llm-applications
6215 строк · 512.0 Кб
1{2"config": {3"experiment_name": "gte-large-fine-tuned-el",4"chunk_size": 700,5"chunk_overlap": 50,6"num_chunks": 13,7"embedding_model_name": "/efs/shared_storage/goku/gte-large-fine-tuned-el",8"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1",9"temperature": 0.0,10"max_context_length": 32768,11"system_content": "Answer the query using the context provided. Be succinct.",12"assistant_content": "",13"docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",14"experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",15"references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",16"num_samples": 17717},18"results": [19{20"question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",21"sources": [22"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",23"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",24"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",25"https://docs.ray.io/en/master/data/key-concepts.html#transforming-data",26"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",27"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.transform_batch.html#ray-data-preprocessor-preprocessor-transform-batch",28"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.MultiHotEncoder.transform_batch.html#ray-data-preprocessors-multihotencoder-transform-batch",29"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.OneHotEncoder.transform_batch.html#ray-data-preprocessors-onehotencoder-transform-batch",30"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.OrdinalEncoder.transform_batch.html#ray-data-preprocessors-ordinalencoder-transform-batch",31"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.SimpleImputer.transform_batch.html#ray-data-preprocessors-simpleimputer-transform-batch",32"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.Categorizer.transform_batch.html#ray-data-preprocessors-categorizer-transform-batch",33"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.LabelEncoder.transform_batch.html#ray-data-preprocessors-labelencoder-transform-batch",34"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing"35],36"document_ids": [377081,386790,396786,406733,4114727,427241,437421,447553,457476,467136,477767,487135,49804650],51"answer": " When using `map_batches`, you can specify the `batch_format` argument to convert the data type of the batches. For example, if you want to convert the batches to pandas DataFrames, you can do:\n\n```\nds = (\n ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .map_batches(increase_brightness, batch_format=\"pandas\")\n)\n```\n\nThis will convert the batches to pandas DataFrames after applying the `increase_brightness` function. By default, batches are represented as dictionaries of NumPy ndarrays.\n\nYou can also return pandas DataFrames from your `map_batches` function to convert the batches to this format. For example:\n\n```\ndef increase_brightness(batch: Dict[str, pd.DataFrame]) -> Dict[str, pd.DataFrame]:\n batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n return batch\n\nds = (\n ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .map_batches(increase_brightness)\n)\n```\n\nThis will convert the batches to dictionaries of pandas DataFrames after applying the `increase_brightness` function.\n\nKeep in mind that the `map`, `flat_map`, and `filter` operations are unvectorized and can be slow. If you're using a vectorized transformation, consider using `.map_batches()` 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/architecture.html#ray-serve-autoscaling",58"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",59"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",60"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",61"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",62"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",63"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#load-metrics-are-based-on-application-semantics",64"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",65"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",66"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",67"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",68"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",69"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling"70],71"document_ids": [7213471,736363,745559,7513448,765969,775953,785971,796364,8013447,8114726,8213449,8310282,84595585],86"answer": " Ray Serve's autoscaling feature automatically adjusts the number of replicas in a deployment based on its load. The Ray autoscaler, which runs on the head node, increases or decreases the number of worker nodes based on the resource demands of tasks, actors, or placement groups. It reacts to task and actor resource requests, not application metrics or physical resource utilization. When Ray Serve scales down, it tries to terminate replica actors in a way that results in the most nodes having no Ray actors or tasks running on them, at which point the Ray autoscaler removes those nodes. The autoscaler only considers logical resource requests for scaling and not physical machine utilization. The Ray Serve Autoscaler is an application-level autoscaler that sits on top of the Ray Autoscaler.",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/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",93"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",94"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",95"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",96"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",97"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",98"https://docs.ray.io/en/master/ray-core/api/doc/ray.nodes.html#ray-nodes",99"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",100"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",101"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",102"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",103"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",104"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints"105],106"document_ids": [107739,108754,1094817,1104452,1116552,112493,1134769,1144187,1156502,1164454,1174824,118695,1195587120],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 'NodeManagerAddress' and 'MetricsExportPort' fields in the returned dictionary can be combined to get the address of the node. Here is an example:\n```\nimport ray\nray.init()\n\nnodes = ray.nodes()\nnode_address = nodes[0]['NodeManagerAddress'] + ':' + str(nodes[0]['MetricsExportPort'])\nprint(node_address)\n```\nThis will print the address of the first node in the Ray cluster.\n\nYou can also use the `ray.util.state.get_node` function to get a node by its id and the `node.node_manager_address` to get the address of the node.\n```\nfrom ray.util.state import get_node\nnode = get_node(nodes[0]['node_id'])\nnode_address = node.node_manager_address\nprint(node_address)\n```\nNote that the above example assumes that you have already initialized Ray by calling `ray.init()` and that you have at least one node in your cluster.",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/ray-core/api/cli.html#ray-core-cli",128"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",129"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",130"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",131"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",132"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",133"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray",134"https://docs.ray.io/en/master/installation.html#installing-ray",135"https://docs.ray.io/en/master/ray-overview/index.html#overview",136"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",137"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",138"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",139"https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations"140],141"document_ids": [1424656,14314403,1444811,1456835,14613974,147284,14814553,14959,150364,1516208,15214255,1535656,1548331155],156"answer": " Yes, Ray does support NCCL (NVIDIA Collective Communications Library) as one of the optional high-performance communication backends in its collective communication library. Ray's collective communication library is suitable for distributed machine learning programs and can operate on both distributed CPUs and GPUs.",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/train/deepspeed.html#running-deepspeed-with-other-frameworks",163"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.deepspeed_engine.html#ray-train-lightning-raydeepspeedstrategy-deepspeed-engine",164"https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",165"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.strategy_name.html#ray-train-lightning-raydeepspeedstrategy-strategy-name",166"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",167"https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",168"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#fine-tune-vicuna-13b-with-lightning-and-deepspeed",169"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",170"https://docs.ray.io/en/master/train/deepspeed.html#training-with-deepspeed",171"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.html#ray-train-lightning-raydeepspeedstrategy",172"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.html#ray-train-lightning-raydeepspeedstrategy",173"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#fine-tuning-the-model-with-ray-train-a-name-train-a",174"https://docs.ray.io/en/master/workflows/basics.html#ray-integration"175],176"document_ids": [1778232,1788996,1798331,1808578,1818881,1828249,1839334,1849393,1858207,1868784,1878780,18810313,1899685190],191"answer": " Yes, Ray is integrated with DeepSpeed through the Ray DeepSpeedStrategy. You can use DeepSpeed with Ray Train for distributed training using frameworks like Lightning, Transformers, and Accelerate. The integration supports features like model parallelism, tensor parallelism, and pipeline parallelism. You can configure DeepSpeed through the Accelerator object in your training function. Ray TorchTrainer allows you to scale your PyTorch Lightning job across a Ray cluster with DeepSpeed integration.",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/actors/async_api.html#objectrefs-as-asyncio-futures",198"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",199"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example",200"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",201"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",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/walkthrough.html#running-a-task",204"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",205"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",206"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle",207"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",208"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",209"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups"210],211"document_ids": [2124600,2134611,2145197,2154599,2165248,2174598,2184470,2194601,2204603,22114277,2224360,22313643,2244637225],226"answer": " If you use AsyncIO's `await` keyword to wait for a Ray future like `await x.remote()`, it will allow you to await on Ray futures in existing concurrent applications. However, you cannot use `await` directly in remote tasks. Instead, you can wrap the async function with a synchronous wrapper function and call it using `.remote()`. Also, keep in mind that ObjectRefs can be translated to asyncio.Futures, which you can use with asyncio patterns like timeouts and `asyncio.gather`.\n\nHere's an example of how to wait for a Ray future using `await`:\n\n```python\nimport asyncio\nimport ray\n\n@ray.remote\ndef some_task():\n return 1\n\nasync def await_obj_ref():\n await some_task.remote()\n await asyncio.wait([some_task.remote()])\n\nasyncio.run(await_obj_ref())\n```\n\nThis code snippet shows how to await a Ray future using asyncio.Futures:\n\n```python\nimport asyncio\nimport ray\n\n@ray.remote\ndef some_task():\n return 1\n\nasync def convert_to_asyncio_future():\n ref = some_task.remote()\n fut: asyncio.Future = asyncio.wrap_future(ref.future())\n print(await fut)\n\nasyncio.run(convert_to_asyncio_future())\n```",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/ray-more-libs/dask-on-ray.html#using-dask-on-ray",233"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",234"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",235"https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",236"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",237"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",238"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",239"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",240"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",241"https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure",242"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",243"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",244"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDDPStrategy.html#ray-train-lightning-rayddpstrategy"245],246"document_ids": [24714477,24814488,2496847,25014661,25114516,25214478,2536846,2546809,25514479,2569809,2576752,25813620,2598556260],261"answer": " Spark, Ray, and Dask are all distributed computing systems, but they have different focuses and use cases.\n\nApache Spark is a general-purpose cluster-computing system that can handle batch processing, stream processing, and machine learning workloads. It has a wide range of supported libraries and integrations, making it a versatile choice for big data processing.\n\nRay is a high-performance distributed execution engine that is designed to handle complex, dynamic workloads. It is particularly well-suited for use cases that involve reinforcement learning, hyperparameter tuning, and large-scale graph processing. Ray can also be used as a scheduler for Dask, allowing you to use Dask's APIs and abstractions with Ray's execution engine.\n\nDask is a flexible parallel computing library for analytic computing, built with the Python ecosystem in mind. It is designed to integrate seamlessly with popular Python libraries like NumPy, Pandas, and Scikit-Learn, making it a good choice for data scientists who want to scale their existing Python code. Dask can be used on its own or with Ray as a scheduler.\n\nIn summary, Spark is a general-purpose big data processing system, Ray is a high-performance distributed execution engine, and Dask is a flexible parallel computing library for analytic computing with Python. The choice between these systems depends on the specific requirements of your use case.",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/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",269"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",270"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",271"https://docs.ray.io/en/master/ray-core/tasks.html#scheduling",272"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",273"https://docs.ray.io/en/master/ray-core/scheduling/index.html#locality-aware-scheduling",274"https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks",275"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",276"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#custom-resources",277"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case",278"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#pattern-using-ray-wait-to-limit-the-number-of-pending-tasks",279"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling"280],281"document_ids": [2825112,2835231,2845104,2855115,2864376,2875230,2885152,2894199,2906504,2915106,2925232,2935246,2946364295],296"answer": " Ray does not automatically limit the number of tasks or actors based on the physical resources of a node. By default, Ray tasks and actors use 1 logical CPU resource for scheduling and 0 for running. If the sum of the resource requirements of all concurrent tasks and actors on a node exceeds the node's total resources, Ray will not schedule additional tasks or actors on that node. Overloading a node with more tasks or actors than its resources allow can lead to issues like out-of-memory errors. To avoid this, you can increase the amount of resources requested by tasks or actors, or manually override the number of CPUs and GPUs available for use by Ray when starting the cluster. Custom resources can also be specified and requested in tasks or actors for special hardware or label-based affinity scheduling.",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/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",303"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",304"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",305"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",306"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",307"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",308"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",309"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",310"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#which-crd-should-you-choose",311"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",312"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",313"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3",314"https://docs.ray.io/en/master/ray-core/walkthrough.html#next-steps"315],316"document_ids": [3176615,3186611,3196608,3206612,3216613,32213974,3234811,3244204,3255649,32613442,3275380,328534,3294479330],331"answer": " You should use Ray Client when you want to connect an interactive Python shell to a remote cluster. It's useful for developing interactively in a local Python shell, but requires a stable connection to the remote cluster. Ray Client is not recommended for long-running workloads on the cluster; instead, use Ray Jobs for those scenarios. To use Ray Client, you need to install the pip package 'ray[client]' and call 'ray.init(\"ray://<head_node_host>:<port>\")'.",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/scheduling/index.html#spread",338"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",339"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",340"https://docs.ray.io/en/master/serve/architecture.html#high-level-view",341"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",342"https://docs.ray.io/en/master/ray-core/scheduling/index.html#default",343"https://docs.ray.io/en/master/ray-core/api/utility.html#utility",344"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",345"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",346"https://docs.ray.io/en/master/data/api/doc/ray.data.ActorPoolStrategy.html#ray-data-actorpoolstrategy",347"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",348"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",349"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources"350],351"document_ids": [3525145,3531027,3545195,35513466,3565391,3575143,3584677,3598114,3606787,3617733,36211596,3634519,36414707365],366"answer": " To scatter actors across the cluster in Ray, you can use the `scheduling_strategy=\"SPREAD\"` option when creating the actors. Here's an example:\n```python\n@ray.remote(scheduling_strategy=\"SPREAD\")\nclass SpreadActor:\n pass\n\nactors = [SpreadActor.remote() for _ in range(10)]\n```\nThis will spread the actors across the available nodes in the cluster. You can also use the `placement_strategy` option to control the placement of the actors on specific nodes or resources.",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/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",373"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",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-more-libs/multiprocessing.html#run-on-a-cluster",376"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",377"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",378"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#connect-to-the-cluster",379"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",380"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#running-on-a-cluster",381"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",382"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",383"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",384"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"385],386"document_ids": [3875681,3884815,3896610,39014685,3914266,3926619,3936526,39413718,395578,3966505,3976620,3984264,3994817400],401"answer": " The connection refused error you're encountering when running `ray debug` on your remote Ray cluster could be due to a few reasons:\n\n1. The dashboard, dashboard agent, or GCS may not have started yet. These components might take a few seconds to start up after the head Pod is ready.\n2. You might be trying to connect to an incorrect address or port. Ensure that you're using the correct address and port for your Ray cluster.\n3. Firewall settings might be preventing access to the Ray cluster. You can use tools like `nmap` or `nc` to check whether each port can be reached from a node.\n\nIf you're still having trouble, you can try setting up Ray Client via SSH port forwarding as an alternative approach. This method requires an open SSH connection but can be useful in a test environment where the head node host often changes.",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/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",408"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#asynchronous-parameter-server-training",409"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",410"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",411"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",412"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",413"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",414"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",415"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#defining-the-parameter-server",416"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",417"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#fine-tuning-dreambooth-with-ray-train",418"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary",419"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#synchronous-parameter-server-training"420],421"document_ids": [4229845,4235338,4249820,42510019,426374,427183,428156,429337,4305329,4319887,43210147,4339867,4345334435],436"answer": " In Ray AIR, model gradient updates are communicated across machines using a data-parallel training approach. This is achieved by sharding the training data and setting up a parameter server to communicate with multiple worker nodes. The workers asynchronously compute gradients given their current weights and send these gradients to the parameter server. The parameter server then applies the new gradient, updates the weights, and sends a copy of the current weights back to the worker. This process repeats until the training is complete. This is facilitated in the code by importing Ray Train and Ray AIR Session, and using the ParameterServer and DataWorker classes.",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/index.html#how-does-serve-compare-to",443"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",444"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",445"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice",446"https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",447"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",448"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",449"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",450"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments",451"https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",452"https://docs.ray.io/en/master/serve/api/index.html#serve",453"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",454"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"455],456"document_ids": [45713621,45813974,4595785,4605739,46113420,46213614,46310282,46413597,46513727,466144,46713983,46813618,46913623470],471"answer": " Use Ray Serve if you want scalability and reliability for model serving on your own infrastructure, and if you value flexibility in terms of machine learning frameworks and deployment locations. Ray Serve is particularly well-suited for model composition and can handle complex inference services. Unlike other options such as Modal or Seldon, Ray Serve does not provide functionality for managing the lifecycle of models or visualizing their performance, so you would need to build these features yourself if needed. Doing it via containers is possible, but Ray Serve offers a more integrated solution for model serving and composition.",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/index.html#how-can-serve-help-me-as-a",478"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",479"https://docs.ray.io/en/master/cluster/vms/user-guides/community/lsf.html#deploying-on-lsf",480"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",481"https://docs.ray.io/en/master/serve/api/index.html#serve",482"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",483"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",484"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",485"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",486"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",487"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",488"https://docs.ray.io/en/master/serve/deploy-many-models/index.html#deploy-many-models",489"https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving"490],491"document_ids": [49213618,49313677,4946382,4955636,49613983,49713416,49813647,49913598,50010282,50113621,50213622,50313676,504144505],506"answer": " To deploy a large language model (LLM) workload on Ray Serve, you can write all the steps (prompt preprocessing, vector database lookups, LLM API calls, and response validation) as a single Python module due to Ray Serve's support for any arbitrary Python code. This enables rapid development and easy testing. You can then deploy your Ray Serve LLM application to production using the Ray Serve CLI, which can deploy a configuration file to a Ray cluster over HTTP. The number of replicas in a deployment can be scaled up or down to match the incoming request load.\n\nHere is an example of deploying the config for the FruitStand example:\n\n1. Start a Ray cluster:\n```bash\n$ ray start --head\n```\n2. Deploy the config for the LLM application:\n```bash\n$ serve deploy fruit_config.yaml --address localhost:8000\n```\n\nThe `fruit_config.yaml` file would contain the configuration for the LLM application, such as the number of replicas, the Python module to use, and any other necessary configuration.\n\nNote: If you have multiple Ray users, it is recommended to start a Ray cluster for each user so that their workloads are isolated.",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/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",513"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",514"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",515"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",516"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",517"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",518"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",519"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",520"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",521"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",522"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",523"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",524"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed"525],526"document_ids": [5274878,52814121,52914137,53014299,53114183,53214221,53314062,53410280,53510260,536855,53710373,53814094,53910380540],541"answer": " The memory requirement depends on the model, but to run the EleutherAI/gpt-j-6B model, you need access to a GPU with at least 16 GB of memory and set the object store memory to at least 1 GB. The actual memory usage will depend on the specific task, as shown in the logs where the memory usage goes up to 11.39 GB.",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/train/examples/tf/tensorflow_mnist_example.html#running-distributed-training-of-a-tensorflow-model-on-mnist-with-ray-train",548"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",549"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",550"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#example-logging-to-mlflow-and-tensorboard",551"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#quickstart",552"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",553"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",554"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-tune-results-on-a-single-node",555"https://docs.ray.io/en/master/rllib/key-concepts.html#key-subconcepts",556"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#aggregating-results",557"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",558"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-concurrent-tune-trials",559"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowTrainer.html#ray-train-tensorflow-tensorflowtrainer"560],561"document_ids": [5629544,56310927,5642354,5659144,5668286,567231,5682411,5692383,57010729,5718301,5729532,5732380,5748541575],576"answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use TensorBoard's `--logdir` flag and point it to the directory where the logs from all the workers are being saved. In the context provided, it looks like the logs are being saved in the `~/ray_results` directory. Therefore, you can start TensorBoard as follows:\n```bash\ntensorboard --logdir=~/ray_results\n```\nThis will aggregate and display the logs from all the workers in a single TensorBoard instance. Note that each worker should be saving its logs to the same directory (`~/ray_results` in this case) for this to work.",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/tune/api/syncing.html#tune-syncing-configuration",583"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",584"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",585"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningConfigBuilder.checkpointing.html#ray-train-lightning-lightningconfigbuilder-checkpointing",586"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",587"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",588"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_timeout.html#ray-train-syncconfig-sync-timeout",589"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.checkpoint_config.html#ray-train-runconfig-checkpoint-config",590"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",591"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.html#ray-train-runconfig",592"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.syncer.html#ray-train-syncconfig-syncer",593"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",594"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_on_checkpoint.html#ray-train-syncconfig-sync-on-checkpoint"595],596"document_ids": [5971138,59810466,5998441,6008371,6011443,60210464,6031852,6049098,6051782,6068430,6071678,6088881,6091550610],611"answer": " To configure `SyncConfig` for a Ray AIR job, you can use the `ray.train.SyncConfig` class which has several attributes that you can set. Here's an example:\n```python\nfrom ray import train\n\nsync_config = train.SyncConfig(\n upload_dir=None, # specify the directory to upload files to\n",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/ray-observability/getting-started.html#logs-view",619"https://docs.ray.io/en/master/ray-observability/getting-started.html#job-profiling",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/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",623"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",624"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",625"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",626"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",627"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/observability.html#method-3-check-logs-of-ray-pods",628"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",629"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs"630],631"document_ids": [6325764,633444,634422,635415,63613523,6376631,6386157,6396181,6406530,641445,6425855,6436569,6446558645],646"answer": " To quickly narrow down the root cause of a failed Ray job, follow these steps:\n\n1. Access the logs using the command `kubectl logs -l=job-name=rayjob-sample`.\n2. Check the logs organized by node and log file name in the Logs view.\n3. Use the provided search functionality to help find specific log messages.\n4. Analyze the driver logs if the Ray Job is submitted by the Job API.\n5. If needed, profile Ray Jobs using the \"Stack Trace\" or \"CPU Flame Graph\" actions.\n6. For further analysis, parse the stored logs with a logging stack such as ELK or Loki.\n7. If 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.\n8. For Task and Actor logs, click the \"Log\" button in the Task and Actor table view to see the stdout and stderr logs.\n9. If the Ray Pods are involved, check the Ray logs directly by accessing the log files on the Pods.\n\nRemember to substitute the Ray Job's submission id in the commands as needed.",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/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",653"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",654"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",655"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",656"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#resource-management-cpus-gpus",657"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",658"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",659"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#usage-example",660"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#resources",661"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-gpus",662"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",663"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#sbatch-directives",664"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment"665],666"document_ids": [66713458,66811062,66913575,67013456,67113778,67211063,67310283,6746420,6755927,6765937,6777799,6786405,67913776680],681"answer": " To specify how many GPUs a Serve deployment needs, you can use the `ray_actor_options` parameter and set the `\"num_gpus\"` value to the desired number of GPUs. This can be a fractional value to pack multiple replicas together on a machine with fewer GPUs than replicas. Here's an example:\n\n```python\n@serve.deployment(ray_actor_options={\"num_gpus\": 1})\ndef func(*args):\n return do_something_with_my_gpu()\n```\n\nIn this example, each replica of the deployment will be allocated 1 GPU. If you specify `num_gpus` and your machine does not have the required number of GPUs available, a `RuntimeError` will be thrown by the respective worker. If you set `num_gpus=0`, your policies will be built solely on the CPU, even if GPUs are available on the machine.",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/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",689"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",690"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",691"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",692"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",693"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",694"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",695"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",696"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",697"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",698"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",699"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer"700],701"document_ids": [7025129,7039493,70410333,705557,706555,7079483,70810339,7099326,71010456,7119325,712551,7139405,7149484715],716"answer": " The worker nodes are dying due to memory pressure (OOM). This is likely because the TensorFlow binary being used is optimized for specific CPU instructions, but the tasks require more memory than available on the nodes. You can debug this by:\n\n1. Ensuring that the TensorFlow binary is built with the appropriate compiler flags to enable the required CPU instructions in all operations, not just performance-critical ones.\n2. Addressing the out-of-memory issue by following the documentation: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>.\n3. Monitoring memory usage on the nodes using Ray memory monitor and logs to identify which tasks are consuming excessive memory.\n4. Considering using larger nodes or distributing the workload across more nodes to prevent memory pressure.\n5. Checking the logs for any specific error messages related to Out-of-Memory (OOM) issues.",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/serve/production-guide/fault-tolerance.html#head-node-failure",723"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",724"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",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/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",728"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",729"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",730"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",731"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",732"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#cluster-setting",733"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",734"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/aws-eks-gpu-cluster.html#step-3-verify-the-node-groups"735],736"document_ids": [73713898,7384530,7394528,7404531,7419524,742554,7435129,7445639,7454790,74613889,7479337,748467,7495897750],751"answer": " Possible reasons for nodes dying in a 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. Worker node failure: A worker node failure can be simulated in a Kubernetes cluster by looking at the nodes and pods running in the cluster and terminating a worker node.\n4. Memory pressure: A worker node may die due to memory pressure, causing the Ray cluster to kill workers to free up memory.\n5. User action: A cluster may terminate due to user action, as indicated by the message \"Cluster is terminating (reason: user action)\".\n\nTo tolerate head node failures, it is necessary to make the GCS fault-tolerant so that when a new head node is started, all the cluster-level data is still available. Node fault tolerance can be improved by ensuring that the Ray cluster can handle raylet failures and worker node failures without impacting tasks, actors, and objects.",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/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",759"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",760"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",761"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",762"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",763"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",764"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",765"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",766"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#upscaling-and-downscaling-speed",767"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",768"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",769"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster"770],771"document_ids": [7726363,7736715,7745953,7756337,7769510,7776433,778851,7796365,78013471,7815965,7825559,7835973,784854785],786"answer": " To programmatically get a Ray remote cluster to a target size immediately without scaling up through the autoscaler, you can use the `ray.autoscaler.sdk.request_resources()` function within a Ray program. This function commands the autoscaler to scale the cluster up to a desired size, bypassing normal upscaling speed constraints.\n\nHere is an example of how to use this function:\n\n```python\nimport ray\nfrom ray.autoscaler.sdk.request_resources import request_resources\n\n# Connect to the Ray cluster\nray.init(address=\"auto\")\n\n# Request resources to scale the cluster up to the desired size\nrequest_resources(num_cpus=desired_size)\n```\n\nNote that this will not work if you are using the Kubernetes autoscaler, as it adjusts the number of nodes (Ray pods) in the cluster based on resource demand. In this case, you would need to manually adjust the number of nodes in your Kubernetes 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/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",793"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",794"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",795"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.streaming_split.html#ray-data-dataset-streaming-split",796"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",797"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches",798"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#define-your-training-function",799"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.default_batch_format.html#ray-data-dataset-default-batch-format",800"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",801"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",802"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",803"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",804"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches"805],806"document_ids": [8077081,8087188,8097379,8107414,8117380,8127811,8139361,8147791,8159906,8167189,8177545,8187328,8196861820],821"answer": " To disable async iter\\_batches with Ray Dataset, you can use the `iter_batches()` method which returns an iterator over batches of data. This method is a synchronous version of `iter_batches()` and does not have the `prefetch_batches` parameter. Here's an example:\n```python\nfor batch in ray.data.range(1000000).iter_batches():\n print(batch)\n```\nNote that this operation will trigger execution of the lazy transformations performed on this dataset.",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-references/glossary.html#ray-glossary",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/data/data-internals.html#datasets-and-blocks",830"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.batch_to_block.html#ray-data-block-blockaccessor-batch-to-block",831"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",832"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#batch-training-with-ray-core",833"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",834"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",835"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches",836"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.take_batch.html#ray-data-dataset-take-batch",837"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.to_batch_format.html#ray-data-block-blockaccessor-to-batch-format",838"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-data",839"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.dataset_format.html#ray-data-dataset-dataset-format"840],841"document_ids": [84214728,8437081,8446872,8457150,84614727,8475274,8486895,84914576,8506902,8517825,8527384,8536859,8547792855],856"answer": " A batch and a block are related concepts in the context of Ray Datasets, but they differ in their level of abstraction and usage.\n\nA block is an internal abstraction representing a partition of data rows within a Ray Dataset. Blocks are created and managed by Ray Data to enable parallel processing of data. On the other hand, a batch is a user-facing object that represents a collection of data rows. Users can create batches from various data formats using the `ray.data.block.BlockAccessor.batch_to_block` function.\n\nIn summary, a block is an internal data structure used by Ray Data for parallel processing, while a batch is a user-facing object created from data formats like PyArrow tables, Pandas DataFrames, or dictionaries of NumPy arrays.",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/ray-contribute/development.html#building-ray-from-source",863"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",864"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",865"https://docs.ray.io/en/master/serve/api/index.html#serve-run",866"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#development-workflow",867"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",868"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher",869"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-on-linux-macos-full",870"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",871"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",872"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#environment-dependencies",873"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",874"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"875],876"document_ids": [877877,878910,87910168,88013990,88113709,8824251,8834250,884889,8856614,8864811,8874247,8889342,8896334890],891"answer": " To set up the development environment for the Ray project, follow these steps:\n\n1. Clone the Ray repository:\n```bash\ngit clone https://github.com/ray-project/ray.git\n```\n2. Go to the example directory and install dependencies:\n```bash\ncd doc/source/templates/05_dreambooth_finetuning\npip install -Ur dreambooth/requirements.txt\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\nFor editing Ray source code, you'll want to checkout the repository and build Ray from source.\n\nAdditionally, you may need to install additional dependencies for development and configure environment variables for different build types (Fast, Debug, or Optimized). The provided context includes detailed instructions for these steps.",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/rllib/rllib-advanced-api.html#rewriting-trajectories",898"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.__init__.html#ray-rllib-evaluation-rollout-worker-rolloutworker-init",899"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",900"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",901"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.set_weights.html#ray-rllib-evaluation-rollout-worker-rolloutworker-set-weights",902"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",903"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.lock.html#ray-rllib-evaluation-rollout-worker-rolloutworker-lock",904"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",905"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.sample.html#ray-rllib-evaluation-rollout-worker-rolloutworker-sample",906"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.unlock.html#ray-rllib-evaluation-rollout-worker-rolloutworker-unlock",907"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.foreach_worker_async.html#ray-rllib-evaluation-worker-set-workerset-foreach-worker-async",908"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",909"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model"910],911"document_ids": [91210845,91313196,91412708,91512707,91612965,917640,91811896,91910211,92012739,92113022,92213127,92314759,92410218925],926"answer": " To debug why Ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`, you can:\n\n1. Check if you have initialized enough workers to handle the tasks. Ray cannot parallelize the tasks if there are not enough workers available.\n2. Use the `RolloutWorker.lock()` and `RolloutWorker.unlock()` methods to control access to the worker, ensuring that it is not being accessed simultaneously by multiple threads or processes.\n3. Use the `WorkerSet.foreach_worker_async()` method to call the given function asynchronously with each worker as the argument. This method does not return results directly, but you can use `fetch_ready_async_reqs()` to pull results in an async manner whenever they are available.\n4. Ensure that there are available CPUs in the Ray cluster for the Dataset job to run. If all CPUs are being used by Actors or Tune trials, the Dataset job will hang.\n\nRefer to the provided context for more information on these methods and their usage.",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/data/examples/index.html#ray-data-examples",933"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",934"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",935"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",936"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",937"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",938"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",939"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",940"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#load-and-preprocess-datasets",941"https://docs.ray.io/en/master/data/preprocessors.html#overview",942"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",943"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-ml-libraries",944"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer"945],946"document_ids": [9478006,9486835,9499188,9507289,9517255,9526809,9537254,9549180,9559348,9566917,95710013,9586756,9598610960],961"answer": " To join two Ray Datasets, you can use the `union()` method. This method concatenates the datasets row-wise, preserving the order of the blocks in the original datasets. Here's an example:\n\n```python\nimport ray\n\nds1 = ray.data.range(2)\nds2 = ray.data.range(3)\n\nresult = ds1.union(ds2)\nprint(result.take_all())\n```\n\nThis will output:\n\n```python\n[{'id': 0}, {'id': 1}, {'id': 0}, {'id': 1}, {'id': 2}]\n```\n\nKeep in mind that the `union()` method doesn't support joining datasets with different row counts. If you need to join datasets column-wise, consider using the `zip()` method instead. This method merges the column sets of the datasets, disambiguating any duplicate column names with suffixes. The datasets must have the same number of rows. Here's an example:\n\n```python\nimport ray\n\nds1 = ray.data.range(5)\nds2 = ray.data.range(5)\n\nresult = ds1.zip(ds2)\nprint(result.take_batch())\n```\n\nThis will output:\n\n```python\n{'id': array([0, 1, 2, 3, 4]), 'id_1': array([0, 1, 2, 3, 4])}\n```",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/ray-core/objects.html#objects",968"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",969"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",970"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",971"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait",972"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",973"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",974"https://docs.ray.io/en/master/ray-core/api/core.html#objects",975"https://docs.ray.io/en/master/ray-core/objects.html#closure-capture-of-objects",976"https://docs.ray.io/en/master/ray-core/objects.html#objects",977"https://docs.ray.io/en/master/ray-core/patterns/return-ray-put.html#anti-pattern-returning-ray-put-objectrefs-from-a-task-harms-performance-and-fault-tolerance",978"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle",979"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get"980],981"document_ids": [9824413,9835426,9844982,98514041,9864750,9874885,9885436,9894662,9904431,9914412,9925173,99314276,9944884995],996"answer": " Yes, you can retrieve an object from its object reference in Ray by using the `ray.get()` function. This function blocks until the object corresponding to the object reference is available in the local object store. If the object is not in the local object store, it will be shipped from an object store that has it. You can also pass a list of object references to `ray.get()` to retrieve multiple objects in one call.",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/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",1005"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#check-the-training-results-and-checkpoints",1006"https://docs.ray.io/en/master/tune/examples/includes/xgboost_dynamic_resources_example.html#xgboost-dynamic-resources-example",1007"https://docs.ray.io/en/master/tune/examples/tune-pytorch-cifar.html#the-train-function",1008"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint",1009"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#enabling-checkpointing-to-retrieve-the-model",1010"https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example",1011"https://docs.ray.io/en/master/rllib/package_ref/algorithm.html#saving-and-restoring",1012"https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#saving-and-loading-your-rl-algorithms-and-policies",1013"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",1014"https://docs.ray.io/en/master/rllib/package_ref/policy.html#id11"1015],1016"document_ids": [10174104,10189037,10198798,10209331,10213964,10223858,10239066,10249857,10254073,102611225,102710538,10289036,1029113191030],1031"answer": " To create a model checkpoint from a model in memory, you can use the `TensorflowCheckpoint.from_model()` method. Here is an example:\n```\nfrom ray.train.tensorflow import TensorflowCheckpoint\ncheckpoint = TensorflowCheckpoint.from_model(model)\n```\nThis will create a new `TensorflowCheckpoint` object that stores the model's weights. You can then write this checkpoint to a directory using the `to_directory()` method:\n```\ncheckpoint.to_directory(\"my_model\")\n```\nThis will save the checkpoint to the `my_model` directory. You can later restore the model from this checkpoint using the `from_directory()` method:\n```\ncheckpoint = Checkpoint.from_directory(\"my_model\")\nmodel = checkpoint.get_model()\n```\nThis will create a new model with the same weights as the original model.\n\nAlternatively, you can use the `from_dict()` method to create a checkpoint from a dictionary containing the model's state dictionary:\n```\ncheckpoint = Checkpoint.from_dict(dict(model=model.state_dict()))\n```\nYou can then write this checkpoint to a directory using the `to_directory()` method:\n```\ncheckpoint.to_directory(\"my_model\")\n```\nThis will save the checkpoint to the `my_model` directory. You can later restore the model from this checkpoint using the `from_directory()` method:\n```\ncheckpoint = Checkpoint.from_directory(\"my_model\")\nmodel = checkpoint.get_model()\n```\nThis will create a new model with the same weights as the original model.\n\nNote that the `Checkpoint.from_directory()` method is part of the `ray` library, which provides distributed training and scaling for TensorFlow. If you are not using this library, you can use the `TensorflowCheckpoint.from_model()` method instead.",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/key-concepts.html#deployment",1038"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-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/api/index.html#serve-deploy",1044"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1045"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",1046"https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",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/api/index.html#put-api-serve-deployments",1049"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api"1050],1051"document_ids": [105213416,105314255,105414732,105514150,105613679,105714544,105813987,105913677,106013776,106113797,106210282,106314000,1064139741065],1066"answer": " A Deployment in Ray Serve is a central concept that contains business logic or an ML model to handle incoming requests. It can be scaled up to run across a Ray cluster, with multiple replicas (individual copies of the class or function) started in separate Ray Actors (processes) to handle the incoming request load. Deployments are defined using the `@serve.deployment` decorator on a Python class or function. A group of deployments can be bound together into a directed acyclic graph (DAG) to handle requests, enabling 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/serve/api/index.html#serve-config",1073"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",1074"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",1075"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#updating-user-config",1076"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#dynamically-changing-parameters-without-restarting-your-replicas-user-config",1077"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",1078"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",1079"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#adding-deleting-and-updating-applications",1080"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1081"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1082"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",1083"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",1084"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"1085],1086"document_ids": [108713986,108813831,108913974,109013687,109113565,109213551,10935983,109413661,109513678,10966615,109713776,109814288,1099138521100],1101"answer": " User config in Ray Serve refers to the configuration that can be set for a deployment in Ray Serve. You can use it to update the deployments by updating the user\\_config and reapplying it to the Ray cluster. The user\\_config can be set when the deployment is created or updated using the reconfigure() method. The reconfigure() method allows lightweight updates to the deployments.\n\nTo update the user\\_config, you can modify the config options in the corresponding entry under the applications field in the Serve config file and reapply it to the Ray cluster.\n\nYou can use the \"serve config\" command to get the latest config file that the Ray Cluster received, which represents the Serve application's goal state. If you have a remote cluster, you can use the \"--address/-a\" argument to access the cluster.",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/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",1108"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",1109"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1110"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1111"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1112"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1113"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",1114"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",1115"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.placement_strategy.html#ray-train-scalingconfig-placement-strategy",1116"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1117"https://docs.ray.io/en/master/ray-core/key-concepts.html#placement-groups",1118"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",1119"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails"1120],1121"document_ids": [11221872,112312053,112411072,11255075,11265078,11275077,11284789,11295145,11308773,11315076,11324202,113314072,1134141031135],1136"answer": " The PACK strategy places bundles on as few nodes as possible, while the SPREAD strategy places bundles across distinct nodes as evenly as possible.",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/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",1143"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",1144"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",1145"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",1146"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",1147"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",1148"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",1149"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",1150"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",1151"https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure",1152"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1153"https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters",1154"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving"1155],1156"document_ids": [11575636,115814710,11594650,11605600,1161372,116213615,11635634,11644186,11654185,11669809,11674207,11684464,1169135981170],1171"answer": " The best way to run Ray across multiple machines is to deploy a Ray cluster, which consists of a head node and worker nodes connected together. This allows for isolated workloads and seamless scaling of workloads from a laptop to a large cluster. However, it's important to note that while multiple jobs from different users can be run simultaneously in a Ray cluster, it's not recommended for production use due to the lack of strong resource isolation and priority support. The Ray runtime, which consists of multiple services/processes for communication, data transfer, scheduling, and more, can be started on a laptop, a single server, or multiple servers. Ray Serve, a deployment tool built on top of Ray, can easily scale to many machines and offers 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/hyperparameter-optimization.html#how-to-configure-a-tuner",1179"https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",1180"https://docs.ray.io/en/master/tune/key-concepts.html#tune-trials",1181"https://docs.ray.io/en/master/tune/examples/pbt_ppo_example.html#example",1182"https://docs.ray.io/en/master/tune/tutorials/tune-search-spaces.html#working-with-tune-search-spaces",1183"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",1184"https://docs.ray.io/en/master/tune/examples/flaml_example.html#incorporating-a-time-budget-to-the-experiment",1185"https://docs.ray.io/en/master/tune/examples/nevergrad_example.html#optional-passing-the-hyper-parameter-space-into-the-search-algorithm",1186"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",1187"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#more-resources",1188"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#scaling-configurations-in-train-scalingconfig",1189"https://docs.ray.io/en/master/tune/examples/optuna_example.html#providing-an-initial-set-of-hyperparameters"1190],1191"document_ids": [11921826,11939126,11941303,1195967,11962741,11972312,11989127,11992860,12002569,12012400,12029256,12039252,120431961205],1206"answer": " To specify the ScalingConfig for a Tuner run, you can include it in the RunConfig and pass it to the Tuner constructor. Here's an example:\n```python\nfrom ray.tune.run import RunConfig\nfrom ray.train. ScalingConfig import ScalingConfig\n\nscaling_config = ScalingConfig(\n num_workers=8,\n resources_per_worker={\"CPU\": 4, \"GPU\": 0.5},\n use_gpu=True,\n)\n\nrun_config = RunConfig(\n name=\"my_tune_run\",\n scaling_config=scaling_config,\n)\n\ntuner = tune.Tuner(\n trainable,\n tune_config=tune.TuneConfig(num_samples=10),\n run_config=run_config,\n)\n\nresults = tuner.fit()\n```\nIn this example, the `ScalingConfig` is passed to the `RunConfig`, which is then passed to the `Tuner` constructor. This will configure the Tuner run with the specified distributed training properties.",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/ray-core/objects/serialization.html#numpy-arrays",1213"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_numpy_refs.html#ray-data-dataset-to-numpy-refs",1214"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",1215"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",1216"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",1217"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",1218"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1219"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy.html#ray-data-from-numpy",1220"https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only",1221"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1222"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.write_numpy.html#ray-data-dataset-write-numpy",1223"https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only",1224"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.to_numpy.html#ray-data-block-blockaccessor-to-numpy"1225],1226"document_ids": [12274942,12287644,12294539,12304945,12314225,12327102,12337085,12347467,12354944,12367495,12377247,12384943,123975741240],1241"answer": " To utilize the zero-copy feature Ray provides for numpy arrays, you should use numpy arrays as read-only objects in your Ray tasks and actors. Ray stores numpy arrays as read-only objects in the object store, and all Ray workers on the same node can read the numpy array in the object store without copying (zero-copy reads). Each numpy array object in the worker process holds a pointer to the relevant array held in shared memory.\n\nTo convert a Ray Dataset to a distributed set of NumPy ndarrays with zero-copy access to the underlying data, you can use the `Dataset.to_numpy_refs()` method. However, keep in mind that this method induces a copy of the data. For true zero-copy access, consider using `Dataset.to_arrow()` or `Dataset.get_internal_block_refs()`.\n\nHere's an example of using numpy arrays with Ray:\n\n```python\nimport numpy as np\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Create a numpy array\na = np.zeros((10, 20))\n\n# Put the numpy array in the object store\na_id = ray.put(a)\n\n# Define a Ray task that reads the numpy array\n@ray.remote\ndef no_work(a_id):\n a = ray.get(a_id)\n # If you need to mutate the array, copy it first\n a = a.copy()\n # ...\n\n# Invoke the Ray task\nno_work.remote(a_id)\n```\n\nIn this example, the numpy array `a` is stored in the Ray object store and can be read by the Ray task `no_work()` without copying. If you need to mutate the array inside the task, you should copy it first.",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/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1249"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1250"https://docs.ray.io/en/master/data/examples/random-access.html#performance",1251"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",1252"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",1253"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",1254"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_random_access_dataset.html#ray-data-dataset-to-random-access-dataset",1255"https://docs.ray.io/en/master/data/api/doc/ray.data.ActorPoolStrategy.html#ray-data-actorpoolstrategy",1256"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",1257"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1258"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#default-concurrency-group",1259"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors"1260],1261"document_ids": [12628000,12638114,12644878,12658002,12668154,12675231,12688101,12697308,12707732,1271641,12725191,12734645,127454411275],1276"answer": " It is not advised to spin up 1 actor per key due to the large number of actors and the overhead associated with creating and managing them. Instead, it would be more efficient to create 'key pools' where an actor can hold many keys and serve lookups for those keys. This approach would reduce the number of actors, and thus the overhead, while still providing good performance for lookups. This is because each actor can serve multiple lookups concurrently, and the number of actors can be scaled up or down based on the workload.",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": [12978414,12981431,12992188,13001645,13013759,13021430,130311460,13041692,13053760,13061474,13079248,13082348,1309109351310],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-more-libs/actors.html#faq-actors-workers-and-resources",1320"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1321"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",1322"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",1323"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",1324"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1325"https://docs.ray.io/en/master/ray-core/patterns/index.html#design-patterns-anti-patterns",1326"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",1327"https://docs.ray.io/en/master/rllib/actors.html#more-about-ray-actors",1328"https://docs.ray.io/en/master/ray-core/actors.html#more-about-ray-actors",1329"https://docs.ray.io/en/master/ray-more-libs/actors.html#more-about-ray-actors"1330],1331"document_ids": [13325192,13334645,133414708,13354520,133611597,13374630,13384641,13394624,13405244,13415066,134211598,13434521,1344147091345],1346"answer": " A singleton actor is an actor with a single instance in the system.\n\nAdvantages:\n\n* Stateful: An actor can maintain its own state, which can be useful in certain scenarios where maintaining state is necessary.\n* Concurrency control: Ray's concurrency groups allow you to limit the concurrency of an actor's methods, providing more fine-grained control over concurrency than tasks.\n\nDisadvantages:\n\n* Performance: If the actor's methods involve large objects, performance may be impacted due to the overhead of serialization and deserialization.\n* Complexity: Actors introduce additional complexity compared to tasks, and may not be necessary if the use case does not require state or concurrency control.\n\nIn general, if you don't need the stateful part of an actor, you're better off using tasks. However, if you do need to maintain state or control concurrency, actors can be a useful tool.",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#get-or-create-a-named-actor",1354"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",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-more-libs/actors.html#faq-actors-workers-and-resources",1358"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1359"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",1360"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1361"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",1362"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1363"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",1364"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources"1365],1366"document_ids": [13674623,13684629,13694625,13704871,13714624,137214708,13734520,137411597,13754880,1376520,13774444,13784200,1379115961380],1381"answer": " The advantages of using a named actor are:\n\n1. Unique name: An actor can be given a unique name within their namespace, allowing you to retrieve the actor from any job in the Ray cluster.\n2. Get-or-create: Ray provides a `get_if_exists` option for actor creation that creates an actor only if it doesn\u2019t exist. If the actor already exists, a handle to the actor will be returned and the arguments will be ignored.\n3. Accessibility: Named actors can be 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.\n4. Customizable logging: You can define the `__repr__` method for the Actor class to replace the Actor name with a more descriptive representation, making it easier to distinguish between log messages from different Actors.\n\nIt's worth noting that if you don\u2019t need the stateful part of an actor, you\u2019re mostly better off using tasks. Also, named actors are scoped by namespace. If no namespace is assigned, they will be placed in an anonymous namespace by default.",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_text.html#ray-data-read-text",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_numpy.html#ray-data-read-numpy",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/working-with-text.html#reading-text-files",1393"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1394"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1395"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1396"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",1397"https://docs.ray.io/en/master/data/loading-data.html#reading-files-from-cloud-storage",1398"https://docs.ray.io/en/master/data/api/doc/ray.data.read_json.html#ray-data-read-json",1399"https://docs.ray.io/en/master/data/working-with-images.html#saving-images"1400],1401"document_ids": [14027565,14037783,14047496,14057261,14066779,14076994,14086996,14097217,14106995,14117909,14126743,14137317,141467291415],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 file as an argument. Here's an example:\n```\nimport ray\n\nds = ray.data.read_text(\"s3://your-bucket-name/path/to/your/textfile.txt\")\n```\nThis will create a Ray Dataset (`ds`) containing the text of the file, with each line in the file being a separate row in the dataset.\n\nYou can also use the `read_json` function to read a JSONL (JSON Lines) file stored on S3. A JSONL file is a text file where each line is a valid JSON object. Here's an example:\n```\nds = ray.data.read_json(\"s3://your-bucket-name/path/to/your/jsonlfile.jsonl\")\n```\nThis will create a Ray Dataset (`ds`) containing the JSON objects from the file, with each object in a separate row in the dataset.\n\nIf you need to read a binary file (e.g. an image or a PDF) from S3, you can use the `read_binary_files` function and provide the S3 path to the file as an argument. Here's an example:\n```\nds = ray.data.read_binary_files(\"s3://your-bucket-name/path/to/your/binaryfile.ext\")\n```\nThis will create a Ray Dataset (`ds`) containing the binary contents of the file. If you need to decode the binary data, you can use the `map` function to apply a decoding function to each row in the dataset.\n\nFor more information, you can refer to the Ray Data documentation on loading data: <https://docs.ray.io/en/latest/data/loading-data.html>",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/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",1424"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",1425"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",1426"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",1427"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",1428"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1429"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",1430"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats",1431"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",1432"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",1433"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",1434"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code"1435],1436"document_ids": [14375631,14386501,1439493,14405587,14414452,14426502,14436552,14445413,14456416,14466409,14476616,1448739,144966191450],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. For example:\n\n```\nray get_head_ip [OPTIONS] CLUSTER_CONFIG_FILE\n```\n\nAlternatively, if you have already started the Ray cluster, you can find the head node IP by looking at the output of the `ray start` command. When you start the head node, the command will print out the Ray cluster address, which includes the head node IP and the port number. For example:\n\n```\nray start --head --port=6379\n```\n\nThis command will print out a message similar to the following:\n\n```\nRay cluster address: 123.45.67.89:6379\n```\n\nIn this example, the head node IP is 123.45.67.89.\n\nYou can also use the `ray nodes` command to get information about the nodes in your Ray cluster, including the head node. The `ray nodes` command will print out a list of nodes in the cluster, along with their IP addresses and other information. For example:\n\n```\nray nodes\n```\n\nThis command will print out a list of nodes in the cluster, similar to the following:\n\n```\n[\n {\n \"node_id\": \"abcdefghijklmnopqrstuvwxyz012345\",\n \"node_manager_address\": \"123.45.67.89:6800\",\n \"object_manager_address\": \"123.45.67.89:6801\",\n \"redis_address\": \"123.45.67.89:6379\",\n \"alive\": true,\n \"resource_key\": \"resource_key\",\n \"node_ip_address\": \"123.45.67.89\",\n \"node_manager_port\": 6800,\n \"object_manager_port\": 6801,\n \"redis_port\": 6379,\n \"redis_shard_ports\": [6802],\n \"metrics_export_port\": 8080,\n \"gcs_address\": \"123.45.67.89:8081\",\n \"plasma_store_address\": \"123.45.67.89:7000\",\n \"plasma_directory\": \"/tmp/ray/plasma_store\",\n \"node_manager_worker_port_range\": [20002, 29999],\n \"node_manager_port_range\": [20002, 29999],\n \"node_spec\": {\n \"cpu_count\": 8,\n \"memory\": 128000000000,\n \"object_store_memory\": 10000000000,\n \"object_spill_enabled\": true,\n \"temp_dir\": \"/tmp\",\n \"liveness_monitor_port\": 8082,\n \"redis_max_memory\": 104857600,\n \"redis_max_memory_policy\": \"volatile-lru\",\n \"system_config\": {\n \"ray_core_worker_port_range\": [20002, 29999],\n \"ray_redis_max_memory\": 104857600,\n \"ray_redis_max_memory_policy\": \"volatile-lru\",\n \"ray_redis_port\": 6379,\n \"ray_object_manager_port\": 6801,\n \"ray_node_manager_port\": 6800,\n \"ray_min_worker_port\": 20002,\n \"ray_max_worker_port\": 29999,\n \"ray_node_ip_address\": \"123.45.67.89\",\n \"ray_raylet_log_level\": \"INFO\",\n \"ray_object_spill_dir\": \"/tmp/ray/session_2022-10-10_10-10-10_123456/spill\",\n \"ray_plasma_directory\": \"/tmp/ray/plasma_store\",\n \"ray_redis_log_level\": \"INFO\",\n \"ray_worker_log_level\": \"INFO\",\n \"ray_redis_shard_ports\": [6802],\n \"ray_metrics_export_port\": 8080,\n \"ray_client_server_port\": 10001,\n \"ray_temp_dir\": \"/tmp/ray/session_2022-10-10_10-10-10_123456\",\n \"ray_num_cpus\": 8,\n \"ray_num_gpus\": 0,\n \"ray_redis_password\": \"...\",\n \"ray_redis_log_file\": \"/tmp/ray/session_2022-10-10_10-10-10_123456/logs/redis_worker_0_0.log\",\n \"ray_object_manager_log_file\": \"/tmp/ray/session_2022-10-10_10-10-10_123456/logs/object_manager_0_0.log\",\n \"ray_node_manager_log_file\": \"/tmp/ray/session_2022-10-10_10-10-10_123456/logs/node_manager_0_0.log\",\n \"ray_raylet_log_file\": \"/tmp/ray/session_2022-10-10_10-10-10_123456/logs/raylet_0_0.log\",\n \"ray_worker_log_file\": \"/tmp/ray/session_2022-10-10_10-10-10_123456/logs/worker_0_0.log\",\n \"ray_log_directory\": \"/tmp/ray/session_2022-10-10_10-10-10_123456/logs\",\n \"ray_log_level\": \"INFO\",\n \"ray_log_to_driver\": true,\n \"ray_log_to_stderr\": true,\n \"ray_log_to_file\": true,\n \"ray_log_file\": \"/tmp/ray/session_2022-10-10_10-10-10_123456/logs/driver_log.txt\",\n \"ray_log_syslog\": false,\n \"ray_log_colors\": true,\n \"ray_log_time_format\": \"%Y-%m-%d %H:%M:%S\",\n \"ray_log_device\": \"file\",\n \"ray_log_system_log_level\": \"WARNING\",\n \"ray_log_system_log_color\": true,\n \"ray_log_system_log_time_format\": \"%Y-%m-%d %H:%M:%S\",\n \"ray_log_system_log_device\": \"file\",\n \"ray_log_driver_log_level\": \"INFO\",\n \"ray_log_driver_log_color\": true,\n \"ray_log_driver_log_time_format\": \"%Y-%m-%d %H:%M:%S\",\n \"ray_log_driver_log_device\": \"file\",\n \"ray_log_worker_log_level\": \"INFO\",\n \"ray_log_worker_log_color\": true,\n \"ray_log_worker_log_time_format\": \"%Y-%m-%d %H:%M:%S\",\n \"ray_log_worker_log_device\": \"file\",\n \"ray_log_object_manager_log_level\": \"INFO\",\n \"ray_log_object_manager_log_color\": true,\n \"ray_log_object_manager_log_time_format\": \"%Y-%m-%d %H:%M:%S\",",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/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1458"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1459"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1460"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1461"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1462"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1463"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1464"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",1465"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1466"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",1467"https://docs.ray.io/en/master/data/examples/batch_training.html#define-training-functions",1468"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_torch_batches.html#ray-data-dataiterator-iter-torch-batches",1469"https://docs.ray.io/en/master/serve/api/doc/ray.serve.batch.html#ray-serve-batch"1470],1471"document_ids": [14727082,14737091,14747640,14757081,14767800,14777959,14788070,14797738,14807086,148110266,14827888,14837679,1484140841485],1486"answer": " To write a map function that returns a list of objects for `map_batches`, you can define a function or class that takes a batch of data as input and yields or returns one or multiple transformed objects. The function should have the correct signature depending on the `compute` strategy used.\n\nHere's an example of a function-based UDF:\n",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-advanced-api.html#customized-evaluation-during-training",1493"https://docs.ray.io/en/master/rllib/rllib-replay-buffers.html#advanced-usage",1494"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",1495"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",1496"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1497"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",1498"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#evolution-strategies-es",1499"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.train_buffered.html#ray-rllib-algorithms-algorithm-algorithm-train-buffered",1500"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",1501"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",1502"https://docs.ray.io/en/master/rllib/rllib-offline.html#getting-started",1503"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",1504"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options"1505],1506"document_ids": [150710817,150810724,150913177,151011048,151111458,151210510,151311891,151412332,151510818,151610507,151710587,151813176,1519110471520],1521"answer": " In RLlib, you can set a maximum episode length by using the `evaluation_duration` parameter in the configuration. However, this is only applicable for evaluation and not for training. During training, RLlib collects experiences until a certain batch size is reached, which can include multiple episodes.\n\nIf you want to limit the length of episodes during training, you can define a custom environment that terminates episodes after a certain number of time steps. Here's an example of how to do this using the `CartPole-v1` environment:\n```python\nimport gym\n\nclass CustomCartPoleEnv(gym.Wrapper):\n def __init__(self, max_episode_length):\n super().__init__(gym.make('CartPole-v1'))\n self.max_episode_length = max_episode_length\n\n def step(self, action):\n obs, reward, terminated, truncated, info = super().step(action)\n if terminated or truncated:\n return obs, reward, terminated, truncated, info\n else:\n if self.step_num >= self.max_episode_length:\n terminated = True\n truncated = False\n reward = -10 # negative reward for exceeding max episode length\n self.step_num += 1\n return obs, reward, terminated, truncated, info\n```\nIn this example, the `CustomCartPoleEnv` environment terminates an episode after `max_episode_length` time steps, even if the pole is still upright. You can adjust this value to set the maximum episode length.",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/api/env.html#environment-variables-used-by-ray-tune",1528"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1529"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_error.html#ray-tune-callback-on-trial-error",1530"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1531"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_recover.html#ray-tune-callback-on-trial-recover",1532"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",1533"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.MedianStoppingRule.html#ray-tune-schedulers-medianstoppingrule",1534"https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.LoggerCallback.on_trial_recover.html#ray-tune-logger-loggercallback-on-trial-recover",1535"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.HyperBandScheduler.html#ray-tune-schedulers-hyperbandscheduler",1536"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.CONTINUE.html#ray-tune-schedulers-trialscheduler-continue",1537"https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.JsonLoggerCallback.on_trial_recover.html#ray-tune-logger-jsonloggercallback-on-trial-recover",1538"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune",1539"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.on_trial_error.html#ray-tune-schedulers-trialscheduler-on-trial-error"1540],1541"document_ids": [15421114,15432326,15441980,15452266,15462152,15471926,15481728,15491966,15501974,15511819,15522104,155311554,155417491555],1556"answer": " To make a Ray Tune trial retry on failures, you can use the `FailureConfig` to configure failure handling of individual trials. Here is an example of how to configure `FailureConfig` to retry a trial on failure:\n\n```python\nfrom ray import tune\n\nconfig = tune.FailureConfig(max_retries=3)\ntuner = tune.Tuner(train_model, param_space=trial_space, failure_config=config)\nresults = tuner.fit()\n```\n\nIn this example, the `max_retries` parameter is set to 3, which means that a trial will be retried up to 3 times if it fails. You can adjust the number of retries according to your needs.\n\nNote that this will only handle trial failures. If you want to handle failures at the experiment level, you can use Ray's fault tolerance and recovery features, which allow you to save the state of the experiment to a persistent storage location and automatically restart a trial from the latest checkpointed state if it fails.",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/fault_tolerance/actors.html#actor-creator-failure",1565"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1566"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",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/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1569"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#advanced-detached-placement-group",1570"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1571"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1572"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#worker-recovery",1573"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1574"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#worker-node-failure"1575],1576"document_ids": [15775208,15784561,15794575,15804631,15815195,15824351,15835191,15845095,15854444,15865192,158711551,15884878,158945291590],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 the actors are nested to multiple levels to form a tree. However, you can make the actor lifetimes decoupled from the job, allowing an actor to persist even after the driver process of the job exits. We call these actors detached. But this needs to be specified during the creation of the actor.",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_text.html#ray-data-read-text",1598"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1599"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1600"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",1601"https://docs.ray.io/en/master/data/loading-data.html#handling-compressed-files",1602"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1603"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1604"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1605"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1606"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",1607"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",1608"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",1609"https://docs.ray.io/en/master/data/key-concepts.html#loading-data"1610],1611"document_ids": [16127565,16137783,16147496,16157261,16166747,16176994,16186996,16197217,16206995,16216993,16226779,16237909,162467321625],1626"answer": " To read a large text file in S3 with Ray, you can use the `read_text` function and provide the S3 path to the file as an argument. Here's an example:\n\n```python\nimport ray\n\nds = ray.data.read_text(\"s3://your-bucket-name/path/to/your/textfile.txt\")\n```\n\nThis will create a Ray Dataset object `ds` that you can use for further processing. If the text file is compressed, you can specify the compression type in `arrow_open_stream_args` as shown in the example:\n\n```python\nds = ray.data.read_text(\"s3://your-bucket-name/path/to/your/compressed-textfile.txt.gz\", \n arrow_open_stream_args={\"compression\": \"gzip\"})\n```\n\nIf you want to read multiple text files in a directory, you can use the `read_text_files` function instead:\n\n```python\nds = ray.data.read_text_files(\"s3://your-bucket-name/path/to/your/directory\")\n```\n\nThis will create a Ray Dataset object `ds` that contains all the text files in the specified directory.",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/data/saving-data.html#converting-datasets-to-pandas",1633"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",1634"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1635"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",1636"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",1637"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",1638"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1639"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_pandas.html#ray-data-dataset-to-pandas",1640"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-rows",1641"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1642"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1643"https://docs.ray.io/en/master/data/api/from_other_data_libs.html#for-pandas-users",1644"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air"1645],1646"document_ids": [16476806,16486901,16496749,16507818,16516752,16527363,165310017,16547302,16556860,16569180,16577216,16587022,1659101401660],1661"answer": " To get a Ray Dataset from a pandas DataFrame, you can use the `ray.data.from_pandas()` function. Here's an example:\n\n```python\nimport pandas as pd\nimport ray\n\n# Create a pandas DataFrame\ndf = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\n\n# Convert the pandas DataFrame to a Ray Dataset\nds = ray.data.from_pandas(df)\n```\n\nThis will create a Ray Dataset `ds` that you can use for distributed processing. Note that if you have a list of pandas DataFrames, you can pass the list directly to `ray.data.from_pandas()` to create a single Ray Dataset.",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/loading-data.html#loading-data-from-single-node-data-libraries",1672"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1673"https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",1674"https://docs.ray.io/en/master/data/api/from_other_data_libs.html#api-guide-for-users-from-other-data-libraries",1675"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",1676"https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",1677"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",1678"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",1679"https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-transforms"1680],1681"document_ids": [16827796,16837274,16849180,16857081,16866749,16876835,16886782,16897020,16908046,16916773,16928006,16936732,169469761695],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\n```python\nimport ray\nds = ray.data.range(10)\n```\n\nThis creates a Dataset with 10 rows of integers from 0 to 9.\n",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/ray-overview/getting-started.html#ray-core-quickstart",1704"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1705"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1706"https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",1707"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1708"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1709"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",1710"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",1711"https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",1712"https://docs.ray.io/en/master/data/working-with-text.html#transforming-text",1713"https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-flat-map",1714"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment"1715],1716"document_ids": [17177796,1718250,17197274,17207636,17217628,17226749,172310014,1724247,172510303,17266782,17276997,17286783,172913871730],1731"answer": " Sure, here's an example of using `ray.data.map` with a callable class as input:\n```python\nimport ray\n\n@ray.remote\nclass Counter(object):\n def __init__(self):\n self.n = 0\n\n def increment(self):\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/env.html#environment-variables-used-by-ray-tune",1738"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1739"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1740"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",1741"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",1742"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",1743"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-trials-individually-with-a-timeout",1744"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",1745"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria",1746"https://docs.ray.io/en/master/tune/faq.html#how-can-i-use-tune-with-kubernetes",1747"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",1748"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",1749"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors"1750],1751"document_ids": [17521114,17532325,17542266,17552406,17562001,17571025,17582204,17592003,17602198,17611062,17626879,17635131,17645581765],1766"answer": " To set the memory limit for each trial in Ray Tuner, you can utilize the resources allocation feature. When configuring the resources, you can specify the amount of memory to be used by each trial. Here's an example of how to set 4 GB of memory for each trial:\n\n```python\n# Set 4 GB of memory for each trial\ntrain_model = tune.with_resources(train_model, {\"memory\": 4000})\n```\n\nKeep in mind that the actual memory limit will depend on the available memory in your cluster and the memory usage of other processes. Ray Tune will automatically manage the allocation of resources to ensure your cluster is not overloaded.\n\nAdditionally, you can configure trial-level fault tolerance using `FailureConfig` to handle individual trial failures, such as running out of memory or network connection issues.\n\n```python\nfrom ray import tune\n\n# Configure FailureConfig\nconfig = tune.FailureConfig(max_retries=3)\n\ntuner = tune.Tuner(\n train_model,\n param_space,\n tune_config=tune.TuneConfig(failure_config=config),\n run_config=train.RunConfig(storage_path=\"s3://your-s3-bucket/durable-trial/\"),\n)\n\ntuner.fit()\n```\n\nThis example sets the maximum number of retries to 3 for each trial. If a trial fails due to running out of memory or other reasons, Ray Tune will automatically relaunch the trial up to the specified number of retries.",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": [17874787,17884931,1789475,1790690,1791436,1792474,1793750,1794476,1795477,17964623,179714324,179814202,17995201800],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/starting-ray.html#launching-a-ray-cluster-ray-up",1810"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1811"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1812"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1813"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#starting-ray",1814"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",1815"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1816"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.__init__.html#ray-data-preprocessor-preprocessor-init",1817"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",1818"https://docs.ray.io/en/master/ray-core/configure.html#java-applications",1819"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure"1820],1821"document_ids": [18224785,18234811,18244194,18254190,18264188,18274189,18285386,18294186,18304204,18317833,18325399,18334404,183445761835],1836"answer": " No, `ray.init()` is not used to check if Ray is already initiated. Instead, you should use `ray.is_initialized()` to check if Ray has been initialized or not. This function returns a boolean value, True if Ray has been initialized 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/data/transforming-data.html#transforming-batches-with-actors",1843"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",1844"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1845"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1846"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1847"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1848"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",1849"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map.html#ray-util-actorpool-map",1850"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",1851"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1852"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1853"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",1854"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"1855],1856"document_ids": [18576787,18586885,18598108,18608070,18616981,18627988,18635465,18644708,18656999,18668114,18676941,18686954,186969441870],1871"answer": " The `compute=actor` argument within `ray.data.map_batches` is used to specify that the function provided to `map_batches` should be run as a long-running Ray actor, as opposed to a short-running Ray task. This can be useful for performance reasons, as actors can cache data between task invocations, reducing the overhead of fetching the data for each task. In the context of the provided text, the `TorchPredictor` class is being used as a Ray actor to perform predictions on batches of data.",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/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",1878"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1879"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1880"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1881"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",1882"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",1883"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1884"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",1885"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1886"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#tune-wandb-api-reference",1887"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",1888"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",1889"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries"1890],1891"document_ids": [18928470,18932432,18949530,18952463,18962431,18971132,18989529,18999136,19002466,19012462,19029137,19039138,190491391905],1906"answer": " To use Weights & Biases (WandB) logger with `accelerateTrainer`, you can follow these steps:\n\n1. Install WandB: `pip install wandb`\n2. Import the necessary libraries, including WandB and the `Trainer` from PyTorch Lightning.\n3. Create a `WandbLogger` instance with your API key, project name, and other desired parameters.\n4. Initialize the `Trainer` instance with the `WandbLogger` included in the logger list.\n\nHere's a code example:\n\n```python\nimport os\nimport pytorch_lightning as pl\nfrom pytorch_lightning.loggers import WandbLogger\nfrom ray import air, tune\nfrom ray.train.lightning import LightningTrainer\n\n# Grab the api key from the head node\nWANDB_API_KEY = os.environ[\"WANDB_API_KEY\"]\n\ndef train_func_per_worker(config):\n wandb_logger = WandbLogger(\n api_key=WANDB_API_KEY,\n project=\"your-project-name\",\n name=\"your-run-name\",\n id=\"your-run-id\",\n )\n\n # Initialize your model, datamodule, and other components here\n\n trainer = pl.Trainer(\n logger=[wandb_logger],\n # Other parameters as needed\n )\n\n # Fit the model\n trainer.fit(model, datamodule)\n\ntune_config = {\n # Your hyperparameters and tuning options\n}\n\ntrainer = LightningTrainer(\n train_func_per_worker,\n scaling_config=ScalingConfig(num_workers=num_workers),\n logger=tune.integration.PyTorchLightningLogger(),\n tune_config=tune_config,\n)\n\ntune_result = trainer.tune(config=tune_config)\n```\n\nReplace `your-project-name`, `your-run-name`, and `your-run-id` with your desired WandB project, run name, and run ID.\n\nRemember to set the `WANDB_API_KEY` environment variable on each worker before initializing the `Trainer`.",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/ray-core/objects/serialization.html#plasma-store",1913"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1914"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1915"https://docs.ray.io/en/master/ray-core/objects.html#objects",1916"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1917"https://docs.ray.io/en/master/data/data-internals.html#object-store-memory",1918"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",1919"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",1920"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",1921"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",1922"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",1923"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",1924"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"1925],1926"document_ids": [19274941,19285013,19295014,19304414,193114748,19326896,193314094,193414122,193514138,193614063,193714184,193814222,1939143001940],1941"answer": " In the context provided, when data is explicitly put into the object store using functions like `ray.put()` in Python, `Ray.put()` in Java, or `ray::Put()` in C++, it will be implicitly put into the local object store of the node where the function is called. This data remains local to the writer until requested by another task or actor on another 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/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1948"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-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-more-libs/actors.html#cancelling-actor-tasks",1954"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1955"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",1956"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1957"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",1958"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1959"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"1960],1961"document_ids": [19624550,19634375,19644894,196511591,19664514,196714702,196814703,19694515,197011592,19714896,197211590,19734513,1974147011975],1976"answer": " To kill or cancel a Ray task that you have already started, you can call `ray.cancel()` on the ObjectRef returned by the task. Here is an example:\n```\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n```\nBy default, this will send a KeyboardInterrupt to the task's worker if it is mid-execution. Passing `force=True` to `ray.cancel()` will force-exit the worker. Note that Ray will not automatically retry tasks that have been cancelled.\n\nIf the task is an Actor Task, the behavior of cancellation depends on the execution model of the Actor. If it is a regular Actor or a threaded Actor, the execution isn't cancelled. Actor Tasks cannot be interrupted because Actors have states. If it is an async Actor, Ray cancels a `asyncio.Task`. The semantic of cancellation is equivalent to asyncio\u2019s cancellation. If the Task has finished, nothing happens. Only `force=False` is allowed for an Actor Task. Otherwise, it raises `ValueError`. Use `ray.kill(actor)` instead to kill an Actor.\n\nTo cancel a task that is hanging, you can call `ray.cancel()` on the ObjectRef returned by the task. By default, this will send a KeyboardInterrupt to the task's worker if it is mid-execution. Passing `force=True` to `ray.cancel()` will force-exit the worker. Note that Ray will not automatically retry tasks that have been cancelled.",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/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1983"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1984"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",1985"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1986"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1987"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1988"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1989"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",1990"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches",1991"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",1992"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1993"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1994"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map"1995],1996"document_ids": [19977082,19987081,19998607,20007959,20018070,20027988,20037640,20046956,20056861,20067738,200710017,20087091,200977962010],2011"answer": " To send extra arguments in the `dataset.map_batches` function, you can use the `fn_kwargs` parameter. This parameter allows you to pass keyword arguments to the user-defined function (UDF) that you specify as the first argument of `map_batches`. Here's an example:\n```python\nextra_arg = \"some_value\"\nds.map_batches(my_function, fn_kwargs={\"extra_arg\": extra_arg})\n```\nIn this example, `my_function` is the UDF that you want to apply to the batches of data in the dataset. The `fn_kwargs` parameter is a dictionary that maps keyword argument names to their corresponding values. In this case, we're passing an `extra_arg` keyword argument with a value of `\"some_value\"`.\n\nYou can replace `\"some_value\"` with any value that you want to pass to `my_function`. For example, you could pass a NumPy array, a pandas DataFrame, or any other Python object that `my_function` expects as an argument.",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/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance",2018"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",2019"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",2020"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",2021"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",2022"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/experimental.html#gcs-fault-tolerance",2023"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",2024"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",2025"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",2026"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",2027"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2028"https://docs.ray.io/en/master/cluster/kubernetes/index.html#about-kuberay",2029"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence"2030],2031"document_ids": [20325830,203313871,203413470,203513870,20365647,20375916,20385642,20395763,20406149,20415658,2042444,20435660,204458602045],2046"answer": " The history of jobs run on a Kuberay cluster in Ray are not stored in a traditional database. Instead, Ray uses an internal key-value store called the Global Control Store (GCS) to store cluster metadata. This GCS runs on the head node by default. However, for fault tolerance and high availability, Ray can write this metadata to an external Redis database. When using KubeRay, you can enable GCS fault tolerance by adding an external Redis server and modifying your RayService Kubernetes object. This setup allows the Ray cluster to recover from head node crashes and tolerate GCS failures without losing connections to worker nodes.",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/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",2053"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",2054"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#verify-and-save-results",2055"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2056"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler",2057"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2058"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",2059"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2060"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",2061"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",2062"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",2063"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2064"https://docs.ray.io/en/master/data/batch_inference.html#handling-cpu-out-of-memory-failures"2065],2066"document_ids": [20676150,20689858,20697992,2070476,20711895,20728108,207310267,20748114,20755465,20769908,20774878,20786787,207969612080],2081"answer": " The ValueError \"The actor ImplicitFunc is too large\" is likely caused by the actor function capturing a large object in scope. To resolve this issue, you can put large objects in the Ray object store using `ray.put()`.\n\nIn the context provided, it is suggested to move the data loader to the training function. However, this is not directly related to the ValueError you are encountering.\n\nThe warning about the actor being very large (52 MiB) is a separate issue and may not be the direct cause of the ValueError. Nonetheless, you can address this by moving the model loading to the `__call__` method of the actor class, so that the model is only loaded when the actor is invoked, rather than being captured in the actor's closure.\n\nHere's an example of how you can modify your actor class:\n\n```python\nclass BatchInferModel:\n def __call__(self, batch: pd.DataFrame, model=None) -> pd.DataFrame:\n if model is None:\n model = load_model()\n # Perform inference using the model and batch\n return model(batch)\n```\n\nThis way, the model is loaded only when the actor is invoked, and not captured in the actor's closure, which should help reduce the size of the actor.",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/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",2088"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2089"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#distributed-training",2090"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2091"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2092"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",2093"https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others",2094"https://docs.ray.io/en/master/train/huggingface-accelerate.html#training-with-huggingface-accelerate",2095"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#define-the-training-loop",2096"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#training",2097"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",2098"https://docs.ray.io/en/master/train/getting-started-pytorch.html#getting-started-with-pytorch",2099"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#how-it-works"2100],2101"document_ids": [21029845,2103226,210410155,21056940,2106228,2107370,210814313,21098247,211010196,21115515,21129820,21138312,2114101502115],2116"answer": " To distribute training for your custom Keras neural net in Databricks using Ray, you can follow these steps:\n\n1. Import Ray Train and Ray AIR Session.\n2. Use `ray.train.torch.prepare_model` and `ray.train.torch.prepare_data_loader` utility functions to set up your model and data for distributed training.\n3. Define a training function that accepts a configuration dict containing hyperparameters.\n4. Load the data shard for each worker with `session.get_dataset_shard(\"train\")`.\n5. Iterate over the dataset with `train_dataset.iter_torch_batches()`.\n\nHere's a code snippet to help you get started:\n\n```python\nimport ray\nfrom ray import train\n\n# Define your custom Keras model\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dense, Input\ndef create_model():\n inputs = Input(shape=(784,))\n outputs = Dense(units=10, activation='softmax')(inputs)\n model = Model(inputs=inputs, outputs=outputs)\n return model\n\n# Define the training function\ndef train_func_distributed(config):\n num_epochs = config[\"num_epochs\"]\n batch_size = config[\"batch_size\"]\n\n # Prepare the model\n model = create_model()\n model = train.torch.prepare_model(model)\n\n # Prepare the data\n dataset = get_dataset()\n dataloader = DataLoader(dataset, batch_size=batch_size)\n dataloader = train.torch.prepare_data_loader(dataloader)\n\n # Train the model\n for epoch in range(num_epochs):\n for batch in dataloader:\n # Perform training step\n pass\n\n# Set up the trainer\ntrainer = TorchTrainer(\n train_func_distributed,\n scaling_config=ScalingConfig(num_workers=4, use_gpu=use_gpu)\n)\n\n# Fit the model\nresults = trainer.fit()\n```\n\nReplace `get_dataset()` with your data loading logic. This example assumes that you have already prepared a Ray Dataset for training.\n\nKeep in mind that this is a simplified example. You might need to adjust the code to fit your specific use case.",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/api/doc/ray.put.html#ray-put",2129"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2130"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",2131"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",2132"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2133"https://docs.ray.io/en/master/ray-core/objects.html#objects",2134"https://docs.ray.io/en/master/ray-core/patterns/return-ray-put.html#anti-pattern-returning-ray-put-objectrefs-from-a-task-harms-performance-and-fault-tolerance"2135],2136"document_ids": [21374662,21385443,21394849,21404205,21414884,21425219,21434775,21444209,214513646,21465240,21474811,21484413,214951722150],2151"answer": " To use `ray.put` and `ray.get`, you need to understand that `ray.put` stores an object in Ray's object store and returns an object reference, while `ray.get` returns the object itself from the object store using the object reference.\n\nHere's how to use them:\n\n1. `ray.put`:\n```python\nimport ray\nray.init()\n\nmy_object = \"some data\"\nobject_ref = ray.put(my_object)\n```\nThis will store `my_object` in Ray's object store and return an object reference (`object_ref`).\n\n2. `ray.get`:\n```python\nretrieved_object = ray.get(object_ref)\n```\nThis will retrieve the object from the object store using the object reference (`object_ref`) and return the original object (`retrieved_object`).\n\nIt's important to note that you should avoid calling `ray.get` unnecessarily for intermediate steps and work with object references directly. Only call `ray.get` at the end to get the final result. This is because when `ray.get` is called, objects must be transferred to the worker/node that calls `ray.get`, which can harm performance.",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/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",2158"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",2159"https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",2160"https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors",2161"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.__init__.html#ray-data-preprocessor-preprocessor-init",2162"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2163"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2164"https://docs.ray.io/en/master/data/user-guide.html#user-guides",2165"https://docs.ray.io/en/master/train/user-guides.html#ray-train-user-guides",2166"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data",2167"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-preprocess-data-for-training",2168"https://docs.ray.io/en/master/data/data.html#ray-data-scalable-datasets-for-ml",2169"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-data"2170],2171"document_ids": [21729178,21739222,21746849,21756914,21767833,21776838,21786835,21796911,21808115,21819212,21828163,21836907,218491892185],2186"answer": " To preprocess many files using Ray Data, you can follow these steps:\n\n1. Load and preprocess data in a streaming fashion with Ray Data, which serves as a bridge from storage or ETL pipeline outputs to distributed applications and libraries in Ray.\n2. If your preprocessed Dataset is small enough to fit in Ray object store memory, materialize the preprocessed dataset in Ray\u2019s built-in object store by calling `materialize()` on the preprocessed dataset. This method computes the entire preprocessed dataset and pins it in the Ray object store memory, so when iterating over the dataset repeatedly, the preprocessing operations do not need to be re-run.\n3. If you're working with tabular data, consider using Ray Data preprocessors, which implement common data preprocessing operations for structured data.\n\nHere's an example of using Ray Data preprocessors with Ray Train Trainers:\n\n```python\nimport ray\nfrom ray.data.preprocessors import MinMaxScaler\nfrom ray.train.xgboost import XGBoostTrainer\nfrom ray.train import ScalingConfig\n\ntrain_dataset = ray.data.from_items([{\"x\": x, \"y\": 2 * x} for x in range(0, 32, 3)])\nvalid_dataset = ray.data.from_items([{\"x\": x, \"y\": 2 * x} for x in range(1, 32, 3)])\n\nscaler = MinMaxScaler(columns=[\"x\"])\nprocessed_train_data = scaler.fit_transform(train_dataset)\nprocessed_valid_data = scaler.transform(valid_dataset)\n\nscaling_config = ScalingConfig(num_workers=2, use_gpu=True)\ntrainer = XGBoostTrainer(\n config={\"objective\": \"reg:squarederror\"},\n label_column=\"y\",\n datasets={\"train\": processed_train_data, \"valid\": processed_valid_data},\n)\nresult = trainer.train(scaling_config=scaling_config)\n```\n\nThis example demonstrates how to use Ray Data preprocessors to preprocess data for training with Ray Train Trainers. The `MinMaxScaler` preprocessor scales the \"x\" column values to a specified range, typically between 0 and 1.",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/ray-core/objects/serialization.html#troubleshooting",2193"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2194"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",2195"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#tls-verification",2196"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2197"https://docs.ray.io/en/master/serve/tutorials/java.html#calling-ray-serve-deployment-with-http",2198"https://docs.ray.io/en/master/ray-core/objects/serialization.html#known-issues",2199"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",2200"https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#checkpoints-are-py-version-specific-but-can-be-converted-to-be-version-independent",2201"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",2202"https://docs.ray.io/en/master/installation.html#installed-python-dependencies",2203"https://docs.ray.io/en/master/ray-overview/installation.html#installed-python-dependencies",2204"https://docs.ray.io/en/master/ray-more-libs/installation.html#installed-python-dependencies"2205],2206"document_ids": [22074953,22084952,22094939,22106592,22114957,221214570,22134958,22144400,221510551,22164260,2217110,2218335,2219146592220],2221"answer": " The error message indicates that `SSLContext` objects cannot be pickled, which is causing the serialization to fail. This is likely due to the use of certain Python versions (3.8 and 3.9) that have a bug in the pickle module, which has been solved in later versions. To resolve this issue, you can try upgrading to a later version of Python or converting the pickle checkpoint to a msgpack one using an RLlib utility function. Additionally, using Ray's customized Pickle protocol version 5 backport can also help get rid of previous limitations in serialization.",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/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",2228"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",2229"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",2230"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",2231"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-3-install-a-kuberay-operator",2232"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-3-install-a-kuberay-operator",2233"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-2-install-the-kuberay-operator",2234"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-2-create-a-raycluster-cr",2235"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-1-install-a-kuberay-cluster",2236"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/kubeflow.html#step-3-install-kuberay-operator",2237"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-1-install-the-kuberay-operator",2238"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-2-install-the-kuberay-operator",2239"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-2-install-the-kuberay-operator"2240],2241"document_ids": [22425720,22436103,22445817,22455647,22465774,22476057,22486044,22495981,22505980,22516092,22526143,22535741,225457592255],2256"answer": " To install Custom Resource Definitions (CRDs) in Kuberay, follow these steps:\n\n1. Add the Kuberay Helm repository:\n```bash\nhelm repo add kuberay https://ray-project.github.io/kuberay-helm/\n```\n\n2. Install the Kuberay operator, which includes CRDs, with Helm:\n```bash\nhelm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n```\n\nThe CRDs are installed as part of the Kuberay operator deployment.",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/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2263"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2264"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",2265"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",2266"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",2267"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",2268"https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",2269"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",2270"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging",2271"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2272"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",2273"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",2274"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"2275],2276"document_ids": [22778107,22786788,2279140,228013697,22817380,22826955,22837632,22847985,2285590,22866998,22877328,22887955,228970822290],2291"answer": " The function for Ray data batch inference is 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, the `__call__()` method is defined in a class that is then passed to the `map_batches()` function. The `__call__()` method takes a batch of data as input and returns the predicted output for that batch. By defining this method in a class, we can encapsulate the model and any necessary preprocessing or postprocessing code in a single reusable object.",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/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2298"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",2299"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",2300"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",2301"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",2302"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2303"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2304"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",2305"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",2306"https://docs.ray.io/en/master/cluster/usage-stats.html#requesting-removal-of-collected-data",2307"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2308"https://docs.ray.io/en/master/ray-core/api/doc/ray.shutdown.html#ray-shutdown",2309"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options"2310],2311"document_ids": [23126615,23136626,23146624,231514522,23166608,23174613,23184894,23196612,23204375,23215569,23224618,23234827,2324145142325],2326"answer": " To disconnect a Ray client, you can use the `disconnect()` method on the client object. Here is an example:\n\n```python\nfrom ray import client\n\n# Connect to the Ray cluster\ncli = client.RayClient()\n\n# Use Ray through the client object\n# ...\n\n# Disconnect from the Ray cluster\ncli.disconnect()\n```\n\nNote that if the client disconnects unexpectedly due to a network failure, the client will attempt to reconnect to the server for a certain period of time before giving up. You can increase this time by setting the environment variable `RAY_CLIENT_RECONNECT_GRACE_PERIOD`.\n\nAlso, when using Ray multi-client, you need to call `disconnect()` explicitly to close the connection, as the client won't be disconnected automatically.",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/running-applications/job-submission/quickstart.html#submitting-a-job",2333"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job",2334"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2335"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2336"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2337"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",2338"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#jobsubmissionclient",2339"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",2340"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2341"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#dependency-management",2342"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",2343"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",2344"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job"2345],2346"document_ids": [23476550,23484255,23496553,23506578,23516579,23526575,23536540,23546595,23556581,23566585,23576567,23585732,235966932360],2361"answer": " To submit a job with Python using local files, you can use the Ray Jobs API. Here's a step-by-step guide:\n\n1. Install Ray: If you haven't already, install Ray by following the instructions in the [official documentation](https://docs.ray.io/en/latest/installation.html).\n\n2. Create a Python script (e.g., `my_ray_script.py`) that you want to run as a job.\n\n3. Create a runtime environment YAML file (e.g., `ray_env.yaml`) to specify the required dependencies and working directory:\n\n ```yaml\n working_dir: \"/path/to/working/directory\"\n python:\n version: \"3.8\"\n packages:\n - numpy\n - pandas\n ```\n\n4. Submit the job using the following command:\n\n ```bash\n ray job submit --runtime-env-file ray_env.yaml -- python my_ray_script.py\n ```\n\nReplace `/path/to/working/directory` with the path to your local working directory and update the `packages` list with any additional Python packages required by your script.\n\nFor more information, refer to the [Ray Jobs API documentation](https://docs.ray.io/en/latest/cluster/jobs.html).",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.html#ray-tune-hyperparameter-tuning",2368"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",2369"https://docs.ray.io/en/master/tune/examples/includes/tune_basic_example.html#tune-basic-example",2370"https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",2371"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2372"https://docs.ray.io/en/master/data/batch_inference.html#using-models-from-ray-train",2373"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",2374"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",2375"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve",2376"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",2377"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",2378"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",2379"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples"2380],2381"document_ids": [2382117,23831080,23844111,2385986,2386234,23876962,23881090,2389127,23902747,2391343,2392189,2393162,23943802395],2396"answer": " To do inference from a model trained by Ray tune.fit(), you can use the checkpoint returned by the `tune.fit()` method to make predictions. Here is an example:\n```python\nfrom ray import tune\n\n# Define the training function\ndef train_model(config):\n # Train the model here\n pass\n\n# Define the search space\nsearch_space = {\n # Add hyperparameters to tune here\n}\n\n# Create a tuner\ntuner = tune.Tuner(train_model, param_space=search_space)\n\n# Train the model\nresult = tuner.fit()\n\n# Get the best checkpoint\nbest_checkpoint = result.get_best_checkpoint()\n\n# Load the model from the checkpoint\nmodel = best_checkpoint.to_local()\n\n# Make predictions using the model\npredictions = model.predict(data)\n```\nIn this example, `train_model` is the function that trains your model, and `search_space` is a dictionary that defines the hyperparameters you want to tune. The `tuner.fit()` method trains the model using the specified search algorithm and returns a `TuneResult` object. You can then use the `get_best_checkpoint()` method to get the best checkpoint and load the model from it. Finally, you can use the loaded model to make predictions on new data.",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/data/working-with-pytorch.html#batch-inference-with-pytorch",2403"https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",2404"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-2-inference-on-a-single-batch",2405"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2406"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",2407"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2408"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",2409"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2410"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2411"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",2412"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2413"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2414"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference"2415],2416"document_ids": [24176978,24186725,24197976,24206980,24218043,24226979,24238010,24246947,24256941,24267970,24276940,24288107,24291402430],2431"answer": " Yes, it is possible to load and run inference without using PyTorch or TensorFlow directly by using a framework like Ray, which provides a unified interface for batch inference across different deep learning frameworks. Ray allows you to define a callable class for inference and then map it over your data using `ds.map_batches()`. This way, you can leverage Ray's scalability and distributed computing capabilities while keeping your inference logic framework-agnostic. The provided context demonstrates how to perform batch inference using PyTorch models and Ray Datasets.",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/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",2441"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",2442"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",2443"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",2444"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",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/serve/api/index.html#ray-serve-api",2448"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2449"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment"2450],2451"document_ids": [24524811,24535380,24546910,245514255,24566208,24576015,24585556,24595656,24604656,24616835,246213974,24636615,246413862465],2466"answer": " Ray is a distributed computing framework that provides a variety of features, including:\n\n* Initialization with `ray.init()`\n* Tutorials and examples for learning how to use Ray\n* Blogs for staying up-to-date on Ray developments\n* Deployment options for Ray on cloud VMs and Kubernetes\n* A Ray Core CLI for interacting with Ray\n* Ray Jobs for managing long-running Ray applications\n* Ray Serve API for building and deploying server applications\n* Ray Tune for hyperparameter tuning\n* Ray Data for distributed data processing\n\nYou can use the Ray Client to interact with Ray clusters remotely. For more information, see the Ray documentation and resources listed in the context.",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/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",2473"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",2474"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",2475"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2476"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2477"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",2478"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",2479"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",2480"https://docs.ray.io/en/master/data/performance-tips.html#configuring-resources-and-locality",2481"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus",2482"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",2483"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#resources",2484"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob"2485],2486"document_ids": [248713457,24881025,24894999,249011062,249111068,249212049,24932966,24945116,24956829,24962730,24976590,24986237,249961452500],2501"answer": " If you specify a fractional GPU in the resource spec, you can use more than that fraction of the GPU, but it is your responsibility to ensure that multiple trials can share GPUs and there is enough memory to do so. Ray does not automatically handle this for you. However, if your script also contains a Ray task defined with @ray.remote(num_gpus=1), then the task will be scheduled to use a different GPU. The actual CPU and GPU utilization is not controlled or limited by Ray.",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-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2508"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",2509"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",2510"https://docs.ray.io/en/master/rllib/actors.html#actors",2511"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",2512"https://docs.ray.io/en/master/ray-core/actors.html#actors",2513"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",2514"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",2515"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",2516"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2517"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",2518"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",2519"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor"2520],2521"document_ids": [25225192,25235194,25244428,252511576,252614687,25274482,25284630,25294241,2530520,25315191,25325066,25334629,253442602535],2536"answer": " In the given context, it is not possible to pickle a variable defined in an actor's `__init__` method directly. However, you can pass objects to actor method calls by value or by reference. If you want to use a mutable object in your actor, consider passing it as an argument to the actor's method instead of defining it in the `__init__` method. Here's an example:\n\n```python\nclass Actor:\n @ray.remote\n def f(self, shared_obj):\n # Manipulate shared_obj here\n return shared_obj\n\nshared_obj = {\"key\": \"value\"}\nactor = Actor.remote()\nresult = ray.get(actor.f.remote(shared_obj))\n```\n\nIn this example, `shared_obj` is passed by reference to the `f` method of the actor. Any changes made to `shared_obj` within the `f` method will be reflected in the original object.",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/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",2543"https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",2544"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",2545"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",2546"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2547"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",2548"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayFSDPStrategy.reduce.html#ray-train-lightning-rayfsdpstrategy-reduce",2549"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#collective-communication",2550"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#shuffling-and-reducing-data",2551"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDDPStrategy.reduce.html#ray-train-lightning-rayddpstrategy-reduce",2552"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.reduce.html#ray-train-lightning-raydeepspeedstrategy-reduce",2553"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",2554"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.summarize_actors.html#ray-util-state-summarize-actors"2555],2556"document_ids": [2557788,2558659,255914457,2560673,25616787,25628101,25638854,256414427,25655353,25668424,25679076,2568641,25696842570],2571"answer": " To perform an all-reduce operation among a list of actors in Ray, you can use the `ray.util.collective.allreduce` function. However, the provided context does not include this function. Instead, it includes `ray.util.collective.reduce`, `ray.util.collective.barrier`, and `ray.util.state.summarize_actors`.\n\nAssuming you have access to the `ray.util.collective.allreduce` function, you can use it as follows:\n\n1. Import the necessary Ray modules:\n```python\nimport ray\nfrom ray.util.collective import allreduce\n```\n2. Create a list of actors.\n3. Perform the all-reduce operation on a list of tensors using the `allreduce` function:\n```python\ntensor_list = [...] # list of tensors to be allreduced, each on a GPU\nallreduce(tensor_list, group_name='my_group_name')\n```\n\nIf you don't have access to `ray.util.collective.allreduce`, you can implement a custom all-reduce using `ray.util.collective.reduce` and a loop:\n\n1. Import the necessary Ray modules:\n```python\nimport ray\nfrom ray.util.collective import reduce\n```\n2. Create a list of actors.\n3. Perform the all-reduce operation using a loop and `reduce`:\n```python\ntensor_list = [...] # list of tensors to be allreduced, each on a GPU\ngroup_name = 'my_group_name'\n\n# Initialize the result tensor\nresult_tensor = tensor_list[0].new_full(tensor_list[0].size(), 0)\n\n# Perform the all-reduce operation\nfor tensor in tensor_list:\n reduced_tensor = reduce(tensor, 0, group_name)\n result_tensor += reduced_tensor\n```\n\nThis will all-reduce the tensors in the `tensor_list` using the `group_name` and the sum operation.",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/api/doc/ray.util.placement_group.html#ray-util-placement-group",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/scheduling/placement-group.html#bundles",2580"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",2581"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",2582"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2583"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",2584"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",2585"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",2586"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",2587"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.PlacementGroup.bundle_count.html#ray-util-placement-group-placementgroup-bundle-count",2588"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",2589"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment"2590],2591"document_ids": [25924790,25935063,25945050,25955051,259612053,259711072,25985076,25995060,26004789,26015069,26024746,26035077,2604142622605],2606"answer": " If you specify a bundle with `{\"CPU\":0}` in the PlacementGroup, it will reserve a bundle with zero CPUs. However, since at least one CPU is required for a bundle to be scheduled on a node, this bundle will not be able to be placed on any node, and the PlacementGroup will not be able to be created successfully.",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/ray-core/api/doc/ray.cancel.html#ray-cancel",2616"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",2617"https://docs.ray.io/en/master/workflows/comparison.html#trip-booking",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/vms/user-guides/community/yarn.html#cleaning-up",2624"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"2625],2626"document_ids": [26274550,26284375,26296559,26304894,26316710,26329757,26339791,263411590,26354513,263614701,26376690,26386486,263966862640],2641"answer": " To cancel a job from the UI, you can use the `ray job stop` command, providing the job ID as an argument. For example:\n\n$ ray job stop raysubmit\\_tUAuCKubPAEXh6CW\n\nThis will request the job to exit asynchronously. If you want to clean up the running job, you can use the `skein application shutdown` command with the application ID.\n\nNote that these commands are not UI-specific and should be executed in the terminal. If you are using a Ray UI that does not provide a way to cancel jobs, you may need to use these commands to stop the job.",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#using-a-remote-cluster",2648"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2649"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",2650"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2651"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#development-workflow-with-serve-run",2652"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",2653"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",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/serve/api/index.html#serve-deploy",2656"https://docs.ray.io/en/master/serve/api/index.html#serve-config",2657"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup",2658"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",2659"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard"2660],2661"document_ids": [266213680,26636562,266413677,26656552,266613659,266713719,26686577,266913831,267013987,267113986,26726549,267313718,2674135142675],2676"answer": " To get your project files on the cluster when using Ray Serve, you can use the `serve run` command with the `--address` flag to specify the address of the Ray cluster and the `--working-dir` flag to specify the local directory containing your project files. Here's an example command:\n```bash\nserve run --address=ray://<head-node-ip-address>:10001 --working-dir=\"./project/src\" local_dev:graph\n```\nReplace `<head-node-ip-address>` with the IP address of the head node of your Ray cluster. This command will connect to the remote cluster via Ray Client, upload the working\\_dir directory, and run your Ray Serve application.\n\nNote that you can also use the `serve deploy` command to deploy your Ray Serve application to a remote cluster, but this command does not upload any local files to the cluster. Instead, it deploys a configuration file that specifies how to create and deploy your application's components on the cluster. If you need to upload local files to the cluster, you should use the `serve run` command as described above.",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-overview/installation.html#installing-from-a-specific-commit",2688"https://docs.ray.io/en/master/ray-more-libs/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/examples/gentle_walkthrough.html#install-ray",2691"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",2692"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2693"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2694"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"2695],2696"document_ids": [269714560,2698288,269963,27006335,270166,2702291,270314566,2704883,27055412,27066103,2707271,27086351,270963172710],2711"answer": " To install the Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use the `pip install` command with the appropriate link to the nightly wheel. For example, for Python 3.8 on a Linux system, you would use:\n\n`pip install -U \"ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp38-cp38-manylinux2014_x86_64.whl\"`\n\nYou can find the appropriate link to the nightly wheel for your system and Python version in the context provided.",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/ray-more-libs/installation.html#daily-releases-nightlies",2718"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2719"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2720"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2721"https://docs.ray.io/en/master/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/ray-more-libs/installation.html#installing-from-a-specific-commit",2724"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",2725"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",2726"https://docs.ray.io/en/master/installation.html#from-wheels",2727"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",2728"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#install-the-latest-ray-release",2729"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only"2730],2731"document_ids": [273214560,2733288,273463,27356335,273666,2737291,273814566,273914557,2740286,274161,27425412,27435820,27448832745],2746"answer": " To install the latest Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use the following pip command with the link to the nightly wheel:\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 nightly wheel you want to install. The links for the latest nightly wheels can be found in the Ray documentation or through other reliable sources.",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/api/doc/ray.init.html#ray-init",2754"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",2755"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",2756"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",2757"https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",2758"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2759"https://docs.ray.io/en/master/ray-core/patterns/return-ray-put.html#code-example",2760"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",2761"https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution",2762"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",2763"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",2764"https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally"2765],2766"document_ids": [27675397,27684811,27695395,27704190,277114546,27724338,27735380,27745179,27756621,27769644,27774204,27785757,2779134352780],2781"answer": " To write unit tests for Ray code, you can follow these tips:\n\n1. Initialize Ray with a fixed number of CPUs for each test using `ray.init(num_cpus=...)`.\n2. If possible, share the Ray cluster across tests to avoid the overhead of creating a new cluster for each test.\n3. Consider creating a mini-cluster with `ray.cluster_utils.Cluster`.\n4. Be careful when running tests in parallel to avoid resource contention.\n5. After each test, call `ray.shutdown()` to release resources and ensure that the Ray cluster is not initialized using `assert not ray.is_initialized()`.\n\nHere's an example of how to write a unit test for Ray code in Python:\n\n```python\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n def setUp(self):\n ray.init(num_cpus=4, num_gpus=0)\n\n def tearDown(self):\n ray.shutdown()\n assert not ray.is_initialized()\n\n def test_my_ray_code(self):\n # Test your Ray code here\n pass\n```\n\nIn this example, `setUp` initializes Ray with 4 CPUs, and `tearDown` shuts down Ray and checks that it is not initialized. You can then write your test case in the `test_my_ray_code` method.",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/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",2788"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#disable-logging-to-the-driver",2789"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#distributed-progress-bars-tqdm",2790"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",2791"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2792"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",2793"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",2794"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",2795"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",2796"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",2797"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",2798"https://docs.ray.io/en/master/cluster/cli.html#ray-stop",2799"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks"2800],2801"document_ids": [28026558,2803523,2804529,2805524,28064209,2807519,2808410,280913929,28105128,28115766,2812525,28135613,281445502815],2816"answer": " To stop Ray from spamming lots of Info updates on stdout, you can try the following steps:\n\n1. Disable logging to the driver by setting `log_to_driver=False` in `ray.init`. This will prevent Task and Actor logs from being copied to the driver stdout.\n2. You can also redirect Worker logs to the Driver by using the `ray.get()` method. This will help users aggregate the logs for the distributed Ray application in a single place.\n3. If you are using the `tqdm_ray` library, you can disable the built-in print patching by setting `RAY_TQDM_PATCH_PRINT=0`. This will avoid progress bar corruption on driver print statements.\n4. By default, Ray deduplicates logs that appear redundantly across multiple processes. You can disable this feature by setting `RAY_DEDUP_LOGS=0` to print all logs without deduplication.\n5. If a task is hanging, you can cancel the task by calling `ray.cancel()` on an ObjectRef returned by the task. This will send a KeyboardInterrupt to the task's worker if it is mid-execution.\n\nIn your specific case, you can try setting `log_to_driver=False` in `ray.init` to prevent the logs from being printed to stdout. You can also try setting `RAY_DEDUP_LOGS=0` to print all logs without deduplication.",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/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",2823"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",2824"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#serve-a-stablediffusion-text-to-image-model-on-kubernetes",2825"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-7-submit-a-request-to-the-ray-serve-application",2826"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",2827"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#code-updates",2828"https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",2829"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",2830"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",2831"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",2832"https://docs.ray.io/en/master/serve/develop-and-deploy.html#deploy-ray-serve-in-production",2833"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",2834"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-4-forward-the-port-of-serve"2835],2836"document_ids": [28376127,283814398,28396124,28405989,28415671,284213692,284313797,28446128,284513677,284613622,284713441,284813987,284961292850],2851"answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install the required packages: `pip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers`\n2. Create a RayService by applying the provided configuration: `kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml`\n3. Test your Ray Serve script locally before deploying it to a RayService.\n4. Forward the port of the Serve application: `kubectl port-forward svc/stable-diffusion-serve-svc 8000`\n\nNote: Make sure to add the recommended taints to the GPU node and ensure that the runtime_env includes diffusers.\n\nFor more information on deploying Ray Serve applications, refer to the Ray documentation and the KubeRay operator documentation.",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-core/actors.html#passing-around-actor-handles",2859"https://docs.ray.io/en/master/ray-more-libs/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-more-libs/actors.html#passing-around-actor-handles",2864"https://docs.ray.io/en/master/ray-core/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/doc/ray.runtime_context.RuntimeContext.current_actor.html#ray-runtime-context-runtimecontext-current-actor",2869"https://docs.ray.io/en/master/ray-core/api/core.html#actors"2870],2871"document_ids": [287211587,28734509,287414698,28754860,28764615,287711588,287814699,28794511,28804200,28814623,28824871,28834874,288446602885],2886"answer": " An actor handle is a reference to a Ray actor, which is a stateful, long-running object in the Ray system. It allows you to call methods on the actor and access its state. In the provided context, actor handles are used to call the `increment` method on the `Counter` actor.",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/ray-core/actors/named-actors.html#actor-lifetimes",2893"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2894"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",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/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",2898"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2899"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2900"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2901"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",2902"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",2903"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",2904"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure"2905],2906"document_ids": [29074633,29084859,29092157,29104613,29114615,29124579,29134848,29144620,29154618,29164561,29174631,29188106,291945752920],2921"answer": " To kill a detached Ray actor in C++, you can use the `ray.kill` function, passing the actor's handle as an argument. Detached actors are not garbage-collected and must be manually destroyed once they are no longer needed. Here's the C++ syntax:\n\n```cpp\nactor_handle.Kill();\n```\n\nKeep in mind that this will not call any exit handlers installed in the actor using `std::atexit`. The actor will immediately exit, causing any current, pending, and future tasks to fail. If you want to allow pending tasks to finish before killing the actor, you can call `actor.__ray_terminate__.remote()` instead.\n\nIn Python, you can also use the `ray.kill` function to manually terminate the actor:\n\n```python\nray.kill(actor_handle)\n```\n\nThis will not go through the normal Python teardown logic, so any exit handlers installed in the actor using `atexit` will not be called.\n\nFor garbage collection, you can delete all references to the actor handle in Python, which will cause them to complete any outstanding work and then shut down. If you want to kill actors immediately, you can call `ray.kill(actor)`.\n\nIn case of a misbehaving actor, you can recover from the failure by manually terminating the actor using `ray.kill` on any handle to the actor. If `max_restarts` is set, you can allow Ray to automatically restart the actor by passing `no_restart=False` to `ray.kill`.",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/ray-core/handling-dependencies.html#debugging",2928"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2929"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2930"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",2931"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2932"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",2933"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2934"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",2935"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2936"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2937"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",2938"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",2939"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging"2940],2941"document_ids": [29424329,29434717,29444327,29454260,29464330,29474267,29484328,29494268,29504283,29514719,29524310,295310281,295443262955],2956"answer": " To force upgrade the pip package in the runtime environment if an old version exists, you can specify the pip version in the runtime\\_env using the \"pip\\_version\" parameter with the \"==\" operator and the desired pip version. For example:\n```python\nRuntimeEnv(\n pip={\"packages\":[\"tensorflow\", \"requests\"], \"pip_check\": False,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"}\n)\n```\nIn this example, the pip version is set to 22.0.2 for Python version 3.8.11. If an old version of a package exists, specifying the pip version in this way will force pip to upgrade the package to the specified version. Note that this will only affect the packages installed in the virtual environment created for the runtime environment, and not any packages installed in the system Python environment.",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/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",2963"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",2964"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2965"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2966"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2967"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2968"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",2969"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2970"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",2971"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition",2972"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2973"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",2974"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility"2975],2976"document_ids": [29777395,29786795,29796827,29809209,29816826,29829206,29836876,29846825,29856864,29867352,29876824,298814516,298992112990],2991"answer": " To do a global shuffle with Ray, you can use the `random_shuffle()` function on a Ray Dataset. Here is an example:\n```\nimport ray\n\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/sms_spam_collection_subset.txt\")\n\n# Do a global shuffle of all rows in this dataset.\n# The dataset will be shuffled on each iteration, unless `.materialize()`\n# is called after the `.random_shuffle()`\nds = ds.random_shuffle()\n```\nKeep in mind that `random_shuffle()` is a global shuffle, which can be slow and requires significant computation, I/O, and communication. If you don't need a full global shuffle, you can consider using the `local_shuffle_buffer_size` parameter when iterating over batches, which shuffles a subset of rows up to a provided buffer size during iteration. This can be more performant because it doesn't require excessive data movement.\n\nYou can also set the `DataContext.use_push_based_shuffle` flag to enable push-based shuffle, which can improve the performance of shuffling operations.\n\nFor more information on shuffling with Ray Data, you can see the [Optimize shuffling guide](https://docs.ray.io/en/latest/data/optimize-shuffling.html).",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-core/namespaces.html#specifying-namespace-for-named-actors",2998"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2999"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3000"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",3001"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",3002"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3003"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3004"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace",3005"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3006"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3007"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3008"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",3009"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"3010],3011"document_ids": [30124496,30134623,30144484,30154871,30164501,30174625,30184626,30194751,30204481,30214493,30224488,30234463,3024147463025],3026"answer": " To find the namespace of an Actor in Ray, you can use the `ray.get_actor` function, which returns an `ActorHandle` to the actor with the given name. The namespace can be specified as an optional parameter. If not provided, it defaults to the current namespace.\n\nHere's an example:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\n# Create an actor with a name in a specified namespace\nactor = Actor.options(name=\"some_name\", namespace=\"my_namespace\").remote()\n\n# Retrieve the actor later somewhere\nfound_actor = ray.get_actor(\"some_name\", namespace=\"my_namespace\")\n```\nIn this example, the actor is created in the \"my\\_namespace\" namespace, and then later retrieved using `ray.get_actor` with the specified 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/actors/async_api.html#asyncio-for-actors",3033"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",3034"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",3035"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3036"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",3037"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",3038"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",3039"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",3040"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",3041"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3042"https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution",3043"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3044"https://docs.ray.io/en/master/ray-core/patterns/concurrent-operations-async-actor.html#pattern-using-asyncio-to-run-actor-methods-concurrently"3045],3046"document_ids": [30474598,30484603,30494599,30504208,30515443,30524360,30534637,30544205,30554611,30564811,30579644,305813974,305951613060],3061"answer": " Ray can work with async.io by using the async/await syntax and integrating with popular async frameworks like aiohttp and aioredis. Ray automatically detects whether an actor supports async calls or not by using async method definitions. You can use asyncio.run() with ray.get() to get the results of the async actor methods. However, Ray doesn't support asyncio for remote tasks, but you can wrap the async function with a synchronous wrapper to run the task. Ray actors run in a single thread and actor method calls are executed sequentially, but by using await, you can yield control from the long-running method call so other method calls can run concurrently.",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/tips-for-first-time.html#tip-1-delay-ray-get",3068"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",3069"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",3070"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",3071"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",3072"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",3073"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",3074"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",3075"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#stepping-between-ray-tasks",3076"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",3077"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",3078"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",3079"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get"3080],3081"document_ids": [30824209,30834416,30845235,30855236,30865219,30875169,308814433,308913646,3090581,30914884,30924417,30934361,309442143095],3096"answer": " To debug a hanging `ray.get()` call, there are a few common anti-patterns to avoid:\n\n1. Calling `ray.get()` in a loop, which harms parallelism. Instead, separate the call to `ray.get()` from the call to remote functions, so all remote functions can spawn and run in parallel in the background.\n2. Calling `ray.get()` unnecessarily for intermediate steps. Work with object references directly and only call `ray.get()` at the end to get the final result.\n3. Fetching too many objects at once with `ray.get()`. Make sure to instantiate the receive side call if using `ray.get()` with object references.\n\nIn your specific case, ensure that you're not calling `ray.get()` in a loop or unnecessarily, and that you're not fetching too many objects at once. If the issue persists, you can use Ray's debugger to step through the code and identify the problem.",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-core/api/doc/ray.shutdown.html#ray-shutdown",3112"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",3113"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",3114"https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor"3115],3116"document_ids": [31174618,31184613,31194619,31204620,31214579,31224859,31234615,31244660,31254781,31264827,3127637,31284565,312944723130],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 resources associated with the actor. Here is an example:\n```\n@ray.remote\nclass Actor:\n def exit(self):\n ray.actor.exit_actor()\n\nactor = Actor.remote()\nactor.exit.remote()\n```\nHowever, it is generally not necessary to manually terminate an actor, as they are automatically garbage collected. The object reference resulting from the task can be waited on to wait for the actor to exit.\n\nTo force-kill a misbehaving actor, you can use the `ray.kill()` function. This will interrupt any running tasks on the actor and cause them to fail immediately. For example:\n```\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nactor_handle = Actor.remote()\n\nray.kill(actor_handle)\n```\nNote that this will not go through the normal Python or C++ teardown logic, so any exit handlers installed in the actor using `atexit` will not be called.",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/user-guides/cli-sdk.html#get-started",3147"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3148"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",3149"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#add-metadata-to-structured-logs"3150],3151"document_ids": [3152414,3153540,3154520,315514324,315614202,315714286,315814295,315914106,3160434,3161477,3162476,3163682,31645373165],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 log messages. 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# Create an instance of the actor and call the log method\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 logging 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 logs actor` command or the `get_log` function from the `ray.util.state` module. For example:\n```python\nfrom ray.util.state import get_log\n\n# In this case, ACTOR_ID is the ID of the actor\nfor line in get_log(actor_id=<ACTOR_ID>):\n print(line)\n```\nNote that if you're using concurrent actors (i.e. async actors or threaded actors), the log of the tasks may be interleaved. In this case, you should use `ray logs actor --id` to get the entire actor log.",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/data/working-with-text.html#performing-inference-on-text",3173"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt",3174"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",3175"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",3176"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#next-steps",3177"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#next-steps",3178"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",3179"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",3180"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",3181"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",3182"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",3183"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",3184"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference"3185],3186"document_ids": [31876999,318810470,31896843,319010259,319110219,319210137,319314575,31946998,31956938,31965292,3197140,31986936,319968453200],3201"answer": " Sure, I can help you write a script to perform batch inference with GPT-2 on text data from an S3 bucket using Ray. Here's an example script that you can modify to suit your needs:\n```python\nimport os\nimport ray\nfrom ray import data_block_api\nfrom transformers import GPT2Tokenizer, GPT2LMHeadModel\n\n# Download the GPT-2 model and tokenizer\nmodel_name = \"gpt2\"\ntokenizer = GPT2Tokenizer.from_pretrained(model_name)\nmodel = GPT2LMHeadModel.from_pretrained(model_name)\n\n# Define a function to generate text from a prompt\ndef generate_text(prompt: str, max_length: int = 50) -> str:\n inputs = tokenizer(prompt, return_tensors=\"pt\")\n outputs = model.generate(inputs[\"input_ids\"], max_length=max_length)\n return tokenizer.decode(outputs[0])\n\n# Define a function to process a batch of text data\n@ray.remote\ndef process_batch(batch: list) -> list:\n results = []\n for text in batch:\n result = generate_text(text)\n results.append(result)\n return results\n\n# Read the text data from an S3 bucket\ns3_bucket = \"s3://your-s3-bucket/path/to/text/data\"\ndata = data_block_api.read_text_block(s3_bucket)\n\n# Convert the data to a list of prompts\nprompts = [row[\"text\"] for row in data.iter_rows()]\n\n# Split the prompts into smaller batches\nbatch_size = 10\nbatches = [prompts[i:i+batch_size] for i in range(0, len(prompts), batch_size)]\n\n# Use Ray to process the batches in parallel\nray.init()\nresults = ray.get([process_batch.remote(batch) for batch in batches])\n\n# Combine the results into a single list\nfinal_results = [result for batch_results in results for result in batch_results]\n\n# Save the results to an S3 bucket\noutput_s3_bucket = \"s3://your-output-s3-bucket/path/to/output/data\"\ndata_block_api.write_text_block(final_results, output_s3_bucket)\n```\nIn this script, we first download the GPT-2 model and tokenizer using the Hugging Face Transformers library. We then define a function to generate text from a prompt using the model and tokenizer.\n\nNext, we define a function to process a batch of text data by calling the `generate_text` function for each prompt in the batch. We use the `ray.remote` decorator to create a remote function that can be executed in parallel by Ray.\n\nWe then read the text data from an S3 bucket using the Ray Data Block API, convert the data to a list of prompts, and split the prompts into smaller batches.\n\nWe use Ray to process the batches in parallel by calling the `process_batch.remote` function for each batch and storing the results in a list.\n\nFinally, we combine the results into a single list and save the results to an S3 bucket using the Ray Data Block API.\n\nNote that this script assumes that you have already installed Ray and the Hugging Face Transformers library. You may need to modify the script to suit your specific use case and data format.",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/user-guides/configure-logging.html#configuring-logging",3208"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3209"https://docs.ray.io/en/master/ray-observability/user-guides/index.html#user-guides",3210"https://docs.ray.io/en/master/cluster/cli.html#ray-start",3211"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#debugger-commands",3212"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-debug",3213"https://docs.ray.io/en/master/ray-contribute/debugging.html#debugging-for-ray-developers",3214"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",3215"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3216"https://docs.ray.io/en/master/ray-observability/index.html#monitoring-and-debugging",3217"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",3218"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#monitoring-with-the-cli-or-sdk",3219"https://docs.ray.io/en/master/ray-observability/key-concepts.html#debugger"3220],3221"document_ids": [3222510,3223680,3224546,32255611,3226579,32274669,3228944,3229573,32304323,3231450,32326631,3233465,32344053235],3236"answer": " To enable Ray debug logs, you can use the `--log-style` flag with the `ray start` or `ray init` command and set it to 'pretty' or 'record' for more detailed logging. Additionally, you can use the Ray debugger by running `ray debug` command to show all active breakpoints and exceptions. For runtime environment debugging, you can set the environment variable `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1` before starting Ray. This will print the full runtime environment setup log messages to the driver.",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/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",3243"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3244"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",3245"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3246"https://docs.ray.io/en/master/ray-observability/key-concepts.html#ray-states",3247"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3248"https://docs.ray.io/en/master/rllib/actors.html#actors",3249"https://docs.ray.io/en/master/ray-core/actors.html#actors",3250"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3251"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.html#ray-util-actorpool",3252"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#getting-a-summary-of-states-of-entities-by-type",3253"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",3254"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor"3255],3256"document_ids": [3257788,3258472,3259673,3260475,3261402,326214686,326311575,32644480,32654623,32664742,3267480,3268488,326946293270],3271"answer": " To list the current Ray actors from Python, you can use the `list_actors` function from the `ray.util.state` module. Here is an example:\n\n```python\nfrom ray.util.state import list_actors\n\nactors = list_actors()\nfor actor in actors:\n print(actor)\n```\n\nThis will print the ID, class name, name, process ID, and state of each actor in the cluster.\n\nYou can also use the `ray cli` to list the actors with the following command:\n\n```\nray list actors\n```\n\nThis will list the ID, class name, name, process ID, and state of each actor in the cluster. The `--detail` option can be used to include additional information about each actor.\n\nThe `ray get actors <ACTOR_ID>` command can be used to get detailed information about a specific actor.\n\nYou can also use the `ray summary actors` command to get a summary of the actors in the cluster. This command will return the number of actors, the number of pending actors, and the number of finished actors.\n\nI hope this helps! Let me know if you have any other questions.",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/serve/production-guide/fault-tolerance.html#deployment-replica-failure",3279"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3280"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3281"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3282"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3283"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3284"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3285"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",3286"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3287"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",3288"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",3289"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure"3290],3291"document_ids": [32924859,329313912,32944848,32954620,32964613,32974860,32984618,32994579,330013916,33014615,33024577,330313905,3304139153305],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's an example:\n```python\nimport ray\n\n# Get the actor handle\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\n\n# Kill the actor\nray.kill(replica_handle, no_restart=True)\n```\nThis will immediately interrupt any running tasks on the actor and cause them to fail. If you want to let pending tasks finish before killing the actor, you can call `actor.__ray_terminate__.remote()` instead.\n\nNote that if you only have one reference to an actor handle, calling `del actor` could trigger actor deletion. However, if your program has multiple references to the same ActorHandle, actor termination will not occur until the reference count goes to 0.\n\nAlso, keep in mind that garbage collection for actors has not been implemented yet, so this is currently the only way to terminate an actor gracefully. The ObjectRef resulting from the task can be waited on to wait for the actor to exit.\n\nFinally, if you want to kill actors immediately, you can also call `ray.kill(actor)`.",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-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3313"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#accelerator-types",3314"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",3315"https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",3316"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3317"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3318"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-workload-scheduling",3319"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",3320"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",3321"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3322"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",3323"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",3324"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors"3325],3326"document_ids": [33274994,33285006,33296590,33304366,33314996,33324997,33336017,33344697,33354702,333614767,33375114,333814475,333949953340],3341"answer": " To specify that a task should run on a V100 GPU, you can use the `@ray.remote(num_gpus=1, accelerator_type=\"NVIDIA_TESLA_V100\")` decorator when defining the remote function. This will tell Ray to schedule the task to a node with a free V100 GPU and assign it to the task.\n\nHere is an example:\n```\n@ray.remote(num_gpus=1, accelerator_type=\"NVIDIA_TESLA_V100\")\ndef train(data):\n return \"This function was run on a node with a Tesla V100 GPU\"\n\nray.get(train.remote(1))\n```\nNote that the `accelerator_type` argument is used to specify the type of GPU that the task should run on. The available accelerator types include Nvidia GPUs, and you can see the currently detected accelerator types by importing `ray.util.accelerators`.\n\nAlso, keep in mind that the `num_gpus` argument only specifies the number of GPUs required for the task, and does not guarantee that the task will be scheduled to a specific GPU. To specify the GPU that the task should run on, you can use the `accelerator_type` argument as described above.",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/cluster/vms/index.html#learn-more",3358"https://docs.ray.io/en/master/train/train.html#get-started",3359"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#walkthrough"3360],3361"document_ids": [33626468,3363220,3364864,336513625,33664469,33671049,336813827,33694185,33706043,33715646,33726210,33738244,337480783375],3376"answer": " To get started with Ray, you should:\n\n1. Install Ray by running `pip install -U ray`.\n2. Import and initialize Ray in your code:\n```python\nimport ray\nray.init()\n```\nIf you're using Ray version >=1.5, `ray.init()` is automatically called on the first use of a Ray remote API.\n\nFor Ray Serve, you can follow the quick start tutorials, learn key concepts, and check out examples in the Ray Serve documentation.\n\nIf you want to contribute to Tune, look for issues labeled \"good first issue\" and \"help wanted\" on the Tune Github project board and include \"[tune]\" in the title when raising a new issue or pull request.\n\nFor Ray on cloud VMs, you can learn how to start a Ray cluster, deploy Ray applications, and find API references in the Ray docs.\n\nCheck the Ray documentation for more information on distributed training with Ray Train and specific frameworks like PyTorch, PyTorch Lightning, and Hugging Face Transformers.",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/ray-core/handling-dependencies.html#debugging",3383"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3384"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3385"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",3386"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",3387"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3388"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",3389"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3390"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",3391"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3392"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.runtime_env.html#ray-job-submission-jobinfo-runtime-env",3393"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",3394"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.set_runtime_env.html#ray-job-config-jobconfig-set-runtime-env"3395],3396"document_ids": [33974330,33984329,33994717,340013856,34014653,34024327,34034253,34044283,34055679,34064712,34076665,34084310,340948263410],3411"answer": " To specify the Python version in the runtime environment, you can use the `env_vars` key in the `runtime_env` dictionary to set the `PYTHON_VERSION` environment variable. Here's an example:\n```python\nruntime_env = {\n \"env_vars\": {\n \"PYTHON_VERSION\": \"3.8\"\n },\n \"pip\": [\"my_package\"]\n}\n```\nNote that this sets the environment variable for the runtime environment, but it doesn't actually control which Python version is used to create the environment. To use a specific Python version with conda, you can create a new conda environment with that version and specify it in the `conda` key of the `runtime_env` dictionary.\n\nAlso note that the `pip_version` key in the `pip` dictionary is used to specify the version of pip to use in the environment, not the version of Python.",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/namespaces.html#specifying-namespace-for-named-actors",3418"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3419"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3420"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3421"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3422"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3423"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3424"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3425"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3426"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",3427"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",3428"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",3429"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"3430],3431"document_ids": [34324496,34334626,34344625,34354623,34364491,34374484,34384488,34394481,34404493,34414501,34424871,34434498,344446273445],3446"answer": " To create an actor in a specific namespace in Ray, you can use the `options` method and set the `name` and `namespace` parameters. Here is an example in Python:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nctx = ray.init(\"ray://localhost:10001\")\n\n# Create an actor with a specified namespace.\nActor.options(name=\"my_actor\", namespace=\"actor_namespace\", lifetime=\"detached\").remote()\n```\n\nIn this example, an actor named \"my\\_actor\" is created in the \"actor\\_namespace\" namespace. The actor will belong to this namespace, regardless of the namespace of the current job.\n\nYou can also specify the namespace when retrieving the actor using the `get_actor` function:\n\n```python\n# Retrieve the actor later somewhere.\nmy_actor = ray.get_actor(\"my_actor\", namespace=\"actor_namespace\")\n```\n\nNamed actors are scoped by namespace. If no namespace is assigned, they will be placed in an anonymous namespace by default.",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/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",3453"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3454"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",3455"https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.PathPartitionParser.html#ray-data-datasource-pathpartitionparser",3456"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3457"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",3458"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3459"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",3460"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",3461"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",3462"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",3463"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3464"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris"3465],3466"document_ids": [34671775,34684277,34694301,34707297,34714273,347210170,34731790,34744305,347510169,34766597,34771066,34786632,347943033480],3481"answer": " No, you cannot specify multiple working directories in Ray. The working directory is a single location where your job's files will be run. You can specify a local directory or a remote URI to a .zip file as your working directory. If you need to access files in the original working directory, you can use relative paths, but you need to ensure that the directory is synced on all nodes if running on multiple machines.",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/rllib/rllib-training.html#specifying-resources",3488"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",3489"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3490"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",3491"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",3492"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#code-example",3493"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3494"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",3495"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.entrypoint_num_cpus.html#ray-job-submission-jobinfo-entrypoint-num-cpus",3496"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",3497"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",3498"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.num_cpus_per_worker.html#ray-train-scalingconfig-num-cpus-per-worker",3499"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application"3500],3501"document_ids": [350211063,3503623,35044850,35055936,35066716,35075234,35084853,35095068,35106703,35114877,35127639,35138998,3514135753515],3516"answer": " If you set `num_cpus=0` for tasks, your tasks will be built solely on the CPU, even if GPUs are available on the machine. However, keep in mind that if your tasks are very small or have variable durations, you might not see speedups. Also, if your tasks require more CPU resources than allocated, you may encounter performance issues or even runtime errors.",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/user-guides/configuring-autoscaling.html#kuberay-autoscaling",3523"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",3524"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",3525"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",3526"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",3527"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",3528"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",3529"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",3530"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",3531"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",3532"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#overview",3533"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",3534"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler"3535],3536"document_ids": [35375953,35385818,35395752,35405969,35415963,354213957,35435656,35445742,35455642,35465958,35475956,35485788,354959753550],3551"answer": " Yes, it is possible to have Ray on K8s without using KubeRay, but you will not be able to use KubeRay's built-in autoscaling feature. Instead, you can use Kubernetes' own autoscaling features, such as the Kubernetes Cluster Autoscaler. This can be configured to work alongside the Ray Autoscaler, which decides when to create or delete Ray pods, while the Kubernetes Cluster Autoscaler provisions or cleans up Kubernetes nodes as needed. However, it is recommended to configure your RayCluster so that only one Ray pod fits per Kubernetes node to ensure a one-to-one correspondence between Ray Autoscaler pod scaling events and cluster autoscaler events.",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/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",3558"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",3559"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",3560"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",3561"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",3562"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",3563"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gke-gcs-bucket.html#create-a-minimal-raycluster-yaml-manifest",3564"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",3565"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide",3566"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",3567"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-2-deploy-a-ray-cluster-on-kubernetes-with-the-kuberay-operator",3568"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",3569"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-2-deploy-a-ray-cluster-on-kubernetes-with-the-kuberay-operator"3570],3571"document_ids": [35725717,35735918,35745917,35755656,35765642,35775605,35786034,35795958,35805784,35815953,35826161,35836500,358461633585],3586"answer": " To manually configure and manage a Ray cluster on Kubernetes, follow these steps:\n\n1. Install `kubectl` and Helm.\n2. Prepare a Kubernetes cluster.\n3. Deploy a KubeRay operator using Helm.\n4. Create a `RayCluster` custom resource YAML file (`raycluster.yaml`) with the desired configuration.\n5. Apply the `RayCluster` custom resource to create the Ray cluster: `kubectl apply -f raycluster.yaml`.\n6. To manage the Ray cluster, use the commands provided in the Cluster Management CLI section, such as `kubectl get rayclusters` to view the list of Ray clusters, or `kubectl delete raycluster <cluster-name>` to delete a Ray cluster.\n7. Optionally, configure KubeRay autoscaling by setting up the Ray autoscaler on Kubernetes.\n\nFor a minimal RayCluster YAML manifest, you can download it using `curl -LO <https://...>` and include the necessary lines in every pod spec of your Ray cluster.\n\nFor more advanced configurations, refer to the RayCluster Configuration and User Guides.",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/scheduling/ray-oom-prevention.html#worker-killing-policy",3593"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",3594"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",3595"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",3596"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options",3597"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",3598"https://docs.ray.io/en/master/serve/api/doc/ray.serve.shutdown.html#ray-serve-shutdown",3599"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",3600"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",3601"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3602"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job",3603"https://docs.ray.io/en/master/cluster/key-concepts.html#worker-node",3604"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors"3605],3606"document_ids": [36075129,36084531,36096216,36104528,361114514,361214522,361314043,36144523,361513448,36166390,36176694,36185558,36195553620],3621"answer": " Shutting down a raylet will not directly kill tasks and workers on that node. However, it can lead to the termination of worker processes if it's due to memory pressure or other reasons that cause the raylet to fail. In such cases, the Ray cluster will mark the node as dead and treat it like a node failure.",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-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",3628"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",3629"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",3630"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management",3631"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#what-s-the-out-of-memory-error",3632"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",3633"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",3634"https://docs.ray.io/en/master/serve/monitoring.html#profiling-memory",3635"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",3636"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3637"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#what-is-the-memory-monitor",3638"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",3639"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#failures-due-to-memory-issues"3640],3641"document_ids": [3642547,3643548,3644559,36455010,3646549,36475118,3648562,364913549,36505016,3651557,36525119,36535024,36546063655],3656"answer": " To debug out-of-memory issues in Ray, you can follow these steps:\n\n1. Familiarize yourself with Ray's Memory Management model.\n2. View the per Task and Actor memory usage graph to locate Tasks and Actors with high memory usage.\n3. Use the CLI command `htop` or the `ray memory` command to get more details about memory usage.\n4. If you notice a memory leak on a Serve actor, use `memray` to debug.\n5. Refer to the documentation on how to address the out of memory issue: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>.\n\nFor more information, you can refer to the following documentation:\n\n* Debugging Memory Issues: <https://docs.ray.io/en/latest/memory-debugging.html>\n* Debugging Out of Memory: <https://docs.ray.io/en/latest/memory-debugging.html#debugging-out-of-memory>\n* Memory Management: <https://docs.ray.io/en/latest/memory-management.html>\n* Out-Of-Memory Prevention: <https://docs.ray.io/en/latest/memory-debugging.html#out-of-memory-prevention>\n* Head node out-of-Memory error: <https://docs.ray.io/en/latest/memory-debugging.html#head-node-out-of-memory-error>\n* Debugging using \u2018ray memory\u2019: <https://docs.ray.io/en/latest/memory-debugging.html#debugging-using-ray-memory>\n* What is the memory monitor?: <https://docs.ray.io/en/latest/memory-debugging.html#what-is-the-memory-monitor>",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/train/examples/transformers/transformers_torch_trainer_basic.html#ray-train-basic-example-for-huggingface-transformers",3663"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",3664"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.html#ray-train-huggingface-transformers-raytrainreportcallback",3665"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",3666"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.html#ray-tune-callback",3667"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_end.html#ray-train-lightning-raytrainreportcallback-on-init-end",3668"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_start.html#ray-train-lightning-raytrainreportcallback-on-init-start",3669"https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",3670"https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune",3671"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_batch_start.html#ray-train-lightning-raytrainreportcallback-on-batch-start",3672"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_load_checkpoint.html#ray-train-lightning-raytrainreportcallback-on-load-checkpoint",3673"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_pretrain_routine_end.html#ray-train-lightning-raytrainreportcallback-on-pretrain-routine-end",3674"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_pretrain_routine_start.html#ray-train-lightning-raytrainreportcallback-on-pretrain-routine-start"3675],3676"document_ids": [36779553,36789165,36798635,36808206,36811405,36828531,36838760,36849934,36852390,36868999,36879107,36888406,368991153690],3691"answer": " To use a callback in the Trainer class from the Hugging Face Transformers library, you can add it to your Trainer instance using the `add_callback` method. For example, to use the `RayTrainReportCallback` from the `ray.train.huggingface.transformers` module, you can do:\n\n```\nfrom ray.train.huggingface.transformers import RayTrainReportCallback\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=small_train_ds,\n eval_dataset=small_eval_ds,\n compute_metrics=compute_metrics,\n)\n\ntrainer.add_callback(RayTrainReportCallback())\n```\n\nAlternatively, you can implement your own custom callback by creating a class that inherits from the `TrainerCallback` class from the `transformers.trainer_callback` module, and implementing the desired behavior in the methods of your class. For example:\n\n```\nfrom transformers.trainer_callback import TrainerCallback\n\nclass MyTrainReportCallback(TrainerCallback):\n def __init__(self):\n super().__init__()\n self.metrics = {}\n\n def on_train_begin(self, args, state, control, **kwargs):\n print(\"Training has begun!\")\n\n def on_train_end(self, args, state, control, **kwargs):\n print(\"Training has ended!\")\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=small_train_ds,\n eval_dataset=small_eval_ds,\n compute_metrics=compute_metrics,\n callbacks=[MyTrainReportCallback()],\n)\n```\n\nThis will cause the `on_train_begin` and `on_train_end` methods of your `MyTrainReportCallback` class to be called at the beginning and end of training, respectively. You can implement other methods to customize the behavior of your callback at other points during training.",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/ray-client.html#uploads",3699"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",3700"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3701"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3702"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",3703"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3704"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3705"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",3706"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",3707"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3708"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",3709"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"3710],3711"document_ids": [37126632,37136633,37144262,37151790,3716511,37176562,37186551,37196552,37206596,37211775,37224385,37234656,372448113725],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\n```python\nimport os\nimport ray\n\n# Create a working directory and add a file to it\nos.makedirs(\"/tmp/my_working_dir\", exist_ok=True)\nwith open(\"/tmp/my_working_dir/hello.txt\", \"w\") as hello_file:\n hello_file.write(\"Hello World!\")\n\n# Specify the runtime environment with the working directory\nray.init(runtime_env={\"working_dir\": \"/tmp/my_working_dir\"})\n```\n\nThis will set the working directory for Ray workers to `/tmp/my_working_dir`. Relative paths in the remote tasks and actors will work on your laptop and on the cluster without any code changes.",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/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",3733"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",3734"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",3735"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3736"https://docs.ray.io/en/master/rllib/actors.html#actors",3737"https://docs.ray.io/en/master/ray-core/actors.html#actors",3738"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",3739"https://docs.ray.io/en/master/ray-core/api/doc/ray.cross_language.java_actor_class.html#ray-cross-language-java-actor-class",3740"https://docs.ray.io/en/master/ray-core/api/core.html#actors",3741"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",3742"https://docs.ray.io/en/master/ray-core/actors.html#actors",3743"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3744"https://docs.ray.io/en/master/rllib/actors.html#actors"3745],3746"document_ids": [37474876,37484629,37495192,375014687,375111576,37524482,37535391,37544916,37554660,37564428,37574487,375814689,3759115783760],3761"answer": " To create an actor instance with a parameter in Ray, you can use the `.remote()` method on the actor class and pass the parameter to the constructor as a keyword argument. Here's an example in Python:\n```python\nimport ray\n\n@ray.remote\nclass Greeter:\n def __init__(self, value):\n self.value = value\n\n def say_hello(self):\n return self.value\n\n# Create an actor instance with a parameter\ngreeter = Greeter.remote(value='Hello, world!')\n\n# Call the actor method\nprint(ray.get(greeter.say_hello.remote()))\n```\nThis will create a new actor instance of the `Greeter` class with the value `'Hello, world!'` and call its `say_hello` method. The `ray.get()` function is used to get the result of the remote function call.",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/ray-core/actors.html#actors",3773"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3774"https://docs.ray.io/en/master/rllib/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/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",3779"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails"3780],3781"document_ids": [37824660,37835443,37844758,3785568,378613459,37874480,378814686,378911575,379014219,37915387,37924472,37934876,3794140923795],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 pass a reference to the module to the Actor's constructor. Here is an example:\n\nFirst, put the custom module into the object store:\n```\nray.put(my_custom_module)\n```\nThen, in the Actor's constructor, retrieve the custom module from the object store:\n```\n@ray.remote\nclass MyActor:\n def __init__(self, custom_module_ref):\n self.custom_module = ray.get(custom_module_ref)\n```\nFinally, when creating the Actor, pass a reference to the custom module:\n```\ncustom_module_ref = ray.put(my_custom_module)\nmy_actor = MyActor.remote(custom_module_ref)\n```\nThis will make the custom module available to the Actor, allowing it to be used by the Actor's methods.",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/ray-observability/user-guides/configure-logging.html#logging-directory",3804"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3805"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#submit-the-workload",3806"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#submit-the-workload",3807"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3808"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",3809"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",3810"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",3811"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",3812"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",3813"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",3814"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files"3815],3816"document_ids": [38176632,3818511,38196551,38206528,38216179,38226633,38236530,38246157,38255732,3826519,38276181,38285757,382942643830],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_dir\"])\n```\nHowever, this will only print the working directory of the Ray cluster, not the working directory of the local machine. If you want to print the local working directory, you can simply use:\n```python\nimport os\nprint(os.getcwd())\n```\nNote that the local working directory is not automatically uploaded to the Ray cluster when you call `ray.init()`. You will need to manually upload any necessary files to the Ray cluster using the `ray upload` command or by specifying a remote cloud storage URI when calling `ray.init()`.",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/getting-started.html#logs-view",3838"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",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/user-guides/configure-logging.html#logging-directory",3841"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",3842"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",3843"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",3844"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",3845"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",3846"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",3847"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3848"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3849"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence"3850],3851"document_ids": [3852444,3853680,38546631,3855511,3856662,3857445,3858530,3859415,38606360,3861535,3862493,3863477,386463593865],3866"answer": " Based on the context provided, you may not be able to see `log.info` messages in the Ray dashboard because the logs are not being written to the default Ray log directory. When using the Ray client, logs can be found at `/tmp/ray/session_latest/logs` on the head node. Additionally, you can use Ray's `list_logs` and `get_log` functions to access logs programmatically. If you are running the Ray job by executing the Ray driver on the head node directly or connecting via Ray Client, the driver logs will not be captured in the Ray logging directory and can be viewed in the terminal or Jupyter Notebook output.",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/data/api/doc/ray.data.read_images.html#ray-data-read-images",3873"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",3874"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",3875"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",3876"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",3877"https://docs.ray.io/en/master/data/data-internals.html#reading-files",3878"https://docs.ray.io/en/master/data/user-guide.html#user-guides",3879"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",3880"https://docs.ray.io/en/master/data/examples/batch_training.html#filtering-a-dataset-on-read-a-class-anchor-id-filter-ds-a",3881"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",3882"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",3883"https://docs.ray.io/en/master/data/examples/batch_training.html#transforming-a-dataset-in-parallel-using-custom-functions-a-class-anchor-id-transform-ds-a",3884"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images"3885],3886"document_ids": [38877362,38889210,38896835,38907865,38917495,38926874,38936911,38947782,38957870,38969188,38977564,38987875,389973603900],3901"answer": " No, when using Ray Dataset to read a file, the order of the data is not guaranteed to be preserved by default. However, you can enable reproducibility and preserve the order of the data by setting the `preserve_order` flag in the DataContext as follows:\n```python\nimport ray\n\nctx = ray.data.DataContext.get_current()\nctx.execution_options.preserve_order = True\n```\nThis ensures that the data is processed in the order in which it appears in the file.",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/tasks.html#fault-tolerance",3908"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3909"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3910"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",3911"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",3912"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3913"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3914"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayTaskError.html#ray-exceptions-raytaskerror",3915"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",3916"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3917"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",3918"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",3919"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure"3920],3921"document_ids": [39224377,39234542,39244538,39254533,39264348,39274546,39284643,39294779,39304564,39314646,39324674,39334897,393445633935],3936"answer": " The sentence \"Ray will not retry tasks upon exceptions thrown by application code\" means that if a task fails due to an exception raised in the application code, Ray will not automatically retry executing that task by default. This behavior can be changed by modifying the `retry_exceptions` argument in `ray.remote()` or `.options()` to specify which application-level errors should be retried.",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/cluster/kubernetes/user-guides/logging.html#mounting-the-ray-log-directory",3946"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",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-core/configure.html#logging-and-debugging",3954"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"3955],3956"document_ids": [3957511,39586360,39595861,39605871,3961680,3962510,39635880,3964530,39654811,3966542,39674385,39684384,3969135173970],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_dir` configuration when calling `ray.init()`. Here's an example:\n\nFor `ray start`:\n```bash\nray start --temp-dir=/path/to/temp/dir --logs-dir=/path/to/log/dir\n```\nFor `ray.init()`:\n```python\nimport ray\n\nray.init(temp_dir=\"/path/to/temp/dir\", log_dir=\"/path/to/log/dir\")\n```\nAlternatively, you can also set the `RAY_TMP_DIR` and `RAY_LOG_DIR` environment variables before starting Ray:\n```bash\nexport RAY_TMP_DIR=/path/to/temp/dir\nexport RAY_LOG_DIR=/path/to/log/dir\nray start\n```\nNote that changing the temp directory will also change the default log directory, as Ray stores log files in a `logs` subdirectory of the temp 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/references/ray-cluster-configuration.html#full-configuration",3988"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",3989"https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters"3990],3991"document_ids": [39926337,39936328,39945615,39956203,39966392,39976522,39984193,3999851,40006501,400114491,40026353,40036411,400444644005],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 the provider and node_config sections with your cloud provider's configuration\nprovider:\n type: aws\n region: us-west-2\n availability_zone: us-west-2a\n\nauth:\n ssh_user: ubuntu\n\n# You can customize the worker_start_ray_commands if needed\nworker_start_ray_commands:\n - ray stop\n - ray start --address=$RAY_HEAD_IP:637",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/cluster/package-overview.html#ray-cluster-management-api",4014"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",4015"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",4016"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",4017"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",4018"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",4019"https://docs.ray.io/en/master/cluster/cli.html#ray-up",4020"https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started",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/cluster/vms/user-guides/launching-clusters/gcp.html#start-ray-with-the-ray-cluster-launcher",4024"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data"4025],4026"document_ids": [40276209,40285604,40294650,403014710,40315600,40326715,40336363,40345615,40356189,40366371,4037368,40386514,403979804040],4041"answer": " To scale up a Ray cluster, you can use the `ray up` command with the `--min-workers` and `--max-workers` options to set the desired number of worker nodes. Alternatively, you can use the `ray.autoscaler.sdk.request_resources()` function within a Ray program to command the autoscaler to scale the cluster up to a desired size. This will immediately attempt to scale to the requested resources, bypassing normal upscaling speed constraints. You can also configure the Ray autoscaler using the Ray cluster launcher to automatically scale the cluster up and down based on resource demand.",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/ray-overview/getting-started.html#ray-cluster-quickstart",4049"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4050"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",4051"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",4052"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents",4053"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",4054"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",4055"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-2-check-ports",4056"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",4057"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",4058"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#launching-a-cluster-ray-up",4059"https://docs.ray.io/en/master/cluster/vms/index.html#overview"4060],4061"document_ids": [40626199,4063273,40646203,40655503,40666491,40676519,40686447,40696441,40706617,40716104,40726448,40736212,407462094075],4076"answer": " To specify a subnet in the Ray cluster deployment file for AWS, you can include the `provider.security_group` field in the cluster configuration file (cluster.yaml or example-full.yaml) and set it to the name of the security group that you want to use. This will configure the security group to allow inbound access for the Ray Client port on the head node from your local machine.\n\nHere is an example of how you can specify a subnet in the cluster configuration file:\n\n```\ncluster_name: my-ray-cluster\nprovider:\n type: aws\n region: us-west-2\n security_group: my-security-group\n```\n\nIn this example, `my-security-group` is the name of the security group that you want to use for the Ray cluster. This security group should be configured to allow inbound access for the Ray Client port (by default, this is port 6379) from your local machine.\n\nYou can find more information about launching Ray clusters on AWS in the Ray documentation: <https://docs.ray.io/en/latest/cluster/aws.html>",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/serve/api/index.html#ray-serve-api",4083"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",4084"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4085"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",4086"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",4087"https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally",4088"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4089"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",4090"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",4091"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4092"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",4093"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4094"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods"4095],4096"document_ids": [409713974,409813541,40996631,41005671,41015747,410213437,410313517,41045802,410514025,410613523,41074694,410814288,410956654110],4111"answer": " To find the HTTP server error code log for Ray serve, you can check the logs stored at /tmp/ray/session\\_latest/logs/serve/. These logs include system-level logs from the Serve controller and proxy, as well as access logs and custom user logs produced from within deployment replicas. You can parse these stored logs with a logging stack such as ELK or Loki to be able to search by deployment or replica.\n\nAdditionally, you can check the logs of Ray Pods by accessing the log files on the pods using the command `kubectl exec -it $RAY_POD -n $YOUR_NAMESPACE -- bash` and checking the logs under /tmp/ray/session\\_latest/logs/serve/.\n\nRay Serve supports log rotation of these logs through setting the environment variables RAY\\_ROTATION\\_MAX\\_BYTES and RAY\\_ROTATION\\_BACKUP\\_COUNT. To silence the replica-level logs or otherwise configure logging, you can configure the \"ray.serve\" logger inside the deployment constructor.",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/ray-observability/getting-started.html#set-up-dashboard",4119"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",4120"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",4121"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",4122"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",4123"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",4124"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",4125"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",4126"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",4127"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#dashboard-host",4128"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",4129"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard"4130],4131"document_ids": [41325530,4133418,41345528,41355526,41366562,41376561,41385543,41395531,414013681,41416223,41425935,4143417,4144135144145],4146"answer": " This issue is not explicitly mentioned as a known problem, but there are several suggestions for how to access the Ray Dashboard in a remote cluster setup. It is recommended to set up port forwarding using the `ray dashboard` command and the cluster configuration file. If the dashboard port is not publicly exposed, you may need to configure it for external access. It is also recommended to set up Prometheus and Grafana for enhanced dashboard experience. The dashboard agent's default port is 52365, but it can be changed using the `--dashboard-agent-listen-port` argument when running `ray start`. The `dashboard-host` field should be set to \"0.0.0.0\" to expose the Ray dashboard outside the Ray cluster.",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/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",4153"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4154"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4155"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",4156"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",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/user-guides/configuring-autoscaling.html#cluster-config-parameters",4159"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",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/cluster/key-concepts.html#head-node",4163"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#max-workers",4164"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"4165],4166"document_ids": [41676292,4168256,41696345,41706392,41716522,41726367,41736366,41746227,41756337,41766328,41775557,41786241,417963114180],4181"answer": " In the Ray cluster launcher YAML, `max_workers` includes only worker nodes, not the head node. The head node is managed separately and has its own configuration options. The `max_workers` option sets the maximum number of worker nodes that can be launched in addition to the head node.",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-configuration.html#full-configuration",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": [42026632,42031775,42041790,42058894,42061068,42076214,4208619,42091066,42104273,42116222,42126331,4213269,421463154215],4216"answer": " To update files in the working directory, you can use the `ray rsync-up` command to upload files to the cluster head node. Here's the general format:\n\n$ ray rsync\\_up cluster.yaml '/local/path' '/path/on/cluster'\n\nThis command will synchronize the files from your local machine to the cluster head node. Note that if you're running a multi-node cluster, you'll need to manually copy the files to each worker node as well.\n\nAdditionally, if you want to continuously sync changes to the worker nodes, you can set the `file_mounts_sync_continuously` configuration option to `True` in your Ray cluster configuration file. This will ensure that any changes to the synchronized files or directories on the head node are automatically reflected on the worker nodes.\n\nIt's important to note that changing the working directory can lead to conflicts and overwriting of files, so it's recommended to use `ray.train.get_context().get_trial_dir()` as the path to save any outputs and avoid writing to the shared working directory.",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/ray-core/configure.html#logging-and-debugging",4224"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4225"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4226"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",4227"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",4228"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",4229"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4230"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",4231"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4232"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",4233"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",4234"https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution"4235],4236"document_ids": [42376632,42384385,42394811,42406555,42411775,42421790,42434263,4244511,42456157,42466633,42476214,42484262,424996444250],4251"answer": " To update the working directory file when Ray is already initiated, you can use the `ray.put()` and `ray.get()` functions to store and retrieve data in the object store, which can be accessed by all workers in the cluster.\n\nFor example, to update a file called \"data.txt\" in the working directory, you can do:\n```python\n# Write data to the object store\nray.put(\"data.txt\", open(\"data.txt\", \"rb\").read())\n\n# Update the file on all nodes\nfor i in range(ray.cluster_size()):\n ray.call_async(update_file, i)\n\n# Define the update_file function\n@ray.remote\ndef update_file(node_id):\n # Get the data from the object store\n data = ray.get(\"data.txt\")\n\n # Write the data to the file on the current node\n with open(f\"/tmp/ray/session_latest/runtime_resources/_ray_pkg_{node_id}/data.txt\", \"wb\") as f:\n f.write(data)\n```\nThis will write the contents of \"data.txt\" to the object store, then call the `update_file` function on each node in the cluster to write the data to the file on that node.\n\nNote that this assumes that the working directory has been uploaded to the cluster as described in the context you provided. If you need to update the working directory itself (i.e., add or remove files), you may need to use a different approach, such as updating the file mounts in the Ray cluster configuration and restarting the cluster.",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/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4258"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",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/references/ray-cluster-configuration.html#full-configuration",4261"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4262"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4263"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4264"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4265"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",4266"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",4267"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4268"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4269"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding"4270],4271"document_ids": [42726330,4273261,42746307,42756340,42766318,4277272,42786314,4279268,42806205,42816502,42826324,42836353,428466204285],4286"answer": " To force the Ray head node to use a custom PEM file for SSHing into the worker node, you can specify the `ssh_private_key` field in the `auth` section of your configuration file. For example:\n```\nauth:\n ssh_user: ubuntu\n ssh_private_key: /path/to/your/custom/key.pem\n```\nMake sure that the specified private key file matches the public key that is specified in the `file_mounts` section and is added to the project-wide metadata. Also, ensure that the corresponding private key is added to the head and worker node configurations.",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-references/glossary.html#ray-glossary",4293"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",4294"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",4295"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",4296"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-operating-system-settings",4297"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",4298"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",4299"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",4300"https://docs.ray.io/en/master/ray-core/configure.html#head-node",4301"https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",4302"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",4303"https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",4304"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error"4305],4306"document_ids": [430714740,43084522,43096375,4310563,43114466,431213870,43134530,43145011,43154390,4316940,431713928,4318939,43195624320],4321"answer": " The GCS (Global Control Service) server is a centralized metadata server for a Ray cluster that runs on the Ray head node. It has functions like managing node membership and actor directory. The GCS server stores all its data in-memory, which means it can take up a significant amount of memory on the head node. If your GCS server is taking up too much memory, you may need to consider increasing the memory capacity of your head node or tuning the operating system settings to support a larger number of network connections. Additionally, if you are running tasks and actors on the head node, you may want to consider specifying --num-cpus=0 when starting a head node to prevent out-of-memory issues.",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/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",4330"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",4331"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",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-overview/getting-started.html#ray-cluster-quickstart",4334"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",4335"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",4336"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",4337"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#troubleshooting",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/kubernetes/user-guides/pod-command.html#solution-1-container-command-recommended"4340],4341"document_ids": [4342467,4343852,43446147,43456215,43469510,43475724,4348254,434910208,4350466,43519898,43522428,435310218,435458474355],4356"answer": " If you encounter nodes that are \"pending\" for a long time when starting a cluster with `ray up`, you can try the following steps to debug:\n\n1. Run `ray status` to view the detailed cluster status. This will show you the status of each node in the cluster, as well as any recent failures.\n2. Check the autoscaler monitor log (`cat /tmp/ray/session_latest/logs/monitor.*`) for any errors or warnings.\n3. If the head node is out of date or mismatched with the specified YAML, consider terminating the out-of-date node and provisioning a new one with the correct setup/file mounts/ray start commands.\n4. If your pods are stuck in the \"Pending\" state, you can check for errors via `kubectl describe pod <pod_name>` and ensure that your Docker resource limits are set high enough.\n5. If you're using Kubernetes, you can also check the status of the pods via `kubectl get pods` and check the logs of the head pod via `kubectl logs <head_pod_name>`.\n6. If your program freezes, you can restart the Ray cluster without running any of the installation commands via `ray up CLUSTER.YAML --restart-only`.\n\nIn your specific case, the warning message suggests that the Ray cluster currently does not have any available CPUs, which could be the reason for the pending nodes. Consider creating fewer actors or adding more nodes to this 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-more-libs/installation.html#daily-releases-nightlies",4363"https://docs.ray.io/en/master/ray-overview/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-overview/installation.html#from-wheels",4370"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",4371"https://docs.ray.io/en/master/installation.html#from-wheels",4372"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",4373"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",4374"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only"4375],4376"document_ids": [437714560,4378288,437963,43806335,438114566,4382291,438366,4384286,438514557,438661,438710168,43885412,43898834390],4391"answer": " To install Ray 2.5.1 from a GitHub commit, you need to find the commit hash and use it in the pip install command with the appropriate Ray version, Python version, and OS version. However, I cannot find a specific commit for Ray 2.5.1 in the Ray GitHub repository.\n\nIf you want to install Ray 2.5.1 from a wheel, you can download the wheel from the following link: <https://github.com/ray-project/ray/releases/tag/v2.5.1>. Then, use the following command to install the wheel:\n```bash\npip uninstall -y ray\npip install -U \"path/to/ray-2.5.1-cp38-cp38-manylinux2014_x86_64.whl\"\n```\nReplace \"path/to/ray-2.5.1-cp38-cp38-manylinux2014_x86_64.whl\" with the actual path to the downloaded wheel file. Note that the wheel file name may vary depending on the Python version and OS version.\n\nAlternatively, you can install Ray 2.5.1 from PyPI using the following command:\n```\npip install \"ray==2.5.1\"\n```\nThis command will install the Ray 2.5.1 package along with its dependencies.",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/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",4398"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",4399"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",4400"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4401"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4402"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#worker-setup-commands",4403"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",4404"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4405"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.__init__.html#ray-rllib-evaluation-rollout-worker-rolloutworker-init",4406"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4407"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",4408"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4409"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment"4410],4411"document_ids": [4412542,441312971,441410768,44154287,44164720,44176259,441810771,44194288,442013199,44216202,44224881,44234272,442493424425],4426"answer": " To use `worker_setup_hook` in a runtime environment to set up on worker node creation, you can specify it when initializing Ray as follows:\n```python\nimport ray\n\ndef logging_setup_func():\n # your logging setup code here\n pass\n\nray.init(runtime_env={\n \"worker_process_setup_hook\": logging_setup_func\n})\n```\nThis will call the `logging_setup_func` function after workers start and before tasks and actors are scheduled.\n\nYou can also specify other configuration options for the runtime environment, such as `setup_timeout_seconds`, `eager_install`, and `worker_path`, in the `runtime_env` dictionary.\n\nNote that `worker_process_setup_hook` is an experimental feature, so it may change in future versions of Ray.",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/data/key-concepts.html#loading-data",4433"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",4434"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",4435"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",4436"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",4437"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",4438"https://docs.ray.io/en/master/data/key-concepts.html#datasets",4439"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",4440"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",4441"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",4442"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4443"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",4444"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv"4445],4446"document_ids": [44476732,44488006,44499188,44507865,44516839,44526779,44536731,44546809,44556835,44566935,44577362,44586441,445972164460],4461"answer": " To use Ray dataset on AWS, you can read data from CSV files in an S3 bucket using the `ray.data.read_csv` function. Here's an example:\n```python\nimport ray\n\nds = ray.data.read_csv(\"s3://my-bucket/path/to/csv-files/*.csv\")\n```\nThis will create a Ray Dataset from the CSV files in the specified S3 location. Ray Datasets support many other data sources and formats, including Parquet, raw images, and more.\n\nYou can also write Ray Datasets to AWS S3 using the `write_json` method:\n```python\nds.write_json(\"s3://my-bucket/path/to/output-folder\")\n```\nThis will write the contents of the Ray Dataset to JSON files in the specified S3 location.\n\nNote that Ray Datasets can be converted to distributed dataframes that can be used with other distributed data processing frameworks like Dask, Spark, and Modin. This can be useful if you want to use Ray Datasets with existing data processing pipelines.",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/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4468"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",4469"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",4470"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",4471"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",4472"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",4473"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",4474"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",4475"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",4476"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",4477"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4478"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",4479"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition"4480],4481"document_ids": [44827081,44836864,44849207,44857395,448610017,44876795,44888046,44896876,44907188,44917796,44927085,44937379,449473524495],4496"answer": " To avoid shuffling of your dataset during `ray.data.map_batches()`, you can set the `local_shuffle_buffer_size` in `iter_batches()` or `iter_torch_batches()` when iterating over your dataset during training. This will enable local shuffling of a subset of rows during iteration, which is more performant than a full global shuffle. Choose the smallest value for `local_shuffle_buffer_size` that achieves sufficient randomness for your use case.",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/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",4503"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4504"https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms",4505"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4506"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",4507"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",4508"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4509"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4510"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",4511"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4512"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",4513"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",4514"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.OrdinalEncoder.transform_batch.html#ray-data-preprocessors-ordinalencoder-transform-batch"4515],4516"document_ids": [45177640,45187081,45196813,45207091,45217800,45227738,45237088,45247085,45257737,45267082,452710266,45287988,452974764530],4531"answer": " Yes, the order of the input data is preserved after a map_batches operation. The map_batches function applies a given function to batches of data, but it does not change the order of the data within those batches. Additionally, the examples provided show that data can be sorted and shuffled before and after map_batches operations, indicating that the order of the data is maintained.",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-core/fault_tolerance/tasks.html#catching-application-level-failures",4539"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4540"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",4541"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",4542"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4543"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",4544"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4545"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",4546"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4547"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#rayservice-troubleshooting",4548"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",4549"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-2-check-rayservice-cr-status"4550],4551"document_ids": [45524349,45534533,455413517,45554348,45565671,455713974,4558595,45594651,456013469,45614646,45625661,45634694,456456644565],4566"answer": " To get more detailed errors or logs when Ray Serve returns a generic internal service error, you can:\n\n1. Check the application-level failures by catching Python-level exceptions and wrapping them in a RayTaskError. This will allow you to see the original exception and traceback information.\n2. Leverage Ray logging to understand system-level behavior and surface application-level details during runtime. Ray Serve uses Python's standard logging module with a logger named \"ray.serve\". By default, logs are emitted to stderr and on disk on each node, and in development, logs are streamed to the driver Ray program.\n3. If you are using RayService, you can check the status and events of the RayService Custom Resource Definition (CR) to see if there are any errors. You can do this by running `kubectl describe rayservice $RAYSERVICE_NAME -n $YOUR_NAMESPACE`.\n\nAdditionally, it is recommended to test your Ray Serve script locally or in a RayCluster before deploying it to a RayService.",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/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",4574"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",4575"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",4576"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",4577"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4578"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception",4579"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4580"https://docs.ray.io/en/master/ray-observability/getting-started.html#ray-dashboard",4581"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",4582"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-debug",4583"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_placement_group.html#ray-util-state-get-placement-group",4584"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api"4585],4586"document_ids": [458713974,45884694,45894533,45905747,45914674,45924646,4593778,45944651,4595416,4596696,45974669,4598701,459946754600],4601"answer": " To track an uncaught exception in Ray Serve, you can use the RayTaskError exception, which wraps the original exception and stores it as the task's return value. When a task on a remote worker or actor fails due to a Python-level exception, Ray surfaces the failure as a RayTaskError.\n\nYou can catch this exception and handle it in your code. Here's an example:\n```python\nimport ray\n\n@ray.remote\ndef f():\n raise Exception(\"the real error\")\n\ntry:\n result = f.remote()\n ray.get(result)\nexcept ray.exceptions.RayTaskError as e:\n print(\"An uncaught exception occurred: \", e.exception)\n```\nIn this example, when the function `f` raises an exception, Ray surfaces it as a RayTaskError, which you can catch and handle in your code.\n\nNote that Ray also provides a web-based dashboard for monitoring and debugging Ray applications, which can help you track the performance of your applications and troubleshoot issues.",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/serve/monitoring.html#ray-logging",4608"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",4609"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4610"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",4611"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4612"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",4613"https://docs.ray.io/en/master/serve/monitoring.html#json-logging-format",4614"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4615"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4616"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4617"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3",4618"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",4619"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs"4620],4621"document_ids": [462213517,4623680,4624445,46254323,462613524,4627519,462813525,4629444,4630493,463113523,4632533,4633411,46344154635],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 on each node at `/tmp/ray/session_latest/logs/serve/`. This includes both system-level logs from the Serve controller and proxy as well as access logs and custom user logs produced from within deployment replicas. In development, logs are streamed to the driver Ray program, so it's convenient to keep the driver running while debugging.\n\nAdditionally, you can use the Ray Log CLI to access logs from the cluster. Note that only the logs from alive nodes are available through this API.\n\nIf you want to view the logs from the Dashboard, you can find the Job logs for Ray Jobs submitted via Jobs API. However, Driver logs are not captured for Ray Jobs run by executing the Ray Driver on the Head node directly or connecting via Ray Client. In this case, you can see the logs in the terminal or Jupyter Notebook output.\n\nTo silence the replica-level logs or otherwise configure logging, you can configure the \"ray.serve\" logger inside the deployment constructor. Ray Serve uses Python\u2019s standard logging module with a logger named \"ray.serve\". The `logger.setLevel(logging.ERROR)` line controls which logs are written to STDOUT or files on disk.",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/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4644"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4645"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",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/monitoring.html#ray-logging",4650"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",4651"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",4652"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",4653"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4654"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring"4655],4656"document_ids": [465713523,46586631,4659444,466013960,466113658,466213517,466313442,466413521,466513537,466613514,46675665,4668445,4669139594670],4671"answer": " You can find the logs for a failed Ray Serve deployment in the following locations:\n\n1. /tmp/ray/session_latest/logs/serve/ on the head node pod and worker node pod. These logs contain information about specific deployment failure reasons and autoscaling events.\n2. Kuberay operator logs, which can be accessed by running `kubectl logs -f <kuberay-operator-pod-name>`. These logs contain information about the Serve application deployment event and Serve application health checks.\n3. If you're using Kubernetes, you can check the logs of Ray Pods directly by accessing the log files on the pods using the command `kubectl exec -it $RAY_POD -n $YOUR_NAMESPACE -- bash # Check the logs under /tmp/ray/session_latest/logs/serve/`.\n\nNote that if you execute the Driver directly on the Head Node of the Ray Cluster or run with Ray Client, the Driver logs are not accessible from the Dashboard. In this case, you can view the Driver logs in the terminal or Jupyter Notebook output.",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/data/examples/index.html#ray-data-examples",4678"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",4679"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",4680"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",4681"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",4682"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#splitting-datasets",4683"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",4684"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",4685"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-ml-libraries",4686"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4687"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",4688"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray",4689"https://docs.ray.io/en/master/data/key-concepts.html#datasets"4690],4691"document_ids": [46928006,46939180,46946835,469510017,46968610,46979198,46989188,46996901,47006756,47017362,470210013,4703143,470467314705],4706"answer": " To take a subset of a Ray Dataset, you can use the `take` method, which returns the first n rows of the dataset. Here's an example:\n```python\nsubset_dataset = train_dataset.take(100) # returns the first 100 rows of the train_dataset\n```\nAlternatively, if you want to take a random subset of the dataset, you can use the `sample` method. Here's an example:\n```python\nsubset_dataset = train_dataset.sample(with_replacement=False, fraction=0.1) # returns a 10% random sample of the train_dataset\n```\nNote that the `sample` method takes an optional `with_replacement` parameter, which determines whether sampling is done with or without replacement, and a `fraction` parameter, which specifies the fraction of the dataset to sample.",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/tutorials/tune-trial-checkpoints.html#summary",4713"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing",4714"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-out-of-tune-using-checkpoints-other-artifacts",4715"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints",4716"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",4717"https://docs.ray.io/en/master/tune/tutorials/overview.html#tune-feature-guides",4718"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#tunecontroller",4719"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",4720"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",4721"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",4722"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",4723"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",4724"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished"4725],4726"document_ids": [47272226,47282217,47292299,47302212,47312253,47322270,47332243,47342348,47352439,47362255,47372350,47382347,473923104740],4741"answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `Tuner.restore()` function. This function can restore the experiment from a local path or a cloud storage location where the experiment was saved. You can then access the results and checkpoints by calling `Tuner.get_results()` to receive the `ResultGrid` object.\n\nIf you used cloud checkpointing for your initial experiment, you will need to download all the checkpoints to analyze them. You can do this by specifying the cloud storage location when calling `Tuner.restore()`. For example, if you used S3 for cloud checkpointing, you can restore the experiment as follows:\n```\ntuner = tune.Tuner.restore(\n \"s3://my-checkpoints-bucket/path/my-tune-exp\",\n trainable=my_trainable,\n resume_errored=True\n)\n```\nYou can then access the results and checkpoints by calling `Tuner.get_results()`:\n```\nresults = tuner.get_results()\n```\nYou can then analyze the results and checkpoints as needed.\n\nFor more information, you can refer to the documentation of `Tuner.restore()` and `Tuner.get_results()`.",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/actors/named-actors.html#actor-lifetimes",4748"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",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/named-actors.html#actor-lifetimes",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/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",4753"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",4754"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",4755"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",4756"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",4757"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",4758"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",4759"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure"4760],4761"document_ids": [47624633,47634859,47644615,47654631,47664613,47674579,47684620,47694848,47704561,47714618,47724614,47732157,477445754775],4776"answer": " To kill a \"detached\" Actor in C++, you can use the `actor_handle.Kill()` method. This will immediately exit the actor's process and cause any current, pending, and future tasks to fail. Detached actors are not garbage-collected by Ray, so they must be manually destroyed once they are no longer needed. In Python, you can use the `ray.kill(actor_handle)` function to achieve the same result.",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/cluster/running-applications/job-submission/ray-client.html#client-arguments",4783"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",4784"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4785"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4786"https://docs.ray.io/en/master/serve/api/index.html#serve-run",4787"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",4788"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",4789"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#set-up-ray-a-name-setup-a",4790"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.init_method.html#ray-train-torch-torchconfig-init-method",4791"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",4792"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",4793"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",4794"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv"4795],4796"document_ids": [47976614,47989343,47994811,48004285,480113990,48024253,4803542,480410295,48059118,480613684,480713850,48086613,480947124810],4811"answer": " To set environment variables in `ray.init()`, you can include the `env_vars` option in the `runtime_env` argument. Here's an example of how to set the `foo` environment variable:\n\n```python\nray.init(\n runtime_env={\n \"env_vars\": {\n \"foo\": \"foo\"\n }\n }\n)\n```\n\nIn this example, the `foo` environment variable is set to the string `\"foo\"`. You can replace this value with the desired value for the `foo` environment variable.",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-core/api/doc/ray.runtime_context.RuntimeContext.node_id.html#ray-runtime-context-runtimecontext-node-id",4818"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",4819"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",4820"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",4821"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",4822"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails",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/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4826"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4827"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4828"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",4829"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails"4830],4831"document_ids": [48324731,4833721,4834754,48354909,48364456,48376657,483814324,483914202,4840492,4841493,484214294,4843720,4844141054845],4846"answer": " The REST API for getting the head node ID is not explicitly mentioned in the provided context. However, you can use the `ray.util.state.get_node()` function to get node information by providing the node ID. To get the node ID of the head node, you can use the `ray cluster` command in the CLI to list the nodes and find the head node's ID.\n\nHere's an example of how to get the node ID of the head node using the CLI:\n\n```bash\n$ ray cluster nodes\n...\nHeadNode-abcdefg:\n Node ID: abcdefg\n Node IP: 10.0.0.1\n ...\nWorker-1:\n Node ID: 1234567\n Node IP: 10.0.0.2\n ...\n```\n\nThen, you can use the `ray.util.state.get_node()` function to get the node information for the head node:\n\n```python\nfrom ray.util.state import get_node\n\nhead_node = get_node(id=\"abcdefg\")\nprint(head_node)\n```\n\nPlease note that the provided context mentions that the `ray.runtime_context.RuntimeContext.node_id` property is deprecated and suggests using `get_node_id()` instead. However, this function is not part of the REST API.",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/api/doc/ray.cancel.html#ray-cancel",4853"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",4854"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",4855"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",4856"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",4857"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",4858"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",4859"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",4860"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",4861"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",4862"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",4863"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",4864"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks"4865],4866"document_ids": [48674897,48684375,48694550,487014702,48714514,487211591,48734515,487414703,487511592,48764894,48774895,48784542,487945384880],4881"answer": " It's not possible to rerun a canceled Ray task as canceled tasks aren't retried in Ray. If a task is misbehaving or causing memory leaks, you can set the `max_calls` option to make progress. If a worker dies unexpectedly, Ray will rerun the task until it succeeds or the maximum number of retries is exceeded.",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.sigopt.SigOptSearch.html#ray-tune-search-sigopt-sigoptsearch",4888"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",4889"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",4890"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.zoopt.ZOOptSearch.set_max_concurrency.html#ray-tune-search-zoopt-zooptsearch-set-max-concurrency",4891"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ConcurrencyLimiter.html#ray-tune-search-concurrencylimiter",4892"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.html#ray-tune-search-optuna-optunasearch",4893"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.skopt.SkOptSearch.set_max_concurrency.html#ray-tune-search-skopt-skoptsearch-set-max-concurrency",4894"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.set_max_concurrency.html#ray-tune-search-optuna-optunasearch-set-max-concurrency",4895"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.bayesopt.BayesOptSearch.set_max_concurrency.html#ray-tune-search-bayesopt-bayesoptsearch-set-max-concurrency",4896"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ax.AxSearch.set_max_concurrency.html#ray-tune-search-ax-axsearch-set-max-concurrency",4897"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.dragonfly.DragonflySearch.set_max_concurrency.html#ray-tune-search-dragonfly-dragonflysearch-set-max-concurrency",4898"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.set_max_concurrency.html#ray-tune-search-searcher-set-max-concurrency",4899"https://docs.ray.io/en/master/tune/examples/optuna_example.html#conditional-search-spaces"4900],4901"document_ids": [49022072,49037738,49041654,49051340,49061464,49071867,49081547,49091610,49101597,49111543,49121434,49131712,491432864915],4916"answer": " To set the maximum parallel concurrent scheduled tasks in `map_batches`, you need to use the SigOpt experiment and space specification. The searcher manages its own concurrency. If the `Searcher` is used in a `ConcurrencyLimiter`, the `max_concurrent` value passed to it will override the value passed here.\n\nYou can set the maximum concurrency for various searchers, like `HyperOptSearch`, `ZOOptSearch`, `SkOptSearch`, `OptunaSearch`, `BayesOptSearch`, `AxSearch`, `DragonflySearch`, and `Searcher`, by using the `set_max_concurrency()` method.\n\nHere's an example of how to set the maximum concurrency for `HyperOptSearch`:\n\n```python\nfrom ray.tune.search import ConcurrencyLimiter, HyperOptSearch\n\nsearcher = HyperOptSearch(metric=\"accuracy\")\nalgo = ConcurrencyLimiter(searcher, max_concurrent=4)\n```\n\nHowever, the `map_batches()` function you provided does not seem to be related to these searchers. If you want to limit the number of concurrent tasks while using `map_batches()`, you can use the `concurrent_tasks` parameter:\n\n```python\nds.map_batches(lambda batch: {\"id\": batch[\"id\"] * 2}, concurrent_tasks=4)\n```\n\nIf you want to use a searcher with `map_batches()`, you might need to create a custom searcher that inherits from `Searcher` and implements the necessary logic.",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/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",4923"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-6-profile-using-ray-dashboard",4924"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",4925"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",4926"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",4927"https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",4928"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",4929"https://docs.ray.io/en/master/data/performance-tips.html#tuning-read-parallelism",4930"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",4931"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",4932"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.head_cpus.html#ray-tune-execution-placement-groups-placementgroupfactory-head-cpus",4933"https://docs.ray.io/en/master/ray-core/miscellaneous.html#resource-information",4934"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer"4935],4936"document_ids": [493710003,49386049,49395936,49406504,49415413,49424380,49435693,49446815,49456013,4946614,49472112,49484459,494995104950],4951"answer": " To get the number of CPUs in a Ray cluster, you can use the `ray.cluster_resources()` function, which returns a dictionary containing the current total cluster resources. The number of CPUs can be found under the key 'CPU'.\n\nAlternatively, if you have access to the Ray dashboard, you can view the number of CPUs available on the node in the dashboard's interface.\n\nFinally, if you are using Kubernetes with Ray, you can use the `kubectl` command to view the allocatable CPU resources for each node.",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/ray-core/api/runtime-env.html#runtime-env-api",4958"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.set_runtime_env.html#ray-job-config-jobconfig-set-runtime-env",4959"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",4960"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-6-runtime-env",4961"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4962"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_runtime_env_string.html#ray-runtime-context-runtimecontext-get-runtime-env-string",4963"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",4964"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.runtime_env.html#ray-job-submission-jobinfo-runtime-env",4965"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",4966"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",4967"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.runtime_env.html#ray-runtime-context-runtimecontext-runtime-env",4968"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",4969"https://docs.ray.io/en/master/serve/api/index.html#serve-run"4970],4971"document_ids": [49724653,49734826,497413856,49755684,49764712,49774912,49784291,49796665,49804319,49814764,49824770,49834308,4984139904985],4986"answer": " To use the `exclude` option with the runtime environment in Ray, you can use the `RuntimeEnvConfig` class to specify configuration options for a runtime environment. However, the `exclude` option is not explicitly mentioned in the provided context.\n\nIf you want to exclude certain packages or dependencies from the runtime environment, you can specify them in the `pip` field of the `RuntimeEnv` class and use a `-` symbol to exclude them. For example:\n```python\nray.runtime_env.RuntimeEnv(pip=[\"pkg1\", \"-pkg2\", \"pkg3\"])\n```\nThis will include `pkg1` and `pkg3` in the runtime environment but exclude `pkg2`.\n\nIf you want to modify the runtime environment of a JobConfig, you can use the `set_runtime_env` method and pass in a runtime environment dictionary. However, the `exclude` option is not available in this method either.\n\nIf you want to exclude certain files or directories from the `working_dir` of the runtime environment, you can specify the `working_dir` as a remote URI to a .zip file that does not include those files or directories.\n\nIn summary, while the `exclude` option is not explicitly mentioned in the provided context, you can still exclude certain packages or dependencies by specifying them in the `pip` field of the `RuntimeEnv` class with a `-` symbol. Excluding files or directories from the `working_dir` can be achieved by specifying a remote URI to a .zip file that does not include those files or directories.",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/rllib/package_ref/doc/ray.rllib.policy.sample_batch.SampleBatch.split_by_episode.html#ray-rllib-policy-sample-batch-samplebatch-split-by-episode",4994"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",4995"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",4996"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",4997"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",4998"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4999"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5000"https://docs.ray.io/en/master/rllib/rllib-offline.html#example-converting-external-experiences-to-batch-format",5001"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",5002"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.default_batch_format.html#ray-data-dataset-default-batch-format",5003"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.to_batch_format.html#ray-data-block-blockaccessor-to-batch-format",5004"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference"5005],5006"document_ids": [50077081,500813299,500910266,501014727,50117640,50126956,50137091,50147088,501510613,50166790,50177791,50187384,501979885020],5021"answer": " Here's an example of using `map_batches()` with `batch_format=\"pandas\"`:\n\n```python\nimport pandas as pd\n\ndef process_batch(batch: pd.DataFrame) -> pd.DataFrame:\n # modify the batch in some way\n batch['new_column'] = batch['existing_column'] * 2\n return batch\n\nds = ray.data.read_csv('path/to/csv')\nprocessed_ds = ds.map_batches(process_batch, batch_format=\"pandas\")\n```\n\nIn this example, `map_batches()` applies the `process_batch()` function to each batch of data in the `ds` dataset, where each batch is represented as a pandas DataFrame. The `process_batch()` function modifies the batch by adding a new column that is a transformation of an existing column. The resulting dataset is stored in `processed_ds`.",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/running-applications/job-submission/quickstart.html#submitting-a-job",5028"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5029"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",5030"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",5031"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",5032"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",5033"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5034"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",5035"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",5036"https://docs.ray.io/en/master/ray-core/configure.html#driver-options",5037"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",5038"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_objects.html#ray-util-state-list-objects",5039"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_placement_groups.html#ray-util-state-list-placement-groups"5040],5041"document_ids": [50426552,50434817,50446531,50456183,50464187,504713988,50484818,504913974,50505413,50514409,5052739,5053757,50547725055],5056"answer": " To find the local Ray address, you can look for the Ray dashboard address, which is displayed in the output when you start a local Ray cluster. The default address for the Ray dashboard is <http://127.0.0.1:8265>. Alternatively, you can find the Ray cluster address by checking the environment variable RAY\\_ADDRESS, or by checking the address of the latest cluster started if available. If no address is provided, Ray will try to find an existing Ray instance to connect to.",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/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",5063"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",5064"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",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-core/starting-ray.html#launching-a-ray-cluster-ray-up",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/vms/getting-started.html#running-applications-on-a-ray-cluster",5069"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",5070"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",5071"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",5072"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-up",5073"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",5074"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview"5075],5076"document_ids": [50776203,50785605,50796616,50806501,50814193,50826510,50836206,50846500,50856622,50866509,5087862,50885600,508946505090],5091"answer": " To start a Ray cluster on multiple nodes via CLI, you can use the `ray up` command with a configuration file that specifies the head node and worker nodes. Here's an example of how to do this:\n\n1. Create a configuration file (e.g. `config.yaml`) that specifies the head node and worker nodes. Here's an example of what this file might look like:\n```\nhead_node:\n node_ip: <head_node_ip>\n",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/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",5101"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",5102"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",5103"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",5104"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",5105"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",5106"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",5107"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",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-air/examples/xgboost_example.html#training-a-model-with-distributed-xgboost"5110],5111"document_ids": [51125068,5113625,511410218,51159397,51166879,5117622,51181113,51199901,5120468,512110384,5122614,51239898,512498825125],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 could 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/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",5133"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",5134"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",5135"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",5136"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",5137"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",5138"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",5139"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",5140"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",5141"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",5142"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",5143"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",5144"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#head-node-commands"5145],5146"document_ids": [51476394,5148563,51495013,51506454,51515109,51526478,51535557,515414491,5155855,5156560,51576375,51585014,515964635160],5161"answer": " Yes, it is recommended to have different object store memory sizes for the Ray head node and worker nodes due to the memory-demanding system components on the head node. The head node should have less object store memory allocated than the worker nodes to prevent out-of-memory issues. The exact memory sizes depend on the specific workload and resources available.",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/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5169"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5170"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",5171"https://docs.ray.io/en/master/serve/api/index.html#serve-start",5172"https://docs.ray.io/en/master/serve/architecture.html#how-does-serve-ensure-horizontal-scalability-and-availability",5173"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",5174"https://docs.ray.io/en/master/serve/develop-and-deploy.html#build-serve-config-files-for-production-deployment",5175"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.ProxyLocation.HeadOnly.html#ray-serve-config-proxylocation-headonly",5176"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",5177"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5178"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#running-behind-a-reverse-proxy",5179"https://docs.ray.io/en/master/serve/architecture.html#high-level-view"5180],5181"document_ids": [518214022,518314055,518414288,518514025,518613994,518713476,518813958,518913439,519014163,519114045,519214287,51935535,5194134655195],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 `http_options` configuration with the `host` property. The options are \"HeadOnly\" to start one HTTP server on the head node, \"EveryNode\" to start one HTTP server per node, and \"NoServer\" to disable the HTTP server. By default, Ray Serve uses \"EveryNode\". You can set this configuration when starting Serve on the Ray cluster using the `--proxy-location` 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/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",5203"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",5204"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",5205"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-grafana-host-location",5206"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",5207"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-11-embed-grafana-panels-in-ray-dashboard",5208"https://docs.ray.io/en/master/cluster/metrics.html#simplist-setting-up-grafana-with-ray-provided-configurations",5209"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",5210"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-prometheus-host-location",5211"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#dashboard-host",5212"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",5213"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",5214"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster"5215],5216"document_ids": [52176061,52185544,52195543,52205547,52215545,52226086,52235597,52246084,52255546,52265935,52276085,52286058,522960625230],5231"answer": " Given that Prometheus and Grafana are not running on your Head node and you are using KubeRay, you should set the `RAY_GRAFANA_HOST` and `RAY_PROMETHEUS_HOST` environment variables to the addresses that the head node can use to access Grafana and Prometheus in your Kubernetes cluster.\n\nFor example, if Grafana is hosted on port 3000 on a node with IP address `55.66.77.88`, and Prometheus is hosted on port 9090 on the same node, you can set:\n\n* `RAY_GRAFANA_HOST=http://55.66.77.88:3000`\n* `RAY_PROMETHEUS_HOST=http://55.66.77.88:9090`\n\nHowever, since the head node IP is changing every time you reconfigure your cluster, you may want to set these variables more intelligently. One way to do this is to use the external URL of the Grafana ingress if both Grafana and the Ray Cluster are on the same Kubernetes cluster.\n\nFor `RAY_PROMETHEUS_NAME`, you can set it to select a different data source to use for the Grafana dashboard panels. The default value is \"Prometheus\".\n\nNote that you do not need to deploy Grafana in the head Pod, so you need to set both `RAY_GRAFANA_IFRAME_HOST` and `RAY_GRAFANA_HOST`. `RAY_GRAFANA_HOST` is used by the head Pod to send health-check requests to Grafana in the backend, while `RAY_GRAFANA_IFRAME_HOST` is used by your browser to fetch the Grafana panels from the Grafana server rather than from the head Pod. In your case, since you forward the port of Grafana to `127.0.0.1:3000`, you can set `RAY_GRAFANA_IFRAME_HOST` to `http://127.0.0.1:3000`.",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/serve/production-guide/kubernetes.html#autoscaling",5238"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",5239"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",5240"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",5241"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",5242"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",5243"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#deploy-a-ray-cluster",5244"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",5245"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",5246"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#experiment-results-kuberay-v0-6-0",5247"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",5248"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-1-create-a-new-kubernetes-cluster",5249"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#worker-init-container"5250],5251"document_ids": [525213957,525313956,52545975,52555953,52565972,52576170,52586175,52595973,526013902,52615779,52625963,52635772,526457085265],5266"answer": " The Google Kubernetes Engine (GKE) determines which Kubernetes pod to kill when using KubeRay autoscaling by utilizing the Kubernetes Cluster Autoscaler. When the Ray autoscaler decides to delete an idle pod, the Kubernetes Cluster Autoscaler can clean up the idle Kubernetes node that remains. It is recommended to configure the RayCluster so that only one Ray pod fits per Kubernetes node. This way, Ray Autoscaler pod scaling events will correspond roughly one-to-one with cluster autoscaler node scaling events.",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/serve/production-guide/config.html#serve-config-files-serve-build",5273"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",5274"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",5275"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",5276"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5277"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",5278"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s",5279"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",5280"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",5281"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5282"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s",5283"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5284"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests"5285],5286"document_ids": [528713854,528813795,528914020,52904287,529114288,529214044,52938481,52944765,529514045,529613852,52978655,529814055,5299137165300],5301"answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you can include it as a field in the `http_options` object like this:\n```\nhttp_options:\n request_timeout_s: <value_in_seconds>\n```\nHere, `<value_in_seconds>` should be replaced with the desired timeout value in seconds. This config is global to your Ray cluster and cannot be updated during runtime.\n\nThe full YAML config file may look something like this:\n```\nhttp_options:\n request_timeout_s: <value_in_seconds>\n\napplications:\n - name: <application_name>\n ...\n```\nNote that the `name` field for each application must be unique.",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/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",5308"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",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/cluster/kubernetes/user-guides/gpu.html#requesting-gpus",5313"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5314"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",5315"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gcp-gke-gpu-cluster.html#step-2-create-a-gpu-node-pool",5316"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",5317"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",5318"https://docs.ray.io/en/master/tune/api/doc/ray.tune.utils.wait_for_gpu.html#ray-tune-utils-wait-for-gpu",5319"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#starting-ray-nodes-with-gpus"5320],5321"document_ids": [53224995,53234997,532473,532514585,5326298,53276020,53284818,53292966,53305950,53316012,5332417,53331487,533449925335],5336"answer": " To make the GPU available for Ray on an M1 laptop, you need to install Ray and the GPU version of TensorFlow in a miniforge environment. Here are the steps:\n\n1. Install miniforge by running the following commands in the terminal:\n```bash\nwget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh\nbash Miniforge3-MacOSX-arm64.sh\nrm Miniforge3-MacOSX-arm64.sh # Cleanup.\nsource ~/.bash_profile\nconda activate\n```\n2. Install Ray by running `pip install ray`.\n3. Install the GPU version of TensorFlow by running `pip install tensorflow-macos`.\n4. Use the `@ray.remote(num_gpus=1)` decorator to create Ray remote functions that use GPUs.\n\nNote that the function defined in the context `use_gpu()` doesn't actually use any GPUs. It just reserves one GPU for the function while it is being executed. You need to use an external library like TensorFlow to make use of the GPU.\n\nAlso, note that multi-node clusters are untested on M1 Macs, and it is up to the function to actually make use of the GPU.",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/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",5343"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",5344"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",5345"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",5346"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-stop",5347"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",5348"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",5349"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.GetTimeoutError.html#ray-exceptions-gettimeouterror",5350"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",5351"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",5352"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",5353"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5354"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api"5355],5356"document_ids": [535713795,53586556,53595757,53606554,53616602,53624416,53636558,53644796,53656569,53664765,53676568,536813854,536966385370],5371"answer": " To add a timeout for a Ray job, you can use the `ray.get()` function with a specified timeout. This function is used to get the result of a remote function execution. If the result is not ready before the specified timeout, it raises a `GetTimeoutError`. Here is an example:\n\n```python\nfrom ray.exceptions import GetTimeoutError\n\n@ray.remote\ndef long_running_function():\n time.sleep(8)\n\nobj_ref = long_running_function.remote()\ntry:\n ray.get(obj_ref, timeout=4)\nexcept GetTimeoutError:\n print(\"`get` timed out.\")\n```\n\nIn this example, the `long_running_function` will sleep for 8 seconds. We call this function remotely and store the returned object reference in `obj_ref`. Then, we call `ray.get(obj_ref, timeout=4)` to get the result of the remote function execution. If the result is not ready before 4 seconds, it raises a `GetTimeoutError`.\n\nHowever, this method only works for getting the result of a remote function execution. If you want to set a timeout for the whole Ray job, you can use the `--no-wait` flag when submitting the job and periodically check the job status using the `ray job status` command. If the job is still running after a certain amount of time, you can stop the job using the `ray job stop` command. Here is an example:\n\n```bash\n$ ray job submit --no-wait -- python script.py & # Submit the job in the background\n$ JOB_ID=<job_id>\n$ sleep 10 # Wait for 10 seconds\n$ ray job status $JOB_ID # Check the job status\n$ ray job stop $JOB_ID # Stop the job if it's still running\n```\n\nIn this example, we submit the Ray job in the background using the `--no-wait` flag. We store the job ID in the `JOB_ID` variable. Then, we wait for 10 seconds using the `sleep` command. After that, we check the job status using the `ray job status` command. If the job is still running, we stop the job using the `ray job stop` command.\n\nNote that the `ray job stop` command may take some time to stop the job, especially if the job is performing I/O operations or communicating with other services. Therefore, it's recommended to set a longer timeout for the `ray.get()` function or the `ray job stop` command if the job is expected to take a long time to complete.",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/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",5379"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",5380"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",5381"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",5382"https://docs.ray.io/en/master/cluster/cli.html#ray-submit",5383"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.restore.html#ray-tune-trainable-restore",5384"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.restore.html#ray-rllib-algorithms-algorithm-algorithm-restore",5385"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-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/train/user-guides/checkpoints.html#saving-checkpoints",5388"https://docs.ray.io/en/master/train/user-guides/persistent-storage.html#persistent-storage",5389"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster"5390],5391"document_ids": [53924273,5393872,53946561,539513680,539613718,53975625,53982081,539912465,54006205,540113719,54029160,54039150,540465625405],5406"answer": " To set a custom `/tmp` directory for a remote cluster in Ray, you can use the `RAY_TEMPDIR` environment variable when initializing the Ray cluster. This variable defines where the temporary directory for the cluster files should be created, and it should be accessible by the host, the outer container, and the inner container.\n\nFor example, if you have a remote cluster with the configuration file `cluster.yaml`, you can set the `RAY_TEMPDIR` environment variable and start the Ray dashboard with the following command:\n\n```\nRAY_TEMPDIR=\"/my/custom/tmp\" ray dashboard cluster.yaml\n```\n\nThis will start the Ray dashboard on your local machine, and you can access it in your web browser at `http://127.0.0.1:8265`. Make sure to set the `RAY_ADDRESS` environment variable to `\"http://127.0.0.1:8265\"` so that you can use the Jobs CLI on your local machine to interact with your remote Ray cluster.\n\nKeep in mind that the `RAY_TEMPDIR` variable can only be set per-job, not per-task or per-actor. This means that it will be the same for all tasks and actors in the Ray cluster.",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/ray-core/objects/object-spilling.html#single-node",5413"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",5414"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",5415"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",5416"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",5417"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",5418"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",5419"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",5420"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",5421"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",5422"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",5423"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",5424"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"5425],5426"document_ids": [54274960,54284385,54294959,5430511,54316632,5432872,54331775,54349160,54356396,54364384,54376360,54384961,543948235440],5441"answer": " Yes, you can set the object spilling directory to a custom directory by using the `object_spilling_config` parameter in `ray.init()`. However, setting the `--temp-dir` flag when starting Ray will not change the object spilling directory. It will only change the root temporary directory for Ray.",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/ray-core/handling-dependencies.html#remote-uris",5448"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",5449"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",5450"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",5451"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",5452"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",5453"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",5454"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5455"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5456"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",5457"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",5458"https://docs.ray.io/en/master/serve/api/index.html#serve-run",5459"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_runtime_env_string.html#ray-runtime-context-runtimecontext-get-runtime-env-string"5460],5461"document_ids": [54624308,54634319,54644253,54654653,546613720,54674712,54684716,546914217,547014236,54714292,54724713,547313990,547449125475],5476"answer": " The `--runtime-env-json` option allows you to specify a JSON-serialized runtime environment dictionary that will be passed to `ray.init()` as the default for deployments. Here's an example:\n\n```json\nserve run --address=ray://<head-node-ip-address>:10001 --runtime-env-json='{\"env_vars\": {\"MY_ENV_VAR\": \"my-value\"}, \"working_dir\": \"./project/src\", \"pip\": [\"requests\", \"chess\"]}' local_dev:graph\n```\n\nIn this example, the runtime environment includes environment variables, a working directory, and Python packages that will be used for the deployment.",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/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5483"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5484"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5485"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5486"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5487"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",5488"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5489"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",5490"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5491"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-autoscaler",5492"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",5493"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#configuring-the-autoscaler-sidecar-container",5494"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#programmatic-scaling"5495],5496"document_ids": [549713449,549814225,549914066,550014187,550114303,55025963,550313450,550414141,550513451,55066377,550714098,55085967,550963705510],5511"answer": " The context does not provide a specific value for `maxConcurrentReplicas` when autoscaling configuration is specified. It only mentions that the `autoscaling_config` parameter is an optional dictionary that can be used to specify autoscaling parameters for the deployment's number of replicas. The actual configuration of autoscaling parameters would depend on the specific use case and requirements.",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/scaling-and-resource-allocation.html#autoscaling-config-parameters",5518"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5519"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5520"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5521"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5522"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",5523"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.zoopt.ZOOptSearch.set_max_concurrency.html#ray-tune-search-zoopt-zooptsearch-set-max-concurrency",5524"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.set_max_concurrency.html#ray-tune-search-repeater-set-max-concurrency",5525"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",5526"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.set_max_concurrency.html#ray-tune-search-optuna-optunasearch-set-max-concurrency",5527"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.dragonfly.DragonflySearch.set_max_concurrency.html#ray-tune-search-dragonfly-dragonflysearch-set-max-concurrency",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.ServeDeploySchema.html#ray-serve-schema-servedeployschema"5530],5531"document_ids": [553213451,553314303,553414187,553514066,553614225,553713561,55381340,55391462,55401654,55411610,55421434,554314302,5544140655545],5546"answer": " The value of `max_concurrent_queries` should be set based on the desired limit of pending queries in a single replica, and it is independent of the `target_num_ongoing_requests_per_replica` setting. The default value is 1, and it should be set to a positive integer with the `exclusiveMinimum` being 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/rllib/rllib-algorithms.html#qmix-monotonic-value-factorisation-qmix-vdn-iqn",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": [556713454,556813455,55693991,55701920,557111144,557211153,557313324,557411786,557511773,557612379,55771735,557811979,5579126755580],5581"answer": " A `smoothing_factor` is a multiplicative factor that speeds up or slows down the autoscaling decision-making process in a system. It can be used to either accelerate the scaling up of resources during high traffic volumes or slow down the pace of downscaling to make the application less sensitive to drops in traffic. The `smoothing_factor` can be applied to both upscaling and downscaling decisions.",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/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",5588"https://docs.ray.io/en/master/serve/api/index.html#get-api-serve-deployments-status",5589"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5590"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",5591"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",5592"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",5593"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",5594"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",5595"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#other-possible-scenarios-that-trigger-a-new-raycluster-preparation",5596"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",5597"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#other-possible-scenarios-that-trigger-a-new-raycluster-preparation",5598"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5599"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema"5600],5601"document_ids": [560213836,560314006,560414294,560514708,56064520,560711597,5608747,560913935,56105814,561114131,56125813,561314306,5614142285615],5616"answer": " Actors can change their state to \"UNHEALTHY\" if there is an error in the deployment's constructor, a crashed replica, or a general system or machine error. This information is provided in the context when discussing the different states an actor can be in, including \"HEALTHY\" and \"UNHEALTHY\".",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/user-guides/configure-logging.html#logging-directory",5628"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",5629"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",5630"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",5631"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",5632"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",5633"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",5634"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log"5635],5636"document_ids": [5637492,5638493,5639662,5640739,5641740,5642512,56436631,564413894,5645681,5646695,5647445,56486359,56496805650],5651"answer": " To access logs for a dead node, you need to have persisted the logs before the node died. Ray does not provide a native storage solution for log data, so you need to manage the lifecycle of the logs by yourself. If you have persisted the logs, you can use the Ray logs command-line interface (CLI) or Python SDK to access the logs from the cluster. However, you cannot access the logs from a dead node directly. You should get the node ID or node IP from `ray list nodes` before the node died and use it to access the logs. Note that the API prints logs from a head node by default, and you should provide the head node ID if you want to list the same logs.",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/serve/production-guide/fault-tolerance.html#worker-node-recovery",5660"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#rescheduling-bundles-on-a-dead-node",5661"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.PlacementGroupState.html#ray-util-state-common-placementgroupstate",5662"https://docs.ray.io/en/master/ray-core/scheduling/index.html#nodeaffinityschedulingstrategy",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/serve/production-guide/fault-tolerance.html#head-node-failure",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/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",5667"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#provider-cache-stopped-nodes",5668"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",5669"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status"5670],5671"document_ids": [56724531,5673554,567413869,56755098,5676724,56775147,56789228,567913898,56805976,56814790,56826283,5683721,56844675685],5686"answer": " In the given context, a node can change its status to dead due to the following reasons:\n\n1. Raylet failure: When the raylet process fails, the corresponding node is marked as dead.\n2. Task killed due to low memory: If a worker node is running low on memory, tasks may be killed.\n3. Worker node failure: When a worker node fails, the actors running on it also fail, and KubeRay attempts to restart the worker pod on another running node.\n4. Rescheduling bundles on a dead node: If nodes that contain some bundles of a placement group die, all the bundles are rescheduled on different nodes by GCS.\n5. Head node failure: The head node can be simulated to fail by killing the head pod or the head node.\n\nAdditionally, the node state can be ALIVE or DEAD, and the node's resources, labels, and start time can be tracked.",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/cli-sdk.html#cli-command-ray-status",5693"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",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/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",5696"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",5697"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",5698"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5699"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5700"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5701"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",5702"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5703"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5704"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training"5705],5706"document_ids": [5707468,5708554,57099227,57106364,5711555,57126375,57133015,57143008,57153002,57165118,57173037,57183029,571930225720],5721"answer": " Spikes in node CPU utilization can be caused by:\n\n1. Increased computational workload due to more tasks or actors being launched.\n2. Bottlenecks in preprocessing compute time, which can be alleviated by adding more CPU-only nodes.\n3. Autoscaling adding nodes to satisfy resource demands in a queue.\n4. Insufficient resources for launching actors, tasks, or placement groups, which can result in queued requests.\n\nHowever, the autoscaler considers only logical resource requests for scaling, not physical machine utilization. Therefore, it's essential to monitor and configure resources accordingly to prevent spikes in CPU utilization.",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/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-aks-microsoft-azure",5728"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",5729"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",5730"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",5731"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",5732"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",5733"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",5734"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",5735"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",5736"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",5737"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#managed-kubernetes-services",5738"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#q-are-you-deploying-on-public-cloud-providers-e-g-aws-gcp-azure",5739"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-eks-cluster-aws"5740],5741"document_ids": [57425887,574313962,5744372,574513622,574613931,574713930,57485656,57496491,57506376,57515642,57525884,57535651,575458865755],5756"answer": " The recommended AWS machine type to deploy a RayService on EKS is at least as good as an r5dn.16xlarge, with 0 logical CPU resources set on the head node due to heavy networking load. This is based on the context provided, which suggests this machine type for Ray clusters deployed using KubeRay.",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/ray-core/configure.html#logging-and-debugging",5763"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5764"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",5765"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",5766"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",5767"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",5768"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5769"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",5770"https://docs.ray.io/en/master/cluster/cli.html#ray-attach",5771"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#developing-ray-serve-python-scripts-on-a-raycluster",5772"https://docs.ray.io/en/master/cluster/usage-stats.html#frequently-asked-questions-faq",5773"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",5774"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"5775],5776"document_ids": [57774384,57784811,577913974,5780511,57816613,57824525,57834823,57844656,57855628,57865979,57875570,578814765,578966155790],5791"answer": " Yes, you can configure the session name generated by Ray by specifying the `temp_dir` argument when calling `ray.init()` or `ray start`. This will change the root temporary directory for the Ray process, and the session name will be based on this directory. Additionally, you can specify a namespace for the session using the `namespace` argument in `ray.init()`. However, there is no direct way to configure the exact format of the session name, it is always based on the timestamp and process ID by default.",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/running-applications/job-submission/sdk.html#python-sdk-overview",5798"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#python-sdk-api-reference",5799"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli",5800"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",5801"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",5802"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.get_job_info.html#ray-job-submission-jobsubmissionclient-get-job-info",5803"https://docs.ray.io/en/master/cluster/running-applications/job-submission/rest.html#ray-jobs-rest-api",5804"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",5805"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",5806"https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",5807"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",5808"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#observe-and-debug-placement-groups",5809"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job"5810],5811"document_ids": [58126575,58136539,58146547,58156640,58166637,58176671,58186571,58196551,58206578,5821275,5822415,58235085,582442555825],5826"answer": " No, the Python SDK for Ray Jobs does not provide a direct method to get a link to the Ray dashboard for a given job. The SDK is used to submit jobs, get job information, and retrieve job logs programmatically. The Ray dashboard is available at the default URL <http://localhost:8265> or <http://hostname:8265> when Ray is installed with pip install \"ray[default]\". You can access the dashboard manually to view information about your jobs.",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-core/scheduling/ray-oom-prevention.html#worker-killing-policy",5833"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",5834"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",5835"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.LocalRayletDiedError.html#ray-exceptions-localrayletdiederror",5836"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.TaskUnschedulableError.html#ray-exceptions-taskunschedulableerror",5837"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",5838"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.WorkerCrashedError.html#ray-exceptions-workercrashederror",5839"https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#understanding-objectlosterrors",5840"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",5841"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",5842"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",5843"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",5844"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors"5845],5846"document_ids": [58475129,58484646,5849554,58504858,58514830,5852557,58534906,58544559,5855597,58564531,5857555,58585118,58595535860],5861"answer": " The node where the task was running crashed unexpectedly due to memory pressure resulting in the OOM (Out-Of-Memory) error. The raylet process, which manages the allocation and deallocation of resources to workers, was killed due to the node running low on memory. This happened because the memory usage on the node exceeded the memory usage threshold of 0.95.",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/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",5869"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",5870"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",5871"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",5872"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",5873"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",5874"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",5875"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",5876"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#getting-the-status-of-the-application",5877"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",5878"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-9-upgrade-from-ray-serves-single-application-api-to-its-multi-application-api-without-downtime",5879"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure"5880],5881"document_ids": [58825638,58836505,588413870,58854522,58864530,588713871,58885709,588914740,58904526,589113947,5892467,58935705,5894138895895],5896"answer": " The error message \"Health check failed for node\" suggests that the worker node is having trouble connecting to the GCS (Global Control Store) on the head node. Here are some steps you can take to troubleshoot this issue:\n\n1. Check that the GCS with a matching version has started successfully at the specified address.\n2. Check for firewall settings that may be preventing access to the head node.\n3. Use the `ip route get` command on the worker node to start debugging routing issues.\n4. Use a tool such as `nmap` or `nc` to check whether each port can be reached from the worker node.\n5. If you are using KubeRay, ensure that the ray.io/external-storage-namespace annotation is unique for each RayCluster custom resource.\n\nIf the head node is inaccessible, some possible causes include the head node not actually running, a different version of Ray running at the specified address, the specified address being wrong, or firewall settings preventing access. In this case, you can use the `nmap` command to check whether each port can be reached from a node.\n\nIf the GCS is not fault tolerant, all the data is stored in-memory and its failure means that the entire Ray cluster fails. To make the GCS fault tolerant, HA Redis is required. When the GCS restarts, it loads all the data from the Redis instance and resumes regular functions. During the recovery period, some functions are not available, such as actor creation, deletion and reconstruction, and placement group creation, deletion and reconstruction.\n\nIf the head node fails, the entire Ray cluster fails. To tolerate head node failures, you need to make GCS fault tolerant so that when you start a new head node, you still have all the cluster-level data. In Ray 2.0, KubeRay added experimental support for GCS fault tolerance, preventing the Ray cluster from crashing if the head node goes down. While the head node is recovering, Serve applications can still handle traffic via worker nodes but cannot be updated or recover from other failures (e.g. actors or worker nodes crashing). Once the GCS is recovered, the cluster will return to normal behavior. You can enable GCS fault tolerance on KubeRay by adding an external Redis server and modifying your RayService Kubernetes object.\n\nIf the worker node fails, you can simulate the failure in the working example by taking a look at the nodes and pods running in your Kubernetes cluster and using the `kubectl delete pod` command to delete a pod. The Ray autoscaler will automatically launch a new pod to replace the failed one.",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/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",5903"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-the-applications",5904"https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph",5905"https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",5906"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5907"https://docs.ray.io/en/master/serve/tutorials/gradio-dag-visualization.html#requirement-driver",5908"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",5909"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",5910"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",5911"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",5912"https://docs.ray.io/en/master/serve/api/doc/ray.serve.run.html#ray-serve-run",5913"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",5914"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"5915],5916"document_ids": [591713741,591813655,591913426,592013502,592113859,592214509,592313652,592413650,592514732,592613647,592714238,592813744,5929136215930],5931"answer": " No, you don't need the DAGDriver to deploy a RayServe application. The DAGDriver is used to route HTTP requests through your call graph and can be configured with an optional HTTP adapter for handling HTTP requests. However, it is not a requirement for deploying a RayServe application. To deploy an application, you can use the `serve deploy` command with a configuration file or use the `ray.serve.run()` function with your deployed RayServe application.",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/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",5938"https://docs.ray.io/en/master/workflows/basics.html#sub-task-results",5939"https://docs.ray.io/en/master/serve/api/index.html#serve-run",5940"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",5941"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",5942"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Application.html#ray-serve-application",5943"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#defining-an-application-builder",5944"https://docs.ray.io/en/master/serve/tutorials/batch.html#define-the-deployment",5945"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",5946"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#walkthrough",5947"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",5948"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-9-create-a-rayservice-yaml-file",5949"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air"5950],5951"document_ids": [595213844,59539670,595413990,59555789,59565678,595714037,595813813,595914577,596013846,59618078,596210168,59635994,5964102815965],5966"answer": " To provide the import path for a simple RayServe deployment, you can use a local or remote YAML file containing a runtime environment definition that includes the import path. For example, if your application is in the \"fruit\" variable of \"fruit.py\" in the \"test\\_dag\" repo, your import path in the config might look like this:\n\n`import_path: test_dag.fruit:fruit`\n\nThis tells RayServe where to import the application from. You can also specify the route prefix for the application in the config. The working directory can be a local directory or a remote URI to a .zip file.\n\nHere is an example of a runtime environment definition in a YAML file:\n\n```yaml\nruntime_env:\n working_dir: \"https://github.com/ray-project/serve_config_examples/archive/HEAD.zip\"\n```\n\nYou can then pass this YAML file to `ray.init()` as the default for deployments.\n\n```python\nimport ray\n\nray.init(runtime_env=\"path/to/runtime_env.yaml\")\n```\n\nAlternatively, you can use the `--runtime-env` or `--runtime-env-json` command-line options when running the RayServe deployment.\n\n```bash\nserve run --runtime-env path/to/runtime_env.yaml my_app.py\n```\n\nFor more details, you can refer to the RayServe documentation on configuring deployments.",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/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",5973"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",5974"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",5975"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",5976"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",5977"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",5978"https://docs.ray.io/en/master/serve/develop-and-deploy.html#deploy-ray-serve-in-production",5979"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",5980"https://docs.ray.io/en/master/cluster/kubernetes/index.html#about-kuberay",5981"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",5982"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",5983"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-is-a-rayjob",5984"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-2-deploy-a-ray-cluster-on-kubernetes-with-the-kuberay-operator"5985],5986"document_ids": [59875751,59885760,59895752,59906144,59915647,59925658,599313441,599413926,59955660,59965763,59975754,59985753,599961626000],6001"answer": " Yes, the context includes an example of using RayJob in Kuberay. You can download the RayJob configuration file using the command:\n\ncurl -LO [https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray\\_v1alpha1\\_rayjob.yaml](https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml)\n\nThen, you can create a RayJob by applying the configuration file using the command:\n\nkubectl apply -f ray_v1alpha1_rayjob.yaml\n\nThis is covered in Step 3 of the context.",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/add-app-metrics.html#adding-application-level-metrics",6008"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",6009"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",6010"https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",6011"https://docs.ray.io/en/master/cluster/metrics.html#scraping-metrics",6012"https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",6013"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",6014"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",6015"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",6016"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",6017"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",6018"https://docs.ray.io/en/master/ray-core/actors.html#actors",6019"https://docs.ray.io/en/master/rllib/actors.html#actors"6020],6021"document_ids": [6022503,6023656,60245573,6025438,60265581,6027403,602811596,60294519,603014707,60315443,60324200,60334480,6034115756035],6036"answer": " No, Ray metrics do not have to be exported via an actor. The example you provided shows an actor exporting metrics, but you can also export metrics in other ways, such as through a standalone process or as part of your application code. The key is to use the `ray.util.metrics` API to define and export the metrics.",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/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",6043"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",6044"https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",6045"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",6046"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6047"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6048"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#out-of-core-data-processing",6049"https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",6050"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6051"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#stats",6052"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#cluster-mode",6053"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6054"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks"6055],6056"document_ids": [60576374,60584959,60594438,606014748,60614960,60624963,606314493,60641247,60654962,60664969,60674968,60684961,606942236070],6071"answer": " To avoid objects not getting spilled, you can increase the capacity of the object store by using high-performance storage such as NVMe SSDs. If object spilling is enabled, Ray will spill objects to disk if necessary. You can also configure the directory where objects are spilled to by using the `object_spilling_config` parameter in the `ray.init()` function. Additionally, you can specify multiple directories for spilling to spread the IO load and disk space usage across multiple physical devices. To prevent running out of disk space, local object spilling will throw an `OutOfDiskError` if the disk utilization exceeds the predefined threshold. It is also recommended to use a large buffer size when using an HDD for object spilling.",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/doc/ray.init.html#ray-init",6080"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",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/serve/api/index.html#ray-serve-api",6085"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",6086"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",6087"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",6088"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",6089"https://docs.ray.io/en/master/ray-core/examples/overview.html#basic-examples"6090],6091"document_ids": [60924656,60934467,60944811,60954673,60965444,60975656,60986835,609913974,6100367,6101246,61026208,61039685,610453836105],6106"answer": " Ray Core is a part of the Ray framework that provides core primitives for building and scaling distributed applications, including tasks, actors, and objects. It allows you to easily turn your functions and classes into Ray tasks and actors, and provides a simple API for building and running distributed applications in Python and Java. You can initialize Ray with `ray.init()`, and then decorate the functions you want to run remotely with `@ray.remote`. Remote function calls made with `.remote()` yield a future that can be fetched with `ray.get()`.",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/serve/api/index.html#ray-serve-api",6113"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",6114"https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",6115"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",6116"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",6117"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",6118"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",6119"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",6120"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",6121"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-api-server",6122"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",6123"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",6124"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide"6125],6126"document_ids": [612713974,61284656,61299634,61306637,61314811,61326208,61335635,61346638,61355656,613613474,61376530,61386181,613957846140],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. The Ray Jobs API is recommended for running jobs on a Ray cluster, which consists of a CLI tool, Python SDK, and a REST API. It allows you to submit locally developed applications to a remote Ray Cluster for execution and handle retries or different runs with different parameters. For following job logs, you can use the `ray job logs` command with the job's submission ID.",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/api/doc/ray.data.read_images.html#ray-data-read-images",6149"https://docs.ray.io/en/master/data/working-with-images.html#reading-images",6150"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",6151"https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",6152"https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",6153"https://docs.ray.io/en/master/installation.html#docker-source-images",6154"https://docs.ray.io/en/master/serve/tutorials/object-detection.html#serving-an-object-detection-model",6155"https://docs.ray.io/en/master/serve/http-guide.html#list-of-built-in-adapters",6156"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset",6157"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",6158"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",6159"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data"6160],6161"document_ids": [61627361,61637360,61646720,616510191,6166306,616714601,616881,616914458,617013511,61718041,61726779,61737947,617479096175],6176"answer": " The dependencies for the `api read_images` function include:\n\n* The `ray` library for data processing and parallelism\n* The `pyarrow` library for handling file system operations\n* Optionally, the `numpy` and `PIL` libraries for reading images in NumPy format and for image processing, respectively\n* The `requests` library for downloading images from the web\n* The `os` and `io` libraries for file path manipulation and byte stream handling, respectively\n\nThese dependencies are required for the `read_images` function to load and process image data from various sources.",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/api/index.html#delete-api-serve-deployments",6185"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",6186"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",6187"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",6188"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",6189"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-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#httpproxy-failure",6192"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#replica-health-checking",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": [619713912,619813911,619914007,620013717,620113448,620213908,620313915,620413892,620514015,620613916,620713865,620813905,6209139016210],6211"answer": " To kill a specific serve replica, you can follow these steps:\n\n1. Get a list of all actors by running `ray summary actors` or `ray list actors` command.\n2. Identify the NAME of the specific replica you want to kill from the list.\n3. Get a handle to the replica by running `ray.get_actor(\"NAME\", namespace=\"serve\")` in a Python interpreter.\n4. Kill the replica by running `ray.kill(replica_handle, no_restart=True)` in the Python interpreter.\n\nFor example, if you want to kill the replica with NAME \"SERVE_REPLICA::SleepyPid#RlRptP\", you can run:\n```python\nimport ray\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nThis will kill the specific replica without restarting it, and you can confirm its death by checking the list of actors again.",6212"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6213}6214]6215}6216