llm-applications

1
{
2
    "config": {
3
        "experiment_name": "chunk-size-700",
4
        "chunk_size": 700,
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,
10
        "max_context_length": 32768,
11
        "system_content": "Answer the query using the context provided. Be succinct.",
12
        "assistant_content": "",
13
        "docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",
14
        "experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",
15
        "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/data-internals.html#execution-memory",
24
                "https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",
25
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",
26
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart"
27
            ],
28
            "document_ids": [
29
                6994,
30
                6965,
31
                6790,
32
                6786,
33
                9180
34
            ],
35
            "answer": " When using `map_batches`, you can specify the batch format as \"numpy\" or \"pandas\" to convert the Ray Data type. To avoid out-of-memory issues, ensure that the batch size is small enough so that the output batch can fit into memory. Additionally, make sure no single item in your Ray Data is too large.\n\nHere's an example of how to use \"numpy\" 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\nAnd here's an example of how to use \"pandas\" batch format:\n\n```python\nimport pandas as pd\nimport ray\n\ndef increase_brightness(batch: Dict[str, pd.Series]) -> Dict[str, pd.Series]:\n    batch[\"image\"] = pd.Series(np.clip(batch[\"image\"].to_numpy() + 4, 0, 255))\n    return batch\n\nds = (\n    ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n    .map_batches(increase_brightness, batch_format=\"pandas\")\n)\n```\n\nMake sure to adjust the batch format and the function accordingly based on your specific use case.",
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/cluster/key-concepts.html#autoscaling",
43
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
44
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
45
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling"
46
            ],
47
            "document_ids": [
48
                13471,
49
                5559,
50
                6451,
51
                5953,
52
                5955
53
            ],
54
            "answer": " Ray Serve's autoscaling feature automatically adjusts the number of replicas in a deployment based on its workload. The Ray autoscaler, which runs on the head node, increases or decreases the number of worker nodes in the cluster when the resource demands of the Ray workload exceed or are below the current capacity. The autoscaler is reactive to task and actor resource requests, not application metrics or physical resource utilization. It can be configured using the Ray cluster launcher or on Kubernetes to scale the number of nodes based on resource demand. Autoscaling can reduce workload costs but adds node launch overheads and can be tricky to configure, so it is recommended to start with non-autoscaling clusters for new Ray users.",
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/launching-clusters/on-premises.html#start-worker-nodes",
62
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
63
                "https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",
64
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"
65
            ],
66
            "document_ids": [
67
                4305,
68
                6367,
69
                4817,
70
                5587,
71
                4824
72
            ],
73
            "answer": " To get the address of a Ray node, you can look at the output of the `ray.nodes()` function, which provides information about the nodes in the Ray cluster. In particular, you can use the 'NodeManagerAddress' field and the 'MetricsExportPort' field to construct the address of the node. For example, if the output shows 'NodeManagerAddress' as '192.168.1.82' and 'MetricsExportPort' as '64860', then the address of the node would be '192.168.1.82:64860'.\n\nAlternatively, if you have already initialized Ray on the node using `ray.init()`, you can get the node's address using the `ray.global_state.node_ip_address` property. This property returns the IP address of the node that Ray is currently running on.\n\nOnce you have the address of the Ray node, you can use it to connect to the Ray cluster by passing it as an argument to the `ray start` command, as described in the context you provided.",
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/ray-more-libs/ray-collective.html#ray-collective-communication-lib",
80
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
81
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",
82
                "https://docs.ray.io/en/master/installation.html#installing-ray",
83
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray"
84
            ],
85
            "document_ids": [
86
                14403,
87
                4469,
88
                284,
89
                59,
90
                14541
91
            ],
92
            "answer": " Yes, Ray does support NCCL (NVIDIA Collective Communications Library) as one of the high-performance communication backends for its collective communication library. NCCL is used for efficient out-of-band communication between Ray actor and task processes on both distributed CPUs and GPUs.",
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/workflows/basics.html#ray-integration",
99
                "https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",
100
                "https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",
101
                "https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",
102
                "https://docs.ray.io/en/master/tune/api/integration.html#external-library-integrations-for-ray-tune"
103
            ],
104
            "document_ids": [
105
                9668,
106
                8356,
107
                8101,
108
                8135,
109
                1306
110
            ],
111
            "answer": " Yes, Ray is integrated with DeepSpeed through the Ray Train component. You can run DeepSpeed with other frameworks like Accelerate, Transformers, and Lightning in Ray Train. To configure DeepSpeed in Ray Train, you can use the Accelerator object and create a DeepSpeedPlugin from a dictionary.",
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-actors",
118
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",
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/tips-for-first-time.html#tip-1-delay-ray-get",
121
                "https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example"
122
            ],
123
            "document_ids": [
124
                4550,
125
                4595,
126
                4564,
127
                4208,
128
                5197
129
            ],
130
            "answer": " If you use `asyncio`'s `await` to wait for a Ray future like `await x.remote()`, you will encounter an error. Ray does not support `asyncio` for remote tasks, so you cannot use `async/await` syntax directly with `@ray.remote` functions. However, Ray object refs can be translated to `asyncio.Futures`, allowing you to await on Ray futures in existing concurrent applications. To maintain parallelism, avoid blocking the driver program by delaying `ray.get()` until you need the actual results.\n\nIn your case, you can create a wrapper function to run the async function synchronously:\n\n```python\nimport ray\nimport asyncio\n\n@ray.remote\nclass AsyncActor:\n    async def run_concurrent(self):\n        print(\"started\")\n        await asyncio.sleep(2) # concurrent workload here\n        print(\"finished\")\n\nactor = AsyncActor.remote()\n\nasync def wrapper():\n    import asyncio\n    await actor.run_concurrent.remote()\n\nray.get([wrapper.remote() for _ in range(4)])\n```\n\nThis way, you can use `async/await` in your actor methods and still use `ray.get()` to collect the results when needed.",
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/ray-more-libs/dask-on-ray.html#using-dask-on-ray",
137
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",
138
                "https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",
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/raydp.html#using-spark-on-ray-raydp"
141
            ],
142
            "document_ids": [
143
                14472,
144
                14484,
145
                6859,
146
                6861,
147
                14653
148
            ],
149
            "answer": " Spark, Ray, and Dask are all distributed computing systems, but they have different focuses and use cases. Spark is a general-purpose cluster computing system with a large ecosystem of libraries and tools. It is well-suited for data processing and machine learning tasks, and can be used with languages such as Python, Scala, and Java. Ray, on the other hand, is a high-performance distributed execution system that is designed to handle large-scale machine learning and AI workloads. It is optimized for low-latency, fault-tolerant task execution and can be used with a variety of deep learning frameworks. Dask is a parallel computing library for Python that is built on top of Ray. It provides big data collections that mimic the APIs of NumPy and Pandas, allowing data analytics and scientific computing workloads to be scaled up to larger-than-memory data and multi-machine clusters. Dask can be used on its own or on top of Ray, and is well-suited for tasks that involve large amounts of data and complex computations.\n\nIn summary, Spark is a general-purpose cluster computing system, Ray is a high-performance distributed execution system optimized for machine learning and AI workloads, and Dask is a parallel computing library for Python that can be used on its own or on top of Ray. Dask provides big data collections that mimic the APIs of NumPy and Pandas, making it well-suited for data analytics and scientific computing tasks.",
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/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",
157
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case",
158
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
159
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources"
160
            ],
161
            "document_ids": [
162
                5231,
163
                5230,
164
                5232,
165
                5115,
166
                5104
167
            ],
168
            "answer": " Ray overloads a node with more tasks than the resources allow when the requested resources by the tasks or actors exceed the node's total resources. This can lead to issues like Out-Of-Memory (OOM) if the tasks or actors use more memory than their proportionate share. To avoid this, you can limit the number of concurrently running tasks or actors by increasing the resources requested by them, or by using the memory resource to limit the number of concurrent tasks. Note that resource requirements in Ray are logical, not physical, so it's your responsibility to ensure tasks or actors use no more resources than specified.",
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#ray-client",
177
                "https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",
178
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments"
179
            ],
180
            "document_ids": [
181
                6597,
182
                6589,
183
                6583,
184
                5636,
185
                6593
186
            ],
187
            "answer": " You should use Ray Client when you want to connect an interactive Python shell to a remote Ray cluster for purposes other than ML workloads, such as Ray Tune or Ray Train. Use the command `ray.init(\"ray://<head_node_host>:10001\")` to connect your shell to the cluster. Note that Ray Client requires the pip package `ray[client]`. It is recommended to start a Ray cluster for each user to ensure workload isolation.",
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/ray-core/scheduling/index.html#spread",
194
                "https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",
195
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",
196
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",
197
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard"
198
            ],
199
            "document_ids": [
200
                5145,
201
                5195,
202
                5441,
203
                7978,
204
                13515
205
            ],
206
            "answer": " To scatter actors across the cluster in Ray, you can use the `scheduling_strategy=\"SPREAD\"` option when creating the actors. Here's an example:\n\n```python\n@ray.remote(num_cpus=1)\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 create 10 actors and spread them across the available nodes in the 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/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",
213
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
214
                "https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",
215
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",
216
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster"
217
            ],
218
            "document_ids": [
219
                5681,
220
                4816,
221
                14683,
222
                6591,
223
                13822
224
            ],
225
            "answer": " The \"connection refused\" error when running `ray debug` on a remote Ray cluster could be due to the dashboard or dashboard agent not being fully operational yet. It's recommended to wait for 1 minute and try again. If the issue persists, it's possible that the dashboard or dashboard agent failed to start.\n\nTo connect to a remote Ray cluster, you can use `ray.init()` with the `ray_address` parameter set to the IP address and port of the cluster. Alternatively, you can set the `RAY_ADDRESS` environment variable to the same value.\n\nWhen testing on a remote cluster, you need to specify the address of the Ray cluster using the `ray://<head-node-ip-address>:10001` format. It's important to ensure that your remote cluster has a similar environment to your local machine, including files, environment variables, and Python packages.",
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/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",
233
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",
234
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",
235
                "https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples"
236
            ],
237
            "document_ids": [
238
                9845,
239
                10019,
240
                9820,
241
                374,
242
                183
243
            ],
244
            "answer": " Ray AIR sets up the model to communicate gradient updates across machines by creating the model, metric, and tokenizer inside a function (trainer\\_init\\_per\\_worker) and returning it. This function is then passed to the TransformersTrainer and will run on every Ray worker. The training will proceed by the means of PyTorch DDP, which handles the communication of gradient updates across machines.",
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/index.html#how-does-serve-compare-to",
252
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
253
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
254
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving"
255
            ],
256
            "document_ids": [
257
                13980,
258
                13622,
259
                13626,
260
                13596,
261
                13598
262
            ],
263
            "answer": " Use Ray Serve instead of Modal or Seldon for scalability and flexibility in model serving on your own infrastructure. It is framework-agnostic, works with any model packaging and registry format, and provides key features for building production-ready ML applications. Unlike Modal or Seldon, Ray Serve does not have built-in functionality for model lifecycle management and visualization, but it offers end-to-end control, high performance, and seamless integration with business logic. Ray Serve is unique in achieving these features with other tools requiring multiple integrations.",
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/index.html#how-can-serve-help-me-as-a",
270
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
271
                "https://docs.ray.io/en/master/serve/key-concepts.html#deployment",
272
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",
273
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm"
274
            ],
275
            "document_ids": [
276
                13618,
277
                14255,
278
                13416,
279
                13731,
280
                13732
281
            ],
282
            "answer": " To deploy a large language model (LLM) workload on Ray Serve, follow these steps:\n\n1. Write your LLM application as a single Python module, including all steps such as prompt preprocessing, vector database lookups, LLM API calls, and response validation.\n2. Use the `@serve.deployment` decorator on a Python class (or function for simple use cases) to define a deployment.\n3. Optionally, bind the deployment with arguments to the constructor for configuration.\n4. Configure your deployment to scale out and assign replica resources like CPUs and GPUs.\n5. Deploy your Ray Serve application to a Ray cluster using the Ray Serve CLI and a configuration file, which can be a local, single-node cluster or a remote, multi-node cluster.\n\nRefer to the context for more information on configuring a deployment and deploying on a VM.",
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/ray-air/examples/gptj_deepspeed_fine_tuning.html#fine-tuning-the-model-with-ray-train-a-name-train-a",
289
                "https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-size",
290
                "https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#task-based-batch-prediction",
291
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",
292
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#initialize-generation-pipeline"
293
            ],
294
            "document_ids": [
295
                10449,
296
                6792,
297
                5463,
298
                855,
299
                9486
300
            ],
301
            "answer": " The model you're interested in using, \"EleutherAI/gpt-j-6B\", is quite large and may not fit in a GPU with less than or equal to 16 GB of GPU RAM (GRAM). To address this issue, the example uses DeepSpeed, a library that optimizes the training process and reduces GRAM usage.\n\nIn terms of memory specifications, the example sets the `object_store_memory` to 1 GB and allows for a maximum of 2 workers. The `max_memory` for the device map is set to \"15GB\" for the meta model.\n\nHowever, the exact memory size you need depends on various factors, such as the batch size and other processes running on your system. If you encounter an out-of-memory error, you can try decreasing the batch size or increasing the `object_store_memory` and `max_memory` settings.\n\nKeep in mind that the actual memory requirements may vary, and you might need to adjust these settings based on your specific use case and available resources.",
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/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",
309
                "https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",
310
                "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",
311
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers"
312
            ],
313
            "document_ids": [
314
                2411,
315
                2354,
316
                10680,
317
                9682,
318
                9660
319
            ],
320
            "answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use TensorBoard's `--logdir` flag to specify the directory where the results are stored. In the context provided, the results are stored in the `~/ray_results/` directory. Therefore, you can start TensorBoard with the following command:\n```\ntensorboard --logdir=~/ray_results/\n```\nIf you are using Ray's Tune, you can customize the directory of results by specifying the `storage_path` parameter in the `RunConfig` object, which is then passed to the `Tuner`. For example:\n```\ntune.RunConfig(\n    storage_path=\"~/my_tune_results\",\n    ...\n)\n```\nThen, you can point TensorBoard to that directory to visualize the results:\n```\ntensorboard --logdir=~/my_tune_results\n```\nIf you are running Ray on a remote multi-user cluster where you do not have sudo access, you can run the following commands to make sure TensorBoard is able to write to the `tmp` directory:\n```\nexport TMPDIR=/tmp/$USER; mkdir -p $TMPDIR; tensorboard --logdir=~/ray_results\n```\nThis will ensure that TensorBoard has the necessary permissions to write to the `tmp` directory.\n\nIn summary, to log the results from multiple distributed workers into a single TensorBoard, you can use the `--logdir` flag to specify the directory where the results are stored. You can customize the directory of results by specifying the `storage_path` parameter in the `RunConfig` object if you are using Ray's Tune. If you are running Ray on a remote multi-user cluster, you can use the `TMPDIR` environment variable to ensure that TensorBoard has the necessary permissions to write to the `tmp` directory.",
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/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig"
331
            ],
332
            "document_ids": [
333
                10910,
334
                8441,
335
                1443,
336
                1138,
337
                1782
338
            ],
339
            "answer": " To configure `SyncConfig` for a Ray AIR job, you can set it in the `RunConfig` object. Here's an example:\n\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  # This is deprecated\n)\n\nrun_config = train.RunConfig(\n    sync_config=sync_config\n)\n```\n\nIn this example, the `sync_config` object specifies various synchronization parameters such as the sync period, timeout, and whether to sync artifacts or not. This `sync_config` object is then passed to the `RunConfig` object, which can be used in a Ray AIR job.\n\nNote that syncing to the head node is deprecated and will be hard-deprecated in Ray 2.7. Instead, you can use the `upload_dir` parameter in `SyncConfig` to specify a custom directory for uploading files. However, this feature is also deprecated and will be removed in a future Ray release. It is recommended to use alternative methods for data synchronization, such as using a shared file system or a distributed database.",
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/ray-observability/getting-started.html#logs-view",
348
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",
349
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view"
350
            ],
351
            "document_ids": [
352
                415,
353
                681,
354
                444,
355
                5764,
356
                445
357
            ],
358
            "answer": " To narrow down the root cause of a failed Ray job, you can access all the logs and:\n\n1. Use `ray logs` command with different parameters (cluster, actor, task) to filter logs by filename, resource identifiers, or node ID.\n2. Check the Driver logs if the Ray Job is submitted by the Job API. The log file follows the format: job-driver-<job\\_submission\\_id>.log.\n3. If you execute the Driver directly on the Head Node or run with Ray Client, view the Driver logs in the terminal or Jupyter Notebook output.\n4. Check the output of the Ray job using `kubectl logs -l=job-name=rayjob-sample`.\n5. For Task and Actor logs, access them from the Task and Actor table view and click the \"Log\" button to see the stdout and stderr logs.",
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/getting_started.html#converting-to-a-ray-serve-application",
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/advanced-guides/managing-java-deployments.html#resource-management-cpus-gpus",
368
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#set-up-autoscaling-and-resource-allocation"
369
            ],
370
            "document_ids": [
371
                13458,
372
                13575,
373
                13456,
374
                13735,
375
                13443
376
            ],
377
            "answer": " To specify how many GPUs a Serve deployment needs, you can use the `ray_actor_options` parameter in the `@serve.deployment` decorator and set the `\"num_gpus\"` key to the desired number of GPUs. This can be a fraction if you want to pack multiple replicas together on a machine with fewer GPUs than replicas. Here's an example:\n\n```python\n@serve.deployment(ray_actor_options={\"num_gpus\": 1.5})\ndef func_2(*args):\n    return do_something_with_my_gpu()\n```\n\nIn this example, each replica of the `func_2` deployment will reserve 1.5 GPUs.",
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-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
384
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
385
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",
386
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
387
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning"
388
            ],
389
            "document_ids": [
390
                5129,
391
                10812,
392
                9410,
393
                10826,
394
                9409
395
            ],
396
            "answer": " Based on the information provided, it appears that your worker node is running out of memory (OOM) and receiving SIGTERM as a result. This is indicated by the message in the raylet log: \"1 Workers (tasks / actors) killed due to memory pressure (OOM)\". The TensorflowTrainer may be using more memory than expected, leading to the OOM error.\n\nTo debug this issue, you can check the memory usage of the worker nodes by using the `ray logs raylet.out -ip <node_ip>` command, as suggested in the raylet log. This will show you more information about the workers that were killed due to memory pressure.\n\nAdditionally, you can monitor the memory usage of the worker nodes during training to see if any of them are approaching the memory limit. You can do this by using a tool like `htop` or `top` to monitor the memory usage of the worker processes.\n\nIf you find that the worker nodes are consistently running out of memory, you may need to adjust the memory settings for the worker nodes or reduce the batch size or other memory-intensive parameters in your TensorflowTrainer. You can also try using gradient accumulation to reduce the memory usage of the worker nodes.\n\nFinally, the warning message about pytorch allocator cache flushes suggests that there is high memory pressure during training. This could be a contributing factor to the OOM errors. You can try reducing the memory consumption of your training code or adding `get_accelerator().empty_cache()` calls in your training loop to ensure that all ranks flush their caches at the same time.",
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/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",
403
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",
404
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
405
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",
406
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure"
407
            ],
408
            "document_ids": [
409
                5639,
410
                4387,
411
                13898,
412
                4391,
413
                4393
414
            ],
415
            "answer": " Based on the provided context, there are several possible reasons for nodes dying in a Ray cluster:\n\n1. Overloading the head node with too many simultaneous connections, which can cause worker nodes to die.\n2. Raylet process failure, which marks the corresponding node as dead and treats it as a new raylet/node to the Ray cluster.\n3. Head node failure, which causes the entire Ray cluster to fail.\n\nTo tolerate head node failures, GCS (Global Control Store) needs to be made fault-tolerant so that when a new head node is started, all the cluster-level data is still available.",
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/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
422
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
423
                "https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",
424
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
425
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction"
426
            ],
427
            "document_ids": [
428
                6451,
429
                5953,
430
                5559,
431
                6455,
432
                5919
433
            ],
434
            "answer": " To set the target size of a Ray remote cluster immediately without scaling up through the autoscaler, you can manually configure the number of worker nodes in the cluster. This can be done by specifying the desired number of nodes in the cluster configuration, outside of the autoscaling settings.\n\nFor example, in the cluster launcher configuration, you can specify the number of worker nodes in the `workerGroupSpec` section. Here is an example configuration snippet:\n```\n...\nworkerGroupSpec:\n",
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.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",
441
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",
442
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",
443
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",
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
                7188,
448
                7543,
449
                7379,
450
                8478,
451
                7811
452
            ],
453
            "answer": " To disable async iter_batches with Ray Dataset, you can set the `prefetch_batches` parameter to 0 in the `iter_batches()` method. For example:\n```python\nfor batch in dataset.iter_batches(prefetch_batches=0, batch_size=256, ...):\n    ...\n```\nThis will disable the asynchronous behavior of the iterator and return batches synchronously. Note that this may affect the performance of your data processing pipeline.",
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/serve/tutorials/batch.html#batching-tutorial"
464
            ],
465
            "document_ids": [
466
                14728,
467
                6919,
468
                6965,
469
                6994,
470
                14578
471
            ],
472
            "answer": " A batch and a block are related concepts in Ray Datasets, but they differ in terms of abstraction level and usage. A block is an internal abstraction that represents a distributed data block containing a disjoint subset of rows, while a batch is a user-facing object. The map\\_batches() function is used to process batches of data, which can help avoid out-of-memory situations by controlling the block size and ensuring it fits into memory.",
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/ray-core/handling-dependencies.html#runtime-environments",
480
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher",
481
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",
482
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#environment-dependencies"
483
            ],
484
            "document_ids": [
485
                910,
486
                4251,
487
                4250,
488
                6595,
489
                4203
490
            ],
491
            "answer": " To set up the development environment for the Ray project, you can follow these steps:\n\n1. Checkout the Ray repository and build Ray from source.\n2. Use runtime environments to install dependencies dynamically while Ray is running. A runtime environment describes the dependencies your Ray application needs, including packages, environment variables, and more. It is installed dynamically on the cluster at runtime and cached for future use.\n3. Alternatively, you can prepare an environment using the Ray Cluster launcher by building all your files and dependencies into a container image and specifying it in your Cluster YAML Configuration. You can also install packages using setup\\_commands in the Ray Cluster configuration file.\n\nThe `ray.init()` function can be used to specify the runtime environment, including the working directory and pip packages. For example:\n```python\nray.init(\n    \"ray://1.2.3.4:10001\",\n    namespace=\"my_namespace\",\n    runtime_env={\"working_dir\": \"files/my_project\", \"pip\": [\"toolz\", \"requests\"]},\n)\n```\nFor more information, you can refer to the User Guide for Environment Dependencies.",
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/rllib-advanced-api.html#rewriting-trajectories",
498
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",
499
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
500
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",
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
                10444,
505
                12707,
506
                14759,
507
                12708,
508
                13196
509
            ],
510
            "answer": " To debug why Ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`, you can start by checking the configuration of the RolloutWorker, including the `env_creator`, `validate_env`, `config`, `worker_index`, `num_workers`, `recreated_worker`, `log_dir`, `spaces`, `default_policy_class`, and `dataset_shards`. You can also look into the implementation of the `__init__` method of the RolloutWorker class for more insights. To get the global worker, you can use `get_global_worker()` function. To scale the data collection process for faster RL training, you can have several replicas of RolloutWorkers running as Ray actors.",
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/user-guides/data-loading-preprocessing.html#quickstart",
518
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",
519
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",
520
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a"
521
            ],
522
            "document_ids": [
523
                6845,
524
                9180,
525
                7255,
526
                10436,
527
                10014
528
            ],
529
            "answer": " To join two Ray Datasets, you can use the `zip()` function provided by Ray Datasets. Here's an example of how you can do it:\n```python\nimport ray\n\ndataset1 = ray.data.range(8)\ndataset2 = ray.data.range(8)\n\n# Zip the two datasets\nzipped_dataset = dataset1.zip(dataset2)\n\n# Take a batch to see the result\nresult = zipped_dataset.take_batch()\nprint(result)  # {'id': array([0, 1, 2, 3, 4, 5, 6, 7]), 'id_1': array([0, 1, 2, 3, 4, 5, 6, 7])}\n```\nIn this example, `zip()` returns a new Ray Dataset that contains columns from both the input datasets, with duplicate column names disambiguated with suffixes like \"\\_1\".\n\nNote that the `zip()` function requires both input datasets to have the same number of rows. If they have different numbers of rows, the resulting dataset will have the number of rows equal to the smaller input dataset.",
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/tasks/generators.html#num-returns-set-by-the-task-executor",
536
                "https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",
537
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle",
538
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",
539
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait"
540
            ],
541
            "document_ids": [
542
                4982,
543
                4984,
544
                14276,
545
                13987,
546
                4750
547
            ],
548
            "answer": " Yes, you can retrieve an object ref from its id in Ray by calling `ray.ObjectID(id_here)`. However, directly working with object IDs is not typically necessary in Ray, as you can pass around and return `ObjectRef` objects directly.\n\nIn the provided context, an ObjectRefGenerator is used to handle multiple object refs returned by a task. The ObjectRefGenerator can be passed to another task or used to iterate over the return values.",
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/tune/examples/includes/pbt_convnet_function_example.html#pbt-convnet-example",
555
                "https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",
556
                "https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",
557
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#check-the-training-results-and-checkpoints",
558
                "https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example"
559
            ],
560
            "document_ids": [
561
                4104,
562
                8798,
563
                9037,
564
                9331,
565
                4073
566
            ],
567
            "answer": " To create a model checkpoint from the model in memory, you can use the `tf.keras.callbacks.Checkpoint` class and its `from_model` method. Here's an example:\n```python\nimport tensorflow as tf\n\n# Create the model\nmodel = tf.keras.applications.resnet.ResNet101()\n\n# Create a checkpoint from the model\ncheckpoint = tf.keras.callbacks.Checkpoint.from_model(model, save_weights_only=True, save_fmt='tf')\n\n# Save the checkpoint to a directory\ncheckpoint.save('my_model')\n```\nThis will create a checkpoint of the model's weights in the `my_model` directory. If you want to save the entire model (including optimizer and training configuration), you can remove the `save_weights_only` argument.\n\nAlternatively, you can also use the `tf.keras.models.save_model` function to save the model to a SavedModel format, and then create a checkpoint from the saved model using the `tf.keras.callbacks.Checkpoint.from_saved_model` method. Here's an example:\n```python\nimport tensorflow as tf\n\n# Create the model\nmodel = tf.keras.applications.resnet.ResNet101()\n\n# Save the model to a SavedModel format\ntf.keras.models.save_model(model, 'my_model')\n\n# Create a checkpoint from the saved model\ncheckpoint = tf.keras.callbacks.Checkpoint.from_saved_model('my_model')\n\n# Save the checkpoint to a directory\ncheckpoint.save('my_checkpoint')\n```\nThis will save the model to the `my_model` directory in SavedModel format, and then create a checkpoint of the model in the `my_checkpoint` directory.",
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/serve/key-concepts.html#deployment",
575
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
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
                14255,
581
                13416,
582
                14732,
583
                13731,
584
                10395
585
            ],
586
            "answer": " A Deployment in Ray Serve is a group of actors that can handle traffic, defined as a single class with a number of options, including the number of \"replicas\" of the deployment. At runtime, a deployment consists of a number of replicas, which are individual copies of the class or function that are started in separate Ray Actors (processes). These replicas can be scaled up or down to match the incoming request load. A deployment is created using the `@serve.deployment` decorator on a Python class or function.",
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/api/index.html#ray-serve-api",
594
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",
595
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serves-recovery-procedures",
596
                "https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide"
597
            ],
598
            "document_ids": [
599
                6597,
600
                13980,
601
                13731,
602
                13883,
603
                13962
604
            ],
605
            "answer": " In Ray Serve, user config refers to the configuration of deployments, such as scaling out by increasing the number of replicas, and assigning resources like CPUs and GPUs. Here's how you can use it:\n\n1. Define the configuration in a `serveConfig` block, for example:\n```yaml\nserveConfig:\n    importPath: \"sleepy_pid:app\"\n    runtimeEnv: |\n      working_dir: \"https://github.com/ray-project/serve_config_examples/archive/42d10bab77741b40d11304ad66d39a4ec2345247.zip\"\n    deployments:\n      - name: SleepyPid\n        numReplicas: 6\n        rayActorOptions:\n          numCpus: 0\n```\n2. Use this configuration when creating a deployment, for example:\n```python\nimport ray\nfrom ray import serve\n\nray.init(serve_config={\n    \"serveConfig\": your_config_here\n})\n\nserve.create_backend(\"sleepy_pid\", your_backend_here)\nserve.create_deployment(\"SleepyPid\", your_deployment_here)\n```\n\nNote: The above example is in Python, but Ray Serve also supports other languages like Java and C++.\n\n3. You can also use the Ray Serve CLI to generate and deploy a config file, as described in the Ray Serve Production Guide.\n\nRegarding the Ray cluster configuration, it is defined in the `rayClusterConfig` block and is used to configure the Ray cluster that Ray Serve runs on. The recommended way to run Ray Serve in production is on Kubernetes using the KubeRay RayService custom resource.",
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/ray-core/scheduling/index.html#spread",
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/rllib/rllib-training.html#specifying-resources",
614
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",
615
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy"
616
            ],
617
            "document_ids": [
618
                5145,
619
                11365,
620
                11115,
621
                1872,
622
                5078
623
            ],
624
            "answer": " The \"SPREAD\" strategy in Ray can be used for both tasks and actors, and it aims to distribute them across available nodes. On the other hand, the \"PACK\" strategy for placement groups tries to place as many resource bundles as possible onto a single node, while \"SPREAD\" strategy places them on distinct nodes as evenly as possible. It's important to note that these two concepts (scheduling strategy and placement strategy) serve different purposes and are used in different parts of Ray's configuration.",
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/serve/index.html#why-choose-serve",
632
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",
633
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
634
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms"
635
            ],
636
            "document_ids": [
637
                5636,
638
                13615,
639
                4186,
640
                372,
641
                6208
642
            ],
643
            "answer": " To run Ray across multiple machines, you can use the Ray cluster launcher (`ray up`) to start a Ray cluster, which will provide isolated workloads for each user. Ray is designed to run on multiple servers, and the Ray runtime can be started explicitly via the cluster launcher. This is the recommended way to deploy Ray for multiple users.",
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/doc/ray.tune.Tuner.html#ray-tune-tuner",
650
                "https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",
651
                "https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",
652
                "https://docs.ray.io/en/master/train/user-guides/using-gpus.html#configuring-scale-and-gpus",
653
                "https://docs.ray.io/en/master/train/api/doc/ray.train.trainer.BaseTrainer.html#ray-train-trainer-basetrainer"
654
            ],
655
            "document_ids": [
656
                1826,
657
                1303,
658
                9126,
659
                9251,
660
                8519
661
            ],
662
            "answer": " To specify the `ScalingConfig` for a Tuner run, you can include it in the `RunConfig` which you pass into the Tuner constructor. Here's an example:\n```python\nfrom ray.tune.scalers import ScalingConfig\n\nscaling_config = ScalingConfig(...)\nrun_config = RunConfig(name=\"my_tune_run\", scaling_config=scaling_config)\n\ntuner = Tuner(...)\nresults = tuner.fit(...)\n```\nIn this example, `ScalingConfig` is configured with the desired scale settings, and then it is passed as a parameter to the `RunConfig` constructor. The `RunConfig` object is then passed into the `Tuner` constructor.",
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/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
670
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",
671
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
672
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks"
673
            ],
674
            "document_ids": [
675
                4944,
676
                7012,
677
                4942,
678
                7005,
679
                4225
680
            ],
681
            "answer": " To utilize the 'zero-copy' feature Ray provides for numpy arrays, you should ensure that the function you're using doesn't mutate the input array. If your function doesn't modify the array, you can set the `zero_copy_batch` argument to `True` when using functions like `map_batches()`. This will allow Ray to use zero-copy, read-only views of the data in the object store, which can decrease memory utilization and improve performance.\n\nHere's an example:\n\n```python\nds = (\n    ray.data.from_items([\n        {\"name\": \"Luna\", \"age\": 4},\n        {\"name\": \"Rory\", \"age\": 14},\n        {\"name\": \"Scout\", \"age\": 9},\n    ])\n    .map_batches(add_dog_years, batch_format=\"numpy\", zero_copy_batch=True)\n)\n```\n\nIn this example, the `add_dog_years` function doesn't modify the input array, so we can set `zero_copy_batch=True` to improve performance and decrease memory utilization.\n\nHowever, if your function needs to mutate the input array, you'll need to manually copy the array at the destination, as the context suggests. This is because Ray stores numpy arrays as read-only objects in the object store, and any writes to the read-only object will require the user to first copy it into the local process memory.",
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-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
691
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks"
692
            ],
693
            "document_ids": [
694
                5441,
695
                7308,
696
                7976,
697
                5192,
698
                5231
699
            ],
700
            "answer": " It is not advised to spin up 1 actor per key due to the large number of actors and the potential for resource contention. Instead, it would be better to create 'key pools' where an actor can hold many keys, reducing the total number of actors needed. This approach can help balance resource usage and improve performance.",
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/train/api/doc/ray.train.Result.html#ray-train-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/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result"
711
            ],
712
            "document_ids": [
713
                8371,
714
                1645,
715
                1431,
716
                2188,
717
                3402
718
            ],
719
            "answer": " To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` method provided by Ray Tune. Here's how you can do it:\n\n1. Import the necessary modules:\n```python\nfrom ray import tune\n```\n2. Assume you have a trial directory and you want to find the best checkpoint based on a specific metric (e.g., 'accuracy') in 'max' mode (finding the highest accuracy):\n```python\nbest_checkpoint = tune.ExperimentAnalysis(path).get_best_checkpoint(metric='accuracy', mode='max')\n```\nIn this example, replace `path` with the path to your local trial directory.\n\n3. If you want to get the best trial's checkpoint directly, you can use the `best_checkpoint` property provided by Ray Tune:\n```python\nbest_checkpoint = tune.ExperimentAnalysis().best_checkpoint\n```\nThis will return the best checkpoint from the latest trial. If you want to get the best checkpoint from a specific trial, you can pass the trial object or its path to the `ExperimentAnalysis` constructor.",
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/key-concepts.html#actors",
727
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
728
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
729
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources"
730
            ],
731
            "document_ids": [
732
                5192,
733
                4200,
734
                4455,
735
                14708,
736
                4371
737
            ],
738
            "answer": " Advantage of using a singleton Actor:\n\n* Allows for managing state between actor instances.\n* Methods of the actor can access and mutate the state of the worker.\n\nDisadvantage of using a singleton Actor:\n\n* Each actor instance is instantiated in its own process, so it requires more resources than using tasks.\n* Actors can fail if the actor process dies or if the owner of the actor dies.\n* If you don't need the stateful part of an actor, you're mostly better off using tasks.\n* Class variables are not supported for managing state between instances of the same class.\n\nNote: Actors are essentially stateful workers or services that extend the Ray API from functions (tasks) to classes. When a new actor is instantiated, a new worker is created, and methods of the actor are scheduled on that specific worker and can access and mutate the state of that worker.",
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-more-libs/actors.html#faq-actors-workers-and-resources",
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-core/user-guide.html#user-guides",
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
                14708,
752
                11813,
753
                4371,
754
                4678,
755
                4880
756
            ],
757
            "answer": " Named actors in Ray provide several advantages:\n\n1. **Specifying required resources:** You can specify the required resources for an actor.\n2. **Calling the actor:** You can call the actor using its unique name.\n3. **Passing Around Actor Handles:** You can pass around actor handles to other tasks or actors.\n4. **Cancelling Actor Tasks:** You can cancel an actor's tasks.\n5. **Scheduling:** Ray schedules actor tasks for you.\n6. **Fault Tolerance:** Ray provides fault tolerance for actors.\n7. **Unique Name:** Each named actor has a unique name that can be used to retrieve the actor via `ray.get_actor(name)` as long as the actor is still alive.\n8. **Lifetime Control:** You can control the actor's lifetime by setting it to \"detached\", meaning it will live as a global object independent of the creator.\n\nHowever, if you don't need the stateful part of an actor, you're mostly better off using tasks.",
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/api/doc/ray.data.read_text.html#ray-data-read-text",
764
                "https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",
765
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
766
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",
767
                "https://docs.ray.io/en/master/data/key-concepts.html#loading-data"
768
            ],
769
            "document_ids": [
770
                7565,
771
                7909,
772
                6993,
773
                7496,
774
                6732
775
            ],
776
            "answer": " To read a text file stored on S3 using Ray Data, you can use the `read_text` function and provide the S3 path of the text file as an argument. Here's an example:\n```python\nimport ray\n\n# Replace `my-s3-bucket` and `my-text-file.txt` with the actual S3 bucket and file names\nds = ray.data.read_text(\"s3://my-s3-bucket/my-text-file.txt\")\n```\nThis will create a Ray Dataset `ds` containing the text data from the specified S3 file. If the S3 bucket is private, you may need to set up authentication using `pyarrow.fs`.",
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/vms/user-guides/community/slurm.html#starting-the-ray-head-node",
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/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",
786
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"
787
            ],
788
            "document_ids": [
789
                5631,
790
                6537,
791
                6365,
792
                6367,
793
                493
794
            ],
795
            "answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command. It is used as follows:\n\n```\nray get_head_ip [OPTIONS] CLUSTER_CONFIG_FILE\n```\n\nwhere `CLUSTER_CONFIG_FILE` is a required argument that specifies the configuration file for the Ray cluster.\n\nAlternatively, you can also get the IP of the head node by running the following command on the head node:\n\n```\necho \"IP Head: $ip_head\"\n```\n\nThis command is used in the script to start the Ray head node and prints out the IP head, which is the IP of the head node.",
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.Dataset.map_batches.html#ray-data-dataset-map-batches",
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.html#ray-data-dataset-map",
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/examples/batch_inference_object_detection.html#model-inference"
806
            ],
807
            "document_ids": [
808
                6994,
809
                7019,
810
                7800,
811
                7979,
812
                8218
813
            ],
814
            "answer": " To write a map function that returns a list of objects for `map_batches`, you should define a class or function as the user-defined function (UDF) and return the transformed batch from it. The `map_batches` function will apply this UDF to each batch of data. Here is an example of how to define a class-based UDF:\n```\nclass MyUDF:\n    def __call__(self, batch):\n        # Transform the batch here\n        transformed_batch = ...\n        return transformed_batch\n\npredictions = ds.map_batches(MyUDF(), batch_size=128)\n```\nIn this example, `MyUDF` is a class with a `__call__` method that defines the transformation to be applied to each batch. The `map_batches` function will apply this transformation to each batch of data in the dataset `ds`, using a batch size of 128.\n\nNote that the size of the batches provided to the UDF may be smaller than the specified batch size if the batch size does not evenly divide the block(s) sent to a given map task. Also, the UDF can return multiple records or rows, depending on the method used (`map_batches` or `flat_map`).",
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/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.reporting.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-reporting",
822
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",
823
                "https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",
824
                "https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data"
825
            ],
826
            "document_ids": [
827
                10410,
828
                13177,
829
                11046,
830
                10411,
831
                11069
832
            ],
833
            "answer": " In RLlib, you can't set a maximum episode length directly during training. However, you can control the number of episodes or timesteps for evaluation using the `evaluation_duration` and `evaluation_duration_unit` configuration.\n\nTo limit the number of timesteps during training, you can use the `min_train_timesteps_per_iteration` configuration to specify the minimum number of training timesteps to accumulate before moving on to the next iteration. Note that this does not guarantee a strict maximum episode length but rather sets a limit on the total number of timesteps accumulated during training.\n\nHere's an example configuration:\n\n```yaml\ntrainer:\n  ...\n  min_train_timesteps_per_iteration: <your_desired_number_of_timesteps>\n```\n\nKeep in mind that this approach may not be suitable for all use cases, and you might need to adjust your training loop or environment to better fit your specific requirements.",
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/ray-core/tasks.html#fault-tolerance",
843
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_recover.html#ray-tune-callback-on-trial-recover"
844
            ],
845
            "document_ids": [
846
                2326,
847
                2267,
848
                2268,
849
                4505,
850
                2152
851
            ],
852
            "answer": " To make a Ray Tune trial retry on failures, you can define the `failure_config` in the `run_config` of the `Tuner`. For example, to retry a trial up to 3 times when it encounters a runtime error, you can use the following configuration:\n\n```python\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```\n\nYou can also use the `ray.tune.Callback.on_trial_recover` method to define a callback function that will be called after a trial instance has failed but is scheduled for retry. The search algorithm and scheduler are not notified of this event. The method has the following signature:\n\n```python\ndef on_trial_recover(iteration: int, trials: List[tune.Trial], trial: tune.Trial, **info)\n```\n\nwhere `iteration` is the number of iterations of the tuning loop, `trials` is a list of all trials, `trial` is the trial that just has errored, and `info` is a kwargs dict for forward compatibility.",
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/fault_tolerance/actors.html#actor-creator-failure",
860
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
861
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
862
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes"
863
            ],
864
            "document_ids": [
865
                5208,
866
                4493,
867
                4455,
868
                5191,
869
                4654
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 if the supervisor dies, or the driver, the worker actors are automatically terminated thanks to actor reference counting. However, you can make the actor \"detached\" to decouple its lifetime from the job, allowing it to persist even after the driver process of the job exits.",
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/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",
879
                "https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",
880
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
881
                "https://docs.ray.io/en/master/data/working-with-text.html#working-with-text"
882
            ],
883
            "document_ids": [
884
                7565,
885
                7496,
886
                7909,
887
                6993,
888
                6991
889
            ],
890
            "answer": " To read a large text file in S3 with Ray, you can use the `read_text` function and provide the S3 path to the file as an argument. Here's an example:\n```\nimport ray\n\n# Replace this with the S3 path to your text file\ns3_path = \"s3://bucket/path/to/your/text/file.txt\"\n\n# Create a Ray dataset from the text file\nds = ray.data.read_text(s3_path)\n\n# You can now use the dataset (ds) to perform various operations\n# such as showing the first few rows, or performing transformations\n# on the text data.\n```\nIf you want to load the data from a private bucket, you have to run `import pyarrow.fs` before reading the data from S3.\n\nNote that `read_text` function creates a row for each line of text in the file.",
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/train/user-guides/data-loading-preprocessing.html#quickstart",
897
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",
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/loading-data.html#loading-data-from-distributed-dataframe-libraries",
900
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-datasets"
901
            ],
902
            "document_ids": [
903
                9180,
904
                6750,
905
                7818,
906
                6752,
907
                6972
908
            ],
909
            "answer": " To create a Ray Dataset from a pandas DataFrame, you can use the `ray.data.from_pandas()` function. Here's an example:\n\n```python\nimport pandas as pd\nimport ray\n\n# Create a pandas DataFrame\ndf = pd.DataFrame({\n    \"food\": [\"spam\", \"ham\", \"eggs\"],\n    \"price\": [9.34, 5.37, 0.94]\n})\n\n# Create a Ray Dataset from the pandas DataFrame\nds = ray.data.from_pandas(df)\n\n# Print the Ray Dataset\nprint(ds)\n```\n\nThis will create a Ray Dataset from the pandas DataFrame `df`. The `ray.data.from_pandas()` function can also take a list of pandas DataFrames to create a Ray Dataset from multiple DataFrames.",
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/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
917
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",
918
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",
919
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data"
920
            ],
921
            "document_ids": [
922
                7039,
923
                6994,
924
                5433,
925
                221,
926
                5352
927
            ],
928
            "answer": " Sure, here's an example of using `ray.data.map`:\n```python\n@ray.remote\ndef process_data(data):\n    # process the data here\n    processed_data = data + 1\n    return processed_data\n\nds = ray.data.read_csv(\"s3://anonymous@ray-example-data/iris.csv\")\n\n# apply the processing function to each batch of data\nprocessed_ds = ds.map(process_data)\n\n# print the processed data\nresult = [print(data) for data in ray.get(processed_ds.ref(()))]\n```\nIn this example, we define a remote function `process_data` that takes in a batch of data and processes it. We then use `ds.map(process_data)` to apply this function to each batch of data in the `ds` dataset. The resulting dataset `processed_ds` contains the processed data. We use `ray.get(processed_ds.ref(()))` to collect the processed data on the driver for printing. Note that in a real use case, you might not want to collect the data on the driver, but instead, continue with another task using the processed dataset.",
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/gentle_walkthrough.html#task-dependencies",
936
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",
937
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",
938
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart"
939
            ],
940
            "document_ids": [
941
                7796,
942
                5433,
943
                6782,
944
                6749,
945
                9180
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\nclass MyCallable:\n    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:\n        data[\"new_col\"] = \"new_value\"\n        return data\n\nif __name__ == \"__main__\":\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/tune/faq.html#how-do-i-set-resources",
954
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#summary",
955
                "https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",
956
                "https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",
957
                "https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#scalability-and-overhead-benchmarks-for-ray-tune"
958
            ],
959
            "document_ids": [
960
                1025,
961
                2268,
962
                2406,
963
                2325,
964
                2376
965
            ],
966
            "answer": " To set the memory limit for each trial in Ray Tuner, you can request custom resources when starting the cluster and control the allocation per trial using the `with_resources` function. However, Ray Tuner automatically manages the memory limit by making half of the GPU memory available to each trial, and you need to ensure your model fits in the allocated memory. The maximum number of concurrent trials can be set using the `max_concurrent_trials` parameter in `TuneConfig`.",
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/user-guides/cli-sdk.html#get-started",
976
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor"
977
            ],
978
            "document_ids": [
979
                4787,
980
                4931,
981
                436,
982
                475,
983
                690
984
            ],
985
            "answer": " To get the actor ID of the current actor in a Ray program, you can use the `get_actor_id()` function from the `ray.runtime_context.RuntimeContext` class. This function returns the current actor id in hex format as an optional string. Here is an example:\n```\ncurrent_actor_id = ray.runtime_context.RuntimeContext.get_actor_id()\n```\nAlternatively, you can use the `actor_id` property from the `ray.runtime_context.RuntimeContext` class, which serves the same purpose.\n\nNote that the `actor_id` property is deprecated and may be removed in future Ray releases, so it is recommended to use the `get_actor_id()` function instead.\n\nTo get the details of an actor, including its metadata, state, and tasks, you can visit the Actor Detail page for that actor. To do this, click on the ID of the actor in your Ray program. On the Actor Detail page, you can see the metadata, state, and tasks for the actor.\n\nYou can also use the `ray get actors` command followed by the actor ID to get the details of an actor from the command line.\n\nFinally, you can use the `get_actor()` function from the `ray.util.state` module to get an actor by its ID. This function returns the actor state as a dictionary, or `None` if the actor is not found. Here is an example:\n```\nactor_state = ray.util.state.get_actor(actor_id)\n```",
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#launching-a-ray-cluster-ray-up",
994
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",
995
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine"
996
            ],
997
            "document_ids": [
998
                4785,
999
                4811,
1000
                4194,
1001
                4190,
1002
                4189
1003
            ],
1004
            "answer": " No, `ray.init()` is used to initialize Ray, while `ray.is_initialized()` checks if Ray has been initialized or not. You can use `ray.is_initialized()` to determine if you need to call `ray.init()`.",
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/data-internals.html#streaming-execution",
1011
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
1012
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
1013
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.filter.html#ray-data-dataset-filter",
1014
                "https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air"
1015
            ],
1016
            "document_ids": [
1017
                6947,
1018
                8218,
1019
                6787,
1020
                7840,
1021
                10144
1022
            ],
1023
            "answer": " The `compute=actor` argument within `ray.data.map_batches` specifies that the user-defined function (UDF) should run as a long-running Ray actor, rather than a short-running Ray task. This allows for more efficient processing of large datasets by reusing the same actor instance for multiple batches. The `ActorPoolStrategy` can be used to configure the number of concurrent actor workers.",
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/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",
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
                1132,
1037
                2432,
1038
                8470,
1039
                2469,
1040
                2431
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. Import the necessary modules, including `wandb` and `WandbLoggerCallback`.\n3. Make sure you're logged in to WandB on all machines: `wandb login`\n4. Initialize the WandB logger with `WandbLoggerCallback()`.\n5. Pass the WandB logger to the AccelerateTrainer when creating an instance of it.\n\nHere's an example of how to use the WandB logger with AccelerateTrainer:\n\n```python\nimport torch\nimport pytorch_lightning as pl\nfrom ray import tune\nfrom ray.train.lightning import LightningTrainer\nfrom pytorch_lightning.loggers import WandbLoggerCallback\n\n# Define your PyTorch Lightning model\nclass MyModel(pl.LightningModule):\n    # Model definition\n\n# Initialize the WandB logger\nwandb_logger = WandbLoggerCallback(project=\"my-project\")\n\n# Initialize the AccelerateTrainer with the Wandb logger\ntrainer = LightningTrainer(\n    logger=wandb_logger,\n    # Other arguments\n)\n\n# Use the trainer to train your model\ntune.run(\n    model=MyModel,\n    train_loop=trainer.tune,\n    # Other arguments\n)\n```\n\nThis example assumes that you have already set up your WandB project and have the necessary credentials to log in. The `WandbLoggerCallback` will automatically log metrics reported to Tune to the WandB API.",
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/objects/serialization.html#plasma-store",
1049
                "https://docs.ray.io/en/master/ray-core/objects.html#objects",
1050
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",
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
                4941,
1056
                4634,
1057
                5013,
1058
                5014,
1059
                14748
1060
            ],
1061
            "answer": " In the context of Ray, when data is explicitly put into an object store using functions like ray.put or Ray.put in Java or Put in C++, it will be implicitly stored in the local object store of the node where the data is put. The object store does not automatically broadcast data to other nodes, and data remains local until requested. Objects in the object store are immutable and have reference counting for garbage collection. Once the capacity of the object store is used up, objects are spilled to external storage, which can result in a performance penalty due to disk I/O.",
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-more-libs/actors.html#cancelling-actor-tasks"
1072
            ],
1073
            "document_ids": [
1074
                4430,
1075
                4498,
1076
                4894,
1077
                4689,
1078
                14703
1079
            ],
1080
            "answer": " To kill or cancel a Ray task that you already started, you can call `ray.cancel()` on the ObjectRef returned by the task. By default, this will send a KeyboardInterrupt to the task's worker if it is mid-execution. If you pass `force=True` to `ray.cancel()`, it will force-exit the worker. Here's an example:\n```python\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n```\nIf you want to cancel all child and Actor Tasks, you can pass `recursive=True` to `ray.cancel()`. Note that cancellation is not guaranteed and you can check if a Task was successfully cancelled using `ray.get(actor_task_ref)`.",
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.Dataset.map_batches.html#ray-data-dataset-map-batches",
1088
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
1089
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",
1090
                "https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches"
1091
            ],
1092
            "document_ids": [
1093
                6994,
1094
                6997,
1095
                8218,
1096
                7979,
1097
                6895
1098
            ],
1099
            "answer": " To send extra arguments in the `dataset.map_batches` function, you can use the `fn_kwargs` parameter. This parameter allows you to pass keyword arguments to the user-defined function (UDF) that you specify as the first argument of `map_batches`. Here's an example:\n```python\nds.map_batches(my_udf, fn_kwargs={\"arg1\": value1, \"arg2\": value2})\n```\nIn this example, `my_udf` is the UDF that you want to apply to the dataset, and `arg1` and `arg2` are the extra arguments that you want to pass to `my_udf`. `value1` and `value2` are the values of `arg1` and `arg2`, respectively.\n\nYou can also use the `fn_args` parameter to pass extra positional arguments to the UDF. However, it is recommended to use `fn_kwargs` for clarity and to avoid potential issues with argument order.\n\nNote that the UDF can be a function or a class, and that you can use the `compute` parameter to specify the compute strategy for class-based UDFs. The `batch_size` parameter allows you to specify the number of images in each batch, and the `batch_format` parameter allows you to specify the format of the batches.",
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/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",
1106
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",
1107
                "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",
1108
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/experimental.html#gcs-fault-tolerance",
1109
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance"
1110
            ],
1111
            "document_ids": [
1112
                13871,
1113
                5754,
1114
                5729,
1115
                5916,
1116
                13870
1117
            ],
1118
            "answer": " Ray GCS in a KubeRay cluster stores the history of jobs run on the cluster in an external Redis server, if GCS fault tolerance is enabled. It uses a Redis database, with the data stored in a Redis database format.",
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/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler",
1126
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
1127
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
1128
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference"
1129
            ],
1130
            "document_ids": [
1131
                9858,
1132
                1895,
1133
                6150,
1134
                6908,
1135
                8367
1136
            ],
1137
            "answer": " The ValueError \"The actor ImplicitFunc is too large\" is likely caused by an actor function capturing a large object in scope. To resolve this issue, you can use `ray.put()` to put large objects in the Ray object store, and then have the actor function retrieve them using `ray.get()`.\n\nAdditionally, the warning about the actor being large (52 MiB) suggests that you should check the definition of the actor function to ensure that it is not implicitly capturing a large array or other object in scope.\n\nIn the context provided, it seems like you are using Ray's Data API to perform some image classification task. To ensure full parallelization across an actor pool, you should set the batch size to be at most the size of the actor pool. In your case, since you are using a actor pool of size 2, you should set the batch size to be at most 2.\n\nHere's an example of how you can modify your code to use `ray.put()` and `ray.get()`:\n```python\n# Put the large object in the Ray object store\nlarge_object = ...\nray.put(large_object)\n\n# Define the actor function to retrieve the large object using ray.get()\n@ray.remote\nclass MyActor:\n    def __init__(self):\n        self.large_object = ray.get(large_object)\n\n    def my_function(self, ...):\n        # Use the large object here\n        ...\n\n# Create the actor and call its function\nmy_actor = MyActor.remote()\nray.get(my_actor.my_function.remote(...))\n```\nAnd here's an example of how you can modify your code to set the batch size to be at most the size of the actor pool:\n```python\npredictions = ds.map_batches(\n    TorchPredictor,\n    num_gpus=1,\n    batch_size=2,  # Set the batch size to be at most the size of the actor pool\n    compute=ray.data.ActorPoolStrategy(size=2)\n)\n```",
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/data/batch_inference.html#quickstart",
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/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others"
1148
            ],
1149
            "document_ids": [
1150
                228,
1151
                6887,
1152
                14653,
1153
                9675,
1154
                14326
1155
            ],
1156
            "answer": " To distribute training for your custom Keras neural net in Ray on Databricks, you can follow these steps:\n\n1. Install the required packages: `!pip install -U \"ray[train]\" tensorflow`\n2. Set up your dataset and model using TensorFlow and Keras.\n3. Use the `TensorflowTrainer` class from `ray.train.tensorflow` to create a distributed training setup.\n\nHere's an example of how you can set this up:\n\n```python\nimport ray\nfrom ray.train.tensorflow import TensorflowTrainer\n\n# Set up your dataset and model\nimport numpy as np\nimport tensorflow as tf\n\n# Create a simple Keras model\nmodel = tf.keras.models.Sequential([\n    tf.keras.layers.Dense(10, activation='relu', input_shape=(10,)),\n    tf.keras.layers.Dense(1)\n])\n\n# Compile the model\nmodel.compile(optimizer='adam', loss='mse')\n\n# Create a Ray Dataset\nds = ray.data.from_numpy(np.random.rand(1000, 10))\n\n# Define the training function\ndef train_func(model, ds):\n    model.fit(ds.train_shuffles(1), epochs=1)\n\n# Set up the trainer\ntrainer = TensorflowTrainer(\n    train_func,\n    scaling_config=ray.train.ScalingConfig(num_workers=4, use_gpu=False)\n)\n\n# Run the training\nresult = trainer.fit()\n```\n\nThis example sets up a simple Keras model, creates a Ray Dataset, and then uses the `TensorflowTrainer` class to distribute the training across 4 workers. The `train_func` function defines the training loop, and the `ScalingConfig` object is used to configure the number of workers and whether to use GPUs.",
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/examples/gentle_walkthrough.html#summary",
1163
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
1164
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
1165
                "https://docs.ray.io/en/master/ray-core/api/core.html#objects",
1166
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory"
1167
            ],
1168
            "document_ids": [
1169
                5443,
1170
                4811,
1171
                6597,
1172
                4693,
1173
                5030
1174
            ],
1175
            "answer": " To use `ray.put` and `ray.get`, you can follow these steps:\n\n1. `ray.put(value)`: This function stores an object in Ray's object store, which can then be accessed from other nodes or processes in the Ray cluster.\n2. `ray.get(object_ref)`: This function retrieves the value of an object from the object store, given its object reference (returned by `ray.put` or a task/actor computation).\n\nHere's an example:\n\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Create an object and put it in the object store\nmy_obj = ray.put(\"Hello, Ray!\")\n\n# Get the object from the object store\nprint(ray.get(my_obj))  # Output: b'Hello, Ray!'\n```\n\nIn the context provided, `ray.put` is used to store a value in the object store, and `f.remote(a)` creates a task that depends on the object stored in `a`. The `ray.wait` function is used to wait for tasks to complete and return their object references.\n\nTo use the Ray Client, you first need to initialize Ray on a cluster, then create a client on the worker nodes to communicate with the Ray cluster. More information on using the Ray Client can be found in the Ray documentation.",
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/train/user-guides/data-loading-preprocessing.html#preprocessing-data",
1183
                "https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",
1184
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#code-example",
1185
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart"
1186
            ],
1187
            "document_ids": [
1188
                9178,
1189
                9189,
1190
                6866,
1191
                5233,
1192
                9180
1193
            ],
1194
            "answer": " To preprocess many files using Ray Data, you can follow these general steps:\n\n1. Initialize Ray and create a Ray Dataset from your files. Ray Data supports creating datasets from various sources and file formats.\n2. Preprocess your Ray Dataset as needed. Ray Data provides a range of preprocessing operations that can be used to transform your data prior to training.\n3. Use the preprocessed dataset in your distributed ML training pipeline.\n\nHere is a simple code example to get you started:\n\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Assume this Ray node has 16 CPUs and 16G memory.\n\n# Step 1: Create a Ray Dataset from files\ntrain_dataset = ray.data.read_csv(\"my_files/*.csv\")\n\n# Step 2: Preprocess your Ray Dataset\ndef preprocess_file(file):\n    # Read the file and process the data as needed\n    pass\n\n# Apply the preprocessing function to each file\ntrain_dataset = ray.data.map(preprocess_file, \"my_files/*.csv\")\n\n# Or, you can perform batch-level preprocessing\ndef preprocess_batch(batch):\n    # Perform preprocessing on each batch of data\n    pass\n\ntrain_dataset = train_dataset.map_batches(preprocess_batch)\n\n# Step 3: Use the preprocessed dataset in your distributed ML training pipeline\ndef train_func(config):\n    # Use the preprocessed dataset for training\n    for batch in train_dataset.iter_batches():\n        # Train your model on each batch of data\n        pass\n```\n\nThis example assumes that you have a set of CSV files in the \"my\\_files\" directory that you want to preprocess and use for distributed ML training. You can replace the `preprocess_file` and `preprocess_batch` functions with your own preprocessing logic as needed.",
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#overview",
1201
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",
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#troubleshooting",
1204
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting"
1205
            ],
1206
            "document_ids": [
1207
                4939,
1208
                4945,
1209
                4947,
1210
                4957,
1211
                4953
1212
            ],
1213
            "answer": " Based on the context provided, Ray, a parallelization framework, uses a customized Pickle protocol version 5 for serialization. However, it seems like SSLContext objects cannot be pickled, as indicated by the error message in the inspection result: \"cannot pickle '_thread.lock' object\". To overcome this limitation, you might need to define a custom serialization process for SSLContext objects, if you have access to the code, by defining a `__reduce__` function inside the corresponding class.",
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/k8s-ecosystem/ingress.html#instructions",
1221
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",
1222
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",
1223
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#getting-started-with-kuberay"
1224
            ],
1225
            "document_ids": [
1226
                5720,
1227
                6103,
1228
                5647,
1229
                5817,
1230
                5646
1231
            ],
1232
            "answer": " To install CRDs in Kuberay, follow the instructions in Step 1 of the 'Instructions' section. Run the following command:\n\n```bash\nhelm repo add kuberay https://ray-project.github.io/kuberay-helm/\nhelm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n```\n\nThis command will install both CRDs and the Kuberay operator.",
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/serve/tutorials/batch.html#batching-tutorial",
1240
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
1241
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",
1242
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors"
1243
            ],
1244
            "document_ids": [
1245
                140,
1246
                14578,
1247
                8038,
1248
                8161,
1249
                6788
1250
            ],
1251
            "answer": " The function for Ray data batch inference is named `__call__()` because it allows the instance of the class to be invoked like a function, taking a batch of data as an argument and returning the batch with predictions. This is a common convention in Python for creating custom callable objects.",
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#client-disconnections",
1259
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",
1260
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",
1261
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-5-change-the-ray-image-from-rayproject-ray-ray-version-to-rayproject-ray-ml-ray-version"
1262
            ],
1263
            "document_ids": [
1264
                6597,
1265
                6613,
1266
                6611,
1267
                6605,
1268
                5985
1269
            ],
1270
            "answer": " To disconnect a Ray client, you can use the `disconnect()` method on the Ray client object. Here's an example:\n```python\nimport ray\n\n# Connect to the Ray cluster\nray.init(\"ray://<head_node_host>:10001\")\n\n# ... Ray code here ...\n\n# Disconnect from the Ray cluster\nray.shutdown()\n```\nNote that calling `ray.init()` without the `allow_multiple` argument will create a default global Ray client, which you can disconnect from using `ray.shutdown()`.\n\nIf you're using multiple Ray clients, you'll need to call `disconnect()` or `shutdown()` on each client object explicitly to close the connection.\n\nIn the context provided, the Ray clients are disconnected using `cli1.disconnect()` and `cli2.disconnect()`.",
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/sdk.html#submitting-a-ray-job",
1278
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
1279
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",
1280
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm-launch.html#slurm-launch-py"
1281
            ],
1282
            "document_ids": [
1283
                6460,
1284
                6516,
1285
                6467,
1286
                6518,
1287
                6575
1288
            ],
1289
            "answer": " To submit a job with Python using local files, follow these steps:\n\n1. Write your Ray task function, for example:\n```python\n# script.py\nimport ray\n\n@ray.remote\ndef hello_world():\n    return \"hello world\"\n```\n2. Initialize Ray:\n```python\nray.init()\n```\n3. Submit the task and print its return value:\n```python\nprint(ray.get(hello_world.remote()))\n```\n4. Export any desired headers (optional):\n```bash\nexport RAY_JOB_HEADERS='{\"KEY\": \"VALUE\"}'\n```\n5. Submit the job using the following command, making sure to replace `your_working_directory` with the actual path:\n```bash\nray job submit --working-dir your_working_directory -- python script.py\n```\n\nFor a local cluster, the `--working-dir` argument is not strictly necessary, but it is required for remote clusters.\n\nThe provided context also includes information on submitting jobs to remote clusters and handling headers, but the query was about submitting jobs with local files.",
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/data/batch_inference.html#using-models-from-ray-train",
1296
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#putting-it-together",
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/bayesopt_example.html#running-tune-experiments-with-bayesopt",
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
                6927,
1303
                3223,
1304
                986,
1305
                3093,
1306
                2774
1307
            ],
1308
            "answer": " To do inference from a model trained by Ray Tune, you can use the Checkpoint returned by Ray Train and use it for batch inference with Ray Data. Here is an example of how you can use the Checkpoint for inference:\n```makefile\ncheckpoint = ray.train.checkpoint.Checkpoint.from_uri(checkpoint_uri)\nmodel = checkpoint.to_local_checkpoint().get_model()\n\ndef predict(input_data):\n    return model.predict(input_data)\n\nray_dataset = ray.data.read_csv(\"path/to/data.csv\")\nresults = ray_dataset.map_batches(predict, batch_size=128)\n```\nIn this example, `checkpoint_uri` is the URI of the trained model's checkpoint. The `to_local_checkpoint()` method is used to download the checkpoint to the local node, and `get_model()` is used to get the actual model object. The `predict` function is then used to define the inference logic, which takes a batch of input data and returns the corresponding predictions. Finally, the `map_batches` method is used to apply the `predict` function to each batch of data in the Ray Dataset.",
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/working-with-pytorch.html#batch-inference-with-pytorch",
1316
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
1317
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-2-inference-on-a-single-batch",
1318
                "https://docs.ray.io/en/master/data/user-guide.html#user-guides"
1319
            ],
1320
            "document_ids": [
1321
                6959,
1322
                6963,
1323
                6899,
1324
                8021,
1325
                6836
1326
            ],
1327
            "answer": " Yes, it is possible to load and run inference without using PyTorch or TensorFlow directly by using a pre-trained model from libraries like Hugging Face and performing inference on a Ray Dataset. The example you provided demonstrates how to do this using PyTorch, where a pre-trained model is mapped over a Ray Dataset for batch inference. The model and data can also be moved to and from a GPU to perform inference using GPUs.",
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/data/data.html#learn-more",
1334
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
1335
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
1336
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
1337
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples"
1338
            ],
1339
            "document_ids": [
1340
                6995,
1341
                4811,
1342
                5556,
1343
                6597,
1344
                5380
1345
            ],
1346
            "answer": " Ray is a universal array and task parallelism library for Python. It allows you to parallelize and distribute computations across multiple CPUs and GPUs in your cluster. You can find out more about Ray by reading the Ray blogs, checking out the documentation on ray.init(), Ray Jobs, Ray Client, and exploring the tutorials and examples available.",
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/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",
1353
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",
1354
                "https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus",
1355
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",
1356
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources"
1357
            ],
1358
            "document_ids": [
1359
                13457,
1360
                4999,
1361
                2738,
1362
                5116,
1363
                1025
1364
            ],
1365
            "answer": " If you specify a fractional GPU in the resource spec, such as 0.5 GPUs, and your program uses more than that, it may result in errors or unexpected behavior due to insufficient GPU resources. Ray supports fractional GPUs to allow multiple tasks and actors to share the same GPU, but each Ray worker will be scheduled on one machine, and you need to ensure that your model fits on the fractional memory.",
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#code-example",
1372
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
1373
                "https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",
1374
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",
1375
                "https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments"
1376
            ],
1377
            "document_ids": [
1378
                5194,
1379
                5192,
1380
                4241,
1381
                637,
1382
                4664
1383
            ],
1384
            "answer": " To pickle a variable defined in an actor's `__init__` method, you can pass the variable as an argument when creating the actor, and then pass it to the variable in the `__init__` method. Here's an example using the provided context:\n\n```python\n@ray.remote\nclass GlobalVarActor:\n    def __init__(self, var):\n        self.var = var\n\n    def set_global_var(self, var):\n        self.var = var\n\n    def get_global_var(self):\n",
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/data/transforming-data.html#transforming-batches-with-actors",
1391
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
1392
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",
1393
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",
1394
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference"
1395
            ],
1396
            "document_ids": [
1397
                6787,
1398
                673,
1399
                788,
1400
                5441,
1401
                8369
1402
            ],
1403
            "answer": " To perform an all-reduce operation among a list of actors in Ray, you can use the `ray.util.state.list_actors()` function to get the actor handles, and then use the `ray.put()` and `ray.get()` functions to share data among the actors. However, Ray does not have a built-in all-reduce operation like some other distributed computing frameworks. You would need to implement the all-reduce logic yourself, such as by having each actor perform a reduction operation on its local data and then averaging the results across all the actors.\n\nHere's some example code to get you started:\n```python\nimport ray\nfrom ray.util.state import list_actors\n\n# Start Ray with 4 worker processes\nray.init(num_workers=4)\n\n# Define a function to perform the all-reduce operation\ndef all_reduce(value):\n    # Get the actor handles\n    actors = list_actctors()\n    # Put the value on the object store\n    obj_id = ray.put(value)\n    # Initialize a list to hold the partial reductions\n    partial_reductions = []\n    # Compute the partial reduction on each actor\n    for actor in actors:\n        partial_reductions.append(ray.get(actor.remote.partial_reduce.remote(obj_id)))\n    # Average the partial reductions\n    reduced_value = sum(partial_reductions) / len(partial_reductions)\n    # Return the reduced value\n    return reduced_value\n\n# Define a function for the actors to perform a partial reduction\n@ray.remote\ndef partial_reduce(obj_id):\n    value = ray.get(obj_id)\n    # Perform a partial reduction on the value\n    partial_reduction = value * 2\n    # Return the partial reduction\n    return partial_reduction\n\n# Create some actors\nactors = [ray.remote(TorchPredictor).remote() for _ in range(4)]\n\n# Perform the all-reduce operation\nreduced_value = all_reduce(10)\n\n# Print the reduced value\nprint(reduced_value)\n\n# Clean up the actors\nray.kill(actors)\n```\nIn this example, we define a `all_reduce()` function that gets the actor handles, puts the value on the object store, computes the partial reductions on each actor, averages the partial reductions, and returns the reduced value. We also define a `partial_reduce()` function for the actors to perform a partial reduction on the value. Finally, we create some actors, perform the all-reduce operation, print the reduced value, and clean up the actors. Note that this is just one way to implement an all-reduce operation in Ray, and there may be more efficient or scalable ways to do it depending on your use case.",
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/api/doc/ray.util.placement_group.html#ray-util-placement-group",
1412
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",
1413
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#bundles"
1414
            ],
1415
            "document_ids": [
1416
                5063,
1417
                4790,
1418
                4789,
1419
                5051,
1420
                5050
1421
            ],
1422
            "answer": " Specifying a bundle with `{\"CPU\":0}` in the PlacementGroup will result in a bundle with no resources reserved for CPU. However, this specific bundle won't cause any issues with the placement group creation. The problem lies in creating bundles that require resources that are not available in the cluster, such as a bundle with `{\"CPU\": 1}` when only `{\"CPU\": 1, \"GPU\": 1}` is left in the cluster. This would prevent the placement group from being created due to insufficient resources.",
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/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",
1431
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
1432
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel"
1433
            ],
1434
            "document_ids": [
1435
                4430,
1436
                6478,
1437
                9791,
1438
                4498,
1439
                4894
1440
            ],
1441
            "answer": " To cancel a job from the UI, you can use the `ray job stop` command followed by the job ID. For example, `ray job stop raysubmit_tUAuCKubPAEXh6CW`. If you want to cancel a specific task, you can call `ray.cancel()` on the returned Object ref. For example, `obj_ref = blocking\\_operation.remote(); ray.cancel(obj\\_ref)`. You can also use `ray.workflow.cancel()` to cancel a workflow.",
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/deploy-many-models/multi-app.html#development-workflow-with-serve-run",
1451
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"
1452
            ],
1453
            "document_ids": [
1454
                13738,
1455
                13732,
1456
                6484,
1457
                13696,
1458
                6465
1459
            ],
1460
            "answer": " To get your project files on the cluster when using Ray Serve, you can use the Ray Cluster Launcher to start a remote, multi-node cluster and then use the `serve deploy` command with the `--address` argument to specify your remote Ray cluster\u2019s dashboard agent address. The address should be of the form `[RAY_CLUSTER_URI]:[DASHBOARD_AGENT_PORT]`.\n\nAlternatively, if you have already started a remote Ray cluster, you can set the `RAY_ADDRESS` environment variable to the address of the Ray Dashboard for your cluster before using the `serve deploy` command.\n\nYou can also use the `serve run` command to run and test your application easily, either locally or on a remote cluster.\n\nFor more information, you can refer to the documentation on deploying Ray Serve applications to a remote cluster.",
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-overview/installation.html#from-wheels",
1470
                "https://docs.ray.io/en/master/installation.html#from-wheels"
1471
            ],
1472
            "document_ids": [
1473
                14549,
1474
                63,
1475
                288,
1476
                286,
1477
                61
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 using the command `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 appropriate link for your system and Python version from the Ray documentation.",
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-overview/installation.html#from-wheels",
1489
                "https://docs.ray.io/en/master/installation.html#from-wheels"
1490
            ],
1491
            "document_ids": [
1492
                14549,
1493
                63,
1494
                288,
1495
                286,
1496
                61
1497
            ],
1498
            "answer": " To install the latest Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use the `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"` command, replacing `LINK_TO_WHEEL.whl` with the actual link to the nightly wheel you want to install. The nightly wheels can be found in the \"Daily Releases (Nightlies)\" section of the Ray documentation.",
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/workflows/basics.html#ray-integration",
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/overview.html#ray-tutorials-and-examples"
1509
            ],
1510
            "document_ids": [
1511
                5397,
1512
                5395,
1513
                9668,
1514
                4403,
1515
                5380
1516
            ],
1517
            "answer": " To write unit tests for Ray code, you can follow these tips:\n\n1. Fix the resource quantity with `ray.init(num_cpus=...)` to ensure consistent resource allocation.\n2. Share the Ray cluster across tests if possible to reduce overhead. You can do this by calling `ray.init()` before the test suite and `ray.shutdown()` after.\n3. Consider creating a mini-cluster with `ray.cluster_utils.Cluster` for more control over the testing environment.\n4. Be cautious when running tests in parallel to avoid resource conflicts.\n5. When testing Ray programs with Java, set the `ray.job.code-search-path` property to the directory of your Python code.\n\nHere's an example of how to set up a test case in Python:\n\n```python\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n    def setUp(self):\n        ray.init(num_cpus=4, num_gpus=0)\n\n    def tearDown(self):\n        ray.shutdown()\n```\n\nRemember to replace the `num_cpus` and `num_gpus` values with the appropriate resource quantities for your tests.",
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/cli.html#ray-up",
1525
                "https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",
1526
                "https://docs.ray.io/en/master/cluster/cli.html#ray-start",
1527
                "https://docs.ray.io/en/master/cluster/cli.html#ray-stop"
1528
            ],
1529
            "document_ids": [
1530
                410,
1531
                5616,
1532
                13929,
1533
                5611,
1534
                5613
1535
            ],
1536
            "answer": " To stop Ray from spamming lots of Info updates on stdout, you can try changing the log style to 'record' using the `--log-style record` command-line option. This will output logs in a record-style without formatting, which might help reduce the amount of output.\n\nAdditionally, you can consider redirecting the output to a file using the `--redirect-command-output` option, which will direct command output to a file.\n\nFinally, if you want to completely disable the logs, you can use the `--disable-usage-stats` option, which will disable the usage stats collection and may help reduce the amount of output.",
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/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-7-submit-a-request-to-the-ray-serve-application",
1546
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment"
1547
            ],
1548
            "document_ids": [
1549
                6127,
1550
                14398,
1551
                6124,
1552
                5989,
1553
                14255
1554
            ],
1555
            "answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install RayService:\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml\n\n2. Install the required libraries:\npip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers\n\n3. Save the Stable Diffusion code to a file named `stable_diffusion.py`.\n\n4. Deploy the Stable Diffusion model with Ray Serve.\n\n5. Forward the serve port of the head Pod:\nkubectl port-forward --address 0.0.0.0 $HEAD_POD 8000\n\n6. Clone the repository on your local machine:\ngit clone https://github.com/ray-project/serve_config_examples.git\n\n7. Prepare a sample image file for the request.\n\nNote: The Python files for the Ray Serve application and its client are in the ray-project/serve_config_examples repo and the Ray documentation.",
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-core/api/doc/ray.runtime_context.RuntimeContext.current_actor.html#ray-runtime-context-runtimecontext-current-actor",
1565
                "https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles"
1566
            ],
1567
            "document_ids": [
1568
                14698,
1569
                4351,
1570
                11795,
1571
                4874,
1572
                4353
1573
            ],
1574
            "answer": " An actor handle is a reference to an actor object, which can be passed into other tasks and used to call the actor's remote functions or methods. It allows for communication and interaction with the actor from other parts of the program. Examples of actor handles are provided in the context for Python, Java, and C++. In Python, an actor handle can be obtained by calling `Counter.remote()`, and then passed to other tasks.",
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.kill.html#ray-kill",
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/api/doc/ray.remote.html#ray-remote",
1584
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor"
1585
            ],
1586
            "document_ids": [
1587
                4859,
1588
                4658,
1589
                4601,
1590
                4848,
1591
                4504
1592
            ],
1593
            "answer": " To kill a detached actor in Ray, you can use the `ray.kill()` function, passing the actor handle as an argument. For example:\n```python\nimport ray\n\nactor_handle = ray.remote(MyActorClass).remote()\n\n# ... do some work with the actor ...\n\nray.kill(actor_handle)\n```\nThis will interrupt any running tasks on the actor and cause them to fail immediately. If you want to let pending tasks finish before terminating the actor, you can call `actor.__ray_terminate__.remote()` instead.\n\nNote that after calling `ray.kill()` on a detached actor, subsequent calls to get its handle via `ray.get_actor` will fail. Also, detached actors must be manually destroyed once you are sure that they are no longer needed, as they are not automatically garbage-collected by Ray.",
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#api-reference",
1601
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
1602
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",
1603
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages"
1604
            ],
1605
            "document_ids": [
1606
                4624,
1607
                4292,
1608
                4380,
1609
                4260,
1610
                4267
1611
            ],
1612
            "answer": " To force upgrade the pip package in the runtime environment if an old version exists, you can specify the pip version in the runtime environment with \"pip\\_version\" parameter. For example:\n\nRuntimeEnv(\npip={\"packages\":[\"tensorflow\", \"requests\"], \"pip\\_check\": False,\n\"pip\\_version\": \"==22.0.2;python\\_version=='3.8.11'\"}\n)\n\nThis will ensure that pip version 22.0.2 is installed for Python version 3.8.11. If an old version of the package exists, it will be upgraded to the specified version. Note that you cannot specify both conda and pip keys in the runtime\\_env at the same time.",
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/data/data-internals.html#shuffling-data",
1620
                "https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",
1621
                "https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",
1622
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling"
1623
            ],
1624
            "document_ids": [
1625
                6826,
1626
                6928,
1627
                6827,
1628
                6825,
1629
                9206
1630
            ],
1631
            "answer": " To perform a global shuffle with Ray, you can use the `random_shuffle()` function provided by Ray Data. Here is an example of how to do it:\n\n```\nimport ray\n\nctx = ray.data.DataContext.get_current()\nctx.use_push_based_shuffle = True\n\nds = (\n    ray.data.range(1000)\n    .random_shuffle()\n)\n```\n\nThis will randomly shuffle the data in the `ds` dataset. Note that shuffling data in Ray materializes the dataset in memory, so it is not streamed through memory.\n\nIf you want to use the experimental push-based shuffle implementation, you can set the `RAY_DATA_PUSH_BASED_SHUFFLE` environment variable to `1` when running your application. For example:\n\n```\n$ RAY_DATA_PUSH_BASED_SHUFFLE=1 python sort.py --num-partitions=10 --partition-size=1e7\n```\n\nThis will enable the push-based shuffle implementation for the `sort.py` script.\n\nFor more information on shuffle performance and tuning, you can refer to the Ray Data documentation on performance tips and tuning.",
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/namespaces.html#specifying-namespace-for-named-actors",
1638
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
1639
                "https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",
1640
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
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
                4719,
1645
                4871,
1646
                4709,
1647
                4880,
1648
                4751
1649
            ],
1650
            "answer": " To find the namespace of an Actor in Ray, you can use the `ray.get_actor()` function, which allows you to specify the namespace of the actor. If you don't know the namespace, you can call this function with `None` as the namespace argument, and it will return the actor handle if it exists in the current namespace. If the actor exists in a different namespace, you will need to specify that namespace when calling `ray.get_actor()`.\n\nHere's an example of how to use `ray.get_actor()` to find an actor in the current namespace:\n```python\nimport ray\n\n# Get a handle to the actor\nactor_handle = ray.get_actor(\"my_actor\")\n\n# Print the namespace of the actor\nprint(actor_handle.namespace)\n```\nNote that if the actor does not exist in the current namespace, this will raise a `ValueError`. To find the actor in a different namespace, you can specify the namespace when calling `ray.get_actor()`:\n```python\nimport ray\n\n# Get a handle to the actor in a different namespace\nactor_handle = ray.get_actor(\"my_actor\", namespace=\"other_namespace\")\n\n# Print the namespace of the actor\nprint(actor_handle.namespace)\n```\nIf you're not sure what namespace the actor is in, you can use the `ray namespace list` command to list all namespaces in the cluster:\n```css\n$ ray namespace list\n",
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/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
1660
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors"
1661
            ],
1662
            "document_ids": [
1663
                4208,
1664
                4550,
1665
                4199,
1666
                6597,
1667
                4553
1668
            ],
1669
            "answer": " Ray's interaction with async.io is achieved by making every remote operation, such as tasks and actor methods, asynchronous. This means that the operation immediately returns a promise/future, which is essentially an identifier of the operation's result. To get the actual results, you need to call `ray.get()` on the IDs of the results, but this call blocks until the results are available, which can hurt parallelism.\n\nHowever, Ray integrates natively with asyncio, and you can use it alongside popular async frameworks like aiohttp, aioredis, etc. For example, you can define an async method in an actor class, and then invoke it using `ray.get()` in an async context, like `asyncio.run(async_get())`. This allows you to run multiple async actor methods concurrently in the event loop without blocking the driver program.",
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-more-libs/ray-collective.html#point-to-point-communication",
1676
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",
1677
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-tasks",
1678
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",
1679
                "https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance"
1680
            ],
1681
            "document_ids": [
1682
                14432,
1683
                5235,
1684
                5423,
1685
                5236,
1686
                5219
1687
            ],
1688
            "answer": " To debug a hanging `ray.get()` call for a hanging `ray.get([A.do_send.remote(target_rank=1)])`, it's likely that the issue is due to the fact that the recv side call was not instantiated. This creates a blocking call that will hang.\n\nHere are some steps to resolve this issue:\n\n1. Avoid calling `ray.get()` in a loop, as it's a blocking call that can harm parallelism.\n2. Instead, separate the call to `ray.get()` from the call to the remote functions. This way, all remote functions can be spawned before waiting for the results and can run in parallel in the background.\n3. Pass a list of object references to `ray.get()` instead of calling it one by one to wait for all of the tasks to finish.\n4. Only call `ray.get()` at the end to get the final result. Unnecessary calls to `ray.get()` for intermediate steps can harm performance.\n5. Make sure that the recv side call has been instantiated to avoid hanging `ray.get()` calls.\n\nBy following these steps, you can ensure that your `ray.get()` calls are not hanging and are running in parallel as intended.",
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-via-an-actor-handle",
1696
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
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
                4615,
1702
                4601,
1703
                4618,
1704
                4622,
1705
                4606
1706
            ],
1707
            "answer": " Sure, here are examples of using `ray.actor.exit_actor()` in Python and C++:\n\nPython:\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```\nC++:\n```cpp\n#include \"ray/ray.h\"\n\nRAY_EXPORT void exit_actor() {\n  ray::Actor::Exit();\n}\n\nint main() {\n  ray::Init();\n  auto actor_handle = ray::Actor::Create<exit_actor>();\n  ray::Actor::Kill(actor_handle);\n  ray::Shutdown();\n  return 0;\n}\n```\nNote that calling `ray.actor.exit_actor()` or `ray::Actor::Exit()` will immediately terminate the actor process and release associated resources. This should be used with caution and only when necessary, as actors are usually garbage collected automatically.",
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/reference/cli.html#ray-logs",
1716
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#add-metadata-to-structured-logs",
1717
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view"
1718
            ],
1719
            "document_ids": [
1720
                414,
1721
                540,
1722
                682,
1723
                537,
1724
                446
1725
            ],
1726
            "answer": " To add logging inside an actor, you can create a logger using the logging module and use it to log messages. Here's an example:\n```python\nimport logging\n\nclass MyActor(Actor):\n    def log(self, msg):\n        logger = logging.getLogger(__name__)\n        logger.info(msg)\n```\nBy default, all stdout and stderr of Tasks and Actors are redirected to the Driver output. If you want to disable this feature, you can view the Configuring Logging documentation for more information.\n\nNote that if you are using concurrent actors (i.e. async actors or threaded actors), the log of the tasks may be interleaved. In this case, you can use `ray logs actor --id` to get the entire actor log.\n\nAdditionally, if you need to add metadata to structured logs, you can fetch the metadata of Jobs, Tasks, or Actors using Ray's `ray.runtime_context.get_runtime_context` API. For example, to get the job ID, you can use:\n```python\nimport ray\n\njob_id = ray.get_runtime_context().get_job_id()\n```\nFinally, note that logs of asynchronous Actor Tasks or threaded Actor Tasks (concurrency>1) are only available as part of the Actor logs. You can view the Actor logs in the Dashboard.",
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-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt",
1733
                "https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",
1734
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",
1735
                "https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",
1736
                "https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text"
1737
            ],
1738
            "document_ids": [
1739
                10923,
1740
                7002,
1741
                5292,
1742
                6853,
1743
                7001
1744
            ],
1745
            "answer": " Sure, here's a script that performs batch inference with GPT-2 on text data from an S3 bucket using Ray Train and Hugging Face's \ud83e\udd17 Transformers pipeline:\n```python\nimport os\nimport ray\nfrom transformers import pipeline, AutoTokenizer, GPT2ForSequenceClassification\n",
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-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",
1754
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
1755
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging"
1756
            ],
1757
            "document_ids": [
1758
                510,
1759
                944,
1760
                573,
1761
                680,
1762
                4372
1763
            ],
1764
            "answer": " To enable Ray debug logs, you can set the environment variable `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1` on each node before starting Ray. This will print the full runtime\\_env setup log messages to the driver. For example, you can add this variable in the setup\\_commands in the Ray Cluster configuration file. Note that this feature requires a full installation of Ray using `pip install \"ray[default]\"`.",
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#get-started",
1771
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",
1772
                "https://docs.ray.io/en/master/ray-core/actors/actor-utils.html#actor-pool",
1773
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#ray-states",
1774
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"
1775
            ],
1776
            "document_ids": [
1777
                475,
1778
                488,
1779
                4543,
1780
                402,
1781
                477
1782
            ],
1783
            "answer": " To list the current Ray actors from Python, you can use the following command:\n```python\nfrom ray.util.state import list_actors\nlist_actors(filters=[(\"state\", \"=\", \"ALIVE\")])\n```\nThis will return a list of 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/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
1791
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-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.kill.html#ray-kill"
1794
            ],
1795
            "document_ids": [
1796
                4848,
1797
                13912,
1798
                4502,
1799
                4615,
1800
                4859
1801
            ],
1802
            "answer": " To kill a replica actor from Python, you can use the `ray.kill()` function and pass the actor handle to it. Here is an example:\n\n```python\nimport ray\n\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\n\nAlternatively, if you want to kill the actor immediately, you can delete all actor handles referring to it, which will cause them to complete any outstanding work and then shut down. If you only have one reference to an actor handle, calling `del actor` could trigger actor deletion. Note that your program may have multiple references to the same ActorHandle, and actor termination will not occur until the reference count goes to 0.\n\nYou can also manually terminate an actor from within one of the actor methods by calling `ray.actor.exit_actor()`. This will kill the actor process and release resources associated/assigned to the actor. However, this approach should generally not be necessary as actors are automatically garbage collected. The ObjectRef resulting from the task can be waited on to wait for the actor to exit.",
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/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",
1809
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",
1810
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",
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/tasks/using-ray-with-gpus.html#fractional-gpus"
1813
            ],
1814
            "document_ids": [
1815
                4994,
1816
                6548,
1817
                4582,
1818
                4997,
1819
                5000
1820
            ],
1821
            "answer": " To specify that a task should run on a V100 GPU type, you can use the `num_gpus` argument in the `@ray.remote` decorator when defining the remote function. However, this only indicates the number of GPUs required for the task, it does not specify the GPU type.\n\nRay uses the CUDA_VISIBLE_DEVICES environment variable to assign GPUs to tasks or actors. The actual assignment of GPUs to tasks is done by Ray and it's not possible to specify a particular GPU type (e.g. V100) in the task declaration.\n\nIt's up to the user to ensure that the task is compatible with the assigned GPU type and that the task does not exceed the GPU memory limits.\n\nHere is an example of how to define a remote function that requires 1 GPU:\n```\n@ray.remote(num_gpus=1)\ndef my_task():\n    # task code here\n    pass\n```\nIt's also important to note that the version of TensorFlow or any other library that uses GPU should be compatible with the GPU type.",
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/cluster/vms/index.html#learn-more",
1829
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",
1830
                "https://docs.ray.io/en/master/serve/index.html#learn-more",
1831
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started"
1832
            ],
1833
            "document_ids": [
1834
                6335,
1835
                6210,
1836
                4702,
1837
                13629,
1838
                6189
1839
            ],
1840
            "answer": " To get started with Ray, follow these steps:\n1. Install Ray using `pip install -U ray`.\n2. Import and initialize Ray: `import ray; ray.init()`.\n3. For Ray Serve, check out the quick start tutorials and examples in the documentation.\n4. If you want to launch a Ray cluster manually, refer to the on-premise cluster setup guide.\n\nFind more information in the Ray docs: [The Ray docs](https://docs.ray.io/en/latest/).",
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#runtime-environments",
1848
                "https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",
1849
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
1850
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"
1851
            ],
1852
            "document_ids": [
1853
                4624,
1854
                4253,
1855
                4466,
1856
                4610,
1857
                13856
1858
            ],
1859
            "answer": " To specify the Python version in a RuntimeEnv, you can include it in the `pip_version` field under the `pip` key, using the syntax `\"pip_version\": \"==22.0.2;python_version=='3.8.11'\"`. This example specifies that pip version 22.0.2 should be used when the Python version is 3.8.11. Here is an example:\n```\nRuntimeEnv(\n    pip={\"packages\":[\"tensorflow\", \"requests\"], \"pip_check\": False,\n         \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"}\n)\n```\nAlternatively, you can use the `conda` key to specify a conda environment that includes a specific Python version. For example:\n```\nRuntimeEnv(\n    conda={\"name\": \"my_conda_env\", \"python_version\": \"3.8.11\"}\n)\n```\nThis creates a new conda environment called `my_conda_env` with Python version 3.8.11.\n\nYou can also use the `env_vars` key to set the `PYTHON_VERSION` environment variable to the desired version. However, this does not actually change the Python version that is used, it only sets the environment variable.\n\nFor more information, see the documentation for RuntimeEnv at <https://docs.ray.io/en/latest/ray-core/handling-dependencies.html#runtime-environments>.",
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#using-namespaces",
1867
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1868
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
1869
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"
1870
            ],
1871
            "document_ids": [
1872
                4719,
1873
                4709,
1874
                4640,
1875
                4871,
1876
                4637
1877
            ],
1878
            "answer": " To create an actor in a specific namespace, you can use the `options` method to specify the `name` and `namespace` when creating the actor. Here's an example in Python:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n    pass\n\nctx = ray.init(\"ray://localhost:10001\")\n\n# Create an actor with a specified namespace.\nActor.options(name=\"my_actor\", namespace=\"actor_namespace\", lifetime=\"detached\").remote()\n```\nNote that the namespace must be set when you first connect to the cluster, and named actors are only accessible within their namespaces. Also, keep in mind that if no namespace is assigned, the actor will be placed in an anonymous namespace by default.",
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/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
1886
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",
1887
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
1888
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable"
1889
            ],
1890
            "document_ids": [
1891
                1775,
1892
                1790,
1893
                4328,
1894
                4282,
1895
                2371
1896
            ],
1897
            "answer": " No, you cannot specify multiple working directories in a single Ray program. However, you can use either local paths or remote URIs to specify a working directory in the runtime environment. If you need to access files relative to the original working directory, you can disable the behavior of changing the working directory by setting the `RAY_CHDIR_TO_TRIAL_DIR` environment variable to 0. In this case, you should use relative paths for read-only purposes and ensure that the directory is synced on all nodes if running on multiple machines.",
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/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
1904
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",
1905
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
1906
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#configuring-parallelism-with-omp-num-threads",
1907
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote"
1908
            ],
1909
            "document_ids": [
1910
                5113,
1911
                5936,
1912
                6511,
1913
                13461,
1914
                4850
1915
            ],
1916
            "answer": " If you set `num_cpus=0` for tasks, it means that you are not reserving any CPU resources for those tasks. However, it's important to note that if you explicitly specify a task's or actor's logical resource requirements, they are required for both scheduling and running. Therefore, setting `num_cpus=0` may prevent the task from being scheduled or running at all.\n\nAdditionally, if you are using Python and Ray's remote function decorator, you can specify the default resource requirements for the function using the `num_cpus` argument. If you do not specify `num_cpus`, Ray will set it to 1 CPU resource by default.\n\nFinally, if you are using the OMP\\_NUM\\_THREADS environment variable to control the number of CPUs used by deep learning models during inference, setting `num_cpus=0` for tasks may not reduce contention between actors/tasks as Ray sets OMP\\_NUM\\_THREADS=1 if num\\_cpus is not specified on the tasks/actors.",
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#ray-autoscaler-with-kubernetes-cluster-autoscaler",
1925
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",
1926
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites"
1927
            ],
1928
            "document_ids": [
1929
                5818,
1930
                5953,
1931
                5975,
1932
                5963,
1933
                5752
1934
            ],
1935
            "answer": " Yes, it is possible to have Ray on K8s without using KubeRay and still enable autoscaling, but it requires additional configuration. You can use the Kubernetes Cluster Autoscaler to provision nodes for Ray pods that the Ray autoscaler decides to create. However, this method doesn't provide the built-in autoscaling feature that KubeRay provides. To enable Ray autoscaling with KubeRay, you need to set `enableInTreeAutoscaling:true` in the KubeRay RayCluster custom resource (CR) config, and set the `minReplicas` and `maxReplicas` fields to constrain the number of replicas of an autoscaling worker group. KubeRay v0.3.0+ is required for this feature.",
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
                5717,
1949
                5917,
1950
                5918,
1951
                5526,
1952
                5783
1953
            ],
1954
            "answer": " To manually configure and manage a Ray cluster on Kubernetes, you can use the RayCluster custom resource (CR) and the KubeRay operator. First, create a RayCluster CR configuration in yaml format, specifying the desired Ray version and other parameters. Then, use `kubectl apply -f raycluster.yaml` to deploy the Ray cluster. You can manage and monitor the cluster using the Ray Dashboard and, optionally, integrations with Prometheus and Grafana. The RayService provides Kubernetes-native support for Ray clusters and Ray Serve applications, allowing you to use `kubectl` for creation and updates.",
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-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",
1961
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
1962
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
1963
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",
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
                14512,
1968
                5130,
1969
                5129,
1970
                4393,
1971
                558
1972
            ],
1973
            "answer": " Yes, if you shutdown a raylet, any tasks and workers currently on that node will also be killed. This is because the raylet is responsible for managing the resources and scheduling tasks on each node in a Ray cluster. If the raylet is shutdown, the node is considered dead and all tasks and workers on that node will be terminated.\n\nIn the context you provided, it seems that the out of memory (OOM) issue is the reason why the raylet and the tasks/workers on that node were killed. To prevent this from happening, you can try provisioning more memory on the node or reducing task parallelism by requesting more CPUs per task. You can also adjust the kill threshold or disable worker killing by setting the appropriate environment variables when starting Ray. The Ray Dashboard's metrics page and event page can also provide more information about OOM killer-specific events and metrics.",
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-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",
1982
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#what-s-the-out-of-memory-error",
1983
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management"
1984
            ],
1985
            "document_ids": [
1986
                548,
1987
                547,
1988
                559,
1989
                549,
1990
                5010
1991
            ],
1992
            "answer": " To debug out-of-memory issues in Ray, first familiarize yourself with Ray's Memory Management model. If you're experiencing issues, locate the source of the memory leak by viewing the per Task and Actor memory usage graph or using the CLI command `htop`. Refer to the \"Find per Task and Actor Memory Usage\" section in the Ray documentation for more details. If you want to profile memory usage, you can also view \"Profile Task and Actor Memory Usage\". For information on the out-of-memory error and how Ray handles it, refer to \"What\u2019s the Out-of-Memory Error?\" and \"Debugging Out of Memory\" sections.",
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/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_end.html#ray-train-lightning-raytrainreportcallback-on-init-end",
2000
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_start.html#ray-train-lightning-raytrainreportcallback-on-init-start",
2001
                "https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune",
2002
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_batch_end.html#ray-train-lightning-raytrainreportcallback-on-batch-end"
2003
            ],
2004
            "document_ids": [
2005
                9704,
2006
                8531,
2007
                8760,
2008
                2390,
2009
                8206
2010
            ],
2011
            "answer": " To use a callback in the Trainer from the context provided, you can add it to the Trainer object like this: `trainer.add_callback(YourCallback())`.\n\nIn the given example, `RayTrainReportCallback()` is added as a callback to the Trainer. This callback reports metrics and checkpoints to Ray Train.\n\nYou can create your own callback by subclassing `pytorch_lightning.callbacks.Callback` and implementing the desired hooks, such as `on_batch_end`, `on_train_batch_end`, etc. These hooks will be called at the corresponding points during training.\n\nFor example, based on the provided context, here's how you can create a simple callback that prints a metric each time a result is received:\n\n```python\nfrom pytorch_lightning import Callback\n\nclass MyCallback(Callback):\n    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):\n        print(f\"Got result: {outputs['metric']}\")\n```\n\nThen, you can add this callback to your Trainer:\n\n```python\ntrainer = Trainer(..., callbacks=[MyCallback()])\n```\n\nPlease note that some of the callback hooks, like `on_init_start` and `on_init_end`, are deprecated and will be removed in future versions. It is recommended to use the alternatives provided in the documentation.",
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#uploads",
2019
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
2020
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
2021
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable"
2022
            ],
2023
            "document_ids": [
2024
                6627,
2025
                6625,
2026
                6597,
2027
                5757,
2028
                2371
2029
            ],
2030
            "answer": " To provide the current working directory to Ray, you can specify the `working_dir` in the runtime environment when calling `ray.init()`. This will upload the working directory on the laptop to the Ray cluster. Once the Ray actor is launched as a remote actor, its current working directory will be set to the corresponding trial directory. Therefore, you can use relative paths in your remote task definitions without needing to modify your code for different environments.",
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/examples/monte_carlo_pi.html#creating-a-progress-actor",
2037
                "https://docs.ray.io/en/master/ray-core/cross-language.html#python-calling-java",
2038
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
2039
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
2040
                "https://docs.ray.io/en/master/rllib/actors.html#actors"
2041
            ],
2042
            "document_ids": [
2043
                5391,
2044
                4398,
2045
                5192,
2046
                4329,
2047
                11774
2048
            ],
2049
            "answer": " To create an actor instance with a parameter, you can use the `.remote()` method with the constructor and arguments. For example, if you have a class `MyActor` with a constructor that takes an integer parameter, you can create an instance of it with a specific value like this:\n```makefile\nmy_actor = MyActor.remote(my_param)\n```\nThis creates and runs the actor on a remote worker process, and the return value is an actor handle that can be used to call its methods. Note that using class variables to manage state between instances of the same class is not supported, as each actor instance is instantiated in its own process and has its own copy of the class variables.",
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/ray-core/api/doc/ray.remote.html#ray-remote",
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/rllib/actors.html#actors",
2059
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors"
2060
            ],
2061
            "document_ids": [
2062
                4844,
2063
                14685,
2064
                4327,
2065
                11772,
2066
                4200
2067
            ],
2068
            "answer": " To push a custom module to Ray, which is used by an Actor, you need to ensure that the module is available on the Ray worker processes where the Actor is running. You can achieve this by using the `put()` function to transfer the module to the Ray workers before creating the Actor.\n\nHere's an example of how to do this:\n\n1. Create your custom module, e.g., `my_module.py`:\n```python\n# my_module.py\ndef my_function():\n    return \"Hello from my_module!\"\n```\n\n2. Modify the Actor class to import and use the custom module:\n```python\n# actor.py\nimport ray\nimport my_module  # Import the custom module\n\n@ray.remote\nclass MyActor:\n    def __init__(self):\n        self.module = my_module  # Save the custom module as an attribute\n\n    def execute_function(self):\n        result = self.module.my_function()  # Use the custom module\n        return result\n```\n\n3. Transfer the custom module to the Ray workers:\n```python\n# main.py\nimport ray\nimport actor\n\n# Initialize Ray\nray.init()\n\n# Transfer the custom module to the Ray workers\nray.put(actor.my_module)\n\n# Create the Actor\nmy_actor = actor.MyActor.remote()\n\n# Call the Actor method that uses the custom module\nresult = ray.get(my_actor.execute_function.remote())\nprint(result)\n```\n\nNow, when you run `main.py`, the custom module `my_module.py` will be available to the Actor running on the Ray worker processes.",
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-observability/user-guides/debug-apps/debug-failures.html#print",
2077
                "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",
2078
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"
2079
            ],
2080
            "document_ids": [
2081
                5757,
2082
                511,
2083
                600,
2084
                5732,
2085
                6597
2086
            ],
2087
            "answer": " To print the working directory of a Ray cluster, you can use the following command:\n```css\nray job submit --address <cluster-address> -- python -c \"import ray; ray.init(); print(ray.global_state.get_current_node_resource().directory)\"\n```\nReplace `<cluster-address>` with the address of your Ray cluster. By default, Ray stores log files in a `/tmp/ray/session_*/logs` directory. The log files are organized within the logs folder, and you can view them to understand how they are structured. Ray uses `/tmp/ray` (for Linux and macOS) as the default temp directory. To change the temp and the logging directory, 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#system-logs",
2095
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",
2096
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
2097
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger"
2098
            ],
2099
            "document_ids": [
2100
                680,
2101
                535,
2102
                6445,
2103
                444,
2104
                530
2105
            ],
2106
            "answer": " The Ray logging system defaults to logging.INFO level, which means it won't display log.info messages unless you change the logging level to display more verbose output. To do this, you can use Python's logging module to get the Ray logger and modify its configuration. Here's an example:\n\n```python\nimport logging\n\nlogger = logging.getLogger(\"ray\")\nlogger.setLevel(logging.DEBUG)  # Set logging level to DEBUG to see log.info messages\n```\n\nKeep in mind that changing the logging level may result in more log messages being displayed, which could affect the readability and performance of the logs.",
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/examples/batch_training.html#filtering-a-dataset-on-read-a-class-anchor-id-filter-ds-a",
2113
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
2114
                "https://docs.ray.io/en/master/data/user-guide.html#user-guides",
2115
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
2116
                "https://docs.ray.io/en/master/data/saving-data.html#saving-data"
2117
            ],
2118
            "document_ids": [
2119
                7870,
2120
                7360,
2121
                6834,
2122
                6839,
2123
                6798
2124
            ],
2125
            "answer": " No, when using Ray Dataset to read a file, the order of the data is not guaranteed to be preserved. The primary focus of Ray Dataset is to provide functionality for efficiently reading, transforming, and saving data, rather than preserving data order. If maintaining data order is a requirement, it may be necessary to implement additional logic to ensure this after reading the data with Ray Dataset.",
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/tasks.html#retrying-failed-tasks",
2133
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",
2134
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",
2135
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions"
2136
            ],
2137
            "document_ids": [
2138
                4505,
2139
                4414,
2140
                4423,
2141
                4460,
2142
                4453
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 is because Ray assumes that exceptions thrown by application code are not recoverable and therefore should not be retried. However, this behavior can be changed by setting the `retry_exceptions` option to `True` in the `ray.remote()` or `.options()` functions, which will enable retries upon application-level errors.",
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/cluster/vms/user-guides/logging.html#ray-log-directory",
2151
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-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
                6445,
2158
                511,
2159
                5861,
2160
                510,
2161
                5880
2162
            ],
2163
            "answer": " To specify the log directory when starting Ray, you can use the `--logs-dir` option with the `ray start` command or set the `logging_dir` parameter when calling `ray.init()`. Here's an example:\n```css\nray start --logs-dir /path/to/your/log/directory\n```\nor\n```python\nimport ray\nray.init(logging_dir='/path/to/your/log/directory')\n```\nBy default, Ray uses `/tmp/ray` as the temp directory and `/tmp/ray/session_*/logs` as the log directory. You can change these directories by specifying the desired path in the `--temp-dir` or `--logs-dir` options for `ray start`, or the `temp_dir` or `logging_dir` parameters for `ray.init()`.",
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/ray-core/examples/testing-tips.html#tip-3-create-a-mini-cluster-with-ray-cluster-utils-cluster",
2171
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
2172
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#walkthrough-using-ray-with-slurm",
2173
                "https://docs.ray.io/en/master/cluster/vms/index.html#overview"
2174
            ],
2175
            "document_ids": [
2176
                6400,
2177
                5401,
2178
                5555,
2179
                6531,
2180
                6209
2181
            ],
2182
            "answer": " To launch a Ray cluster with 1",
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/ray-core/miscellaneous.html#tuning-ray-settings",
2190
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
2191
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
2192
                "https://docs.ray.io/en/master/ray-overview/index.html#overview"
2193
            ],
2194
            "document_ids": [
2195
                5604,
2196
                4321,
2197
                6451,
2198
                5953,
2199
                364
2200
            ],
2201
            "answer": " To use the Ray API to scale up a cluster, you can use the `ray.autoscaler.sdk.request_resources()` function in the Python SDK. Additionally, you can configure the Ray autoscaler using the Ray cluster launcher or on Kubernetes to automatically scale the cluster up and down based on resource demand. For data scientists and machine learning practitioners, Ray allows for easily parallelizing and distributing ML workloads across multiple nodes and GPUs.",
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-overview/getting-started.html#ray-cluster-quickstart",
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/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",
2211
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws"
2212
            ],
2213
            "document_ids": [
2214
                6199,
2215
                273,
2216
                6629,
2217
                5503,
2218
                6304
2219
            ],
2220
            "answer": " To specify a subnet in the Ray cloud launcher deployment file for AWS, you can add the `subnet_id` field under the `provider` section of the YAML file. Here is an example:\n```makefile\ncluster_name: my-aws-cluster\n\nprovider:\n  type: aws\n  region: us-west-2\n  subnet_id: subnet-01234567890123456\n```\nReplace `subnet-01234567890123456` with the actual ID of your desired subnet. Make sure that your AWS credentials are properly configured in `~/.aws/credentials` and that you have installed the `boto3` library.",
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/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",
2229
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
2230
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications"
2231
            ],
2232
            "document_ids": [
2233
                13980,
2234
                13517,
2235
                5802,
2236
                14255,
2237
                5747
2238
            ],
2239
            "answer": " HTTP server error code logs for Ray Serve can be found in the logs at /tmp/ray/session\\_latest/logs/serve/ on each node's disk. These logs are also streamed to the driver Ray program during development, such as when using the serve.run() function or the serve run CLI command. Additionally, the Ray dashboard, accessible at http://localhost:8265/#/serve, provides visibility into Serve applications and their logs.",
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/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster"
2250
            ],
2251
            "document_ids": [
2252
                5530,
2253
                5528,
2254
                6223,
2255
                5526,
2256
                13740
2257
            ],
2258
            "answer": " Yes, it's a known issue when accessing the Ray Dashboard from outside the head node. This could be due to the dashboard port not being publicly exposed or an internal refresh failure.\n\nTo access the Ray Dashboard from outside the head node, you need to set up port forwarding. When starting a remote Ray cluster, the dashboard launches on the head node, but it may not be publicly exposed. You can customize the port using the `--dashboard-port` argument when starting the cluster.\n\nIf you're experiencing an internal refresh failure, it might be related to the dashboard agent's HTTP server failing to start if the specified port is unavailable. In this case, you can try setting the dashboard agent's listen port to a different value using the `--dashboard-agent-listen-port` argument when running `ray start`.\n\nFor security reasons, avoid exposing the Ray Dashboard publicly without proper authentication.\n\nIf the issue persists, you can refer to the Ray Dashboard documentation for more information on configuring and managing the dashboard: <https://docs.ray.io/en/latest/cluster/dashboard.html>",
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/key-concepts.html#ray-cluster",
2266
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",
2267
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
2268
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis"
2269
            ],
2270
            "document_ids": [
2271
                5557,
2272
                5555,
2273
                6299,
2274
                256,
2275
                6509
2276
            ],
2277
            "answer": " In the Ray cluster launcher YAML, `max_workers` in the `available_node_types.<node_type_name>.node_type.max_workers` field refers to the maximum number of worker nodes, and does not include the head node. The head node is managed separately and its workers are controlled by the autoscaler. The `max_workers` field in `cluster.yaml` specifies the maximum number of worker nodes to launch in addition to 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/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
2284
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
2285
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",
2286
                "https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",
2287
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads"
2288
            ],
2289
            "document_ids": [
2290
                1775,
2291
                1790,
2292
                2369,
2293
                8894,
2294
                6625
2295
            ],
2296
            "answer": " To update files in the working directory, you can use the `to_directory()` function to write checkpoint data to a specific directory. Additionally, you can use the `update_metadata(metadata)` function to update the metadata stored with the checkpoint. If you want to write to a file directly, you can use the `open()` function with the appropriate file path. When writing to files, be mindful of multiple workers saving to the same location and use `ray.train.get_context().get_trial_dir()` as the path to save any outputs. Also, note that the working directory is set to the trial directory, so you don't need to worry about specifying the full path when writing to files within the trial directory.",
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/ray-core/configure.html#logging-and-debugging",
2304
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",
2305
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
2306
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments"
2307
            ],
2308
            "document_ids": [
2309
                6625,
2310
                4531,
2311
                2371,
2312
                1790,
2313
                6595
2314
            ],
2315
            "answer": " To update the working directory file when Ray is already initiated, you can't directly change the working directory. However, you can access and modify the files within the existing working directory.\n\nWhen you initialize Ray with a `working_dir` in the `runtime_env`, Ray uploads the working directory to a temporary directory on the cluster. In your code, you can access the files within that directory using the `ray.train.get_context().get_trial_dir()` function, which returns the path to the trial directory where you can find your uploaded working directory.\n\nHere's an example of how to access and modify a file within the working directory:\n\n```python\nimport os\nimport ray\n\n# Initialize Ray with a working_dir in the runtime_env\nray.init(\n    \"ray://1.2.3.4:10001\",\n    namespace=\"my_namespace\",\n    runtime_env={\n        \"working_dir\": \"files/my_project\",\n        \"pip\": [\"toolz\", \"requests\"],\n    },\n)\n\n# Get the path to the trial directory\ntrial_dir = ray.train.get_context().get_trial_dir()\n\n# Update a file within the working directory\nfile_path = os.path.join(trial_dir, \"my_file.txt\")\nwith open(file_path, \"a\") as f:\n    f.write(\"Appended text.\")\n```\n\nThis example appends text to a file named `my_file.txt` within the working directory. Note that this method does not change the working directory itself but allows you to access and modify the files within it.",
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/ray-overview/getting-started.html#ray-cluster-quickstart"
2326
            ],
2327
            "document_ids": [
2328
                6385,
2329
                6205,
2330
                6406,
2331
                6356,
2332
                272
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 path to your custom PEM file in the `ssh_private_key` field of the `auth` section in the Ray cluster configuration file (`config.yaml` in this case). Here's an example:\n```yaml\nauth:\n    ssh_user: ubuntu\n    ssh_private_key: /path/to/your/custom/key.pem\n```\nMake sure to replace `/path/to/your/custom/key.pem` with the actual path to your custom PEM file. Also, ensure that the specified key has been added to the project-wide metadata if you're using a cloud-based environment.",
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-references/glossary.html#ray-glossary",
2341
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",
2342
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#running-out-of-file-descriptors-too-may-open-files",
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
                14740,
2348
                4375,
2349
                603,
2350
                563,
2351
                4315
2352
            ],
2353
            "answer": " The Global Control Service (GCS) is a centralized metadata server for a Ray cluster, running on the Ray head node. It manages node membership and actor directory, and handles actor, placement groups, and node management. However, it stores all data in-memory, making it not fault-tolerant by default.\n\nThe head node, which runs GCS and other cluster-level processes, can consume significant memory due to memory-demanding system components like GCS or the dashboard. If the head node has the same memory capacity as worker nodes and runs the same number of tasks and actors, it can easily face out-of-memory issues.\n\nIn your case, the GCS server might be taking up so much memory because of more than 1024 connections, which can cause \"too many open files\" error messages. To handle this, adjust the max file descriptors limit per process via the ulimit command, like ulimit -n 65536, for Ray components or the host configuration.\n\nAdditionally, consider offloading tasks and actors from the head node by specifying --num-cpus=0 when starting a head node with ray start --head, or if you use Kuberay, follow the provided link.",
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/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
2360
                "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",
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/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource"
2364
            ],
2365
            "document_ids": [
2366
                467,
2367
                5692,
2368
                2428,
2369
                2421,
2370
                5724
2371
            ],
2372
            "answer": " If some nodes are \"pending\" for a long time when starting a cluster with `ray up`, it might be due to insufficient resources in the Kubernetes cluster. Here are some steps to debug:\n\n1. Check if your Kubernetes cluster has enough resources. You can check the current resource usage and allocate more resources if necessary.\n2. Check the recent failures with `kubectl describe pod <pod-name>`. This can help you identify any issues that might be causing the pods to fail.\n3. Try increasing the Docker resource limits if the pods are stuck in the \"Pending\" state.\n4. You can also try restarting the Ray cluster with `ray up CLUSTER.YAML --restart-only` and then killing a random node with `ray kill-random-node tune-default.yaml --hard`. This can help you check if the Ray cluster is working correctly.\n\nIf the issue persists, you can refer to the Ray documentation for more troubleshooting steps.",
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-more-libs/installation.html#from-wheels",
2379
                "https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",
2380
                "https://docs.ray.io/en/master/installation.html#from-wheels",
2381
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
2382
                "https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies"
2383
            ],
2384
            "document_ids": [
2385
                14545,
2386
                286,
2387
                61,
2388
                63,
2389
                288
2390
            ],
2391
            "answer": " To install Ray 2.5.1, you can use the wheels provided in the \"Daily Releases (Nightlies)\" section of the context. However, Ray has already released 2.6.0, and it is recommended to use the latest version. Here are the steps to install Ray 2.6.0:\n\nFor general Python applications:\n```bash\npip install -U \"ray[default]\"\n```\nIf you don't want Ray Dashboard or Cluster Launcher, install Ray with minimal dependencies instead:\n```bash\npip install -U \"ray\"\n```\nIf you still want to install Ray 2.5.1, you can check the corresponding wheel in the \"Daily Releases (Nightlies)\" section of the context and use the following command:\n```bash\npip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"\n```\nReplace `LINK_TO_WHEEL.whl` with the actual link to the Ray 2.5.1 wheel.",
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-observability/user-guides/configure-logging.html#system-component-logs",
2399
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
2400
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
2401
                "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"
2402
            ],
2403
            "document_ids": [
2404
                542,
2405
                518,
2406
                4632,
2407
                4610,
2408
                13199
2409
            ],
2410
            "answer": " To use `worker_setup_hook` in a runtime environment for setting up worker node creation, you can define a callable function and pass it to the `worker_process_setup_hook` parameter when initializing Ray using `ray.init()`. Here's an example:\n\n```python\ndef logging_setup_func():\n    # Your setup code here\n    pass\n\nray.init(runtime_env={\"worker_process_setup_hook\": logging_setup_func})\n```\n\nAlternatively, you can create a runtime environment using the `RuntimeEnv` class and set the `worker_process_setup_hook` parameter:\n\n```python\nfrom ray.runtime_env import RuntimeEnv\n\ndef logging_setup_func():\n    # Your setup code here\n    pass\n\nray_env = RuntimeEnv(worker_process_setup_hook=logging_setup_func)\nray.init(runtime_env=ray_env)\n```\n\nThe `worker_process_setup_hook` function will be called after the worker starts and before tasks and actors are scheduled. Make sure the function is serializable, as it will be sent to the worker processes.\n\nFor more information, refer to the documentation: <https://docs.ray.io/en/latest/ray-core/runtime-envs.html#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/key-concepts.html#loading-data",
2417
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",
2418
                "https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",
2419
                "https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",
2420
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset"
2421
            ],
2422
            "document_ids": [
2423
                6732,
2424
                7959,
2425
                6846,
2426
                7865,
2427
                8159
2428
            ],
2429
            "answer": " To use Ray dataset on AWS, you can read data from an S3 bucket using the `ray.data.read_csv` function, as shown in the example `ds = ray.data.read_csv(\"s3://anonymous@air-example-data/iris.csv\")`. Before using Ray 2.5, you could also create datasets from Python objects, but this is no longer supported in Ray 2.5. Instead, Ray Data now requires schemas for all datasets, and the default batch format is fixed to NumPy.\n\nAdditionally, you can use the Ray cluster launcher to start a Ray cluster on AWS, GCP, or Azure clouds, and use any ML framework of your choice. Ray Data supports a wide variety of data formats, including CSV, Parquet, and raw images.\n\nHere's an example of how to create a dataset using Ray:\n\n```python\nimport ray\n\n# Define the S3 link to the dataset\ns3_link = \"s3://anonymous@air-example-data/iris.csv\"\n\n# Create a Ray dataset from the S3 link\nds = ray.data.read_csv(s3_link)\n\n# Show the first batch of the dataset\nds.show(limit=1)\n```\n\nNote that you may need to configure your AWS credentials to access the S3 bucket. You can do this by setting the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` environment variables, or by using the AWS CLI's `aws configure` command.",
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.random_shuffle.html#ray-data-dataset-random-shuffle",
2438
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
2439
                "https://docs.ray.io/en/master/data/data-internals.html#execution-memory"
2440
            ],
2441
            "document_ids": [
2442
                6994,
2443
                6928,
2444
                7395,
2445
                6841,
2446
                6965
2447
            ],
2448
            "answer": " To avoid dataset shuffling during a `ray.data.map_batches()` operation, you should avoid calling functions like `random_shuffle()`, `sort()`, or `groupby()` before `map_batches()`. These functions shuffle the dataset in a map-reduce style, which might not be desired in your use case. Instead, you can process the data in its original order during the `map_batches()` operation. However, keep in mind the best practices for `map_batches()`, such as setting an appropriate batch size to avoid out-of-memory situations.",
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.Dataset.html#ray-data-dataset",
2456
                "https://docs.ray.io/en/master/data/data-internals.html#execution-memory",
2457
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
2458
                "https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms"
2459
            ],
2460
            "document_ids": [
2461
                6994,
2462
                7738,
2463
                6965,
2464
                8218,
2465
                6813
2466
            ],
2467
            "answer": " No, the order of the input data is not preserved after a `map_batches` operation. In the provided context, the dataset is sorted by \"id\" after being randomly shuffled, demonstrating that the order can change. However, `map_batches` is designed to be faster than `map()` when the transformation is vectorized.",
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#issue-1-ray-serve-script-is-incorrect",
2474
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",
2475
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#common-issues",
2476
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
2477
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray"
2478
            ],
2479
            "document_ids": [
2480
                5671,
2481
                4423,
2482
                5670,
2483
                13980,
2484
                595
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, as recommended in Issue 1. This can help you catch any issues early on.\n2. Implement fault tolerance mechanisms provided by Ray to handle application-level failures, such as catching errors, retrying failed code, and handling misbehaving code, as described in the Fault Tolerance section.\n3. Check for common issues such as incorrect Ray Serve script, incorrect serveConfigV2, missing dependencies, incorrect import\\_path, and issues with creating or updating Serve applications, as listed in the Common issues section.\n4. Refer to the Ray Serve API documentation for more information on how to use the API and handle errors.\n5. Understand the kinds of failures that can exist in Ray, such as Task or Actor failures, and how to identify and fix them, as explained in the What Kind of Failures Exist in Ray? section.\n\nBy following these steps, you can potentially emit more detailed errors or logs and troubleshoot any internal failures in Ray Serve.",
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/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception",
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/api.html#state-apis-exceptions"
2497
            ],
2498
            "document_ids": [
2499
                4560,
2500
                13980,
2501
                778,
2502
                4779,
2503
                665
2504
            ],
2505
            "answer": " To track an uncaught exception in Ray Serve, you can use the RayStateApiException and RayTaskError exceptions provided by Ray. These exceptions are thrown when Ray encounters a system error or a task throws an exception during execution. You can catch these exceptions in your code and handle them appropriately to track uncaught exceptions. However, Ray Serve itself does not provide a specific exception for uncaught exceptions. You might need to implement additional logging or monitoring to track uncaught exceptions in your Ray Serve application.",
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/ray-observability/reference/cli.html#log",
2513
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
2514
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",
2515
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs"
2516
            ],
2517
            "document_ids": [
2518
                13517,
2519
                680,
2520
                445,
2521
                415,
2522
                535
2523
            ],
2524
            "answer": " To view logs for your Ray Serve endpoint in the Ray cluster, you can check the logs on disk at the location `/tmp/ray/session_latest/logs/serve/`. These logs are also emitted to stderr. In development, logs are streamed to the driver Ray program, which is the Python script that calls `serve.run()` or the `serve run` CLI command.\n\nIf you're using Ray Jobs, worker logs are always captured in the Ray logging directory, and driver logs are captured only for Ray Jobs submitted via Jobs API. You can access these logs using the Ray Dashboard UI, CLI, or Python SDK.\n\nNote that if you execute the driver directly on the head node of the Ray cluster or run with Ray Client, the driver logs are not accessible from the Dashboard. In this case, you can view the driver logs in the terminal or Jupyter Notebook output.",
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/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
2532
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
2533
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",
2534
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"
2535
            ],
2536
            "document_ids": [
2537
                13960,
2538
                14255,
2539
                13523,
2540
                13514,
2541
                13517
2542
            ],
2543
            "answer": " To view logs for a failed Ray serve deployment, you can follow these steps:\n\n1. Check the Kuberay operator logs by running `kubectl logs -f <kuberay-operator-pod-name>`. These logs contain information about the Serve application deployment event and health checks.\n2. Examine the controller log and deployment log, which are located under `/tmp/ray/session_latest/logs/serve/` in both the head node pod and worker node pod. These logs contain information about specific deployment failure reasons and autoscaling events.\n3. You can also find a copy of these logs at `/tmp/ray/session_latest/logs/serve/`. You can parse these stored logs with a logging stack such as ELK or Loki to be able to search by deployment or replica.\n4. Use the Ray dashboard to get a high-level overview of your Ray cluster and Ray Serve application\u2019s states. The dashboard includes details such as the number of deployment replicas currently running, logs for your Serve controller, deployment replicas, and proxies, and the Ray nodes running in your Ray cluster. You can access the Ray dashboard at port 8265 at your cluster\u2019s URI.\n\nNote that Ray Serve uses Python\u2019s standard logging module with a logger named \"ray.serve\", and logs are emitted from actors both to stderr and on disk on each node at `/tmp/ray/session_latest/logs/serve/`.",
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/train/user-guides/data-loading-preprocessing.html#quickstart",
2550
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",
2551
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",
2552
                "https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",
2553
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview"
2554
            ],
2555
            "document_ids": [
2556
                9180,
2557
                10014,
2558
                10436,
2559
                7274,
2560
                6839
2561
            ],
2562
            "answer": " To take a subset of a Ray Dataset, you can use the `ray.data.range()` function, which allows for easy creation of synthetic datasets for testing or benchmarking. You can also use the `take()` method to get a specified number of rows from the dataset.\n\nFor example, to create a Ray Dataset with the first 100 integers, you can use:\n\n```python\nsubset_dataset = ray.data.range(100)\n```\n\nAnd to get the first 10 rows from this dataset, you can use:\n\n```python\nsubset_first_10 = subset_dataset.take(10)\n```\n\nAlternatively, if you have an existing Ray Dataset and want to take a subset of it, you can use the `map_batches()` method to filter the dataset based on your desired criteria. For example, to take a subset of a Ray Dataset where the `x` column is less than 100, you can use:\n\n```python\ndef filter_func(batch):\n    return batch[\"x\"] < 100\n\nsubset_dataset = train_dataset.map_batches(filter_func, batch_format=\"pandas\")\n```\n\nNote that the `batch_format` argument is set to `\"pandas\"` to ensure that the `batch` object is a Pandas DataFrame, which allows for easy indexing and filtering.",
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/examples/tune-wandb.html#running-a-weights-biases-example",
2570
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",
2571
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",
2572
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis"
2573
            ],
2574
            "document_ids": [
2575
                2226,
2576
                2439,
2577
                2347,
2578
                2253,
2579
                1473
2580
            ],
2581
            "answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `get_all_configs()` and `get_best_checkpoint()` functions from the `ExperimentAnalysis` class.\n\nFirst, you need to create an `ExperimentAnalysis` object by providing the path to the experiment's state.json file using the `experiment_checkpoint_path` parameter.\n\nThen, you can use `get_all_configs()` to get a list of all configurations and iterate through them. For each configuration, use `get_best_checkpoint()` to get the best persistent checkpoint path.\n\nHere's an example:\n\n```python\nfrom ray import tune\nfrom ray.tune.analysis import ExperimentAnalysis\n\n# Assuming your experiment was run with the name \"my_exp\" and the local_dir is set to a valid directory\nanalysis = ExperimentAnalysis(experiment_checkpoint_path=\"<path_to>/my_exp/state.json\")\n\n# Get all configurations\nconfigs = analysis.get_all_configs()\n\nfor config in configs:\n    # Get the best checkpoint path for the current config\n    checkpoint_path = analysis.get_best_checkpoint(config, metric=\"loss\", mode=\"min\")\n    print(f\"Best checkpoint path for config {config}: {checkpoint_path}\")\n```\n\nReplace `<path_to>` with the actual path to the directory where your experiment's state.json file is located.\n\nKeep in mind that this will only download the best checkpoints for each trial. If you need to download all checkpoints, you might need to implement a custom solution using the `load_checkpoint()` function provided in the context and the cloud storage API.",
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/actors/named-actors.html#actor-lifetimes",
2588
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
2589
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
2590
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
2591
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance"
2592
            ],
2593
            "document_ids": [
2594
                4658,
2595
                4859,
2596
                4502,
2597
                4493,
2598
                4455
2599
            ],
2600
            "answer": " To kill a \"detached\" Actor in Ray using C++, you can't do it directly in C++ as of now because customizing the lifetime of an actor hasn't been implemented in C++ yet. However, you can kill a detached actor using Ray's Python API.\n\nFirst, you need to get the `ActorHandle` of the detached actor. Then, you can call `ray.kill(actor)` to terminate the actor forcefully. Here's an example:\n\n```python\nactor_handle = ray.actor_handle(detached_actor_id)\nray.kill(actor_handle)\n```\n\nThis will interrupt any running tasks on the actor and cause them to fail immediately. Note that atexit handlers installed in the actor will not be run. If you want to kill the actor but let pending tasks finish, you can call `actor.__ray_terminate__.remote()` instead. Any atexit handlers installed in the actor will be run in this case.\n\nKeep in mind that if you kill a detached actor, subsequent calls to get its handle via `ray.get_actor` will fail.",
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/cluster/running-applications/job-submission/ray-client.html#client-arguments",
2607
                "https://docs.ray.io/en/master/rllib-env.html#configuring-environments",
2608
                "https://docs.ray.io/en/master/rllib/rllib-env.html#configuring-environments",
2609
                "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",
2610
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment"
2611
            ],
2612
            "document_ids": [
2613
                6595,
2614
                3,
2615
                10502,
2616
                609,
2617
                9343
2618
            ],
2619
            "answer": " To set environment variables in `ray.init()`, you can use the `runtime_env` parameter and set the `env_vars` key to a dictionary of variable names and values. For example:\n\n```\nray.init(runtime_env={\"env_vars\": {\"foo\": \"foo\"}})\n```\n\nIn the context provided, this would allow you to set the `foo` environment variable for the Ray workers.",
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/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",
2626
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",
2627
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",
2628
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_node_id.html#ray-runtime-context-runtimecontext-get-node-id",
2629
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"
2630
            ],
2631
            "document_ids": [
2632
                6535,
2633
                721,
2634
                754,
2635
                4909,
2636
                493
2637
            ],
2638
            "answer": " The rest API for getting the head node ID is not explicitly mentioned in the provided context. However, you can obtain the head node ID using `ray.runtime_context.RuntimeContext.get_node_id()` in the Ray Python SDK. Alternatively, you can use the `ray list nodes` command in the CLI to get the node ID/IP and then use it with the `ray logs` command to get logs from a specific node.",
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/api/doc/ray.cancel.html#ray-cancel",
2645
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
2646
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
2647
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
2648
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"
2649
            ],
2650
            "document_ids": [
2651
                4897,
2652
                4498,
2653
                4359,
2654
                11802,
2655
                14702
2656
            ],
2657
            "answer": " Based on the provided context, it's not possible to rerun a canceled Ray task as cancelled tasks aren't retained for retrying. Moreover, the `max_task_retries` parameter isn't applicable to cancelled tasks. If you want to stop a task that hasn't started yet, you can cancel its scheduling, but for running tasks, Ray does not provide a mechanism to interrupt them, except for async actor tasks where Ray attempts to cancel the associated asyncio.Task.",
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/data/api/doc/ray.data.Dataset.html#ray-data-dataset",
2664
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
2665
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#setting-concurrency-in-async-actors",
2666
                "https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#pattern-using-ray-wait-to-limit-the-number-of-pending-tasks",
2667
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#dividing-data-into-batches-and-spawning-a-ray-task-for-each-batch-to-be-ran-in-parallel"
2668
            ],
2669
            "document_ids": [
2670
                7738,
2671
                1787,
2672
                4581,
2673
                5246,
2674
                5289
2675
            ],
2676
            "answer": " In the provided context, there is no direct way to set the maximum parallel concurrent scheduled tasks in `map_batches()`. However, you can control the concurrency of Ray tasks using the `max_concurrency` flag in the actor options when defining a Ray remote function. By default, 1",
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/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",
2685
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",
2686
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core"
2687
            ],
2688
            "document_ids": [
2689
                5936,
2690
                5693,
2691
                10003,
2692
                614,
2693
                5413
2694
            ],
2695
            "answer": " To get the number of CPUs in a Ray cluster, you can check the Ray dashboard which displays information such as the number of CPU cores available. Alternatively, you can use the `ray.cluster_resources()` function in Python which returns a dictionary containing the resources of the cluster, including the number of CPUs, which you can print using the `pprint()` function. In the provided context, the Ray cluster has 4 physical CPUs on the head pod and 1 CPU on the worker pod.",
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/ray-core/api/runtime-env.html#runtime-env-api",
2702
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",
2703
                "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",
2704
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
2705
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig"
2706
            ],
2707
            "document_ids": [
2708
                4466,
2709
                13856,
2710
                4826,
2711
                4610,
2712
                4764
2713
            ],
2714
            "answer": " To use the \"exclude\" option with the runtime environment in Ray, you can create a RuntimeEnvConfig object with the desired configuration options and set the \"exclude\" option when calling the set\\_runtime\\_env method of the JobConfig object. However, it seems there is no direct \"exclude\" option available in the RuntimeEnv or RuntimeEnvConfig classes based on the provided context.\n\nYou can achieve a similar effect by specifying the files or directories to be excluded in the .whl file or .tar.gz archive that you use for your py\\_modules or working\\_dir. This can be done using the appropriate command-line options when creating the archive with tools like pip or conda.\n\nHere's an example of how to create a runtime environment with excluded files in a .whl package:\n\n1. Create a virtual environment and install the packages you need.\n2. Use the following command to create a .whl package, excluding the files or directories you don't want:\n\n```bash\npip wheel --wheel-dir=/path/to/output/directory /path/to/your/virtual/environment/ -w /path/to/output/wheel/file.whl --no-deps --exclude=path/to/excluded/file --exclude=path/to/excluded/directory\n```\n\n3. Use the created .whl file in your py\\_modules or working\\_dir when defining the runtime environment.\n\nNote that this method excludes the specified files or directories from the package, but it doesn't directly provide an \"exclude\" option in the runtime environment configuration.",
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/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
2721
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
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-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air"
2725
            ],
2726
            "document_ids": [
2727
                6994,
2728
                14727,
2729
                7019,
2730
                6786,
2731
                10365
2732
            ],
2733
            "answer": " Here's an example of using `map_batches()` with `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=\"dict\")\n)\n```\n\nIn this example, we read image data and increase the brightness of each image in the dataset. The `batch_format` is set to \"dict\", which specifies that the batches should be represented as dictionaries. The `map_batches()` function applies the `increase_brightness` function to each batch of data.",
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/vms/examples/ml-example.html#ray-dashboard",
2741
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",
2742
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
2743
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"
2744
            ],
2745
            "document_ids": [
2746
                4817,
2747
                6422,
2748
                6183,
2749
                6484,
2750
                6465
2751
            ],
2752
            "answer": " To find the local Ray cluster address, you can look for the address of the latest cluster started in the /tmp/ray/ray_current_cluster directory. If that's empty, you can also check the environment variable RAY\\_ADDRESS. If you're using a Ray cluster started on VMs or Kubernetes, you'll need to follow instructions for setting up network access from a client. Once you have the Ray Dashboard address, you can set the RAY\\_ADDRESS environment variable to it for the Ray Jobs CLI to find your Ray cluster.",
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/examples/testing-tips.html#tip-3-create-a-mini-cluster-with-ray-cluster-utils-cluster",
2759
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
2760
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",
2761
                "https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",
2762
                "https://docs.ray.io/en/master/cluster/vms/index.html#overview"
2763
            ],
2764
            "document_ids": [
2765
                5401,
2766
                6203,
2767
                6599,
2768
                5636,
2769
                6209
2770
            ],
2771
            "answer": " To start a Ray cluster on multiple nodes via CLI, you can use the Ray cluster launcher's `ray up` command with a configuration file that specifies the head node and worker nodes. Here's an example of how to use this command:\n\n1. Set up your Ray cluster: If you have a running Ray cluster, Ray Client server is likely already running on port 10001 of the head node by default. Otherwise, you can start a Ray cluster locally by running `ray start --head`, or remotely by following the directions in Getting Started.\n2. Create a configuration file (e.g. `config.yaml`) that specifies the head node and worker nodes for your Ray cluster. Here's an example of what this file might look like:\n```\ncluster_name: my_ray_cluster\nprovider:\n  type: aws\n  region: us-west-2\n  availability_zone: us-west-2a\n  instance_type: m5.xlarge\n  key_pair: my_key_pair\n  subnet_id: my_subnet\nhead_node:\n  instance_type: m5.xlarge\n  count: 1\nworker_nodes:\n  instance_type: m5.xlarge\n  count: 2\n```\n3. Use the Ray CLI to start the cluster:\n```\n$ ray up -y config.yaml\n```\nThis will start a Ray cluster with a head node and two worker nodes on AWS. You can modify the configuration file to suit your needs, such as changing the cloud provider, instance type, and number of nodes.\n\nIt's recommended to start a Ray cluster for each user if you have multiple Ray users, so that their workloads are isolated.\n\nFor more information, you can refer to the Ray documentation on launching Ray clusters on Cloud VMs.",
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/data/data-internals.html#ray-data-and-tune",
2779
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",
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/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup"
2782
            ],
2783
            "document_ids": [
2784
                5068,
2785
                6934,
2786
                10283,
2787
                9898,
2788
                622
2789
            ],
2790
            "answer": " The Ray tuner shows \"running\" but CPU usage is almost 0% because the Ray actor and Ray Data both require CPUs to run tasks, and by default, Ray Tune tries to fully utilize cluster CPUs. If there are not enough free CPUs for Ray Data to run on, it can prevent Ray Data from scheduling tasks, reducing performance or causing workloads to hang. 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-core/scheduling/memory-management.html#concepts",
2799
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",
2800
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources"
2801
            ],
2802
            "document_ids": [
2803
                563,
2804
                5557,
2805
                5013,
2806
                5014,
2807
                5109
2808
            ],
2809
            "answer": " No, the Ray head node and worker nodes do not need to have the same object store memory size allocated. In fact, it is recommended to allocate less object store memory to the head node to avoid out-of-memory issues, as the head node has more memory-demanding system components and may run additional processes such as the GCS or the dashboard. The object store memory size can be controlled using the `--object-store-memory` flag when starting a Ray cluster. Additionally, the object store memory is not a logical resource for scheduling, but users can manually specify the quantities of pre-defined resources and add custom resources when starting the Ray cluster.",
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/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",
2817
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",
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.config.ProxyLocation.html#ray-serve-config-proxylocation"
2820
            ],
2821
            "document_ids": [
2822
                14075,
2823
                14081,
2824
                13958,
2825
                14010,
2826
                14279
2827
            ],
2828
            "answer": " In Ray Serve, you can specify whether to set up an HTTP proxy on each node or just the head node using the `ProxyLocation` configuration. The options are:\n\n- `HeadOnly`: start one HTTP server on the head node (this is the default)\n- `EveryNode`: start one HTTP server per node\n- `NoServer`: disable HTTP server\n\nYou can set this configuration using the `ray.serve.config.ProxyLocation` enum. For example:\n```\nfrom ray.serve.config import ProxyLocation\n\nray.serve.config.proxy_location = ProxyLocation.EVERY_NODE\n```\nThis configuration determines where the HTTP proxies will be run to receive ingress traffic to the cluster. Note that this is different from the `num_cpus` configuration, which is used to reserve CPU cores for each internal Serve HTTP proxy actor. The `num_cpus` configuration is now deprecated.",
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/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",
2838
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster"
2839
            ],
2840
            "document_ids": [
2841
                5544,
2842
                6086,
2843
                5543,
2844
                5545,
2845
                6061
2846
            ],
2847
            "answer": " Given that Prometheus and Grafana are not running on your Head node and you are using KubeRay, you should set `RAY_PROMETHEUS_HOST` to the address that the head node can use to access Prometheus in your KubeRay cluster, and `RAY_GRAFANA_HOST` to the address that the head node can use to access Grafana in your KubeRay cluster.\n\nTo make these settings more intelligent and avoid hardcoding the IP of the head node, which changes every time you reconfigure your cluster, you can use the Kubernetes service discovery mechanism to dynamically discover the IP addresses of Prometheus and Grafana.\n\nFor example, you can set `RAY_PROMETHEUS_HOST` to the DNS name of the Prometheus service in your KubeRay cluster, and `RAY_GRAFANA_HOST` to the DNS name of the Grafana service in your KubeRay cluster.\n\nHere's an example of how to set these variables:\n```makefile\nRAY_PROMETHEUS_HOST=http://prometheus-service.prometheus-namespace.svc:9090\nRAY_GRAFANA_HOST=http://grafana-service.grafana-namespace.svc:3000\n```\nReplace `prometheus-service` and `prometheus-namespace` with the actual names of your Prometheus service and namespace, and replace `grafana-service` and `grafana-namespace` with the actual names of your Grafana service and namespace.\n\nNote that you don't need to set `RAY_PROMETHEUS_NAME` or `RAY_GRAFANA_IFRAME_HOST` in this case, as they are not relevant to your setup.",
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#experiment-results-kuberay-v0-6-0",
2854
                "https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-1-create-a-new-kubernetes-cluster",
2855
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",
2856
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
2857
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp"
2858
            ],
2859
            "document_ids": [
2860
                5779,
2861
                5772,
2862
                5975,
2863
                13902,
2864
                6170
2865
            ],
2866
            "answer": " The GCS (KubeRay) determines which Kubernetes pod to kill when using KubeRay autoscaling based on the memory usage of the KubeRay operator Pod, which is highly correlated to the number of Pods in the Kubernetes cluster. The Kubernetes Cluster Autoscaler complements the Ray Autoscaler by provisioning or cleaning up Kubernetes nodes to accommodate Ray pod creation or deletion. It is recommended to configure the RayCluster so that only one Ray pod fits per Kubernetes node. If GCS fault tolerance is configured, worker pods can continue serving traffic without restarting when the head pod crashes and recovers.",
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-core/handling-dependencies.html#api-reference",
2876
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema"
2877
            ],
2878
            "document_ids": [
2879
                13854,
2880
                13762,
2881
                13852,
2882
                4300,
2883
                13991
2884
            ],
2885
            "answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you can include the following in your YAML config:\n\n```yaml\nhttp_options:\n  request_timeout_s: <your_timeout_value>\n```\n\nReplace `<your_timeout_value>` with the number of seconds you want to set as the end-to-end timeout for HTTP requests. 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/ray-core/tasks/using-ray-with-gpus.html#gpu-support",
2892
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",
2893
                "https://docs.ray.io/en/master/installation.html#installing-ray",
2894
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",
2895
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray"
2896
            ],
2897
            "document_ids": [
2898
                4991,
2899
                5105,
2900
                59,
2901
                284,
2902
                14541
2903
            ],
2904
            "answer": " To make the GPU available on your M1 laptop for Ray, you don't need to do anything specific since Ray natively supports GPU as a pre-defined resource type for Apple silicon (M1) hardware. Ray also provides GPU isolation by automatically setting the CUDA\\_VISIBLE\\_DEVICES environment variable, which most machine learning frameworks will respect for GPU assignment.",
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/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",
2913
                "https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",
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
                5757,
2918
                8655,
2919
                13762,
2920
                4639,
2921
                8481
2922
            ],
2923
            "answer": " To add a timeout for a Ray job, you can use the `timeout_s` parameter in the configuration classes such as `HorovodConfig` or `TorchConfig`. For example, you can set `ray.train.horovod.HorovodConfig.timeout_s = 300` to set an end-to-end timeout of 300 seconds for a Horovod job.\n\nAdditionally, you can set a timeout for the `ray.get()` function to return early from a blocking call that takes too long. For example, you can set `timeout=4` in the `ray.get()` function to raise a `GetTimeoutError` after 4 seconds.\n```python\nfrom ray.exceptions import GetTimeoutError\n\n@ray.remote\ndef long_running_function():\n    time.sleep(8)\n\nobj_ref = long_running_function.remote()\ntry:\n    ray.get(obj_ref, timeout=4)\nexcept GetTimeoutError:\n    print(\"`get` timed out.\")\n```\nHowever, note that the HTTP request timeout configuration is global to the Ray cluster and cannot be updated during runtime.",
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/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
2931
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
2932
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#cluster-setting",
2933
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"
2934
            ],
2935
            "document_ids": [
2936
                872,
2937
                6482,
2938
                6517,
2939
                9337,
2940
                6465
2941
            ],
2942
            "answer": " To set a custom `/tmp` directory for a remote cluster, you can use the `ray_temp_root_dir` configuration when starting the Ray cluster. This will create a subdirectory under the specified path for the cluster's temporary data.\n\nFor example, if you want to set the temporary directory to `/mnt/mytmp` on the remote cluster, you can use the following command:\n\n```\nray dashboard --ray-temp-root-dir=/mnt/mytmp cluster.yaml\n```\n\nThis will create a subdirectory like `ray-{head_port}-{random_suffix}` under `/mnt/mytmp` for the cluster's temporary data.\n\nNote that this directory needs to be accessible by the host, the outer container, and the inner container. You can set the `RAY_TEMPDIR` environment variable in the inner container to control the directory name.\n\nFor example, in the inner container, you can set `RAY_TEMPDIR` to `/mnt/mytmp/ray-mount` to use the custom temporary directory.\n\n```\nRAY_TEMPDIR=\"/mnt/mytmp/ray-mount\"\n```\n\nThis way, you can set a custom `/tmp` directory for the remote cluster.",
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-core/configure.html#logging-and-debugging",
2950
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
2951
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",
2952
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment"
2953
            ],
2954
            "document_ids": [
2955
                4960,
2956
                4531,
2957
                511,
2958
                4959,
2959
                872
2960
            ],
2961
            "answer": " Yes, if you set the `--temp-dir` flag to a different directory, Ray object spilling will occur in the custom directory. However, to configure the directory for object spilling specifically (regardless of the value of `--temp-dir`), you should use the `object_spilling_config` parameter in `ray.init()`, as shown in the context.",
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/ray-core/handling-dependencies.html#remote-uris",
2968
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",
2969
                "https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",
2970
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",
2971
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster"
2972
            ],
2973
            "document_ids": [
2974
                4342,
2975
                4253,
2976
                4466,
2977
                4310,
2978
                13826
2979
            ],
2980
            "answer": " The `--runtime-env-json` flag is used to specify a runtime environment when running a task or actor using Ray. The value for this flag is a JSON string that describes the runtime environment. Here's an example of using this flag:\n```bash\nserve run --address=ray://<head-node-ip-address>:10001 \\\n  --runtime-env-json='{\"env_vars\": {\"MY_ENV_VAR\": \"my-value\"}, \"working_dir\": \"./project/src\", \"pip\": [\"requests\", \"chess\"]}' \\\n  local_dev:graph\n```\nIn this example, the runtime environment includes the following:\n\n* An environment variable `MY_ENV_VAR` set to `my-value`.\n* The working directory set to `./project/src`.\n* Two Python packages, `requests` and `chess`, installed using pip.\n\nNote that you can also specify the runtime environment via a YAML file using the `serve run` command.",
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/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-autoscaler",
2987
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
2988
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
2989
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
2990
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"
2991
            ],
2992
            "document_ids": [
2993
                6479,
2994
                13450,
2995
                14225,
2996
                14187,
2997
                14303
2998
            ],
2999
            "answer": " The context does not provide a specific value for `maxConcurrentReplicas` when autoscaling configuration is specified. It should be set based on the needs of the specific use case, considering the available resources and the desired level of concurrency.",
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/scaling-and-resource-allocation.html#autoscaling-config-parameters",
3006
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
3007
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
3008
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
3009
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema"
3010
            ],
3011
            "document_ids": [
3012
                13451,
3013
                14303,
3014
                14187,
3015
                14225,
3016
                14032
3017
            ],
3018
            "answer": " The value of `max_concurrent_queries` is not directly dependent on `target_num_ongoing_requests_per_replica`. The former is the maximum number of pending queries in a single replica, while the latter is the expected number of ongoing requests to run concurrently per replica. The default value for `max_concurrent_queries` is 1, and it can be configured based on the system's requirements, with a minimum value of 0 and an integer type.",
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/examples/includes/pb2_example.html#pb2-example",
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/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining"
3029
            ],
3030
            "document_ids": [
3031
                13455,
3032
                13454,
3033
                3900,
3034
                1925,
3035
                1920
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. A higher value for the `smoothing_factor` increases the sensitivity of the autoscaler to changes in the metric, resulting in more rapid upscaling or downscaling decisions. Conversely, a lower value for the `smoothing_factor` makes the autoscaler less sensitive to changes in the metric, resulting in more gradual upscaling or downscaling decisions. The `smoothing_factor` can be adjusted to control the aggressiveness of the autoscaler and to prevent rapid, potentially unnecessary changes to the number of replicas.",
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/ray-core/actors.html#faq-actors-workers-and-resources",
3044
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
3045
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
3046
                "https://docs.ray.io/en/master/serve/api/index.html#get-api-serve-deployments-status",
3047
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status"
3048
            ],
3049
            "document_ids": [
3050
                4371,
3051
                11813,
3052
                14708,
3053
                14047,
3054
                13836
3055
            ],
3056
            "answer": " Actors in this context appear to be related to deployments that can have a state of \"HEALTHY\" or \"UNHEALTHY.\" Actors may change to an \"UNHEALTHY\" state if there's an error in the deployment's constructor, a crashed replica, or a general system or machine error. If an actor does not require a stateful part, it is typically better to use tasks.",
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/cluster/vms/user-guides/logging.html#log-persistence",
3065
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
3066
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy"
3067
            ],
3068
            "document_ids": [
3069
                492,
3070
                493,
3071
                6443,
3072
                680,
3073
                5129
3074
            ],
3075
            "answer": " Based on the provided context, you cannot access logs from a dead node. The Ray State API and CLI allow you to fetch logs from a head node or a specific node, but this is only possible for alive nodes. When a node is dead, the logs are not available through the Ray API or CLI. If you need to access logs from a dead node for troubleshooting purposes, you may need to rely on other methods of log collection, depending on your specific environment and log management system.",
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/fault_tolerance/nodes.html#raylet-failure",
3082
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",
3083
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3084
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",
3085
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.PlacementGroupState.html#ray-util-state-common-placementgroupstate"
3086
            ],
3087
            "document_ids": [
3088
                4393,
3089
                13869,
3090
                4625,
3091
                4307,
3092
                724
3093
            ],
3094
            "answer": " A node in a Ray cluster can change its status to \"dead\" due to two main reasons:\n\n1. Raylet failure: When a raylet process fails, the corresponding node is marked as dead and treated as a new node upon restart.\n2. Worker node failure: When a worker node fails, the actors running on it also fail. Serve attempts to respawn the actors on the remaining healthy nodes, and KubeRay attempts to restart the worker pod on another running node.\n\nIn the provided context, the 'death\\_cause' details specify that an actor's worker process has died due to an intended user exit, which could also be a reason for a node to be considered \"dead.\"",
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/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3101
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3102
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
3103
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",
3104
                "https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training"
3105
            ],
3106
            "document_ids": [
3107
                555,
3108
                554,
3109
                468,
3110
                3273,
3111
                3249
3112
            ],
3113
            "answer": " The reasons for spikes in node CPU utilization in this context could include:\n\n1. A task or trial requiring high CPU resources.\n2. Multiple tasks or trials running simultaneously and competing for CPU resources.\n3. Autoscaling of resources due to memory usage, which can indirectly affect CPU usage.\n\nHowever, the provided context does not show any direct correlation between CPU utilization and the spikes. The primary issue indicated is the node running low on memory, which led to the killing of a worker. To get more information, running `ray logs raylet.out -ip 10.0.62.231` and `ray logs worker-6f2ec5c8b0d5f5a66572859faf192d36743536c2e9702ea58084b037*out -ip 10.0.62.231` is recommended.",
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/serve/production-guide/kubernetes.html#deploy-on-kubernetes",
3120
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",
3121
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-eks-cluster-aws",
3122
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploying-a-serve-application",
3123
                "https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide"
3124
            ],
3125
            "document_ids": [
3126
                13931,
3127
                13930,
3128
                5886,
3129
                13938,
3130
                13962
3131
            ],
3132
            "answer": " The recommended AWS machine type for deploying a RayService on EKS would be one with GPUs, as suggested in the \"Setting up an EKS cluster (AWS)\" section of the context. However, the specific machine type is not specified in the context. It is important to provide sufficient resources to the Serve application, as the example RayService uses low numCpus values for demonstration purposes. Additional dependencies can be added to the runtime\\_env in the Deployment code.",
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/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
3140
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
3141
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
3142
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory"
3143
            ],
3144
            "document_ids": [
3145
                4528,
3146
                6597,
3147
                4811,
3148
                1782,
3149
                511
3150
            ],
3151
            "answer": " Yes, you can configure the session name generated by Ray by specifying the `temp_dir` or `log_directory` parameter when calling `ray.init()` or `ray start`. The session name will be based on the name of the directory specified. For example, if you specify `temp_dir='/my/ray/session'`, the session name will be `session_<timestamp>_<pid>`, and the session directory will be `/my/ray/session/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/sdk.html#submitting-a-ray-job",
3160
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",
3161
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli"
3162
            ],
3163
            "document_ids": [
3164
                6640,
3165
                6510,
3166
                6516,
3167
                275,
3168
                6454
3169
            ],
3170
            "answer": " No, the Python SDK does not provide a direct method to get a link to the Ray dashboard for a given job. The dashboard can be accessed through the default URL, http://localhost:8265, or by providing the Ray cluster head node address and the port used by the Ray Dashboard when initializing the JobSubmissionClient object. However, the SDK does not generate a specific link for a particular job in the dashboard.",
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-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
3177
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
3178
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3179
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
3180
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure"
3181
            ],
3182
            "document_ids": [
3183
                5129,
3184
                4456,
3185
                554,
3186
                5130,
3187
                4393
3188
            ],
3189
            "answer": " The node where the task was running crashed unexpectedly could have been caused by:\n\n1. The instance where the node was running failed.\n2. The raylet process crashed unexpectedly, possibly due to memory pressure (OOM) or node preemption.\n3. The task ran out of memory, resulting in the task being killed.\n4. Raylet failure, which would mark the corresponding node as dead and treat it 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/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",
3196
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",
3197
                "https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",
3198
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",
3199
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"
3200
            ],
3201
            "document_ids": [
3202
                4375,
3203
                5709,
3204
                5638,
3205
                6372,
3206
                13899
3207
            ],
3208
            "answer": " The error message you're seeing is related to the Global Control Service (GCS) health check failing, but the node is still up and running. This could be due to the GCS server process failing in the head Pod, or the ray executable not being included in the $PATH for the image.\n\nSince the node is still running, it's possible that the GCS server process failed but the node was able to recover. You can check the log directory /tmp/ray/session\\_latest/logs/ in the head Pod for errors related to the GCS server to confirm this.\n\nIf the ray executable is not included in the $PATH for the image, you can modify the image to include it or specify the full path to the ray executable in the init container.\n\nAdditionally, the error message suggests that during the recovery period, certain functions such as actor creation, deletion, and reconstruction, as well as placement group creation, deletion, and reconstruction, are not available. This is expected behavior during the recovery period when the GCS is loading data from the Redis instance.\n\nIf the issue persists, you can also check for firewall settings preventing access to the specified address and use a tool such as nmap or nc to check whether each port can be reached from a node.",
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/http-guide.html#serve-deployment-graph-dagdriver",
3217
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-the-applications",
3218
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started"
3219
            ],
3220
            "document_ids": [
3221
                13660,
3222
                13426,
3223
                13502,
3224
                13690,
3225
                13680
3226
            ],
3227
            "answer": " No, you don't necessarily need the DAGDriver to deploy a RayServe application. The DAGDriver is used when you want to create a deployment graph and route HTTP requests through your call graph. However, if your application doesn't require a deployment graph, you can directly deploy your Ray Serve applications using the `serve.deployment` decorator and `serve deploy` command. The context provided gives examples of using the DAGDriver, but it's not a strict requirement for every RayServe deployment.",
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/workflows/basics.html#sub-task-results",
3234
                "https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",
3235
                "https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",
3236
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",
3237
                "https://docs.ray.io/en/master/serve/tutorials/batch.html#define-the-deployment"
3238
            ],
3239
            "document_ids": [
3240
                9636,
3241
                13844,
3242
                13850,
3243
                5678,
3244
                14580
3245
            ],
3246
            "answer": " To import a simple RayServe deployment, you need to provide the import path in the following format: `<directory_name>.<file_name>:<variable_name>`. For example, if you have a file called `app.py` in a directory called `mobilenet`, and the Ray Serve application is assigned to the variable `app` in the file, the import path would be `mobilenet.app:app`.\n\nIn the example you provided, the import path would be `tutorial_batch.py:app` if you have a Ray Serve application assigned to the variable `app` in the `tutorial_batch.py` file.\n\nHere is an example of how you might use this import path in your code:\n\n```python\nfrom ray import serve\n\nserve.start(import_path='tutorial_batch.py:app')\n```",
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/rayjob-quick-start.html#step-3-install-a-rayjob",
3254
                "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",
3255
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",
3256
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status"
3257
            ],
3258
            "document_ids": [
3259
                5751,
3260
                5760,
3261
                5729,
3262
                5762,
3263
                5763
3264
            ],
3265
            "answer": " Yes, the context includes an example of using RayJob in Kuberay. Here are the steps to follow:\n\n1. Download the `ray_v1alpha1_rayjob.yaml` file using the following command:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml\n```\n2. Create a RayJob by applying the `ray_v1alpha1_rayjob.yaml` file using the following command:\n```\nkubectl apply -f ray_v1alpha1_rayjob.yaml\n```\n3. Check the status of the RayJob by running the following command:\n```bash\nkubectl get rayjobs.ray.io rayjob-sample -o json | jq '.status.jobStatus'\n```\nThe output should show `\"SUCCEEDED\"` if the job has finished successfully.\n\nNote that RayJob support in KubeRay v0.x is in alpha.",
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/getting-started.html#metrics-view",
3274
                "https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",
3275
                "https://docs.ray.io/en/master/serve/monitoring.html#exporting-metrics-into-arize"
3276
            ],
3277
            "document_ids": [
3278
                503,
3279
                5573,
3280
                438,
3281
                656,
3282
                13550
3283
            ],
3284
            "answer": " No, Ray metrics do not have to be exported via an actor. Ray provides a convenient API for defining and exporting custom metrics at the application level, and it also exports default metrics that can be viewed in the Metrics view. Additionally, Ray has the flexibility to export metrics into other observability platforms like Arize.",
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/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",
3292
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
3293
                "https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",
3294
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node"
3295
            ],
3296
            "document_ids": [
3297
                4959,
3298
                6473,
3299
                14748,
3300
                4674,
3301
                4960
3302
            ],
3303
            "answer": " To avoid objects from getting spilled, you can increase the capacity of the object store by using high-performance storage such as NVMe SSDs. If you're using Ray, you can configure the directory where objects are spilled to by using the `object_spilling_config` parameter in the `ray.init()` function. Additionally, you can specify multiple directories for spilling to distribute the IO load and disk space usage across multiple physical devices. However, keep in mind that object spilling can cause significant performance problems since data needs to be written to disk.",
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-core/api/cli.html#ray-core-cli",
3310
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",
3311
                "https://docs.ray.io/en/master/data/data.html#learn-more",
3312
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
3313
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice"
3314
            ],
3315
            "document_ids": [
3316
                4469,
3317
                4701,
3318
                6995,
3319
                5656,
3320
                5780
3321
            ],
3322
            "answer": " Ray Core is a distributed computing system that provides primitives like tasks, actors, and objects for building and scaling applications. It offers a Command Line Interface (CLI) and can be used with Kubernetes. You can also find Ray blogs for more information and read about RayService.",
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/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
3329
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
3330
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",
3331
                "https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",
3332
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs"
3333
            ],
3334
            "document_ids": [
3335
                5757,
3336
                13980,
3337
                410,
3338
                9574,
3339
                415
3340
            ],
3341
            "answer": " No, Ray does not directly support cron jobs. However, you can use an external job scheduler that can interact with your Ray cluster to trigger workflow runs according to a cron schedule. Ray Workflows does not have a built-in job scheduler.\n\nFor Ray Job logs, Worker logs are always captured in the Ray logging directory, while Driver logs are captured only for Ray Jobs submitted via Jobs 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-more-libs/installation.html#docker-source-images",
3348
                "https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",
3349
                "https://docs.ray.io/en/master/installation.html#docker-source-images",
3350
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",
3351
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning"
3352
            ],
3353
            "document_ids": [
3354
                14593,
3355
                306,
3356
                81,
3357
                10238,
3358
                6011
3359
            ],
3360
            "answer": " The `read_images` function depends on the `numpy`, `PIL`, `os`, `requests`, and `io` libraries, which are not specific to Ray. When running this function in a Ray-based environment, you can use the `rayproject/ray` or `rayproject/ray-ml` Docker images, which include Ray and various versions of Python, as well as additional ML libraries in the case of `rayproject/ray-ml`. These images come with most of the required dependencies, but you might need to add custom dependencies using one of the methods suggested in the \"Dependencies for GPU-based machine learning\" context, such as building a Docker image with additional libraries or using Ray Runtime environments.",
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/production-guide/fault-tolerance.html#deployment-replica-failure",
3369
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
3370
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure"
3371
            ],
3372
            "document_ids": [
3373
                13912,
3374
                13905,
3375
                13911,
3376
                13901,
3377
                13903
3378
            ],
3379
            "answer": " To kill a specific serve replica using the provided context, you can follow these steps:\n\n1. First, list the available actors using `ray list actors` command in the terminal.\n2. Identify the NAME of the specific ServeReplica you want to kill. For example, \"SERVE_REPLICA::SleepyPid#PHvdJW\".\n3. Get a handle to the ServeReplica using the following command in the Python interpreter:\n```python\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#PHvdJW\", namespace=\"serve\")\n```\n4. Kill the ServeReplica using the following command in the Python interpreter:\n```python\nray.kill(replica_handle, no_restart=True)\n```\nThis will kill the specific ServeReplica without restarting it.",
3380
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3381
        }
3382
    ]
3383
}
3384