llm-applications

1
{
2
    "config": {
3
        "experiment_name": "gte-large-fine-tuned-fp",
4
        "chunk_size": 700,
5
        "chunk_overlap": 50,
6
        "num_chunks": 13,
7
        "embedding_model_name": "/efs/shared_storage/goku/gte-large-fine-tuned-fp",
8
        "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1",
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        "temperature": 0.0,
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        "max_context_length": 32768,
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        "system_content": "Answer the query using the context provided. Be succinct.",
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        "assistant_content": "",
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/examples/batch_inference_object_detection.html#preprocessing",
23
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
24
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
25
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",
26
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data",
27
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
28
                "https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",
29
                "https://docs.ray.io/en/master/data/data-internals.html#execution-memory",
30
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
31
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
32
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",
33
                "https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",
34
                "https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors"
35
            ],
36
            "document_ids": [
37
                8046,
38
                6835,
39
                7081,
40
                6782,
41
                9212,
42
                5347,
43
                7633,
44
                6895,
45
                7360,
46
                7796,
47
                6609,
48
                7865,
49
                6914
50
            ],
51
            "answer": " When using `map_batches`, you'll need to make sure your function is vectorized and can process a batch of rows at once, instead of individual rows. This is because `map_batches` applies the function to batches of data, which can be more efficient.\n\nIn your function, you should return a batch of rows, where each row is a dictionary. The batch should have the same size as the input batch.\n\nHere's an example of how you can use `map_batches`:\n```python\nimport numpy as np\n\ndef process_batch(batch):\n    # process the entire batch of rows here\n    # for example, add a new column with the mean of a column\n    batch[\"new_column\"] = np.mean(batch[\"existing_column\"], axis=0)\n    return batch\n\nds = ds.map_batches(process_batch)\n```\nIn this example, `process_batch` takes a batch of rows as input, computes the mean of the `existing_column` for each row in the batch, and adds a new column `new_column` with the result.\n\nNote that the size of the batch should be small enough to fit into memory, to avoid out-of-memory situations. You can adjust the batch size by passing the `batch_size` argument to `map_batches`.\n\nAlso, keep in mind that `map_batches` is not applicable for unstructured data. If you're working with unstructured data, you should use Ray Data operations such as `map` instead.",
52
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
53
        },
54
        {
55
            "question": "How does autoscaling work in a Ray Serve application?",
56
            "sources": [
57
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
58
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
59
                "https://docs.ray.io/en/master/workflows/basics.html#ray-integration",
60
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
61
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
62
                "https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#dynamic-request-batching",
63
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",
64
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
65
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
66
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice",
67
                "https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",
68
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
69
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving"
70
            ],
71
            "document_ids": [
72
                13974,
73
                4811,
74
                9685,
75
                5380,
76
                6615,
77
                13693,
78
                5648,
79
                13596,
80
                13598,
81
                5780,
82
                13420,
83
                14255,
84
                144
85
            ],
86
            "answer": " Ray Serve, a model serving library built on Ray, allows for dynamic request batching to improve service throughput. It can take advantage of vectorized computation in ML frameworks like Tensorflow, PyTorch, and Scikit-Learn to evaluate multiple samples at once. Autoscaling in Ray Serve is achieved through Ray's inherent scalability and flexible scheduling support, such as fractional GPUs, enabling efficient resource utilization and low-cost serving of multiple machine learning models. Additionally, Ray Serve allows for model composition and flexible scaling by enabling multiple independent deployments to call into each other.",
87
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
88
        },
89
        {
90
            "question": "how do I get the address of a ray node",
91
            "sources": [
92
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",
93
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",
94
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
95
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.nodes.html#ray-nodes",
96
                "https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#system-metrics",
97
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
98
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
99
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
100
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",
101
                "https://docs.ray.io/en/master/ray-contribute/profiling.html#running-microbenchmarks",
102
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
103
                "https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",
104
                "https://docs.ray.io/en/master/workflows/basics.html#ray-integration"
105
            ],
106
            "document_ids": [
107
                6224,
108
                4438,
109
                493,
110
                4769,
111
                654,
112
                4662,
113
                6835,
114
                4824,
115
                4440,
116
                942,
117
                739,
118
                5587,
119
                9685
120
            ],
121
            "answer": " To get the address of a Ray node, you can use the `ray.nodes()` function, which returns information about the nodes in the Ray cluster. The information returned includes the `NodeManagerAddress` and `MetricsExportPort` for each node. You can combine these to get the address of the node. Here is an example:\n```\nimport ray\nray.init()\n\nnodes = ray.nodes()\nfor node in nodes:\n    node_address = node['NodeManagerAddress'] + ':' + str(node['MetricsExportPort'])\n    print(node_address)\n```\nThis will print the address for each node in the Ray cluster.\n\nIf you want to get the address of a specific node, you can use the `node_id` to filter the nodes returned by `ray.nodes()`. For example:\n```\nnode_id = '2691a0c1aed6f45e262b2372baf58871734332d7'\nnode = next((n for n in nodes if n['NodeID'] == node_id), None)\nif node:\n    node_address = node['NodeManagerAddress'] + ':' + str(node['MetricsExportPort'])\n    print(node_address)\n```\nThis will print the address of the node with the given `node_id`.\n\nNote that the address returned is the address of the metrics agent for the node, not the address of the node itself. If you want to get the IP address of the node, you can use the `NodeManagerHostname` field instead of the `NodeManagerAddress` field.\n\nAlso, the `MetricsExportPort` is used for the metrics agent, if you want to get the address of the object store, you can use the `ObjectManagerPort` field.\n\nFinally, the above examples are using the Python SDK, if you are using the CLI, you can use the `ray node get-address` command to get the address of a node.\n\nFor example:\n```\nray node get-address <node_id>\n```\nThis will return the address of the node with the given `node_id`.\n\nYou can also use the `ray node get-addresses` command to get the address of all nodes in the cluster.\n\nFor example:\n```\nray node get-addresses\n```\nThis will return the address of all nodes in the cluster.\n\nYou can also use the `ray node ls` command to get the information of all nodes in the cluster, including the `NodeManagerAddress` and `MetricsExportPort` fields.\n\nFor example:\n```\nray node ls\n```\nThis will return the information of all nodes in the cluster.\n\nYou can then use the `NodeManagerAddress` and `MetricsExportPort` fields to construct the address of the metrics agent for each node.\n\nI hope this helps! Let me know if you have any questions.",
122
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
123
        },
124
        {
125
            "question": "Does Ray support NCCL?",
126
            "sources": [
127
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
128
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
129
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
130
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
131
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
132
                "https://docs.ray.io/en/master/data/data.html#learn-more",
133
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment",
134
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#where-to-find-log-to-file-files",
135
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
136
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
137
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
138
                "https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",
139
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart"
140
            ],
141
            "document_ids": [
142
                6015,
143
                6208,
144
                4811,
145
                5556,
146
                5656,
147
                6910,
148
                1386,
149
                2363,
150
                6835,
151
                4662,
152
                5380,
153
                8331,
154
                245
155
            ],
156
            "answer": " Yes, Ray does support NCCL (NVIDIA Collective Communications Library) for distributed training. You can find more information about this in ticket [this ticket](https://github.com/ray-project/ray/issues/32142). Ray also provides integrations with other distributed training systems through Ray Train.",
157
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
158
        },
159
        {
160
            "question": "Is Ray integrated with DeepSpeed?",
161
            "sources": [
162
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
163
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
164
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
165
                "https://docs.ray.io/en/master/workflows/basics.html#ray-integration",
166
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
167
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
168
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
169
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice",
170
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
171
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",
172
                "https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",
173
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
174
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig"
175
            ],
176
            "document_ids": [
177
                4811,
178
                6015,
179
                10128,
180
                9685,
181
                6835,
182
                4662,
183
                13974,
184
                5780,
185
                6208,
186
                10058,
187
                8331,
188
                14255,
189
                1782
190
            ],
191
            "answer": " Based on the provided context, there is no direct mention of Ray being integrated with DeepSpeed. The context includes various Ray components, integrations, and APIs, but DeepSpeed is not mentioned.",
192
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
193
        },
194
        {
195
            "question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",
196
            "sources": [
197
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
198
                "https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example",
199
                "https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",
200
                "https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",
201
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",
202
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",
203
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",
204
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
205
                "https://docs.ray.io/en/master/ray-core/patterns/concurrent-operations-async-actor.html#example-use-case",
206
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#running-a-task",
207
                "https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",
208
                "https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",
209
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle"
210
            ],
211
            "document_ids": [
212
                4205,
213
                5197,
214
                13646,
215
                5248,
216
                4600,
217
                4599,
218
                5236,
219
                4208,
220
                5167,
221
                4486,
222
                13643,
223
                4598,
224
                14277
225
            ],
226
            "answer": " Using `asyncio`'s `await` keyword to wait for a Ray future like `await x.remote()` is valid, as Ray methods return ObjectRefs that can be awaited. However, keep in mind that `ray.get(x.remote())` is a blocking operation, so it may affect the concurrency of your application. If you want to maintain concurrency, consider using async Ray methods like `ray.get(x.remote())` with the `async/await` pattern.",
227
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
228
        },
229
        {
230
            "question": "How would you compare Spark, Ray, Dask?",
231
            "sources": [
232
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDDPStrategy.html#ray-train-lightning-rayddpstrategy",
233
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayFSDPStrategy.html#ray-train-lightning-rayfsdpstrategy",
234
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.html#ray-train-lightning-raydeepspeedstrategy",
235
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
236
                "https://docs.ray.io/en/master/ray-overview/ray-libraries.html#the-ray-ecosystem",
237
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
238
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.FINISHED.html#ray-tune-search-searcher-finished",
239
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
240
                "https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",
241
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#fine-tune-vicuna-13b-with-lightning-and-deepspeed",
242
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#fine-tune-vicuna-13b-with-lightning-and-deepspeed",
243
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
244
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDDPStrategy.strategy_name.html#ray-train-lightning-rayddpstrategy-strategy-name"
245
            ],
246
            "document_ids": [
247
                8556,
248
                9020,
249
                8780,
250
                6015,
251
                216,
252
                4811,
253
                1324,
254
                5556,
255
                14653,
256
                9335,
257
                9336,
258
                14730,
259
                8932
260
            ],
261
            "answer": " Spark, Ray, and Dask are all distributed computing frameworks, but they have different focuses and use cases.\n\nSpark is a general-purpose cluster computing system that can handle batch processing, interactive queries, and streaming data. It has a large and active community, and many big data tools and libraries integrate with it. RayDP (\"Spark on Ray\") enables you to use Spark inside a Ray program, combining the data processing capabilities of Spark with the machine learning and scalability of Ray.\n\nRay is a scalable and flexible framework for building and running distributed applications. It is designed to be easy to use and efficient for a wide range of use cases, including machine learning, reinforcement learning, and high-performance computing. Ray has integrations with popular deep learning frameworks like TensorFlow and PyTorch, and it also has its own machine learning library, Ray AIR.\n\nDask is a flexible parallel computing library for analytic computing, built with the Python ecosystem in mind. It is designed to integrate with existing Python libraries and tools, and it can be used for tasks like parallel computing, distributed data processing, and machine learning. Dask has integrations with popular data science libraries like Pandas, NumPy, and Scikit-learn.\n\nIn summary, Spark is a general-purpose data processing framework with a large community and many integrations, Ray is a flexible and scalable framework for building distributed applications, and Dask is a parallel computing library for analytic computing in Python. RayDP enables you to use Spark inside a Ray program, combining the strengths of both frameworks.",
262
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
263
        },
264
        {
265
            "question": "why would ray overload a node w/ more task that the resources allow ?",
266
            "sources": [
267
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
268
                "https://docs.ray.io/en/master/ray-core/tasks.html#scheduling",
269
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",
270
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",
271
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#locality-aware-scheduling",
272
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks",
273
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",
274
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#custom-resources",
275
                "https://docs.ray.io/en/master/ray-core/actors.html#scheduling",
276
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#scheduling",
277
                "https://docs.ray.io/en/master/rllib/actors.html#scheduling",
278
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#scheduling",
279
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks"
280
            ],
281
            "document_ids": [
282
                5112,
283
                4376,
284
                5231,
285
                5104,
286
                5152,
287
                4199,
288
                6504,
289
                5106,
290
                4500,
291
                14704,
292
                11593,
293
                5137,
294
                5230
295
            ],
296
            "answer": " Ray's scheduling decisions are based on a task or actor's resource requirements and the available resources on a node. By default, Ray tasks use 1 logical CPU resource and Ray actors use 1 logical CPU for scheduling, and 0 logical CPU for running. If a task or actor requests more resources than available on a node, Ray will not schedule it on that node to avoid overloading the node's resources.\n\nHowever, Ray's resources are logical, not physical, meaning that specifying resource requirements does not impose limits on actual physical resource usage. It's the user's responsibility to ensure that tasks or actors use no more resources than specified via resource requirements.\n\nTherefore, if Ray is overloading a node with more tasks than the resources allow, it may be due to misconfiguration of resource requirements or exceeding the physical resource limits. In this case, reducing the number of concurrently running tasks or actors on each node by increasing the amount of resources requested by them can help prevent overloading the node's resources.",
297
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
298
        },
299
        {
300
            "question": "when should I use Ray Client?",
301
            "sources": [
302
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
303
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
304
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment",
305
                "https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#key-concepts",
306
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
307
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
308
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",
309
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
310
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTrainingReplay.NOOP.html#ray-tune-schedulers-populationbasedtrainingreplay-noop",
311
                "https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve",
312
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
313
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.NOOP.html#ray-tune-schedulers-trialscheduler-noop",
314
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-6-repeat-step-3-and-step-4"
315
            ],
316
            "document_ids": [
317
                4811,
318
                6615,
319
                1386,
320
                9123,
321
                1782,
322
                5380,
323
                6611,
324
                6208,
325
                1410,
326
                2748,
327
                1113,
328
                1495,
329
                5987
330
            ],
331
            "answer": " You should use the Ray Client when you want to connect an interactive Python shell to a remote cluster. It is not recommended to use Ray Client for ML workloads like Ray Tune or Ray Train. Use `ray.init(\"ray://<head_node_host>:10001\")` to connect your shell to the cluster.",
332
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
333
        },
334
        {
335
            "question": "how to scatter actors across the cluster?",
336
            "sources": [
337
                "https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",
338
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",
339
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
340
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
341
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",
342
                "https://docs.ray.io/en/master/ray-core/api/utility.html#utility",
343
                "https://docs.ray.io/en/master/data/batch_inference.html#handling-cpu-out-of-memory-failures",
344
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
345
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
346
                "https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",
347
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",
348
                "https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",
349
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors"
350
            ],
351
            "document_ids": [
352
                5195,
353
                5391,
354
                6787,
355
                6954,
356
                8157,
357
                4677,
358
                6961,
359
                6949,
360
                8114,
361
                6952,
362
                8101,
363
                659,
364
                788
365
            ],
366
            "answer": " To scatter actors across a cluster, you can use the `ActorClass.remote()` method to create an instance of the actor on a remote worker process. The return value of this method is an actor handle that can be used to call the actor's methods. For example:\n```\n# Create the progress actor.\nprogress_actor = ProgressActor.remote(TOTAL_NUM_SAMPLES)\n```\nYou can also use the `ray.data.ActorPoolStrategy` class to specify a fixed pool of actors to use for a computation. This can be useful if you have multiple tasks that need to wait on some condition or synchronize across tasks and actors on a cluster. For example:\n```\n# Set the ActorPool size to the number of GPUs in your cluster.\ncompute=ray.data.ActorPoolStrategy(size=2)\n```\nAdditionally, you can use the `ray.util.ActorPool` utility class to operate on a fixed pool of actors. This can be useful for managing a pool of actors and their resources. For example:\n```\n# The actor pool will have at least 2 workers and at most 8 workers.\nstrategy = ActorPoolStrategy(min_size=2, max_size=8)\n```\nIt's also a good idea to consider the number of CPUs in your cluster when setting the number of actors and the CPUs per actor. A common approach is to set the number of actors to the greatest common divisor (GCD) of the number of CPUs in your cluster, and the CPUs per actor to the GCD. This can help ensure that the actors are evenly distributed across the cluster and that they have enough resources to perform their tasks. For example:\n```\n# Use 6 actors, each actor using 4 CPUs. 4 * 6 = 24 CPUs total.\nactors = [Actor.remote() for _ in range(6)]\nstrategy = ActorPoolStrategy(actors=actors, cpus_per_actor=4)\n```\nYou can then use the `map_batches()` method to transform batches with the actors in the pool. For example:\n```\n# Transform batches with the actors in the pool.\nds.map_batches(MyActor, compute=strategy).take()\n```\nThis will distribute the batches across the actors in the pool and apply the `MyActor` class to each batch. The results will be returned as a Ray Dataset.\n\nIn summary, to scatter actors across a cluster, you can use the `ActorClass.remote()` method to create instances of the actor on remote worker processes, and the `ray.data.ActorPoolStrategy` class to specify a fixed pool of actors to use for a computation. You can also use the `ray.util.ActorPool` utility class to manage a pool of actors and their resources. It's a good idea to consider the number of CPUs in your cluster when setting the number of actors and the CPUs per actor, and to use the `map_batches()` method to transform batches with the actors in the pool.",
367
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
368
        },
369
        {
370
            "question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",
371
            "sources": [
372
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",
373
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",
374
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",
375
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",
376
                "https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#helpful-ray-commands",
377
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",
378
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#using-ray-cli-tools-from-outside-the-cluster",
379
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",
380
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",
381
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#id1",
382
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
383
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",
384
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages"
385
            ],
386
            "document_ids": [
387
                6436,
388
                6609,
389
                6610,
390
                4264,
391
                5525,
392
                6510,
393
                501,
394
                5671,
395
                6619,
396
                5940,
397
                6562,
398
                14419,
399
                4266
400
            ],
401
            "answer": " The `ray debug` command is likely giving a connection refused error because the Ray cluster is not able to establish a connection with the head node at the specified address (ray://<head\\_node\\_host>:10001). This could be due to several reasons such as incorrect head node host, firewall settings, or the cluster not being up and running. You can try checking if the cluster is up and running, and if the head node host is correct. Additionally, you can check if there are any firewall settings blocking the connection.",
402
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
403
        },
404
        {
405
            "question": "How does Ray AIR set up the model to communicate gradient updates across machines?",
406
            "sources": [
407
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",
408
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",
409
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-scale-out-training",
410
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide",
411
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",
412
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#adding-deleting-and-updating-applications",
413
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
414
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",
415
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningConfigBuilder.checkpointing.html#ray-train-lightning-lightningconfigbuilder-checkpointing",
416
                "https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",
417
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
418
                "https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#fine-tuning-dreambooth-with-ray-train",
419
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tune-a-transformers-model"
420
            ],
421
            "document_ids": [
422
                10019,
423
                4185,
424
                8153,
425
                5784,
426
                9819,
427
                13661,
428
                4818,
429
                5974,
430
                8371,
431
                8331,
432
                4662,
433
                10147,
434
                9999
435
            ],
436
            "answer": " Ray AIR sets up model communication for gradient updates across machines using PyTorch DDP (Distributed Data-Parallel). This is done by creating a Transformers Trainer inside a function, trainer\\_init\\_per\\_worker, which is then passed to the TransformersTrainer and runs on every Ray worker. The training then proceeds with PyTorch DDP, with the model, metric, and tokenizer initialized inside the function. Ray automatically handles the distribution of workers and checkpoints the model at every epoch for manual pushing to the hub.",
437
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
438
        },
439
        {
440
            "question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",
441
            "sources": [
442
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
443
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
444
                "https://docs.ray.io/en/master/serve/index.html#why-choose-serve",
445
                "https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#dynamic-request-batching",
446
                "https://docs.ray.io/en/master/ray-air/getting-started.html#ray-for-ml-infrastructure",
447
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
448
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
449
                "https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",
450
                "https://docs.ray.io/en/master/ray-air/getting-started.html#pick-and-choose-your-own-libraries",
451
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
452
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#ml-platform",
453
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",
454
                "https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve"
455
            ],
456
            "document_ids": [
457
                13974,
458
                13623,
459
                13614,
460
                13693,
461
                9807,
462
                13597,
463
                13598,
464
                13619,
465
                9814,
466
                13620,
467
                152,
468
                144,
469
                2748
470
            ],
471
            "answer": " Ray Serve is a framework-agnostic model serving library that allows you to serve various types of models, including deep learning models and Scikit-Learn models, and supports features such as dynamic request batching, response streaming, and multi-node/multi-GPU serving. It is well-suited for model composition and can integrate with any model optimization technique. In contrast, Modal and Seldon are focused on serving and scaling models independently and may not offer the same level of flexibility and features as Ray Serve. While you could use containers to serve your models, Ray Serve provides additional features such as best-in-class autoscaling and natural integration with business logic, which can be beneficial for building production-ready machine learning applications.",
472
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
473
        },
474
        {
475
            "question": "How do I deploy an LLM workload on top of Ray Serve?",
476
            "sources": [
477
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
478
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
479
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
480
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments",
481
                "https://docs.ray.io/en/master/serve/production-guide/docker.html#adding-your-serve-application-to-the-docker-image",
482
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
483
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",
484
                "https://docs.ray.io/en/master/serve/api/index.html#serve-start",
485
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",
486
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice",
487
                "https://docs.ray.io/en/master/serve/key-concepts.html#deployment",
488
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
489
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment"
490
            ],
491
            "document_ids": [
492
                13974,
493
                14255,
494
                13677,
495
                13727,
496
                13924,
497
                6615,
498
                13832,
499
                13994,
500
                5785,
501
                5739,
502
                13416,
503
                13596,
504
                13776
505
            ],
506
            "answer": " To deploy an LLM (large language model) workload on top of Ray Serve, you can follow these steps:\n\n1. Create a Ray Serve config file (e.g., `llm_config.yaml`) that defines your deployment, including the number of replicas and required resources.\n2. Use the `ray serve deploy` command to deploy the config file to a Ray cluster. This can be a local, single-node cluster or a remote, multi-node cluster started with the Ray Cluster Launcher.\n3. If you're using a Docker image for production, add your Serve application to the Dockerfile using the `WORKDIR` and `COPY` commands.\n4. Once the deployment is created, you can use the Ray Client to interact with the Ray Serve API and send requests to your LLM workload.\n\nHere's an example of how to deploy the `llm_config.yaml` file:\n\n```bash\n$ ray start --head\n$ serve deploy llm_config.yaml\n...",
507
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
508
        },
509
        {
510
            "question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",
511
            "sources": [
512
                "https://docs.ray.io/en/master/serve/deploy-many-models/model-multiplexing.html#writing-a-multiplexed-deployment",
513
                "https://docs.ray.io/en/master/serve/deploy-many-models/model-multiplexing.html#writing-a-multiplexed-deployment",
514
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
515
                "https://docs.ray.io/en/master/serve/deploy-many-models/model-multiplexing.html#writing-a-multiplexed-deployment",
516
                "https://docs.ray.io/en/master/rllib/rllib-catalogs.html#catalog-alpha",
517
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
518
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",
519
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.multiplexed.html#ray-serve-multiplexed",
520
                "https://docs.ray.io/en/master/rllib/rllib-models.html#more-examples-for-building-custom-models",
521
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.get_multiplexed_model_id.html#ray-serve-get-multiplexed-model-id",
522
                "https://docs.ray.io/en/master/rllib/rllib-models.html#built-in-models",
523
                "https://docs.ray.io/en/master/rllib/rllib-models.html#default-model-config-settings",
524
                "https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint"
525
            ],
526
            "document_ids": [
527
                13671,
528
                13673,
529
                9953,
530
                13675,
531
                11484,
532
                4878,
533
                9290,
534
                14253,
535
                11365,
536
                14035,
537
                11247,
538
                11245,
539
                8799
540
            ],
541
            "answer": " Based on the context provided, the model size for \"EleutherAI/gpt-j-6B\" is approximately 402 MB. This information can be found in the logs where the model parameters are displayed. When setting the `model_id` to this value, you should allocate at least this amount of memory to ensure the model can be loaded and cached properly.",
542
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
543
        },
544
        {
545
            "question": "How do I log the results from multiple distributed workers into a single tensorboard?",
546
            "sources": [
547
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#caveats",
548
                "https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#how-to-obtain-and-aggregate-results-from-different-workers",
549
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",
550
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
551
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",
552
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-component-logs",
553
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
554
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#application-logs",
555
                "https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#monitoring-and-logging-metrics",
556
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3",
557
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",
558
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.rollouts.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-rollouts",
559
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers"
560
            ],
561
            "document_ids": [
562
                2361,
563
                9265,
564
                411,
565
                445,
566
                519,
567
                517,
568
                539,
569
                514,
570
                9260,
571
                533,
572
                6530,
573
                12628,
574
                10999
575
            ],
576
            "answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use the Ray Train's `train.report()` function to report metrics from the training function executed on distributed workers. The results will be collected from the distributed workers and passed to the driver to be logged and displayed in TensorBoard. Note that only the results from rank 0 worker will be used, but `train.report()` has to be called on each worker to ensure consistency.\n\nAdditionally, you can customize the worker process loggers by customizing the logger configuration when defining the Tasks or Actors. This can be done by setting the severity level of the logger to INFO or higher to print info logs to stdout.\n\nFor more detailed logs, you can check the log files generated by Ray workers, such as `python-core-worker-[worker_id]_[pid].log`, `raylet.[out|err]`, `redis-shard_[shard_index].[out|err]`, `redis.[out|err]`, and `runtime_env_agent.log`. These log files can be found in the Ray cluster's log directory.\n\nIf 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 see the Driver logs in the terminal or Jupyter Notebook output. Task and Actor logs are accessible from the Task and Actor table view in the Ray Dashboard.",
577
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
578
        },
579
        {
580
            "question": "how do you config SyncConfig for a Ray AIR job?",
581
            "sources": [
582
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
583
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",
584
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",
585
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#run-the-workload",
586
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",
587
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
588
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",
589
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-jobs",
590
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",
591
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
592
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
593
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-scale-out-training",
594
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"
595
            ],
596
            "document_ids": [
597
                5757,
598
                10027,
599
                10019,
600
                6525,
601
                5760,
602
                5556,
603
                9820,
604
                5560,
605
                6637,
606
                5380,
607
                14255,
608
                8153,
609
                4811
610
            ],
611
            "answer": " To configure `SyncConfig` for a Ray AIR job, you need to create a function (e.g., `trainer_init_per_worker`) that initializes the model, metric, and tokenizer, and returns the `TransformersTrainer`. This function will be passed to the `TransformersTrainer` and run on every Ray worker. The training will then proceed by the means of PyTorch DDP.\n\nHere is an example of how to define `SyncConfig` in a Ray AIR job:\n\n```python\nfrom ray import tune\n\nsync_config = tune.SyncConfig(\n    backend=\"nccl\",\n    gradient_aggregation_rule=\"sum\"\n)\n```\n\nYou can then pass this `sync_config` to the `TransformersTrainer` when creating it inside the `trainer_init_per_worker` function.\n\nNote that you should initialize the model, metric, and tokenizer inside the `trainer_init_per_worker` function, and make sure that `push_to_hub=True` is not supported in Ray AIR. Ray will, however, checkpoint the model at every epoch, allowing you to push it to the hub manually after the training.",
612
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
613
        },
614
        {
615
            "question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",
616
            "sources": [
617
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",
618
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
619
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
620
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",
621
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
622
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#job-profiling",
623
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
624
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",
625
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/observability.html#method-3-check-logs-of-ray-pods",
626
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",
627
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
628
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
629
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures"
630
            ],
631
            "document_ids": [
632
                5764,
633
                6157,
634
                444,
635
                415,
636
                13523,
637
                422,
638
                6631,
639
                6181,
640
                5855,
641
                6530,
642
                4541,
643
                445,
644
                4533
645
            ],
646
            "answer": " To quickly narrow down the root cause of a failed Ray job, follow these steps:\n\n1. Access the Ray job logs using the command `kubectl logs -l=job-name=rayjob-sample`.\n2. Alternatively, use the command `ray job logs 'raysubmit_xxxxxxxxxxxxxxxx' --address http://127.0.0.1:8265 --follow` to follow the logs in real-time.\n3. Check the logs of Ray Pods by accessing the log files on the Pods with the command `kubectl exec -it $RAY_POD -n $YOUR_NAMESPACE -- bash` and then checking the logs under `/tmp/ray/session_latest/logs/`.\n4. Look for any error messages or exceptions in the logs. Ray surfaces application-level failures as Python-level exceptions, so pay close attention to any wrapped exceptions in the logs.\n5. If necessary, use log parsing tools like ELK or Loki to search the logs by deployment or replica.\n6. For Ray Jobs submitted via Jobs API, you can also view the Driver logs in the Dashboard UI, CLI, or Python SDK.\n7. If you're running Ray Jobs by executing the Ray Driver on the Head node directly or connecting via Ray Client, view the Driver logs in your terminal or Jupyter Notebooks.\n\nRemember to replace `raysubmit_xxxxxxxxxxxxxxxx` with the actual submission ID of your Ray Job.",
647
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
648
        },
649
        {
650
            "question": "How do I specify how many GPUs a serve deployment needs?",
651
            "sources": [
652
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",
653
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",
654
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#scaling-out",
655
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",
656
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",
657
                "https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",
658
                "https://docs.ray.io/en/master/serve/api/index.html#put-api-serve-deployments",
659
                "https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",
660
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#cli-best-practices",
661
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#build-serve-config-files-for-production-deployment",
662
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",
663
                "https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#in-place-updates-to-serve",
664
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment"
665
            ],
666
            "document_ids": [
667
                13561,
668
                13559,
669
                13777,
670
                13551,
671
                13776,
672
                13987,
673
                14000,
674
                13575,
675
                13830,
676
                13438,
677
                13560,
678
                13685,
679
                14260
680
            ],
681
            "answer": " To specify the number of GPUs a Serve deployment needs, you can use the `num_gpus` parameter in the `@serve.deployment` decorator or in the Serve config file. This parameter represents the logical number of GPUs each replica should reserve. It can be a fraction to pack multiple replicas together on a machine with fewer GPUs than replicas.\n\nHere is an example of using `num_gpus` in the `@serve.deployment` decorator:\n\n```python\n@serve.deployment(num_replicas=2, num_gpus=1.0)\nclass MyDeployment:\n    pass\n```\n\nAnd here is an example of using `num_gpus` in the Serve config file:\n\n```yaml\ndeployments:\n  - name: MyDeployment\n    num_replicas: 2\n    num_gpus: 1.0\n```\n\nRemember to use `serve build` command to generate a Serve config YAML file for production deployment.",
682
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
683
        },
684
        {
685
            "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?",
686
            "sources": [
687
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
688
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
689
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
690
                "https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",
691
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",
692
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#worker-node-failure",
693
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
694
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
695
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
696
                "https://docs.ray.io/en/master/ray-core/patterns/generators.html#code-example",
697
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
698
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
699
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure"
700
            ],
701
            "document_ids": [
702
                5129,
703
                13889,
704
                555,
705
                5638,
706
                14481,
707
                4527,
708
                467,
709
                13892,
710
                557,
711
                5229,
712
                5124,
713
                13898,
714
                13894
715
            ],
716
            "answer": " Based on the information provided, it seems like your worker node is running out of memory and is being killed due to memory pressure. You can debug this issue by:\n\n1. Checking the logs of the raylet on the affected node using the command: `ray logs raylet.out -ip 172.31.183.172`\n2. Checking the memory usage on the node by using `ray logs raylet.out -ip 10.0.62.231`\n3. Referring to the Ray documentation on how to address out-of-memory issues: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>\n4. Adjusting the workload to make it pass if it fails due to memory issues.\n\nThe error message also suggests that a SIGTERM signal was received by the raylet, which is a process that manages resources on the worker node. This could be due to the node running out of memory or other system resources.\n\nIt's also worth noting that the issue may not necessarily be with TensorflowTrainer itself, but rather with the resources available on the worker node. It's possible that the workload is too large for the available resources, or that there is a memory leak in the code.\n\nFinally, you may want to consider adjusting the worker killing policy to avoid killing workers due to memory pressure. The memory monitor in Ray avoids infinite loops of task retries by ensuring at least one task is able to run for each caller on each node. If it is unable to ensure this, the workload will fail with an OOM error. However, this is only an issue for tasks, since the memory monitor will not indefinitely retry actors. If the workload fails, you may need to adjust the workload to make it pass.",
717
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
718
        },
719
        {
720
            "question": "what are the possible reasons for nodes dying in a cluster?",
721
            "sources": [
722
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",
723
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",
724
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
725
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",
726
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
727
                "https://docs.ray.io/en/master/rllib/rllib-cli.html#inspecting-examples",
728
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#cluster-setting",
729
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
730
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
731
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
732
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
733
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",
734
                "https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like"
735
            ],
736
            "document_ids": [
737
                4525,
738
                4529,
739
                13898,
740
                4531,
741
                9524,
742
                11453,
743
                9337,
744
                368,
745
                5129,
746
                554,
747
                467,
748
                4790,
749
                5639
750
            ],
751
            "answer": " Based on the provided context, possible reasons for nodes dying in a Ray cluster include:\n\n1. Head node failure: When the head node fails, the entire Ray cluster fails. This can be simulated by killing the head pod or the head node.\n2. Raylet failure: When a raylet process fails, the corresponding node is marked as dead and treated as a new raylet/node to the Ray cluster.\n3. Memory pressure: Workers can be killed due to memory pressure or low memory on the node.\n4. User action: The cluster can be terminated due to user action.\n5. Overloading the head node: The head node can be overloaded with too many simultaneous connections, causing worker nodes to fail to connect to the cluster.\n\nTo mitigate these issues, it is important to ensure that the head node is fault-tolerant, monitor and manage memory usage, and start worker nodes slowly to avoid overloading the head node.",
752
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
753
        },
754
        {
755
            "question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",
756
            "sources": [
757
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
758
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",
759
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",
760
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
761
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",
762
                "https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",
763
                "https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",
764
                "https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",
765
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",
766
                "https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",
767
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
768
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
769
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster"
770
            ],
771
            "document_ids": [
772
                6365,
773
                851,
774
                6616,
775
                6363,
776
                6510,
777
                4644,
778
                5600,
779
                14710,
780
                6436,
781
                6715,
782
                9510,
783
                368,
784
                854
785
            ],
786
            "answer": " To programmatically set the size of a Ray remote cluster immediately, you can use the `ray.autoscaler.sdk.request_resources()` function within a Ray program. This function bypasses normal upscaling speed constraints and commands the autoscaler to scale the cluster up to the desired size.\n\nAlternatively, if you're using the Ray cluster launcher, you can configure the cluster's autoscaling settings using a YAML file and the `ray up` command. However, this method will not set the cluster size immediately and will follow the normal upscaling speed constraints.\n\nNote that these methods do not directly set the cluster size but rather request resources that trigger the autoscaler to adjust the number of nodes in the cluster.",
787
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
788
        },
789
        {
790
            "question": "how do you disable async iter_batches with Ray Dataset?",
791
            "sources": [
792
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
793
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment",
794
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
795
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
796
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
797
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.FINISHED.html#ray-tune-search-searcher-finished",
798
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
799
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
800
                "https://docs.ray.io/en/master/data/data.html#learn-more",
801
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#where-to-find-log-to-file-files",
802
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.bohb.TuneBOHB.FINISHED.html#ray-tune-search-bohb-tunebohb-finished",
803
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
804
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms"
805
            ],
806
            "document_ids": [
807
                4811,
808
                1386,
809
                5380,
810
                6015,
811
                6615,
812
                1324,
813
                1782,
814
                14255,
815
                6910,
816
                2363,
817
                1981,
818
                475,
819
                6208
820
            ],
821
            "answer": " To disable async iter_batches with Ray Dataset, you can set the `async_parallelism` parameter to 1 when creating the dataset. This will ensure that the dataset is processed synchronously.\n\nHere is an example:\n```python\ndataset = ray.data.read_parquet('s3://my-bucket/data/*.parquet', async_parallelism=1)\n```\nAlternatively, you can set the `parallelism` parameter to 1 when creating the dataset, which will also ensure synchronous processing.\n```python\ndataset = ray.data.read_parquet('s3://my-bucket/data/*.parquet', parallelism=1)\n```\nNote that setting `async_parallelism` or `parallelism` to a value greater than 1 can improve performance by processing multiple batches in parallel, but at the cost of increased memory usage.\n\nThe context provided does not contain any information about disabling `async_iter_batches` specifically, but the above solution should achieve the desired result.",
822
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
823
        },
824
        {
825
            "question": "what is the different between a batch and a block, for ray datasets?",
826
            "sources": [
827
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
828
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
829
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.rollouts.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-rollouts",
830
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers",
831
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
832
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.sample_batch.SampleBatch.INFOS.html#ray-rllib-policy-sample-batch-samplebatch-infos",
833
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.sample_batch.SampleBatch.VF_PREDS.html#ray-rllib-policy-sample-batch-samplebatch-vf-preds",
834
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
835
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#batch-training-with-ray-core",
836
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.sample_batch.SampleBatch.T.html#ray-rllib-policy-sample-batch-samplebatch-t",
837
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.batch.html#ray-serve-batch",
838
                "https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",
839
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.sample_batch.SampleBatch.DONES.html#ray-rllib-policy-sample-batch-samplebatch-dones"
840
            ],
841
            "document_ids": [
842
                10089,
843
                7081,
844
                12631,
845
                11002,
846
                14727,
847
                11868,
848
                12355,
849
                7087,
850
                5274,
851
                12873,
852
                14083,
853
                7632,
854
                12435
855
            ],
856
            "answer": " In the context of Ray Datasets, a \"batch\" refers to a set of data records that are processed together in memory, with a specified \"batch size\" determining the number of records in each batch. On the other hand, a \"block\" is a unit of data storage in Ray Datasets, which may contain a variable number of records. When using methods like `map_batches()` or `iter_batches()`, you can specify the `batch_format` to determine how batches are created from blocks.\n\nIn the context of reinforcement learning with Ray RLlib, a `SampleBatch` is a namedtuple that contains data about a batch of experiences, including features, actions, rewards, and other relevant information. The `batch_size` in this context refers to the number of experiences in the batch. The `INFOS`, `VF_PREDS`, `T`, and `DONES` attributes of a `SampleBatch` are used to store additional information about the batch.\n\nRay Serve's `batch()` function is used to apply a function to a group of records in a batch, with the `compute` strategy determining how the function is executed. The `batch_format` parameter can be used to specify the format of the input and output data.",
857
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
858
        },
859
        {
860
            "question": "How to setup the development environments for ray project?",
861
            "sources": [
862
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#development-workflow",
863
                "https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",
864
                "https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",
865
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
866
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-4-prepare-your-ray-serve-python-scripts-and-run-the-ray-serve-application",
867
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",
868
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
869
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",
870
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-4-run-an-application-on-a-raycluster",
871
                "https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-on-linux-macos-full",
872
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#walkthrough",
873
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
874
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart"
875
            ],
876
            "document_ids": [
877
                13709,
878
                10168,
879
                910,
880
                4811,
881
                5983,
882
                5760,
883
                6334,
884
                5788,
885
                5726,
886
                889,
887
                8078,
888
                6351,
889
                271
890
            ],
891
            "answer": " To set up the development environment for the Ray project, follow these steps:\n\n1. Clone the Ray repository and go to the example directory:\n```bash\ngit clone https://github.com/ray-project/ray.git\ncd doc/source/templates/05_dreambooth_finetuning\n```\n\n2. Install dependencies:\n```bash\npip install -Ur dreambooth/requirements.txt\n```\n\n3. Prepare some directories and environment variables:\n```bash\nexport DATA_PREFIX=\"/tmp\"\nexport ORIG_MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport ORIG_MODEL_HASH=\"b95be7d6f134c3a9e62ee616f310733567f069ce\"\nexport ORIG_MODEL_DIR=\"$DATA_PREFIX/model-orig\"\nexport ORIG_MODEL_PATH=\"$ORIG_MODEL_DIR/models--${ORIG_MODEL_NAME/\\\\//--}/snapshots/$ORIG_MODEL_HASH\"\n```\n\n4. Checkout the Ray repository and build Ray from source:\n```bash\n# Replace <your_sha> with your desired commit SHA\ngit checkout <your_sha>\n# Recompile if necessary\n# pip install -e .\n```\n\n5. Set up Ray Serve Python scripts and run the Ray Serve application:\n```bash\ngit clone https://github.com/ray-project/serve_config_examples.git\ncd serve_config_examples\n```\n\n6. (Optional) If you're working on a Kubernetes cluster, install a RayJob or RayService:\n   - For RayJob, follow steps 3.1 and 3.2 in the context.\n   - For RayService, follow steps 3.1 and 3.2 in the context.\n\n7. Build Ray for Linux & MacOS (full), if necessary:\n   - Follow the instructions in the context for building the dashboard and Ray for Python.\n\n8. Import Ray and initialize a local Ray cluster in your Python script:\n```python\nimport ray\nray.init()\n```",
892
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
893
        },
894
        {
895
            "question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",
896
            "sources": [
897
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",
898
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-jobs-cli-api-reference",
899
                "https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",
900
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
901
                "https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.list_all.html#ray-workflow-list-all",
902
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
903
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
904
                "https://docs.ray.io/en/master/data/examples/batch_training.html#walkthrough",
905
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
906
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",
907
                "https://docs.ray.io/en/master/ray-core/patterns/pipelining.html#code-example",
908
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",
909
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.GetTimeoutError.html#ray-exceptions-gettimeouterror"
910
            ],
911
            "document_ids": [
912
                6637,
913
                6593,
914
                4673,
915
                541,
916
                9793,
917
                4843,
918
                542,
919
                7863,
920
                675,
921
                6596,
922
                5189,
923
                6570,
924
                4796
925
            ],
926
            "answer": " Based on the context provided, the Ray Jobs API is recommended for running a job on a Ray cluster. The issue you are facing with ray rollout workers deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py` is not directly addressed in the provided context. However, the context suggests using the Ray Jobs API for submitting jobs and specifying the runtime environment in the Jobs API, not via `ray.init(runtime_env=...)` in the driver script.\n\nTo debug the deadlock issue, you can try enabling debug logging for Ray by setting the log level for the Ray logger to DEBUG in a `logging_setup_func()` function and using `worker_process_setup_hook` to apply the logging configuration to all worker processes within a job.\n\nHere's an example of how to do this:\n\n1. Define the `logging_setup_func()` function:\n```python\nimport logging\n\ndef logging_setup_func():\n    logger = logging.getLogger(\"ray\")\n    logger.setLevel(logging.DEBUG)\n    warnings.simplefilter(\"always\")\n```\n2. Use `worker_process_setup_hook` to apply the logging configuration to all worker processes within a job:\n```python\nray.init(runtime_env={\"worker_process_setup_hook\": logging_setup_func})\n```\n3. Run the job using the Ray Jobs API and specify the runtime environment in the Jobs API, not via `ray.init(runtime_env=...)` in the driver script.\n\nEnabling debug logging should provide more information about what's causing the deadlock issue and help you debug it.",
927
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
928
        },
929
        {
930
            "question": "how do I join two ray datasets?",
931
            "sources": [
932
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",
933
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
934
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
935
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
936
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
937
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",
938
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",
939
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
940
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
941
                "https://docs.ray.io/en/master/ray-contribute/getting-involved.html#more-resources-for-getting-involved",
942
                "https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",
943
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",
944
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"
945
            ],
946
            "document_ids": [
947
                6622,
948
                6615,
949
                4811,
950
                6015,
951
                14255,
952
                4193,
953
                4185,
954
                6208,
955
                4662,
956
                932,
957
                8331,
958
                4519,
959
                4814
960
            ],
961
            "answer": " To join two Ray datasets, you need to connect to multiple Ray clusters first. You can do this by initializing Ray clients with different cluster head nodes. Here is an example:\n```python\nimport ray\n\n# Create a default client.\nray.init(\"ray://<head_node_host_cluster>:10001\")\n\n# Connect to other clusters.\ncli1 = ray.init(\"ray://<head_node_host_cluster_1>:10001\", allow_multiple=True)\ncli2 = ray.init(\"ray://<head_node_host_cluster_2>:10001\", allow_multiple=True)\n\n# Put data into different clusters.\nobj = ray.put(\"obj\") # this goes to the default cluster\nwith cli1:\n    obj1 = ray.put(\"obj1\") # this goes to cluster 1\nwith cli2:\n    obj2 = ray.put(\"obj2\") # this goes to cluster 2\n\n# Now you can join datasets from different clusters using their object IDs.\n# For example, to join obj and obj1:\nobj_joined = ray.put(ray.get(obj) + ray.get(obj1))\n```\nNote that data is put into the default cluster, and you need to use the `with` statement to switch to other clusters to put data there. Once you have put data into different clusters, you can join their datasets using their object IDs and the `ray.put()` function.",
962
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
963
        },
964
        {
965
            "question": "Is there a way to retrieve an object ref from its id?",
966
            "sources": [
967
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",
968
                "https://docs.ray.io/en/master/ray-core/objects.html#objects",
969
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#dependency-management",
970
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle",
971
                "https://docs.ray.io/en/master/cluster/usage-stats.html#requesting-removal-of-collected-data",
972
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#set-up-ray-a-name-setup-a",
973
                "https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",
974
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
975
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",
976
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.trial_id.html#ray-tune-trainable-trial-id",
977
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",
978
                "https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",
979
                "https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor"
980
            ],
981
            "document_ids": [
982
                14041,
983
                4418,
984
                6586,
985
                14276,
986
                5569,
987
                10300,
988
                8609,
989
                4898,
990
                5039,
991
                2153,
992
                5426,
993
                4982,
994
                4984
995
            ],
996
            "answer": " Yes, object references (obj\\_refs) can be retrieved from their IDs in Ray by using the `ray.ObjectRef.from_object` class method or by storing the obj\\_refs in a data structure such as a list or dictionary and accessing them using their IDs. However, it is important to note that directly retrieving an obj\\_ref from its ID is not typically necessary, as obj\\_refs are usually returned by remote function calls or by calling `ray.put()`.",
997
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
998
        },
999
        {
1000
            "question": "how to create model Checkpoint from the model in memory?",
1001
            "sources": [
1002
                "https://docs.ray.io/en/master/tune/examples/includes/pbt_convnet_function_example.html#pbt-convnet-example",
1003
                "https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",
1004
                "https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#enabling-checkpointing-to-retrieve-the-model",
1005
                "https://docs.ray.io/en/master/tune/examples/tune-pytorch-cifar.html#the-train-function",
1006
                "https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",
1007
                "https://docs.ray.io/en/master/rllib/package_ref/algorithm.html#saving-and-restoring",
1008
                "https://docs.ray.io/en/master/rllib/package_ref/policy.html#id11",
1009
                "https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#what-s-a-checkpoint",
1010
                "https://docs.ray.io/en/master/tune/examples/includes/xgboost_dynamic_resources_example.html#xgboost-dynamic-resources-example",
1011
                "https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example",
1012
                "https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#class-api-checkpointing",
1013
                "https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint",
1014
                "https://docs.ray.io/en/master/train/user-guides/checkpoints.html#loading-checkpoints"
1015
            ],
1016
            "document_ids": [
1017
                4104,
1018
                9037,
1019
                9857,
1020
                3858,
1021
                8798,
1022
                11244,
1023
                11344,
1024
                10540,
1025
                3964,
1026
                4073,
1027
                2219,
1028
                9066,
1029
                9173
1030
            ],
1031
            "answer": " To create a model checkpoint from a model in memory, you can use the `tf.keras.callbacks.ModelCheckpoint` class in TensorFlow. Here's an example:\n```python\nfrom tensorflow.keras.callbacks import ModelCheckpoint\n\n# Create a callback that saves the model weights to a checkpoint file\ncheckpoint_callback = ModelCheckpoint(filepath=\"my_model_checkpoint.h5\", save_weights_only=True, save_best_only=True)\n\n# Define the model\nmodel = tf.keras.applications.ResNet101()\n",
1032
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1033
        },
1034
        {
1035
            "question": "what is Deployment in Ray Serve?",
1036
            "sources": [
1037
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
1038
                "https://docs.ray.io/en/master/serve/key-concepts.html#deployment",
1039
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
1040
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment",
1041
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
1042
                "https://docs.ray.io/en/master/serve/tutorials/java.html#converting-to-a-ray-serve-deployment",
1043
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
1044
                "https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",
1045
                "https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",
1046
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
1047
                "https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",
1048
                "https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",
1049
                "https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment"
1050
            ],
1051
            "document_ids": [
1052
                14255,
1053
                13416,
1054
                14732,
1055
                14150,
1056
                13679,
1057
                14560,
1058
                13677,
1059
                13776,
1060
                13987,
1061
                13974,
1062
                10282,
1063
                13797,
1064
                14562
1065
            ],
1066
            "answer": " A Deployment in Ray Serve is a group of actors that handle traffic. It is defined as a single class with various options, including the number of \"replicas\" which map to individual Ray actors at runtime. Requests to a deployment are load balanced across its replicas. A deployment is created using the `@serve.deployment` decorator on a Python class, and its number of replicas can be scaled up or down to match the incoming request load. Deployments can be combined into a Deployment graph for model composition.",
1067
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1068
        },
1069
        {
1070
            "question": "What is user config in Ray Serve? how do I use it?",
1071
            "sources": [
1072
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
1073
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
1074
                "https://docs.ray.io/en/master/serve/api/index.html#serve",
1075
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
1076
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",
1077
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice",
1078
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",
1079
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
1080
                "https://docs.ray.io/en/master/serve/architecture.html#ray-serve-api-server",
1081
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
1082
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
1083
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
1084
                "https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a"
1085
            ],
1086
            "document_ids": [
1087
                6615,
1088
                13974,
1089
                13983,
1090
                4811,
1091
                5785,
1092
                5739,
1093
                6613,
1094
                13596,
1095
                13474,
1096
                13517,
1097
                14255,
1098
                13597,
1099
                13618
1100
            ],
1101
            "answer": " User config in Ray Serve refers to the configuration settings that allow users to customize the behavior of their Ray Serve applications. To use it, you can specify the desired configurations when creating a deployment or when initializing the Ray Serve application.\n\nFor example, you can use the `runtime_env` argument to set the runtime environment for your deployment, which allows you to specify environment variables, packages, and local files. Here's an example:\n```python\nfrom ray import serve\n\nserve.deployment(\"my_deployment\",\n                runtime_env={\n                    \"env\": {\"VAR1\": \"value1\", \"VAR2\": \"value2\"},\n                    \"packages\": [\"package1\", \"package2\"],\n                    \"local_files\": [\"file1.txt\", \"file2.txt\"]\n                },\n                my_function)\n```\nIn this example, the `runtime_env` argument is used to set the environment variables `VAR1` and `VAR2`, install the packages `package1` and `package2`, and include the local files `file1.txt` and `file2.txt` in the runtime environment for the deployment `my_deployment`.\n\nYou can also use the `client()` function from the Ray Serve API to create a Ray Serve client with a specific namespace and runtime environment. Here's an example:\n```python\nfrom ray import serve\n\nclient = serve.client(namespace=\"my_namespace\", runtime_env={\n    \"env\": {\"VAR1\": \"value1\", \"VAR2\": \"value2\"},\n    \"packages\": [\"package1\", \"package2\"],\n    \"local_files\": [\"file1.txt\", \"file2.txt\"]\n})\n```\nIn this example, the `client()` function is used to create a Ray Serve client with the namespace `my_namespace` and the same runtime environment as in the previous example.\n\nNote that the `runtime_env` argument is optional, and you can omit it if you don't need to customize the runtime environment.",
1102
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1103
        },
1104
        {
1105
            "question": "What is the difference between PACK and SPREAD strategy?",
1106
            "sources": [
1107
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",
1108
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1109
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1110
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1111
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1112
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1113
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1114
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1115
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1116
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1117
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1118
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1119
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer"
1120
            ],
1121
            "document_ids": [
1122
                5077,
1123
                9990,
1124
                9982,
1125
                9972,
1126
                9968,
1127
                9986,
1128
                9985,
1129
                9981,
1130
                9974,
1131
                9975,
1132
                9988,
1133
                9977,
1134
                9991
1135
            ],
1136
            "answer": " The PACK and SPREAD strategies refer to the distribution of tasks across resources. In the provided context, the SPREAD strategy is demonstrated, where training is spread across three different nodes with each having a CPU bundle of 2. On the other hand, a PACK strategy would involve concentrating the training on a single node, utilizing all available resources on that node. The choice between PACK and SPREAD depends on the specific requirements and constraints of the system and tasks.",
1137
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1138
        },
1139
        {
1140
            "question": "What\u2019s the best way to run ray across multiple machines?",
1141
            "sources": [
1142
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",
1143
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",
1144
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
1145
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#run-the-workload",
1146
                "https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",
1147
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
1148
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-scale-out-training",
1149
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",
1150
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
1151
                "https://docs.ray.io/en/master/serve/index.html#why-choose-serve",
1152
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",
1153
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",
1154
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling"
1155
            ],
1156
            "document_ids": [
1157
                4185,
1158
                6373,
1159
                4662,
1160
                6525,
1161
                5636,
1162
                4207,
1163
                8153,
1164
                622,
1165
                13598,
1166
                13615,
1167
                614,
1168
                6436,
1169
                6365
1170
            ],
1171
            "answer": " To run Ray across multiple machines, you should start a Ray cluster for each user so that their workloads are isolated. You can adjust the number of workers for each cluster, and Ray will handle the rest automatically. Additionally, make sure to configure your system with a high `ulimit -n` value and a sufficiently large `/dev/shm` to avoid performance bottlenecks. Ray Serve, which is built on top of Ray, can also easily scale to multiple machines and offer flexible scheduling support.",
1172
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1173
        },
1174
        {
1175
            "question": "how do I specify ScalingConfig for a Tuner run?",
1176
            "sources": [
1177
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",
1178
                "https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#automatically-recover-from-train-worker-failures",
1179
                "https://docs.ray.io/en/master/train/user-guides/persistent-storage.html#run-configuration-in-train-runconfig",
1180
                "https://docs.ray.io/en/master/train/user-guides/persistent-storage.html#run-configuration-in-train-runconfig",
1181
                "https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",
1182
                "https://docs.ray.io/en/master/tune/api/internals.html#trial",
1183
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",
1184
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments",
1185
                "https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.name.html#ray-train-runconfig-name",
1186
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm-launch.html#slurm-launch-py",
1187
                "https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-python-api",
1188
                "https://docs.ray.io/en/master/rllib/rllib-cli.html#getting-help",
1189
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobStatus.RUNNING.html#ray-job-submission-jobstatus-running"
1190
            ],
1191
            "document_ids": [
1192
                1826,
1193
                9231,
1194
                9146,
1195
                9147,
1196
                9128,
1197
                1259,
1198
                2348,
1199
                13727,
1200
                8934,
1201
                6428,
1202
                10933,
1203
                11462,
1204
                6712
1205
            ],
1206
            "answer": " To specify the `ScalingConfig` for a Tuner run, you can include it in the `run_config` argument of the `Tuner` object. Here's an example:\n```python\nfrom ray.train. ScalingConfig import ScalingConfig\n\nscaling_config = ScalingConfig(\n    # configure the number of CPUs per trial here\n)\n\ntuner = ray.tune.Tuner(\n    \"PPO\",\n    param_space=config,\n    run_config=train.RunConfig(\n        stop={\"episode_reward_mean\": 150},\n        checkpoint_config=train.CheckpointConfig(checkpoint_at_end=True),\n        scaling_config=scaling_config,  # add ScalingConfig here\n    ),\n)\n```\nIn the `ScalingConfig` object, you can configure the number of CPUs or GPUs to allocate to each trial, as well as other settings related to resource allocation. For more information, you can refer to the Ray Train documentation on ScalingConfig: <https://docs.ray.io/en/latest/train/advanced.html#scalingconfig>.",
1207
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1208
        },
1209
        {
1210
            "question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",
1211
            "sources": [
1212
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.zero_stage_3.html#ray-train-lightning-raydeepspeedstrategy-zero-stage-3",
1213
                "https://docs.ray.io/en/master/workflows/basics.html#ray-integration",
1214
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
1215
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
1216
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
1217
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
1218
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDDPStrategy.is_global_zero.html#ray-train-lightning-rayddpstrategy-is-global-zero",
1219
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayFSDPStrategy.is_global_zero.html#ray-train-lightning-rayfsdpstrategy-is-global-zero",
1220
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.is_global_zero.html#ray-train-lightning-raydeepspeedstrategy-is-global-zero",
1221
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
1222
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
1223
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
1224
                "https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations"
1225
            ],
1226
            "document_ids": [
1227
                8505,
1228
                9685,
1229
                4662,
1230
                6835,
1231
                4811,
1232
                5380,
1233
                8961,
1234
                8351,
1235
                8530,
1236
                1782,
1237
                9952,
1238
                1113,
1239
                8331
1240
            ],
1241
            "answer": " The 'zero-copy' feature in Ray is related to data transfer between processes or nodes. However, the context provided does not contain specific information on how to utilize the 'zero-copy' feature for numpy.\n\nIn the context, there are properties related to Ray DeepSpeed Strategy like `zero_stage_3`, `is_global_zero`, which might use zero-copy feature. But there are no clear instructions or examples on how to utilize this for numpy.\n\nTo get more information, you can check the official Ray documentation on Data Overview (<https://docs.ray.io/en/latest/data.html#zero-copy-transfer>), Ray Core CLI, and Ray Tutorials and Examples. Also, you can ask in Ray community forums.",
1242
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1243
        },
1244
        {
1245
            "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?",
1246
            "sources": [
1247
                "https://docs.ray.io/en/master/data/examples/random-access.html#architecture",
1248
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
1249
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",
1250
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
1251
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
1252
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
1253
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
1254
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
1255
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",
1256
                "https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",
1257
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",
1258
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",
1259
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks"
1260
            ],
1261
            "document_ids": [
1262
                8000,
1263
                4878,
1264
                8101,
1265
                5192,
1266
                4508,
1267
                14708,
1268
                11597,
1269
                5191,
1270
                641,
1271
                5195,
1272
                8155,
1273
                8114,
1274
                5231
1275
            ],
1276
            "answer": " It would not be advised to spin up 1=1 actors for O(millions) of keys, as it may lead to resource contention and inefficient use of resources. Instead, it would be better to create 'key pools' where an actor can hold many keys, to better parallelize the workload and efficiently utilize resources. This can be achieved by splitting the dataset into shards and ensuring that the shards are of equal size, and assigning each shard to an actor. This way, each actor can handle multiple keys, and the workload is distributed more evenly across the actors.",
1277
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1278
        },
1279
        {
1280
            "question": "How to find the best checkpoint from the trial directory?",
1281
            "sources": [
1282
                "https://docs.ray.io/en/master/train/api/doc/ray.train.Result.html#ray-train-result",
1283
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",
1284
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",
1285
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",
1286
                "https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",
1287
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",
1288
                "https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",
1289
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_trial_checkpoints_paths.html#ray-tune-experimentanalysis-get-trial-checkpoints-paths",
1290
                "https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",
1291
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis",
1292
                "https://docs.ray.io/en/master/train/user-guides/results.html#other-checkpoints",
1293
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",
1294
                "https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-python-api"
1295
            ],
1296
            "document_ids": [
1297
                8414,
1298
                1431,
1299
                2188,
1300
                1645,
1301
                3759,
1302
                1430,
1303
                11459,
1304
                1692,
1305
                3760,
1306
                1474,
1307
                9248,
1308
                2348,
1309
                10935
1310
            ],
1311
            "answer": " To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` function, which is part of the Ray Tune library. This function allows you to specify a metric and a mode (either \"min\" or \"max\") to determine the best checkpoint. Here's an example of how to use this function:\n```python\nfrom ray import tune\n\n# Assuming `path` is the path to your trial directory\nanalysis = tune.ExperimentAnalysis(path)\nbest_checkpoint = analysis.get_best_checkpoint(metric=\"your_metric\", mode=\"max\")\n```\nIn this example, replace \"your_metric\" with the name of the metric you want to use to determine the best checkpoint. The `mode` parameter should be set to \"max\" if you want to find the checkpoint with the highest value for the specified metric, or \"min\" if you want to find the checkpoint with the lowest value for the metric.\n\nNote that any checkpoints without an associated metric value will be filtered out. If there is no valid checkpoint associated with the metric, the function will return `None`.\n\nAdditionally, you can use the `best_checkpoint` object to load the associated trial or get the checkpoint path. If you need the path, you can use the `.path` attribute.",
1312
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1313
        },
1314
        {
1315
            "question": "what are the advantage and disadvantage of using singleton Actor ?",
1316
            "sources": [
1317
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
1318
                "https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#default-concurrency-group",
1319
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
1320
                "https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",
1321
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
1322
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
1323
                "https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",
1324
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1325
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
1326
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
1327
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
1328
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
1329
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure"
1330
            ],
1331
            "document_ids": [
1332
                5192,
1333
                4641,
1334
                4561,
1335
                4638,
1336
                5066,
1337
                4878,
1338
                4459,
1339
                4624,
1340
                14687,
1341
                4464,
1342
                11576,
1343
                4630,
1344
                4575
1345
            ],
1346
            "answer": " Advantages of using Singleton Actor:\n\n* Each actor instance is instantiated in its own process, so each actor will have its own copy of the class variables. This ensures that the state of the actor is not shared between instances, providing a form of encapsulation and avoiding issues with shared mutable state.\n\nDisadvantages of using Singleton Actor:\n\n* If the actor process dies, or if the owner of the actor dies, the actor will also die and will not be automatically recovered, even if it has a nonzero max\\_restarts.\n* Detached actors, which do not have an owner, will still be restarted by Ray if their original creator dies, but they will continue to be automatically restarted until the maximum restarts is exceeded, at which point they will be destroyed.\n\nIn the context provided, the Singleton Actor is used in Java and C++ to create actors from regular classes. The actor is created using the Ray.actor or ray::Actor method, which takes a factory method that can produce a Counter object. The actor is then remote, allowing it to be accessed and used from other parts of the program.",
1347
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1348
        },
1349
        {
1350
            "question": "what are the advantages of using a named actor?",
1351
            "sources": [
1352
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1353
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1354
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
1355
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
1356
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1357
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",
1358
                "https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",
1359
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
1360
                "https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",
1361
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
1362
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
1363
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
1364
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources"
1365
            ],
1366
            "document_ids": [
1367
                4623,
1368
                4625,
1369
                4629,
1370
                4871,
1371
                4624,
1372
                4200,
1373
                4515,
1374
                4880,
1375
                4459,
1376
                4561,
1377
                11596,
1378
                4506,
1379
                14707
1380
            ],
1381
            "answer": " Named actors offer the following advantages:\n\n1. Unique identification: Actors can be given a unique name within their namespace, allowing you to retrieve the actor from any job in the Ray cluster.\n2. Accessibility: You can retrieve the actor later somewhere in the same job or in another job, making it useful if you cannot directly pass the actor handle to the task that needs it or if you are trying to access an actor launched by another driver.\n3. Get-or-create functionality: Ray provides a get\\_if\\_exists option for actor creation that creates an actor only if it doesn\u2019t exist, allowing you to get a handle to the actor if it already exists and create a new actor with specified arguments otherwise.\n4. Namespace specification: You can specify a namespace for a named actor while creating it, placing the actor in the specified namespace, no matter what namespace of the current job is.\n\nNamed actors are particularly useful when you need to maintain state between tasks, access an actor launched by another driver, or share an actor between multiple tasks or jobs.",
1382
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1383
        },
1384
        {
1385
            "question": "How do I read a text file stored on S3 using Ray Data?",
1386
            "sources": [
1387
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",
1388
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",
1389
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",
1390
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",
1391
                "https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",
1392
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",
1393
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
1394
                "https://docs.ray.io/en/master/data/working-with-images.html#saving-images",
1395
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_json.html#ray-data-read-json",
1396
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
1397
                "https://docs.ray.io/en/master/data/key-concepts.html#loading-data",
1398
                "https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",
1399
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv"
1400
            ],
1401
            "document_ids": [
1402
                7496,
1403
                7783,
1404
                7565,
1405
                7261,
1406
                6779,
1407
                7217,
1408
                6996,
1409
                6729,
1410
                7317,
1411
                6995,
1412
                6732,
1413
                7909,
1414
                7216
1415
            ],
1416
            "answer": " To read a text file stored on S3 using Ray Data, you can use the `read_text` function and provide the S3 path to the text file as an argument. Here's an example:\n```python\nimport ray\n\npath = \"s3://bucket/path/to/textfile.txt\"\nds = ray.data.read_text(path)\n```\nThis will create a Ray Dataset `ds` that contains the text data from the file at the given S3 path. Note that you may need to configure your AWS credentials to access the S3 bucket.",
1417
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1418
        },
1419
        {
1420
            "question": "how do I get the IP of the head node for my Ray cluster?",
1421
            "sources": [
1422
                "https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",
1423
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",
1424
                "https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",
1425
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
1426
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
1427
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",
1428
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",
1429
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats",
1430
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
1431
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
1432
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",
1433
                "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",
1434
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.nodes.html#ray-nodes"
1435
            ],
1436
            "document_ids": [
1437
                5631,
1438
                4438,
1439
                5587,
1440
                493,
1441
                6552,
1442
                5413,
1443
                6501,
1444
                6416,
1445
                6561,
1446
                739,
1447
                6619,
1448
                5731,
1449
                4769
1450
            ],
1451
            "answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command and provide the path to your cluster configuration file as an argument. The command will return the head node IP of the Ray cluster.\n\nFor example:\n\n```\nray get_head_ip [OPTIONS] CLUSTER_CONFIG_FILE\n```\n\nYou can also use the `ray.nodes()` function to get information about the current nodes in your cluster. This function returns information about the Ray clients in the cluster, which includes the IP addresses of the nodes.\n\nHere is an example of how to use the `ray.nodes()` function:\n\n```\nimport ray\nray.init()\n\nfrom pprint import pprint\npprint(ray.nodes())\n```\n\nThis will print information about the nodes in your Ray cluster, including the IP addresses.\n\nNote: The `ray.nodes()` function is intended for debugging purposes and may change in future releases of Ray.",
1452
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1453
        },
1454
        {
1455
            "question": "How to write a map function that returns a list of object for `map_batches`?",
1456
            "sources": [
1457
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
1458
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
1459
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
1460
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows",
1461
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-flat-map",
1462
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
1463
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1464
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",
1465
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
1466
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",
1467
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1468
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
1469
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"
1470
            ],
1471
            "document_ids": [
1472
                5347,
1473
                5346,
1474
                7800,
1475
                6781,
1476
                6783,
1477
                5348,
1478
                7081,
1479
                6782,
1480
                5350,
1481
                7640,
1482
                7086,
1483
                7796,
1484
                7091
1485
            ],
1486
            "answer": " To write a map function that returns a list of objects for `map_batches`, you can define a function that takes a batch of data as input and yields one or more objects as output. Here's an example using the `map_batches` method and a simple `map_function` that takes a batch of documents and returns a list of pairs (word, 1) for every word found in the documents:\n\n```python\n@ray.remote\ndef apply_map(corpus, num_partitions=3):\n    map_results = [list() for _ in range(num_partitions)]\n    for batch in corpus:\n        for document in batch:\n            for result in map_function(document):\n                first_letter = result[0].decode(\"utf-8\")[0]\n                word_index = ord(first_letter) % num_partitions\n                map_results[word_index].append(result)\n    return map_results\n\ndef map_function(documents):\n    for document in documents:\n        for word in document.lower().split():\n            yield word, 1\n\n# Apply the map phase to the corpus in parallel\nmap_results = apply_map.remote(corpus, num_partitions=3)\nfor i in range(3):\n    mapper_results = ray.get(map_results[i])\n    for j, result in enumerate(mapper_results):\n        print(f\"Mapper {i}, return value {j}: {result[:2]}\")\n```\n\nIn this example, `map_function` takes a batch of documents and returns a list of pairs (word, 1) for every word found in the documents. The `apply_map` function applies `map_function` to each batch of documents and collects the results in separate lists for each partition. The `apply_map` function is decorated with `@ray.remote` to enable parallel execution.",
1487
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1488
        },
1489
        {
1490
            "question": "How do I set a maximum episode length when training with Rllib?",
1491
            "sources": [
1492
                "https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",
1493
                "https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-python-api",
1494
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.HyperBandScheduler.html#ray-tune-schedulers-hyperbandscheduler",
1495
                "https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",
1496
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.torch_policy_v2.TorchPolicyV2.postprocess_trajectory.html#ray-rllib-policy-torch-policy-v2-torchpolicyv2-postprocess-trajectory",
1497
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.rollouts.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-rollouts",
1498
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers",
1499
                "https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-python-api",
1500
                "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",
1501
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",
1502
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.exploration.random_encoder.RE3.on_episode_end.html#ray-rllib-utils-exploration-random-encoder-re3-on-episode-end",
1503
                "https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",
1504
                "https://docs.ray.io/en/master/tune/examples/includes/hyperband_function_example.html#hyperband-function-example"
1505
            ],
1506
            "document_ids": [
1507
                10507,
1508
                10931,
1509
                1970,
1510
                10829,
1511
                13016,
1512
                12631,
1513
                11002,
1514
                10933,
1515
                13177,
1516
                11048,
1517
                13094,
1518
                10783,
1519
                3928
1520
            ],
1521
            "answer": " To set a maximum episode length when training with Rllib, you can use the `evaluation_duration` and `evaluation_duration_unit` parameters in your config. For example, if you want to limit episodes to 100 timesteps, you can set `evaluation_duration: 100` and `evaluation_duration_unit: \"timesteps\"`. Note that if the evaluation duration is not divisible by the number of evaluation workers, RLlib will round up the number of time-steps specified.\n\nAdditionally, you can use the `stop` parameter in the `RunConfig` to specify early stopping conditions for your trials. For example, you can set `stop={\"episode_reward_mean\": 150}` to stop training when the mean episode reward reaches 150.\n\nHere is an example of how to set these parameters:\n\n```\ntuner = tune.Tuner(\n    \"PPO\",\n    run_config=train.RunConfig(\n        stop={\"episode_reward_mean\": 150},\n        evaluation_duration=100,\n        evaluation_duration_unit=\"timesteps\"\n    ),\n    param_space=config,\n)\n```\n\nThis will train the PPO algorithm with a maximum episode length of 100 timesteps and will stop training when the mean episode reward reaches 150.",
1522
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1523
        },
1524
        {
1525
            "question": "how do I make a Ray Tune trial retry on failures?",
1526
            "sources": [
1527
                "https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",
1528
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",
1529
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
1530
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_error.html#ray-tune-callback-on-trial-error",
1531
                "https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-on-trial-failures",
1532
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_recover.html#ray-tune-callback-on-trial-recover",
1533
                "https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune",
1534
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.on_trial_error.html#ray-tune-schedulers-trialscheduler-on-trial-error",
1535
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
1536
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.HyperBandScheduler.html#ray-tune-schedulers-hyperbandscheduler",
1537
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.fit.html#ray-tune-tuner-fit",
1538
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.MedianStoppingRule.html#ray-tune-schedulers-medianstoppingrule",
1539
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#how-to-enable-fault-tolerance-in-ray-tune"
1540
            ],
1541
            "document_ids": [
1542
                2326,
1543
                2266,
1544
                1114,
1545
                1980,
1546
                2207,
1547
                2152,
1548
                11554,
1549
                1749,
1550
                14738,
1551
                1974,
1552
                1607,
1553
                1728,
1554
                2249
1555
            ],
1556
            "answer": " To make a Ray Tune trial retry on failures, you can use the `FailureConfig` to configure failure handling of individual trials. Here's an example of how to configure it:\n\n```python\nfrom ray import tune\n\n# ...\n\nconfig = tune.FailureConfig(\n    max_retries=3,  # retry up to 3 times\n    retry_delay=10  # delay between retries in seconds\n)\ntuner = tune.Tuner(train_model, param_space=trial_space, failure_config=config)\nresults = tuner.fit()\n```\n\nIn this example, the `max_retries` parameter sets the number of retries to 3, and the `retry_delay` parameter sets the delay between retries to 10 seconds. You can adjust these parameters based on your needs.\n\nAdditionally, you can use the `ray.tune.Callback.on_trial_error` and `ray.tune.Callback.on_trial_recover` callbacks to handle trial errors and recoveries, respectively. These callbacks allow you to customize the behavior of your trials when they encounter errors or are scheduled for retry.",
1557
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1558
        },
1559
        {
1560
            "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?",
1561
            "sources": [
1562
                "https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",
1563
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
1564
                "https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",
1565
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
1566
                "https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#worker-recovery",
1567
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#how-to-write-fault-tolerant-ray-applications",
1568
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
1569
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
1570
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#worker-node-failure",
1571
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
1572
                "https://docs.ray.io/en/master/ray-core/actors.html#fault-tolerance",
1573
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#fault-tolerance",
1574
                "https://docs.ray.io/en/master/rllib/actors.html#fault-tolerance"
1575
            ],
1576
            "document_ids": [
1577
                5208,
1578
                4561,
1579
                5195,
1580
                4575,
1581
                11551,
1582
                4351,
1583
                4631,
1584
                5191,
1585
                4527,
1586
                4878,
1587
                4502,
1588
                14705,
1589
                11594
1590
            ],
1591
            "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. This is true even if actors are nested to multiple levels to form a tree. However, you can make the actor \"detached\" to allow it to persist even after the driver process of the job exits.",
1592
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1593
        },
1594
        {
1595
            "question": "How do I read a large text file in S3 with Ray?",
1596
            "sources": [
1597
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",
1598
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",
1599
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",
1600
                "https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",
1601
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",
1602
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",
1603
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_json.html#ray-data-read-json",
1604
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
1605
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",
1606
                "https://docs.ray.io/en/master/data/key-concepts.html#loading-data",
1607
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.serialize_lineage.html#ray-data-dataset-serialize-lineage",
1608
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
1609
                "https://docs.ray.io/en/master/data/loading-data.html#handling-compressed-files"
1610
            ],
1611
            "document_ids": [
1612
                7783,
1613
                7496,
1614
                7565,
1615
                6779,
1616
                7261,
1617
                7217,
1618
                7317,
1619
                6996,
1620
                7497,
1621
                6732,
1622
                7539,
1623
                6995,
1624
                6747
1625
            ],
1626
            "answer": " To read a large text file in S3 with Ray, you can use the `ray.data.read_text` function and provide the S3 path to the text file as an argument. Here's an example:\n```python\nimport ray\n\npath = \"s3://bucket/path/to/text-file.txt\"\nds = ray.data.read_text(path)\n```\nThis will create a Ray Dataset `ds` that contains the text data from the file in S3. If the text file is very large, Ray will automatically stream the data and process it in a memory-efficient manner.",
1627
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1628
        },
1629
        {
1630
            "question": "how do I get a ray dataset from pandas",
1631
            "sources": [
1632
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
1633
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice",
1634
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
1635
                "https://docs.ray.io/en/master/ray-core/api/core.html#objects",
1636
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
1637
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
1638
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",
1639
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
1640
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
1641
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
1642
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
1643
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
1644
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment"
1645
            ],
1646
            "document_ids": [
1647
                6015,
1648
                5780,
1649
                4811,
1650
                4651,
1651
                5380,
1652
                6615,
1653
                5412,
1654
                475,
1655
                13974,
1656
                5556,
1657
                4662,
1658
                10128,
1659
                14255
1660
            ],
1661
            "answer": " To get a Ray dataset from a pandas DataFrame, you can first convert the DataFrame to a Ray object using `ray.put()`, and then create a Ray dataset using `ray.data.from_objects()`. Here's an example:\n```python\nimport pandas as pd\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Create a pandas DataFrame\ndf = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})\n\n# Convert the DataFrame to a Ray object\ndf_obj = ray.put(df)\n\n# Create a Ray dataset from the Ray object\nds = ray.data.from_objects(df_obj)\n```\nNote that this example assumes that Ray is already installed and initialized. If you haven't installed Ray yet, you can do so using `pip install ray`.",
1662
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1663
        },
1664
        {
1665
            "question": "can you give me an example of using `ray.data.map` ?",
1666
            "sources": [
1667
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
1668
                "https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",
1669
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",
1670
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1671
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
1672
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",
1673
                "https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",
1674
                "https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",
1675
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",
1676
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",
1677
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",
1678
                "https://docs.ray.io/en/master/data/api/from_other_data_libs.html#api-guide-for-users-from-other-data-libraries",
1679
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-transforms"
1680
            ],
1681
            "document_ids": [
1682
                7796,
1683
                7274,
1684
                9180,
1685
                7081,
1686
                6835,
1687
                8046,
1688
                8006,
1689
                6773,
1690
                7971,
1691
                6749,
1692
                6782,
1693
                7020,
1694
                6976
1695
            ],
1696
            "answer": " Sure, here's an example of using `ray.data.map`:\n\nFirst, let's create a Ray Dataset using `ray.data.range`:\n```python\nimport ray\nds = ray.data.range(10)\n```\nThis creates a Dataset with the numbers 0 to 9.\n\nNow, let's say we want to square each number in the Dataset. We can use `map` to apply a function to each row:\n```python\ndef square(row):\n    return row * row\n\nds = ds.map(square)\n```\nNow, `ds` contains the squares of the numbers 0 to 9.\n\nNote that `map` is an unvectorized operation, so it can be slow for large datasets. If your transformation is vectorized, you can use `map_batches` instead for better performance.",
1697
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1698
        },
1699
        {
1700
            "question": "can you give me an example of using `ray.data.map` , with a callable class as input?",
1701
            "sources": [
1702
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",
1703
                "https://docs.ray.io/en/master/data/working-with-text.html#transforming-text",
1704
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",
1705
                "https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",
1706
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-transforms",
1707
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",
1708
                "https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",
1709
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
1710
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
1711
                "https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",
1712
                "https://docs.ray.io/en/master/data/api/doc/ray.data.from_huggingface.html#ray-data-from-huggingface",
1713
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",
1714
                "https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs"
1715
            ],
1716
            "document_ids": [
1717
                7796,
1718
                6997,
1719
                8046,
1720
                8610,
1721
                6976,
1722
                6782,
1723
                7274,
1724
                5346,
1725
                7081,
1726
                6749,
1727
                7128,
1728
                7636,
1729
                7102
1730
            ],
1731
            "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, Any\n\nclass MyCallable:\n    def __call__(self, row: Dict[str, Any]) -> Dict[str, Any]:\n        row[\"new_col\"] = \"new_value\"\n        return row\n",
1732
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1733
        },
1734
        {
1735
            "question": "How to set memory limit for each trial in Ray Tuner?",
1736
            "sources": [
1737
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.HyperBandScheduler.html#ray-tune-schedulers-hyperbandscheduler",
1738
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
1739
                "https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria",
1740
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTrainingReplay.STOP.html#ray-tune-schedulers-populationbasedtrainingreplay-stop",
1741
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.STOP.html#ray-tune-schedulers-trialscheduler-stop",
1742
                "https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-trials-individually-with-a-timeout",
1743
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTrainingReplay.CONTINUE.html#ray-tune-schedulers-populationbasedtrainingreplay-continue",
1744
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
1745
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",
1746
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.STOP.html#ray-tune-schedulers-populationbasedtraining-stop",
1747
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.CONTINUE.html#ray-tune-schedulers-trialscheduler-continue",
1748
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.MedianStoppingRule.CONTINUE.html#ray-tune-schedulers-medianstoppingrule-continue",
1749
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater"
1750
            ],
1751
            "document_ids": [
1752
                1974,
1753
                1788,
1754
                2198,
1755
                1694,
1756
                1623,
1757
                2204,
1758
                1934,
1759
                1114,
1760
                1926,
1761
                1695,
1762
                1819,
1763
                1412,
1764
                2001
1765
            ],
1766
            "answer": " To set a memory limit for each trial in Ray Tuner, you can use the `run_config` argument in the `Tuner` constructor to specify a `stop` dictionary with a `memory` key. The value of the `memory` key should be a dictionary containing the memory limit in the format `{\"unit\": \"GB\", \"threshold\": limit}`, where `limit` is the desired memory limit in gigabytes. Here is an example:\n```\nfrom ray import train, tune\n\ntuner = tune.Tuner(\n    my_trainable,\n    run_config=train.RunConfig(stop={\"memory\": {\"unit\": \"GB\", \"threshold\": limit}}),\n)\n```\nThis will stop each trial when the memory usage exceeds the specified limit. Note that the memory limit is applied to the entire trial, not to individual tasks within the trial.\n\nIf you want to set a memory limit per task, you can use the `resources_per_task` argument in the `train.RunConfig` constructor to specify the maximum amount of memory that each task is allowed to use. For example:\n```\nfrom ray import train, tune\n\ntuner = tune.Tuner(\n    my_trainable,\n    run_config=train.RunConfig(resources_per_task={\"cpu\": 1, \"memory\": limit}),\n)\n```\nThis will limit the memory usage of each task to the specified limit. Note that the memory limit is specified in bytes, so you will need to convert it to bytes from gigabytes (or whatever unit you are using) before passing it to `resources_per_task`.\n\nI hope this helps! Let me know if you have any other questions.",
1767
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1768
        },
1769
        {
1770
            "question": "how do I get the actor id of an actor",
1771
            "sources": [
1772
                "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",
1773
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",
1774
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
1775
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor",
1776
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#actor-detail-page",
1777
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
1778
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",
1779
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
1780
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
1781
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
1782
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
1783
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
1784
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs"
1785
            ],
1786
            "document_ids": [
1787
                4787,
1788
                4931,
1789
                475,
1790
                690,
1791
                436,
1792
                474,
1793
                750,
1794
                476,
1795
                477,
1796
                4623,
1797
                14323,
1798
                14202,
1799
                520
1800
            ],
1801
            "answer": " To get the actor ID of the current actor in a Ray application, you can use the `RuntimeContext.get_actor_id()` function or the `RuntimeContext.actor_id` property. These functions will return the ID of the actor in the current worker process, in hex format. If there is no actor in the current process, they will return `None`.\n\nHere is an example of how to use these functions:\n\n```\nfrom ray.runtime_context import RuntimeContext\n\nactor_id = RuntimeContext.get_actor_id()\nprint(actor_id)\n\nactor_id = RuntimeContext.actor_id\nprint(actor_id)\n```\n\nAlternatively, you can use the `ray.util.state.get_actor()` function to get the state of an actor by its ID. This function returns an `ActorState` object, which contains information about the actor, including its ID.\n\nHere is an example of how to use this function:\n\n```\nfrom ray.util.state import get_actor\n\nactor_state = get_actor(id=\"<ACTOR_ID>\")\nactor_id = actor_state.actor_id\nprint(actor_id)\n```\n\nNote that the `RuntimeContext.actor_id` property is deprecated and may be removed in future Ray releases. It is recommended to use the `RuntimeContext.get_actor_id()` function instead.\n\nYou can also use the `ray logs actor` command in the Ray CLI to get the logs of a specific actor, using its ID.\n\nHere is an example of how to use this command:\n\n```\nray logs actor --id <ACTOR_ID>\n```\n\nFor more information about actors in Ray, you can refer to the Ray documentation:\n\n* [Actors in Ray](https://docs.ray.io/en/latest/actors.html)\n* [Ray CLI reference](https://docs.ray.io/en/latest/ray-cli.html)",
1802
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1803
        },
1804
        {
1805
            "question": "can ray.init() can check if ray is all-ready initiated ?",
1806
            "sources": [
1807
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",
1808
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
1809
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-4-pipeline-data-processing",
1810
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.is_fully_executed.html#ray-data-dataset-is-fully-executed",
1811
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",
1812
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
1813
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.PlacementGroup.ready.html#ray-util-placement-group-placementgroup-ready",
1814
                "https://docs.ray.io/en/master/cluster/usage-stats.html#how-does-it-work",
1815
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",
1816
                "https://docs.ray.io/en/master/ray-core/api/core.html#objects",
1817
                "https://docs.ray.io/en/master/ray-core/tasks/nested-tasks.html#nested-remote-functions",
1818
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",
1819
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-too-many-objects.html#anti-pattern-fetching-too-many-objects-at-once-with-ray-get-causes-failure"
1820
            ],
1821
            "document_ids": [
1822
                4785,
1823
                4811,
1824
                4232,
1825
                7405,
1826
                14506,
1827
                4206,
1828
                4864,
1829
                5566,
1830
                5056,
1831
                4651,
1832
                4972,
1833
                4194,
1834
                5169
1835
            ],
1836
            "answer": " No, `ray.init()` is used to initialize Ray on a node, it does not check if Ray is already initiated. To check if Ray is already initialized, you can use `ray.is_initialized()`. This function returns `True` if `ray.init()` has already been called and `False` otherwise.",
1837
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1838
        },
1839
        {
1840
            "question": "What does the `compute=actor`  argument do within `ray.data.map_batches` ?",
1841
            "sources": [
1842
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
1843
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map.html#ray-util-actorpool-map",
1844
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
1845
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
1846
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
1847
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
1848
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",
1849
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map_unordered.html#ray-util-actorpool-map-unordered",
1850
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.html#ray-util-actorpool",
1851
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#memory-profiling-ray-tasks-and-actors",
1852
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#verify-and-save-results",
1853
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
1854
                "https://docs.ray.io/en/master/ray-core/actors.html#actors"
1855
            ],
1856
            "document_ids": [
1857
                4650,
1858
                4708,
1859
                6787,
1860
                11575,
1861
                4462,
1862
                14685,
1863
                4488,
1864
                4936,
1865
                4742,
1866
                568,
1867
                8073,
1868
                8070,
1869
                4468
1870
            ],
1871
            "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, using an `ActorPoolStrategy` with a specified number of parallel actors. This can be useful for UDFs that perform complex, time-consuming computations, as actors can maintain their state between different calls.",
1872
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1873
        },
1874
        {
1875
            "question": "how do I use wandb logger with accelerateTrainer?",
1876
            "sources": [
1877
                "https://docs.ray.io/en/master/train/examples/deepspeed/deepspeed_example.html#deepspeed-zero-3-distributed-training-example-with-ray-train",
1878
                "https://docs.ray.io/en/master/train/examples/accelerate/accelerate_example.html#hugging-face-accelerate-distributed-training-example-with-ray-train",
1879
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.FINISHED.html#ray-tune-search-searcher-finished",
1880
                "https://docs.ray.io/en/master/ray-core/tasks/nested-tasks.html#nested-remote-functions",
1881
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#usage",
1882
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.stopper.Stopper.__init__.html#ray-tune-stopper-stopper-init",
1883
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.with_resources.html#ray-tune-with-resources",
1884
                "https://docs.ray.io/en/master/train/deepspeed.html#training-with-deepspeed",
1885
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tune-a-transformers-model",
1886
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",
1887
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.FINISHED.html#ray-tune-search-repeater-finished",
1888
                "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",
1889
                "https://docs.ray.io/en/master/tune/examples/flaml_example.html#running-tune-experiments-with-blendsearch-and-cfo"
1890
            ],
1891
            "document_ids": [
1892
                9618,
1893
                9573,
1894
                1324,
1895
                4972,
1896
                14397,
1897
                1849,
1898
                1879,
1899
                8211,
1900
                9999,
1901
                9211,
1902
                2015,
1903
                5702,
1904
                2805
1905
            ],
1906
            "answer": " To use Weights & Biases (WandB) logger with AccelerateTrainer, you can follow these steps:\n\n1. Import the WandB library: `import wandb`\n2. Initialize a WandB run at the beginning of your script: `wandb.init(project=\"your-project-name\")`\n3. Wrap your `AccelerateTrainer` with a custom trainer class that logs relevant metrics to WandB after each training step or epoch. Here's an example:\n\n```python\nimport torch\nimport torch.nn as nn\nimport transformers\nfrom accelerate import Accelerator\nfrom ray import tune\nfrom ray.train.trainer import Trainer\n\nclass WandBTrainer(Trainer):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.accelerator = Accelerator()\n\n    def train_step(self, batch):\n        inputs = self.accelerator.prepare(batch)\n        outputs = self.model(**inputs)\n        loss = outputs.loss\n        logits = outputs.logits\n        labels = inputs[\"labels\"]\n        loss = loss.mean()  # Mean over all GPUs\n        logits = logits.mean()  # Mean over all GPUs\n        preds = torch.argmax(logits, dim=-1)\n        acc = (preds == labels).float().mean()\n\n        # Log metrics to WandB\n        wandb.log({\"train_loss\": loss, \"train_accuracy\": acc})\n\n        return loss\n```\n\n4. Use your custom `WandBTrainer` class with Ray Train:\n\n```python\ntune.run(\n    WandBTrainer,\n    config={\n        # Your training configs here\n    },\n)\n```\n\nThis example assumes that you have already set up a WandB project and replaced \"your-project-name\" with the name of your project. The `WandBTrainer` class logs the training loss and accuracy to WandB after each training step. You can modify the logging frequency and metrics as needed.",
1907
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1908
        },
1909
        {
1910
            "question": "What will be implicitly put into object store?",
1911
            "sources": [
1912
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#preparation",
1913
                "https://docs.ray.io/en/master/rllib/key-concepts.html#sample-batches",
1914
                "https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",
1915
                "https://docs.ray.io/en/master/rllib/package_ref/policy.html#miscellaneous",
1916
                "https://docs.ray.io/en/master/workflows/advanced.html#advanced-topics",
1917
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#text-generation-with-huggingface-pipeline",
1918
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#plasma-store",
1919
                "https://docs.ray.io/en/master/serve/api/index.html#get-api-serve-deployments-status",
1920
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#configuring-log-processing",
1921
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
1922
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#dreamer",
1923
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers",
1924
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.rollouts.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-rollouts"
1925
            ],
1926
            "document_ids": [
1927
                5819,
1928
                10697,
1929
                1247,
1930
                11356,
1931
                9780,
1932
                9998,
1933
                4940,
1934
                14006,
1935
                5868,
1936
                4878,
1937
                11871,
1938
                11002,
1939
                12631
1940
            ],
1941
            "answer": " In the context provided, objects are implicitly put into the Plasma Object Store, which is an in-memory object store used to efficiently transfer objects across different processes and nodes. The objects in the Plasma object store are immutable and held in shared memory for efficient access by many workers on the same node.",
1942
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1943
        },
1944
        {
1945
            "question": "How do I kill or cancel a ray task that I already started?",
1946
            "sources": [
1947
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
1948
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",
1949
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
1950
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
1951
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
1952
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",
1953
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
1954
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",
1955
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
1956
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
1957
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
1958
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
1959
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"
1960
            ],
1961
            "document_ids": [
1962
                4375,
1963
                4550,
1964
                4894,
1965
                11591,
1966
                4496,
1967
                14702,
1968
                4896,
1969
                14703,
1970
                4498,
1971
                11592,
1972
                4859,
1973
                11590,
1974
                14701
1975
            ],
1976
            "answer": " To kill or cancel a Ray task that you already started, you can call `ray.cancel()` on the returned Object ref. This will send a KeyboardInterrupt to the task's worker if it is mid-execution. If the task is an async actor task, Ray will seek to cancel the associated asyncio.Task. If the task hasn't been scheduled yet, Ray will attempt to cancel the scheduling. If the task is currently executing and is a regular actor or threaded actor task, Ray offers no mechanism for interruption. If the task is an async actor task, Ray will cancel the associated asyncio.Task. If the task has finished, nothing happens. Only `force=False` is allowed for an actor task. If you want to kill the actor but let pending tasks finish, you can call `actor.__ray_terminate__.remote()` instead. Recursive cancellation is also available by passing `recursive=True` to `ray.cancel()`, which cancels all child and actor tasks.",
1977
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
1978
        },
1979
        {
1980
            "question": "how to send  extra arguments in dataset.map_batches function?",
1981
            "sources": [
1982
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
1983
                "https://docs.ray.io/en/master/data/working-with-images.html#saving-images",
1984
                "https://docs.ray.io/en/master/data/working-with-text.html#transforming-text",
1985
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",
1986
                "https://docs.ray.io/en/master/data/working-with-text.html#saving-text",
1987
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
1988
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
1989
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
1990
                "https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",
1991
                "https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",
1992
                "https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-transforms",
1993
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",
1994
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files"
1995
            ],
1996
            "document_ids": [
1997
                5346,
1998
                6729,
1999
                6997,
2000
                6782,
2001
                7000,
2002
                5348,
2003
                5347,
2004
                5350,
2005
                6779,
2006
                4439,
2007
                6976,
2008
                14419,
2009
                6996
2010
            ],
2011
            "answer": " To send extra arguments in the `dataset.map_batches` function, you can include them in the function definition of the mapper function. Here's an example:\n\n```python\ndef my_mapper(batch, arg1, arg2):\n    # process the batch and use arg1 and arg2\n    pass\n\nds.map_batches(my_mapper, batch_size=128, arg1=value1, arg2=value2)\n```\n\nIn this example, `arg1` and `arg2` are the extra arguments you want to pass to the `my_mapper` function. Note that you need to include the extra arguments in the function definition (`def my_mapper(batch, arg1, arg2):`), and then pass their values when calling `map_batches` (`ds.map_batches(my_mapper, batch_size=128, arg1=value1, arg2=value2)`).",
2012
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2013
        },
2014
        {
2015
            "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?",
2016
            "sources": [
2017
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",
2018
                "https://docs.ray.io/en/master/data/data-internals.html#object-store-memory",
2019
                "https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",
2020
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#objects",
2021
                "https://docs.ray.io/en/master/train/user-guides/persistent-storage.html#persistent-storage",
2022
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#on-a-single-node-cluster",
2023
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
2024
                "https://docs.ray.io/en/master/workflows/management.html#storage-configuration",
2025
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",
2026
                "https://docs.ray.io/en/master/data/loading-data.html#reading-databases",
2027
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#how-to-configure-persistent-storage-in-ray-tune",
2028
                "https://docs.ray.io/en/master/ray-core/objects.html#objects",
2029
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview"
2030
            ],
2031
            "document_ids": [
2032
                5425,
2033
                6896,
2034
                5013,
2035
                4201,
2036
                9148,
2037
                2342,
2038
                6396,
2039
                9636,
2040
                6373,
2041
                6758,
2042
                2334,
2043
                4416,
2044
                6835
2045
            ],
2046
            "answer": " Ray GCS (a Ray cluster) stores the history of jobs run on a Kuberay cluster in a distributed object store, which is implemented as part of the Ray memory system. This object store uses a key-value store for data blocks, and objects are reference counted and evicted when they fall out of scope. Object spilling to disk is also supported when the object store memory is full. Ray utilizes pyarrow internally as the storage engine, and supports various storage options, including GCS (Google Cloud Storage). The default temporary storage location is /tmp/ray/workflow\\_data, which works for single-node Ray clusters.",
2047
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2048
        },
2049
        {
2050
            "question": "How to resolve ValueError: The actor ImplicitFunc is too large?",
2051
            "sources": [
2052
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
2053
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
2054
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
2055
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",
2056
                "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",
2057
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
2058
                "https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",
2059
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",
2060
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",
2061
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
2062
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
2063
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#task-and-actor-breakdown",
2064
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle"
2065
            ],
2066
            "document_ids": [
2067
                4508,
2068
                14708,
2069
                11597,
2070
                8101,
2071
                9858,
2072
                5071,
2073
                5208,
2074
                520,
2075
                4579,
2076
                475,
2077
                4629,
2078
                423,
2079
                4616
2080
            ],
2081
            "answer": " The error message suggests that an actor is too large, which can be resolved by avoiding the use of stateful actors if their stateful functionality is not required. Instead, consider using tasks. If you need to manage a collection of worker actors, consider using a supervising actor to manage them. If a specific actor is causing issues, you can force-kill it using `ray.kill()`. To check the death cause of dead actors, use `ray list actors --detail`.",
2082
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2083
        },
2084
        {
2085
            "question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",
2086
            "sources": [
2087
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
2088
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
2089
                "https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchTrainer.html#ray-train-torch-torchtrainer",
2090
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
2091
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",
2092
                "https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others",
2093
                "https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#training",
2094
                "https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#parameter-server",
2095
                "https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#analyzing-tune-experiment-results",
2096
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tuning-a-torch-object-detection-model",
2097
                "https://docs.ray.io/en/master/serve/tutorials/aws-neuron-core-inference.html#serving-an-inference-model-on-aws-neuroncores-using-fast-api-experimental",
2098
                "https://docs.ray.io/en/master/tune/getting-started.html#setting-up-a-pytorch-model-to-tune",
2099
                "https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve"
2100
            ],
2101
            "document_ids": [
2102
                6940,
2103
                6937,
2104
                8489,
2105
                6942,
2106
                9180,
2107
                14313,
2108
                5515,
2109
                5324,
2110
                3685,
2111
                10177,
2112
                14381,
2113
                988,
2114
                2747
2115
            ],
2116
            "answer": " To distribute training for your custom neural net written using Keras in Databricks using Ray, you can follow these steps:\n\n1. Create a Ray Dataset from your data.\n2. Define a custom Ray Trainable class that inherits from `ray.train.torch.TorchTrainer`.\n3. Implement the `setup` and `train_loop` methods in your custom Ray Trainable class.\n4. Use `ray.train.run` to launch the training.\n\nHere's an example of how you can define your custom Ray Trainable class:\n\n```python\nimport ray\nfrom ray.train import Checkpoint, CheckpointConfig, RunConfig, ScalingConfig\nfrom ray.train.torch import TorchTrainer\n\nclass KerasTrainer(TorchTrainer):\n    def __init__(self, model_definition, num_workers=4, use_gpu=False):\n        super().__init__(num_workers=num_workers, use_gpu=use_gpu)\n        self.model_definition = model_definition\n\n    def setup(self, config):\n        from tensorflow import keras\n\n        # Create the model\n        self.model = self.model_definition()\n\n    def train_loop(self, config, data_loader):\n        for batch in data_loader:\n            # Perform training on the batch\n            self.model.train_on_batch(batch[0], batch[1])\n\n# Define your Keras model\ndef create_model():\n    model = keras.Sequential([\n        keras.layers.Dense(32, input_shape=(1,), activation='relu'),\n        keras.layers.Dense(1)\n    ])\n    model.compile(optimizer='adam', loss='mse')\n    return model\n\n# Initialize Ray\nray.init()\n\n# Create a Ray Dataset\nds = ray.data.from_items([{\"x\": [x], \"y\": [2 * x]} for x in range(200)])\n\n# Preprocess your Ray Dataset\nds = ds.map_batches(increment)\n\n# Define the train config\ntrain_config = RunConfig(\n    checkpoint_config=CheckpointConfig(\n        num_to_keep=3\n    ),\n    scaling_config=ScalingConfig(\n        num_workers=4,\n        use_gpu=False\n    )\n)\n\n# Create the KerasTrainer instance\ntrainer = KerasTrainer(create_model, num_workers=4, use_gpu=False)\n\n# Run the training\nresult = trainer.train(train_config, ds.train_shuffle_split())\n```\n\nThis example demonstrates how to use Ray to distribute training for a custom Keras neural net. You can adapt this code to your specific use case and data.",
2117
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2118
        },
2119
        {
2120
            "question": "how to use ray.put and ray,get?",
2121
            "sources": [
2122
                "https://docs.ray.io/en/master/ray-core/api/core.html#objects",
2123
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",
2124
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
2125
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",
2126
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",
2127
                "https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",
2128
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
2129
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",
2130
                "https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",
2131
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
2132
                "https://docs.ray.io/en/master/ray-core/objects.html#objects",
2133
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
2134
                "https://docs.ray.io/en/master/ray-core/patterns/pass-large-arg-by-value.html#anti-pattern-passing-the-same-large-argument-by-value-repeatedly-harms-performance"
2135
            ],
2136
            "document_ids": [
2137
                4651,
2138
                5443,
2139
                4849,
2140
                4205,
2141
                4884,
2142
                5219,
2143
                4209,
2144
                4775,
2145
                13646,
2146
                4811,
2147
                4418,
2148
                5380,
2149
                5240
2150
            ],
2151
            "answer": " To use `ray.put` and `ray.get`, you need to understand that these functions are used to store and retrieve objects in Ray's object store.\n\n`ray.put(value)` is used to store an object in the object store and returns an object reference (`ObjectRef`). This object reference can be used to pass the object as an argument to any remote function or method call.\n\n`ray.get(object_refs)` is used to return an object or list of objects from the object ID or list of object IDs. This is a synchronous (i.e., blocking) operation.\n\nHere's an example:\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Put an object in the object store\nobj_ref = ray.put(\"hello world\")\n\n# Get the object from the object store\nobj = ray.get(obj_ref)\n\n# Print the object\nprint(obj)\n```\nIn this example, we first initialize Ray using `ray.init()`. Then, we store the string \"hello world\" in the object store using `ray.put()` and get an object reference. We then use `ray.get()` to retrieve the object from the object store and print it.\n\nIt's important to note that calling `ray.get()` unnecessarily can harm performance, so it's best practice to wait as long as possible before calling `ray.get()` or even design your program to avoid having to call `ray.get()` at all. Instead, you can work with object references directly.\n\nAdditionally, you should avoid passing large arguments to remote task or method calls repeatedly. Instead, use `ray.put()` to create a copy of the object in the object store and pass the object reference to tasks. This way, you can ensure that all tasks use the same copy of the argument, which is faster and uses less object store memory.",
2152
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2153
        },
2154
        {
2155
            "question": "how do I use Ray Data to pre process many files?",
2156
            "sources": [
2157
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
2158
                "https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",
2159
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",
2160
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart",
2161
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",
2162
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",
2163
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray",
2164
                "https://docs.ray.io/en/master/data/working-with-images.html#working-with-images",
2165
                "https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",
2166
                "https://docs.ray.io/en/master/data/user-guide.html#user-guides",
2167
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",
2168
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
2169
                "https://docs.ray.io/en/master/data/key-concepts.html#loading-data"
2170
            ],
2171
            "document_ids": [
2172
                6835,
2173
                6993,
2174
                9178,
2175
                6935,
2176
                9222,
2177
                9188,
2178
                143,
2179
                6719,
2180
                6849,
2181
                6911,
2182
                7980,
2183
                7360,
2184
                6732
2185
            ],
2186
            "answer": " To preprocess many files using Ray Data, follow these steps:\n\n1. Install Ray Data: `pip install -U \"ray[data]\"`\n2. Load your data into a Ray Dataset: `ds = ray.data.read_csv(\"path/to/your/files/*.csv\")`\n3. Define any necessary data transformations using Ray Data's transformation methods, such as `ds.map_batches()`.\n4. If your preprocessed Dataset is small enough, materialize it in Ray's object store memory by calling `ds.materialize()`.\n\nRemember that Ray Data supports various data sources and formats, not just CSV files. You can read images, other file formats, and even data from cloud storage services like S3. For more information on loading data, refer to the 'Loading Data' user guide and the 'Ray Data Key Concepts' user guide.",
2187
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2188
        },
2189
        {
2190
            "question": "can\u2019t pickle SSLContext objects",
2191
            "sources": [
2192
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#certificate-authority-ca-error",
2193
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#tls-verification",
2194
                "https://docs.ray.io/en/master/ray-core/configure.html#step-4-verify-tls-authentication",
2195
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",
2196
                "https://docs.ray.io/en/master/ray-core/configure.html#step-3-set-the-environment-variables-for-both-ray-head-and-worker-to-enable-tls",
2197
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/tls.html#tls-authentication",
2198
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/tls.html#step-4-verify-tls-authentication",
2199
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",
2200
                "https://docs.ray.io/en/master/ray-core/configure.html#step-4-verify-tls-authentication",
2201
                "https://docs.ray.io/en/master/ray-core/configure.html#step-2-generate-individual-private-keys-and-self-signed-certificates-for-the-ray-head-and-workers",
2202
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/tls.html#step-3-configure-environment-variables-for-ray-tls-authentication",
2203
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/tls.html#step-3-configure-environment-variables-for-ray-tls-authentication",
2204
                "https://docs.ray.io/en/master/ray-core/configure.html#step-3-set-the-environment-variables-for-both-ray-head-and-worker-to-enable-tls"
2205
            ],
2206
            "document_ids": [
2207
                5551,
2208
                6592,
2209
                4403,
2210
                4306,
2211
                4400,
2212
                5898,
2213
                5913,
2214
                6507,
2215
                4402,
2216
                4399,
2217
                5912,
2218
                5911,
2219
                4401
2220
            ],
2221
            "answer": " The error you're encountering, \"can't pickle SSLContext objects,\" is not directly related to the context provided, which discusses TLS/SSL certificates and their usage in Ray and Grafana. This error is typically raised when attempting to serialize Python objects that contain unpicklable components, such as SSLContext objects. To resolve this issue, you may need to consider alternative ways of implementing the functionality that's causing the error, or find a way to avoid using SSLContext objects in the pickling process.",
2222
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2223
        },
2224
        {
2225
            "question": "How do I install CRDs in Kuberay?",
2226
            "sources": [
2227
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#test-the-generated-code-snippets",
2228
                "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",
2229
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",
2230
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/text-summarizer-rayservice.html#step-3-install-a-rayservice",
2231
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
2232
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
2233
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
2234
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
2235
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
2236
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.FINISHED.html#ray-tune-search-searcher-finished",
2237
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#optional-optimizing-for-runtime-over-memory-with-centralized-data-loading",
2238
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
2239
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status"
2240
            ],
2241
            "document_ids": [
2242
                9459,
2243
                9548,
2244
                5295,
2245
                6134,
2246
                14307,
2247
                14229,
2248
                14191,
2249
                14070,
2250
                14132,
2251
                1324,
2252
                5318,
2253
                6996,
2254
                6224
2255
            ],
2256
            "answer": " To install Custom Resource Definitions (CRDs) in Kuberay, you can follow these steps:\n\n1. Download the CRD file for the Text Summarizer service:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.text-summarizer.yaml\n```\n2. Apply the CRD using `kubectl`:\n```bash\nkubectl apply -f ray-service.text-summarizer.yaml\n```\n\nThis will install the necessary CRDs for the Text Summarizer service in Kuberay.",
2257
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2258
        },
2259
        {
2260
            "question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",
2261
            "sources": [
2262
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
2263
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
2264
                "https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",
2265
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
2266
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
2267
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
2268
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
2269
                "https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",
2270
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",
2271
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.take_batch.html#ray-data-dataset-take-batch",
2272
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.batch.html#ray-serve-batch",
2273
                "https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",
2274
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches"
2275
            ],
2276
            "document_ids": [
2277
                6788,
2278
                8048,
2279
                7632,
2280
                6789,
2281
                7082,
2282
                14727,
2283
                7985,
2284
                13697,
2285
                7328,
2286
                7825,
2287
                14083,
2288
                6727,
2289
                7380
2290
            ],
2291
            "answer": " The function for Ray data batch inference has to be named as `__call__()` because it is a special method in Python classes that allows an instance of the class to be called like a function. In the context of Ray data batch inference, this method is used to define the function that will be applied to each batch of data. By naming it `__call__()`, we can use the resulting class object as a callable in the `map_batches()` method.",
2292
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2293
        },
2294
        {
2295
            "question": "How to disconnnect ray client?",
2296
            "sources": [
2297
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment",
2298
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
2299
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.FINISHED.html#ray-tune-search-searcher-finished",
2300
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
2301
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
2302
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
2303
                "https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#tuning-the-configuration-parameters",
2304
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
2305
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.bohb.TuneBOHB.FINISHED.html#ray-tune-search-bohb-tunebohb-finished",
2306
                "https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#key-concepts",
2307
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTrainingReplay.NOOP.html#ray-tune-schedulers-populationbasedtrainingreplay-noop",
2308
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.nevergrad.NevergradSearch.FINISHED.html#ray-tune-search-nevergrad-nevergradsearch-finished",
2309
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#where-to-find-log-to-file-files"
2310
            ],
2311
            "document_ids": [
2312
                1386,
2313
                5556,
2314
                1324,
2315
                4811,
2316
                6015,
2317
                1782,
2318
                2670,
2319
                6208,
2320
                1981,
2321
                9123,
2322
                1410,
2323
                1984,
2324
                2363
2325
            ],
2326
            "answer": " Based on the provided context, there doesn't seem to be a direct method to disconnect a Ray client. However, you can simply shut down the Ray cluster or the Ray process to disconnect the client. Here's how you can do it:\n\n1. If you are using Ray on Cloud VMs, follow the instructions to terminate the cluster.\n2. If you are running Ray locally, you can shut down Ray using the following Python command:\n\n```python\nimport ray\nray.shutdown()\n```\n\nThis command will terminate the Ray process and disconnect any connected clients.",
2327
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2328
        },
2329
        {
2330
            "question": "how to submit job with python with local files?",
2331
            "sources": [
2332
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-3-run-the-pytorch-image-training-benchmark",
2333
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
2334
                "https://docs.ray.io/en/master/ray-core/cluster/index.html#what-s-next",
2335
                "https://docs.ray.io/en/master/cluster/getting-started.html#what-s-next",
2336
                "https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#what-s-next",
2337
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",
2338
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",
2339
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.JobState.html#ray-util-state-common-jobstate",
2340
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails",
2341
                "https://docs.ray.io/en/master/train/api/doc/ray.train.gbdt_trainer.GBDTTrainer.restore.html#ray-train-gbdt-trainer-gbdttrainer-restore",
2342
                "https://docs.ray.io/en/master/train/api/doc/ray.train.xgboost.XGBoostTrainer.restore.html#ray-train-xgboost-xgboosttrainer-restore",
2343
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightgbm.LightGBMTrainer.restore.html#ray-train-lightgbm-lightgbmtrainer-restore",
2344
                "https://docs.ray.io/en/master/train/api/doc/ray.train.trainer.BaseTrainer.restore.html#ray-train-trainer-basetrainer-restore"
2345
            ],
2346
            "document_ids": [
2347
                6166,
2348
                6157,
2349
                4647,
2350
                5603,
2351
                14713,
2352
                6530,
2353
                6181,
2354
                776,
2355
                6653,
2356
                8435,
2357
                8617,
2358
                8582,
2359
                8706
2360
            ],
2361
            "answer": " To submit a job with Python using local files in Ray, you can use the `ray submit` command with a Python script that specifies the training job. For example:\n```bash\nray submit -- Ray cluster --python3 /path/to/pytorch_training_e2e_submit.py --address http://127.0.0.1:8265\n```\nIn the Python script, you can specify the local files to be used in the training job using relative or absolute paths. For example:\n```python\nimport torch\nfrom torchvision import transforms\n\n# Load a local dataset\ndataset = torch.load('/path/to/local/dataset.pt')\n\n# Define a data transform\ntransform = transforms.Compose([transforms.ToTensor()])\n\n# Use the dataset and transform in the training job\n# ...\n```\nTo follow the job's logs, you can use the `ray job logs` command with the submission ID:\n```bash\nray job logs 'raysubmit_xxxxxxxxxxxxxxxx' --address http://127.0.0.1:8265 --follow\n```\nNote that the `ray submit` command returns the submission ID, which you can use in the `ray job logs` command. Also, the `--address` option specifies the Ray cluster's address, which is `http://127.0.0.1:8265` for a local cluster.",
2362
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2363
        },
2364
        {
2365
            "question": "How do I do inference from a model trained by Ray tune.fit()?",
2366
            "sources": [
2367
                "https://docs.ray.io/en/master/tune/examples/includes/tune_basic_example.html#tune-basic-example",
2368
                "https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",
2369
                "https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",
2370
                "https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",
2371
                "https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#configuring-the-search-space-and-starting-ray-tune",
2372
                "https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",
2373
                "https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",
2374
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",
2375
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",
2376
                "https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",
2377
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",
2378
                "https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve",
2379
                "https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#analyzing-tune-experiment-results"
2380
            ],
2381
            "document_ids": [
2382
                4111,
2383
                986,
2384
                1090,
2385
                127,
2386
                2767,
2387
                1080,
2388
                117,
2389
                380,
2390
                162,
2391
                189,
2392
                343,
2393
                2747,
2394
                3685
2395
            ],
2396
            "answer": " To do inference from a model trained by Ray tune.fit(), you can use the trained model in your inference function. Here's an example of how you can do it:\n\n1. First, you need to get the best configuration and the corresponding trained model. You can do this by using the `tune.run()` method's `stop` argument, which is a function that takes a `tune.Trial` object as an argument and returns `True` if the trial should be stopped. You can use this argument to stop all trials except the best one. Here's an example:\n```python\nfrom functools import partial\n\nbest_config = None\nbest_model = None\n\ndef checkpoint_callback(trial):\n    global best_config, best_model\n    if not best_config or trial.last_result[\"mean_loss\"] < best_config[\"mean_loss\"]:\n        best_config = trial.config\n        best_model = trial.checkpoint\n",
2397
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2398
        },
2399
        {
2400
            "question": "is there a way to load and run inference without using pytorch or tensorflow directly?",
2401
            "sources": [
2402
                "https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",
2403
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
2404
                "https://docs.ray.io/en/master/serve/api/index.html#command-line-interface-cli",
2405
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#running-shell-commands-on-the-cluster-ray-exec",
2406
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-via-the-cli-ray-start",
2407
                "https://docs.ray.io/en/master/ray-contribute/docs.html#creating-a-notebook-example",
2408
                "https://docs.ray.io/en/master/cluster/cli.html#ray-exec",
2409
                "https://docs.ray.io/en/master/serve/api/index.html#serve-run",
2410
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
2411
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#running-shell-commands-on-the-cluster-ray-exec",
2412
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#library-development",
2413
                "https://docs.ray.io/en/master/workflows/comparison.html#id1",
2414
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node"
2415
            ],
2416
            "document_ids": [
2417
                13850,
2418
                5757,
2419
                13982,
2420
                6219,
2421
                4191,
2422
                821,
2423
                5620,
2424
                13989,
2425
                4324,
2426
                6218,
2427
                4271,
2428
                9768,
2429
                6410
2430
            ],
2431
            "answer": " Yes, it is possible to load and run inference without using PyTorch or TensorFlow directly by using Ray, a distributed computing framework. Ray provides a feature called \"runtime environments\" that allows you to run commands on a Ray cluster. You can use this feature to load and run inference using PyTorch or TensorFlow without directly importing them in your code.\n\nFor example, you can create a virtual environment with PyTorch or TensorFlow installed and then use the `ray exec` command to run your inference code in that environment. Here's an example of how to do this:\n\n1. Create a virtual environment with PyTorch or TensorFlow installed:\n```bash\n$ conda create -n my_env pytorch\n```\nor\n```bash\n$ conda create -n my_env tensorflow\n",
2432
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2433
        },
2434
        {
2435
            "question": "what does ray do",
2436
            "sources": [
2437
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
2438
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
2439
                "https://docs.ray.io/en/master/data/data.html#learn-more",
2440
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
2441
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
2442
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
2443
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
2444
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
2445
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
2446
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
2447
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
2448
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
2449
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary"
2450
            ],
2451
            "document_ids": [
2452
                4811,
2453
                5380,
2454
                6910,
2455
                6208,
2456
                14255,
2457
                6015,
2458
                5656,
2459
                5556,
2460
                4662,
2461
                6835,
2462
                6615,
2463
                13974,
2464
                5443
2465
            ],
2466
            "answer": " Ray is a distributed computing framework that enables the execution of tasks and actors on a cluster. The `ray.init()` function is used to initialize a Ray cluster. You can use `@ray.remote` decorator to turn functions into tasks and classes into actors. The `ray.put()` and `ray.get()` functions are used to put values into and get values from Ray's object store. The `.remote()` method is used to run actor methods or tasks on the Ray cluster. The `ray.wait()` function returns two lists of object references, one with finished tasks and one with unfinished tasks. Ray can be used on cloud VMs, Kubernetes, and with a client. More information can be found in the Ray tutorials, blogs, and GitHub repository.",
2467
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2468
        },
2469
        {
2470
            "question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",
2471
            "sources": [
2472
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#id1",
2473
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",
2474
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
2475
                "https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",
2476
                "https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",
2477
                "https://docs.ray.io/en/master/data/performance-tips.html#configuring-resources-and-locality",
2478
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
2479
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",
2480
                "https://docs.ray.io/en/master/rllib/actors.html#specifying-required-resources",
2481
                "https://docs.ray.io/en/master/ray-core/actors.html#specifying-required-resources",
2482
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#specifying-required-resources",
2483
                "https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",
2484
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#resources"
2485
            ],
2486
            "document_ids": [
2487
                5939,
2488
                5104,
2489
                5115,
2490
                5230,
2491
                4366,
2492
                6829,
2493
                5112,
2494
                5108,
2495
                11580,
2496
                4473,
2497
                14691,
2498
                4382,
2499
                5928
2500
            ],
2501
            "answer": " If you specify a fractional GPU in the resource spec, and your task uses more than that, Ray won't prevent it. It's your responsibility to ensure that tasks or actors use no more resources than specified via resource requirements. Ray doesn't provide GPU isolation for tasks or actors, and it lets the operating system schedule and run the task instead. The actual physical resource usage is not limited by the resource requirements of tasks or actors.",
2502
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2503
        },
2504
        {
2505
            "question": "how to pickle a variable defined in actor\u2019s init method",
2506
            "sources": [
2507
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",
2508
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",
2509
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
2510
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
2511
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",
2512
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
2513
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",
2514
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
2515
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
2516
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
2517
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
2518
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#actor-detail-page",
2519
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate"
2520
            ],
2521
            "document_ids": [
2522
                520,
2523
                8101,
2524
                14101,
2525
                4878,
2526
                4200,
2527
                14723,
2528
                751,
2529
                14132,
2530
                14323,
2531
                14202,
2532
                475,
2533
                436,
2534
                750
2535
            ],
2536
            "answer": " To pickle a variable defined in an actor's `__init__` method, you can pickle the actor object itself, which will include all of its state, including the variables defined in `__init__`. Here's an example of how to do this:\n```python\nimport ray\nimport pickle\n\n@ray.remote\nclass MyActor:\n    def __init__(self, index):\n        self.index = index\n",
2537
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2538
        },
2539
        {
2540
            "question": "how do I do an all_reduce operation among a list of actors",
2541
            "sources": [
2542
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
2543
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",
2544
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",
2545
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
2546
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",
2547
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorSummaries.html#ray-util-state-common-actorsummaries",
2548
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
2549
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#task-and-actor-breakdown",
2550
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
2551
                "https://docs.ray.io/en/master/data/loading-data.html#reading-sql-databases",
2552
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
2553
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
2554
                "https://docs.ray.io/en/master/data/batch_inference.html#quickstart"
2555
            ],
2556
            "document_ids": [
2557
                673,
2558
                8101,
2559
                788,
2560
                475,
2561
                276,
2562
                727,
2563
                473,
2564
                424,
2565
                14132,
2566
                6766,
2567
                14101,
2568
                472,
2569
                6944
2570
            ],
2571
            "answer": " To perform an all\\_reduce operation among a list of actors, you can use the Ray's actor system to create a distributed computation pattern. However, Ray does not provide an all\\_reduce function out-of-the-box for actors. Instead, you can implement a custom reduction logic using Ray's remote functions and actor system.\n\nHere's a high-level outline of how you can implement an all\\_reduce operation using Ray:\n\n1. Create a remote function that performs the reduction operation.\n2. Use Ray's `put()` function to store the initial values on the object store.\n3. Use Ray's `get()` function to retrieve the final reduced value.\n\nHere's a simple example of implementing an all\\_reduce using a sum reduction function:\n\n```python\n@ray.remote\nclass Reducer:\n    def __init__(self):\n        self.value = 0\n\n    def add(self, value):\n        self.value += value\n\n    def get_value(self):\n        return self.value\n\n# Initialize reducers\nreducers = [Reducer.remote() for _ in range(num_actors)]\n\n# Perform the reduction\nfor i in range(num_iterations):\n    for reducer_id, reducer in enumerate(reducers):\n        value = get_value_from_actor(reducer_id)  # Fetch the value from the actor\n        ray.get(reducer.add.remote(value))  # Perform the reduction\n\n# Get the final reduced value\nfinal_value = ray.get(reducers[0].get_value.remote())\n```\n\nReplace `num_actors` and `num_iterations` with the appropriate values for your use case. The `get_value_from_actor()` function should fetch the value from the actor, which can be implemented using Ray's `get()` function.\n\nKeep in mind that this is a simple example and you might need to adapt it to your specific use case. For instance, you can replace the `add()` function with a more complex reduction logic depending on your requirements.",
2572
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2573
        },
2574
        {
2575
            "question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",
2576
            "sources": [
2577
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#bundles",
2578
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",
2579
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",
2580
                "https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",
2581
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
2582
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
2583
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
2584
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
2585
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",
2586
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",
2587
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
2588
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
2589
                "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"
2590
            ],
2591
            "document_ids": [
2592
                5050,
2593
                5063,
2594
                4790,
2595
                6717,
2596
                14298,
2597
                14136,
2598
                14220,
2599
                14182,
2600
                5936,
2601
                1873,
2602
                14093,
2603
                14061,
2604
                12053
2605
            ],
2606
            "answer": " Specifying a bundle with `{\"CPU\":0}` in the PlacementGroup will result in a bundle with no CPU resources. However, it is important to note that a bundle must be able to fit on a single node and have sufficient resources to run the tasks assigned to it. A bundle with no CPU resources may not be able to run any tasks and may cause the PlacementGroup to fail. Additionally, the current cluster has only `{\"CPU\": 2, \"GPU\": 2}` resources available, so creating a bundle with `{\"CPU\":0}` may still result in insufficient resources for scheduling the bundle.",
2607
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2608
        },
2609
        {
2610
            "question": "How to cancel job from UI?",
2611
            "sources": [
2612
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",
2613
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
2614
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",
2615
                "https://docs.ray.io/en/master/workflows/comparison.html#trip-booking",
2616
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
2617
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",
2618
                "https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",
2619
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
2620
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
2621
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",
2622
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobStatus.STOPPED.html#ray-job-submission-jobstatus-stopped",
2623
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.stop_job.html#ray-job-submission-jobsubmissionclient-stop-job",
2624
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#cleaning-up"
2625
            ],
2626
            "document_ids": [
2627
                4550,
2628
                4375,
2629
                6559,
2630
                9757,
2631
                4894,
2632
                6710,
2633
                9791,
2634
                11590,
2635
                4494,
2636
                14701,
2637
                6690,
2638
                6686,
2639
                6471
2640
            ],
2641
            "answer": " To cancel a job from the UI, you can use the `ray job stop` command, where `job_id` is the ID of the job you want to cancel. For example:\n\n```\n$ ray job stop raysubmit_tUAuCKubPAEXh6CW\n```\n\nThis will request the job to exit asynchronously. If you want to delete the job and all of its associated data, you can use the `ray delete job` command instead.\n\nIf you are trying to cancel a task rather than a job, you can use the `ray.cancel()` function, passing it the `ObjectRef` returned by the task. For example:\n\n```\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n```\n\nThis will send a `KeyboardInterrupt` to the task's worker if it is mid-execution. If you want to force-exit the worker, you can pass `force=True` to `ray.cancel()`.\n\nTo cancel an actor task, you can also use the `ray.cancel()` function, passing it the `ObjectRef` returned by the task. For example:\n\n```\nref = actor.f.remote()\nray.cancel(ref)\n```\n\nThis will raise an `asyncio.CancelledError` in the actor task, which you can catch and handle as needed.",
2642
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2643
        },
2644
        {
2645
            "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>`",
2646
            "sources": [
2647
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
2648
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",
2649
                "https://docs.ray.io/en/master/serve/api/index.html#serve",
2650
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments",
2651
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
2652
                "https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",
2653
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",
2654
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",
2655
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",
2656
                "https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",
2657
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
2658
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#development-workflow-with-serve-run",
2659
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"
2660
            ],
2661
            "document_ids": [
2662
                13677,
2663
                13680,
2664
                13983,
2665
                13727,
2666
                13719,
2667
                13987,
2668
                4262,
2669
                13831,
2670
                5671,
2671
                13962,
2672
                13718,
2673
                13659,
2674
                4280
2675
            ],
2676
            "answer": " To get your project files on the cluster when using Ray Serve, you can use the `serve run` command with the `--working-dir` option. This command connects to the remote cluster via Ray Client, uploads the specified working directory, and runs your Ray Serve application. For example, you can use the following command to connect to a remote cluster with the head node IP address substituted for `<head-node-ip-address>`:\n```\nserve run --address=ray://<head-node-ip-address>:10001 --working-dir=\"./project/src\" local_dev:graph\n",
2677
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2678
        },
2679
        {
2680
            "question": "how do i install ray nightly wheel",
2681
            "sources": [
2682
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",
2683
                "https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",
2684
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
2685
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2686
                "https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",
2687
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",
2688
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",
2689
                "https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",
2690
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",
2691
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.stopper.Stopper.__init__.html#ray-tune-stopper-stopper-init",
2692
                "https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",
2693
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",
2694
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit"
2695
            ],
2696
            "document_ids": [
2697
                14545,
2698
                288,
2699
                63,
2700
                6335,
2701
                66,
2702
                14551,
2703
                291,
2704
                883,
2705
                4185,
2706
                1849,
2707
                67,
2708
                292,
2709
                14553
2710
            ],
2711
            "answer": " To install the Ray nightly wheel, first cleanly remove any previous Ray installation using `pip uninstall -y ray`. Then, use the `pip install` command with the appropriate link to the wheel file. You can find the links for the nightly wheels on the Ray documentation's \"Daily Releases (Nightlies)\" page. The general format for the install command is:\n\n```bash\npip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"\n```\n\nReplace `LINK_TO_WHEEL.whl` with the actual link to the wheel file you want to install.",
2712
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2713
        },
2714
        {
2715
            "question": "how do i install the latest ray nightly wheel?",
2716
            "sources": [
2717
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
2718
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",
2719
                "https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",
2720
                "https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",
2721
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",
2722
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",
2723
                "https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",
2724
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",
2725
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",
2726
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
2727
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",
2728
                "https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",
2729
                "https://docs.ray.io/en/master/installation.html#from-wheels"
2730
            ],
2731
            "document_ids": [
2732
                63,
2733
                14545,
2734
                288,
2735
                67,
2736
                14553,
2737
                292,
2738
                66,
2739
                291,
2740
                14551,
2741
                6335,
2742
                14540,
2743
                286,
2744
                61
2745
            ],
2746
            "answer": " To install the latest Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use pip to install the nightly wheel with support for the dashboard and cluster launcher by 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 from the Ray website. Make sure to replace `LINK_TO_WHEEL.whl` with the actual link to the wheel file.",
2747
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2748
        },
2749
        {
2750
            "question": "how can I write unit tests for Ray code?",
2751
            "sources": [
2752
                "https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",
2753
                "https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",
2754
                "https://docs.ray.io/en/master/ray-contribute/writing-code-snippets.html#ignoring-code-block-style-outputs",
2755
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",
2756
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",
2757
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
2758
                "https://docs.ray.io/en/master/ray-contribute/docs.html#creating-a-notebook-example",
2759
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",
2760
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",
2761
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",
2762
                "https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",
2763
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
2764
                "https://docs.ray.io/en/master/ray-contribute/docs.html#adding-code-to-an-rst-or-md-file"
2765
            ],
2766
            "document_ids": [
2767
                5397,
2768
                5395,
2769
                845,
2770
                6621,
2771
                9327,
2772
                5380,
2773
                819,
2774
                350,
2775
                387,
2776
                169,
2777
                196,
2778
                1788,
2779
                814
2780
            ],
2781
            "answer": " To write unit tests for Ray code, you can follow these tips:\n\n1. Fix the resource quantity with `ray.init(num_cpus=...)`.\n2. Share the Ray cluster across tests if possible.\n3. Create a mini-cluster with `ray.cluster_utils.Cluster`.\n4. Be careful when running tests in parallel.\n\nHere's an example of how to set up a test case with a shared Ray cluster:\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 test_my_ray_code(self):\n        # Test your Ray code here.\n        pass\n```\n\nMake sure to replace the comment with your actual test code. The `setUp` method is called before each test method, ensuring a fresh Ray cluster for every test.\n\nAdditionally, if your code contains non-deterministic output, you can use the provided \"testcode\" and \"testoutput\" blocks to display a sample output or replace problematic sections with ellipses.",
2782
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2783
        },
2784
        {
2785
            "question": "How I stop Ray from spamming lots of Info updates on stdout?",
2786
            "sources": [
2787
                "https://docs.ray.io/en/master/data/data.html#learn-more",
2788
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
2789
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",
2790
                "https://docs.ray.io/en/master/cluster/usage-stats.html#requesting-removal-of-collected-data",
2791
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
2792
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
2793
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",
2794
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
2795
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-disable-usage-stats",
2796
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",
2797
                "https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-a-tune-experiment-manually",
2798
                "https://docs.ray.io/en/master/cluster/cli.html#ray-stop",
2799
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs"
2800
            ],
2801
            "document_ids": [
2802
                6910,
2803
                4811,
2804
                14506,
2805
                5569,
2806
                6208,
2807
                9510,
2808
                4550,
2809
                558,
2810
                4671,
2811
                6569,
2812
                2196,
2813
                5613,
2814
                6558
2815
            ],
2816
            "answer": " To stop Ray from spamming lots of Info updates on stdout, you can set the environment variable `RAY_SCHEDULER_EVENTS=0`. This will disable scheduler events from being printed to stdout. Alternatively, you can use the `ray disable-usage-stats` command to disable usage stats collection, which may also reduce the amount of output.",
2817
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2818
        },
2819
        {
2820
            "question": "how to deploy stable diffusion 2.1 with Ray Serve?",
2821
            "sources": [
2822
                "https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",
2823
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
2824
                "https://docs.ray.io/en/master/serve/api/index.html#serve-start",
2825
                "https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",
2826
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",
2827
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-4-prepare-your-ray-serve-python-scripts-and-run-the-ray-serve-application",
2828
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
2829
                "https://docs.ray.io/en/master/serve/api/index.html#serve",
2830
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",
2831
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
2832
                "https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",
2833
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
2834
                "https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment"
2835
            ],
2836
            "document_ids": [
2837
                13797,
2838
                13677,
2839
                13994,
2840
                5671,
2841
                13832,
2842
                5983,
2843
                13974,
2844
                13983,
2845
                13647,
2846
                14255,
2847
                13987,
2848
                13678,
2849
                14594
2850
            ],
2851
            "answer": " To deploy stable diffusion 2.1 with Ray Serve, you can follow these steps:\n\n1. Prepare your Ray Serve Python scripts. You can clone the example scripts using the command `git clone https://github.com/ray-project/serve_config_examples.git`.\n2. Ensure that your Ray Serve script is correct and test it locally or in a RayCluster before deploying it to a RayService.\n3. Use the Ray Serve CLI to deploy your application. You can start a local Ray cluster with `ray start --head`, and then deploy the config file using `serve deploy <config_file>.yaml`.\n4. In your case, you would run `$ ray start --head` and then `$ serve deploy stable_diffusion_config.yaml`.\n5. To check the status of the deployments, you can use the command `serve status`.\n\nNote that the message \"Sent deploy request successfully!\" only indicates that the Ray cluster has received the config file successfully. It does not mean that the application has been deployed yet. Use `serve status` to check the status of the deployments.",
2852
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2853
        },
2854
        {
2855
            "question": "what is actor_handle?",
2856
            "sources": [
2857
                "https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",
2858
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",
2859
                "https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",
2860
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
2861
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
2862
                "https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",
2863
                "https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",
2864
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",
2865
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",
2866
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
2867
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
2868
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
2869
                "https://docs.ray.io/en/master/ray-core/actors.html#actors"
2870
            ],
2871
            "document_ids": [
2872
                11587,
2873
                14698,
2874
                4487,
2875
                4860,
2876
                4615,
2877
                11588,
2878
                4489,
2879
                14699,
2880
                4200,
2881
                4623,
2882
                4871,
2883
                4650,
2884
                4462
2885
            ],
2886
            "answer": " An actor handle is a reference to a specific actor instance, which allows you to call methods on that actor from other tasks or functions. It is used in the context of Ray, a distributed computing framework. The handle can be passed around to various tasks, enabling them to use the actor's methods and access its state.",
2887
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2888
        },
2889
        {
2890
            "question": "how to kill a r detached actors?",
2891
            "sources": [
2892
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",
2893
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
2894
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",
2895
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
2896
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
2897
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
2898
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
2899
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
2900
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
2901
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",
2902
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
2903
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
2904
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor"
2905
            ],
2906
            "document_ids": [
2907
                2157,
2908
                4859,
2909
                4579,
2910
                4616,
2911
                4615,
2912
                4620,
2913
                4860,
2914
                4633,
2915
                4848,
2916
                8106,
2917
                4613,
2918
                4650,
2919
                4618
2920
            ],
2921
            "answer": " To kill a detached Ray actor, you can call `ray.kill(actor)` on the actor handle. This will interrupt any running tasks on the actor and immediately cause it to exit. If you want to let pending tasks finish before killing the actor, you can call `actor.__ray_terminate__.remote()` instead. If the actor is a detached actor, subsequent calls to get its handle via `ray.get_actor` will fail.",
2922
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2923
        },
2924
        {
2925
            "question": "How to force upgrade the pip package in the runtime environment if an old version exists?",
2926
            "sources": [
2927
                "https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#which-algorithm-checkpoint-versions-can-i-use",
2928
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
2929
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#versioning-requirements",
2930
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
2931
                "https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-python-api",
2932
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.get_version.html#ray-job-submission-jobsubmissionclient-get-version",
2933
                "https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#putting-everything-together",
2934
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
2935
                "https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",
2936
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",
2937
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",
2938
                "https://docs.ray.io/en/master/serve/advanced-guides/migration.html#migrate-from-1-x-to-2-x",
2939
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor"
2940
            ],
2941
            "document_ids": [
2942
                10555,
2943
                4329,
2944
                6627,
2945
                4327,
2946
                10935,
2947
                6674,
2948
                2794,
2949
                10128,
2950
                2455,
2951
                5295,
2952
                14467,
2953
                13796,
2954
                4260
2955
            ],
2956
            "answer": " To force upgrade the pip package in the runtime environment if an old version exists, you can use the following command in your terminal:\n```\npip install --upgrade package-name\n```\nReplace \"package-name\" with the name of the pip package you want to upgrade. This command will upgrade the package to the latest version available on PyPI.\n\nIn the context provided, there is no specific mention of forcing an upgrade of a pip package in a runtime environment. However, the information given is about Ray, a distributed computing framework, and its compatibility with different versions of Dask and Python.",
2957
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2958
        },
2959
        {
2960
            "question": "How do I do global shuffle with Ray?",
2961
            "sources": [
2962
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
2963
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
2964
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
2965
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment",
2966
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#deploying-on-slurm",
2967
                "https://docs.ray.io/en/master/workflows/basics.html#ray-integration",
2968
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
2969
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
2970
                "https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#key-concepts",
2971
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
2972
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",
2973
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",
2974
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api"
2975
            ],
2976
            "document_ids": [
2977
                5380,
2978
                4811,
2979
                6615,
2980
                1386,
2981
                6400,
2982
                9685,
2983
                4662,
2984
                6015,
2985
                9123,
2986
                1782,
2987
                6621,
2988
                5412,
2989
                13974
2990
            ],
2991
            "answer": " To perform a global shuffle with Ray, you can use the `ray.data.BlockList` and `ray.data.Dataset.shuffle` functions. Here's a simple example:\n\n```python\n# First, create a BlockList from a list of filenames or datasets\nblocklist = ray.data.BlockList(filenames)\n\n# Then, create a new shuffled dataset from the BlockList\nshuffled_dataset = ray.data.Dataset.from_blocks(blocklist).shuffle()\n```\n\nHowever, if you're looking to perform a global shuffle on Ray actors or tasks, you might need to implement a custom solution, as there isn't a built-in function specifically for that.\n\nFor more information on Ray and its features, you can refer to the Ray Tutorials and Examples, Ray Core CLI, Ray Integration, and Ray Tune documentation. The Ray project's GitHub repository (`ray-project/ray`) and issue #29753 may also be helpful.\n\nTo install Ray, you can use the following command:\n\n```bash\npip install ray\n```\n\nKeep in mind that the Ray Serve API is for building web services with Ray, and it doesn't directly relate to the global shuffle functionality.",
2992
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
2993
        },
2994
        {
2995
            "question": "How to find namespace of an Actor?",
2996
            "sources": [
2997
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
2998
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#actor-detail-page",
2999
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor",
3000
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#actors-view",
3001
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
3002
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3003
                "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",
3004
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
3005
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",
3006
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
3007
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
3008
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
3009
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors"
3010
            ],
3011
            "document_ids": [
3012
                475,
3013
                436,
3014
                690,
3015
                434,
3016
                673,
3017
                4616,
3018
                4787,
3019
                4629,
3020
                4931,
3021
                5071,
3022
                14323,
3023
                14202,
3024
                788
3025
            ],
3026
            "answer": " To find the namespace of an Actor in Ray, you can use the `ray.util.state.get_actor` function and inspect the `ray_namespace` field in the returned `ActorState` dictionary. Here is an example:\n\n```python\nfrom ray.util.state import get_actor\n\nactor_state = get_actor(actor_id)\nif actor_state:\n    namespace = actor_state['ray_namespace']\n    print(f\"The namespace of the actor is: {namespace}\")\nelse:\n    print(\"Actor not found\")\n```\n\nAlternatively, you can use the `ray list actors --detail` command in the Ray CLI to list the actors in the cluster along with their namespaces. The `--detail` flag provides detailed information about each actor, including its namespace. Here is an example:\n\n```bash\nray list actors --detail\n```\n\nThis will print a list of actors in the cluster along with their details, including their namespaces. You can then search for the actor you are interested in and find its namespace.",
3027
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3028
        },
3029
        {
3030
            "question": "How does Ray work with async.io ?",
3031
            "sources": [
3032
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",
3033
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
3034
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
3035
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
3036
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#where-to-find-log-to-file-files",
3037
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
3038
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#next-steps",
3039
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
3040
                "https://docs.ray.io/en/master/tune/examples/tune-aim.html#tune-aim-logger-api",
3041
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.stop_job.html#ray-job-submission-jobsubmissionclient-stop-job",
3042
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
3043
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",
3044
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.get_job_status.html#ray-job-submission-jobsubmissionclient-get-job-status"
3045
            ],
3046
            "document_ids": [
3047
                5443,
3048
                6615,
3049
                4811,
3050
                372,
3051
                2363,
3052
                13974,
3053
                4495,
3054
                5656,
3055
                3791,
3056
                6687,
3057
                4857,
3058
                4872,
3059
                6664
3060
            ],
3061
            "answer": " Ray doesn't directly work with async/io. However, you can use asyncio with Ray by using the `get_gpu_local_actor` function to create a local GPU-accelerated actor, and then use asyncio within the method of that actor. This way, you can take advantage of both Ray's distributed computing capabilities and asyncio's concurrency model. Note that this approach requires careful management of object references and results between tasks and actors.",
3062
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3063
        },
3064
        {
3065
            "question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",
3066
            "sources": [
3067
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",
3068
                "https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",
3069
                "https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",
3070
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",
3071
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",
3072
                "https://docs.ray.io/en/master/ray-core/api/core.html#objects",
3073
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
3074
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-tasks",
3075
                "https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",
3076
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-too-many-objects.html#anti-pattern-fetching-too-many-objects-at-once-with-ray-get-causes-failure",
3077
                "https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",
3078
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#library-development",
3079
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users"
3080
            ],
3081
            "document_ids": [
3082
                5235,
3083
                13646,
3084
                5219,
3085
                4884,
3086
                14419,
3087
                4651,
3088
                4209,
3089
                5423,
3090
                4424,
3091
                5169,
3092
                5236,
3093
                4271,
3094
                4205
3095
            ],
3096
            "answer": " To debug a hanging `ray.get()` call, avoid calling `ray.get()` in a loop as it's a blocking call. Instead, fetch the results of remotely executed functions using `ray.get()` only for the final result. If you call `ray.get()` unnecessarily for intermediate steps, it can harm performance. Work with object references directly and call `ray.get()` at the end to get the final result.\n\nIn your specific case, you should separate the call to `ray.get()` from the call to the remote functions. Spawn all remote functions before waiting for the results, so they can run in parallel in the background. Pass a list of object references to `ray.get()` instead of calling it one by one, to wait for all tasks to finish.\n\nHere's an example of how to properly use `ray.get()`:\n\n```python\n# Spawn remote functions\nobject_refs = [function.remote() for function in function_list]\n\n# Wait for all tasks to finish\nresults = ray.get(object_refs)\n```",
3097
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3098
        },
3099
        {
3100
            "question": "can you show me an example of ray.actor.exit_actor()",
3101
            "sources": [
3102
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3103
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3104
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3105
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3106
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",
3107
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
3108
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3109
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
3110
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.AsyncioActorExit.html#ray-exceptions-asyncioactorexit",
3111
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",
3112
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.shutdown.html#ray-shutdown",
3113
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",
3114
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure"
3115
            ],
3116
            "document_ids": [
3117
                4618,
3118
                4613,
3119
                4620,
3120
                4619,
3121
                4579,
3122
                4859,
3123
                4615,
3124
                4650,
3125
                4781,
3126
                637,
3127
                4827,
3128
                4488,
3129
                4565
3130
            ],
3131
            "answer": " The `ray.actor.exit_actor()` function can be used to manually terminate a Ray actor from within one of the actor methods. This will kill the actor process and release associated resources. Here is an example:\n\n```python\n@ray.remote\nclass Actor:\n    def exit(self):\n        ray.actor.exit_actor()\n\nactor = Actor.remote()\nactor.exit.remote()\n```\n\nHowever, it is generally not necessary to manually terminate actors, as they are automatically garbage collected. The object ref resulting from the task can be waited on to wait for the actor to exit.\n\nTo forcefully terminate an actor, you can use the `ray.kill()` function. This should be reserved for cases where an actor is unexpectedly hanging or leaking resources, and for detached actors, which must be manually destroyed. Here is an example:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n    pass\n\nactor_handle = Actor.remote()\n\nray.kill(actor_handle)\n```\n\nNote that this will not go through the normal Python sys.exit teardown logic, so any exit handlers installed in the actor using `atexit` will not be called.\n\nGarbage collection for actors has not been implemented yet, so this is currently the only way to terminate an actor gracefully. The object ref resulting from the task can be waited on to wait for the actor to exit. This method of termination waits until any previously submitted tasks finish executing and then exits the process gracefully with sys.exit. You can see the actor is dead as a result of the user's `exit_actor()` call by using the `ray list actors --detail` command.\n\nIn C++, you can use the `actor_handle.Kill()` function to forcefully terminate an actor. This will cause the actor to immediately exit its process, causing any current, pending, and future tasks to fail with a RayActorError. If you would like Ray to automatically restart the actor, make sure to set a nonzero max\\_restarts in the `@ray.remote` options for the actor, then pass the flag `no_restart=False` to `ray.kill`. For named and detached actors, calling `ray.kill` on an actor handle destroys the actor and allows the name to be reused.",
3132
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3133
        },
3134
        {
3135
            "question": "how to add log inside actor?",
3136
            "sources": [
3137
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",
3138
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
3139
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",
3140
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
3141
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
3142
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
3143
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
3144
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
3145
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#actors-view",
3146
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",
3147
                "https://docs.ray.io/en/master/rllib/rllib-catalogs.html#write-a-catalog-from-scratch",
3148
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
3149
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"
3150
            ],
3151
            "document_ids": [
3152
                414,
3153
                540,
3154
                520,
3155
                14323,
3156
                14202,
3157
                14286,
3158
                14295,
3159
                14106,
3160
                434,
3161
                682,
3162
                11539,
3163
                477,
3164
                476
3165
            ],
3166
            "answer": " To add logging inside an actor, you can define a log method within the actor class that uses the logging library to add a log message. Here's an example:\n```python\nimport logging\n\n@ray.remote\nclass MyActor:\n    def __init__(self):\n        self.logger = logging.getLogger(__name__)\n\n    def log(self, msg):\n        self.logger.info(msg)\n\n# Use the log method on the actor\nactor = MyActor.remote()\nray.get(actor.log.remote(\"A log message for an actor.\"))\n```\nBy default, all stdout and stderr of Tasks and Actors are redirected to the Driver output. If you want to customize the prefixes for Actor logs, you can define the `__repr__` method for the Actor class to replace the Actor name with a custom representation.\n\nYou can also access the logs for an Actor using the Ray CLI or the Python SDK. For example, you can use `ray logs actor --id <ACTOR_ID>` to view the logs for a specific Actor.",
3167
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3168
        },
3169
        {
3170
            "question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",
3171
            "sources": [
3172
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",
3173
                "https://docs.ray.io/en/master/tune/faq.html#how-can-i-use-tune-with-kubernetes",
3174
                "https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches",
3175
                "https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",
3176
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",
3177
                "https://docs.ray.io/en/master/ray-air/examples/batch_tuning.html#define-how-to-load-and-prepare-parquet-data-a-class-anchor-id-prepare-data-a",
3178
                "https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",
3179
                "https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",
3180
                "https://docs.ray.io/en/master/ray-core/examples/automl_for_time_series.html#walkthrough",
3181
                "https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",
3182
                "https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",
3183
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#full-customization-advanced",
3184
                "https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial"
3185
            ],
3186
            "document_ids": [
3187
                5292,
3188
                1062,
3189
                6861,
3190
                6843,
3191
                6786,
3192
                10230,
3193
                7866,
3194
                9128,
3195
                5497,
3196
                6999,
3197
                6791,
3198
                9202,
3199
                14580
3200
            ],
3201
            "answer": " Here is a script to do batch inference with GPT-2 on text data from an S3 bucket:\n\n```python\nfrom typing import Dict\nimport numpy as np\nimport ray\nfrom ray import tune, train\nimport torch\n\n# Define a function to preprocess the data\ndef preprocess_data(batch: str) -> Dict[str, np.ndarray]:\n    # Preprocess the data as needed for GPT-2\n    # ...\n    return {\"input\": np.array(preprocessed_data)}\n\n# Define a function to run inference with GPT-2\ndef run_inference(batch: Dict[str, np.ndarray]) -> Dict[str, torch.Tensor]:\n    # Load the GPT-2 model\n    model = GPT2Model.from_pretrained(\"gpt2\")\n    # Run inference with the model\n    input_ids = torch.tensor(batch[\"input\"]).unsqueeze(0)\n    outputs = model(input_ids)\n    # Format the outputs as needed\n    # ...\n    return {\"output\": outputs.last_hidden_state}\n\n# Read the data from the S3 bucket\nds = ray.data.read_text(\"s3://your-s3-bucket/text_data\")\n\n# Preprocess the data\nds = ds.map_batches(preprocess_data, batch_format=\"numpy\")\n\n# Run inference on the data\nds = ds.map_batches(run_inference, batch_format=\"python\")\n\n# Write the results to another S3 bucket\nds.write_json(\"s3://your-s3-bucket/inference_results\")\n```\n\nThis script reads text data from an S3 bucket, preprocesses it as needed for GPT-2, runs inference with GPT-2, and writes the results to another S3 bucket. You can adjust the preprocessing and inference functions to suit your specific needs. Note that you will need to install the `transformers` library to use GPT-2.",
3202
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3203
        },
3204
        {
3205
            "question": "How do I enable Ray debug logs?",
3206
            "sources": [
3207
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
3208
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-collection",
3209
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
3210
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
3211
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",
3212
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
3213
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
3214
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",
3215
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",
3216
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
3217
                "https://docs.ray.io/en/master/tune/examples/index.html#other-examples",
3218
                "https://docs.ray.io/en/master/tune/examples/other-examples.html#other-examples",
3219
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job"
3220
            ],
3221
            "document_ids": [
3222
                443,
3223
                6362,
3224
                13523,
3225
                444,
3226
                510,
3227
                6157,
3228
                680,
3229
                6530,
3230
                6181,
3231
                6631,
3232
                3798,
3233
                3322,
3234
                5764
3235
            ],
3236
            "answer": " To enable Ray debug logs, you can configure the \"ray.serve\" logger inside the deployment constructor and set its level to debug. Here's an example:\n```python\nimport logging\nlogging.getLogger('ray.serve').setLevel(logging.DEBUG)\n```\nThis will enable debug logs for the Ray Serve component. If you want to enable debug logs for the entire Ray cluster, you can set the root logger's level to debug:\n```python\nimport logging\nlogging.getLogger().setLevel(logging.DEBUG)\n```\nNote that enabling debug logs may produce a large amount of output and consume more disk space. Make sure to configure log rotation to avoid filling up the disk.",
3237
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3238
        },
3239
        {
3240
            "question": "How do I list the current Ray actors from python?",
3241
            "sources": [
3242
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
3243
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
3244
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
3245
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
3246
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
3247
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",
3248
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
3249
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
3250
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ReplicaDetails.html#ray-serve-schema-replicadetails",
3251
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3252
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#actors-view",
3253
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
3254
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.FINISHED.html#ray-tune-search-searcher-finished"
3255
            ],
3256
            "document_ids": [
3257
                475,
3258
                673,
3259
                14286,
3260
                14295,
3261
                14106,
3262
                788,
3263
                14132,
3264
                14101,
3265
                14243,
3266
                4622,
3267
                434,
3268
                472,
3269
                1324
3270
            ],
3271
            "answer": " To list the current Ray actors from Python, you can use the following command in your terminal or command line:\n```bash\nray list actors\n```\nAlternatively, if you are using the Ray Python SDK, you can use the `ray.util.state.list_actors()` function as follows:\n```python\nfrom ray.util.state import list_actors\n\nactors = list_actors()\nfor actor in actors:\n    print(actor)\n```\nThis will print out information about all the actors currently running in the Ray cluster.",
3272
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3273
        },
3274
        {
3275
            "question": "I want to kill the replica actor from Python. how do I do it?",
3276
            "sources": [
3277
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
3278
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",
3279
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",
3280
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3281
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
3282
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
3283
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
3284
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3285
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3286
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
3287
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
3288
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
3289
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure"
3290
            ],
3291
            "document_ids": [
3292
                4859,
3293
                4579,
3294
                2157,
3295
                4615,
3296
                4860,
3297
                4633,
3298
                4650,
3299
                4616,
3300
                4614,
3301
                4620,
3302
                5071,
3303
                4613,
3304
                13905
3305
            ],
3306
            "answer": " To kill a replica actor in Ray from Python, you can use the `ray.kill()` function, passing in the actor handle as the argument. Here is an example:\n```\nimport ray\n\n@ray.remote\nclass Actor:\n    pass\n",
3307
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3308
        },
3309
        {
3310
            "question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",
3311
            "sources": [
3312
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
3313
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",
3314
                "https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",
3315
                "https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",
3316
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",
3317
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",
3318
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",
3319
                "https://docs.ray.io/en/master/ray-core/api/core.html#tasks",
3320
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",
3321
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#running-a-task",
3322
                "https://docs.ray.io/en/master/ray-core/tasks.html#more-about-ray-tasks",
3323
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",
3324
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements"
3325
            ],
3326
            "document_ids": [
3327
                14767,
3328
                4697,
3329
                4639,
3330
                4366,
3331
                4259,
3332
                412,
3333
                4258,
3334
                4649,
3335
                5114,
3336
                4486,
3337
                4378,
3338
                4714,
3339
                5115
3340
            ],
3341
            "answer": " To specify that a task should run on a V100 GPU, you can use the `num_gpus` parameter in the `options()` function when invoking the remote function. Here's an example:\n```python\n@ray.remote\ndef my_function():\n    # This function will run on a V100 GPU\n    ...\n\nobject_ref = my_function.options(num_gpus=1).remote()\n```\nIn this example, `my_function` is defined as a remote function using the `@ray.remote` decorator. The `options()` function is then used to specify that the function should run on a GPU. The `num_gpus` parameter is set to 1, indicating that a single GPU should be used for this task. Finally, the remote function is invoked using the `remote()` method, which returns a object reference that can be used to track the progress and retrieve the result of the task.",
3342
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3343
        },
3344
        {
3345
            "question": "How do I get started?",
3346
            "sources": [
3347
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",
3348
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#getting-started",
3349
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-autoscaler-private-fake-multi-node-test-utils-dockercluster",
3350
                "https://docs.ray.io/en/master/serve/index.html#learn-more",
3351
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",
3352
                "https://docs.ray.io/en/master/tune/faq.html#how-can-i-get-started-contributing-to-tune",
3353
                "https://docs.ray.io/en/master/serve/advanced-guides/index.html#advanced-guides",
3354
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",
3355
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-1-create-a-kind-cluster",
3356
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#getting-started-with-kuberay",
3357
                "https://docs.ray.io/en/master/serve/key-concepts.html#whats-next",
3358
                "https://docs.ray.io/en/master/train/train.html#get-started",
3359
                "https://docs.ray.io/en/master/cluster/vms/index.html#learn-more"
3360
            ],
3361
            "document_ids": [
3362
                6478,
3363
                220,
3364
                864,
3365
                13625,
3366
                4484,
3367
                1049,
3368
                13827,
3369
                4185,
3370
                6043,
3371
                5646,
3372
                13428,
3373
                8244,
3374
                6210
3375
            ],
3376
            "answer": " To get started with Ray, first install it using pip by running `pip install -U ray`. Then, import and initialize Ray in your code with `import ray` and `ray.init()`. If you're new to Ray Serve, start with the Ray Serve Quickstart. For more options and configurations, refer to the advanced guides. If you're interested in contributing to Tune, look for issues labeled \"good first issue\" and \"help wanted\" on the GitHub page. To learn about distributed training with Ray Train, check out the Ray Train documentation.",
3377
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3378
        },
3379
        {
3380
            "question": "How to specify python version in runtime_env?",
3381
            "sources": [
3382
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#versioning-requirements",
3383
                "https://docs.ray.io/en/master/installation.html#install-ray-java-with-maven",
3384
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#install-ray-java-with-maven",
3385
                "https://docs.ray.io/en/master/ray-overview/installation.html#install-ray-java-with-maven",
3386
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#image",
3387
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ReplicaDetails.html#ray-serve-schema-replicadetails",
3388
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
3389
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#the-ray-version",
3390
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
3391
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
3392
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
3393
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
3394
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema"
3395
            ],
3396
            "document_ids": [
3397
                6627,
3398
                69,
3399
                14557,
3400
                294,
3401
                5931,
3402
                14243,
3403
                14101,
3404
                5922,
3405
                14132,
3406
                4330,
3407
                14134,
3408
                14133,
3409
                14219
3410
            ],
3411
            "answer": " To specify the Python version in runtime\\_env, you can include it in the runtime\\_env section of your configuration. Here is an example:\n\n```\n\"runtime_env\": {\n  \"python\": \"python3.8\"\n}\n```\n\nIt's important to ensure that the Python version is consistent between the client and server, as mentioned in the context.",
3412
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3413
        },
3414
        {
3415
            "question": "how to create a Actor in a namespace?",
3416
            "sources": [
3417
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
3418
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",
3419
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
3420
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3421
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
3422
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",
3423
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
3424
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3425
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
3426
                "https://docs.ray.io/en/master/rllib/rllib-catalogs.html#write-a-catalog-from-scratch",
3427
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",
3428
                "https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",
3429
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors"
3430
            ],
3431
            "document_ids": [
3432
                4629,
3433
                5391,
3434
                14687,
3435
                4464,
3436
                11576,
3437
                4200,
3438
                14689,
3439
                4468,
3440
                11578,
3441
                11539,
3442
                4623,
3443
                4515,
3444
                6787
3445
            ],
3446
            "answer": " To create an actor in a namespace in Ray, you can use the `options` method to set the name and namespace of the actor when calling the `remote` method. Here's an example in Python:\n```python\nimport ray\n\n@ray.remote\nclass MyActor:\n    def __init__(self):\n        pass\n",
3447
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3448
        },
3449
        {
3450
            "question": "Can I specify multiple working directories?",
3451
            "sources": [
3452
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
3453
                "https://docs.ray.io/en/master/ray-core/tasks/nested-tasks.html#nested-remote-functions",
3454
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers",
3455
                "https://docs.ray.io/en/master/tune/api/logging.html#other-integrations",
3456
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.rollouts.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-rollouts",
3457
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers",
3458
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
3459
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",
3460
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",
3461
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
3462
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
3463
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
3464
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a"
3465
            ],
3466
            "document_ids": [
3467
                10128,
3468
                4972,
3469
                10990,
3470
                1135,
3471
                12628,
3472
                10999,
3473
                10087,
3474
                10046,
3475
                2371,
3476
                10077,
3477
                10080,
3478
                10112,
3479
                10118
3480
            ],
3481
            "answer": " No, you cannot specify multiple working directories in the given context. The current working directory of both functional and class trainables is set to the corresponding trial directory once it\u2019s been launched as a remote Ray actor.",
3482
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3483
        },
3484
        {
3485
            "question": "what if I set num_cpus=0 for tasks",
3486
            "sources": [
3487
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
3488
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
3489
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",
3490
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",
3491
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",
3492
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
3493
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#recovering-from-data-loss",
3494
                "https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#system-metrics",
3495
                "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",
3496
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
3497
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
3498
                "https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",
3499
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks"
3500
            ],
3501
            "document_ids": [
3502
                4853,
3503
                4895,
3504
                644,
3505
                525,
3506
                276,
3507
                4878,
3508
                4555,
3509
                649,
3510
                9548,
3511
                4538,
3512
                471,
3513
                656,
3514
                4547
3515
            ],
3516
            "answer": " If you set `num_cpus=0` for tasks, it will not allocate any CPU resources for those tasks. However, this does not force GPU tasks to release resources after finishing. To release GPU resources, you need to manage GPU resources explicitly, such as using NVIDIA's GPU management API.\n\nThe `num_cpus` parameter is used to specify the number of CPUs to allocate for a task or actor. A value of 0 means that no CPUs will be allocated. This can be useful for tasks that only use GPUs or other resources, and you don't want to waste CPU resources on them.\n\nHowever, it's important to note that setting `num_cpus=0` may not necessarily result in lower resource usage or better performance, as other factors such as memory usage, I/O, and network traffic can also affect resource utilization and performance.",
3517
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3518
        },
3519
        {
3520
            "question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",
3521
            "sources": [
3522
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
3523
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",
3524
                "https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",
3525
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
3526
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-status",
3527
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",
3528
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",
3529
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
3530
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
3531
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",
3532
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
3533
                "https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",
3534
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource"
3535
            ],
3536
            "document_ids": [
3537
                5656,
3538
                5752,
3539
                13622,
3540
                4811,
3541
                6184,
3542
                5817,
3543
                5760,
3544
                4662,
3545
                6615,
3546
                5742,
3547
                5380,
3548
                13962,
3549
                5725
3550
            ],
3551
            "answer": " Yes, it is possible to have Ray on Kubernetes (k8s) without using KubeRay, but it would require using the Ray cluster's command-line interface (CLI) and manually configuring the Ray job and service. However, using KubeRay is recommended, especially for production scenarios, as it simplifies deployment, scaling, and management of Ray applications on Kubernetes.\n\nAdditionally, when using k8s with autoscaler enabled, it is important to note that there might be brief API server unavailability during cluster resizing events. However, the Ray workload should be uninterrupted.\n\nIn summary, while it is possible to use Ray on k8s without KubeRay, using KubeRay is recommended for ease of use and to handle important production requirements.",
3552
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3553
        },
3554
        {
3555
            "question": "how to manually configure and manage Ray cluster on Kubernetes",
3556
            "sources": [
3557
                "https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",
3558
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",
3559
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",
3560
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",
3561
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
3562
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
3563
                "https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#helpful-ray-commands",
3564
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#using-ray-cluster-launcher",
3565
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",
3566
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",
3567
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
3568
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-1-execute-a-ray-job-in-the-head-pod",
3569
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster"
3570
            ],
3571
            "document_ids": [
3572
                5605,
3573
                6500,
3574
                6436,
3575
                5717,
3576
                9510,
3577
                6363,
3578
                5525,
3579
                6508,
3580
                6510,
3581
                6619,
3582
                4814,
3583
                5727,
3584
                6616
3585
            ],
3586
            "answer": " To manually configure and manage a Ray cluster on Kubernetes, you can follow these steps:\n\n1. Install the Ray command line tool with cluster launcher support by running `pip install -U \"ray[default]\"`.\n2. Use the Ray cluster launcher to start, stop, and attach to a running Ray cluster using commands such as `ray up`, `ray down`, and `ray attach`.\n3. To create a Ray cluster on Kubernetes, you can use a configuration file with the `ray up` command, such as `ray up example-full.yaml`.\n4. To interact with the Ray cluster, you can use the `ray.init()` function in your Ray code and specify the head node host and port.\n5. To manage and interact with the Ray cluster on Kubernetes, you can use the RayCluster Quickstart guide and the helpful Ray commands provided in the context.\n\nYou can also configure autoscaling for the Ray cluster using the Ray cluster launcher and the Ray autoscaler.\n\nTo manually configure and manage a Ray cluster on machines, you can follow these steps:\n\n1. Install Ray on each machine using pip.\n2. Use the Ray cluster launcher to start, stop, and attach to a running Ray cluster using commands such as `ray up`, `ray down`, and `ray attach`.\n3. You can also use the RayCluster Quickstart guide and the helpful Ray commands provided in the context to manage and interact with the Ray cluster.\n\nYou can also configure autoscaling for the Ray cluster using the Ray cluster launcher and the Ray autoscaler.\n\nNote: The context provided assumes that you have a list of machines and that the nodes in the cluster share the same network. It also assumes that Ray is installed on each machine.",
3587
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3588
        },
3589
        {
3590
            "question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",
3591
            "sources": [
3592
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#worker-node-failure",
3593
                "https://docs.ray.io/en/master/cluster/key-concepts.html#worker-node",
3594
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
3595
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
3596
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3597
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
3598
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",
3599
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",
3600
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3601
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
3602
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
3603
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-autoscaler-private-fake-multi-node-test-utils-dockercluster",
3604
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters"
3605
            ],
3606
            "document_ids": [
3607
                4527,
3608
                5558,
3609
                5127,
3610
                5124,
3611
                554,
3612
                5129,
3613
                4514,
3614
                6216,
3615
                555,
3616
                5125,
3617
                553,
3618
                867,
3619
                6367
3620
            ],
3621
            "answer": " Shutting down a raylet will not directly kill tasks and workers on that node. However, if the node runs out of memory, the worker killer policy may terminate tasks or actors to free up resources. This could potentially impact running tasks and workers on that node. It's important to note that if the raylet process is killed, it will affect the overall Ray cluster's operation and may disrupt running workloads.",
3622
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3623
        },
3624
        {
3625
            "question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",
3626
            "sources": [
3627
                "https://docs.ray.io/en/master/ray-contribute/docs.html#where-to-go-from-here",
3628
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",
3629
                "https://docs.ray.io/en/master/ray-contribute/docs.html#creating-a-notebook-example",
3630
                "https://docs.ray.io/en/master/rllib/rllib-env.html#logging-off-policy-actions",
3631
                "https://docs.ray.io/en/master/rllib-env.html#logging-off-policy-actions",
3632
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#optional-optimizing-for-runtime-over-memory-with-centralized-data-loading",
3633
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#storage-options-in-tune",
3634
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/text-summarizer-rayservice.html#step-7-uninstall-your-kuberay-operator",
3635
                "https://docs.ray.io/en/master/serve/advanced-guides/migration.html#migrating-the-1-x-deployment",
3636
                "https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#and-what-about-exporting-my-nn-models-in-onnx-format",
3637
                "https://docs.ray.io/en/master/cluster/usage-stats.html#requesting-removal-of-collected-data",
3638
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.export_policy_checkpoint.html#ray-rllib-algorithms-algorithm-algorithm-export-policy-checkpoint",
3639
                "https://docs.ray.io/en/master/ray-contribute/docs.html#building-the-ray-documentation"
3640
            ],
3641
            "document_ids": [
3642
                828,
3643
                5295,
3644
                820,
3645
                10917,
3646
                52,
3647
                5318,
3648
                2337,
3649
                6139,
3650
                13798,
3651
                10581,
3652
                5569,
3653
                12356,
3654
                802
3655
            ],
3656
            "answer": " To debug out-of-memory errors, you can look at the Ray memory usage documentation: <https://docs.ray.io/en/latest/memory-management.html>. This page provides information on how to monitor and manage Ray's memory usage.\n\nAdditionally, if you're working with large datasets, you might want to consider using Ray's data management tools, such as the `ray.data` API, which can help you load and process data more efficiently. You can find the documentation for `ray.data` here: <https://docs.ray.io/en/latest/data.html>.\n\nIf you're still having trouble, you can also try adjusting Ray's memory limits or using Ray's memory profiling tools to identify any memory leaks or bottlenecks in your application.",
3657
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3658
        },
3659
        {
3660
            "question": "How to use callback in Trainer?",
3661
            "sources": [
3662
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
3663
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
3664
                "https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#aws-neuron-core-accelerator-experimental",
3665
                "https://docs.ray.io/en/master/rllib/rllib-models.html#default-model-config-settings",
3666
                "https://docs.ray.io/en/master/rllib/rllib-catalogs.html#catalog-alpha",
3667
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
3668
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3669
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
3670
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.rollouts.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-rollouts",
3671
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers",
3672
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#re3-random-encoders-for-efficient-exploration",
3673
                "https://docs.ray.io/en/master/rllib/rllib-models.html#implementing-custom-recurrent-networks",
3674
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#asynchronous-proximal-policy-optimization-appo"
3675
            ],
3676
            "document_ids": [
3677
                4650,
3678
                1788,
3679
                5009,
3680
                11222,
3681
                11487,
3682
                11578,
3683
                4468,
3684
                14689,
3685
                12628,
3686
                10999,
3687
                12031,
3688
                11311,
3689
                11660
3690
            ],
3691
            "answer": " To use a callback in a Trainer with Ray, you can define a custom class that inherits from `ray.rllib.agents.callbacks.Callbacks` and override the relevant methods. Then, you can pass an instance of this class to the `callbacks` parameter when creating the Trainer.\n\nHere's an example of how to define a custom callback class:\n```python\nfrom ray.rllib.agents.callbacks import Callbacks\n\nclass MyCallbacks(Callbacks):\n    def on_training_start(self):\n        # Code to run at the beginning of training\n        pass\n\n    def on_episode_start(self, episode, worker):\n        # Code to run at the beginning of each episode\n        pass\n\n    def on_episode_step(self, episode, step, reward, done):\n        # Code to run at each step of an episode\n        pass\n\n    def on_episode_end(self, episode, worker):\n        # Code to run at the end of each episode\n        pass\n\n    def on_training_end(self):\n        # Code to run at the end of training\n        pass\n```\nOnce you've defined your custom callback class, you can pass an instance of it to the `callbacks` parameter when creating the Trainer:\n```python\ntrainer = PPOTrainer(\n    config={\n        # Other config options\n        \"callbacks\": MyCallbacks()\n    }\n)\n```\nYou can then use the methods of the `MyCallbacks` class to implement your desired callback functionality.",
3692
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3693
        },
3694
        {
3695
            "question": "How to provide current working directory to ray?",
3696
            "sources": [
3697
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
3698
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
3699
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
3700
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
3701
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
3702
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",
3703
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
3704
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
3705
                "https://docs.ray.io/en/master/ray-core/runtime_env_auth.html#running-on-vms-the-netrc-file",
3706
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
3707
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
3708
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
3709
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster"
3710
            ],
3711
            "document_ids": [
3712
                6632,
3713
                6551,
3714
                6314,
3715
                268,
3716
                6330,
3717
                4262,
3718
                4662,
3719
                6633,
3720
                4180,
3721
                1790,
3722
                511,
3723
                6157,
3724
                6562
3725
            ],
3726
            "answer": " To provide the current working directory to Ray, you can specify it in the runtime environment when calling `ray.init()`. Here's an example:\n```python\nimport os\nimport ray\n\nos.makedirs(\"/tmp/runtime_env_working_dir\", exist_ok=True)\nwith open(\"/tmp/runtime_env_working_dir/hello.txt\", \"w\") as hello_file:\n    hello_file.write(\"Hello World!\")\n\nray.init(runtime_env={\"working_dir\": \"/tmp/runtime_env_working_dir\"})\n```\nThis will set the working directory for the Ray job to `/tmp/runtime_env_working_dir`. Note that all workers on the same node will share the same working directory.",
3727
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3728
        },
3729
        {
3730
            "question": "how to create an actor instance with parameter?",
3731
            "sources": [
3732
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",
3733
                "https://docs.ray.io/en/master/rllib/rllib-catalogs.html#write-a-catalog-from-scratch",
3734
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
3735
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",
3736
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3737
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
3738
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
3739
                "https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",
3740
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
3741
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3742
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
3743
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
3744
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources"
3745
            ],
3746
            "document_ids": [
3747
                5391,
3748
                11539,
3749
                4629,
3750
                4200,
3751
                4464,
3752
                14687,
3753
                11576,
3754
                6787,
3755
                14689,
3756
                4468,
3757
                11578,
3758
                11596,
3759
                4506
3760
            ],
3761
            "answer": " To create an actor instance with a parameter, you can use the `.remote()` method on the actor class and pass the parameter as an argument to this method. Here's an example:\n```python\nimport ray\n\nclass MyActor:\n    def __init__(self, param):\n        self.param = param\n\n    def my_method(self):\n        return self.param\n\n# Create an instance of MyActor with the parameter `param_value`\nmy_actor = MyActor.remote(param_value)\n\n# Call the `my_method` method on the actor instance\nresult = ray.get(my_actor.my_method.remote())\n```\nIn this example, `param_value` is the parameter that you want to pass to the actor instance. You can replace this with any value or object that you want to use as a parameter.\n\nThe `.remote()` method creates a remote reference to the actor instance, which you can use to call its methods as shown in the example. The `ray.get()` function is used to retrieve the result of the method call from the remote actor.",
3762
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3763
        },
3764
        {
3765
            "question": "how to push a custom module to ray which is using by Actor ?",
3766
            "sources": [
3767
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
3768
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",
3769
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.push.html#ray-util-actorpool-push",
3770
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#memory-profiling-ray-tasks-and-actors",
3771
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#custom-resources-accelerator-types-and-more",
3772
                "https://docs.ray.io/en/master/rllib/actors.html#actors",
3773
                "https://docs.ray.io/en/master/ray-core/actors.html#actors",
3774
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",
3775
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
3776
                "https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#defining-the-progress-actor",
3777
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",
3778
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
3779
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources"
3780
            ],
3781
            "document_ids": [
3782
                4650,
3783
                5443,
3784
                4758,
3785
                568,
3786
                13459,
3787
                11575,
3788
                4462,
3789
                14685,
3790
                14219,
3791
                5387,
3792
                4488,
3793
                4506,
3794
                14707
3795
            ],
3796
            "answer": " To push a custom module to Ray which is used by an Actor, you can use the `ray.put()` function to put the module into Ray's object store, and then import the module in the Actor's code using `ray.get()`. Here is an example:\n\nFirst, put the module into the object store:\n\n```python\nray.put(my_module)\n```\n\nThen, in the Actor's code, import the module using `ray.get()`:\n\n```python\nimport ray\n\n@ray.remote\nclass MyActor:\n    def __init__(self):\n        self.my_module = ray.get(my_module_ref)\n\n    def do_something(self):\n        # Use the custom module\n        result = self.my_module.some_function()\n        return result\n```\n\nIn this example, `my_module_ref` is the object reference returned by `ray.put()`.\n\nNote that this method should only be used for small modules, as large modules can cause the object store to become bloated and slow down the Ray cluster. For larger modules, consider using a more complex deployment strategy, such as using a custom Ray image with the module pre-installed.\n\nAlso, make sure that the custom module is serializable and can be imported in the Actor's code. If the module has dependencies that are not installed in the Ray cluster, you may need to install them first before putting the module into the object store.",
3797
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3798
        },
3799
        {
3800
            "question": "how to print ray working directory?",
3801
            "sources": [
3802
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
3803
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
3804
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
3805
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
3806
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
3807
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",
3808
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
3809
                "https://docs.ray.io/en/master/workflows/basics.html#sub-task-results",
3810
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-jobs-cli-api-reference",
3811
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
3812
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
3813
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#development-workflow",
3814
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster"
3815
            ],
3816
            "document_ids": [
3817
                6632,
3818
                1790,
3819
                6633,
3820
                6551,
3821
                1775,
3822
                4262,
3823
                4385,
3824
                9670,
3825
                6593,
3826
                511,
3827
                6353,
3828
                13709,
3829
                6562
3830
            ],
3831
            "answer": " To print the working directory where Ray is running, you can use the following code:\n```python\nimport ray\nprint(ray.global_state.get_runtime_context().working_directory)\n```\nNote that all Ray workers on the same node share the same working directory, which is set to a temporary directory on the cluster when running `ray.init()`. You can use `ray.train.get_context().get_trial_dir()` as the path to save any outputs to ensure they are stored in a unique location for each trial.",
3832
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3833
        },
3834
        {
3835
            "question": "why I can not see log.info in ray log?",
3836
            "sources": [
3837
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
3838
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
3839
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
3840
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
3841
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
3842
                "https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",
3843
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",
3844
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",
3845
                "https://docs.ray.io/en/master/tune/examples/includes/logging_example.html#logging-example",
3846
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",
3847
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
3848
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",
3849
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory"
3850
            ],
3851
            "document_ids": [
3852
                680,
3853
                444,
3854
                6631,
3855
                445,
3856
                511,
3857
                662,
3858
                6359,
3859
                530,
3860
                4091,
3861
                6360,
3862
                477,
3863
                415,
3864
                5861
3865
            ],
3866
            "answer": " The reason you can't see `log.info` in Ray logs might be because the default logging level for Ray's logger is `logging.INFO`. However, it's also possible that the logs haven't been flushed to the files yet, or there was an issue with the log file rotation configuration.\n\nYou can try checking the logs directly from the file system on the Ray nodes or using Ray's log APIs to retrieve the logs programmatically. Additionally, you can modify the Ray logger's configuration to change the logging level or add a log handler to redirect the logs to a different location.\n\nHere's an example of how to modify the Ray logger's configuration:\n```python\nimport logging\n\nlogger = logging.getLogger(\"ray\")\nlogger.setLevel(logging.DEBUG)  # Change logging level to DEBUG\n\n# Add a log handler to redirect logs to a file\nfile_handler = logging.FileHandler(\"/path/to/log/file.log\")\nfile_handler.setLevel(logging.DEBUG)\nformatter = logging.Formatter(\"%(asctime)s - %(levelname)s - %(message)s\")\nfile_handler.setFormatter(formatter)\nlogger.addHandler(file_handler)\n```\nThis will change the logging level to `DEBUG` and add a file handler to redirect the logs to a file at `/path/to/log/file.log`. You can adjust the logging level and file path as needed.",
3867
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3868
        },
3869
        {
3870
            "question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",
3871
            "sources": [
3872
                "https://docs.ray.io/en/master/ray-core/objects.html#fault-tolerance",
3873
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
3874
                "https://docs.ray.io/en/master/data/data-internals.html#fault-tolerance",
3875
                "https://docs.ray.io/en/master/data/loading-data.html#reading-files",
3876
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
3877
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",
3878
                "https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",
3879
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#object-fault-tolerance",
3880
                "https://docs.ray.io/en/master/data/data-internals.html#reading-files",
3881
                "https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",
3882
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",
3883
                "https://docs.ray.io/en/master/data/working-with-text.html#saving-text",
3884
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset"
3885
            ],
3886
            "document_ids": [
3887
                4453,
3888
                6835,
3889
                6889,
3890
                6739,
3891
                7360,
3892
                7783,
3893
                6993,
3894
                4552,
3895
                6874,
3896
                6994,
3897
                9222,
3898
                7000,
3899
                7736
3900
            ],
3901
            "answer": " No, when using Ray Dataset to read a file, the order of the data is not guaranteed to be preserved. Ray Data reads files in parallel, which can result in the data being processed in a different order than it appears in the file. If you need to preserve the order of the data, you may need to sort the dataset after reading it in. Additionally, if you are reading a large file, you may want to consider materializing the dataset in Ray's object store to avoid re-running preprocessing operations when iterating over the dataset repeatedly.",
3902
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3903
        },
3904
        {
3905
            "question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",
3906
            "sources": [
3907
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
3908
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",
3909
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",
3910
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",
3911
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
3912
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.nevergrad.NevergradSearch.FINISHED.html#ray-tune-search-nevergrad-nevergradsearch-finished",
3913
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#where-to-find-log-to-file-files",
3914
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",
3915
                "https://docs.ray.io/en/master/ray-core/tasks/nested-tasks.html#nested-remote-functions",
3916
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
3917
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayTaskError.html#ray-exceptions-raytaskerror",
3918
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
3919
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy"
3920
            ],
3921
            "document_ids": [
3922
                4811,
3923
                14506,
3924
                596,
3925
                6621,
3926
                4894,
3927
                1984,
3928
                2363,
3929
                4194,
3930
                4972,
3931
                4660,
3932
                4779,
3933
                4547,
3934
                6015
3935
            ],
3936
            "answer": " The statement \"Ray will not retry tasks upon exceptions thrown by application code\" means that if a task in Ray fails due to an exception caused by user-defined code, Ray will not automatically retry executing that task. Instead, Ray will raise a RayTaskError that includes the exception from the remote process. It's worth noting that Ray handles intentional system failures differently, such as when cancellation APIs like ray.cancel or ray.kill are called.",
3937
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3938
        },
3939
        {
3940
            "question": "how do I specify the log directory when starting Ray?",
3941
            "sources": [
3942
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
3943
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",
3944
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",
3945
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
3946
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#mounting-the-ray-log-directory",
3947
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",
3948
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",
3949
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",
3950
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
3951
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
3952
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
3953
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-rotation",
3954
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging"
3955
            ],
3956
            "document_ids": [
3957
                511,
3958
                6360,
3959
                5861,
3960
                680,
3961
                5871,
3962
                510,
3963
                5880,
3964
                530,
3965
                4811,
3966
                542,
3967
                4385,
3968
                543,
3969
                4384
3970
            ],
3971
            "answer": " To specify the log directory when starting Ray, you can use the `--temp-dir` or `--logs-dir` option with the `ray start` command or set the `ray.temp_dir` or `ray.log_directory` configuration when calling `ray.init()` in your application code.\n\nFor example, to set the temp and log directories to `/path/to/my/ray/directory` when starting Ray from the command line, you can use:\n```css\nray start --temp-dir=/path/to/my/ray/directory --logs-dir=/path/to/my/ray/directory\n```\nAlternatively, to set the same directories in your application code, you can use:\n```python\nimport ray\n\nray.init(temp_dir=\"/path/to/my/ray/directory\", log_directory=\"/path/to/my/ray/directory\")\n```\nNote that the `temp_dir` option sets the root temporary directory for Ray, while the `log_directory` option sets the directory where Ray writes log files. By default, Ray uses `/tmp/ray` as the root temporary directory and `/tmp/ray/session_*/logs` as the log directory.",
3972
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
3973
        },
3974
        {
3975
            "question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",
3976
            "sources": [
3977
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
3978
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
3979
                "https://docs.ray.io/en/master/cluster/cli.html#ray-up",
3980
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
3981
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
3982
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",
3983
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",
3984
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",
3985
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",
3986
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",
3987
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",
3988
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
3989
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters"
3990
            ],
3991
            "document_ids": [
3992
                6337,
3993
                6328,
3994
                5615,
3995
                6203,
3996
                6392,
3997
                6522,
3998
                4193,
3999
                851,
4000
                6501,
4001
                14481,
4002
                6411,
4003
                6353,
4004
                4448
4005
            ],
4006
            "answer": " To launch a Ray cluster with 10 nodes without setting the minimum worker count as 10, you can create or update a Ray cluster by using the `ray up` command with a configuration file (e.g., `config.yaml`). Here's a sample `config.yaml`:\n\n```yaml\ncluster_name: my-ray-cluster\nmax_workers: 9\n\n# Replace this with the appropriate cloud provider configuration\nprovider:\n  type: aws\n  region: us-west-2\n  availability_zone: us-west-2a\n\n# Command to start ray on worker nodes. You don't need to change this.\nworker_start_ray_commands:\n    - ray stop\n    - ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076\n\n# Other configurations like authentication, Docker, etc.\n```\n\nThen, execute the following command to start the Ray cluster with 10 nodes (1 head node and 9 worker nodes):\n\n```bash\nray up -y --min-workers 1 --max-workers 9 config.yaml\n```\n\nThis command will start a Ray cluster with a minimum of 1 worker node and a maximum of 9 worker nodes, which makes a total of 10 nodes in the cluster.",
4007
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4008
        },
4009
        {
4010
            "question": "how to use ray api to scale up a cluster",
4011
            "sources": [
4012
                "https://docs.ray.io/en/master/cluster/vms/index.html#overview",
4013
                "https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",
4014
                "https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",
4015
                "https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",
4016
                "https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",
4017
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
4018
                "https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",
4019
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started",
4020
                "https://docs.ray.io/en/master/cluster/cli.html#ray-up",
4021
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#best-practices-for-deploying-large-clusters",
4022
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
4023
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",
4024
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/gcp.html#start-ray-with-the-ray-cluster-launcher"
4025
            ],
4026
            "document_ids": [
4027
                6209,
4028
                14710,
4029
                4644,
4030
                5600,
4031
                5604,
4032
                6363,
4033
                6715,
4034
                6189,
4035
                5615,
4036
                6371,
4037
                368,
4038
                7980,
4039
                6514
4040
            ],
4041
            "answer": " To use Ray API to scale up a cluster, you can follow these steps:\n\n1. Configure and set up Ray in public clouds, creating a head node and a collection of worker nodes.\n2. Optionally, enable autoscaling support to size the cluster according to the requirements of your Ray workload, adding and removing worker nodes as needed.\n3. Within your Ray program, use the `ray.autoscaler.sdk.request_resources()` function to command the autoscaler to scale the cluster up to a desired size. This will immediately attempt to scale the cluster to accommodate the requested resources, bypassing normal upscaling speed constraints.\n\nFor example, you can use the following code to request 10 CPUs:\n```python\nray.autoscaler.sdk.request_resources(num_cpus=10)\n```\nYou can also request specific resources using a list of bundles. For more information, refer to the `ray.autoscaler.sdk.request_resources()` documentation.\n\nAdditionally, you can use the Ray cluster management CLI commands `ray up` and `ray down` to create or update a Ray cluster and to stop the cluster, respectively. You can specify the minimum and maximum number of workers, as well as other options, using the `ray up` command. For more information, refer to the `ray up` documentation.",
4042
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4043
        },
4044
        {
4045
            "question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",
4046
            "sources": [
4047
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
4048
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
4049
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
4050
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",
4051
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#launching-a-cluster-ray-up",
4052
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
4053
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",
4054
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",
4055
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",
4056
                "https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",
4057
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",
4058
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",
4059
                "https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them"
4060
            ],
4061
            "document_ids": [
4062
                6199,
4063
                6203,
4064
                273,
4065
                6436,
4066
                6212,
4067
                13677,
4068
                6477,
4069
                13678,
4070
                6458,
4071
                5503,
4072
                4193,
4073
                6227,
4074
                5636
4075
            ],
4076
            "answer": " To specify a subnet in the Ray cloud launcher deployment file for AWS, you can add the `subnet_id` field in the `provider` section of the configuration file. Here is an example:\n\n```yaml\ncluster_name: aws-example\n\nprovider:\n  type: aws\n  region: us-west-2\n  subnet_id: subnet-01234567890123456  # specify the subnet ID here\n```\n\nThen, you can use the `ray up` command to start the cluster:\n\n```bash\n$ ray up config.yaml\n```\n\nMake sure to replace `config.yaml` with the path to your actual configuration file.",
4077
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4078
        },
4079
        {
4080
            "question": "where I can find HTTP server error code log for Ray serve",
4081
            "sources": [
4082
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
4083
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4084
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4085
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
4086
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",
4087
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-component-logs",
4088
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",
4089
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",
4090
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
4091
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
4092
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
4093
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
4094
                "https://docs.ray.io/en/master/serve/getting_started.html#next-steps"
4095
            ],
4096
            "document_ids": [
4097
                6631,
4098
                13523,
4099
                13517,
4100
                444,
4101
                13442,
4102
                518,
4103
                13514,
4104
                535,
4105
                680,
4106
                445,
4107
                14325,
4108
                14203,
4109
                13595
4110
            ],
4111
            "answer": " The HTTP server error code logs for Ray serve can be found in the log files located at /tmp/ray/session\\_latest/logs/serve/ on the head node. You can also find a copy of these logs at the same location with the additional directory serve/. These logs can be parsed using logging stacks such as ELK or Loki and can be filtered by deployment or replica. Ray Serve supports log rotation of these logs through setting the environment variables RAY\\_ROTATION\\_MAX\\_BYTES and RAY\\_ROTATION\\_BACKUP\\_COUNT. The logs include system-level logs from the Serve controller and proxy, as well as access logs and custom user logs produced from within deployment replicas.",
4112
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4113
        },
4114
        {
4115
            "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?",
4116
            "sources": [
4117
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",
4118
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
4119
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-4-verify-the-kubernetes-cluster-status",
4120
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-4-verify-the-kubernetes-cluster-status",
4121
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
4122
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",
4123
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",
4124
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",
4125
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-2-check-ports",
4126
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
4127
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",
4128
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
4129
                "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"
4130
            ],
4131
            "document_ids": [
4132
                5530,
4133
                6561,
4134
                5745,
4135
                5794,
4136
                4818,
4137
                6436,
4138
                5531,
4139
                5528,
4140
                6617,
4141
                4817,
4142
                6223,
4143
                9510,
4144
                5731
4145
            ],
4146
            "answer": " Yes, it seems like you are experiencing a known issue with accessing the Ray Dashboard from outside the head node. When you start a remote Ray cluster, the dashboard port may not be publicly exposed, and you need an additional setup to access it from outside the head node.\n\nBased on the context provided, you can use the `ray dashboard` command to set up port forwarding automatically. Here's what you can do:\n\n1. Run the following command on your local machine, where `cluster.yaml` is the configuration file you used to launch your cluster:\n```css\nray dashboard cluster.yaml\n",
4147
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4148
        },
4149
        {
4150
            "question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",
4151
            "sources": [
4152
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
4153
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",
4154
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",
4155
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",
4156
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4157
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",
4158
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4159
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#max-workers",
4160
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4161
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4162
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
4163
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",
4164
                "https://docs.ray.io/en/master/cluster/cli.html#ray-up"
4165
            ],
4166
            "document_ids": [
4167
                6392,
4168
                6367,
4169
                6292,
4170
                6522,
4171
                6345,
4172
                6366,
4173
                6328,
4174
                6241,
4175
                6337,
4176
                6311,
4177
                265,
4178
                14481,
4179
                5615
4180
            ],
4181
            "answer": " In the Ray cluster launcher YAML, `max_workers` refers to the maximum number of worker nodes, and does not include the head node. This is consistent across different versions and cloud providers.",
4182
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4183
        },
4184
        {
4185
            "question": "How to update files in working directory ?",
4186
            "sources": [
4187
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
4188
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
4189
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
4190
                "https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",
4191
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",
4192
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",
4193
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#outdated-function-definitions",
4194
                "https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",
4195
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
4196
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#synchronizing-files-from-the-cluster-ray-rsync-up-down",
4197
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#common-workflow-syncing-git-branches",
4198
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
4199
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"
4200
            ],
4201
            "document_ids": [
4202
                6632,
4203
                1775,
4204
                1790,
4205
                8894,
4206
                1068,
4207
                6214,
4208
                619,
4209
                1066,
4210
                4273,
4211
                6222,
4212
                6225,
4213
                269,
4214
                6331
4215
            ],
4216
            "answer": " To update files in the working directory, you can use the `to_directory()` function to write checkpoint data to a directory. Additionally, you can use the `update_metadata(metadata)` function to update the metadata stored with the checkpoint. If you need to access relative filepaths in your Tune training function, you can configure Tune to not change the working directory to the trial directory, giving access to paths relative to the original working directory. To sync files between the cluster head node and worker nodes, you can use `ray rsync_down` or `ray rsync_up` commands.",
4217
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4218
        },
4219
        {
4220
            "question": "How I can update working directory file when ray allready initiated ?",
4221
            "sources": [
4222
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
4223
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
4224
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
4225
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
4226
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",
4227
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",
4228
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
4229
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
4230
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",
4231
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
4232
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",
4233
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",
4234
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig"
4235
            ],
4236
            "document_ids": [
4237
                6632,
4238
                1775,
4239
                1790,
4240
                6157,
4241
                6214,
4242
                6181,
4243
                511,
4244
                4385,
4245
                6530,
4246
                10128,
4247
                7948,
4248
                4331,
4249
                1789
4250
            ],
4251
            "answer": " To update the working directory file when Ray is already initiated, you can use the `ray.train.get_context().get_trial_dir()` function as the path to save any outputs. Note that all workers on the same node now share the same working directory. If you want to update the cluster configuration, you can run `ray up` again on the existing cluster. This command checks if the local configuration differs from the applied configuration of the cluster and uploads the new files and config. Also, be sure to use unique directory names for each trial to prevent contention between workers saving trial-level outputs.",
4252
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4253
        },
4254
        {
4255
            "question": "how can I force ray head node to use custom pem file to ssh worker node?",
4256
            "sources": [
4257
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
4258
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4259
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4260
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",
4261
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
4262
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4263
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",
4264
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4265
                "https://docs.ray.io/en/master/ray-core/scheduling/resources.html#custom-resources",
4266
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
4267
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#tpu-configuration",
4268
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",
4269
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"
4270
            ],
4271
            "document_ids": [
4272
                268,
4273
                6314,
4274
                6330,
4275
                6502,
4276
                272,
4277
                6318,
4278
                14481,
4279
                6353,
4280
                5106,
4281
                539,
4282
                6355,
4283
                6501,
4284
                6337
4285
            ],
4286
            "answer": " You can force the Ray head node to use a custom PEM file to SSH into the worker node by modifying the `file_mounts` configuration in the Ray cluster launch configuration. However, Ray does not directly support specifying a custom PEM file for SSH. Instead, you can copy your custom PEM file to the head node and worker nodes using the `file_mounts` configuration. Here's an example:\n\n1. Copy your custom PEM file to the local machine (the machine where you run the Ray cluster launch command) at a known location, e.g., `~/my-custom-pem.pem`.\n2. Modify the `file_mounts` configuration in the Ray cluster launch configuration to include the custom PEM file:\n\n   ```\n   file_mounts: {\n     \"~/.ssh/id_rsa.pub\": \"~/.ssh/id_rsa.pub\",\n     \"~/my-custom-pem.pem\": \"~/my-custom-pem.pem\"\n   }\n   ```\n\n3. Ensure that the `~/.ssh/config` file on the local machine is configured to use the custom PEM file for SSH connections to the worker nodes. For example:\n\n   ```\n   Host *\n     IdentityFile ~/my-custom-pem.pem\n   ```\n\n4. After launching the Ray cluster, the custom PEM file will be available on the head node and worker nodes at `~/my-custom-pem.pem`. You can then use it for SSH connections to the worker nodes as needed.\n\nNote that this approach only copies the custom PEM file to the head node and worker nodes. You will still need to configure the SSH settings on the local machine and worker nodes to use the custom PEM file for SSH connections.",
4287
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4288
        },
4289
        {
4290
            "question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",
4291
            "sources": [
4292
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#arp-cache",
4293
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
4294
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
4295
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",
4296
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
4297
                "https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",
4298
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",
4299
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",
4300
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",
4301
                "https://docs.ray.io/en/master/cluster/key-concepts.html#worker-node",
4302
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",
4303
                "https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",
4304
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling"
4305
            ],
4306
            "document_ids": [
4307
                4454,
4308
                6364,
4309
                555,
4310
                14022,
4311
                468,
4312
                5559,
4313
                13448,
4314
                5954,
4315
                855,
4316
                5558,
4317
                6375,
4318
                13472,
4319
                13447
4320
            ],
4321
            "answer": " The GCS server, or Global Control Store server, is a component of Ray that runs on the head node and handles additional system processes. In your case, it's taking up a significant amount of memory on the head node. This is normal for a large cluster, as all worker nodes connect to the head node, adding many entries to the ARP table. To accommodate this, ensure that the ARP cache size is large enough.\n\nIn Ubuntu, you can tune the ARP cache size in /etc/sysctl.conf by increasing the values of net.ipv4.neigh.default.gc_thresh1 through net.ipv4.neigh.default.gc_thresh3. For more information, refer to the OS manual.\n\nAdditionally, when configuring the head node for a large cluster, consider using a machine with 8 CPUs and 32 GB memory as a starting point. Adjust the hardware specification based on metrics like CPU usage, memory usage, and network bandwidth usage. Make sure the head node has sufficient outbound bandwidth, as it is the most heavily stressed resource on the head node.",
4322
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4323
        },
4324
        {
4325
            "question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",
4326
            "sources": [
4327
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
4328
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",
4329
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
4330
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
4331
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",
4332
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource",
4333
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters",
4334
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",
4335
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
4336
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",
4337
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
4338
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",
4339
                "https://docs.ray.io/en/master/cluster/cli.html#ray-up"
4340
            ],
4341
            "document_ids": [
4342
                467,
4343
                852,
4344
                254,
4345
                9510,
4346
                6215,
4347
                5724,
4348
                4448,
4349
                4193,
4350
                6147,
4351
                6501,
4352
                6203,
4353
                10208,
4354
                5615
4355
            ],
4356
            "answer": " If you're encountering nodes that are \"pending\" for a long time when starting a cluster with `ray up`, you can debug this issue by following these steps:\n\n1. Run `ray status` to view the status of your cluster. Check if there are any pending nodes or recent failures.\n2. If you suspect that the issue might be related to autoscaling, you can check the autoscaler monitor log by running `cat /tmp/ray/session_latest/logs/monitor.*`.\n3. If you're using Kubernetes to launch your Ray cluster, you can check for errors via `kubectl describe pod <pod_name>` and ensure that your Docker resource limits are set high enough.\n4. If you're running a large Ray cluster with more than 1k nodes, you may need to tune some system settings to enable communication between a large number of machines.\n5. If you're still having trouble, you can try restarting the cluster by running `ray up` again. This will restart all Ray services even if there are no config changes.\n\nIn some cases, the issue might be due to resource contention, where the requested resources cannot be scheduled due to all cluster resources being claimed by actors. In this case, you can consider creating fewer actors or adding more nodes to the Ray cluster.",
4357
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4358
        },
4359
        {
4360
            "question": "how to install Ray 2.5.1 from github or wheel?",
4361
            "sources": [
4362
                "https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",
4363
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",
4364
                "https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",
4365
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4366
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",
4367
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",
4368
                "https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",
4369
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",
4370
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",
4371
                "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",
4372
                "https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",
4373
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",
4374
                "https://docs.ray.io/en/master/installation.html#from-wheels"
4375
            ],
4376
            "document_ids": [
4377
                288,
4378
                14545,
4379
                63,
4380
                6335,
4381
                14551,
4382
                291,
4383
                66,
4384
                4185,
4385
                5412,
4386
                5985,
4387
                286,
4388
                14540,
4389
                61
4390
            ],
4391
            "answer": " To install Ray 2.5.1 from a GitHub wheel, you need to first find the appropriate wheel for your system and Python version on the Ray wheels directory on GitHub (<https://github.com/ray-project/ray/tree/master/python/ray/pkg/ray_wheels>). Once you have the wheel URL, you can use the following pip command to install it:\n\n```bash\npip uninstall -y ray  # Clean removal of previous install\npip install -U \"ray[default]\" @ WHEEL_URL\n```\n\nReplace `WHEEL_URL` with the URL of the wheel you found. Note that Ray 2.5.1 may not be available as a wheel, so you might need to build it from source if a suitable wheel is not provided.",
4392
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4393
        },
4394
        {
4395
            "question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",
4396
            "sources": [
4397
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.__init__.html#ray-rllib-evaluation-worker-set-workerset-init",
4398
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#worker-setup-commands",
4399
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-worker-setup-commands",
4400
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4401
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
4402
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",
4403
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#setup-commands",
4404
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",
4405
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
4406
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4407
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",
4408
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
4409
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads"
4410
            ],
4411
            "document_ids": [
4412
                12971,
4413
                6259,
4414
                6293,
4415
                6318,
4416
                272,
4417
                542,
4418
                6254,
4419
                6502,
4420
                6202,
4421
                6352,
4422
                6523,
4423
                6353,
4424
                14481
4425
            ],
4426
            "answer": " To use `worker_setup_hook` in a runtime environment to set up worker nodes, you can define a function called `logging_setup_func` and pass it to the `worker_process_setup_hook` parameter in the `ray.init()` call. This function will be run on the worker nodes after common setup.\n\nHere is an example of how you can use `worker_setup_hook`:\n\n```\nimport ray\n\ndef logging_setup_func():\n    # Set up logging here\n    pass\n\nif __name__ == \"__main__\":\n    ray.init(runtime_env={\"worker_process_setup_hook\": logging_setup_func})\n    # Your code here\n```\n\nYou can also use `worker_setup_commands` to specify a list of commands to run to set up worker nodes. These commands will be merged with the general setup commands.\n\nHere is an example of how you can use `worker_setup_commands`:\n\n```\n# Custom commands that will be run on worker nodes after common setup.\nworker_setup_commands:\n  - command1\n  - command2\n```\n\nYou can also specify commands for a specific type of worker node using `available_node_types.<node_type_name>.node_type.worker_setup_commands`. These commands will replace the general worker setup commands for the node.\n\nHere is an example of how you can use `available_node_types.<node_type_name>.node_type.worker_setup_commands`:\n\n```\navailable_node_types:\n  node_type1:\n    node_type:\n      worker_setup_commands:\n        - command1\n        - command2\n```",
4427
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4428
        },
4429
        {
4430
            "question": "how to use Ray dataset on aws",
4431
            "sources": [
4432
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
4433
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
4434
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",
4435
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
4436
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment",
4437
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
4438
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",
4439
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",
4440
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",
4441
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#deploying-on-slurm",
4442
                "https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#key-concepts",
4443
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",
4444
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-4-prepare-your-ray-serve-python-scripts-and-run-the-ray-serve-application"
4445
            ],
4446
            "document_ids": [
4447
                6615,
4448
                5380,
4449
                6436,
4450
                4811,
4451
                1386,
4452
                4662,
4453
                6127,
4454
                5742,
4455
                5760,
4456
                6400,
4457
                9123,
4458
                5412,
4459
                5983
4460
            ],
4461
            "answer": " To use Ray dataset on AWS, you can follow these general steps:\n\n1. Set up a Ray cluster on AWS using the Ray cluster launcher or Kubernetes (kubectl) with the Ray operator. You can refer to the Ray tutorials and examples for more guidance.\n2. Install Ray using pip: `! pip install ray`\n3. Prepare your Ray and Ray Serve Python scripts. You can clone the example scripts from the Ray GitHub repository: `git clone https://github.com/ray-project/serve_config_examples.git`\n4. Initialize Ray using `ray.init()` and create a Ray dataset using `ray.data.read_csv()` or other methods.\n5. Use the Ray dataset in your Ray and Ray Serve applications.\n\nNote that there is no specific guide for using Ray dataset on AWS, but the general Ray and Ray Serve documentation should provide sufficient guidance. For more information, you can refer to the Ray Tune documentation and the Ray Slurm walkthrough.",
4462
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4463
        },
4464
        {
4465
            "question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",
4466
            "sources": [
4467
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",
4468
                "https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",
4469
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
4470
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",
4471
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",
4472
                "https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only",
4473
                "https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",
4474
                "https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",
4475
                "https://docs.ray.io/en/master/ray-contribute/writing-code-snippets.html#ignoring-doctest-style-outputs",
4476
                "https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",
4477
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
4478
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",
4479
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data"
4480
            ],
4481
            "document_ids": [
4482
                8046,
4483
                6827,
4484
                7081,
4485
                9209,
4486
                9207,
4487
                4944,
4488
                6795,
4489
                6826,
4490
                844,
4491
                4476,
4492
                1113,
4493
                4209,
4494
                9212
4495
            ],
4496
            "answer": " To avoid shuffling of your dataset during `ray.data.map_batches()`, you can set the `use_push_based_shuffle` flag to `True` in the current `DataContext`. However, note that `map_batches()` does not shuffle the data by default. The warning about shuffling is related to other operations like `map()`, `flat_map()`, and `filter()`. Here's how to set the flag:\n\n```python\nctx = ray.data.DataContext.get_current()\nctx.use_push_based_shuffle = True\n```\n\nIf you want to shuffle the data during training, consider randomizing each block of your dataset and enabling local shuffling during iteration. This can be done using `randomize_block_order` and specifying a `local_shuffle_buffer_size` when calling `iter_batches()` or `iter_torch_batches()`.\n\nFor example:\n\n```python\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/sms_spam_collection_subset.txt\")\nds = ds.randomize_block_order()\n```\n\nThen, during training:\n\n```python\nfor batch in ds.iter_batches(batch_size=128, local_shuffle_buffer_size=100_000):\n    # Process the batch\n    pass\n```\n\nThis will shuffle the data within each block and provide better performance compared to shuffling all rows.",
4497
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4498
        },
4499
        {
4500
            "question": "Is the order of the input data preserved after a map_batches operation?",
4501
            "sources": [
4502
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#verify-and-save-results",
4503
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#a-simple-mapreduce-example-with-ray-core",
4504
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",
4505
                "https://docs.ray.io/en/master/rllib/rllib-models.html#built-in-preprocessors",
4506
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#soft-actor-critic-sac",
4507
                "https://docs.ray.io/en/master/data/data-internals.html#fault-tolerance",
4508
                "https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#trial-results",
4509
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#recovering-from-data-loss",
4510
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",
4511
                "https://docs.ray.io/en/master/data/examples/ocr_example.html#saving-and-loading-the-result-of-the-ocr-run",
4512
                "https://docs.ray.io/en/master/ray-core/objects.html#fault-tolerance",
4513
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#multi-agent-deep-deterministic-policy-gradient-maddpg",
4514
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#tune-experiment-restoration-with-ray-object-references-advanced"
4515
            ],
4516
            "document_ids": [
4517
                7994,
4518
                5342,
4519
                5346,
4520
                11215,
4521
                11836,
4522
                6889,
4523
                2231,
4524
                4554,
4525
                9210,
4526
                7911,
4527
                4453,
4528
                11994,
4529
                2261
4530
            ],
4531
            "answer": " No, the order of the input data is not preserved after a map_batches operation. The map_batches operation is a part of the map phase, which applies a specified function to transform or map elements within a set of data, producing key-value pairs. The shuffle phase collects all the outputs from the map phase and organizes them by key, which might result in a different order of data.",
4532
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4533
        },
4534
        {
4535
            "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?",
4536
            "sources": [
4537
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",
4538
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",
4539
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",
4540
                "https://docs.ray.io/en/master/serve/monitoring.html#monitor-your-application",
4541
                "https://docs.ray.io/en/master/ray-observability/user-guides/index.html#user-guides",
4542
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-component-logs",
4543
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",
4544
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",
4545
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4546
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",
4547
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4548
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",
4549
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence"
4550
            ],
4551
            "document_ids": [
4552
                4349,
4553
                535,
4554
                13442,
4555
                13513,
4556
                546,
4557
                515,
4558
                13516,
4559
                6359,
4560
                13517,
4561
                510,
4562
                13523,
4563
                13960,
4564
                5860
4565
            ],
4566
            "answer": " To get more detailed errors or logs when Ray Serve returns a generic internal service error, you can:\n\n1. Check the Ray Serve logs in the Ray dashboard, which provides a high-level overview of the Ray Cluster and Ray Serve application\u2019s states.\n2. Use Ray logging, which emits logs from actors to stderr and on disk on each node at `/tmp/ray/session_latest/logs/serve/`. You can also configure the \"ray.serve\" logger inside the deployment constructor to silence or configure replica-level logs.\n3. Check the Kuberay operator logs, the controller log, and the 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.\n4. For Ray Clusters running on Kubernetes, follow the instructions on how to collect logs from Ray Clusters that are running on Kubernetes.\n\nAdditionally, you can configure logging by modifying the configuration of Ray\u2019s logging system using the guide provided.",
4567
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4568
        },
4569
        {
4570
            "question": "how do i track an uncaught exception in ray serve",
4571
            "sources": [
4572
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
4573
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
4574
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
4575
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
4576
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#building-the-call-graph-methodnodes-and-functionnodes",
4577
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
4578
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
4579
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",
4580
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice",
4581
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
4582
                "https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",
4583
                "https://docs.ray.io/en/master/serve/index.html#why-choose-serve",
4584
                "https://docs.ray.io/en/master/serve/advanced-guides/direct-ingress.html#use-serves-schema"
4585
            ],
4586
            "document_ids": [
4587
                13974,
4588
                6615,
4589
                4811,
4590
                13596,
4591
                13734,
4592
                14255,
4593
                4662,
4594
                5785,
4595
                5739,
4596
                5380,
4597
                13597,
4598
                13614,
4599
                13758
4600
            ],
4601
            "answer": " To track uncaught exceptions in Ray Serve, you can create a custom error handler and use it in your Ray Serve deployment. Here's an example of how you can do this:\n\n1. Create a custom error handler function that logs the exception and returns a response indicating an error.\n```python\nimport traceback\n\ndef custom_error_handler(ray_serve_request):\n    try:\n        return your_serve_function(ray_serve_request)\n    except Exception as e:\n        # Log the exception\n        print(f\"Encountered an exception: {e}\")\n        traceback.print_exc()\n        # Return a response indicating an error\n        return {\"status\": \"error\", \"message\": str(e)}\n```\n2. Use the custom error handler in your Ray Serve deployment.\n```python\nfrom ray.serve import Deployment\n\n# Create a deployment with your custom error handler\nDeployment.bind(\n    your_serve_function,\n    route_prefix=\"/your_api\",\n    error_handler=custom_error_handler,\n).deploy()\n```\nThis will ensure that any uncaught exceptions in your Ray Serve deployment are logged and a response indicating an error is returned to the client.",
4602
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4603
        },
4604
        {
4605
            "question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",
4606
            "sources": [
4607
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
4608
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
4609
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
4610
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
4611
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4612
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",
4613
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",
4614
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",
4615
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
4616
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",
4617
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4618
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
4619
                "https://docs.ray.io/en/master/cluster/cli.html#ray-monitor"
4620
            ],
4621
            "document_ids": [
4622
                444,
4623
                6631,
4624
                680,
4625
                445,
4626
                13523,
4627
                6360,
4628
                5861,
4629
                6359,
4630
                511,
4631
                5860,
4632
                13517,
4633
                739,
4634
                5632
4635
            ],
4636
            "answer": " To view the logs emitted by your Ray Serve endpoint in the Ray cluster, you can look for them in the logs directory. By default, Ray writes logs to files in the directory `/tmp/ray/session_*/logs` on each Ray node's file system, including application logs and system logs. For your specific use case, you can find the Serve logs at `/tmp/ray/session_latest/logs/serve/`.\n\nAdditionally, you can use the Ray Log CLI to access the logs from the cluster. Note that this feature requires a full installation of Ray using `pip install \"ray[default]\"`.\n\nFor more information on Ray logging, you can refer to the Ray logging documentation: https://docs.ray.io/en/latest/logging.html#logging-directory",
4637
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4638
        },
4639
        {
4640
            "question": "where can I see logs for a failed ray serve deployment",
4641
            "sources": [
4642
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4643
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
4644
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
4645
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4646
                "https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#inspect-deeper",
4647
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",
4648
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",
4649
                "https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",
4650
                "https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",
4651
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",
4652
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
4653
                "https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",
4654
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log"
4655
            ],
4656
            "document_ids": [
4657
                13523,
4658
                444,
4659
                6631,
4660
                13517,
4661
                13658,
4662
                13521,
4663
                13442,
4664
                13960,
4665
                13537,
4666
                5860,
4667
                445,
4668
                13514,
4669
                680
4670
            ],
4671
            "answer": " You can see the logs for a failed Ray serve deployment in the following locations:\n\n1. /tmp/ray/session\\_latest/logs/serve/ on the head node and worker node pods. These logs contain information about specific deployment failure reasons and autoscaling events.\n2. Kuberay operator logs by running `kubectl logs -f <kuberay-operator-pod-name>`. The Kuberay operator logs contain information about the Serve application deployment event and Serve application health checks.\n\nAdditionally, Ray Serve supports log rotation of these logs through setting the environment variables RAY\\_ROTATION\\_MAX\\_BYTES and RAY\\_ROTATION\\_BACKUP\\_COUNT. The logs can be parsed with a logging stack such as ELK or Loki to be able to search by deployment or replica.",
4672
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4673
        },
4674
        {
4675
            "question": "How to take a subset of a Ray Dataset?",
4676
            "sources": [
4677
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
4678
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
4679
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment",
4680
                "https://docs.ray.io/en/master/workflows/basics.html#ray-integration",
4681
                "https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#key-concepts",
4682
                "https://docs.ray.io/en/master/workflows/basics.html#sub-task-results",
4683
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",
4684
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
4685
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice",
4686
                "https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",
4687
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#walkthrough",
4688
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",
4689
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy"
4690
            ],
4691
            "document_ids": [
4692
                5380,
4693
                6615,
4694
                1386,
4695
                9685,
4696
                9123,
4697
                9670,
4698
                5742,
4699
                4811,
4700
                5780,
4701
                6901,
4702
                8078,
4703
                5760,
4704
                6015
4705
            ],
4706
            "answer": " To take a subset of a Ray Dataset, you can use the `take()` or `take_all()` methods. For example:\n```\nimport ray\n\nds = ray.data.read_csv(\"s3://anonymous@air-example-data/iris.csv\")\n\n# Get the first 5 rows\nsubset = ds.take(5)\n\n# Get all rows\nsubset = ds.take_all()\n```\nThese methods return a list of dictionaries, where each dictionary represents a row in the dataset.\n\nYou can find more information on working with rows in the Ray Data documentation, specifically in the sections \"Inspecting rows\", \"Transforming rows\", and \"Iterating over rows.\"",
4707
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4708
        },
4709
        {
4710
            "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.",
4711
            "sources": [
4712
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",
4713
                "https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-in-and-out-of-tune",
4714
                "https://docs.ray.io/en/master/tune/api/result_grid.html#experimentanalysis-tune-experimentanalysis",
4715
                "https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#analyzing-tune-experiment-results",
4716
                "https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-in-and-out-of-tune",
4717
                "https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#on-a-single-node-cluster",
4718
                "https://docs.ray.io/en/master/tune/key-concepts.html#tune-trials",
4719
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-configure-logging-in-tune",
4720
                "https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",
4721
                "https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#logging-metrics-with-tune-callbacks",
4722
                "https://docs.ray.io/en/master/tune/faq.html#how-can-i-upload-my-tune-results-to-cloud-storage",
4723
                "https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune",
4724
                "https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#the-execution-of-a-trainable-in-tune"
4725
            ],
4726
            "document_ids": [
4727
                1826,
4728
                2271,
4729
                1313,
4730
                3681,
4731
                2272,
4732
                2343,
4733
                967,
4734
                2353,
4735
                2253,
4736
                2295,
4737
                1057,
4738
                2391,
4739
                2236
4740
            ],
4741
            "answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you should use the `ExperimentAnalysis` class, passing it the path to the experiment's checkpoint directory. Then, you can access the saved checkpoints using the `get_checkpoints` method.\n\nHere's an example:\n\n```python\nfrom ray.tune.analysis import ExperimentAnalysis\n\nexperiment_checkpoint_path = \"/path/to/experiment/checkpoint\"\nanalysis = ExperimentAnalysis(experiment_checkpoint_path)\ncheckpoints = analysis.get_checkpoints()\n\n# Now you can load the checkpoints as needed\nfor checkpoint in checkpoints:\n    # Load the checkpoint\n    ...\n```\n\nReplace `\"/path/to/experiment/checkpoint\"` with the actual path to your experiment's checkpoint directory.\n\nKeep in mind that this method assumes you have enabled checkpointing during your Tune experiment. If you used cloud checkpointing, you would need to download the checkpoints from the cloud to your local machine before analyzing them.",
4742
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4743
        },
4744
        {
4745
            "question": "How can I kill a \"detached\" Actor ?",
4746
            "sources": [
4747
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
4748
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",
4749
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
4750
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",
4751
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
4752
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",
4753
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",
4754
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",
4755
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
4756
                "https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",
4757
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
4758
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",
4759
                "https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor"
4760
            ],
4761
            "document_ids": [
4762
                4859,
4763
                4579,
4764
                4615,
4765
                4620,
4766
                4616,
4767
                2157,
4768
                4633,
4769
                4860,
4770
                4614,
4771
                8106,
4772
                4848,
4773
                4613,
4774
                4618
4775
            ],
4776
            "answer": " To kill a \"detached\" Actor in Ray, you can use the `ray.kill()` function and provide the handle to the actor as an argument. Here is an example:\n```\nimport ray\n\n@ray.remote\nclass Actor:\n    pass\n\ndetached_actor_handle = Actor.remote() # This actor is detached\n\n# Later, when you want to kill the actor\nray.kill(detached_actor_handle)\n```\nNote that this will immediately terminate the actor and any tasks running on it, and atexit handlers installed in the actor will not be run. If you want to allow pending tasks to finish before terminating the actor, you can call `actor.__ray_terminate__.remote()` instead.\n\nAdditionally, if you want to allow Ray to automatically restart the actor, you can pass the `no_restart=False` argument to `ray.kill()`.\n\nFor named and detached actors, calling `ray.kill()` on an actor handle destroys the actor and allows the name to be reused.",
4777
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4778
        },
4779
        {
4780
            "question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",
4781
            "sources": [
4782
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
4783
                "https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.verbose.html#ray-train-horovod-horovodconfig-verbose",
4784
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
4785
                "https://docs.ray.io/en/master/workflows/basics.html#ray-integration",
4786
                "https://docs.ray.io/en/master/tune/faq.html#how-can-i-use-nested-conditional-search-spaces",
4787
                "https://docs.ray.io/en/master/data/preprocessors.html#built-in-preprocessors",
4788
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",
4789
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
4790
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.NOOP.html#ray-tune-schedulers-trialscheduler-noop",
4791
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.mode.html#ray-tune-tuneconfig-mode",
4792
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.hyperopt.HyperOptSearch.FINISHED.html#ray-tune-search-hyperopt-hyperoptsearch-finished",
4793
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTrainingReplay.NOOP.html#ray-tune-schedulers-populationbasedtrainingreplay-noop",
4794
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.CKPT_FILE_TMPL.html#ray-tune-search-searcher-ckpt-file-tmpl"
4795
            ],
4796
            "document_ids": [
4797
                4811,
4798
                8930,
4799
                675,
4800
                9685,
4801
                1016,
4802
                6922,
4803
                1782,
4804
                5380,
4805
                1495,
4806
                1456,
4807
                1874,
4808
                1410,
4809
                1997
4810
            ],
4811
            "answer": " To set environment variables in `ray.init()`, you can use the `runtime_env` argument which allows you to specify environment variables. Here's an example:\n```python\nimport ray\n\nray.init(runtime_env={\"env_vars\": {\"foo\": \"foo\"}})\n```\nIn this example, the `env_vars` dictionary contains the environment variable `foo` set to the value `foo`. You can replace the key-value pairs with your desired environment variables.",
4812
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4813
        },
4814
        {
4815
            "question": "What is the rest api for getting the head node id?",
4816
            "sources": [
4817
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",
4818
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.node_id.html#ray-runtime-context-runtimecontext-node-id",
4819
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",
4820
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",
4821
                "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",
4822
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
4823
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
4824
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
4825
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
4826
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",
4827
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.DriverInfo.html#ray-job-submission-driverinfo",
4828
                "https://docs.ray.io/en/master/serve/api/index.html#v1-rest-api-single-application",
4829
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails"
4830
            ],
4831
            "document_ids": [
4832
                754,
4833
                4731,
4834
                4442,
4835
                13745,
4836
                4909,
4837
                14294,
4838
                14323,
4839
                14202,
4840
                739,
4841
                721,
4842
                6683,
4843
                13999,
4844
                14105
4845
            ],
4846
            "answer": " The rest API for getting the head node ID is not explicitly stated in the provided context. However, you can use the `ray.util.state.get_node` function to get the node state by specifying the node ID. To get the current node ID for the worker or driver, you can use the `ray.runtime_context.RuntimeContext.get_node_id` function, which returns the node ID in hex format. Note that the API `ray.runtime_context.RuntimeContext.node_id` is deprecated and will be removed in future Ray releases.\n\nHere's an example of how to use these functions in Python:\n\n```python\nimport ray\nfrom ray.util.state import get_node\nfrom ray.runtime_context import RuntimeContext\n\n# Initialize Ray\nray.init()\n\n# Get the current node ID for this worker or driver\nnode_id = RuntimeContext.get_node_id()\n\n# Get the node state by specifying the node ID\nnode_state = get_node(node_id)\n\n# Print the node state\nprint(node_state)\n```\n\nTo determine if the node is the head node, you can check the `is_head_node` attribute of the `NodeState` object returned by the `get_node` function.\n\nIf you want to use the REST API, you can make an HTTP request to the Ray API server's `/node/info` endpoint, which returns information about all the nodes in the cluster, including the head node. You can then parse the response to extract the information you need. Note that this requires the Ray API server to be running and accessible over HTTP.\n\nHere's an example of how to make the HTTP request using the `requests` library in Python:\n\n```python\nimport requests\n\n# Make the HTTP request to the Ray API server's /node/info endpoint\nresponse = requests.get('http://localhost:8265/node/info')\n\n# Parse the response to extract the information about the nodes\nnodes = response.json()['nodes']\nhead_node = next((node for node in nodes if node['is_head_node']), None)\n\n# Print the information about the head node\nprint(head_node)\n```\n\nNote that the URL for the Ray API server may be different in your setup. You can find the URL by checking the `redis_address` argument passed to the `ray.init` function or by checking the Ray configuration.",
4847
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4848
        },
4849
        {
4850
            "question": "how to rerun a canceled ray task",
4851
            "sources": [
4852
                "https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",
4853
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",
4854
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
4855
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
4856
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",
4857
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
4858
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",
4859
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
4860
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",
4861
                "https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",
4862
                "https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",
4863
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",
4864
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel"
4865
            ],
4866
            "document_ids": [
4867
                4375,
4868
                4894,
4869
                11591,
4870
                4496,
4871
                14702,
4872
                4498,
4873
                14703,
4874
                11592,
4875
                4550,
4876
                11590,
4877
                4494,
4878
                14701,
4879
                4897
4880
            ],
4881
            "answer": " To rerun a canceled Ray task, you can simply call the remote function again to create a new object ref, and then use `ray.get()` to retrieve the result. Note that Ray won't automatically retry tasks that have been canceled. Here's an example:\n```python\nobj_ref = blocking_operation.remote()  # Create a new object ref\nresult = ray.get(obj_ref)  # Retrieve the result\n```\nIf the task is an async actor task, you can catch the `asyncio.CancelledError` exception and re-run the task in the exception handler. Here's an example:\n```python\nimport ray\nimport asyncio\n\nclass Actor:\n    async def f(self):\n        try:\n            await asyncio.sleep(5)\n        except asyncio.CancelledError:\n            print(\"Actor task canceled.\")\n            # Rerun the task here\n            return await self.f()\n\nactor = Actor.remote()\nref = actor.f.remote()\n\n# Wait until task is scheduled.\ntime.sleep(1)\nray.cancel(ref)\n\nresult = ray.get(ref)  # Get the result of the rerun task\n```\nNote that Ray tracks all child and actor tasks. When the `recursive=True` argument is given to `ray.cancel()`, it cancels all child and actor tasks.",
4882
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4883
        },
4884
        {
4885
            "question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",
4886
            "sources": [
4887
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.html#ray-tune-search-optuna-optunasearch",
4888
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.sigopt.SigOptSearch.html#ray-tune-search-sigopt-sigoptsearch",
4889
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",
4890
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_json.html#ray-data-read-json",
4891
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_mongo.html#ray-data-read-mongo",
4892
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
4893
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
4894
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
4895
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",
4896
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",
4897
                "https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#updating-your-training-function",
4898
                "https://docs.ray.io/en/master/tune/examples/includes/dragonfly_example.html#dragonfly-example",
4899
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.hyperopt.HyperOptSearch.set_max_concurrency.html#ray-tune-search-hyperopt-hyperoptsearch-set-max-concurrency"
4900
            ],
4901
            "document_ids": [
4902
                1867,
4903
                2072,
4904
                7568,
4905
                7320,
4906
                7122,
4907
                9521,
4908
                9513,
4909
                9508,
4910
                9517,
4911
                7223,
4912
                8290,
4913
                3947,
4914
                1654
4915
            ],
4916
            "answer": " To set the max parallel concurrent scheduled tasks in `map_batches`, you need to use the SigOpt experiment and space specification. The searcher manages its own concurrency, and you can set the max concurrent trials using the `set_max_concurrency` method of the searcher. However, if you use the searcher in a `ConcurrencyLimiter`, the `max_concurrent` value passed to it will override the value passed to `set_max_concurrency`. The parallelism is automatically determined and you should not need to manually set this value in most cases.\n\nIn the context you provided, it mentions `df_search = ConcurrencyLimiter(df_search, max_concurrent=4)`, which is setting the max concurrent trials to 4.\n\nAdditionally, it is not recommended to manually set the parallelism through `tf.config.threading` as it is automatically determined and can be tuned using the guidance provided in the documentation.",
4917
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4918
        },
4919
        {
4920
            "question": "How do I get the number of cpus from ray cluster?",
4921
            "sources": [
4922
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",
4923
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",
4924
                "https://docs.ray.io/en/master/cluster/faq.html#faq",
4925
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-6-collect-worker-node-metrics-with-podmonitors",
4926
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
4927
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",
4928
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
4929
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
4930
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",
4931
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
4932
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-1-execute-a-ray-job-in-the-head-pod",
4933
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
4934
                "https://docs.ray.io/en/master/ray-core/miscellaneous.html#resource-information"
4935
            ],
4936
            "document_ids": [
4937
                6436,
4938
                6609,
4939
                5633,
4940
                6072,
4941
                6835,
4942
                6365,
4943
                14730,
4944
                368,
4945
                5961,
4946
                6147,
4947
                5727,
4948
                13896,
4949
                4444
4950
            ],
4951
            "answer": " To get the number of CPUs in a Ray cluster, you can use the `ray cluster_resources()` function, which returns a dictionary of the current total cluster resources. The number of CPUs can be found under the key `'CPU'`. Here's an example of how to use this function:\n\n```python\nimport ray\nray.init()\ncpu_count = ray.cluster_resources()['CPU']\nprint(f\"Number of CPUs in the cluster: {cpu_count}\")\n```\n\nKeep in mind that the information returned by `ray.cluster_resources()` can grow stale as nodes are added to or removed from the cluster.",
4952
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4953
        },
4954
        {
4955
            "question": "How to use the exclude option to the runtime_env",
4956
            "sources": [
4957
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
4958
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
4959
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
4960
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/text-summarizer-rayservice.html#step-3-install-a-rayservice",
4961
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",
4962
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
4963
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#re3-random-encoders-for-efficient-exploration",
4964
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",
4965
                "https://docs.ray.io/en/master/rllib/rllib-cli.html#training-from-configuration-options",
4966
                "https://docs.ray.io/en/master/workflows/comparison.html#exit-handler",
4967
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",
4968
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
4969
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning"
4970
            ],
4971
            "document_ids": [
4972
                14132,
4973
                14101,
4974
                6395,
4975
                6134,
4976
                5760,
4977
                1113,
4978
                12031,
4979
                5742,
4980
                11475,
4981
                9721,
4982
                6103,
4983
                675,
4984
                9395
4985
            ],
4986
            "answer": " To use the \"exclude\" option for \"runtime\\_env\" in the given context, you can define it in the \"worker\\_node\\_options\" or \"ray\\_actor\\_options\" as a dict. The keys in this dict should be the Ray start options in snake\\_case format (e.g., \"foo\\_bar\" for \"--foo-bar\"). For flag options like '--disable-usage-stats', set the value to None. Here's an example for \"worker\\_node\\_options\":\n\n```yaml\nworker_node_options:\n  exclude_option_1: None\n  exclude_option_2: None\n```\n\nReplace \"exclude\\_option\\_1\" and \"exclude\\_option\\_2\" with the actual options you want to exclude. Note that short name options (e.g., '-v') are not supported.",
4987
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
4988
        },
4989
        {
4990
            "question": "show a map batch example with batch_format",
4991
            "sources": [
4992
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
4993
                "https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",
4994
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",
4995
                "https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",
4996
                "https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",
4997
                "https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",
4998
                "https://docs.ray.io/en/master/data/examples/batch_training.html#run-batch-training-using-map-groups",
4999
                "https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",
5000
                "https://docs.ray.io/en/master/rllib/rllib-examples.html#environments-and-adapters",
5001
                "https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",
5002
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",
5003
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",
5004
                "https://docs.ray.io/en/master/rllib/rllib-offline.html#example-converting-external-experiences-to-batch-format"
5005
            ],
5006
            "document_ids": [
5007
                7081,
5008
                10266,
5009
                7640,
5010
                7988,
5011
                10143,
5012
                7091,
5013
                7890,
5014
                7959,
5015
                10646,
5016
                6956,
5017
                10308,
5018
                8070,
5019
                10612
5020
            ],
5021
            "answer": " Here's an example of using `map_batches` with `batch_format=\"pandas\"` in Ray:\n```python\nfrom ray.data.preprocessors import MapCallable\n",
5022
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5023
        },
5024
        {
5025
            "question": "how to find local ray address",
5026
            "sources": [
5027
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
5028
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
5029
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.FINISHED.html#ray-tune-search-searcher-finished",
5030
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5031
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
5032
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice",
5033
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",
5034
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",
5035
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",
5036
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
5037
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
5038
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
5039
                "https://docs.ray.io/en/master/workflows/basics.html#ray-integration"
5040
            ],
5041
            "document_ids": [
5042
                6015,
5043
                4662,
5044
                1324,
5045
                4811,
5046
                5556,
5047
                5780,
5048
                6531,
5049
                6183,
5050
                5380,
5051
                14255,
5052
                13974,
5053
                6835,
5054
                9685
5055
            ],
5056
            "answer": " To find the local Ray address, you can use the Ray command-line interface (CLI) or check the Ray dashboard. Here are the steps to find the local Ray address:\n\n1. Start Ray using `ray.init()` in your Python script or Jupyter notebook.\n2. Access the Ray dashboard by opening a web browser and navigating to `localhost:8265`.\n3. In the Ray dashboard, you can find the Ray address under the \"Cluster\" tab, which shows the Ray cluster's information.\n4. Alternatively, you can use the Ray CLI to check the Ray address by running `ray status` in the terminal.\n\nThe Ray address typically follows the format `<address>:<port>`, where `<address>` is the IP address or hostname of the machine running Ray, and `<port>` is the port number used by Ray for communication.",
5057
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5058
        },
5059
        {
5060
            "question": "How to start ray cluster on multiple node via CLI?",
5061
            "sources": [
5062
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",
5063
                "https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",
5064
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",
5065
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",
5066
                "https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",
5067
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",
5068
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",
5069
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#launching-an-on-premise-cluster",
5070
                "https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",
5071
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-up",
5072
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5073
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",
5074
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster"
5075
            ],
5076
            "document_ids": [
5077
                6616,
5078
                4193,
5079
                6203,
5080
                6501,
5081
                14683,
5082
                6509,
5083
                6619,
5084
                6499,
5085
                2410,
5086
                862,
5087
                4816,
5088
                6510,
5089
                6500
5090
            ],
5091
            "answer": " To start a Ray cluster on multiple nodes via CLI, you can use the `ray up` command with the Ray cluster launcher. This command creates a designated \"head node\" and worker nodes on the cloud. Here are the steps:\n\n1. Define your cluster configuration in a YAML file.\n2. Use the `ray up` command to start the Ray cluster, specifying the YAML file as an argument.\n\nFor example, if you have a YAML file called `my_cluster.yaml`, you can start the Ray cluster with the following command:\n```css\nray up -y my_cluster.yaml\n```\nMake sure to include the necessary configuration in the YAML file, such as the head node IP address, worker node IP addresses, and SSH user.\n\nAfter starting the Ray cluster, you can connect to it by calling `ray.init()` from one of the machines in the cluster.\n\nNote: If you want to start a Ray cluster locally, you can use the `ray start` command with the `--head` option. To start worker nodes, you can use the Ray cluster address printed out by the `ray start` command.",
5092
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5093
        },
5094
        {
5095
            "question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",
5096
            "sources": [
5097
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
5098
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",
5099
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",
5100
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
5101
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",
5102
                "https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",
5103
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",
5104
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
5105
                "https://docs.ray.io/en/master/ray-air/examples/xgboost_example.html#training-a-model-with-distributed-xgboost",
5106
                "https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",
5107
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
5108
                "https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",
5109
                "https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks"
5110
            ],
5111
            "document_ids": [
5112
                5068,
5113
                625,
5114
                10218,
5115
                468,
5116
                622,
5117
                6879,
5118
                614,
5119
                10384,
5120
                9882,
5121
                9397,
5122
                1113,
5123
                9898,
5124
                4226
5125
            ],
5126
            "answer": " The Ray tuner shows \"running\" but CPU usage is almost 0% because the Ray actor only requires 1 CPU when it is scheduled, but after it is created, it occupies 0 CPU. Additionally, there might be no available CPUs in the Ray cluster as other processes or Tune trials might be using them. To ensure there are enough free CPUs for Ray Data to run on, you can limit the number of concurrent Tune trials with the max\\_concurrent\\_trials Tune option.",
5127
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5128
        },
5129
        {
5130
            "question": "should the Ray head node and all workers have the same object store memory size allocated?",
5131
            "sources": [
5132
                "https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",
5133
                "https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",
5134
                "https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",
5135
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
5136
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#running-out-of-file-descriptors-too-may-open-files",
5137
                "https://docs.ray.io/en/master/cluster/key-concepts.html#key-concepts",
5138
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
5139
                "https://docs.ray.io/en/master/cluster/key-concepts.html#worker-node",
5140
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",
5141
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",
5142
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",
5143
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",
5144
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"
5145
            ],
5146
            "document_ids": [
5147
                14481,
5148
                5557,
5149
                6367,
5150
                6392,
5151
                604,
5152
                5554,
5153
                5555,
5154
                5558,
5155
                4525,
5156
                6375,
5157
                6441,
5158
                855,
5159
                6328
5160
            ],
5161
            "answer": " Yes, it is recommended to have the same object store memory size allocated for the Ray head node and all workers for consistent performance. However, in large-scale clusters, it might be beneficial to adjust the memory allocation for the head node based on its additional system processes.",
5162
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5163
        },
5164
        {
5165
            "question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",
5166
            "sources": [
5167
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",
5168
                "https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",
5169
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments",
5170
                "https://docs.ray.io/en/master/serve/api/index.html#serve-start",
5171
                "https://docs.ray.io/en/master/serve/architecture.html#how-does-serve-ensure-horizontal-scalability-and-availability",
5172
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#building-the-call-graph-methodnodes-and-functionnodes",
5173
                "https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",
5174
                "https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",
5175
                "https://docs.ray.io/en/master/ray-core/configure.html#configuring-ray",
5176
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5177
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",
5178
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
5179
                "https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview"
5180
            ],
5181
            "document_ids": [
5182
                14022,
5183
                10282,
5184
                13727,
5185
                13994,
5186
                13476,
5187
                13734,
5188
                4960,
5189
                4381,
5190
                4379,
5191
                14055,
5192
                14025,
5193
                13974,
5194
                5600
5195
            ],
5196
            "answer": " In Ray Serve, you can specify whether to set up an HTTP proxy on each node or just the head node using the `--proxy-location` option with `serve start`. The options are \"HeadOnly\" (default), \"EveryNode\", and \"NoServer\". The number of CPU cores to reserve for each internal Serve HTTP proxy actor can be specified using the deprecated `num_cpus` option.",
5197
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5198
        },
5199
        {
5200
            "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?",
5201
            "sources": [
5202
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats",
5203
                "https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",
5204
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5205
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",
5206
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5207
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#provider-use-internal-ips",
5208
                "https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",
5209
                "https://docs.ray.io/en/master/cluster/faq.html#what-is-the-difference-between-node-ip-address-and-address",
5210
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",
5211
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
5212
                "https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",
5213
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",
5214
                "https://docs.ray.io/en/master/serve/api/index.html#serve-start"
5215
            ],
5216
            "document_ids": [
5217
                6416,
5218
                14683,
5219
                4816,
5220
                6708,
5221
                4817,
5222
                6284,
5223
                2414,
5224
                5637,
5225
                6552,
5226
                13718,
5227
                5528,
5228
                6619,
5229
                13994
5230
            ],
5231
            "answer": " Given that Prometheus and Grafana are not running on your Head node and you are using KubeRay, you should set `RAY_GRAFANA_HOST` and `RAY_PROMETHEUS_HOST` to the IP addresses or hostnames of the nodes where Prometheus and Grafana are running.\n\nTo make these settings more intelligent and avoid having to manually update the head node IP, you could use a DNS name that is mapped to the head node IP, or use a service discovery tool like Consul or Kubernetes service discovery.\n\nHere's an example of how you could set these variables using Kubernetes service discovery:\n\n1. Deploy Prometheus and Grafana as Kubernetes services in the same namespace as your KubeRay cluster.\n2. Set `RAY_PROMETHEUS_HOST` to the name of the Prometheus service, and `RAY_GRAFANA_HOST` to the name of the Grafana service.\n\nFor example:\n```\nexport RAY_PROMETHEUS_HOST=prometheus.kube-system.svc.cluster.local\nexport RAY_GRAFANA_HOST=grafana.kube-system.svc.cluster.local\n```\nThis way, even if the head node IP changes, the `RAY_PROMETHEUS_HOST` and `RAY_GRAFANA_HOST` variables will still point to the correct services.",
5232
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5233
        },
5234
        {
5235
            "question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",
5236
            "sources": [
5237
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#nodeselector-and-tolerations",
5238
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",
5239
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#deploying-a-network-policy-for-the-static-ray-cluster",
5240
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#node-selectors-and-node-labels",
5241
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#timing-1-before-ray-start",
5242
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",
5243
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",
5244
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#specify-container-commands-for-ray-head-worker-pods",
5245
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#pod-and-container-lifecyle-prestophook",
5246
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
5247
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-vs-horizontal-pod-autoscaler",
5248
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#vertical-pod-autoscaler",
5249
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure"
5250
            ],
5251
            "document_ids": [
5252
                5930,
5253
                5972,
5254
                5829,
5255
                6025,
5256
                5841,
5257
                5975,
5258
                5964,
5259
                5839,
5260
                5947,
5261
                13901,
5262
                5970,
5263
                5977,
5264
                13894
5265
            ],
5266
            "answer": " The GCS (GKE Autopilot Container-attached Storage) or Kubernetes cluster autoscaler determines which Kubernetes pod to kill when using KubeRay autoscaling based on the nodeSelector and tolerations fields of the pod spec. These fields determine on which Kubernetes nodes the pods may be scheduled. The Ray Autoscaler has more fine-grained control over scale-down than the Horizontal Pod Autoscaler, allowing it to select precisely which pods to scale down. The KubeRay operator then deletes that pod.",
5267
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5268
        },
5269
        {
5270
            "question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",
5271
            "sources": [
5272
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",
5273
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
5274
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment",
5275
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
5276
                "https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",
5277
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment",
5278
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",
5279
                "https://docs.ray.io/en/master/ray-core/configure.html#driver-options",
5280
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",
5281
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle",
5282
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
5283
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5284
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-jobs-cli-api-reference"
5285
            ],
5286
            "document_ids": [
5287
                6395,
5288
                14132,
5289
                14159,
5290
                14101,
5291
                13562,
5292
                14155,
5293
                14040,
5294
                4407,
5295
                675,
5296
                14275,
5297
                4876,
5298
                4811,
5299
                6594
5300
            ],
5301
            "answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you can use the following configuration:\n```yaml\nworker_node_options:\n  http_options:\n    request_timeout_s: <value>\n```\nReplace `<value>` with the desired timeout value in seconds.\n\nThe configuration above sets the `request_timeout_s` option in the `http_options` section, which is passed to the Ray worker node's start script. The Ray start options key is converted from the `--foo-bar` format to the `foo_bar` format.\n\nNote that short name options (e.g., `-v`) are not supported. Also, remember that the `ray_actor_options` dictionary in the config file overrides the entire `ray_actor_options` dictionary from the graph code. If there are individual options within `ray_actor_options` (e.g., `runtime_env`, `num_gpus`, `memory`) that are set in the code but not in the config, Serve still won't use the code settings if the config has a `ray_actor_options` dictionary. It treats these missing options as though the user never set them and uses defaults instead.",
5302
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5303
        },
5304
        {
5305
            "question": "How do I make the GPU available on my M1 laptop to ray?",
5306
            "sources": [
5307
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
5308
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",
5309
                "https://docs.ray.io/en/master/installation.html#m1-mac-apple-silicon-support",
5310
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#m1-mac-apple-silicon-support",
5311
                "https://docs.ray.io/en/master/ray-overview/installation.html#m1-mac-apple-silicon-support",
5312
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",
5313
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#developing-ray-serve-python-scripts-on-a-raycluster",
5314
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-6-repeat-step-3-and-step-4",
5315
                "https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",
5316
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",
5317
                "https://docs.ray.io/en/master/installation.html#installing-ray",
5318
                "https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray",
5319
                "https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray"
5320
            ],
5321
            "document_ids": [
5322
                6615,
5323
                417,
5324
                73,
5325
                14572,
5326
                298,
5327
                5412,
5328
                5979,
5329
                5987,
5330
                6436,
5331
                4519,
5332
                59,
5333
                14536,
5334
                284
5335
            ],
5336
            "answer": " To make the GPU available on your M1 laptop for Ray, you need to ensure that you have installed Ray while using the miniforge environment on your M1 Mac. Here are the steps to follow:\n\n1. Install miniforge by running the following commands in your terminal:\n```bash\nwget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh\nbash Miniforge3-MacOSX-arm64.sh\nrm Miniforge3-MacOSX-arm64.sh # Cleanup.\nsource ~/.bash_profile\nconda activate\n```\n2. Install Ray by running the following command:\n```\npip install ray\n```\n3. To use the Ray Client and access the dashboard, use the following commands:\n```python\nimport ray\n# Access the dashboard with the URL that Ray prints when it initializes\n# (the default URL is http://localhost:8265) or with the context object returned by ray.init().\n```\n\nBy following these steps, you can make the GPU available on your M1 laptop for Ray. Note that multi-node clusters are untested on M1 Macs, so it is recommended to use Ray for local development only.",
5337
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5338
        },
5339
        {
5340
            "question": "How can I add a timeout for the Ray job?",
5341
            "sources": [
5342
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
5343
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",
5344
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup",
5345
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-scale-out-training",
5346
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5347
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",
5348
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",
5349
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",
5350
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",
5351
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",
5352
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#run-the-workload",
5353
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
5354
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples"
5355
            ],
5356
            "document_ids": [
5357
                5757,
5358
                5760,
5359
                6548,
5360
                8153,
5361
                4811,
5362
                6615,
5363
                5412,
5364
                6144,
5365
                6576,
5366
                5742,
5367
                6525,
5368
                6157,
5369
                5380
5370
            ],
5371
            "answer": " To add a timeout for a Ray job, you can specify a timeout value in the RayJob configuration file. Here's an example of how to do this:\n\n1. Download the RayJob configuration file:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml\n```\n2. Edit the configuration file and add the following line to the `spec` section of the RayJob:\n```yaml\ntimeoutSeconds: <timeout_value>\n```\nReplace `<timeout_value>` with the number of seconds you want to set as the timeout for the RayJob.\n\n3. Apply the RayJob configuration:\n```bash\nkubectl apply -f ray_v1alpha1_rayjob.yaml\n```\n\nThis will create a RayJob with a specified timeout value. Once the timeout is reached, the RayJob will be automatically terminated.",
5372
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5373
        },
5374
        {
5375
            "question": "how do I set custom /tmp directory for remote cluster?",
5376
            "sources": [
5377
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
5378
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
5379
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",
5380
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
5381
                "https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",
5382
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",
5383
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5384
                "https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats",
5385
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",
5386
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",
5387
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.restore.html#ray-tune-trainable-restore",
5388
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.restore.html#ray-rllib-algorithms-algorithm-algorithm-restore",
5389
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files"
5390
            ],
5391
            "document_ids": [
5392
                4273,
5393
                13718,
5394
                13680,
5395
                6561,
5396
                6205,
5397
                6616,
5398
                4816,
5399
                6416,
5400
                6562,
5401
                13719,
5402
                2081,
5403
                12465,
5404
                4264
5405
            ],
5406
            "answer": " Based on the provided context, it is not directly mentioned how to set a custom /tmp directory for a remote cluster in Ray. However, it is mentioned that certain configurations like local directories, environment variables, and Python packages can be set per-job using `ray.init()`. Therefore, you may try setting a custom /tmp directory using the `temp_directory` argument in `ray.init()`. Here's an example:\n```python\nimport ray\n\nray.init(temp_directory=\"/path/to/custom/tmp\")\n# continue with your Ray program\n```\nHowever, it's important to note that this may not work as expected since the `temp_directory` argument is intended for use with local Ray clusters, and its behavior with remote clusters is not explicitly mentioned in the Ray documentation. If this does not work, you may need to contact Ray support or consult their community for further assistance.",
5407
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5408
        },
5409
        {
5410
            "question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",
5411
            "sources": [
5412
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",
5413
                "https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.PartitionStyle.DIRECTORY.html#ray-data-datasource-partitionstyle-directory",
5414
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
5415
                "https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",
5416
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",
5417
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",
5418
                "https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",
5419
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
5420
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",
5421
                "https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",
5422
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",
5423
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.chdir_to_trial_dir.html#ray-tune-tuneconfig-chdir-to-trial-dir",
5424
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.local_dir.html#ray-tune-experiment-local-dir"
5425
            ],
5426
            "document_ids": [
5427
                1775,
5428
                7232,
5429
                4273,
5430
                4385,
5431
                6318,
5432
                272,
5433
                4277,
5434
                511,
5435
                6632,
5436
                10170,
5437
                6360,
5438
                1439,
5439
                2170
5440
            ],
5441
            "answer": " No, setting the `--temp-dir` option to a different directory than /tmp will not cause Ray object spill to the custom directory. The `--temp-dir` option only changes the root temporary directory used by Ray for internal purposes. It does not affect the location where Ray objects are spilled. Object spilling in Ray is determined by the `object_spill_dir` configuration option, which defaults to a subdirectory of the root temporary directory. If you want to change the location of object spilling, you would need to set the `object_spill_dir` configuration option to a subdirectory of your custom `--temp-dir` directory.",
5442
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5443
        },
5444
        {
5445
            "question": "can you give me an example for *`--runtime-env-json`*",
5446
            "sources": [
5447
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#examples",
5448
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#wrapping-up",
5449
                "https://docs.ray.io/en/master/tune/examples/index.html#other-examples",
5450
                "https://docs.ray.io/en/master/tune/examples/other-examples.html#other-examples",
5451
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.FINISHED.html#ray-tune-search-searcher-finished",
5452
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
5453
                "https://docs.ray.io/en/master/ray-contribute/writing-code-snippets.html#types-of-examples",
5454
                "https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",
5455
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#text-generation-with-huggingface-pipeline",
5456
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",
5457
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt",
5458
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",
5459
                "https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples"
5460
            ],
5461
            "document_ids": [
5462
                6299,
5463
                5359,
5464
                3798,
5465
                3322,
5466
                1324,
5467
                9956,
5468
                830,
5469
                14599,
5470
                9998,
5471
                10419,
5472
                10472,
5473
                5295,
5474
                373
5475
            ],
5476
            "answer": " I'm sorry, the provided context does not contain an example for the `--runtime-env-json` flag. The context includes examples for custom loggers and trial directory naming, PyTorch FSDP tutorial, Hugging Face model card, and Ray examples, but none of them demonstrate the use of `--runtime-env-json`.",
5477
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5478
        },
5479
        {
5480
            "question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",
5481
            "sources": [
5482
                "https://docs.ray.io/en/master/train/examples/pytorch/tune_cifar_torch_pbt_example.html#tuning-hyperparameters-of-a-distributed-pytorch-model-with-pbt-using-ray-train-tune",
5483
                "https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",
5484
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
5485
                "https://docs.ray.io/en/master/train/api/doc/ray.train.CheckpointConfig.checkpoint_score_order.html#ray-train-checkpointconfig-checkpoint-score-order",
5486
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",
5487
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
5488
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.JupyterNotebookReporter.html#ray-tune-jupyternotebookreporter",
5489
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.HyperBandScheduler.html#ray-tune-schedulers-hyperbandscheduler",
5490
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ax.AxSearch.html#ray-tune-search-ax-axsearch",
5491
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.html#ray-tune-search-searcher",
5492
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.max_concurrent_trials.html#ray-tune-tuneconfig-max-concurrent-trials",
5493
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5494
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails"
5495
            ],
5496
            "document_ids": [
5497
                9595,
5498
                1115,
5499
                4878,
5500
                8922,
5501
                4853,
5502
                14144,
5503
                1667,
5504
                1970,
5505
                1743,
5506
                1399,
5507
                1939,
5508
                14066,
5509
                14187
5510
            ],
5511
            "answer": " The value of `maxConcurrentReplicas` should be specified based on the configuration of the system and the desired level of concurrency. If autoscaling is specified, `maxConcurrentReplicas` will be used as the maximum number of replicas that can be running concurrently. It is important to set this value appropriately to ensure that the system has sufficient resources to run all of the replicas, while also avoiding overwhelming the system with too many concurrent tasks. It is also subject to the constraint of `max_replicas_per_node` which is the max number of deployment replicas can run on a single node. The default value is no limitation, but a value in the range of [1, 100] can be set.",
5512
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5513
        },
5514
        {
5515
            "question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",
5516
            "sources": [
5517
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",
5518
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5519
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5520
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
5521
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
5522
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5523
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5524
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",
5525
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",
5526
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5527
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",
5528
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5529
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"
5530
            ],
5531
            "document_ids": [
5532
                14144,
5533
                13450,
5534
                13451,
5535
                14225,
5536
                14187,
5537
                14066,
5538
                14303,
5539
                14103,
5540
                14231,
5541
                14072,
5542
                14193,
5543
                14309,
5544
                14145
5545
            ],
5546
            "answer": " The value of `max_concurrent_queries` is not directly specified by the `target_num_ongoing_requests_per_replica`. `max_concurrent_queries` is the max number of pending queries in a single replica, while `target_num_ongoing_requests_per_replica` is how many ongoing requests are expected to run concurrently per replica. The default value for `max_concurrent_queries` is 1, and it can be set with an exclusive minimum of 0.",
5547
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5548
        },
5549
        {
5550
            "question": "what is a `smoothing_factor`",
5551
            "sources": [
5552
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5553
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",
5554
                "https://docs.ray.io/en/master/tune/examples/includes/pb2_example.html#pb2-example",
5555
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",
5556
                "https://docs.ray.io/en/master/rllib/package_ref/utils.html#tensorflow-utilities",
5557
                "https://docs.ray.io/en/master/rllib/package_ref/utils.html#numpy-utilities",
5558
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.numpy.fc.html#ray-rllib-utils-numpy-fc",
5559
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#twin-delayed-ddpg-td3",
5560
                "https://docs.ray.io/en/master/rllib/rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg",
5561
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.AsyncSampler.html#ray-rllib-evaluation-sampler-asyncsampler",
5562
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.AsyncHyperBandScheduler.html#ray-tune-schedulers-asynchyperbandscheduler",
5563
                "https://docs.ray.io/en/master/data/examples/batch_training.html#run-batch-training-using-map-groups",
5564
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.exploration.gaussian_noise.GaussianNoise.html#ray-rllib-utils-exploration-gaussian-noise-gaussiannoise"
5565
            ],
5566
            "document_ids": [
5567
                13454,
5568
                13455,
5569
                3991,
5570
                1920,
5571
                11146,
5572
                11158,
5573
                13324,
5574
                11812,
5575
                11783,
5576
                12379,
5577
                1735,
5578
                7897,
5579
                12675
5580
            ],
5581
            "answer": " A `smoothing_factor` is a multiplicative factor that can be used to speed up or slow down the pace of upscaling or downscaling decisions in an autoscaling algorithm. A higher value for the `smoothing_factor` will make the algorithm more responsive to changes in traffic, while a lower value will make it more conservative. It can be used to either amplify the response of the autoscaling algorithm or to make it less sensitive to drops in traffic.",
5582
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5583
        },
5584
        {
5585
            "question": "What is the reason actors change their state to unhealthy?",
5586
            "sources": [
5587
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
5588
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
5589
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
5590
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",
5591
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
5592
                "https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",
5593
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
5594
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",
5595
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",
5596
                "https://docs.ray.io/en/master/ray-observability/reference/api.html#state-apis-schema",
5597
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",
5598
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",
5599
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-checkpointing"
5600
            ],
5601
            "document_ids": [
5602
                14708,
5603
                4508,
5604
                11597,
5605
                747,
5606
                5192,
5607
                5191,
5608
                4575,
5609
                4561,
5610
                4200,
5611
                663,
5612
                4878,
5613
                4562,
5614
                4569
5615
            ],
5616
            "answer": " Actors in Ray, a distributed computing framework, can have state managed by their instance variables. However, using global variables to share state between tasks and actors is not recommended, as different processes do not share the same address space. Actors can fail due to actor process death or owner process death, and Ray can automatically restart actors with a specified maximum number of restarts. Actors can also manually checkpoint their state and recover upon restart, but this must be managed by the actor's creator. The reason an actor's state might be changed unhealthily could be due to unexpected crashes, max\\_restarts being exceeded, or checkpoint/recovery mismanagement.",
5617
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5618
        },
5619
        {
5620
            "question": "How do I access logs for a dead node?",
5621
            "sources": [
5622
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
5623
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",
5624
                "https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",
5625
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
5626
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",
5627
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",
5628
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",
5629
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",
5630
                "https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",
5631
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",
5632
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",
5633
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
5634
                "https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence"
5635
            ],
5636
            "document_ids": [
5637
                492,
5638
                493,
5639
                662,
5640
                739,
5641
                740,
5642
                680,
5643
                695,
5644
                512,
5645
                681,
5646
                6631,
5647
                445,
5648
                13894,
5649
                6359
5650
            ],
5651
            "answer": " To access logs for a dead node, you need to have persisted the logs before the node went down. Ray does not provide a native storage solution for log data, so you need to manage log persistence yourself. If you have persisted the logs, you can use the `ray logs` command with the `--node-id` flag to get the logs of a specific node.\n\nHere's the command you can use:\n\n```bash\nray logs --node-id <NODE_ID>\n```\n\nReplace `<NODE_ID>` with the ID of the dead node. Note that you can get the node ID from `ray list nodes` before the node goes down.\n\nIf you haven't persisted the logs, you won't be able to access them for a dead node.",
5652
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5653
        },
5654
        {
5655
            "question": "What are the reasons for a node to change it\u2019s status to dead?",
5656
            "sources": [
5657
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",
5658
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
5659
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#nodeaffinityschedulingstrategy",
5660
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",
5661
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
5662
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#rescheduling-bundles-on-a-dead-node",
5663
                "https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#handling-failures-and-node-preemption",
5664
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",
5665
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",
5666
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#provider-cache-stopped-nodes",
5667
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy.html#ray-util-scheduling-strategies-nodeaffinityschedulingstrategy",
5668
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.PlacementGroupState.html#ray-util-state-common-placementgroupstate",
5669
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate"
5670
            ],
5671
            "document_ids": [
5672
                4531,
5673
                554,
5674
                5147,
5675
                13869,
5676
                13898,
5677
                5098,
5678
                9228,
5679
                4790,
5680
                5976,
5681
                6283,
5682
                4686,
5683
                724,
5684
                721
5685
            ],
5686
            "answer": " A node in a Ray cluster can change its status to dead for the following reasons:\n\n1. Raylet failure: If the raylet process fails, the corresponding node will be marked as dead.\n2. Node running low on memory: If a task is killed due to the node running low on memory, the node can be considered dead.\n3. Head node failure: If the head node is killed, it can be considered dead.\n4. Node scaling events: If nodes are stopped when the cluster scales down, they can be considered dead.\n\nAdditionally, nodes can be marked as dead if some of their bundles are dead because they were on dead nodes, causing the placement group to reschedule the bundles on different nodes.",
5687
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5688
        },
5689
        {
5690
            "question": "What are the reasons for spikes in node CPU utilization",
5691
            "sources": [
5692
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
5693
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
5694
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#adding-cpu-only-nodes-to-your-cluster",
5695
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
5696
                "https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",
5697
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",
5698
                "https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",
5699
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
5700
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
5701
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",
5702
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",
5703
                "https://docs.ray.io/en/master/rllib/rllib-cli.html#inspecting-examples",
5704
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model"
5705
            ],
5706
            "document_ids": [
5707
                554,
5708
                468,
5709
                9227,
5710
                467,
5711
                4381,
5712
                855,
5713
                6375,
5714
                5129,
5715
                555,
5716
                625,
5717
                563,
5718
                11453,
5719
                10208
5720
            ],
5721
            "answer": " The reasons for spikes in node CPU utilization in the given context can be:\n\n1. Running low on memory, which can cause the system to kill tasks and workers, potentially leading to increased CPU usage.\n2. Preprocessing compute time that can be parallelized by adding more CPU-only nodes to the cluster.\n3. System processes running on the head node, such as GCS or the dashboard, which can cause extra stress on the head node's memory and CPU resources.\n4. A single process using most of the CPU resources, while other processes use a small amount.\n5. Running tasks and actors from the head node, which can cause out-of-memory problems if the head node has the same memory capacity as worker nodes.\n\nIt is essential to monitor the CPU usage, memory usage, and network bandwidth usage to decide the hardware specification for the head node and the cluster. In case of issues, logs can be checked to understand the root cause.",
5722
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5723
        },
5724
        {
5725
            "question": "What AWS machine type is recommended to deploy a RayService on EKS?",
5726
            "sources": [
5727
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",
5728
                "https://docs.ray.io/en/master/serve/api/index.html#deployment-decorators",
5729
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
5730
                "https://docs.ray.io/en/master/serve/key-concepts.html#deployment",
5731
                "https://docs.ray.io/en/master/serve/api/index.html#object-types",
5732
                "https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",
5733
                "https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",
5734
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
5735
                "https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",
5736
                "https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",
5737
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment",
5738
                "https://docs.ray.io/en/master/serve/advanced-guides/migration.html#migrating-handle-pass-between-deployments",
5739
                "https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers"
5740
            ],
5741
            "document_ids": [
5742
                13717,
5743
                13977,
5744
                14732,
5745
                13416,
5746
                13978,
5747
                12707,
5748
                13420,
5749
                14255,
5750
                2371,
5751
                13542,
5752
                14150,
5753
                13801,
5754
                10999
5755
            ],
5756
            "answer": " The context provided discusses RayServe, a tool for deploying and managing machine learning models and business logic in a Ray cluster. However, it does not provide specific recommendations for AWS machine types to use with RayServe or EKS.\n\nWhen deploying a RayService on EKS, the choice of machine type will depend on a number of factors, including the size and complexity of your models, the expected request load, and your budget constraints. In general, you will want to choose a machine type with sufficient CPU and memory resources to handle the demands of your RayService.\n\nFor guidance on selecting the appropriate machine type for your use case, you may want to refer to the Ray documentation or consult with a cloud architect or other expert.",
5757
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5758
        },
5759
        {
5760
            "question": "Is there a way to configure the session name generated by ray?",
5761
            "sources": [
5762
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#deploying-a-raycluster-with-logging-sidecar",
5763
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
5764
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
5765
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",
5766
                "https://docs.ray.io/en/master/cluster/usage-stats.html#requesting-removal-of-collected-data",
5767
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
5768
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-4-verify-the-kubernetes-cluster-status",
5769
                "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",
5770
                "https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice",
5771
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.sigopt.SigOptSearch.FINISHED.html#ray-tune-search-sigopt-sigoptsearch-finished",
5772
                "https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-6-collect-worker-node-metrics-with-podmonitors",
5773
                "https://docs.ray.io/en/master/ray-overview/use-cases.html#llms-and-gen-ai",
5774
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli"
5775
            ],
5776
            "document_ids": [
5777
                5879,
5778
                4811,
5779
                10128,
5780
                6015,
5781
                5569,
5782
                6835,
5783
                5798,
5784
                5702,
5785
                5780,
5786
                2014,
5787
                6072,
5788
                139,
5789
                4662
5790
            ],
5791
            "answer": " Based on the provided context, there is no direct way to configure the session name generated by Ray. However, after the Ray session is completed, you can find the session ID in the file /tmp/ray/session\\_xxx/usage\\_stats.json. This session ID can be used to request removal of collected data by emailing usage\\_stats@ray.io.\n\nAdditionally, when deploying Ray on Kubernetes using RayService, metadata.labels are defined by manually adding ray.io/cluster: <ray-cluster-name> and then instructing the PodMonitors resource to add that label in the scraped metrics via spec.podTargetLabels[\"ray.io/cluster\"]. This approach could potentially allow for some customization of the Ray session name as it appears in the Kubernetes environment.",
5792
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5793
        },
5794
        {
5795
            "question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",
5796
            "sources": [
5797
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",
5798
                "https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",
5799
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#job-profiling",
5800
                "https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",
5801
                "https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.JobState.html#ray-util-state-common-jobstate",
5802
                "https://docs.ray.io/en/master/ray-observability/getting-started.html#jobs-view",
5803
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
5804
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails",
5805
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-logs",
5806
                "https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#add-metadata-to-structured-logs",
5807
                "https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",
5808
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-status",
5809
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-list"
5810
            ],
5811
            "document_ids": [
5812
                6181,
5813
                6530,
5814
                422,
5815
                6157,
5816
                776,
5817
                421,
5818
                5556,
5819
                6650,
5820
                6604,
5821
                537,
5822
                5757,
5823
                6600,
5824
                6606
5825
            ],
5826
            "answer": " No, the Python SDK for Ray does not provide a direct method to get a link to the Ray dashboard for a given job. However, you can access the Ray dashboard through a web browser at the address \"http://localhost:8265\" (or the appropriate IP address and port for your Ray cluster), and then use the \"Jobs\" view to monitor and view details about your Ray jobs. The command \"ray job logs 'raysubmit_xxxxxxxxxxxxxxxx' --address http://127.0.0.1:8265 --follow\" allows you to follow the logs for a specific Ray job.",
5827
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5828
        },
5829
        {
5830
            "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).",
5831
            "sources": [
5832
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",
5833
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#worker-node-failure",
5834
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",
5835
                "https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#handling-failures-and-node-preemption",
5836
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
5837
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#how-to-write-fault-tolerant-ray-applications",
5838
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.TaskUnschedulableError.html#ray-exceptions-taskunschedulableerror",
5839
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#task-fault-tolerance",
5840
                "https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",
5841
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy.html#ray-util-scheduling-strategies-nodeaffinityschedulingstrategy",
5842
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",
5843
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",
5844
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#nodeaffinityschedulingstrategy"
5845
            ],
5846
            "document_ids": [
5847
                554,
5848
                4527,
5849
                597,
5850
                9228,
5851
                5129,
5852
                4357,
5853
                4830,
5854
                4532,
5855
                4658,
5856
                4686,
5857
                4568,
5858
                4529,
5859
                5147
5860
            ],
5861
            "answer": " The node where the task was running crashed unexpectedly could be due to:\n\n1. The instance where the node was running failed.\n2. Raylet, which is responsible for managing resources on a node, crashed unexpectedly due to reasons like running out of memory (OOM), preemption, or other system failures.\n3. The task was killed due to the node running low on memory.\n\nThe logs suggest that one or more workers were killed due to memory pressure, and the task could not be scheduled because the node specified through NodeAffinitySchedulingStrategy was dead.",
5862
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5863
        },
5864
        {
5865
            "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.",
5866
            "sources": [
5867
                "https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",
5868
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",
5869
                "https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",
5870
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
5871
                "https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#handling-failures-and-node-preemption",
5872
                "https://docs.ray.io/en/master/ray-core/fault-tolerance.html#how-to-write-fault-tolerant-ray-applications",
5873
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",
5874
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
5875
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
5876
                "https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",
5877
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
5878
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",
5879
                "https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed"
5880
            ],
5881
            "document_ids": [
5882
                5638,
5883
                467,
5884
                852,
5885
                13898,
5886
                9228,
5887
                4357,
5888
                13899,
5889
                13890,
5890
                13895,
5891
                5129,
5892
                13889,
5893
                4549,
5894
                10391
5895
            ],
5896
            "answer": " The error message \"Health check failed for node\" suggests that there is a problem with the connection between the worker node and the head node in a Ray cluster. Here are some steps you can take to troubleshoot this issue:\n\n1. Check if the worker node can access the head node's IP address and port by running `ip route get <head_node_ip>` and `telnet <head_node_ip> <port>` on the worker node.\n2. Verify that the head node is running and accessible by running `ray status` or checking the logs of the head node.\n3. Ensure that there are no firewall settings preventing access between the worker node and the head node.\n4. If the worker node is running inside a Kubernetes cluster, check if the worker node is running and if there are any issues with the pod or node it is running on.\n5. If the worker node has failed due to memory pressure, you can adjust the memory limits or increase the resources available to the worker node.\n\nBased on the context provided, it seems that the worker node is still up and running, so it's possible that there is a connectivity issue between the worker node and the head node. It may be helpful to check the logs of the worker node and the head node for any error messages or indications of the problem.",
5897
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5898
        },
5899
        {
5900
            "question": "Do you need the DAGDriver to deploy a serve application using RayServe?",
5901
            "sources": [
5902
                "https://docs.ray.io/en/master/serve/model_composition.html#composing-deployments-using-servehandles",
5903
                "https://docs.ray.io/en/master/serve/api/index.html#object-types",
5904
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
5905
                "https://docs.ray.io/en/master/serve/api/index.html#deployment-decorators",
5906
                "https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#in-place-updates-to-serve",
5907
                "https://docs.ray.io/en/master/serve/tutorials/java.html#converting-to-a-ray-serve-deployment",
5908
                "https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#serving-tuned-models-with-ray-serve",
5909
                "https://docs.ray.io/en/master/serve/production-guide/best-practices.html#cli-best-practices",
5910
                "https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",
5911
                "https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",
5912
                "https://docs.ray.io/en/master/serve/architecture.html#how-do-servehandles-work",
5913
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",
5914
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment"
5915
            ],
5916
            "document_ids": [
5917
                13628,
5918
                13978,
5919
                14763,
5920
                13977,
5921
                13685,
5922
                14560,
5923
                2778,
5924
                13830,
5925
                13420,
5926
                13616,
5927
                13477,
5928
                14255,
5929
                14150
5930
            ],
5931
            "answer": " No, you don't need the DAGDriver to deploy a RayServe application. The `serve.handle.RayServeHandle` is used to make requests from one deployment to another within a RayServe application. The `DAGDriver` is not mentioned or used in the provided context.",
5932
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5933
        },
5934
        {
5935
            "question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",
5936
            "sources": [
5937
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
5938
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",
5939
                "https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",
5940
                "https://docs.ray.io/en/master/serve/tutorials/index.html#examples",
5941
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#next-steps",
5942
                "https://docs.ray.io/en/master/serve/tutorials/gradio-integration.html#parallelize-using-ray-serve",
5943
                "https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#experimental-deployment-graphs",
5944
                "https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",
5945
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
5946
                "https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",
5947
                "https://docs.ray.io/en/master/serve/key-concepts.html#ingress-deployment-http-handling",
5948
                "https://docs.ray.io/en/master/serve/develop-and-deploy.html#build-serve-config-files-for-production-deployment",
5949
                "https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm"
5950
            ],
5951
            "document_ids": [
5952
                14732,
5953
                14290,
5954
                14057,
5955
                14686,
5956
                13755,
5957
                14470,
5958
                13722,
5959
                13844,
5960
                14733,
5961
                13855,
5962
                13422,
5963
                13438,
5964
                13677
5965
            ],
5966
            "answer": " To provide the import path for a simple RayServe deployment, you can use the class name of the deployment as the import path. The import path should be importable by Ray Serve at runtime. If you're running the deployment locally, the path might be in your current working directory. However, when running on a cluster, you need to make sure the path is importable by building the code into the cluster's container image or using a runtime environment with a remote URI that hosts the code in remote storage.\n\nFor example, if you have a deployment class named `MyDeployment`, the import path would be `my_deployment:deployment`.\n\nIn a RayServe config file, the import path would look like this:\n```yaml\ndeployments:\n  - import_path: my_deployment:deployment\n    init_args:\n      ...\n```\nAdditionally, you can specify the root path for the deployment, which is the mount path for the Ray Serve application. All deployment routes will be prefixed with this path. The root path defaults to `/`.\n\nIn the config file, the root path would look like this:\n```yaml\ndeployments:\n  - import_path: my_deployment:deployment\n    init_args:\n      ...\n    route_prefix: \"/my_app\"\n```\nThis would mount the Ray Serve application at `/my_app`.",
5967
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
5968
        },
5969
        {
5970
            "question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",
5971
            "sources": [
5972
                "https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#examples",
5973
                "https://docs.ray.io/en/master/workflows/comparison.html#conditionals",
5974
                "https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#usage",
5975
                "https://docs.ray.io/en/master/rllib/rllib-catalogs.html#basic-usage",
5976
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",
5977
                "https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#wrapping-up",
5978
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.nevergrad.NevergradSearch.FINISHED.html#ray-tune-search-nevergrad-nevergradsearch-finished",
5979
                "https://docs.ray.io/en/master/workflows/comparison.html#conditionals",
5980
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.FINISHED.html#ray-tune-search-searcher-finished",
5981
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#optional-optimizing-for-runtime-over-memory-with-centralized-data-loading",
5982
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#define-a-custom-datasource",
5983
                "https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",
5984
                "https://docs.ray.io/en/master/rllib/package_ref/policy.html#miscellaneous"
5985
            ],
5986
            "document_ids": [
5987
                6299,
5988
                9706,
5989
                14397,
5990
                11512,
5991
                5295,
5992
                5359,
5993
                1984,
5994
                9707,
5995
                1324,
5996
                5318,
5997
                10182,
5998
                5297,
5999
                11356
6000
            ],
6001
            "answer": " No, the provided context does not contain any documents or examples specifically showing the usage of RayJob in Kuberay. The context includes examples of Ray Job templates, usage of Ray search and Searcher, and Ray Data examples, but there is no direct connection to Kuberay.",
6002
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6003
        },
6004
        {
6005
            "question": "Does Ray metrics have to be exported via an actor?",
6006
            "sources": [
6007
                "https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",
6008
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",
6009
                "https://docs.ray.io/en/master/ray-core/api/core.html#actors",
6010
                "https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",
6011
                "https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",
6012
                "https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",
6013
                "https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",
6014
                "https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",
6015
                "https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",
6016
                "https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",
6017
                "https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",
6018
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
6019
                "https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"
6020
            ],
6021
            "document_ids": [
6022
                475,
6023
                4629,
6024
                4650,
6025
                5556,
6026
                4506,
6027
                14707,
6028
                11596,
6029
                4575,
6030
                4200,
6031
                14723,
6032
                5071,
6033
                4811,
6034
                4625
6035
            ],
6036
            "answer": " No, Ray metrics do not have to be exported via an actor. Actors are a way to manage stateful objects in Ray, but metrics can be collected and exported using other methods, such as Ray's built-in metric collection or through third-party tools. The provided context mainly discusses Ray actors and their usage.",
6037
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6038
        },
6039
        {
6040
            "question": "how can I avoid objects not getting spilled?",
6041
            "sources": [
6042
                "https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",
6043
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
6044
                "https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",
6045
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#running-out-of-file-descriptors-too-may-open-files",
6046
                "https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",
6047
                "https://docs.ray.io/en/master/tune/examples/includes/mnist_pytorch.html#mnist-pytorch-example",
6048
                "https://docs.ray.io/en/master/train/getting-started-pytorch-lightning.html#preparing-your-lightning-trainer",
6049
                "https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#performance-tips",
6050
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_before_accelerator_backend_setup.html#ray-train-lightning-raytrainreportcallback-on-before-accelerator-backend-setup",
6051
                "https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_before_optimizer_step.html#ray-train-lightning-raytrainreportcallback-on-before-optimizer-step",
6052
                "https://docs.ray.io/en/master/train/getting-started-pytorch-lightning.html#getting-started-with-pytorch-lightning",
6053
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#basic-training-with-tree-based-models-in-train",
6054
                "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"
6055
            ],
6056
            "document_ids": [
6057
                9897,
6058
                9938,
6059
                9934,
6060
                604,
6061
                9282,
6062
                4005,
6063
                8145,
6064
                9217,
6065
                8645,
6066
                8416,
6067
                8116,
6068
                8140,
6069
                9548
6070
            ],
6071
            "answer": " To avoid objects not getting spilled, you can increase the number of open files limit using the `ulimit` command before starting the Ray head node. For example, to set the limit to 65536, you can run `ulimit -n 65536 ray start --head`. Additionally, you can consider using a higher number than 65536 if you have more workers.\n\nIn the context you provided, there is no direct mention of how to avoid objects not getting spilled in LightGBM or PyTorch Lightning training. However, increasing the open files limit should help in general to avoid such issues.",
6072
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6073
        },
6074
        {
6075
            "question": "what\u2019s ray core",
6076
            "sources": [
6077
                "https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",
6078
                "https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",
6079
                "https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",
6080
                "https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",
6081
                "https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#want-to-learn-more",
6082
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
6083
                "https://docs.ray.io/en/master/data/overview.html#ray-data-overview",
6084
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",
6085
                "https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",
6086
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",
6087
                "https://docs.ray.io/en/master/ray-core/examples/overview.html#basic-examples",
6088
                "https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-for-developers",
6089
                "https://docs.ray.io/en/master/ray-overview/index.html#ray-framework"
6090
            ],
6091
            "document_ids": [
6092
                4662,
6093
                4482,
6094
                4673,
6095
                4811,
6096
                5444,
6097
                5656,
6098
                6835,
6099
                367,
6100
                13974,
6101
                246,
6102
                5383,
6103
                646,
6104
                371
6105
            ],
6106
            "answer": " Ray Core is a part of the Ray framework that provides a small set of primitives for building and scaling distributed applications, including tasks, actors, and objects. It has a CLI, API, and utilities for scheduling, runtime environment configuration, and debugging. Ray Core is the foundation of Ray's unified compute framework and enables the development of scalable, distributed systems. It can be used for parallelizing functions, scaling ML workloads, and integrating tooling from the broader ML ecosystem.",
6107
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6108
        },
6109
        {
6110
            "question": "Does ray support cron job",
6111
            "sources": [
6112
                "https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",
6113
                "https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-scale-out-training",
6114
                "https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",
6115
                "https://docs.ray.io/en/master/ray-core/runtime_env_auth.html#running-on-vms-the-netrc-file",
6116
                "https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.NOOP.html#ray-tune-schedulers-trialscheduler-noop",
6117
                "https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",
6118
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",
6119
                "https://docs.ray.io/en/master/ray-core/scheduling/index.html#scheduling",
6120
                "https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",
6121
                "https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",
6122
                "https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",
6123
                "https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",
6124
                "https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api"
6125
            ],
6126
            "document_ids": [
6127
                410,
6128
                8153,
6129
                6208,
6130
                4180,
6131
                1495,
6132
                10128,
6133
                6560,
6134
                5137,
6135
                5656,
6136
                13929,
6137
                9634,
6138
                276,
6139
                6638
6140
            ],
6141
            "answer": " No, Ray does not directly support cron jobs. However, you can use an external job scheduler to interact with your Ray cluster and trigger workflow runs at specified intervals. Ray jobs run once to completion or failure, and are bound to the lifetime of a Ray cluster. For more information, you can refer to the Ray Jobs API quickstart guide.",
6142
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6143
        },
6144
        {
6145
            "question": "can you give me the dependencies list for api read_images?",
6146
            "sources": [
6147
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
6148
                "https://docs.ray.io/en/master/data/working-with-images.html#reading-images",
6149
                "https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset",
6150
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
6151
                "https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",
6152
                "https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",
6153
                "https://docs.ray.io/en/master/data/working-with-images.html#working-with-images",
6154
                "https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",
6155
                "https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",
6156
                "https://docs.ray.io/en/master/serve/http-guide.html#list-of-built-in-adapters",
6157
                "https://docs.ray.io/en/master/data/loading-data.html#reading-files",
6158
                "https://docs.ray.io/en/master/data/api/input_output.html#images",
6159
                "https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches"
6160
            ],
6161
            "document_ids": [
6162
                7360,
6163
                6720,
6164
                8041,
6165
                7361,
6166
                10191,
6167
                6779,
6168
                6719,
6169
                7909,
6170
                7364,
6171
                13511,
6172
                6739,
6173
                7057,
6174
                6902
6175
            ],
6176
            "answer": " The `read_images` function in Ray Data reads images from a given path or list of paths and returns a dataset where each image is represented as a numpy ndarray. The dependencies for this function include the `ray` library and the `numpy` library for handling arrays, and `PIL` (Pillow) for opening and processing image files. If you need to read images from a private bucket or S3, you would also need to use `pyarrow.fs` to handle the filesystem.",
6177
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6178
        },
6179
        {
6180
            "question": "how do I kill a specific serve replica",
6181
            "sources": [
6182
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
6183
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
6184
                "https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",
6185
                "https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-deployments",
6186
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",
6187
                "https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",
6188
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",
6189
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",
6190
                "https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-applications",
6191
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#replica-health-checking",
6192
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",
6193
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",
6194
                "https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"
6195
            ],
6196
            "document_ids": [
6197
                13912,
6198
                13911,
6199
                13717,
6200
                14007,
6201
                13908,
6202
                13448,
6203
                13892,
6204
                13915,
6205
                14015,
6206
                13865,
6207
                13916,
6208
                13905,
6209
                13901
6210
            ],
6211
            "answer": " To kill a specific serve replica, you can follow these steps:\n\n1. List the available actors using the command `ray summary actors` or `ray list actors --filter \"class_name=ServeReplica:SleepyPid\"` in the Ray terminal.\n2. Identify the NAME of the replica you want to kill from the list.\n3. Get a handle to the replica using the command `replica_handle = ray.get_actor(\"NAME\", namespace=\"serve\")` in a Python Ray terminal.\n4. Kill the replica using the command `ray.kill(replica_handle, no_restart=True)` in the Python Ray terminal.\n\nFor example, if your replica's NAME is `SERVE_REPLICA::SleepyPid#RlRptP`, you can kill it using the following commands:\n```python\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```",
6212
            "llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"
6213
        }
6214
    ]
6215
}
6216