. However, Ray Tuner does not directly support setting memory limits in the same way it does with CPUs. Instead, you can use the `RAY_memory_usage_threshold` environment variable to set a global memory threshold for Ray. For example:\n```makefile\nimport os\n\nos.environ[\"RAY_memory_usage_threshold\"] = \"0.4\"\ntuner = tune.TuneConfig(num_samples=10)\ntuner.fit(tune.with_resources(trainable, {\"cpu\": 1}))\n```\nThis will set the memory threshold to 40% of the system's total memory and allow Ray Tuner to run one trial at a time using one CPU. Note that this does not strictly enforce a memory limit for each trial, but it will prevent Ray from using more than the specified amount of memory in total. If a trial exceeds its allocated resources, Ray Tuner will automatically stop the trial.",
967
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
968
        },
969
        {
970
            "question": "how do I get the actor id of an actor",
971
            "sources": [
972
                "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",
973
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",
974
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#actor-detail-page",
975
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor",
976
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#add-metadata-to-structured-logs"
977
            ],
978
            "document_ids": [
979
                6376,
980
                6664,
981
                581,
982
                753,
983
                730
984
            ],
985
            "answer": " To get the actor ID of the current actor in Ray, you can use the `get_actor_id()` function from the `ray.runtime_context.RuntimeContext` class. Here's an example:\n```python\nimport ray\nfrom ray.runtime_context import RuntimeContext\n\n# Initiate a driver\nray.init()\n\n# Define a remote class\n@ray.remote\nclass MyActor:\n    pass\n\n# Create an instance of the remote class\nmy_actor = MyActor.remote()\n\n# Get the actor ID\nactor_id = RuntimeContext.get_actor_id()\nprint(actor_id)\n```\nThis will print the actor ID in hex format. Note that `get_actor_id()` should not be used in a driver process.\n\nAlternatively, you can use the `get_actor()` function from the `ray.util.state` module to get the actor by its ID:\n```python\nimport ray\nfrom ray.util.state import get_actor\n\n# Initiate a driver\nray.init()\n\n# Get the actor by its ID\nactor = get_actor(<actor_id>)\nprint(actor)\n```\nReplace `<actor_id>` with the actual actor ID. This will return a dictionary containing the actor's metadata, state, and tasks.",
986
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
987
        },
988
        {
989
            "question": "can ray.init() can check if ray is all-ready initiated ?",
990
            "sources": [
991
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",
992
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
993
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",
994
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",
995
                "https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance"
996
            ],
997
            "document_ids": [
998
                6372,
999
                6424,
1000
                5594,
1001
                5589,
1002
                7052
1003
            ],
1004
            "answer": " No, `ray.init()` is used to initialize Ray, not to check if it is already initialized. To check if Ray is already initialized, you can use `ray.is_initialized()` in Python, `Ray.isInitialized()` in Java, or `ray::is_initialized()` in C++. These functions return a boolean value indicating whether Ray has been initialized or not.",
1005
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1006
        },
1007
        {
1008
            "question": "What does the `compute=actor`  argument do within `ray.data.map_batches` ?",
1009
            "sources": [
1010
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
1011
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
1012
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",
1013
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
1014
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors"
1015
            ],
1016
            "document_ids": [
1017
                11208,
1018
                10799,
1019
                10719,
1020
                11102,
1021
                9110
1022
            ],
1023
            "answer": " The `compute=actor` argument within `ray.data.map_batches` specifies that the user-defined function (UDF) should be executed as long-running Ray actors, using the `ActorPoolStrategy` with the number of parallel actors determined by the `ActorPoolStrategy` configuration. This is used when the UDF is implemented as a class.",
1024
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1025
        },
1026
        {
1027
            "question": "how do I use wandb logger with accelerateTrainer?",
1028
            "sources": [
1029
                "https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",
1030
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",
1031
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",
1032
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#setup-wandb",
1033
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune"
1034
            ],
1035
            "document_ids": [
1036
                1530,
1037
                3218,
1038
                3262,
1039
                3268,
1040
                3215
1041
            ],
1042
            "answer": " To use Weights & Biases (WandB) logger with AccelerateTrainer, you need to follow these steps:\n\n1. Install WandB via pip: `pip install wandb`\n2. Configure WandB by logging in: `wandb login`\n3. Use the `WandbLoggerCallback` from `tune.logger.wandb` to log metrics automatically.\n\nHere's an example of how to use `WandbLoggerCallback` with AccelerateTrainer:\n\n```python\nfrom transformers import Trainer, TrainingArguments\nimport tune\nfrom tune.logger import WandbLoggerCallback\n\n# Training arguments\ntraining_args = TrainingArguments(\n    output_dir='./results',\n    num_train_epochs=3,\n    per_device_train_batch_size=16,\n    save_steps=10_000,\n    save_total_limit=2,\n)\n\n# Initialize WandB logger\nwandb_logger = WandbLoggerCallback(project=\"your-project-name\")\n\n# Initialize Trainer\ntrainer = Trainer(\n    model=your_model,\n    args=training_args,\n    train_dataset=your_train_dataset,\n    callbacks=[wandb_logger],\n)\n\n# Train the model\ntrainer.train()\n```\n\nReplace `your_model` and `your_train_dataset` with your actual model and dataset. Make sure to replace `\"your-project-name\"` with the name of your WandB project.",
1043
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1044
        },
1045
        {
1046
            "question": "What will be implicitly put into object store?",
1047
            "sources": [
1048
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",
1049
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
1050
                "https://docs.ray.io/en/master/ray-core/objects.html#objects",
1051
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",
1052
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"
1053
            ],
1054
            "document_ids": [
1055
                6768,
1056
                19815,
1057
                6457,
1058
                6770,
1059
                19816
1060
            ],
1061
            "answer": " In the context of Ray, putting an object in the object store can be done using functions like `ray.put()` or `Ray.put()` in different languages. When an object is put in the object store, a reference to that object is created and stored in the cluster's memory. This reference, called an ObjectRef, can be used to retrieve the object later. Objects in the store are reference counted and evicted when they fall out of scope. By default, the object store reserves 30% of available memory for storing objects.",
1062
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1063
        },
1064
        {
1065
            "question": "How do I kill or cancel a ray task that I already started?",
1066
            "sources": [
1067
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",
1068
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
1069
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
1070
                "https://docs.ray.io/en/master/ray-core/api/core.html#tasks",
1071
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel"
1072
            ],
1073
            "document_ids": [
1074
                5934,
1075
                6342,
1076
                6620,
1077
                6328,
1078
                6624
1079
            ],
1080
            "answer": " To kill or cancel a Ray task that you already started, you can call `ray.cancel()` on the Object Ref returned by the task. Here's an example:\n```python\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n```\nBy default, this will send a KeyboardInterrupt to the task's worker if it is mid-execution. Passing `force=True` to `ray.cancel()` will force-exit the worker. Note that cancelling a task in Ray will not automatically retry the task, and only async actor tasks can be interrupted. If the task has already finished, calling `ray.cancel()` will have no effect.",
1081
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1082
        },
1083
        {
1084
            "question": "how to send  extra arguments in dataset.map_batches function?",
1085
            "sources": [
1086
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1087
                "https://docs.ray.io/en/master/data/api/doc/ray.data.range_tensor.html#ray-data-range-tensor",
1088
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1089
                "https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches",
1090
                "https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-execution-statistics"
1091
            ],
1092
            "document_ids": [
1093
                9358,
1094
                9666,
1095
                9362,
1096
                9292,
1097
                9412
1098
            ],
1099
            "answer": " To send extra arguments in the `dataset.map_batches` function, you can include them as additional parameters in the function you pass to `map_batches`. Here's an example:\n\n```python\ndef my_map_function(row, multiplier):\n    return {\"data\": row[\"data\"] * multiplier}\n\nds.map_batches(lambda row: my_map_function(row, 2), batch_size=1024).take(2)\n```\n\nIn this example, `my_map_function` takes two arguments: `row` and `multiplier`. We pass `my_map_function` to `map_batches`, along with the value `2` for the `multiplier` argument. The `lambda` function is used to create a function with a single argument `row`, which then calls `my_map_function` with `row` and the specified `multiplier` value.",
1100
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1101
        },
1102
        {
1103
            "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?",
1104
            "sources": [
1105
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",
1106
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#overview",
1107
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
1108
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",
1109
                "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"
1110
            ],
1111
            "document_ids": [
1112
                7650,
1113
                8026,
1114
                8269,
1115
                7769,
1116
                7742
1117
            ],
1118
            "answer": " Based on the provided context, Ray GCS (Global Control Service) does not store the history of jobs run on a KubeRay cluster. Instead, Kubernetes keeps track of the job history. You can view the job history with the `kubectl get jobs` command.\n\nThe RayJob controller creates a Kubernetes job when the RayCluster is ready, and the Ray job submission SDK allows submitting Ray jobs to the RayCluster via the Ray Dashboard port. The KubeRay operator configures a Kubernetes service targeting the Ray head Pod.\n\nIn summary, Kubernetes manages the history of jobs run on a KubeRay cluster, and it uses its built-in database and format for storing this information.",
1119
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1120
        },
1121
        {
1122
            "question": "How to resolve ValueError: The actor ImplicitFunc is too large?",
1123
            "sources": [
1124
                "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",
1125
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
1126
                "https://docs.ray.io/en/master/train/api/doc/ray.train.data_parallel_trainer.DataParallelTrainer.html#ray-train-data-parallel-trainer-dataparalleltrainer",
1127
                "https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",
1128
                "https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction"
1129
            ],
1130
            "document_ids": [
1131
                13187,
1132
                8272,
1133
                11876,
1134
                13728,
1135
                7412
1136
            ],
1137
            "answer": " The ValueError you're encountering might be due to the large size of the actor, as suggested by the warning message. To resolve this issue, you can try the following steps:\n\n1. Move the data loader to the training function, as suggested in the first line of the context. This can help reduce the size of the actor.\n2. Consider using `ray.put()` to put large objects in the Ray object store, as suggested in the warning message.\n3. You can also try reducing the batch size to better saturate the GPUs, as suggested in the context.\n\nHere's an example of how you can modify your code to implement these suggestions:\n```python\ndef train_loop_per_worker():\n    model = ray.get(model_ref)  # get the model from the object store\n    # load the data here instead of at the top level\n    input_files = [\n        f\"s3://anonymous@air-example-data/ursa-labs-taxi-data/downsampled_2009_full_year_data.parquet\",\n        f\"/fe41422b01c04169af2a65a83b753e0f_{i:06d}.parquet}\" for i in range(12)\n    ]\n    for file in input_files:\n        # submit the prediction task with the file as input\n        pool.submit(lambda a, v: a.predict.remote(v), file)\n\nmodel = load_model()\nmodel_ref = ray.put(model)\nnum_actors = 4\nactors = [BatchPredictor.remote(model_ref) for _ in range(num_actors)]\npool = ActorPool(actors)\n\n# move the data loading to the train_loop_per_worker function\nfor _ in range(num_iterations):\n    ray.get([train_loop_per_worker.remote() for _ in range(num_actors)])\n```\nBy moving the data loading to the `train_loop_per_worker` function, you can reduce the size of the actor and avoid capturing large arrays or objects in scope. Additionally, by using `ray.put()` to put the model in the object store, you can share it across multiple actors and avoid replicating it for each actor. Finally, by reducing the batch size, you can better saturate the GPUs and avoid running out of memory.",
1138
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1139
        },
1140
        {
1141
            "question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",
1142
            "sources": [
1143
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",
1144
                "https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#data-loading-and-preprocessing",
1145
                "https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",
1146
                "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",
1147
                "https://docs.ray.io/en/master/ray-air/getting-started.html#existing-ml-platform-integration"
1148
            ],
1149
            "document_ids": [
1150
                320,
1151
                11078,
1152
                19671,
1153
                13074,
1154
                13137
1155
            ],
1156
            "answer": " To distribute training for your custom Keras neural network in Databricks using Ray, follow these steps:\n\n1. Install Ray Train and TensorFlow packages in your Databricks environment:\n```bash\npip install -U \"ray[train]\" tensorflow\n```\n\n2. Set up your dataset and model using TensorFlow and Keras.\n\n3. Convert your Ray Data dataset shard to a TensorFlow dataset in your training function to use the Keras API for model training.\n\n4. Create a training function that utilizes Ray Train's `prepare_dataset_shard` function.\n\n5. Set up the Ray Trainer with a specified number of workers and GPUs if needed.\n\nHere's a full example you can refer to for distributed data loading and training:\n\n```python\nimport numpy as np\nimport tensorflow as tf\nfrom ray import train\nfrom ray.train.tensorflow import prepare_dataset_shard\n\n# Set up your dataset and model\ndef train_func(ray_dataset):\n    # Convert ray_dataset to a TensorFlow dataset\n    tf_dataset = prepare_dataset_shard(ray_dataset)\n\n    # Set up your model\n    model = ...\n\n    # Compile the model\n    model.compile(...)\n\n    # Train the model\n    model.fit(tf_dataset, epochs=...)\n\n# Set up Ray Trainer\ntrainer = TorchTrainer(\n    train_func,\n    scaling_config=ScalingConfig(num_workers=4, use_gpu=False)\n)\n\n# Fit the model\nresults = trainer.fit()\n```\n\nRemember to replace the `...` with your specific dataset, model, and training configurations.\n\nRay clusters can be created for interactive use in Databricks Notebooks, and Ray Train provides integration with tracking services such as MLFlow and Weights & Biases.",
1157
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1158
        },
1159
        {
1160
            "question": "how to use ray.put and ray,get?",
1161
            "sources": [
1162
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
1163
                "https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",
1164
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
1165
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#customized-serialization",
1166
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"
1167
            ],
1168
            "document_ids": [
1169
                5629,
1170
                7092,
1171
                6424,
1172
                6691,
1173
                8825
1174
            ],
1175
            "answer": " To use `ray.put()`, you store an object in the object store and it returns the object's ID, which can be used to pass the object as an argument to any remote function or method call. This is a synchronous operation.\n\nTo use `ray.get()`, you return an object or list of objects from the object ID or list of object IDs. This is also a synchronous operation.\n\nFor example, `ray.get(ray.put(SerializationHelperForA(A(1))))` stores the object A(1) in the object store and returns its ID, then retrieves the object from the ID using `ray.get()`.\n\nYou can also use `ray.wait()` to wait for a list of object references to be ready.\n\n`ray.get(ready_refs)` can be used to get the objects from a list of ready object references.\n\nRegarding the Ray Client, it is not explicitly mentioned in the provided context. However, it is typically used to enable Ray applications to communicate with a Ray cluster, and it requires Ray to be initialized with a specific cluster address.",
1176
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1177
        },
1178
        {
1179
            "question": "how do I use Ray Data to pre process many files?",
1180
            "sources": [
1181
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",
1182
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
1183
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-data",
1184
                "https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",
1185
                "https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors"
1186
            ],
1187
            "document_ids": [
1188
                12217,
1189
                9231,
1190
                12229,
1191
                9262,
1192
                9111
1193
            ],
1194
            "answer": " To preprocess many files using Ray Data, you can use Ray Data's support for a wide range of preprocessing operations. Specifically, for unstructured data, it is recommended to use existing Ray Data operations instead of preprocessors. Ray Data can load and preprocess data for distributed machine learning training pipelines in a streaming fashion. It serves as a last-mile bridge from storage or ETL pipeline outputs to distributed applications and libraries in Ray. You can find more details on how to use Ray Data for preprocessing and ingest for ML training in the Data loading for ML training section.",
1195
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1196
        },
1197
        {
1198
            "question": "can\u2019t pickle SSLContext objects",
1199
            "sources": [
1200
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",
1201
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",
1202
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#customized-serialization",
1203
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",
1204
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting"
1205
            ],
1206
            "document_ids": [
1207
                6694,
1208
                6674,
1209
                6683,
1210
                6681,
1211
                6693
1212
            ],
1213
            "answer": " Based on the context provided, the error message \"can't pickle SSLContext objects\" is not explicitly mentioned. However, the error message \"cannot pickle '_thread.lock' object\" is mentioned, which suggests that there is an issue with serializing certain types of objects that are not compatible with the pickle protocol used by Ray.\n\nIt is possible that the SSLContext object is one of those incompatible objects. In such cases, customizing the serialization process as described in the context may help. Specifically, defining a `__reduce__` method for the type of objects that are causing issues can allow for a customized serialization process that is compatible with Ray.\n\nAlternatively, it may be possible to modify the code to avoid using objects that are not compatible with the pickle protocol used by Ray. For example, in the case of the `_thread.lock` object, it may be possible to use a different locking mechanism that is compatible with pickle.",
1214
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1215
        },
1216
        {
1217
            "question": "How do I install CRDs in Kuberay?",
1218
            "sources": [
1219
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",
1220
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
1221
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#manually-setting-up-nginx-ingress-on-kind",
1222
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",
1223
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds"
1224
            ],
1225
            "document_ids": [
1226
                7733,
1227
                8281,
1228
                8228,
1229
                8212,
1230
                7649
1231
            ],
1232
            "answer": " To install CRDs in Kuberay, you can follow these steps:\n\n1. Add the Kuberay Helm repository:\n```\nhelm repo add kuberay https://ray-project.github.io/kuberay-helm/\n```\n2. Install the Kuberay operator and CRDs using the following command:\n```css\nhelm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n```\n\nThe CRDs are installed as part of the Kuberay operator installation.",
1233
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1234
        },
1235
        {
1236
            "question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",
1237
            "sources": [
1238
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",
1239
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
1240
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",
1241
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1242
                "https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images"
1243
            ],
1244
            "document_ids": [
1245
                195,
1246
                10791,
1247
                10999,
1248
                9366,
1249
                9028
1250
            ],
1251
            "answer": " The function for Ray data batch inference is named `__call__()` because it allows the function to be treated like a callable object, taking a batch of data as an argument and returning the processed results. This is particularly useful for performing data preprocessing and inference in a batch-oriented manner.",
1252
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1253
        },
1254
        {
1255
            "question": "How to disconnnect ray client?",
1256
            "sources": [
1257
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
1258
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",
1259
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",
1260
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",
1261
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-via-the-cli-ray-start"
1262
            ],
1263
            "document_ids": [
1264
                8825,
1265
                8848,
1266
                8856,
1267
                8828,
1268
                5599
1269
            ],
1270
            "answer": " To disconnect a Ray client, you can use the `disconnect()` method on the client object. Here's an example:\n```python\ncli1.disconnect()\ncli2.disconnect()\n```\nNote that the client will not be disconnected automatically, so you need to call `disconnect()` explicitly to close the connection. If the client disconnects unexpectedly due to a network failure, it will attempt to reconnect to the server for 30 seconds before dropping all references. You can increase this time by setting the `RAY_CLIENT_RECONNECT_GRACE_PERIOD` environment variable to the number of seconds you want the client to spend trying to reconnect.",
1271
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1272
        },
1273
        {
1274
            "question": "how to submit job with python with local files?",
1275
            "sources": [
1276
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
1277
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
1278
                "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",
1279
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",
1280
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job"
1281
            ],
1282
            "document_ids": [
1283
                8650,
1284
                8642,
1285
                8989,
1286
                8700,
1287
                8726
1288
            ],
1289
            "answer": " To submit a job with Python using local files, you can use the `ray job submit` command with the `--working-dir` argument to specify the path to the working directory. Here's an example:\n```css\nray job submit --working-dir your_working_directory -- python script.py\n",
1290
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1291
        },
1292
        {
1293
            "question": "How do I do inference from a model trained by Ray tune.fit()?",
1294
            "sources": [
1295
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#putting-it-together",
1296
                "https://docs.ray.io/en/master/data/batch_inference.html#using-models-from-ray-train",
1297
                "https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",
1298
                "https://docs.ray.io/en/master/tune/examples/tune-sklearn.html#using-bayesian-optimization",
1299
                "https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve"
1300
            ],
1301
            "document_ids": [
1302
                4279,
1303
                9232,
1304
                1336,
1305
                3521,
1306
                3714
1307
            ],
1308
            "answer": " To do inference from a model trained by Ray tune.fit(), you can use the Checkpoint returned by Ray Train for batch inference with Ray Data. First, train a model with Ray Train and then use the returned Checkpoint to perform inference. Here's an example using XGBoostTrainer:\n\n```python\nimport ray\nfrom ray.train import ScalingConfig\nfrom ray.train.xgboost import XGBoostTrainer\n\n# Train a model with Ray Train\ndataset = ray.data.read_csv(\"s3://anonymous@air-example-data/breast_cancer.csv\")\ntrain_dataset, valid_dataset = dataset.train_test_split(test_size=0.3)\n\nscaling_config = ScalingConfig(num_workers=2, use_gpu=False, trainer_resources=\"CPU=1\")\nxgboost_trainer = XGBoostTrainer(\n    scaling_config=scaling_config,\n    label_column=\"target\",\n    train_dataset=train_dataset,\n    validation_dataset=valid_dataset,\n    params={\n        \"objective\": \"binary:logistic\",\n        \"tree_method\": \"hist\",\n        \"grow_policy\": \"depthwise\",\n        \"max_depth\": 6,\n        \"learning_rate\": 0.1,\n        \"num_boost_round\": 100,\n    },\n)\nresult = xgboost_trainer.train()\n\n# Perform inference using the returned Checkpoint\ncheckpoint = result.checkpoint\n```\n\nIn this example, `checkpoint` contains the trained model, which you can use for inference. Note that the specifics of using the Checkpoint for inference depend on the model and the framework you are using.",
1309
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1310
        },
1311
        {
1312
            "question": "is there a way to load and run inference without using pytorch or tensorflow directly?",
1313
            "sources": [
1314
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",
1315
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
1316
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",
1317
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
1318
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data"
1319
            ],
1320
            "document_ids": [
1321
                9282,
1322
                9174,
1323
                9287,
1324
                9187,
1325
                10999
1326
            ],
1327
            "answer": " Yes, it is possible to load and run inference without using PyTorch or TensorFlow directly by using a pre-trained model with Ray Datasets. In the provided context, a PyTorch model is used, but Ray Datasets support serving models from various frameworks. The model can be loaded and mapped over the data using the `ds.map_batches()` function, and the preprocessing logic can be implemented within the `__call__()` function. Additionally, Ray Datasets allow for using GPUs for inference by specifying the number of GPUs to use in the `ds.map_batches()` call.",
1328
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1329
        },
1330
        {
1331
            "question": "what does ray do",
1332
            "sources": [
1333
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
1334
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
1335
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",
1336
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#jobs-view",
1337
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples"
1338
            ],
1339
            "document_ids": [
1340
                6424,
1341
                8825,
1342
                359,
1343
                537,
1344
                7283
1345
            ],
1346
            "answer": " Ray is a distributed computing framework. The `ray.init()` function is used to initialize a Ray cluster. The Ray Client allows you to connect to and use a remote Ray cluster. For more information, you can refer to the Ray Core documentation and Ray Job Overview section, and there are also Ray Tutorials and Examples available to learn more.",
1347
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1348
        },
1349
        {
1350
            "question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",
1351
            "sources": [
1352
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",
1353
                "https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus",
1354
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",
1355
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",
1356
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus"
1357
            ],
1358
            "document_ids": [
1359
                1394,
1360
                3682,
1361
                18070,
1362
                14893,
1363
                6753
1364
            ],
1365
            "answer": " If you specify a fractional GPU in the resource spec, and your trial uses more than that allocated fraction, it could lead to resource contention and impact the performance of your trial. It's important to ensure that your trial's resource requirements fit within the allocated fractional GPU memory. Ray supports fractional GPU quantities, allowing multiple tasks and actors to share the same GPU.",
1366
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1367
        },
1368
        {
1369
            "question": "how to pickle a variable defined in actor\u2019s init method",
1370
            "sources": [
1371
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
1372
                "https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",
1373
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
1374
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#add-metadata-to-structured-logs",
1375
                "https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles"
1376
            ],
1377
            "document_ids": [
1378
                7017,
1379
                5811,
1380
                733,
1381
                730,
1382
                16272
1383
            ],
1384
            "answer": " In Ray, you cannot directly pickle a variable defined in an actor's `__init__` method. However, you can use actor's instance variables to hold the state and pass the actor handle to places where the state needs to be modified or accessed. Here's an example:\n\n```python\n@ray.remote\nclass Actor:\n    def __init__(self, init_value):\n        self.i = init_value\n\n    def inc(self, x):\n        self.i += x\n\n    def get(self):\n        return self.i\n\na1 = Actor.bind(10)  # Instantiate Actor with init_value 1",
1385
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1386
        },
1387
        {
1388
            "question": "how do I do an all_reduce operation among a list of actors",
1389
            "sources": [
1390
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",
1391
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",
1392
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
1393
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
1394
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors"
1395
            ],
1396
            "document_ids": [
1397
                19370,
1398
                7367,
1399
                9110,
1400
                971,
1401
                1071
1402
            ],
1403
            "answer": " To perform an all\\_reduce operation among a list of actors, you can use the `col.allreduce()` function from the `collective` module in Ray. Here's an example:\n\n1. Import the necessary modules and create two actors:\n```python\nfrom ray import Worker, put, get\nfrom ray.data.collective import CommunicationGroup\n\n# Create two actors\nA = Worker.remote()\nB = Worker.remote()\n```\n2. Put the actors in a collective group:\n```python\ncol = CommunicationGroup([A, B], options={\"rank\": [0, 1]})\n```\n3. Define a function to perform the all\\_reduce operation:\n```python\n@ray.remote\nclass Worker:\n    def __init__(self):\n        self.buffer = put(0)\n\n    def do_allreduce(self):\n        col.allreduce(self.buffer)\n        return self.buffer\n```\n4. Perform the all\\_reduce operation:\n```python\nray.get([A.do_allreduce.remote(), B.do_allreduce.remote()])\n```\n\nThis will perform an all\\_reduce operation on the initial value of 0 among the two actors. Note that this is just an example, and you can replace the initial value and the function `do_allreduce()` with your own logic.",
1404
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1405
        },
1406
        {
1407
            "question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",
1408
            "sources": [
1409
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",
1410
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",
1411
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",
1412
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
1413
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy"
1414
            ],
1415
            "document_ids": [
1416
                6840,
1417
                6380,
1418
                6839,
1419
                6853,
1420
                6858
1421
            ],
1422
            "answer": " If you specify a bundle with `{\"CPU\":0}` in the PlacementGroup, it will not be created because there is no available bundle with 0 CPU resources. The `PlacementGroup` creation will fail if it cannot create all the specified bundles due to insufficient resources or other reasons.",
1423
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1424
        },
1425
        {
1426
            "question": "How to cancel job from UI?",
1427
            "sources": [
1428
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",
1429
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",
1430
                "https://docs.ray.io/en/master/workflows/comparison.html#trip-booking",
1431
                "https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",
1432
                "https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel"
1433
            ],
1434
            "document_ids": [
1435
                5934,
1436
                8668,
1437
                12945,
1438
                13098,
1439
                13099
1440
            ],
1441
            "answer": " To cancel a job from the UI, you can use the `ray.cancel()` function and pass the ObjectRef returned by the task you want to cancel. If the task is currently executing, this will send a KeyboardInterrupt. If you want to force-exit the worker, you can pass `force=True`.\n\nAlternatively, if you are using Ray workflows, you can use the `ray.workflow.cancel()` function and pass the workflow ID as an argument. This will cancel the workflow and any tasks that are still running.\n\nHere is an example of how to use `ray.cancel()` to cancel a task:\n```\n# Assume task_ref is an ObjectRef returned by the task you want to cancel\nray.cancel(task_ref)\n```\nHere is an example of how to use `ray.workflow.cancel()` to cancel a workflow:\n```\nworkflow.cancel(workflow_id=\"some_workflow\")\n```\nNote that cancelling a task or workflow will not automatically retry it. If you want to retry a cancelled task, you will need to do so manually.",
1442
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1443
        },
1444
        {
1445
            "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>`",
1446
            "sources": [
1447
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",
1448
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
1449
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
1450
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
1451
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster"
1452
            ],
1453
            "document_ids": [
1454
                18567,
1455
                18548,
1456
                8684,
1457
                18759,
1458
                18571
1459
            ],
1460
            "answer": " To get your project files on the cluster when using Ray Serve, you can use the `serve run` command with the `--working-dir` option. This command connects to the remote cluster via Ray Client, uploads the specified directory, and runs your Serve application. For example:\n\n`serve run --address=ray://<head-node-ip-address>:10001 --working-dir=\"./project/src\" local_dev:graph`\n\nHere, the local directory specified by `--working-dir` must contain `local_dev.py` so that it can be uploaded to the cluster and imported by Ray Serve.\n\nAdditionally, when deploying your Serve application to a remote cluster using the `serve deploy` command, you can specify the remote Ray cluster's dashboard agent address using the `--address` or `-a` option. This address should be of the form `[RAY_CLUSTER_URI]:[DASHBOARD_AGENT_PORT]`. For example:\n\n`serve deploy config.yaml --address <remote dashboard agent address>`\n\nWhere `<remote dashboard agent address>` is the address of the remote Ray cluster's dashboard agent.",
1461
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1462
        },
1463
        {
1464
            "question": "how do i install ray nightly wheel",
1465
            "sources": [
1466
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",
1467
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
1468
                "https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",
1469
                "https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",
1470
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart"
1471
            ],
1472
            "document_ids": [
1473
                19550,
1474
                95,
1475
                405,
1476
                1198,
1477
                385
1478
            ],
1479
            "answer": " To install the Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use pip to install the nightly wheel with support for the dashboard and cluster launcher by running `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`, replacing `LINK_TO_WHEEL.whl` with the actual link to the nightly wheel you want to install.\n\nFor example, if you are using Python 3.8, you can install the nightly wheel with the following command:\n```\npip install -U \"ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp38-cp38-manylinux2014_x86_64.whl\"\n```",
1480
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1481
        },
1482
        {
1483
            "question": "how do i install the latest ray nightly wheel?",
1484
            "sources": [
1485
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",
1486
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
1487
                "https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",
1488
                "https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",
1489
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart"
1490
            ],
1491
            "document_ids": [
1492
                19550,
1493
                95,
1494
                405,
1495
                1198,
1496
                385
1497
            ],
1498
            "answer": " To install the latest Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use pip to install the nightly wheel with support for the dashboard and cluster launcher by running `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`, where `LINK_TO_WHEEL.whl` is the link to the nightly wheel file. You can find the latest nightly wheel link in the Ray documentation or by following the instructions in the context provided.",
1499
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1500
        },
1501
        {
1502
            "question": "how can I write unit tests for Ray code?",
1503
            "sources": [
1504
                "https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",
1505
                "https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",
1506
                "https://docs.ray.io/en/master/ray-core/cross-language.html#cross-language-data-serialization",
1507
                "https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",
1508
                "https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible"
1509
            ],
1510
            "document_ids": [
1511
                7308,
1512
                7305,
1513
                6206,
1514
                6187,
1515
                7310
1516
            ],
1517
            "answer": " To write unit tests for Ray code, you can follow these tips:\n\n1. Use `ray.init(num_cpus=...)` to fix the resource quantity.\n2. Ideally, share the Ray cluster across tests if possible to reduce overhead.\n3. Consider creating a mini-cluster with `ray.cluster_utils.Cluster`.\n4. Be cautious when running tests in parallel.\n5. For Java Ray code, you can use `ObjectRef` and `Ray.task()` to test remote functions.\n6. Reusing a Ray cluster across tests can provide significant speedups.\n\nHere's an example of setting up a Ray cluster for a test suite in Python:\n\n```python\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        ray.init(num_cpus=4, num_gpus=0)\n\n    def setUp(self):\n        # ... other set up steps ...\n```\n\nRemember to be cautious when running tests in parallel to avoid resource conflicts.",
1518
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1519
        },
1520
        {
1521
            "question": "How I stop Ray from spamming lots of Info updates on stdout?",
1522
            "sources": [
1523
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",
1524
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",
1525
                "https://docs.ray.io/en/master/cluster/cli.html#ray-start",
1526
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",
1527
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#configuring-log-processing"
1528
            ],
1529
            "document_ids": [
1530
                619,
1531
                7785,
1532
                7605,
1533
                18749,
1534
                7917
1535
            ],
1536
            "answer": " To stop Ray from spamming lots of Info updates on stdout, you can consider changing the log style to 'record' using the `--log-style record` command-line option. This will output record-style logs without formatting, which might reduce the amount of output. Additionally, you can try redirecting the output to a file or disable the usage stats collection using the `--disable-usage-stats` option. If you're running Ray on a local machine, you can also try using the `--ray-debugger-external` option to make the Ray debugger available externally, which might help you diagnose the issue.",
1537
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1538
        },
1539
        {
1540
            "question": "how to deploy stable diffusion 2.1 with Ray Serve?",
1541
            "sources": [
1542
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",
1543
                "https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",
1544
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#serve-a-stablediffusion-text-to-image-model-on-kubernetes",
1545
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
1546
                "https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed"
1547
            ],
1548
            "document_ids": [
1549
                8242,
1550
                19359,
1551
                8239,
1552
                19150,
1553
                18414
1554
            ],
1555
            "answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install the required packages:\n```\npip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers\n```\n2. Download the RayService configuration for Stable Diffusion:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml\n```\n3. Save the Stable Diffusion code to a file named `stable_diffusion.py`.\n4. Follow the Ray documentation and the example in the `ray-project/serve_config_examples` repo to set up the Ray Serve application and its client on Kubernetes.\n\nNote that the new Ray Serve 2.0 deployment API has been released, and the 1.x deployment API will be deprecated in the future. Make sure to use the new API for long-term compatibility.",
1556
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1557
        },
1558
        {
1559
            "question": "what is actor_handle?",
1560
            "sources": [
1561
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",
1562
                "https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",
1563
                "https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",
1564
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ObjectState.html#ray-util-state-common-objectstate",
1565
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill"
1566
            ],
1567
            "document_ids": [
1568
                19745,
1569
                5810,
1570
                16272,
1571
                1068,
1572
                6547
1573
            ],
1574
            "answer": " An actor handle is a reference to an actor in Ray, a distributed computing framework. It can be passed into other tasks and used to define remote functions or actor methods.",
1575
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1576
        },
1577
        {
1578
            "question": "how to kill a r detached actors?",
1579
            "sources": [
1580
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
1581
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
1582
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
1583
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
1584
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill"
1585
            ],
1586
            "document_ids": [
1587
                6523,
1588
                6254,
1589
                6169,
1590
                6179,
1591
                6546
1592
            ],
1593
            "answer": " To kill a detached Ray actor, you can use the `ray.kill()` function and provide the actor handle as an argument. Here is an example:\n\n```python\nactor = ...  # Assume this is your detached actor handle\nray.kill(actor)\n```\n\nThis will immediately terminate the actor and release any associated resources. Note that any running tasks on the actor will be interrupted and will fail immediately. If you want to allow pending tasks to finish before terminating the actor, you can call `actor.__ray_terminate__.remote()` instead.\n\nYou can also use the `ray list actors --detail` command from the Ray State API to see the death cause of dead actors.",
1594
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1595
        },
1596
        {
1597
            "question": "How to force upgrade the pip package in the runtime environment if an old version exists?",
1598
            "sources": [
1599
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
1600
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",
1601
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
1602
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
1603
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging"
1604
            ],
1605
            "document_ids": [
1606
                6089,
1607
                5901,
1608
                5952,
1609
                6152,
1610
                6150
1611
            ],
1612
            "answer": " To force upgrade the pip package in the runtime environment if an old version exists, you can specify the `pip_version` parameter in the `RuntimeEnv` configuration with the desired pip version, and include the `--upgrade` flag in the `pip_check` parameter. Here is an example:\n```python\nRuntimeEnv(\n    pip={\"packages\":[\"tensorflow\", \"requests\"], \"pip_check\": '--upgrade',\n    \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"}\n)\n```\nNote that the `pip_check` parameter is set to `'--upgrade'`, which will force pip to upgrade the packages to the specified version. Also, the syntax of the `pip_version` parameter is defined in PEP 508, and it will be installed in the Ray workers at runtime.",
1613
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1614
        },
1615
        {
1616
            "question": "How do I do global shuffle with Ray?",
1617
            "sources": [
1618
                "https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",
1619
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",
1620
                "https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",
1621
                "https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",
1622
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"
1623
            ],
1624
            "document_ids": [
1625
                9214,
1626
                12253,
1627
                9329,
1628
                9216,
1629
                8825
1630
            ],
1631
            "answer": " To do a global shuffle with Ray, you can use the push-based shuffle. Here's how you can enable it:\n\n1. Set the `use_push_based_shuffle` flag to `True` in your `DataContext`. Here's an example:\n\n   ```python\n   ctx = ray.data.DataContext.get_current()\n   ctx.use_push_based_shuffle = True\n   ```\n\n2. Randomly shuffle your dataset using the `random_shuffle()` function. Here's an example:\n\n   ```python\n   ds = (\n       ray.data.range(1000)\n       .random_shuffle()\n   )\n   ```\n\nNote that global shuffle is more expensive than local shuffle, as it fully decorrelates row iteration order from the original dataset order, at the cost of significantly more computation, I/O, and communication. For most cases, local shuffling is sufficient. To enable local shuffling, specify a `local_shuffle_buffer_size` when calling `iter_batches()` or `iter_torch_batches()`.",
1632
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1633
        },
1634
        {
1635
            "question": "How to find namespace of an Actor?",
1636
            "sources": [
1637
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
1638
                "https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",
1639
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
1640
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1641
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace"
1642
            ],
1643
            "document_ids": [
1644
                6573,
1645
                6586,
1646
                6568,
1647
                6215,
1648
                6277
1649
            ],
1650
            "answer": " To find the namespace of an Actor in Ray, you can use the `ray.get_actor` function, which returns the `ActorHandle` to the actor. The name of the actor must be unique within the namespace. If no namespace is assigned to the actor, it will be placed in an anonymous namespace by default.\n\nYou can specify a namespace for a named actor while creating it using the `namespace` parameter in the `@ray.remote` decorator.\n\nTo set your application's namespace, it should be specified when you first connect to the cluster using `ray.init(namespace=\"your_namespace\")` in Python or `System.setProperty(\"ray.job.namespace\", \"your_namespace\")` in Java before `Ray.init()`.\n\nHere's an example in Python:\n\n```python\nimport ray\n\nray.init(namespace=\"your_namespace\")\n\n@ray.remote(namespace=\"your_namespace\")\nclass Actor:\n    pass\n\nactor_handle = ray.get_actor(\"some_name\")\n```\n\nIn this example, the `actor_handle` belongs to the namespace \"your_namespace\".",
1651
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1652
        },
1653
        {
1654
            "question": "How does Ray work with async.io ?",
1655
            "sources": [
1656
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
1657
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",
1658
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks",
1659
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",
1660
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"
1661
            ],
1662
            "document_ids": [
1663
                5637,
1664
                6133,
1665
                5615,
1666
                6147,
1667
                8825
1668
            ],
1669
            "answer": " Ray's remote operations, such as tasks and actor methods, are inherently asynchronous, returning a promise/future that can be used to launch multiple operations in parallel. To integrate Ray with asyncio, you can use async/await syntax and popular async frameworks like aiohttp, aioredis, etc. However, in async actors, running blocking ray.get or ray.wait is not allowed because it will block the execution of the event loop. Only one task can be running at any point in time in async actors. The Ray client can be used to execute tasks and operations on a remote Ray cluster.",
1670
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1671
        },
1672
        {
1673
            "question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",
1674
            "sources": [
1675
                "https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",
1676
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",
1677
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",
1678
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
1679
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#stepping-between-ray-tasks"
1680
            ],
1681
            "document_ids": [
1682
                7052,
1683
                19372,
1684
                7074,
1685
                5663,
1686
                845
1687
            ],
1688
            "answer": " To debug a hanging `ray.get()` call, first note that it's best practice to wait as long as possible before calling `ray.get()` and avoid calling it in a loop. `ray.get()` is a blocking call, which means that it waits until the requested result is available. If you call `ray.get()` in a loop, the loop will not continue to run until the call to `ray.get()` is resolved.\n\nIn your specific case, you can use Ray's built-in debugger to investigate the issue. After running the program by executing the Python file and calling `ray debug`, you can select the breakpoint by pressing 0 and enter. This will allow you to jump into the call with the `remote` command in Ray's debugger. Inside the function, print the value of the relevant variables with `p(variable_name)` to help identify the cause of the hanging `ray.get()` call.",
1689
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1690
        },
1691
        {
1692
            "question": "can you show me an example of ray.actor.exit_actor()",
1693
            "sources": [
1694
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
1695
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
1696
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
1697
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
1698
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle"
1699
            ],
1700
            "document_ids": [
1701
                6183,
1702
                6186,
1703
                6163,
1704
                6192,
1705
                6167
1706
            ],
1707
            "answer": " The `ray.actor.exit_actor()` function is not a direct method in the Ray API. However, you can exit an actor gracefully by using the ObjectRef resulting from the task that created the actor. For example, in Python:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n    pass\n\nactor_handle = Actor.remote()\n\n# ... perform work with the actor ...\n\nactor_handle.kill.remote()  # This will cause the actor to exit\n```\n\nKeep in mind that manual termination should be reserved for cases where an actor is unexpectedly hanging or leaking resources, and for detached actors, which must be manually destroyed. Ray will automatically terminate actors that have gone out of scope in most cases.",
1708
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1709
        },
1710
        {
1711
            "question": "how to add log inside actor?",
1712
            "sources": [
1713
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",
1714
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
1715
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
1716
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
1717
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs"
1718
            ],
1719
            "document_ids": [
1720
                626,
1721
                733,
1722
                607,
1723
                19192,
1724
                711
1725
            ],
1726
            "answer": " To add log inside an actor, you can use the Python `logging` module. Here's an example of how you can do it:\n\n1. Import the `logging` module in your actor class.\n2. Configure the logging settings using `logging.basicConfig()`. This will automatically stream logs to stdout and stderr.\n3. Define a method in your actor class that uses the `logging.getLogger()` method to get a logger instance and logs the message using the `info()` method.\n\nHere's some sample code that demonstrates this:\n```python\nimport logging\n\n@ray.remote\nclass Actor:\n    def __init__(self):\n        logging.basicConfig(level=logging.INFO)\n\n    def log(self, msg):\n        logger = logging.getLogger(__name__)\n        logger.info(msg)\n\nactor = Actor.remote()\nray.get(actor.log.remote(\"A log message for an actor.\"))\n```\nBy default, all stdout and stderr of Tasks and Actors are redirected to the Driver output. However, you can configure logging to redirect logs to a file or to a custom location.\n\nAdditionally, you can customize the prefixes for Actor logs by defining the `__repr__` method for the Actor class. This can be useful if you have a large number of worker Actors and want to easily see the index of the Actor that logged a particular message. For example:\n```python\nimport ray\n\nclass Actor:\n    def __init__(self, index):\n        self.index = index\n\n    def __repr__(self):\n        return f\"Actor-{self.index}\"\n\n    # ... rest of the actor code ...\n```\nThis will replace the Actor name with the Actor repr in the log messages.",
1727
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1728
        },
1729
        {
1730
            "question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",
1731
            "sources": [
1732
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",
1733
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",
1734
                "https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",
1735
                "https://docs.ray.io/en/master/data/user-guide.html#user-guides",
1736
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt"
1737
            ],
1738
            "document_ids": [
1739
                197,
1740
                7154,
1741
                9348,
1742
                9103,
1743
                15148
1744
            ],
1745
            "answer": " Sure, here's a script that should do batch inference with GPT-2 on text data from an S3 bucket using Ray:\n```python\nimport os\nimport ray\nfrom transformers import pipeline, AutoTokenizer, GPT2ForSequenceClassification\n\n# Set up Ray\nray.init()\n\n# Read data from S3 bucket\ndataset = ray.data.read_text(\"s3://your-s3-bucket/your-text-files\")\n\n# Define the model and tokenizer\nmodel = GPT2ForSequenceClassification.from_pretrained(\"/path/to/your/checkpoint\")\ntokenizer = AutoTokenizer.from_pretrained(\"/path/to/your/checkpoint\")\n\n# Define the inference function\n@ray.remote\ndef infer_on_batch(batch):\n    inputs = tokenizer(list(batch[\"text\"]), padding=True, truncation=True, return_tensors=\"pt\")\n    outputs = model(**inputs)\n    batch[\"predictions\"] = outputs.logits.argmax(dim=-1).tolist()\n    return batch\n\n# Perform batch inference\nresults = dataset.map_batches(infer_on_batch, batch_size=10).fetch()\n\n# Save the results\nray.data.write_json(results, \"s3://your-s3-bucket/your-output-folder\")\n```\nHere's how the script works:\n\n1. We first set up Ray and initialize a Ray cluster.\n2. We then read the text data from the S3 bucket using `ray.data.read_text()`.\n3. We define the GPT-2 model and tokenizer using the `transformers` library.\n4. We define a Ray remote function `infer_on_batch()` that takes a batch of text data, tokenizes it, and generates predictions using the GPT-2 model.\n5. We use `dataset.map_batches()` to apply the `infer_on_batch()` function to each batch of text data.\n6. We save the results to the S3 bucket using `ray.data.write_json()`.\n\nNote that you will need to replace `\"s3://your-s3-bucket/your-text-files\"` and `\"s3://your-s3-bucket/your-output-folder\"` with the actual S3 bucket and folder names for your text data and output files. You will also need to replace `\"/path/to/your/checkpoint\"` with the actual path to your GPT-2 checkpoint.",
1746
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1747
        },
1748
        {
1749
            "question": "How do I enable Ray debug logs?",
1750
            "sources": [
1751
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",
1752
                "https://docs.ray.io/en/master/ray-contribute/debugging.html#debugging-for-ray-developers",
1753
                "https://docs.ray.io/en/master/ray-contribute/debugging.html#backend-logging",
1754
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",
1755
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log"
1756
            ],
1757
            "document_ids": [
1758
                698,
1759
                1280,
1760
                1285,
1761
                820,
1762
                980
1763
            ],
1764
            "answer": " To enable Ray debug logs, you can set the `RAY_BACKEND_LOG_LEVEL` environment variable to `debug` before starting Ray. This can be done by adding the following line to your script before initializing Ray:\n\n```python\nimport os\nos.environ['RAY_BACKEND_LOG_LEVEL'] = 'debug'\n```\n\nAlternatively, you can set the environment variable manually in your terminal before running the script.\n\nNote that this will set the log level for the Ray backend to debug, which includes raylet and ray processes. The Ray Debugger is an experimental feature and may not be suitable for production use.",
1765
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1766
        },
1767
        {
1768
            "question": "How do I list the current Ray actors from python?",
1769
            "sources": [
1770
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",
1771
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
1772
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
1773
                "https://docs.ray.io/en/master/ray-core/examples/automl_for_time_series.html#walkthrough",
1774
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a"
1775
            ],
1776
            "document_ids": [
1777
                670,
1778
                656,
1779
                18748,
1780
                7432,
1781
                13404
1782
            ],
1783
            "answer": " To list the current Ray actors from Python, you can use the `list_actors` function from the `ray.util.state` module and filter by state equal to \"ALIVE\". Here is an example:\n\n```python\nfrom ray.util.state import list_actors\nactors = list_actors(filters=[(\"state\", \"=\", \"ALIVE\")])\nfor actor in actors:\n    print(actor)\n```\n\nThis will print out information about all the currently alive actors in Ray.",
1784
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1785
        },
1786
        {
1787
            "question": "I want to kill the replica actor from Python. how do I do it?",
1788
            "sources": [
1789
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
1790
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
1791
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
1792
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
1793
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote"
1794
            ],
1795
            "document_ids": [
1796
                6523,
1797
                6061,
1798
                18751,
1799
                6179,
1800
                6521
1801
            ],
1802
            "answer": " To kill a replica actor from Python, you can use the `ray.kill()` function and provide the actor handle as an argument. Here's an example:\n```python\nimport ray\n\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nThis will immediately kill the actor and release any associated resources. Note that the actor will not be restarted after being killed.\n\nAlternatively, you can also manually terminate an actor from within one of the actor methods using the `ray.actor.exit_actor()` function. Here's an example:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n    def exit(self):\n        ray.actor.exit_actor()\n\nactor = Actor.remote()\nactor.exit.remote()\n```\nThis will kill the actor process and release any associated resources from within the actor itself.",
1803
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1804
        },
1805
        {
1806
            "question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",
1807
            "sources": [
1808
                "https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",
1809
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",
1810
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
1811
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",
1812
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options"
1813
            ],
1814
            "document_ids": [
1815
                6298,
1816
                6915,
1817
                6912,
1818
                6746,
1819
                6037
1820
            ],
1821
            "answer": " To specify that a task should run on a V10",
1822
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1823
        },
1824
        {
1825
            "question": "How do I get started?",
1826
            "sources": [
1827
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",
1828
                "https://docs.ray.io/en/master/serve/index.html#learn-more",
1829
                "https://docs.ray.io/en/master/cluster/vms/index.html#learn-more",
1830
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",
1831
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started"
1832
            ],
1833
            "document_ids": [
1834
                8478,
1835
                18382,
1836
                8353,
1837
                6551,
1838
                8325
1839
            ],
1840
            "answer": " To get started with Ray Serve, you can follow these steps:\n\n1. Install Ray by running `pip install -U ray`.\n2. Import and initialize Ray using `import ray` and `ray.init()`.\n3. Check out the \"Getting Started\" section to deploy a single model locally.\n4. Convert an existing model into a Ray Serve deployment.\n5. Alternatively, you can head over to the Examples to get started building your Ray Serve applications.\n\nYou can also refer to the Ray documentation for information on running Ray workloads on cloud VMs, starting a Ray cluster, and deploying Ray applications in the cloud.",
1841
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1842
        },
1843
        {
1844
            "question": "How to specify python version in runtime_env?",
1845
            "sources": [
1846
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
1847
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
1848
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",
1849
                "https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",
1850
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging"
1851
            ],
1852
            "document_ids": [
1853
                6089,
1854
                5919,
1855
                5847,
1856
                5891,
1857
                6150
1858
            ],
1859
            "answer": " To specify the Python version in the runtime environment, you can include the `python_version` parameter in the `pip` field of the `RuntimeEnv` dictionary or class. Here's an example:\n\nFor the dictionary format:\n```python\nruntime_env = {\n    \"pip\": {\n        \"packages\": [\"tensorflow\", \"requests\"],\n        \"pip_check\": False,\n        \"pip_version\": \"==22.0.2\",\n        \"python_version\": \"==3.8.11\"\n    }\n}\n```\n\nFor the class format:\n```python\nfrom ray.runtime_env import RuntimeEnv\n\nruntime_env = RuntimeEnv(\n    pip={\n        \"packages\": [\"tensorflow\", \"requests\"],\n        \"pip_check\": False,\n        \"pip_version\": \"==22.0.2\",\n        \"python_version\": \"==3.8.11\"\n    }\n)\n```\n\nThis will ensure that the specified Python version is used when the runtime environment is created.",
1860
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1861
        },
1862
        {
1863
            "question": "how to create a Actor in a namespace?",
1864
            "sources": [
1865
                "https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",
1866
                "https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",
1867
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
1868
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
1869
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"
1870
            ],
1871
            "document_ids": [
1872
                6586,
1873
                6588,
1874
                6573,
1875
                6568,
1876
                6225
1877
            ],
1878
            "answer": " To create an actor in a specified namespace, you can use the `setName()` function with the `ray.actor()` call and provide the namespace as an argument. Here's an example in Java:\n```java\nRay.init();\nRay.actor(Actor::new).setName(\"my_actor\", \"actor_namespace\").remote();\n```\nIn this example, \"my\\_actor\" is the name of the actor and \"actor\\_namespace\" is the namespace where the actor will be created.\n\nBefore creating the actor, make sure to set the namespace for your job. In Java, you can do this by setting the `ray.job.namespace` system property before calling `Ray.init()`. Here's an example:\n```java\nSystem.setProperty(\"ray.job.namespace\", \"hello\");\nRay.init();\n```\nIn this example, the namespace for the job is set to \"hello\". Any actors created in this job will belong to this namespace.",
1879
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1880
        },
1881
        {
1882
            "question": "Can I specify multiple working directories?",
1883
            "sources": [
1884
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
1885
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
1886
                "https://docs.ray.io/en/master/ray-core/configure.html#driver-options",
1887
                "https://docs.ray.io/en/master/serve/api/index.html#serve-run",
1888
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment"
1889
            ],
1890
            "document_ids": [
1891
                2363,
1892
                5922,
1893
                6448,
1894
                18955,
1895
                1183
1896
            ],
1897
            "answer": " Based on the provided context, Ray's configuration allows you to specify a single working directory using the `working_dir` parameter or the `--working-dir` command-line option. However, there is no direct support for specifying multiple working directories.\n\nIf you need to access files from multiple directories, you can consider merging the contents of those directories into a single directory or using relative paths to access files from the original working directory.\n\nAdditionally, you can use the `TUNE_ORIG_WORKING_DIR` environment variable as a workaround to access paths relative to the original working directory. But this is not the same as having multiple working directories.",
1898
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1899
        },
1900
        {
1901
            "question": "what if I set num_cpus=0 for tasks",
1902
            "sources": [
1903
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
1904
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
1905
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",
1906
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",
1907
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads"
1908
            ],
1909
            "document_ids": [
1910
                6527,
1911
                5633,
1912
                7998,
1913
                8709,
1914
                19452
1915
            ],
1916
            "answer": " If you set `num_cpus=0` for tasks, it means that those tasks will not utilize any CPU resources. This can be useful in certain situations, such as when you want to prevent tasks from being scheduled on a head node that already has a heavy networking load. However, keep in mind that this means the tasks will not be able to execute any computations or processing. It's important to use this setting judiciously and only when it makes sense for your specific use case.",
1917
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1918
        },
1919
        {
1920
            "question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",
1921
            "sources": [
1922
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",
1923
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
1924
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",
1925
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",
1926
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler"
1927
            ],
1928
            "document_ids": [
1929
                7852,
1930
                8021,
1931
                8033,
1932
                18882,
1933
                8048
1934
            ],
1935
            "answer": " No, it is not possible to have Ray on K8s with autoscaler enabled without using KubeRay. While you can deploy a static Ray cluster to Kubernetes without KubeRay, this method does not include the built-in autoscaling feature that KubeRay provides. To enable Ray autoscaling on Kubernetes, you need to use KubeRay and configure the Ray autoscaler in the RayCluster custom resource (CR) config. The Kubernetes Cluster Autoscaler complements the Ray Autoscaler by provisioning and cleaning up Kubernetes nodes as needed for Ray pods.",
1936
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1937
        },
1938
        {
1939
            "question": "how to manually configure and manage Ray cluster on Kubernetes",
1940
            "sources": [
1941
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",
1942
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",
1943
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",
1944
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",
1945
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide"
1946
            ],
1947
            "document_ids": [
1948
                7730,
1949
                7977,
1950
                7978,
1951
                7497,
1952
                7806
1953
            ],
1954
            "answer": " To manually configure and manage a Ray cluster on Kubernetes, you can follow these steps:\n\n1. Learn the basics of RayCluster configuration on Kubernetes by reading the RayCluster Configuration guide.\n2. Create a RayCluster custom resource (CR) using a YAML file (e.g., `raycluster.yaml`) and apply it using `kubectl apply -f raycluster.yaml`.\n3. To configure Ray Dashboard, refer to the Configuring and Managing Ray Dashboard guide. Note that the configuration may differ depending on how you launch Ray Clusters.\n4. Use the Kubernetes-native RayService to manage Ray clusters and Ray Serve applications. After defining the Ray cluster and its Ray Serve applications in a Kubernetes config, you can use `kubectl` to create and manage the cluster and its applications.\n5. For in-place updates of Ray Serve applications, update the RayServe config in the RayService CR config and use `kubectl apply` to update the applications.\n\nRefer to the provided context for more details.",
1955
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1956
        },
1957
        {
1958
            "question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",
1959
            "sources": [
1960
                "https://docs.ray.io/en/master/ray-core/cross-language.html#cross-language-data-serialization",
1961
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
1962
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",
1963
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
1964
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors"
1965
            ],
1966
            "document_ids": [
1967
                6208,
1968
                774,
1969
                19499,
1970
                6935,
1971
                776
1972
            ],
1973
            "answer": " Shutting down the raylet with `ray.shutdown()` will not directly kill tasks and workers on that node. However, if your node is experiencing out-of-memory (OOM) issues, as indicated in the context, the raylet may have already killed workers due to memory pressure. To address the OOM issue, consider providing more memory, reducing task parallelism, or adjusting the kill threshold as suggested in the context.",
1974
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1975
        },
1976
        {
1977
            "question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",
1978
            "sources": [
1979
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",
1980
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",
1981
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",
1982
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management",
1983
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage"
1984
            ],
1985
            "document_ids": [
1986
                748,
1987
                745,
1988
                6918,
1989
                6765,
1990
                780
1991
            ],
1992
            "answer": " To debug out-of-memory issues, first familiarize yourself with Ray's Memory Management model. Then, check out the Debugging Out of Memory section for information on how to detect and resolve memory issues. The memory monitor can be particularly helpful for identifying high memory usage. Additionally, the Find per Task and Actor Memory Usage section provides guidance on locating tasks and actors with high memory usage. The relevant documentation to look at includes 'Debugging Out of Memory', 'Memory Management', 'What is the memory monitor and how it works', and 'Find per Task and Actor Memory Usage'.",
1993
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1994
        },
1995
        {
1996
            "question": "How to use callback in Trainer?",
1997
            "sources": [
1998
                "https://docs.ray.io/en/master/train/examples/transformers/transformers_torch_trainer_basic.html#ray-train-basic-example-for-huggingface-transformers",
1999
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#define-your-training-function",
2000
                "https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",
2001
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_end.html#ray-train-lightning-raytrainreportcallback-on-init-end",
2002
                "https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#monitoring-and-logging-metrics"
2003
            ],
2004
            "document_ids": [
2005
                13091,
2006
                12914,
2007
                10745,
2008
                11418,
2009
                12329
2010
            ],
2011
            "answer": " To use a callback in the Trainer from the transformers library, you can add a callback object to the Trainer object using the `add_callback` method. For example, to use the `RayTrainReportCallback` callback, you can do:\n\n```\nfrom transformers import Trainer, TrainingArguments\n\ntraining_args = TrainingArguments(...)\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=small_train_ds,\n    eval_dataset=small_eval_ds,\n)\ntrainer.add_callback(RayTrainReportCallback())\n```\n\nThe `RayTrainReportCallback` callback has a method called `on_train_epoch_end` that is called at the end of each training epoch. This method computes the training metrics and reports them to Ray Train using the `train.report` method.\n\nYou can also use the `RayTrainReportCallback` callback with the PyTorch Lightning Trainer by creating a subclass of `Callback` and defining the `on_train_epoch_end` method in the subclass. Here is an example:\n\n```\nimport pytorch_lightning as pl\n\nclass MyRayTrainReportCallback(pl.Callback):\n    def on_train_epoch_end(self, trainer, pl_module):\n        metrics = trainer.callback_metrics\n        metrics = {k: v.item() for k, v in metrics.items()}\n\n        train.report(metrics=metrics)\n\ntrainer = pl.Trainer(\n    max_epochs=max_epochs,\n    accelerator=\"auto\",\n    devices=\"auto\",\n    strategy=RayDDPStrategy(),\n    plugins=[RayLightningEnvironment()],\n    callbacks=[MyRayTrainReportCallback()],\n    enable_progress_bar=False,\n)\n```\n\nThis will cause the `on_train_epoch_end` method of the `MyRayTrainReportCallback` callback to be called at the end of each training epoch, where the training metrics are computed and reported to Ray Train using the `train.report` method.",
2012
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2013
        },
2014
        {
2015
            "question": "How to provide current working directory to ray?",
2016
            "sources": [
2017
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
2018
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
2019
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",
2020
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
2021
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"
2022
            ],
2023
            "document_ids": [
2024
                8872,
2025
                8825,
2026
                3133,
2027
                8874,
2028
                5922
2029
            ],
2030
            "answer": " To provide the current working directory to Ray, you can specify the `working_dir` argument when calling `ray.init()`. This can be the path to an existing local directory, a zipped file, or a URI to a remotely-stored zip file. The specified working directory will be uploaded to the Ray cluster and used as the working directory for the Ray workers. Relative paths in the remote tasks and actors will work on the laptop and on the cluster without any code changes.",
2031
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2032
        },
2033
        {
2034
            "question": "how to create an actor instance with parameter?",
2035
            "sources": [
2036
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
2037
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",
2038
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
2039
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
2040
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor"
2041
            ],
2042
            "document_ids": [
2043
                7017,
2044
                7299,
2045
                6205,
2046
                6516,
2047
                7298
2048
            ],
2049
            "answer": " To create an actor instance with a parameter, you can use the `.remote()` method with arguments to the constructor when instantiating the actor. Here's an example using the ProgressActor:\n\n```python\nprogress_actor = ProgressActor.remote(TOTAL_NUM_SAMPLES)\n```\n\nIn this example, `TOTAL_NUM_SAMPLES` is being passed as a parameter to the `ProgressActor` constructor.",
2050
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2051
        },
2052
        {
2053
            "question": "how to push a custom module to ray which is using by Actor ?",
2054
            "sources": [
2055
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
2056
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
2057
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
2058
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
2059
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors"
2060
            ],
2061
            "document_ids": [
2062
                16241,
2063
                19709,
2064
                5755,
2065
                6512,
2066
                5617
2067
            ],
2068
            "answer": " To push a custom module to Ray which is used by an Actor, you can use the `ray.put()` function to store the module in the object store, and then use `ray.get()` to retrieve it within the Actor. Here's an example:\n\nSuppose you have a custom module named `my_module.py` that you want to use in your Actor.\n\nFirst, you need to import the module and any necessary functions or classes within the module:\n```python\nimport ray\nimport my_module\n```\nThen, you can use `ray.put()` to store the module in the object store:\n```python\nmy_module_id = ray.put(my_module)\n```\nNow, you can define your Actor class and use `my_module_id` to access the module within the Actor:\n```python\n@ray.remote\nclass MyActor:\n    def __init__(self, my_module_id):\n        self.my_module = ray.get(my_module_id)\n\n    def my_method(self):\n        # Use my_module here\n        result = self.my_module.my_function()\n        return result\n```\nFinally, you can create an instance of the Actor and call `my_method()` to use the custom module:\n```python\nmy_actor = MyActor.remote(my_module_id)\nresult = ray.get(my_actor.my_method.remote())\n```\nNote that you need to pass `my_module_id` to the Actor constructor so that the Actor can access the module within its own worker process.",
2069
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2070
        },
2071
        {
2072
            "question": "how to print ray working directory?",
2073
            "sources": [
2074
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
2075
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
2076
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
2077
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
2078
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#print"
2079
            ],
2080
            "document_ids": [
2081
                7772,
2082
                699,
2083
                5922,
2084
                2384,
2085
                875
2086
            ],
2087
            "answer": " To print the working directory of Ray, you can use the following code:\n```python\nprint(ray.global_state.worker.current_resource_key.to_str() + \": \" + ray.worker.global_worker.directories['working_dir'])\n```\nHowever, to print the logging directory of Ray where log files are stored, you can use the following code:\n```python\nprint(ray.worker.global_worker.log_dir)\n```\nThis will print the default logging directory for Ray, which is `/tmp/ray/session_*/logs`. To change the logging directory, you can specify it when you call `ray start` or `ray.init()`.",
2088
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2089
        },
2090
        {
2091
            "question": "why I can not see log.info in ray log?",
2092
            "sources": [
2093
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
2094
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
2095
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
2096
                "https://docs.ray.io/en/master/ray-contribute/debugging.html#backend-logging",
2097
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs"
2098
            ],
2099
            "document_ids": [
2100
                980,
2101
                699,
2102
                996,
2103
                1285,
2104
                727
2105
            ],
2106
            "answer": " Based on the information provided, Ray's log files are stored in a /tmp/ray/session\\_*\\logs directory by default. The command `ray util state get_log` can be used to access the logs from the Ray cluster. However, the query mentions `log.info`, which seems to be related to Python's logging. If you cannot see the `log.info` messages in the Ray logs, ensure that the Python logger is correctly configured and emitting logs at the desired level.\n\nAdditionally, it's essential to understand that Ray uses the environment variable `RAY_BACKEND_LOG_LEVEL` to set the log level. Make sure this variable is set correctly to include `INFO` logs. If it's set to a higher level (e.g., WARNING, ERROR, or CRITICAL), `INFO` logs might not be displayed.\n\nLastly, the log format for Python logs in Ray includes the timestamp, levelname, filename, lineno, and message. If you still cannot find the `log.info` messages, double-check the log files for the relevant information, as the logs might be present but in a different format.",
2107
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2108
        },
2109
        {
2110
            "question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",
2111
            "sources": [
2112
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
2113
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_tfrecords.html#ray-data-read-tfrecords",
2114
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",
2115
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",
2116
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview"
2117
            ],
2118
            "document_ids": [
2119
                9838,
2120
                9675,
2121
                9647,
2122
                10397,
2123
                9229
2124
            ],
2125
            "answer": " No, when using Ray Datasets to read files, the order of the data is not guaranteed to be preserved. This is because Ray Datasets are designed to process data in parallel, which may result in the data being returned in an arbitrary order. If maintaining the original order of the data is important, you may need to consider using a different method or applying a sorting operation after reading the data.",
2126
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2127
        },
2128
        {
2129
            "question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",
2130
            "sources": [
2131
                "https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",
2132
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",
2133
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
2134
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
2135
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance"
2136
            ],
2137
            "document_ids": [
2138
                6347,
2139
                5985,
2140
                5903,
2141
                5873,
2142
                6227
2143
            ],
2144
            "answer": " The statement \"Ray will not retry tasks upon exceptions thrown by application code\" means that if a task fails due to an exception caused by the user's code, Ray will not automatically retry the task by default. This behavior can be changed by setting the `max_retries` and `retry_exceptions` options in `ray.remote()` or `.options()`. Ray provides mechanisms to handle application-level failures, such as catching errors and retrying failed code, but by default, Ray will not retry tasks that fail due to user code exceptions.",
2145
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2146
        },
2147
        {
2148
            "question": "how do I specify the log directory when starting Ray?",
2149
            "sources": [
2150
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
2151
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",
2152
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",
2153
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",
2154
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr"
2155
            ],
2156
            "document_ids": [
2157
                699,
2158
                8673,
2159
                7909,
2160
                698,
2161
                7930
2162
            ],
2163
            "answer": " To specify the log directory when starting Ray, you can use the `--log-dir` option with the `ray start` or `ray.init()` commands. This will allow you to change the default log directory, which is `/tmp/ray/session_*/logs`. You can specify this option when you first initialize Ray or at any time while Ray is running.\n\nFor example, you can use the following command to start Ray and specify a custom log directory:\n\n```\nray start --log-dir /path/to/custom/log/directory\n```\n\nOr, if you are using `ray.init()` in your code, you can specify the log directory as follows:\n\n```\nray.init(log_dir=\"/path/to/custom/log/directory\")\n```\n\nThis will tell Ray to write logs to the specified directory instead of the default log directory.",
2164
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2165
        },
2166
        {
2167
            "question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",
2168
            "sources": [
2169
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2170
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",
2171
                "https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#experiments",
2172
                "https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",
2173
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration"
2174
            ],
2175
            "document_ids": [
2176
                8571,
2177
                8428,
2178
                7799,
2179
                3192,
2180
                8885
2181
            ],
2182
            "answer": " To launch a Ray cluster with 10 nodes without setting the minimum worker nodes as 10, you can use the Ray cluster launcher and configure the `max_workers` field in the cluster config file to 10. Here is an example of how to set up the config file:\n\n```\ncluster_name: my-ray-cluster\nmax_workers: 10\n\nservices:\n    ray-head:\n        instances: 1\n        resources:\n            vcores: 1\n            memory: 2048\n\n    ray-worker:\n        instances: 0  # The workers will be started by the Ray cluster launcher.\n        resources:\n            vcores: 1\n            memory: 204",
2183
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2184
        },
2185
        {
2186
            "question": "how to use ray api to scale up a cluster",
2187
            "sources": [
2188
                "https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",
2189
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",
2190
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-ray-settings",
2191
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
2192
                "https://docs.ray.io/en/master/ray-references/api.html#api-references"
2193
            ],
2194
            "document_ids": [
2195
                7596,
2196
                18882,
2197
                5741,
2198
                8677,
2199
                19779
2200
            ],
2201
            "answer": " To scale up a cluster using the Ray API, you can use `ray.autoscaler.sdk.request_resources`. Ray also has built-in autoscaling features that can be configured using the Ray cluster launcher. The autoscaler adjusts the number of nodes in the cluster based on resource demand. Note that there is an ongoing project to improve Ray's scalability and stability, and several parameters need to be tuned for running a large cluster. You can refer to the Ray documentation for more information on the Cluster Management API, Python SDK API Reference, and Ray Core API.",
2202
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2203
        },
2204
        {
2205
            "question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",
2206
            "sources": [
2207
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
2208
                "https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",
2209
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",
2210
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",
2211
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart"
2212
            ],
2213
            "document_ids": [
2214
                8338,
2215
                7464,
2216
                8914,
2217
                8213,
2218
                387
2219
            ],
2220
            "answer": " To specify a subnet in the Ray cloud launcher deployment file when launching a cluster in AWS, you can include the 'subnet\\_id' parameter in the 'head' and 'worker' sections of the YAML file. Here's an example:\n```yaml\ncluster_name: aws-example\nprovider:\n  type: aws\n  region: us-west-2\n  avail_zone: us-west-2a\n  subnet_id: subnet-12345678  # specify the subnet ID here\nhead:\n  instance_type: m5.large\n  node_config:\n    Metadata:\n      ssh_public_key: <your-ssh-key>\nworker:\n  instance_type: m5.large\n  node_config:\n    Metadata:\n      ssh_public_key: <your-ssh-key>\n",
2221
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2222
        },
2223
        {
2224
            "question": "where I can find HTTP server error code log for Ray serve",
2225
            "sources": [
2226
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
2227
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
2228
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",
2229
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",
2230
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"
2231
            ],
2232
            "document_ids": [
2233
                18927,
2234
                18156,
2235
                18152,
2236
                7830,
2237
                18163
2238
            ],
2239
            "answer": " Ray Serve error logs related to HTTP server errors can be found in the logs at /tmp/ray/session\\_latest/logs/serve/ on each node in the Ray cluster. By default, logs are also emitted to stderr. Additionally, you can check the Serve page in the Ray dashboard at http://localhost:8265/#/serve for important information about your application, including access logs and custom user logs. The logs include details such as the logging level, timestamp, deployment name, replica tag, request ID, route, file name, and line number. For log rotation, you can set the environment variables RAY\\_ROTATION\\_MAX\\_BYTES and RAY\\_ROTATION\\_BACKUP\\_COUNT.",
2240
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2241
        },
2242
        {
2243
            "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?",
2244
            "sources": [
2245
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",
2246
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",
2247
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",
2248
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",
2249
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#state"
2250
            ],
2251
            "document_ids": [
2252
                7502,
2253
                7499,
2254
                8371,
2255
                7497,
2256
                960
2257
            ],
2258
            "answer": " Yes, the issue you're facing is not uncommon. The Ray Dashboard's internal refresh failing could be due to the dashboard port not being properly forwarded or accessible.\n\nWhen you run a Ray cluster on Amazon, the dashboard typically launches on the head node, but the dashboard port may not be publicly exposed by default for security reasons. To access the Ray Dashboard from outside the head node, you need to set up port forwarding.\n\nHere are some steps to follow:\n\n1. Start the Ray cluster with the necessary configurations for port forwarding.\n2. Tunnel the dashboard port from the head node to your local machine using SSH.\n3. Access the Ray Dashboard via your local browser.\n\nIf the dashboard opens and then disappears, it might be due to misconfiguration or connection issues. Make sure you have set up port forwarding correctly and that the dashboard port is properly exposed.\n\nAdditionally, you can try changing the Ray Dashboard port using the `--dashboard-port` argument with `ray start` in the command line or by passing the `dashboard_port` keyword argument in your call to `ray.init()`. This might help resolve any port conflicts or connection issues.\n\nIf you continue to experience issues, you can check the dashboard log at `<RAY_LOG_DIR>/dashboard.log` for more in-depth debugging information.",
2259
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2260
        },
2261
        {
2262
            "question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",
2263
            "sources": [
2264
                "https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",
2265
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2266
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
2267
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
2268
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"
2269
            ],
2270
            "document_ids": [
2271
                7534,
2272
                8571,
2273
                8771,
2274
                7532,
2275
                8590
2276
            ],
2277
            "answer": " In the Ray cluster launcher YAML, `max_workers` does include the head node along with additional worker nodes. This means that if `max_workers` is set to 2, the cluster will have a head node and 2 worker nodes, for a total of 3 nodes. This is not desired in large-scale clusters, as mentioned in the context, because tasks and actors may be scheduled on the head node, which can impact the performance of cluster management processes. To avoid this, it is recommended to set `max_workers` to a value that is lower than the total number of nodes you want to include in the cluster, excluding the head node.",
2278
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2279
        },
2280
        {
2281
            "question": "How to update files in working directory ?",
2282
            "sources": [
2283
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
2284
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
2285
                "https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#loading-data-in-a-tune-trainable",
2286
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
2287
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable"
2288
            ],
2289
            "document_ids": [
2290
                5922,
2291
                2363,
2292
                3020,
2293
                2383,
2294
                3131
2295
            ],
2296
            "answer": " To update files in the working directory, you can write to a file directly in the trial directory using the open() function, as shown in the context. The working directory is set to the trial directory, so you don't need to worry about multiple workers saving to the same location. If you need to access files relative to the original working directory, you can do so for read-only purposes by setting the RAY\\_CHDIR\\_TO\\_TRIAL\\_DIR environment variable to 0 and using relative paths. However, make sure that the directory is synced on all nodes if running on multiple machines.",
2297
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2298
        },
2299
        {
2300
            "question": "How I can update working directory file when ray allready initiated ?",
2301
            "sources": [
2302
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
2303
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",
2304
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
2305
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
2306
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable"
2307
            ],
2308
            "document_ids": [
2309
                8872,
2310
                3133,
2311
                2384,
2312
                8874,
2313
                2363
2314
            ],
2315
            "answer": " To update the working directory file when Ray is already initiated, you can use the `ray.put()` and `ray.get()` functions to transfer files between the local file system and the working directory on the cluster.\n\nFor example, to upload a file called `data.txt` to the working directory, you can use the following code:\n\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Upload the file to the working directory\nray.put(\"data.txt\", \"/tmp/ray/session_latest/runtime_resources/_ray_pkg_<hash of directory contents>/data.txt\")\n```\n\nAnd to download the file from the working directory, you can use the following code:\n\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Download the file from the working directory\ndata_txt = ray.get(\"/tmp/ray/session_latest/runtime_resources/_ray_pkg_<hash of directory contents>/data.txt\")\n\n# Save the file to the local file system\nwith open(\"downloaded_data.txt\", \"w\") as f:\n    f.write(data_txt)\n```\n\nNote that the `ray.put()` function returns a unique object ID that can be used to retrieve the file later using the `ray.get()` function.\n\nAlso, keep in mind that the working directory on the cluster is not the same as the original working directory on your laptop. If you need 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, in this case, you should use relative paths for read-only purposes and make sure that the directory is synced on all nodes if running on multiple machines.",
2316
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2317
        },
2318
        {
2319
            "question": "how can I force ray head node to use custom pem file to ssh worker node?",
2320
            "sources": [
2321
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2322
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",
2323
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2324
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2325
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"
2326
            ],
2327
            "document_ids": [
2328
                8596,
2329
                8347,
2330
                8622,
2331
                8612,
2332
                8538
2333
            ],
2334
            "answer": " To force the Ray head node to use a custom PEM file for SSHing into the worker node, you can specify the `ssh_private_key` field in the `auth` section of your configuration file (e.g., `config.yaml`). Here's an example:\n\n```yaml\n# How Ray will authenticate with newly launched nodes.\nauth:\n    ssh_user: ubuntu\n    ssh_private_key: /path/to/your/custom/key.pem\n```\n\nMake sure to replace `/path/to/your/custom/key.pem` with the actual path to your custom PEM file. This configuration will make Ray use your custom PEM file when SSHing into worker nodes.",
2335
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2336
        },
2337
        {
2338
            "question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",
2339
            "sources": [
2340
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",
2341
                "https://docs.ray.io/en/master/ray-core/configure.html#head-node",
2342
                "https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",
2343
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",
2344
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-operating-system-settings"
2345
            ],
2346
            "document_ids": [
2347
                5843,
2348
                6388,
2349
                18814,
2350
                792,
2351
                5729
2352
            ],
2353
            "answer": " The GCS (Global Control Service) server, running on the head node of a Ray cluster, manages cluster-level metadata and provides operations like actor, placement groups, and node management. It stores data in-memory, which can cause the GCS server to take up a significant amount of memory. To make the GCS fault-tolerant, HA Redis is required.\n\nThe head node has more memory-demanding system components, such as GCS and the dashboard, which can lead to out-of-memory issues if the head node has the same memory capacity as worker nodes and is executing the same number of tasks and actors. To avoid this, you can specify --num-cpus=0 when starting a head node with ray start --head to prevent running tasks and actors on the head node.\n\nThe high memory usage can also be due to the operating system not being able to support the number of connections created by all nodes and workers connecting to the GCS.",
2354
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2355
        },
2356
        {
2357
            "question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",
2358
            "sources": [
2359
                "https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",
2360
                "https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",
2361
                "https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#troubleshooting",
2362
                "https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-for-using-tune-with-spot-instances-aws",
2363
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#solution-1-container-command-recommended"
2364
            ],
2365
            "document_ids": [
2366
                7640,
2367
                3192,
2368
                3211,
2369
                3203,
2370
                7892
2371
            ],
2372
            "answer": " If you see some nodes \"pending\" for a long time when starting a Ray cluster with \"ray up\", it might be due to overloading the head node with too many simultaneous connections. To debug this issue, you can try starting the worker nodes more slowly. Also, check if you are using the correct Ray address and if there are any routing issues. If your program freezes, you can restart the Ray cluster without running any of the installation commands using the \"--restart-only\" flag. You can also specify a storage path to upload results to cloud storage in case you want to start and stop your cluster automatically.",
2373
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2374
        },
2375
        {
2376
            "question": "how to install Ray 2.5.1 from github or wheel?",
2377
            "sources": [
2378
                "https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",
2379
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",
2380
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
2381
                "https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",
2382
                "https://docs.ray.io/en/master/installation.html#from-wheels"
2383
            ],
2384
            "document_ids": [
2385
                406,
2386
                19552,
2387
                96,
2388
                402,
2389
                92
2390
            ],
2391
            "answer": " To install Ray 2.5.1 from a wheel, first find the appropriate wheel for your system and use pip to install it with the following command:\n\n`pip install -U LINK_TO_WHEEL.whl`\n\nHowever, Ray version 2.5.1 is not available as a wheel on PyPI. The latest version of Ray available as a wheel is 2.3.0.\n\nIf you need to install Ray 2.5.1 specifically, you can build it from source by cloning the Ray repository from GitHub:\n\n`git clone https://github.com/ray-project/ray.git`\n\nThen, navigate to the Ray directory and install it with pip:\n\n`cd ray && pip install -e .`\n\nThis will install Ray 2.5.1 with minimal dependencies. Note that building Ray from source may take some time and requires additional dependencies to be installed.",
2392
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2393
        },
2394
        {
2395
            "question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",
2396
            "sources": [
2397
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
2398
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
2399
                "https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",
2400
                "https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",
2401
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-component-logs"
2402
            ],
2403
            "document_ids": [
2404
                736,
2405
                6097,
2406
                3186,
2407
                12161,
2408
                708
2409
            ],
2410
            "answer": " To use `worker_setup_hook` in a runtime environment for setting up worker nodes, you can define a callable function that contains the setup steps you want to perform. Here's an example:\n\n```python\ndef worker_setup_func():\n    # Your setup code here\n    pass\n\nray.init(runtime_env={\"worker_process_setup_hook\": worker_setup_func})\n```\n\nThis function will be called after worker processes start and before tasks and actors are scheduled. You can replace the `pass` statement with the code you want to execute during worker node creation.\n\nKeep in mind that `worker_process_setup_hook` is an experimental feature, so its behavior might change in future Ray versions. Also, refer to the Ray libraries documentation if you are using any of them, as they might have specific instructions for using `worker_process_setup_hook`.",
2411
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2412
        },
2413
        {
2414
            "question": "how to use Ray dataset on aws",
2415
            "sources": [
2416
                "https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",
2417
                "https://docs.ray.io/en/master/data/key-concepts.html#loading-data",
2418
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#starting-with-pytorch-data",
2419
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",
2420
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset"
2421
            ],
2422
            "document_ids": [
2423
                9238,
2424
                9034,
2425
                12236,
2426
                10691,
2427
                10994
2428
            ],
2429
            "answer": " To use Ray Dataset on AWS, you can start a Ray cluster on AWS using the Ray cluster launcher. You can then create Ray Datasets from on-disk files, Python objects, or cloud storage services like S3. Here's an example of reading a CSV file from S3:\n\n```python\nds = ray.data.read_csv(\"s3://anonymous@air-example-data/iris.csv\")\nds.show(limit=1)\n```\n\nNote that Ray Datasets require schemas for all datasets in Ray 2.5, which means standalone Python objects are no longer supported. The default batch format is fixed to NumPy. If you want to revert to legacy behavior temporarily, set the environment variable `RAY_DATA_STRICT_MODE=0` on all cluster processes.",
2430
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2431
        },
2432
        {
2433
            "question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",
2434
            "sources": [
2435
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
2436
                "https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",
2437
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",
2438
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",
2439
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview"
2440
            ],
2441
            "document_ids": [
2442
                9358,
2443
                9329,
2444
                9862,
2445
                12253,
2446
                9231
2447
            ],
2448
            "answer": " To avoid your dataset shuffling during a `ray.data.map_batches()`, you don't need to do anything specific since `map_batches()` does not shuffle the data by default. Shuffling occurs during operations like `random_shuffle()`, `sort()`, or `groupby()`.\n\nIf you want to shuffle your dataset but avoid a full global shuffle (which is more expensive), you can randomize each block of your dataset using `randomize_block_order()` and then enable local shuffling during batch iteration by specifying a `local_shuffle_buffer_size` to `iter_batches()` or `iter_torch_batches()`.\n\nHere's an example:\n\n```python\nds = ray.data.read_text('path/to/your/dataset')\nds_shuffled = ds.randomize_block_order()\n\n# When iterating over your dataset during training, enable local shuffling\nfor batch in ds_shuffled.iter_batches(batch_size=128, local_shuffle_buffer_size=10000000):\n    # Your training code here\n    pass\n```\n\nThis approach will shuffle data within each block but avoid a full global shuffle, providing a balance between data randomization and performance.",
2449
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2450
        },
2451
        {
2452
            "question": "Is the order of the input data preserved after a map_batches operation?",
2453
            "sources": [
2454
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
2455
                "https://docs.ray.io/en/master/data/api/doc/ray.data.range_tensor.html#ray-data-range-tensor",
2456
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",
2457
                "https://docs.ray.io/en/master/data/data-internals.html#execution-memory",
2458
                "https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms"
2459
            ],
2460
            "document_ids": [
2461
                9358,
2462
                9666,
2463
                10717,
2464
                9380,
2465
                9181
2466
            ],
2467
            "answer": " Yes, the order of the input data is preserved after a `map_batches` operation in Ray Data. This is because `map_batches` applies a user-defined function to each batch of data, and the batches maintain their original order throughout the operation. This is evident in the example provided, where the output maintains the same order as the input.",
2468
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2469
        },
2470
        {
2471
            "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?",
2472
            "sources": [
2473
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#common-issues",
2474
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",
2475
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",
2476
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
2477
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance"
2478
            ],
2479
            "document_ids": [
2480
                7674,
2481
                7676,
2482
                6226,
2483
                18927,
2484
                6227
2485
            ],
2486
            "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. Test your Ray Serve script locally or in a RayCluster before deploying it to a RayService. This can help you catch any issues early.\n2. Check the `serveConfigV2` for any incorrect configurations.\n3. Ensure that the Ray image includes all the required dependencies. If not, you can refer to examples for troubleshooting dependency issues.\n4. Verify the `import_path` to ensure that it is correct.\n5. Handle application-level failures by catching errors, retrying failed code, and handling misbehaving code using Ray's fault tolerance mechanisms.\n6. Configure the `runtime_env` to ensure that it meets your application's requirements.\n7. Check the status of Serve applications to ensure that they are running correctly.\n\nAdditionally, Ray provides mechanisms to automatically recover from internal system-level failures like node failures. However, if you continue to experience issues, you can refer to the Ray Serve API documentation and the Ray fault tolerance documentation for more information.",
2487
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2488
        },
2489
        {
2490
            "question": "how do i track an uncaught exception in ray serve",
2491
            "sources": [
2492
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",
2493
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
2494
                "https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests",
2495
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayTaskError.html#ray-exceptions-raytaskerror",
2496
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception"
2497
            ],
2498
            "document_ids": [
2499
                6011,
2500
                18927,
2501
                18697,
2502
                6359,
2503
                1056
2504
            ],
2505
            "answer": " To track an uncaught exception in Ray Serve, you can handle the `ray.exceptions.RayTaskError` or `ray.exceptions.RaySystemError` exceptions in your Ray Serve deployment's function. These exceptions are subclasses of `Exception`, so you can catch them using a standard `try`-`except` block. Here's an example:\n\n```python\nfrom ray import serve\n\n@serve.deployment\nclass MyDeployment:\n    def __init__(self):\n        self.counter = 0\n\n    async def handle_request(self, request):\n        try:\n            # Your request handling code here\n            self.counter += 1\n            if self.counter > 5:\n                raise ValueError(\"Counter exceeded 5\")\n            return {\"result\": self.counter}\n        except Exception as e:\n            # Log or handle the exception here\n            print(f\"Uncaught exception: {e}\")\n            raise e\n```\n\nHowever, Ray Serve does not provide a built-in way to track uncaught exceptions globally across all deployments. If you need to track uncaught exceptions in a production environment, consider integrating Ray with a logging or monitoring service, such as Loggly, Datadog, or Sentry. These services can capture and aggregate exception information, making it easier to track and debug issues.\n\nFor example, to integrate Ray with Sentry, you can use the `sentry-sdk` package to instrument Ray and automatically capture exceptions:\n\n```python\nimport sentry_sdk\nfrom ray import serve\n\nsentry_sdk.init(\"your-sentry-dsn\")\n\n@serve.deployment\nclass MyDeployment:\n    def __init__(self):\n        self.counter = 0\n\n    async def handle_request(self, request):\n        self.counter += 1\n        if self.counter > 5:\n            raise ValueError(\"Counter exceeded 5\")\n        return {\"result\": self.counter}\n```\n\nWith this integration, Sentry will automatically capture any uncaught exceptions in your Ray Serve deployments and provide detailed information about the error, including the stack trace, request information, and deployment metadata.",
2506
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2507
        },
2508
        {
2509
            "question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",
2510
            "sources": [
2511
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
2512
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
2513
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
2514
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",
2515
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs"
2516
            ],
2517
            "document_ids": [
2518
                18156,
2519
                18157,
2520
                980,
2521
                630,
2522
                629
2523
            ],
2524
            "answer": " To view the logs emitted by your Ray Serve endpoint in the Ray cluster, you can look for them on each node's disk at `/tmp/ray/session_latest/logs/serve/`. These logs include system-level logs from the Serve controller and proxy, as well as access logs and custom user logs produced from within deployment replicas.\n\nIn development, logs are also streamed to the driver Ray program, so you can keep the driver running while debugging.\n\nIf you're running Ray Jobs, worker logs of Ray Jobs are always captured in the Ray logging directory, and driver logs are captured only for Ray Jobs submitted via Jobs API. You can use the Ray Job CLI command `ray job logs` or the Python SDK's `JobSubmissionClient.get_logs()` or `JobSubmissionClient.tail_job_logs()` to access these logs.",
2525
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2526
        },
2527
        {
2528
            "question": "where can I see logs for a failed ray serve deployment",
2529
            "sources": [
2530
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",
2531
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
2532
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
2533
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
2534
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard"
2535
            ],
2536
            "document_ids": [
2537
                18889,
2538
                18163,
2539
                19150,
2540
                18156,
2541
                18151
2542
            ],
2543
            "answer": " You can see logs for a failed Ray serve deployment in the controller log and deployment log, located under /tmp/ray/session_latest/logs/serve/ in both the head node pod and worker node pod. You can also access the Ray dashboard at port 8265 at your cluster\u2019s URI to view logs and get a high-level overview of your Ray cluster and Ray Serve application\u2019s states.",
2544
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2545
        },
2546
        {
2547
            "question": "How to take a subset of a Ray Dataset?",
2548
            "sources": [
2549
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
2550
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",
2551
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",
2552
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.split_at_indices.html#ray-data-dataset-split-at-indices",
2553
                "https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range"
2554
            ],
2555
            "document_ids": [
2556
                9163,
2557
                14472,
2558
                13421,
2559
                10428,
2560
                9731
2561
            ],
2562
            "answer": " To take a subset of a Ray Dataset, you can use the `split_at_indices()` method. This method splits the dataset into multiple datasets at the specified indices. Here's an example:\n```python\nds = ray.data.range(10)\nd1, d2, d3 = ds.split_at_indices([2, 5])\n```\nIn this example, `d1` will contain the first two elements of the dataset, `d2` will contain the next four elements, and `d3` will contain the remaining elements.\n\nYou can also create your own list of subsets by using the `take()` method to extract specific ranges of rows from the dataset. For example:\n```python\nsubset1 = ds.take(2)\nsubset2 = ds.take(4)[1:5]\nsubset3 = ds.take(6)[5:]\n```\nIn this example, `subset1` will contain the first two elements of the dataset, `subset2` will contain the third to sixth elements, and `subset3` will contain the last four elements. Note that the `take()` method returns a list of batches, so you may need to concatenate the batches to get a single dataset.",
2563
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2564
        },
2565
        {
2566
            "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.",
2567
            "sources": [
2568
                "https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",
2569
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",
2570
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",
2571
                "https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing",
2572
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler"
2573
            ],
2574
            "document_ids": [
2575
                2935,
2576
                3102,
2577
                3226,
2578
                2923,
2579
                2514
2580
            ],
2581
            "answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `get_trial_dataframes` function to get a DataFrame of trial information, including the checkpoint paths. Since you used cloud checkpointing, you'll need to download the checkpoints from the specified cloud storage.\n\nHere's an example of how to load checkpoints for each trial:\n\n```python\nfrom ray import tune\n\n# Get the experiment results\nexp_name = \"my-tune-exp\"\nexperiment = tune.experiment.Experiment.from_name(exp_name)\ntrial_dataframes = tune.experiment.get_trial_dataframes(experiment)\n\n# Loop through trials and load checkpoints\nfor trial_df in trial_dataframes:\n    for row in trial_df.itertuples():\n        checkpoint_path = row.checkpoint\n        if checkpoint_path:\n            # Download the checkpoint from the cloud storage\n            # Then, load the checkpoint using your custom load_checkpoint function\n            load_checkpoint(checkpoint_path)\n```\n\nReplace `load_checkpoint` with your custom function to load the checkpoint. Make sure to download the checkpoint file from the cloud storage before loading it.",
2582
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2583
        },
2584
        {
2585
            "question": "How can I kill a \"detached\" Actor ?",
2586
            "sources": [
2587
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
2588
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
2589
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
2590
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
2591
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote"
2592
            ],
2593
            "document_ids": [
2594
                6547,
2595
                6254,
2596
                655,
2597
                5976,
2598
                6523
2599
            ],
2600
            "answer": " To kill a \"detached\" actor, you can use `ray.kill(actor_id)`. In your case, it would be `ray.kill('31405554844820381c2f0f8501000000')`. Detached actors in Ray are not automatically garbage-collected and must be manually destroyed once they are no longer needed.",
2601
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2602
        },
2603
        {
2604
            "question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",
2605
            "sources": [
2606
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#environment-variables-are-not-passed-from-the-driver-process-to-worker-processes",
2607
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#environment-variables-are-not-passed-from-the-driver-process-to-worker-processes",
2608
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
2609
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
2610
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces"
2611
            ],
2612
            "document_ids": [
2613
                886,
2614
                887,
2615
                6071,
2616
                5971,
2617
                6570
2618
            ],
2619
            "answer": " To set environment variables in `ray.init()`, you can use Runtime Environments to pass environment variables explicitly. Here's an example:\n```python\nray.init(runtime_env={\"env_vars\": {\"FOO\": \"bar\"}})\n```\nThis will set the `FOO` environment variable for the Ray workers. Note that any existing environment variables on the cluster will still be visible to the Ray workers, so there's no need to include `os.environ` or similar in the `env_vars` field.\n\nIn your specific example, you can modify the `myfunc()` function to get the `foo` environment variable using `os.environ.get(\"FOO\")`, and it will return `\"bar\"` if you set the `FOO` environment variable using the above `ray.init()` call.\n\nTherefore, to set the `foo` environment variable and use it in the `myfunc()` function, you can do the following:\n```python\nray.init(runtime_env={\"env_vars\": {\"FOO\": \"foo\"}})\n\n@ray.remote\ndef myfunc():\n    myenv = os.environ.get(\"FOO\")\n    print(f\"myenv is {myenv}\")\n    return 1\n\nray.get(myfunc.remote())  # prints: \"myenv is foo\"\n```",
2620
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2621
        },
2622
        {
2623
            "question": "What is the rest api for getting the head node id?",
2624
            "sources": [
2625
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",
2626
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",
2627
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
2628
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2629
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails"
2630
            ],
2631
            "document_ids": [
2632
                841,
2633
                8807,
2634
                19806,
2635
                8643,
2636
                8940
2637
            ],
2638
            "answer": " Based on the context provided, there isn't a specific REST API for getting the head node ID directly. However, you can obtain the head node ID by making a REST API call to list all nodes and then extracting the ID of the head node from the response. The 'node_id' field represents the ID of the node. The 'is_head_node' field can be used to identify the head node.\n\nFor getting the HTTP address and node ID of the driver node (which can be the head node), you can refer to the properties \"driver_agent_http_address\" and \"driver_node_id\" in the REST API response.",
2639
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2640
        },
2641
        {
2642
            "question": "how to rerun a canceled ray task",
2643
            "sources": [
2644
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
2645
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
2646
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",
2647
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
2648
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks"
2649
            ],
2650
            "document_ids": [
2651
                6342,
2652
                16287,
2653
                19752,
2654
                5823,
2655
                5827
2656
            ],
2657
            "answer": " To rerun a canceled Ray task, you can simply call the task function again and get a new object reference. There is no built-in way to directly rerun a canceled task using the same object reference. Here's an example:\n\n```python\n# Define the remote function\n@ray.remote\ndef blocking_operation():\n    time.sleep(10e6)\n\n# Cancel the previous task\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n\n# Rerun the task\nnew_obj_ref = blocking_operation.remote()\n```\n\nKeep in mind that Ray attempts to cancel tasks on a best-effort basis, and cancellation might not always be successful. You can check if a task was successfully canceled by using `ray.get(actor_task_ref)` and catching `ray.exceptions.TaskCancelledError`.",
2658
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2659
        },
2660
        {
2661
            "question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",
2662
            "sources": [
2663
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ConcurrencyLimiter.html#ray-tune-search-concurrencylimiter",
2664
                "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",
2665
                "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",
2666
                "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",
2667
                "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"
2668
            ],
2669
            "document_ids": [
2670
                1953,
2671
                1787,
2672
                2060,
2673
                2153,
2674
                2068
2675
            ],
2676
            "answer": " To set the maximum parallel concurrent scheduled tasks in `map_batches`, you can use the `max_concurrent` parameter. This parameter determines the maximum number of concurrent trials. Here is an example of how to use it:\n\n```\nmap_batches(func, data, max_concurrent=10)\n```\n\nThis will allow up to 10 concurrent tasks to be executed in parallel.",
2677
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2678
        },
2679
        {
2680
            "question": "How do I get the number of cpus from ray cluster?",
2681
            "sources": [
2682
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",
2683
                "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",
2684
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#create-a-basic-python-application",
2685
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
2686
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources"
2687
            ],
2688
            "document_ids": [
2689
                7998,
2690
                7704,
2691
                8336,
2692
                16302,
2693
                5835
2694
            ],
2695
            "answer": " To get the number of CPUs in a Ray cluster, you can use the `ray.cluster_resources()` function, which returns a dictionary of available resources in the cluster. The number of CPUs can be obtained by accessing the \"CPU\" key in this dictionary, like this: `ray.cluster_resources()['CPU']`.\n\nIn the provided context, the Ray cluster has 1 head Pod with 4 physical CPUs and 1 worker Pod with 1 CPU. Therefore, the total number of CPUs in the cluster is 5. This can be verified by running `ray.cluster_resources()['CPU']`, which would return 5.",
2696
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2697
        },
2698
        {
2699
            "question": "How to use the exclude option to the runtime_env",
2700
            "sources": [
2701
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
2702
                "https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",
2703
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",
2704
                "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",
2705
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"
2706
            ],
2707
            "document_ids": [
2708
                18763,
2709
                5891,
2710
                18552,
2711
                6462,
2712
                5919
2713
            ],
2714
            "answer": " To use the `exclude` option in the `runtime_env`, you can define the `runtime_env` using the `RuntimeEnvConfig` class or the Python dictionary, and include the `exclude` field in it. The `exclude` field specifies a list of packages or modules to exclude when creating the runtime environment.\n\nHere is an example of how to use the `exclude` option in the `runtime_env` using the `RuntimeEnvConfig` class:\n```\nfrom ray.runtime_env import RuntimeEnvConfig\n\nruntime_env = RuntimeEnvConfig(\n    working_dir=\"/path/to/working/dir\",\n    python_packages=[\"package1\", \"package2\"],\n    export_formats=[\"pickle\"],\n    exclude=[\"package3\", \"module4\"]\n)\n```\nYou can then use the `runtime_env` when creating a job or task by passing it to the `set_runtime_env` method of the `JobConfig` class.\n\nHere is an example of how to use the `exclude` option in the `runtime_env` using a Python dictionary:\n```\nruntime_env = {\n    \"working_dir\": \"/path/to/working/dir\",\n    \"python_packages\": [\"package1\", \"package2\"],\n    \"export_formats\": [\"pickle\"],\n    \"exclude\": [\"package3\", \"module4\"]\n}\n\njob_config = JobConfig().set_runtime_env(runtime_env)\n```\nNote that the `exclude` field is only supported in Ray version 1.12.0 and later.",
2715
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2716
        },
2717
        {
2718
            "question": "show a map batch example with batch_format",
2719
            "sources": [
2720
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
2721
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
2722
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
2723
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",
2724
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference"
2725
            ],
2726
            "document_ids": [
2727
                19788,
2728
                9358,
2729
                9390,
2730
                9109,
2731
                197
2732
            ],
2733
            "answer": " Here's an example of using `map_batches()` with a specified `batch_format`:\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\nIn this example, we read image data and then increase the brightness of each image in the batches. The `batch_format` is set to \"numpy\" to specify the type of batch.",
2734
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2735
        },
2736
        {
2737
            "question": "how to find local ray address",
2738
            "sources": [
2739
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
2740
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",
2741
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",
2742
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",
2743
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient"
2744
            ],
2745
            "document_ids": [
2746
                6434,
2747
                8318,
2748
                8605,
2749
                7329,
2750
                9005
2751
            ],
2752
            "answer": " To find the local Ray address, you can start a local Ray cluster by running the following commands in your terminal:\n```python\nimport ray\nray.init()\n```\nIn the output, you will see a line that says \"View the Ray dashboard at <http://127.0.0.1:8265>\" or similar, indicating that the Ray cluster is running locally and providing the address of the Ray dashboard.\n\nAlternatively, you can access the Ray dashboard directly by visiting \"localhost:8265\" in your web browser.\n\nIf you have already started a Ray cluster and want to find its address, you can check the output of the `ray status` command or look for the `node_ip_address` field in the output of the `ray info` command.",
2753
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2754
        },
2755
        {
2756
            "question": "How to start ray cluster on multiple node via CLI?",
2757
            "sources": [
2758
                "https://docs.ray.io/en/master/ray-core/configure.html#configuring-ray",
2759
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
2760
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
2761
                "https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-3-create-a-mini-cluster-with-ray-cluster-utils-cluster",
2762
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart"
2763
            ],
2764
            "document_ids": [
2765
                6353,
2766
                6080,
2767
                8345,
2768
                7313,
2769
                387
2770
            ],
2771
            "answer": " To start a Ray cluster on multiple nodes via CLI, follow these steps:\n\n1. Prepare a configuration file (e.g., `cluster.yaml`) with the appropriate settings for your head node and worker nodes.\n2. In the `cluster.yaml` file, ensure that the `worker_start_ray_commands` section contains the correct `ray start` command to join the cluster.\n3. Run `ray up -y cluster.yaml` to start the Ray cluster, creating a designated head node and worker nodes.\n4. On each worker node, execute the `ray start` command from step 2 to join the cluster.\n\nBefore running your Ray application, you can use `ray.init(address=cluster.address)` in your Python script to connect to the cluster services.",
2772
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2773
        },
2774
        {
2775
            "question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",
2776
            "sources": [
2777
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
2778
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",
2779
                "https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",
2780
                "https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",
2781
                "https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#network-overhead-in-ray-tune"
2782
            ],
2783
            "document_ids": [
2784
                6848,
2785
                903,
2786
                9336,
2787
                13247,
2788
                3151
2789
            ],
2790
            "answer": " The Ray tuner showing \"running\" but with almost 0% CPU usage could be due to the following reasons:\n\n1. Ray by default determines the number of CPUs on each machine with `psutil.cpu_count()` and usually will not schedule more tasks in parallel than the number of CPUs.\n2. When using Ray Data in conjunction with Ray Tune, it's important to ensure there are enough free CPUs for Ray Data to run on. By default, Tune tries to fully utilize cluster CPUs, which can prevent Ray Data from scheduling tasks, reducing performance or causing workloads to hang.\n3. Running Ray Tune on a distributed setup leads to network communication overhead, which can cause blocking operations and affect CPU usage.\n\nIn your case, it's possible that Ray Tune is using all available CPUs, leaving none for the Ray tuner to utilize, causing it to show almost 0% CPU usage. To ensure CPU resources are always available for Ray Data execution, you can limit the number of concurrent Tune trials with the `max_concurrent_trials` Tune option.",
2791
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2792
        },
2793
        {
2794
            "question": "should the Ray head node and all workers have the same object store memory size allocated?",
2795
            "sources": [
2796
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",
2797
                "https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",
2798
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",
2799
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",
2800
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster"
2801
            ],
2802
            "document_ids": [
2803
                792,
2804
                7534,
2805
                786,
2806
                6770,
2807
                7532
2808
            ],
2809
            "answer": " No, the Ray head node and worker nodes should not have the same object store memory size allocated. The head node has more memory-demanding system components and may experience out-of-memory issues if running tasks and actors like worker nodes. It's recommended to specify --num-cpus=0 when starting a head node to avoid running tasks and actors on it. The object store memory allows for efficient sharing of objects among tasks and actors, and its usage won't exceed 30% of the host memory due to object spilling.",
2810
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2811
        },
2812
        {
2813
            "question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",
2814
            "sources": [
2815
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",
2816
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",
2817
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.ProxyLocation.EveryNode.html#ray-serve-config-proxylocation-everynode",
2818
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
2819
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"
2820
            ],
2821
            "document_ids": [
2822
                18994,
2823
                18883,
2824
                18852,
2825
                18645,
2826
                19193
2827
            ],
2828
            "answer": " In Ray Serve, you can specify whether to set up an HTTP proxy on each node or just the head node in the Serve config by setting the `ProxyLocation` configuration option to \"EveryNode\" or \"HeadOnly\", respectively. By default, Ray Serve runs an HTTP proxy on every node in the cluster that has at least one replica actor. The number of CPU cores to reserve for each internal Serve HTTP proxy actor can be specified using the `num_cpus` option, but this option is now deprecated. The default HTTP proxy traffic port is 8000, and it can be changed by setting the `http_options` in the Serve config.",
2829
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2830
        },
2831
        {
2832
            "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?",
2833
            "sources": [
2834
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",
2835
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-11-embed-grafana-panels-in-ray-dashboard",
2836
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",
2837
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",
2838
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard"
2839
            ],
2840
            "document_ids": [
2841
                7518,
2842
                8192,
2843
                7520,
2844
                8159,
2845
                7517
2846
            ],
2847
            "answer": " To embed Grafana into the Ray Dashboard when using KubeRay, you should set `RAY_GRAFANA_HOST` to the address of the Grafana server that can be accessed by the head node. Since the head node IP is changing every time you reconfigure the cluster, you can use the DNS name of the Grafana service in your Kubernetes cluster instead.\n\nFor example, if the Grafana service is named `grafana`, you can set `RAY_GRAFANA_HOST` to `http://grafana.default.svc.cluster.local`.\n\nSimilarly, you should set `RAY_PROMETHEUS_HOST` to the address of the Prometheus server that can be accessed by the head node. If you are using the Prometheus server provided by KubeRay, you can set `RAY_PROMETHEUS_HOST` to `http://prometheus-kube-prometheus-prometheus.prometheus-system.svc:9090`.\n\nTo make these settings more intelligent, you can use Kubernetes ConfigMaps or Secrets to store these values and inject them into the Ray cluster as environment variables at runtime. This way, you can easily update these values without modifying the Ray cluster configuration directly.\n\nHere's an example of how to create a ConfigMap with these values:\n\n```yaml\napiVersion: v1\nkind: ConfigMap\nmetadata:\n  name: ray-cluster-env\ndata:\n  RAY_GRAFANA_HOST: http://grafana.default.svc.cluster.local\n  RAY_PROMETHEUS_HOST: http://prometheus-kube-prometheus-prometheus.prometheus-system.svc:9090\n```\n\nThen, you can mount this ConfigMap as an environment variable in your Ray cluster:\n\n```yaml\nspec:\n  containers:\n  - name: ray-head\n    image: rayproject/ray:latest\n    envFrom:\n    - configMapRef:\n        name: ray-cluster-env\n```\n\nThis way, you can easily update the Grafana and Prometheus addresses by modifying the ConfigMap without changing the Ray cluster configuration.",
2848
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2849
        },
2850
        {
2851
            "question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",
2852
            "sources": [
2853
                "https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-1-create-a-new-kubernetes-cluster",
2854
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
2855
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",
2856
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",
2857
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling"
2858
            ],
2859
            "document_ids": [
2860
                7792,
2861
                18708,
2862
                18880,
2863
                8048,
2864
                8021
2865
            ],
2866
            "answer": " When using KubeRay autoscaling in a GKE cluster with Kubernetes autoscaling enabled, if the Ray Autoscaler decides to create a new Ray pod but there are no available nodes, the Kubernetes Cluster Autoscaler provisions a new node for the Ray pod to be placed. Conversely, when the Ray Autoscaler decides to delete an idle pod, the Kubernetes Cluster Autoscaler can clean up the idle node that remains. However, the GCS (Global Cluster Silo) in KubeRay determines which Kubernetes pod to kill when autoscaling based on its own fault tolerance configuration and the need to restart or clean up idle pods.",
2867
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2868
        },
2869
        {
2870
            "question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",
2871
            "sources": [
2872
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",
2873
                "https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",
2874
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",
2875
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-summary-objects",
2876
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-summary-actors"
2877
            ],
2878
            "document_ids": [
2879
                18547,
2880
                18411,
2881
                18541,
2882
                969,
2883
                965
2884
            ],
2885
            "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  request_timeout_s: <value_in_seconds>\n```\nThis will set the end-to-end timeout for HTTP requests to the specified number of seconds before terminating the request. Note that this config is global to your Ray cluster and cannot be updated during runtime.",
2886
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2887
        },
2888
        {
2889
            "question": "How do I make the GPU available on my M1 laptop to ray?",
2890
            "sources": [
2891
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",
2892
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#gpu-support",
2893
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.filter.html#ray-data-dataset-filter",
2894
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.add_column.html#ray-data-dataset-add-column",
2895
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources"
2896
            ],
2897
            "document_ids": [
2898
                4277,
2899
                6743,
2900
                10479,
2901
                10435,
2902
                6899
2903
            ],
2904
            "answer": " To make the GPU available for Ray on your M1 laptop, you need to explicitly request GPU resources when creating Ray tasks or actors, for example, by specifying `num_gpus=1`. Ray automatically handles GPU isolation by setting the CUDA\\_VISIBLE\\_DEVICES environment variable, which most machine learning frameworks respect for GPU assignment. However, Ray does not automatically handle the use of multiple GPUs per trial for you. You may need to use operating system APIs to pin tasks to specific CPUs if required.",
2905
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2906
        },
2907
        {
2908
            "question": "How can I add a timeout for the Ray job?",
2909
            "sources": [
2910
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
2911
                "https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s",
2912
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",
2913
                "https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",
2914
                "https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s"
2915
            ],
2916
            "document_ids": [
2917
                7772,
2918
                11566,
2919
                8660,
2920
                18411,
2921
                11353
2922
            ],
2923
            "answer": " To add a timeout for a Ray job, you can use the `HorovodConfig.timeout_s` or `TorchConfig.timeout_s` configuration options, depending on the type of job. These options allow you to set the maximum number of seconds that a job can run before it is automatically stopped. Here's an example of how to use `HorovodConfig.timeout_s`:\n```\nfrom ray.train.horovod import HorovodConfig\n\n# Create a HorovodConfig object\nconfig = HorovodConfig()\n\n# Set the timeout to 300 seconds (5 minutes)\nconfig.timeout_s = 300\n\n# Use the config when submitting the job\nray job submit --config $config script.py\n```\nNote that this config is global to your Ray cluster, and it cannot be updated during runtime. If you want to check on the job's status without waiting for it to finish, you can pass the `--no-wait` flag to `ray job submit` and use other CLI commands to check on the job's status.\n\nAlternatively, if you are using Torch, you can use the `TorchConfig.timeout_s` option in a similar way.\n```\nfrom ray.train.torch import TorchConfig\n\n# Create a TorchConfig object\nconfig = TorchConfig()\n\n# Set the timeout to 1800 seconds (30 minutes)\nconfig.timeout_s = 1800\n\n# Use the config when submitting the job\nray job submit --config $config script.py\n```\nI hope this helps! Let me know if you have any other questions.",
2924
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2925
        },
2926
        {
2927
            "question": "how do I set custom /tmp directory for remote cluster?",
2928
            "sources": [
2929
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",
2930
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",
2931
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
2932
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
2933
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher"
2934
            ],
2935
            "document_ids": [
2936
                1182,
2937
                3112,
2938
                8678,
2939
                8682,
2940
                5836
2941
            ],
2942
            "answer": " To set a custom `/tmp` directory for a remote Ray cluster, you can set the `RAY_TEMPDIR` environment variable to the desired directory path. This directory should be accessible by the host and the outer container.\n\nFor example, to set the `RAY_TEMPDIR` environment variable to `/ray-mount`, you can use the following command:\n\n```\nRAY_TEMPDIR=\"/ray-mount\"\n```\n\nAdditionally, you can use the `ray rsync_up` command to push local files to the cluster. This can be useful for transferring necessary files to the cluster for production settings.\n\nFor more information on using a remote Ray cluster and monitoring the cluster status, you can refer to the provided context.",
2943
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2944
        },
2945
        {
2946
            "question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",
2947
            "sources": [
2948
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",
2949
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
2950
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
2951
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
2952
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling"
2953
            ],
2954
            "document_ids": [
2955
                6701,
2956
                699,
2957
                6375,
2958
                700,
2959
                6700
2960
            ],
2961
            "answer": " Yes, if you set --temp-dir to a different directory, Ray object spill will be directed to the custom directory instead of the default /tmp. This can be useful for managing disk space or separating Ray's temporary files from other system files.",
2962
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2963
        },
2964
        {
2965
            "question": "can you give me an example for *`--runtime-env-json`*",
2966
            "sources": [
2967
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
2968
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
2969
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",
2970
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",
2971
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments"
2972
            ],
2973
            "document_ids": [
2974
                18760,
2975
                5919,
2976
                6062,
2977
                6070,
2978
                6121
2979
            ],
2980
            "answer": " Sure! The `--runtime-env-json` argument allows you to specify complex dependencies for your Ray job, including environment variables, working directory, and Python packages. Here's an example:\n\n`serve 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\nIn this example, the `--runtime-env-json` argument is set to a JSON object that includes the following fields:\n\n* `env_vars`: A dictionary of environment variables. In this case, we're setting `MY_ENV_VAR` to `\"my-value\"`.\n* `working_dir`: The working directory for the job. Here, we're setting it to `./project/src`.\n* `pip`: A list of Python packages to install using pip. Here, we're installing `requests` and `chess`.\n\nYou can customize these fields to fit the needs of your specific job.",
2981
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2982
        },
2983
        {
2984
            "question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",
2985
            "sources": [
2986
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
2987
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
2988
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",
2989
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
2990
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-autoscaler"
2991
            ],
2992
            "document_ids": [
2993
                18059,
2994
                18920,
2995
                18207,
2996
                18061,
2997
                8711
2998
            ],
2999
            "answer": " The value of `maxConcurrentReplicas` should be specified in the `user_config` dictionary as part of the `autoscaling_config` parameter. It is not specified directly as a separate parameter. The `maxConcurrentReplicas` parameter is used to configure the autoscaling behavior of a deployment in Ray Serve.",
3000
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3001
        },
3002
        {
3003
            "question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",
3004
            "sources": [
3005
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",
3006
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
3007
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
3008
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
3009
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings"
3010
            ],
3011
            "document_ids": [
3012
                18207,
3013
                18062,
3014
                18886,
3015
                18930,
3016
                18221
3017
            ],
3018
            "answer": " When `target_num_ongoing_requests_per_replica` is specified, the value of `max_concurrent_queries` can be configured based on performance tuning requirements, and it doesn't have a default dependency on `target_num_ongoing_requests_per_replica`. The default value for `max_concurrent_queries` is 100, and it should be a positive integer with no replica reusing.",
3019
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3020
        },
3021
        {
3022
            "question": "what is a `smoothing_factor`",
3023
            "sources": [
3024
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
3025
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
3026
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",
3027
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",
3028
                "https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#learning-rate-eta"
3029
            ],
3030
            "document_ids": [
3031
                18067,
3032
                18066,
3033
                2553,
3034
                2554,
3035
                3547
3036
            ],
3037
            "answer": " A `smoothing_factor` is a multiplicative factor that speeds up or slows down the pace of upscaling or downscaling decisions made by an autoscaler. It can be used to make an application less sensitive to drops in traffic and scale down more conservatively (downscale_smoothing_factor), or to scale up the resource quickly during high traffic volume (upscale_smoothing_factor). The value of the smoothing factor typically ranges between 0.01 and 0.3.",
3038
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3039
        },
3040
        {
3041
            "question": "What is the reason actors change their state to unhealthy?",
3042
            "sources": [
3043
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",
3044
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",
3045
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",
3046
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
3047
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.html#ray-rllib-evaluation-worker-set-workerset"
3048
            ],
3049
            "document_ids": [
3050
                18504,
3051
                1011,
3052
                1012,
3053
                652,
3054
                17908
3055
            ],
3056
            "answer": " Actors can become unhealthy in the deployment if there's an error in the deployment's constructor, a crashed replica, or a general system or machine error. The system will try to restore their states if they become unhealthy.",
3057
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3058
        },
3059
        {
3060
            "question": "How do I access logs for a dead node?",
3061
            "sources": [
3062
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
3063
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
3064
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
3065
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",
3066
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log"
3067
            ],
3068
            "document_ids": [
3069
                674,
3070
                675,
3071
                1000,
3072
                8672,
3073
                980
3074
            ],
3075
            "answer": " Based on the provided context, you cannot access the logs from a dead node as it is not available through the Ray logs API. The API only allows you to access logs from alive nodes in the Ray cluster.",
3076
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3077
        },
3078
        {
3079
            "question": "What are the reasons for a node to change it\u2019s status to dead?",
3080
            "sources": [
3081
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3082
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",
3083
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",
3084
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",
3085
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure"
3086
            ],
3087
            "document_ids": [
3088
                6173,
3089
                1154,
3090
                18598,
3091
                1018,
3092
                5863
3093
            ],
3094
            "answer": " A node in a Ray cluster can change its status to 'dead' for the following reasons:\n\n1. The actor running on the node has died, as reported in the `death_cause.actor_died_error_context` field of the node's status.\n2. The worker node has failed, as detected by KubeRay, which attempts to restart the worker pod on another running node and bring up a new healthy node to replace it.\n3. A raylet process failure, which results in the corresponding node being marked as dead and treated as a new raylet/node to the Ray cluster.",
3095
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3096
        },
3097
        {
3098
            "question": "What are the reasons for spikes in node CPU utilization",
3099
            "sources": [
3100
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",
3101
                "https://docs.ray.io/en/master/ray-core/examples/web-crawler.html#speed-up-your-web-crawler-by-parallelizing-it-with-ray",
3102
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3103
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3104
                "https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources"
3105
            ],
3106
            "document_ids": [
3107
                8707,
3108
                7321,
3109
                764,
3110
                766,
3111
                9014
3112
            ],
3113
            "answer": " The reasons for spikes in node CPU utilization can be:\n\n1. Insufficient outbound bandwidth on the head node, especially for large clusters.\n2. Heavy CPU usage by running tasks. In the provided context, the CPU times were 19.3 seconds for user and 340 ms for sys, with a total of 19.7 seconds.\n3. Requesting high resources for tasks, which can cause the cluster to scale and add nodes to accommodate the requested resources.\n\nMemory-related issues can also indirectly affect CPU utilization. In the provided context, a task was killed due to low memory on the node, which might have led to increased CPU usage as the system tried to manage memory resources.",
3114
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3115
        },
3116
        {
3117
            "question": "What AWS machine type is recommended to deploy a RayService on EKS?",
3118
            "sources": [
3119
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",
3120
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",
3121
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
3122
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-eks-cluster-aws",
3123
                "https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide"
3124
            ],
3125
            "document_ids": [
3126
                8724,
3127
                18822,
3128
                517,
3129
                7937,
3130
                18893
3131
            ],
3132
            "answer": " The recommended AWS machine type to deploy a RayService on EKS is not explicitly stated in the provided context. However, it is recommended to use the RayService controller that is provided as part of KubeRay, which can be set up on an EKS cluster. Therefore, the machine type would depend on your specific workload requirements and the AWS EKS instance types available to you.",
3133
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3134
        },
3135
        {
3136
            "question": "Is there a way to configure the session name generated by ray?",
3137
            "sources": [
3138
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
3139
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
3140
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
3141
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
3142
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments"
3143
            ],
3144
            "document_ids": [
3145
                6375,
3146
                6373,
3147
                8825,
3148
                6424,
3149
                8821
3150
            ],
3151
            "answer": " Yes, you can configure the session name generated by Ray by using the `namespace` argument when calling `ray.init()`. This argument allows you to set the namespace for the session, which will be included in the session name. Note that this is only available when using the Ray Client, which requires specifying the address of the Ray cluster in the `ray.init()` call. The default session name format is `session_{timestamp}_{pid}`.",
3152
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3153
        },
3154
        {
3155
            "question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",
3156
            "sources": [
3157
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",
3158
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",
3159
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli",
3160
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
3161
                "https://docs.ray.io/en/master/ray-air/examples/batch_tuning.html#walkthrough"
3162
            ],
3163
            "document_ids": [
3164
                8894,
3165
                8719,
3166
                8636,
3167
                8684,
3168
                14056
3169
            ],
3170
            "answer": " Based on the provided context, the Ray dashboard's URL or a link to it cannot be directly obtained using the Python SDK. The SDK is used to submit jobs programmatically, and the Ray dashboard's address must be set manually through the environment variable `RAY_ADDRESS`. You can access the dashboard at the provided URL (<http://console.anyscale-staging.com/api/v2/sessions/ses_b5q8xHd42BTdukSgFqTxejLT/services?redirect_to=dashboard>) or set up a local Ray cluster to view the dashboard at <http://127.0.0.1:8265>.",
3171
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3172
        },
3173
        {
3174
            "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).",
3175
            "sources": [
3176
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3177
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
3178
                "https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",
3179
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3180
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure"
3181
            ],
3182
            "document_ids": [
3183
                774,
3184
                6934,
3185
                7640,
3186
                764,
3187
                5863
3188
            ],
3189
            "answer": " The node where the task was running could have crashed unexpectedly due to:\n\n1. The instance where the node was running failing.\n2. Raylet crashing unexpectedly, which can be caused by memory pressure (OOM) or other reasons.\n3. Overloading the head node with too many simultaneous connections.\n4. Raylet failure, where the corresponding node is marked as dead and treated as a node failure.",
3190
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3191
        },
3192
        {
3193
            "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.",
3194
            "sources": [
3195
                "https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",
3196
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",
3197
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",
3198
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
3199
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails"
3200
            ],
3201
            "document_ids": [
3202
                7639,
3203
                8535,
3204
                5843,
3205
                19114,
3206
                19059
3207
            ],
3208
            "answer": " Based on the information provided, the error message \"Health check failed for node\" suggests an issue with the connection between the worker node and the Global Control Service (GCS). This could be caused by various factors, such as the GCS not running, a version mismatch, an incorrect address, or firewall settings preventing access. To troubleshoot, you can check if the GCS is running, verify the specified address, and ensure there are no firewall settings blocking access. Additionally, you can use tools like nmap or nc to check if each port can be reached from a node.\n\nThe context also mentions that the GCS is not fault-tolerant by default, so if the GCS fails, the entire Ray cluster will fail. To make the GCS fault-tolerant, HA Redis is required. However, this may not be directly related to the \"Health check failed for node\" error.",
3209
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3210
        },
3211
        {
3212
            "question": "Do you need the DAGDriver to deploy a serve application using RayServe?",
3213
            "sources": [
3214
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",
3215
                "https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph",
3216
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#building-the-call-graph-methodnodes-and-functionnodes",
3217
                "https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",
3218
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice"
3219
            ],
3220
            "document_ids": [
3221
                18271,
3222
                18029,
3223
                18264,
3224
                18135,
3225
                7815
3226
            ],
3227
            "answer": " Yes, the DAGDriver is needed to deploy a RayServe application, as it processes the call graph and routes incoming requests through the deployments based on that graph. It can also accept an HTTP adapter for parsing JSON in the HTTP body.",
3228
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3229
        },
3230
        {
3231
            "question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",
3232
            "sources": [
3233
                "https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",
3234
                "https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",
3235
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",
3236
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",
3237
                "https://docs.ray.io/en/master/serve/index.html#quickstart"
3238
            ],
3239
            "document_ids": [
3240
                18531,
3241
                18436,
3242
                18211,
3243
                7815,
3244
                18312
3245
            ],
3246
            "answer": " To import RayServe in a simple deployment, you need to use the following import statement:\n\n`from ray import serve`\n\nThis import statement provides the necessary components to create and deploy a RayServe application. Make sure to install the required packages by running `pip install \"ray[default]\"` in your environment.",
3247
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3248
        },
3249
        {
3250
            "question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",
3251
            "sources": [
3252
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",
3253
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",
3254
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",
3255
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#overview",
3256
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob"
3257
            ],
3258
            "document_ids": [
3259
                7766,
3260
                7650,
3261
                7780,
3262
                8026,
3263
                7775
3264
            ],
3265
            "answer": " Yes, the context provides an example of using RayJob in Kuberay. You can download the sample RayJob configuration file using the curl command provided in Step 3.1, and then create the RayJob using the kubectl command in Step 3.2. The RayJob creates a RayCluster and submits a job to it, as defined in the entrypoint field of the RayJob custom resource. The KubeRay operator manages the creation and scaling of the RayCluster and worker pods.",
3266
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3267
        },
3268
        {
3269
            "question": "Does Ray metrics have to be exported via an actor?",
3270
            "sources": [
3271
                "https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",
3272
                "https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",
3273
                "https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",
3274
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",
3275
                "https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics"
3276
            ],
3277
            "document_ids": [
3278
                689,
3279
                7556,
3280
                946,
3281
                586,
3282
                690
3283
            ],
3284
            "answer": " No, Ray metrics do not have to be exported via an actor. While the example provided shows an actor exporting metrics using the Ray metrics API, metrics can also be exported at the application level without using an actor. Ray exports default metrics, including task and actor metrics, which can be viewed in the Metrics view if Prometheus and Grafana are set up. Additionally, system metrics are also exported by Ray. The Dashboard agent process is responsible for aggregating and reporting these metrics to the endpoints for Prometheus to scrape.",
3285
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3286
        },
3287
        {
3288
            "question": "how can I avoid objects not getting spilled?",
3289
            "sources": [
3290
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",
3291
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
3292
                "https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",
3293
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",
3294
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node"
3295
            ],
3296
            "document_ids": [
3297
                6700,
3298
                19816,
3299
                6503,
3300
                6701,
3301
                6704
3302
            ],
3303
            "answer": " To avoid objects from getting spilled in Ray, you can increase the object store's capacity by adjusting the buffer size or using a faster storage device like an SSD. Additionally, you can manage the objects' size and number in the object store to prevent it from getting full. However, keep in mind that object spilling is essential for out-of-core data processing in memory-intensive distributed applications, and avoiding it might not be possible or desirable in some cases. To configure the spilling directory, you can use the provided code snippet in the context.",
3304
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3305
        },
3306
        {
3307
            "question": "what\u2019s ray core",
3308
            "sources": [
3309
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",
3310
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
3311
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",
3312
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
3313
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework"
3314
            ],
3315
            "document_ids": [
3316
                359,
3317
                5894,
3318
                6548,
3319
                515,
3320
                524
3321
            ],
3322
            "answer": " Ray Core is an open-source, general purpose, distributed computing library for Python that enables scaling of applications and acceleration of machine learning workloads. It provides primitives like tasks, actors, and objects for building and scaling distributed applications. Ray Core can run on a laptop, cluster, cloud, or Kubernetes, and is the foundation for Ray AI libraries and third-party integrations.",
3323
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3324
        },
3325
        {
3326
            "question": "Does ray support cron job",
3327
            "sources": [
3328
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",
3329
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
3330
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
3331
                "https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",
3332
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers"
3333
            ],
3334
            "document_ids": [
3335
                8819,
3336
                7772,
3337
                18927,
3338
                12613,
3339
                735
3340
            ],
3341
            "answer": " No, Ray does not directly support cron jobs. However, you can use external job schedulers that can interact with your Ray cluster via job submission to trigger workflow runs. Ray Workflows currently does not have a built-in job scheduler with cron job functionality. Additionally, Ray Core, an experimental feature, does not support Ray Client and has limitations regarding the use of runtime environments when using Ray Job Submission API.",
3342
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3343
        },
3344
        {
3345
            "question": "can you give me the dependencies list for api read_images?",
3346
            "sources": [
3347
                "https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",
3348
                "https://docs.ray.io/en/master/installation.html#docker-source-images",
3349
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",
3350
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning",
3351
                "https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning"
3352
            ],
3353
            "document_ids": [
3354
                426,
3355
                116,
3356
                19592,
3357
                8093,
3358
                1479
3359
            ],
3360
            "answer": " The dependencies for the `read_images` API are included in the `rayproject/ray` and `rayproject/ray-ml` Docker images from the Ray Docker Hub. These images come with Ray and all required dependencies, including various versions of Python and additional ML libraries. For GPU-based machine learning, CUDA-based container images with Ray and necessary machine learning libraries are available on the Ray Docker Hub. The Ray ML images are packaged with dependencies needed for the Ray Libraries.",
3361
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3362
        },
3363
        {
3364
            "question": "how do I kill a specific serve replica",
3365
            "sources": [
3366
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
3367
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",
3368
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",
3369
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",
3370
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"
3371
            ],
3372
            "document_ids": [
3373
                18751,
3374
                18724,
3375
                18749,
3376
                18711,
3377
                18706
3378
            ],
3379
            "answer": " To kill a specific serve replica, you can follow these steps:\n\n1. Get the actor handle for the replica you want to kill using `ray.get_actor()` and the replica's ACTOR_ID.\n2. Use `ray.kill()` to kill the replica, passing in the replica's handle and setting `no_restart=True`.\n\nFor example, if you want to kill the replica with ACTOR_ID `SERVE_REPLICA::SleepyPid#RlRptP`, you can use the following commands:\n```python\nimport ray\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nNote that you'll need to replace `\"SERVE_REPLICA::SleepyPid#RlRptP\"` with the actual ACTOR_ID of the replica you want to kill. You can find the ACTOR_ID of your replicas by running `ray list actors`.\n\nAdditionally, if you want to kill the Serve controller, you can do so by getting a handle to it using its NAME and then calling `ray.kill()` on that handle. The NAME of the Serve controller can be found in the output of `ray list actors`. For example:\n```python\nimport ray\ncontroller_handle = ray.get_actor(\"SERVE_CONTROLLER_ACTOR\", namespace=\"serve\")\nray.kill(controller_handle, no_restart=True)\n```\nAgain, you'll need to replace `\"SERVE_CONTROLLER_ACTOR\"` with the actual NAME of your Serve controller.",
3380
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3381
        }
3382
    ]
3383
}
3384