llm-applications
6569 строк · 538.0 Кб
1{2"config": {3"experiment_name": "rerank-0.9",4"chunk_size": 700,5"chunk_overlap": 50,6"num_chunks": 30,7"embedding_model_name": "thenlper/gte-large",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/cluster/kubernetes/user-guides/logging.html#configuring-log-processing",24"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",25"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",26"https://docs.ray.io/en/master/data/key-concepts.html#transforming-data",27"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",28"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",29"https://docs.ray.io/en/master/data/working-with-tensors.html#transforming-tensor-data",30"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.MultiHotEncoder.transform_batch.html#ray-data-preprocessors-multihotencoder-transform-batch",31"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.transform_batch.html#ray-data-preprocessor-preprocessor-transform-batch",32"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",33"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.OneHotEncoder.transform_batch.html#ray-data-preprocessors-onehotencoder-transform-batch",34"https://docs.ray.io/en/master/data/working-with-images.html#transforming-images",35"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.OrdinalEncoder.transform_batch.html#ray-data-preprocessors-ordinalencoder-transform-batch"36],37"document_ids": [387081,395867,406790,416786,426733,4314727,448046,456777,467421,477241,487085,497553,506724,51747652],53"answer": " The `map_batches()` function in Ray Data allows you to transform batches of data using a user-defined function. The function you provide to `map_batches()` should take a batch of data as input and return a transformed batch of data. By default, Ray Data represents batches as dictionaries of NumPy ndarrays, but you can specify a different batch format using the `batch_format` argument in `map_batches()`.\n\nHere is an example of using `map_batches()` to increase the brightness of images in a dataset:\n\n```\nimport numpy as np\nimport ray\n\ndef increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n return batch\n\nds = (\n ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .map_batches(increase_brightness, batch_format=\"numpy\")\n)\n```\n\nIf your function doesn't mutate the input batch, you can improve performance and decrease memory utilization by setting `zero_copy_batch=True` in `map_batches()`.\n\nI hope this helps! Let me know if you have any other questions.",54"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"55},56{57"question": "How does autoscaling work in a Ray Serve application?",58"sources": [59"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",60"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",61"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",62"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",63"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",64"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",65"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",66"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#load-metrics-are-based-on-application-semantics",67"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",68"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",69"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",70"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",71"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",72"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling"73],74"document_ids": [7513471,7613471,776363,785559,7913448,805969,815953,825971,8314726,8413447,856364,8610282,8713449,88595589],90"answer": " Ray Serve's autoscaling feature automatically adjusts the number of replicas in a deployment based on its load. It's configured using the Ray autoscaler, which is a Ray cluster process that scales the cluster up and down based on resource demand. The autoscaler reacts to task and actor resource requests, and 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, not physical machine utilization. Autoscaling can reduce workload costs but adds node launch overheads and can be tricky to configure.",91"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"92},93{94"question": "how do I get the address of a ray node",95"sources": [96"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",97"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",98"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",99"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",100"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",101"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",102"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",103"https://docs.ray.io/en/master/ray-core/api/doc/ray.nodes.html#ray-nodes",104"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",105"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",106"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",107"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",108"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",109"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs"110],111"document_ids": [112739,1136411,1144817,115754,1164446,1176552,118493,1194769,1206502,1215587,1224448,1234824,1244187,125695126],127"answer": " To get the address of a Ray node, you can use the `ray.nodes()` function, which returns information about the nodes in the Ray cluster. The node's address can be found in the `NodeManagerAddress` field of the returned dictionary. Here is an example:\n```\nimport ray\nray.init()\nprint(ray.nodes())\n```\nThis will print a list of dictionaries, one for each node in the cluster. The `NodeManagerAddress` field of each dictionary contains the node's address.\n\nAlternatively, if you have the node ID, you can use the `ray.util.state.get_node` function to get the node's information, including its address:\n```\nfrom ray.util.state import get_node\nnode = get_node(node_id)\nprint(node.node_manager_address)\n```\nThis will print the node manager address of the node with the given ID.\n\nNote that the node address is not the same as the Ray cluster address. The node address is the address of the node running the Raylet process, while the Ray cluster address is the address of the Ray cluster's head node. To connect to a Ray cluster, you need to use the cluster address, not the node address.",128"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"129},130{131"question": "Does Ray support NCCL?",132"sources": [133"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",134"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#starting-a-connection-on-older-ray-versions",135"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",136"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",137"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray",138"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",139"https://docs.ray.io/en/master/installation.html#installing-ray",140"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",141"https://docs.ray.io/en/master/ray-overview/index.html#overview",142"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",143"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",144"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",145"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",146"https://docs.ray.io/en/master/ray-overview/use-cases.html#llms-and-gen-ai"147],148"document_ids": [1494662,1506628,15114391,1524811,15314537,154284,15559,1566835,157364,15813974,1596208,160370,1615656,162139163],164"answer": " Yes, Ray supports NCCL through its collective communication library, which uses NCCL and GLOO as optional high-performance communication backends. NCCL is used for efficient out-of-band communication between Ray actor and task processes, and it operates on both distributed CPUs and GPUs.",165"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"166},167{168"question": "Is Ray integrated with DeepSpeed?",169"sources": [170"https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",171"https://docs.ray.io/en/master/rllib/rllib-dev.html#contributing-algorithms",172"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.deepspeed_engine.html#ray-train-lightning-raydeepspeedstrategy-deepspeed-engine",173"https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",174"https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",175"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",176"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.strategy_name.html#ray-train-lightning-raydeepspeedstrategy-strategy-name",177"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#fine-tune-vicuna-13b-with-lightning-and-deepspeed",178"https://docs.ray.io/en/master/train/deepspeed.html#training-with-deepspeed",179"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",180"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.html#ray-train-lightning-raydeepspeedstrategy",181"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#fine-tuning-the-model-with-ray-train-a-name-train-a",182"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",183"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.DEEPSPEED_ENV_VAR.html#ray-train-lightning-raydeepspeedstrategy-deepspeed-env-var"184],185"document_ids": [1868232,18710595,1888996,1898331,1908249,1918881,1928578,1939334,1948207,1959393,1968784,19710313,1989685,1998633200],201"answer": " Yes, Ray is integrated with DeepSpeed through the Ray DeepSpeedStrategy. This strategy provides a simple interface to configure DeepSpeed and automatically trigger the training process with the DeepSpeed Engine. It is compatible with Ray's cluster management, autoscaling, and distributed process group settings. The integration supports custom models and preprocessors and uses RLlib abstractions for distributed execution. You can use the RayTorchTrainer to scale your PyTorch Lightning job with DeepSpeed across a Ray cluster.",202"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"203},204{205"question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",206"sources": [207"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",208"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",209"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example",210"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",211"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",212"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",213"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",214"https://docs.ray.io/en/master/ray-core/walkthrough.html#running-a-task",215"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",216"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",217"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle",218"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",219"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",220"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups"221],222"document_ids": [2234600,2244600,2255197,2264611,2274599,2285248,2294598,2304485,2314603,2324601,23314277,2344360,23513643,2364636237],238"answer": " If you use AsyncIO's `await` keyword to wait for a Ray future, like `await x.remote()`, it will allow you to use Ray futures in existing concurrent applications that use AsyncIO. However, it's important to note that Ray does not support `asyncio` for remote tasks. Instead, you can wrap the async function with a wrapper to run the task synchronously. Also, when using `asyncio.wait()` with Ray futures, it will wait for the completion of the Ray tasks.",239"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"240},241{242"question": "How would you compare Spark, Ray, Dask?",243"sources": [244"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",245"https://docs.ray.io/en/master/tune/examples/tune-sklearn.html#walkthrough",246"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",247"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",248"https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",249"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",250"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",251"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",252"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",253"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",254"https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure",255"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",256"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",257"https://docs.ray.io/en/master/data/api/api.html#ray-data-api"258],259"document_ids": [26014465,2612640,26214479,2636847,26414647,26514502,2666846,26714467,2686809,26914469,2709809,2716752,27213620,2737048274],275"answer": " Spark, Ray, and Dask are all distributed computing systems, but they have different focuses and use cases.\n\nApache Spark is a general-purpose cluster-computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general computation graphs. It's well-suited for ETL, machine learning, and graph processing tasks.\n\nRay is a distributed execution engine that focuses on scaling and simplifying machine learning and AI applications. It's designed to be general-purpose and supports a wide range of use cases, including deep learning, reinforcement learning, and large-scale data processing. Ray's unique feature is its ability to seamlessly integrate with various machine learning frameworks and libraries.\n\nDask is a flexible parallel computing library for analytic computing, built with the Python ecosystem in mind. It's designed to integrate with existing Python libraries like Pandas and NumPy, making it easy to scale data science workflows. Dask is particularly well-suited for tasks that require dynamic task scheduling and data parallelism.\n\nIn summary, Spark is a general-purpose distributed computing system, Ray is a distributed execution engine tailored for machine learning and AI applications, and Dask is a parallel computing library that extends the Python data science stack. The choice between these systems depends on the specific requirements of your project.",276"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"277},278{279"question": "why would ray overload a node w/ more task that the resources allow ?",280"sources": [281"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",282"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",283"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",284"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",285"https://docs.ray.io/en/master/ray-core/tasks.html#scheduling",286"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",287"https://docs.ray.io/en/master/ray-core/scheduling/index.html#locality-aware-scheduling",288"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",289"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",290"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case",291"https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks",292"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",293"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#custom-resources",294"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#pattern-using-ray-wait-to-limit-the-number-of-pending-tasks"295],296"document_ids": [2975231,2985230,2995112,3005104,3014376,3025230,3035152,3046504,3055115,3065232,3074199,3086364,3095106,3105246311],312"answer": " Ray's scheduler limits the number of concurrently running tasks based on the resources required by each task. If a node is overloaded with more tasks than its resources allow, it could be due to incorrect resource specifications for the tasks. To prevent this, you can reduce the number of tasks per node by increasing the resources requested by each task, or by specifying a custom resource and limiting the number of tasks that can access it. However, it's important to note that resource requirements only impose logical limits and it's the user's responsibility to ensure tasks use no more resources than specified.",313"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"314},315{316"question": "when should I use Ray Client?",317"sources": [318"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",319"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet_bulk.html#ray-data-read-parquet-bulk",320"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",321"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",322"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",323"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",324"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",325"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",326"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",327"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",328"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#which-crd-should-you-choose",329"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",330"https://docs.ray.io/en/master/ray-core/walkthrough.html#next-steps",331"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started"332],333"document_ids": [3346615,3357828,3366611,3376608,3386612,3396613,3404811,34113974,3424204,34313442,3445649,3455380,3464496,3474483348],349"answer": " You should use the Ray Client when you want to connect an interactive Python shell to a remote Ray cluster. It is useful for developing interactively in a local Python shell, but requires a stable connection to the remote cluster. If the connection is lost for more than 30 seconds, the workload will terminate. If you have a long-running workload that you want to run on your cluster, it is recommended to use Ray Jobs instead. To use Ray Client, you need to install the pip package 'ray[client]' and call 'ray.init(\"ray://<head_node_host>:<port>\")' in your Python script.",350"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"351},352{353"question": "how to scatter actors across the cluster?",354"sources": [355"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",356"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.__init__.html#ray-train-lightning-raydeepspeedstrategy-init",357"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",358"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",359"https://docs.ray.io/en/master/serve/architecture.html#high-level-view",360"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",361"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",362"https://docs.ray.io/en/master/ray-core/scheduling/index.html#default",363"https://docs.ray.io/en/master/ray-core/api/utility.html#utility",364"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",365"https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",366"https://docs.ray.io/en/master/data/api/doc/ray.data.ActorPoolStrategy.html#ray-data-actorpoolstrategy",367"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",368"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources"369],370"document_ids": [3715145,3729056,3731027,3745195,37513466,3765391,3776787,3785143,3794677,3808119,381659,3827733,3836949,3845065385],386"answer": " To scatter actors across the cluster in Ray, you can use the `scheduling_strategy` parameter when creating the actor and set it to `\"SPREAD\"`. Here's an example:\n\n```python\n@ray.remote(scheduling_strategy=\"SPREAD\")\nclass SpreadActor:\n pass\n\n# Spread actors across the cluster.\nactors = [SpreadActor.remote() for _ in range(10)]\n```\n\nThis will tell Ray to spread the actors across the available nodes in the cluster. You can also specify other resources such as `num_cpus` or `num_gpus` if needed.\n\nNote that actors do not necessarily have to live on the same node as your main trainable. You can control the placement of actors using the `strategy` parameter or placement groups. Refer to the documentation on placement groups for more information.",387"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"388},389{390"question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",391"sources": [392"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",393"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",394"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",395"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",396"https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",397"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",398"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",399"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#connect-to-the-cluster",400"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",401"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",402"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",403"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",404"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",405"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting"406],407"document_ids": [4085681,4091005,4104815,4116610,41214678,4134266,4146619,4156526,41613718,4176551,4184817,4194264,4206620,4216505422],423"answer": " The connection refused error you're encountering when running `ray debug` on your remote Ray cluster is likely due to the Dashboard, Dashboard Agent, or GCS not being fully operational yet. These components may take a few seconds to start up after the head Pod is ready. If the issue persists after waiting for 1 minute, it's possible that the dashboard or dashboard agent may have failed to start.\n\nYou can try waiting for a while and then running the command again. If the problem persists, you might want to check the status of the dashboard and dashboard agent to see if they are running correctly.",424"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"425},426{427"question": "How does Ray AIR set up the model to communicate gradient updates across machines?",428"sources": [429"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",430"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",431"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#asynchronous-parameter-server-training",432"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",433"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",434"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",435"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",436"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",437"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",438"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#defining-the-parameter-server",439"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary",440"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#synchronous-parameter-server-training",441"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",442"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#fine-tuning-dreambooth-with-ray-train"443],444"document_ids": [4459845,4469845,4475338,4489820,449156,450374,451337,452183,45310019,4545329,4559867,4565334,4579887,45810147459],460"answer": " Ray AIR sets up model communication for gradient updates across machines using a data-parallel training approach. It does this by sharding the training data and setting up a parameter server to communicate with worker nodes. The workers asynchronously compute gradients given their current weights and send these gradients to the parameter server. The parameter server then applies the new gradients and sends back a copy of the current weights to the worker. The worker updates its weights and repeats the process. This is facilitated by importing Ray Train and Ray AIR Session, and using the `ParameterServer` and `DataWorker` classes in Ray.",461"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"462},463{464"question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",465"sources": [466"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",467"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",468"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",469"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",470"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",471"https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",472"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",473"https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",474"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",475"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice",476"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",477"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments",478"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",479"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"480],481"document_ids": [48213621,4831005,48413974,48513614,48613597,48713420,48810282,489144,4905785,4915739,49213618,49313727,49413596,49513623496],497"answer": " Ray Serve is a scalable model serving library that allows you to serve models built with various frameworks and libraries, and is not tied to a specific cloud provider or machine learning library. It is framework-agnostic and focuses on model composition, providing a general-purpose scalable serving layer. Unlike Modal or Seldon, Ray Serve does not have built-in functionality for managing the lifecycle of your models or visualizing their performance. It is primarily focused on model serving and providing the primitives for you to build your own ML platform on top. If you have your own infrastructure and want to avoid being locked into a specific solution, Ray Serve is a good choice. If you are looking for a full-fledged ML platform with more features for managing and visualizing models, you may want to consider Modal or Seldon.",498"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"499},500{501"question": "How do I deploy an LLM workload on top of Ray Serve?",502"sources": [503"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",504"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",505"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",506"https://docs.ray.io/en/master/cluster/vms/user-guides/community/lsf.html#deploying-on-lsf",507"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",508"https://docs.ray.io/en/master/serve/api/index.html#serve",509"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",510"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",511"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",512"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",513"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",514"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",515"https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",516"https://docs.ray.io/en/master/serve/deploy-many-models/index.html#deploy-many-models"517],518"document_ids": [51913618,52013618,52113677,5226382,5235636,52413983,52513598,52613416,52713647,52810282,52913621,53013622,531144,53213676533],534"answer": " To deploy a large language model (LLM) workload on top of Ray Serve, follow these steps:\n\n1. Write your LLM application as a single Python module, including prompt preprocessing, vector database lookups, LLM API calls, and response validation. Ray Serve supports any arbitrary Python code, enabling rapid development and easy testing.\n2. Use the `@serve.deployment` decorator to define a deployment for your Python class or function. This will create a deployment that contains the business logic or ML model to handle incoming requests.\n3. Deploy your Ray Serve LLM application to production using the Ray Serve CLI. You can deploy to a local, single-node cluster or a remote, multi-node cluster started with the Ray Cluster Launcher.\n4. Ray Serve allows for independent autoscaling of each application step, efficiently accommodating user traffic without wasting resources.\n5. Utilize Ray Serve features for batching and integration to improve the performance of your LLM applications.\n\nRefer to the provided context for more information on deploying Ray Serve config files, updating applications, and deploying to remote clusters.",535"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"536},537{538"question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",539"sources": [540"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",541"https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",542"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",543"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",544"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",545"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",546"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",547"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",548"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",549"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",550"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",551"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",552"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",553"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#how-do-i-configure-the-memory-monitor"554],555"document_ids": [5564878,55714606,55814121,55914137,56014299,56114183,56214221,56314062,56410280,56510260,566855,56710373,56810380,5695121570],571"answer": " The memory requirement for setting `model_id` to \u201cEleutherAI/gpt-j-6B\u201d would depend on the specifications of your GPU. The example provided requires at least one GPU with 16 GB or more memory. However, the context does not provide a direct answer to your query about the `object_store_memory` configuration. It is recommended to allocate memory keeping in mind the GPU specifications and the potential memory usage of other system resources.",572"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"573},574{575"question": "How do I log the results from multiple distributed workers into a single tensorboard?",576"sources": [577"https://docs.ray.io/en/master/train/examples/tf/tensorflow_mnist_example.html#running-distributed-training-of-a-tensorflow-model-on-mnist-with-ray-train",578"https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#monitoring-and-logging-metrics",579"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",580"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",581"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#example-logging-to-mlflow-and-tensorboard",582"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#quickstart",583"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",584"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",585"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-tune-results-on-a-single-node",586"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-concurrent-tune-trials",587"https://docs.ray.io/en/master/rllib/key-concepts.html#key-subconcepts",588"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#aggregating-results",589"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",590"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowTrainer.html#ray-train-tensorflow-tensorflowtrainer"591],592"document_ids": [5939544,5949260,59510927,5962354,5979144,5988286,599231,6002411,6012383,6022380,60310730,6048301,6059532,6068541607],608"answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use TensorFlow's built-in MultiWorkerMirroredStrategy for training your model. The results from all workers will be automatically aggregated in TensorBoard. Here's an example:\n\n1. Define your training function using the `MultiWorkerMirroredStrategy`.\n2. Train your model using the `strategy.fit` method.\n3. Monitor and log your results with TensorBoard by running `tensorboard --logdir=<path_to_log_directory>`.\n\nIn the context you provided, the code for training with `MultiWorkerMirroredStrategy` is already given. To log the results in TensorBoard, you can add the following lines at the end of your script:\n\n```python\n# ... (your existing code)\n\n# Specify the log directory for TensorBoard\nlog_dir = \"/path/to/log/directory\"\n\n# Write a checkpoint to TensorBoard\ncheckpoint_callback = ReportCheckpointCallback()\ncheckpoint_callback.on_training_end(results)\n\n# Log the results in TensorBoard\ntensorboard_cmd = \"tensorboard --logdir={}\".format(log_dir)\nos.system(tensorboard_cmd)\n```\n\nReplace `\"/path/to/log_directory\"` with the desired directory for logging TensorBoard events. The `ReportCheckpointCallback` writes a checkpoint at the end of training, which will be visible in TensorBoard. The `os.system` command runs the TensorBoard server, so you can access it in your web browser at `http://localhost:6006`.",609"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"610},611{612"question": "how do you config SyncConfig for a Ray AIR job?",613"sources": [614"https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration",615"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary",616"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",617"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",618"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningConfigBuilder.checkpointing.html#ray-train-lightning-lightningconfigbuilder-checkpointing",619"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",620"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",621"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_timeout.html#ray-train-syncconfig-sync-timeout",622"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",623"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.checkpoint_config.html#ray-train-runconfig-checkpoint-config",624"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.syncer.html#ray-train-syncconfig-syncer",625"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.html#ray-train-runconfig",626"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",627"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_on_checkpoint.html#ray-train-syncconfig-sync-on-checkpoint"628],629"document_ids": [6301138,6319867,63210466,6338441,6348371,6351443,63610464,6371852,6381782,6399098,6401678,6418430,6428881,6431550644],645"answer": " To configure `SyncConfig` for a Ray AIR job, you can use the `ray.train.SyncConfig` class which provides various options for syncing files and checkpoints to the storage path specified in `RunConfig`.\n\nHere's an example of how to use `SyncConfig`:\n```python\nfrom ray import train\n\nsync_config = train.SyncConfig(\n sync_period=300, # sync every 300 seconds\n sync_timeout=1800, # timeout after 1800 seconds\n sync_artifacts=False, # don't sync artifacts\n sync_artifacts_on_checkpoint=True, # sync artifacts on checkpoint\n sync_on_checkpoint=False # don't sync on checkpoint\n)\n\nrun_config = train.RunConfig(\n sync_config=sync_config,\n # other config options\n)\n```\nNote that syncing checkpoints and other artifacts from worker nodes to the head node is no longer supported in Ray AIR. This means that the checkpoints and artifacts saved by trials scheduled on worker nodes will not be accessible during the run or after the run.\n\nAdditionally, the `syncer` argument is deprecated and will not take effect in resumed runs.",646"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"647},648{649"question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",650"sources": [651"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",652"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",653"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",654"https://docs.ray.io/en/master/ray-observability/getting-started.html#job-profiling",655"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",656"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",657"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",658"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",659"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/observability.html#method-3-check-logs-of-ray-pods",660"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",661"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",662"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",663"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",664"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions"665],666"document_ids": [6675764,6682309,669444,670422,671415,67213523,6736631,6746157,6755855,6765665,6776181,678445,6796530,6804658681],682"answer": " To quickly narrow down the root cause of a failed Ray job, you can check the output of the Ray job using the command `kubectl logs -l=job-name=rayjob-sample`. Additionally, you can access the Ray logs in the cluster, which are organized by node and log file name. The logs view provides search functionality to help you find specific log messages. Ray job logs can be found at `/tmp/ray/session_latest/logs` on the head node. If the Ray job is submitted by the Job API, the job logs are available from the Dashboard. You can also check the Ray logs directly by accessing the log files on the Pods using the command `kubectl exec -it $RAY_POD -n $YOUR_NAMESPACE -- bash`.\n\nIn the logs, look for Ray exceptions such as `ray.exceptions.LocalRayletDiedError`, `ray.exceptions.WorkerCrashedError`, `ray.exceptions.TaskPlacementGroupRemoved`, `ray.exceptions.ActorPlacementGroupRemoved`, `ray.exceptions.ObjectStoreFullError`, `ray.exceptions.OutOfDiskError`, and `ray.exceptions.ObjectLostError(...)` which may indicate the root cause of the failed job.",683"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"684},685{686"question": "How do I specify how many GPUs a serve deployment needs?",687"sources": [688"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",689"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",690"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",691"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",692"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",693"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#resource-management-cpus-gpus",694"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",695"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",696"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#usage-example",697"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#resources",698"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",699"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#sbatch-directives",700"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-gpus",701"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment"702],703"document_ids": [70413458,7051006,70611062,70713575,70813456,70913778,71010283,71111063,7126420,7135927,7147799,7156405,7165937,71713776718],719"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 is an example:\n```\n@serve.deployment(ray_actor_options={\"num_gpus\": 0.5})\ndef func_2(*args):\n return do_something_with_my_gpu()\n```\nThis will allocate 0.5 GPUs to each replica of the deployment. You can also use `num_cpus` to reserve CPUs in a similar way.",720"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"721},722{723"question": "One of my worker nodes keeps dying on using TensorflowTrainer with around 1500 workers, I observe SIGTERM has been received to the died node's raylet. How can I debug this?",724"sources": [725"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",726"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",727"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",728"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",729"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",730"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",731"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",732"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",733"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",734"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",735"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",736"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",737"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",738"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed"739],740"document_ids": [7415129,7421006,7439493,74410333,745557,746555,74710339,7489483,74910456,7509326,7519325,7529405,75310443,75410350755],756"answer": " The worker node is dying due to memory pressure (OOM), which means the node is running out of memory. To debug this issue, you can:\n\n1. Check the memory usage on the node using `ray logs raylet.out -ip <node_ip>` as suggested in the context.\n2. Refer to the Ray documentation on how to address the out-of-memory issue: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>.\n3. Monitor the memory usage using Ray memory monitor, which periodically prints the aggregated out-of-memory killer summary to Ray drivers.\n4. Review the logs of the worker that was killed due to memory pressure to understand the cause better.\n\nTo avoid bottlenecks and optimize memory usage, consider the following:\n\n1. Adjust the `--memory` flag for Ray workers to allocate appropriate memory for each worker.\n2. Tune the batch size and learning rate to reduce memory consumption during training.\n3. Use gradient accumulation to fit larger models and larger batches on limited hardware resources.\n4. Utilize mixed precision training to reduce memory consumption and speed up training.\n5. Enable model parallelism to distribute the model across multiple GPUs.\n\nIn this case, focusing on memory management and optimizing TensorFlow training settings should help prevent the worker node from dying due to memory pressure.",757"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"758},759{760"question": "what are the possible reasons for nodes dying in a cluster?",761"sources": [762"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",763"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",764"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",765"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",766"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",767"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",768"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",769"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",770"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",771"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",772"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",773"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#cluster-setting",774"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",775"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/aws-eks-gpu-cluster.html#step-3-verify-the-node-groups"776],777"document_ids": [77813898,77911075,7804522,7814519,7824524,7839524,784554,7855639,7865129,7874790,78813889,7899337,790467,7915897792],793"answer": " There are several possible reasons for nodes dying in a cluster:\n\n1. Head node failure: When the head node fails, the entire Ray cluster fails. This can be simulated by killing the head pod or the head node. To tolerate head node failures, GCS (Global Control Store) needs to be made fault-tolerant so that when a new head node starts, all the cluster-level data is still available.\n2. Raylet failure: When a raylet process fails, the corresponding node is marked as dead and treated the same as a node failure. Each raylet is associated with a unique ID, so even if it restarts on the same physical machine, it will be 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. When a worker node fails, it can cause tasks, actors, and objects to be affected.\n4. Memory pressure: If a node experiences memory pressure, it may kill workers due to memory pressure (OOM), causing tasks and actors to be affected.\n5. User action: A cluster may terminate due to user action, which can cause nodes to die.\n6. Overloading the head node: Overloading the head node with too many simultaneous connections can cause the worker nodes to fail to connect to the cluster, leading to node death.\n\nTo prevent node death, it is essential to monitor the cluster's health, ensure that there is enough memory and CPU resources, and avoid overloading the head node with too many simultaneous connections.",794"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"795},796{797"question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",798"sources": [799"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",800"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",801"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",802"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",803"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",804"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",805"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",806"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",807"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",808"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",809"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#upscaling-and-downscaling-speed",810"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",811"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",812"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview"813],814"document_ids": [8156363,8166715,8176715,8186365,8195953,8209510,8216337,822851,8236457,824854,8255965,8265973,8275559,8285600829],830"answer": " To programmatically set the size of a Ray remote cluster 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 with immediate effect, bypassing normal upscaling speed constraints.\n\nHere's an example usage of the function:\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 set the target cluster size\nrequest_resources(num_cpus=desired_cluster_size)\n```\nNote that this function only sets the target cluster size and does not guarantee that the cluster will reach that size immediately. The actual time taken to reach the target size will depend on various factors, such as the availability of resources in the cloud provider and the current cluster size.\n\nAlso, keep in mind that manually setting the cluster size may lead to resource underutilization or overprovisioning, depending on the workload. It's generally recommended to use autoscaling to ensure that the cluster size matches the workload's resource requirements.",831"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"832},833{834"question": "how do you disable async iter_batches with Ray Dataset?",835"sources": [836"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",837"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#how-do-i-disable-the-memory-monitor",838"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",839"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",840"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.streaming_split.html#ray-data-dataset-streaming-split",841"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",842"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches",843"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#define-your-training-function",844"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",845"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",846"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",847"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",848"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",849"https://docs.ray.io/en/master/data/working-with-pytorch.html#pytorch-dataloader"850],851"document_ids": [8527081,8535120,8547379,8557188,8567414,8577380,8587811,8599361,8609906,8617328,86213697,8637545,8647189,8656990866],867"answer": " To disable async iter\\_batches with Ray Dataset, you can set the `prefetch_batches` parameter to 0 when calling `iter_batches()` method. By default, `prefetch_batches` is set to 1, which means one batch is prefetched in advance for asynchronous execution. Setting it to 0 will disable this feature and make it synchronous.\n\nHere's an example:\n\n```python\ndataset = ray.data.read_csv(\"path/to/csv\")\nfor batch in dataset.iter_batches(prefetch_batches=0):\n # process the batch\n pass\n```\n\nKeep in mind that disabling async iter\\_batches might affect the performance of your data processing pipeline, as it reduces the level of parallelism.",868"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"869},870{871"question": "what is the different between a batch and a block, for ray datasets?",872"sources": [873"https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",874"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",875"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.batch_to_block.html#ray-data-block-blockaccessor-batch-to-block",876"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",877"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-data",878"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches",879"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.take_batch.html#ray-data-dataset-take-batch",880"https://docs.ray.io/en/master/data/examples/batch_training.html#contents",881"https://docs.ray.io/en/master/data/examples/batch_training.html#transforming-a-dataset-in-parallel-using-custom-functions-a-class-anchor-id-transform-ds-a",882"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",883"https://docs.ray.io/en/master/data/working-with-tensors.html#batches-of-variable-shape-tensors",884"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",885"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",886"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.to_batch_format.html#ray-data-block-blockaccessor-to-batch-format"887],888"document_ids": [8896872,8907081,8917150,8926895,8936859,8946902,8957825,8967861,8977875,8987735,8996775,9006846,9017988,9027384903],904"answer": " A batch and a block in Ray Datasets refer to different levels of data organization. A block is a disjoint subset of rows in the dataset, and Ray Data loads and transforms these blocks in parallel. On the other hand, a batch contains data from multiple rows, and it can be created from user-facing data formats like PyArrow tables, Pandas DataFrames, or dictionaries of NumPy ndarrays. You can iterate over batches or rows in a dataset, and you can change the type of the returned batch by setting the batch\\_format.",905"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"906},907{908"question": "How to setup the development environments for ray project?",909"sources": [910"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-from-source",911"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#environment-dependencies",912"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",913"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",914"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#development-workflow",915"https://docs.ray.io/en/master/serve/api/index.html#serve-run",916"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-on-linux-macos-full",917"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",918"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher",919"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",920"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",921"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",922"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#environment-dependencies",923"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"924],925"document_ids": [926877,9274247,928910,92910168,93013709,93113990,932889,9334251,9344250,9356614,9364811,9379342,9384247,9396334940],941"answer": " To set up the development environment for the Ray project, follow these steps:\n\n1. Clone the Ray repository:\n ```\n git clone https://github.com/ray-project/ray.git\n ```\n2. Go to the example directory and install dependencies:\n ```\n cd doc/source/templates/05_dreambooth_finetuning\n pip install -Ur dreambooth/requirements.txt\n ```\n3. Prepare some directories and environment variables.\n4. Build Ray from source by following the instructions for your operating system (Linux, MacOS, or Windows) found in the provided context.\n\nFor dynamic development and experimentation, Ray recommends using runtime environments to install dependencies on the fly. This can be done using the `--runtime-env` or `--runtime-env-json` options with `ray.init()`. For production usage or non-changing environments, install dependencies into a container image and specify the image using the Cluster Launcher.",942"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"943},944{945"question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",946"sources": [947"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#rewriting-trajectories",948"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.__init__.html#ray-rllib-evaluation-rollout-worker-rolloutworker-init",949"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",950"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",951"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.set_weights.html#ray-rllib-evaluation-rollout-worker-rolloutworker-set-weights",952"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.sample.html#ray-rllib-evaluation-rollout-worker-rolloutworker-sample",953"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.lock.html#ray-rllib-evaluation-rollout-worker-rolloutworker-lock",954"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.foreach_worker_async.html#ray-rllib-evaluation-worker-set-workerset-foreach-worker-async",955"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.SyncSampler.html#ray-rllib-evaluation-sampler-syncsampler",956"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.sample_with_count.html#ray-rllib-evaluation-rollout-worker-rolloutworker-sample-with-count",957"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.unlock.html#ray-rllib-evaluation-rollout-worker-rolloutworker-unlock",958"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.AsyncSampler.html#ray-rllib-evaluation-sampler-asyncsampler",959"https://docs.ray.io/en/master/rllib/rllib-training.html#accessing-policy-state",960"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers"961],962"document_ids": [96310845,96413196,96512708,96612707,96712965,96812739,96911794,97013127,97112797,97212242,97313022,97412378,97510940,97610991977],978"answer": " To debug why Ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`, you can try the following steps:\n\n1. Check if the `RolloutWorker` is being correctly initialized with the required parameters such as `env_creator`, `validate_env`, `config`, `worker_index`, `num_workers`, `recreated_worker`, `log_dir`, `spaces`, `default_policy_class`, and `dataset_shards`.\n2. Use the `lock()` and `unlock()` methods of the `RolloutWorker` class to ensure that the worker is properly locked and unlocked during the execution of the sample API.\n3. If you are using `SyncSampler` or `AsyncSampler`, make sure that the required parameters such as `worker`, `env`, `clip_rewards`, `rollout_fragment_length`, `count_steps_by`, `callbacks`, `multiple_episodes_in_batch`, `normalize_actions`, `clip_actions`, `observation_fn`, `sample_collector_class`, `render`, `policies`, `policy_mapping_fn`, `preprocessors`, `obs_filters`, `tf_sess`, `horizon`, and `soft_horizon` are correctly passed.\n4. Check if the `WorkerSet.foreach_worker_async()` method is being used correctly to call the given function asynchronously with each worker as the argument.\n5. Make sure that the `get_weights()` and `set_weights()` methods are being used correctly to get and set the weights of the worker.\n6. If you have configured `num_rollout_workers` > 0, note that updates here may not be immediately reflected in your rollout workers.\n7. You can also use the `ray.timeline()` function to profile and debug the Ray application and identify any performance bottlenecks or deadlocks.\n\nBy following these steps, you can debug why Ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`.",979"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"980},981{982"question": "how do I join two ray datasets?",983"sources": [984"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",985"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",986"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",987"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",988"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",989"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",990"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",991"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#load-and-preprocess-datasets",992"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-ml-libraries",993"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",994"https://docs.ray.io/en/master/data/preprocessors.html#overview",995"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",996"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",997"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray"998],999"document_ids": [10008006,10011006,10026835,10039180,10049188,10057255,10067289,10079348,10086756,10096809,10106917,10117254,101210013,10131431014],1015"answer": " To join two Ray datasets, you can use the `union()` function, which materializes and concatenates the datasets across rows. Here's an example:\n```python\ndataset1 = ray.data.from_items([{\"x\": [x]} for x in range(5)])\ndataset2 = ray.data.from_items([{\"y\": [x]} for x in range(5)])\nmerged_dataset = dataset1.union(dataset2)\n```\nThe `union()` function preserves the order of the blocks in the datasets and the relative ordering between the datasets passed in the argument list. However, keep in mind that unioned datasets aren't lineage-serializable, meaning they can't be used as a tunable hyperparameter in Ray Tune.",1016"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1017},1018{1019"question": "Is there a way to retrieve an object ref from its id?",1020"sources": [1021"https://docs.ray.io/en/master/ray-core/objects.html#objects",1022"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",1023"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",1024"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",1025"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait",1026"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",1027"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",1028"https://docs.ray.io/en/master/ray-core/api/core.html#objects",1029"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",1030"https://docs.ray.io/en/master/ray-core/objects.html#objects",1031"https://docs.ray.io/en/master/ray-core/objects.html#closure-capture-of-objects",1032"https://docs.ray.io/en/master/ray-core/patterns/return-ray-put.html#anti-pattern-returning-ray-put-objectrefs-from-a-task-harms-performance-and-fault-tolerance",1033"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",1034"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor"1035],1036"document_ids": [10374413,10385432,10395426,10404982,10414750,10424885,104314041,10444651,10455436,10464412,10474439,10485173,10494884,105049841051],1052"answer": " Yes, you can retrieve an object by its object reference (`ObjectRef`) using the `ray.get()` function. This function blocks until the object corresponding to the object reference is available in the local object store.\n\nHere's an example from the context:\n\n```python\nobj_ref = handle.other_method.remote(*args)\nretrieved_object = ray.get(obj_ref)\n```\n\nIn this example, `handle.other_method.remote(*args)` returns an object reference, which is then passed to `ray.get()` to retrieve the actual object.",1053"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1054},1055{1056"question": "how to create model Checkpoint from the model in memory?",1057"sources": [1058"https://docs.ray.io/en/master/tune/examples/includes/pbt_convnet_function_example.html#pbt-convnet-example",1059"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",1060"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",1061"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#check-the-training-results-and-checkpoints",1062"https://docs.ray.io/en/master/tune/examples/tune-pytorch-cifar.html#the-train-function",1063"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",1064"https://docs.ray.io/en/master/tune/examples/includes/xgboost_dynamic_resources_example.html#xgboost-dynamic-resources-example",1065"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#enabling-checkpointing-to-retrieve-the-model",1066"https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example",1067"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint",1068"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#class-api-checkpointing",1069"https://docs.ray.io/en/master/tune/examples/includes/pbt_memnn_example.html#memory-nn-example",1070"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#class-api-checkpointing",1071"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model"1072],1073"document_ids": [10744104,10758798,10769037,10779331,10783858,10798798,10803964,10819857,10824073,10839066,10842220,10854027,10862219,108790361088],1089"answer": " To create a model checkpoint from a model in memory, you can use the `TensorflowCheckpoint.from_model` method provided by TensorFlow. Here's an example:\n```python\nimport tensorflow as tf\nfrom ray.train.tensorflow import TensorflowCheckpoint\n\n# Create a Keras model\nmodel = tf.keras.applications.resnet.ResNet101()\n\n# Create a checkpoint from the model\ncheckpoint = TensorflowCheckpoint.from_model(model)\n```\nThis creates a checkpoint object that you can save to disk using the `to_directory` method. Note that this method is specific to TensorFlow and Keras models. If you're using a different deep learning framework, you'll need to use the appropriate method for creating a checkpoint from a model in memory.",1090"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1091},1092{1093"question": "what is Deployment in Ray Serve?",1094"sources": [1095"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",1096"https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",1097"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",1098"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment",1099"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1100"https://docs.ray.io/en/master/serve/tutorials/java.html#converting-to-a-ray-serve-deployment",1101"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1102"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",1103"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",1104"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",1105"https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",1106"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",1107"https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",1108"https://docs.ray.io/en/master/serve/api/index.html#put-api-serve-deployments"1109],1110"document_ids": [111114255,111213797,111313416,111414150,111513679,111614560,111713677,111813776,111913987,112013974,112113797,112214561,112314598,1124140001125],1126"answer": " In Ray Serve, a deployment is a central concept that contains business logic or an ML model to handle incoming requests. It can be scaled up to run across a Ray cluster, with multiple replicas (individual copies of the class or function) started in separate Ray Actors (processes). The number of replicas can be scaled up or down to match the incoming request load. To define a deployment, use the `@serve.deployment` decorator on a Python class or function. Deployments can be created and managed using the Ray Serve API or CLI.",1127"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1128},1129{1130"question": "What is user config in Ray Serve? how do I use it?",1131"sources": [1132"https://docs.ray.io/en/master/serve/api/index.html#serve-config",1133"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1134"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",1135"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",1136"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#updating-user-config",1137"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",1138"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-4-prepare-your-ray-serve-python-scripts-and-run-the-ray-serve-application",1139"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#dynamically-changing-parameters-without-restarting-your-replicas-user-config",1140"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#adding-deleting-and-updating-applications",1141"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1142"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1143"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",1144"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",1145"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"1146],1147"document_ids": [114813986,11491005,115013831,115113974,115213687,115313551,11545983,115513565,115613661,11576615,115813678,115914288,116013776,1161138521162],1163"answer": " User config in Ray Serve refers to the configuration parameters that are configurable on a Ray Serve deployment. These parameters can be configured either in the Serve config file or on the `@serve.deployment` decorator.\n\nTo use user config, you can define the `user_config` parameter in the Serve config file or in the decorator for a specific deployment. Once defined, you can update the user config by updating the Serve config file with a new `user_config` and reapplying it to your Ray cluster.\n\nHere is an example of a YAML snippet defining a deployment with a `user_config` parameter:\n```yaml\ndeployments:\n - name: Model\n user_config:\n threshold: 1.5\n```\nIn this example, the `threshold` parameter is defined in the `user_config` for the `Model` deployment.\n\nTo update the `user_config`, you can modify the `user_config` in the corresponding entry under the `applications` field in the Serve config file and reapply it to your Ray cluster.\n\nFor more information, you can refer to the Ray Serve documentation on configuring Ray Serve deployments.",1164"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1165},1166{1167"question": "What is the difference between PACK and SPREAD strategy?",1168"sources": [1169"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",1170"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1171"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",1172"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1173"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1174"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1175"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1176"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",1177"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",1178"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.placement_strategy.html#ray-train-scalingconfig-placement-strategy",1179"https://docs.ray.io/en/master/ray-core/key-concepts.html#placement-groups",1180"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1181"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",1182"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema"1183],1184"document_ids": [11851872,11865078,118712053,118811075,11895075,11905078,11915077,11924789,11935145,11948773,11954202,11965076,119714193,1198140721199],1200"answer": " The PACK strategy places bundles onto as few nodes as possible, while the SPREAD strategy places bundles across distinct nodes as evenly as possible. PACK tries to minimize the number of nodes used, while SPREAD tries to maximize the distribution of bundles across nodes.",1201"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1202},1203{1204"question": "What\u2019s the best way to run ray across multiple machines?",1205"sources": [1206"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",1207"https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",1208"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",1209"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",1210"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",1211"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",1212"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",1213"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",1214"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",1215"https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure",1216"https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters",1217"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",1218"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1219"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray"1220],1221"document_ids": [12225636,12231237,12245600,12254644,122614710,1227372,122813615,12294186,12305634,12319809,12324455,123313598,12344207,123541851236],1237"answer": " The best way to run Ray across multiple machines is to deploy a Ray cluster. A Ray cluster consists of a head node and worker nodes connected together. You can deploy a fixed-size Ray cluster or one that autoscales based on the resources requested by applications running on the cluster. While it is possible to run multiple jobs from different users simultaneously in a Ray cluster, it is not recommended for production due to the lack of strong resource isolation and priority support.",1238"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1239},1240{1241"question": "how do I specify ScalingConfig for a Tuner run?",1242"sources": [1243"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",1244"https://docs.ray.io/en/master/tune/faq.html#how-do-i-configure-search-spaces",1245"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",1246"https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",1247"https://docs.ray.io/en/master/tune/key-concepts.html#tune-trials",1248"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",1249"https://docs.ray.io/en/master/tune/examples/pbt_ppo_example.html#example",1250"https://docs.ray.io/en/master/tune/examples/nevergrad_example.html#optional-passing-the-hyper-parameter-space-into-the-search-algorithm",1251"https://docs.ray.io/en/master/tune/examples/flaml_example.html#incorporating-a-time-budget-to-the-experiment",1252"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#more-resources",1253"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#scaling-configurations-in-train-scalingconfig",1254"https://docs.ray.io/en/master/tune/tutorials/tune-search-spaces.html#working-with-tune-search-spaces",1255"https://docs.ray.io/en/master/tune/examples/optuna_example.html#providing-an-initial-set-of-hyperparameters",1256"https://docs.ray.io/en/master/tune/examples/bayesopt_example.html#running-tune-experiments-with-bayesopt"1257],1258"document_ids": [12591826,12601063,12619126,12621303,1263967,12649127,12652741,12662569,12672860,12689256,12699252,12702312,12713140,127234541273],1274"answer": " To specify the ScalingConfig for a Tuner run, you can pass it as an argument to the `RunConfig` object, which is then passed to the `Tuner` constructor. Here's an example:\n\n```python\nfrom ray import tune\nfrom ray.train.ScalingConfig import ScalingConfig\n\nscaling_config = ScalingConfig(\n num_workers=8,\n resources_per_worker={\n \"CPU\": 4,\n \"GPU\": 0.5,\n }\n use_gpu=True,\n)\n\nrun_config = tune.RunConfig(\n name=\"my_tune_run\",\n scaling_config=scaling_config,\n)\n\ntuner = tune.Tuner(\n trainable,\n param_space=space,\n tune_config=tune.TuneConfig(num_samples=10),\n run_config=run_config,\n)\n\ntuner.fit()\n```\n\nIn this example, the `ScalingConfig` is passed to the `RunConfig` object, which is then passed to the `Tuner` constructor. This will configure the Tuner run to use 8 workers, with each worker using 4 CPUs and 0.5 GPUs.",1275"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1276},1277{1278"question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",1279"sources": [1280"https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",1281"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#gpu-profiling",1282"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_numpy_refs.html#ray-data-dataset-to-numpy-refs",1283"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",1284"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",1285"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",1286"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1287"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",1288"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy.html#ray-data-from-numpy",1289"https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only",1290"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1291"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.write_numpy.html#ray-data-dataset-write-numpy",1292"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1293"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map"1294],1295"document_ids": [12964942,1297645,12987644,12994537,13004945,13014225,13027085,13037102,13047467,13054944,13067495,13077247,13089180,130976371310],1311"answer": " To utilize the zero-copy feature Ray provides for numpy arrays, you should use the Pickle protocol 5 for serialization and store numpy arrays as read-only objects in the object store. When working with Ray workers on the same node, you can read the numpy array in the object store without copying. Each numpy array object in the worker process will hold a pointer to the relevant array in shared memory. However, if you need to write to the read-only object, you must first copy it into the local process memory.\n\nTo convert a Ray Dataset into a distributed set of NumPy ndarrays with zero-copy access to the underlying data, you can use `Dataset.to_arrow()` or `Dataset.get_internal_block_refs()` methods instead of `Dataset.to_numpy_refs()`.\n\nAdditionally, when using `map_batches()` function, you can set `zero_copy_batch=True` to improve performance and decrease memory utilization if the function does not mutate its input.\n\nTo avoid copying array 'a' every time `no_work()` is invoked, you can explicitly call `ray.put(a)` and then pass 'a's ID to `no_work()`.\n\nHere's an example of how to use zero-copy feature with numpy arrays in Ray:\n```python\nimport numpy as np\nimport ray\n\n# Create a numpy array and put it in the object store\na = np.zeros((100, 100))\na_ref = ray.put(a)\n\n# Pass the object reference to a function and read the array with zero-copy\n@ray.remote\ndef no_work(a_ref):\n a = ray.get(a_ref)\n # Now you can work with the array 'a' without copying it\n\n# Call the function with the object reference\nno_work.remote(a_ref)\n```",1312"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1313},1314{1315"question": "if there are O(millions) of keys that all have state, is it ok to spin up 1=1 actors? Or would it be advised to create \u2018key pools\u2019 where an actor can hold 1=many keys?",1316"sources": [1317"https://docs.ray.io/en/master/data/examples/random-access.html#architecture",1318"https://docs.ray.io/en/master/ray-more-libs/modin/index.html#machine-learning",1319"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1320"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1321"https://docs.ray.io/en/master/data/examples/random-access.html#performance",1322"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",1323"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",1324"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",1325"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_random_access_dataset.html#ray-data-dataset-to-random-access-dataset",1326"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",1327"https://docs.ray.io/en/master/data/api/doc/ray.data.ActorPoolStrategy.html#ray-data-actorpoolstrategy",1328"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",1329"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1330"https://docs.ray.io/en/master/data/batch_inference.html#handling-cpu-out-of-memory-failures"1331],1332"document_ids": [13338000,133414718,13358119,13364878,13378002,13388146,13395231,13408101,13417308,1342641,13437732,13445441,13455191,134669611347],1348"answer": " It is not advised to spin up 1=1 actors for each of O(millions) of keys, as it can lead to resource allocation and management issues. Instead, it is recommended to create 'key pools' where an actor can hold many keys. This approach can help manage resources more efficiently and improve performance. This is similar to how Modin uses Ray Actors for machine learning support, where one actor can hold and aggregate data for multiple partitions on a node.",1349"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1350},1351{1352"question": "How to find the best checkpoint from the trial directory?",1353"sources": [1354"https://docs.ray.io/en/master/train/api/doc/ray.train.Result.html#ray-train-result",1355"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1356"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1357"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",1358"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",1359"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",1360"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1361"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1362"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_trial_checkpoints_paths.html#ray-tune-experimentanalysis-get-trial-checkpoints-paths",1363"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",1364"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis",1365"https://docs.ray.io/en/master/train/user-guides/results.html#other-checkpoints",1366"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",1367"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-python-api"1368],1369"document_ids": [13708414,13711431,13721431,13732188,13741645,13753759,13761430,137711460,13781692,13793760,13801474,13819248,13822348,1383109351384],1385"answer": " To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` function, which is part of the Ray Tune library. This function allows you to specify a metric and a mode (either \"min\" or \"max\") to determine the best checkpoint. Any checkpoints without an associated metric value will be filtered out.\n\nHere is an example of how to use this function:\n```\nfrom ray.tune.experiment import ExperimentAnalysis\n\nanalysis = ExperimentAnalysis.from_directory(path)\nbest_checkpoint = analysis.get_best_checkpoint(metric=\"your_metric\", mode=\"max\")\n```\nIn this example, `path` is the path to the trial directory. `metric` is the key for checkpoints to order on, and `mode` is one of [\"min\", \"max\"]. The function will return the best checkpoint based on the specified metric and mode.\n\nNote: This API is currently in alpha and may change before becoming stable.",1386"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1387},1388{1389"question": "what are the advantage and disadvantage of using singleton Actor ?",1390"sources": [1391"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1392"https://docs.ray.io/en/master/tune/faq.html#what-are-hyperparameters",1393"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",1394"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1395"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",1396"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#default-concurrency-group",1397"https://docs.ray.io/en/master/ray-core/patterns/index.html#design-patterns-anti-patterns",1398"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",1399"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",1400"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1401"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",1402"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1403"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1404"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks"1405],1406"document_ids": [14075192,14081007,140914708,14104505,141111597,14124641,14135244,14144638,14154630,14164624,14175066,14184468,14198108,142052311421],1422"answer": " Actor is a stateful object that can manage its own state between method invocations. The advantages of using a singleton Actor include:\n\n* Only one instance of the Actor is created, ensuring that the state is shared and consistent across all invocations.\n* Actors can be used to implement complex concurrent systems with ease.\n\nThe disadvantages of using a singleton Actor include:\n\n* If the stateful part of an Actor is not needed, using tasks is a better option.\n* Class variables cannot be used to manage state between instances of the same class, as each Actor instance is instantiated in its own process.\n* Overuse of Actor's can lead to performance issues due to the overhead of creating and managing Actor instances.\n* Anti-pattern: Closure capturing large objects harms performance and Using global variables to share state between tasks and actors should be avoided.\n\nIt is important to note that if the number of concurrently running tasks and actors on a given node exceeds the node's total resources, it can lead to performance issues. To avoid this, the number of concurrently running tasks or actors on each node can be reduced by increasing the amount of resources requested by them.",1423"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1424},1425{1426"question": "what are the advantages of using a named actor?",1427"sources": [1428"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1429"https://docs.ray.io/en/master/rllib/rllib-concepts.html#building-policies-in-tensorflow",1430"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",1431"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1432"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1433"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1434"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1435"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",1436"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1437"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",1438"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1439"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",1440"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",1441"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources"1442],1443"document_ids": [14444623,144510781,14464629,14474625,14484871,14494624,14504880,145114708,14524505,145311597,14544451,14554200,1456520,1457115961458],1459"answer": " Named actors in Ray have the following advantages:\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. Accessibility: This 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.\n3. Get-Or-Create: Ray provides a get\\_if\\_exists option for actor creation that creates an actor only if it doesn\u2019t exist.\n4. Namespace: Named actors are scoped by namespace. If no namespace is assigned, they will be placed in an anonymous namespace by default.\n5. Lifetime: Named actors can have a lifetime that is independent of the creator, allowing for more flexible scheduling of tasks and actors.\n\nIn the context of your query, using a named actor for MyTFPolicy can provide a unique name for the policy, allowing it to be retrieved and used later in the Ray cluster. This can be useful for accessing the actor launched by another driver or if you cannot directly pass the actor handle to the task that needs it.",1460"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1461},1462{1463"question": "How do I read a text file stored on S3 using Ray Data?",1464"sources": [1465"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1466"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1467"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1468"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1469"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",1470"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1471"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",1472"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1473"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1474"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",1475"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1476"https://docs.ray.io/en/master/data/api/doc/ray.data.read_json.html#ray-data-read-json",1477"https://docs.ray.io/en/master/data/loading-data.html#reading-files-from-cloud-storage",1478"https://docs.ray.io/en/master/data/working-with-images.html#saving-images"1479],1480"document_ids": [14817565,14827565,14837783,14847496,14857261,14866994,14876779,14886996,14897217,14907909,14916995,14927317,14936743,149467291495],1496"answer": " To read a text file stored on S3 using Ray Data, you can use the `read_text` function and provide the S3 path to the file as an argument. Here's an example:\n\n```python\nimport ray\n\n# S3 path to the text file\ns3_path = \"s3://anonymous@ray-example-data/this.txt\"\n\n# Read the text file from S3\nds = ray.data.read_text(s3_path)\n\n# Print the schema of the DataBlock\nprint(ds.schema())\n```\n\nThis will create a Ray DataBlock containing the text data from the provided S3 path.",1497"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1498},1499{1500"question": "how do I get the IP of the head node for my Ray cluster?",1501"sources": [1502"https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",1503"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",1504"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",1505"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",1506"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",1507"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",1508"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",1509"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",1510"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats",1511"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",1512"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1513"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",1514"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",1515"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient"1516],1517"document_ids": [15185631,15196411,15206501,1521493,15225587,15234446,15246502,15256409,15266416,15276616,15286552,15295413,15306619,153167081532],1533"answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command. It is used as follows:\n```css\nray get_head_ip [OPTIONS] CLUSTER_CONFIG_FILE\n```\nHere, `CLUSTER_CONFIG_FILE` is a required argument that specifies the configuration file for the Ray cluster. The `--cluster-name` option can be used to override the configured cluster name.\n\nIf you don't have the cluster configuration file, you can programmatically discover the endpoints by reading the Ray Cluster information. Here's an example Python script that uses the `ray.nodes` API to find the metrics agents\u2019 URLs:\n```python\nimport ray\nray.init()\nprint(ray.nodes())\n```\nThis will print information about the current nodes in your cluster, including the IP address of the head node.",1534"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1535},1536{1537"question": "How to write a map function that returns a list of object for `map_batches`?",1538"sources": [1539"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1540"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.write_sql.html#ray-data-dataset-write-sql",1541"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1542"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1543"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1544"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1545"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1546"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1547"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",1548"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1549"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",1550"https://docs.ray.io/en/master/data/examples/batch_training.html#define-training-functions",1551"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_torch_batches.html#ray-data-dataiterator-iter-torch-batches",1552"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"1553],1554"document_ids": [15557082,15567662,15577091,15587640,15597081,15607800,15617959,15627086,15637738,15648070,156510266,15667888,15677679,156870851569],1570"answer": " To write a map function that returns a list of objects for `map_batches`, you can define a function that takes a batch of data as input and yields the desired objects in a list. Here's an example using a Python function:\n```python\nfrom typing import Dict, Iterator\nimport numpy as np\nimport ray\n\ndef map_fn_with_large_output(batch: Dict[str, np.ndarray]) -> Iterator[Dict[str, np.ndarray]]:\n for i in range(3):\n yield {\"large_output_\" + str(i): np.ones((100, 1000))}\n\nds = (\n ray.data.from_items([1])\n .map_batches(map_fn_with_large_output)\n)\n```\nIn this example, the `map_fn_with_large_output` function takes a batch of data as input and yields three dictionaries with large numpy arrays as values. The `map_batches` function applies this function to each batch of data in the dataset and returns an iterator over the transformed batches.",1571"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1572},1573{1574"question": "How do I set a maximum episode length when training with Rllib?",1575"sources": [1576"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",1577"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1578"https://docs.ray.io/en/master/rllib/rllib-replay-buffers.html#advanced-usage",1579"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",1580"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1581"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",1582"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",1583"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#evolution-strategies-es",1584"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",1585"https://docs.ray.io/en/master/rllib/rllib-offline.html#getting-started",1586"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",1587"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",1588"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",1589"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli"1590],1591"document_ids": [159210821,15931006,159410724,159510510,159611458,159713177,159811048,159911925,160012332,160110594,160210823,160313176,160411047,1605109261606],1607"answer": " To set a maximum episode length in RLlib, you can use the `sample_async` argument in the `Trainer` constructor. This argument allows you to specify a function that determines when an episode should be terminated. For example, you can use a function that terminates an episode after a certain number of steps have been taken. Here is an example of how you can use this argument to set a maximum episode length of 100 steps:\n```\ndef should_terminate(episode, **kwargs):\n return len(episode.last_timestep().steps) >= 100\n\ntrainer = Trainer(\n env=\"YourEnv\",\n config={\n \"sample_async\": True,\n \"sample_async_args\": {\n \"should_terminate\": should_terminate,\n }\n }\n)\n```\nAlternatively, you can also set the maximum episode length by modifying the environment's `reset` method to return a done flag after a certain number of steps. Here is an example of how you can do this:\n```\nclass YourEnv(gym.Env):\n def __init__(self, max_episode_length=100):\n self.max_episode_length = max_episode_length\n\n def reset(self, **kwargs):\n self.current_step = 0\n observation = self.observation_space.sample()\n return observation, False\n\n def step(self, action):\n observation, reward, done, info = super().step(action)\n self.current_step += 1\n done = self.current_step >= self.max_episode_length\n return observation, reward, done, info\n```\nThis will terminate the episode after `max_episode_length` steps have been taken.\n\nI hope this helps! Let me know if you have any other questions.",1608"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1609},1610{1611"question": "how do I make a Ray Tune trial retry on failures?",1612"sources": [1613"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1614"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1615"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1616"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1617"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_error.html#ray-tune-callback-on-trial-error",1618"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_recover.html#ray-tune-callback-on-trial-recover",1619"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",1620"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.MedianStoppingRule.html#ray-tune-schedulers-medianstoppingrule",1621"https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.LoggerCallback.on_trial_recover.html#ray-tune-logger-loggercallback-on-trial-recover",1622"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune",1623"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.HyperBandScheduler.html#ray-tune-schedulers-hyperbandscheduler",1624"https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.JsonLoggerCallback.on_trial_recover.html#ray-tune-logger-jsonloggercallback-on-trial-recover",1625"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.on_trial_error.html#ray-tune-schedulers-trialscheduler-on-trial-error",1626"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance"1627],1628"document_ids": [16292326,16301006,16311114,16322266,16331980,16342152,16351926,16361728,16371966,163811554,16391974,16402104,16411749,164243771643],1644"answer": " To make a Ray Tune trial retry on failures, you can use the `FailureConfig` to configure failure handling of individual trials. Here is an example of how to configure `FailureConfig` to retry a trial on errors:\n\n```python\nfrom ray import tune\n\n# Define the FailureConfig\nfail_config = tune.FailureConfig(max_retries=3)\n\n# Create the Tuner with the FailureConfig\ntuner = tune.Tuner(train_model, param_space=trial_space, failure_config=fail_config)\n\n# Run the Tuner\nresults = tuner.fit()\n```\n\nIn this example, the `max_retries` parameter is set to 3, meaning that if a trial fails, it will be retried up to 3 times before being marked as failed. You can adjust the number of retries according to your needs.\n\nAdditionally, you can use the `ray.tune.Callback.on_trial_error` and `ray.tune.Callback.on_trial_recover` callbacks to customize the behavior when a trial fails or is recovered. These callbacks are called after a trial instance has failed or errored but is scheduled for retry, respectively. You can use these callbacks to implement custom behavior, such as logging or notifications, when a trial fails or is recovered.",1645"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1646},1647{1648"question": "For the supervised actor pattern, can we keep the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor, to allow the worker actor to remain even on Supervisor / Driver failure?",1649"sources": [1650"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",1651"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",1652"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",1653"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1654"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1655"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",1656"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#how-to-write-fault-tolerant-ray-applications",1657"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1658"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#advanced-detached-placement-group",1659"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#worker-recovery",1660"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1661"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1662"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1663"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#worker-node-failure"1664],1665"document_ids": [16665208,16675208,16684561,16694575,16704631,16715195,16724351,16735191,16745095,167511551,16765192,16774451,16784878,167945201680],1681"answer": " No, keeping the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor will not prevent the worker actor from being terminated on Supervisor/Driver failure. This is because of actor reference counting - if the supervisor dies, the worker actors are automatically terminated. This pattern is simplified in the driver, as it manages only a few supervisors and does not deal with failures from worker actors directly.",1682"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1683},1684{1685"question": "How do I read a large text file in S3 with Ray?",1686"sources": [1687"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1688"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",1689"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1690"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1691"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",1692"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1693"https://docs.ray.io/en/master/data/loading-data.html#handling-compressed-files",1694"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1695"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1696"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",1697"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1698"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",1699"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",1700"https://docs.ray.io/en/master/data/key-concepts.html#loading-data"1701],1702"document_ids": [17037565,17046993,17057783,17067496,17077261,17086994,17096747,17106996,17117217,17126993,17136995,17147909,17156779,171667321717],1718"answer": " To read a large text file in S3 with Ray, you can use the `read_text` function and provide the S3 path to the text file as an argument. Here's an example:\n```python\nimport ray\n\nds = ray.data.read_text(\"s3://your-bucket-name/path/to/your/textfile.txt\")\n```\nThis will create a Ray Dataset (`ds`) containing the text data from the file. If the text file is compressed (e.g., `.gz`), you can specify the compression in `arrow_open_stream_args`:\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```\nIf you need to read multiple text files from a directory in S3, you can use `ray.data.read_text_split_files`:\n```python\nds = ray.data.read_text_split_files(\"s3://your-bucket-name/path/to/text_files_directory/\")\n```\nThis will create a Ray Dataset with the text data from all the text files in the specified directory.",1719"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1720},1721{1722"question": "how do I get a ray dataset from pandas",1723"sources": [1724"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-pandas",1725"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1726"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1727"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",1728"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",1729"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1730"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",1731"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1732"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",1733"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_pandas.html#ray-data-dataset-to-pandas",1734"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-rows",1735"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",1736"https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-datasets",1737"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv"1738],1739"document_ids": [17406806,17411006,17426749,17436901,17446752,17459180,17467818,174710017,17487363,17497302,17506860,175110140,17526984,175372161754],1755"answer": " To convert a pandas DataFrame to a Ray Dataset, you can use the `ray.data.from_pandas()` function. Here's an example:\n\n```python\nimport pandas as pd\nimport ray\n\ndf = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\nds = ray.data.from_pandas(df)\n```\n\nThis will create a Ray Dataset `ds` from the pandas DataFrame `df`. Note that the entire DataFrame must fit in memory on the head node.\n\nIf you have a large dataset that doesn't fit in memory, you can consider using distributed data processing frameworks like Dask, Spark, Modin, or Mars, which Ray Data interoperates with. For example, you can create a Ray Dataset from a Dask DataFrame as follows:\n\n```python\nimport dask.dataframe as dd\nimport pandas as pd\nimport ray\n\ndf = pd.DataFrame({\"col1\": list(range(10000)), \"col2\": list(map(str, range(10000)))})\nddf = dd.from_pandas(df, npartitions=4)\nds = ray.data.from_dask(ddf)\n```\n\nThis will create a Ray Dataset `ds` backed by the distributed Pandas DataFrame partitions that underly the Dask DataFrame.",1756"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1757},1758{1759"question": "can you give me an example of using `ray.data.map` ?",1760"sources": [1761"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1762"https://docs.ray.io/en/master/rllib/rllib-examples.html#custom-and-complex-models",1763"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1764"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1765"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1766"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1767"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",1768"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",1769"https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",1770"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",1771"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1772"https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",1773"https://docs.ray.io/en/master/data/api/from_other_data_libs.html#api-guide-for-users-from-other-data-libraries",1774"https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-transforms"1775],1776"document_ids": [17777796,177810636,17797274,17809180,17817081,17826835,17838046,17848006,17856773,17867971,17876749,17886782,17897020,179069761791],1792"answer": " Sure, here's an example of using `ray.data.Dataset.map`:\n\nSuppose we have a Ray Dataset with some filenames and corresponding image paths, and we want to read the images and add them as a new column to the dataset. We can define a function to read the image using PIL and return a new row with the image added:\n```python\nfrom PIL import Image\nimport ray\n\ndef read_image(row: dict) -> dict:\n path = row[\"path\"]\n image = Image.open(path)\n image_array = np.array(image)\n return {\"path\": path, \"image\": image_array}\n\nds = ray.data.from_items([\n {\"path\": \"path/to/image1.jpg\"},\n {\"path\": \"path/to/image2.jpg\"},\n # ... more rows with image paths\n])\n\n# Apply the transform over the dataset.\ntransformed_ds = ds.map(read_image)\n\n# The resulting dataset will have a new column \"image\" with the image data.\nprint(transformed_ds.schema())\n```\nThis will create a new column \"image\" in the dataset with the image data as a NumPy array. Note that `map` applies the function to each row of the dataset individually, so it can be slower than `map_batches` for vectorized transformations.",1793"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1794},1795{1796"question": "can you give me an example of using `ray.data.map` , with a callable class as input?",1797"sources": [1798"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1799"https://docs.ray.io/en/master/rllib/rllib-examples.html#custom-and-complex-models",1800"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",1801"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1802"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1803"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1804"https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",1805"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",1806"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1807"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",1808"https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",1809"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",1810"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1811"https://docs.ray.io/en/master/data/working-with-text.html#transforming-text"1812],1813"document_ids": [18147796,181510636,1816250,18177274,18187636,18196749,18207628,1821247,182210014,182310303,18246782,18257102,18269180,182769971828],1829"answer": " Sure, here's an example of using `ray.data.map` with a callable class as input:\n```python\nimport ray\n\n@ray.remote\nclass Adder(object):\n def __init__(self):\n self.total = 0\n\n",1830"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1831},1832{1833"question": "How to set memory limit for each trial in Ray Tuner?",1834"sources": [1835"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1836"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#pattern-using-ray-wait-to-limit-the-number-of-pending-tasks",1837"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1838"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",1839"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1840"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",1841"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-trials-individually-with-a-timeout",1842"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria",1843"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",1844"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",1845"https://docs.ray.io/en/master/tune/examples/pbt_guide.html#replaying-a-pbt-run",1846"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",1847"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",1848"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-aware-scheduling"1849],1850"document_ids": [18512325,18525246,18531114,18542406,18552266,18562001,18572204,18582198,18591025,18602003,18613167,1862558,18636879,186450431865],1866"answer": " To set a memory limit for each trial in Ray Tuner, you can use the `resources` parameter in the `tune.with_resources()` function to specify the amount of memory required for each trial. However, Ray Tune automatically decides task parallelism based on resource availability, so it's recommended to adjust the resource requirements of each trial instead of setting a fixed limit on the number of concurrent trials.\n\nHere's an example of how to set the memory limit to 2GB for each trial:\n```python\nimport ray\nfrom ray.tune import with_resources\n\ndef train_model(config):\n # Train the model here\n pass\n\n# Set the memory limit to 2GB for each trial\ntrain_model = with_resources(train_model, {\"memory\": 2e9})\n\ntuner = tune.Tuner(\n train_model,\n tune_config=tune.TuneConfig(\n num_samples=10,\n max_concurrent_trials=8,\n ),\n)\nresults_grid = tuner.fit()\n```\nNote that the memory limit is specified in bytes, so we use `2e9` to represent 2GB. Also, keep in mind that the actual parallelism can be less than `max_concurrent_trials` and will be determined by how many trials can fit in the cluster at once.",1867"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1868},1869{1870"question": "how do I get the actor id of an actor",1871"sources": [1872"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_actor_id.html#ray-runtime-context-runtimecontext-get-actor-id",1873"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",1874"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",1875"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1876"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor",1877"https://docs.ray.io/en/master/ray-observability/getting-started.html#actor-detail-page",1878"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1879"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",1880"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1881"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1882"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1883"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",1884"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",1885"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs"1886],1887"document_ids": [18884787,1889682,18904931,1891475,1892690,1893436,1894474,1895750,1896476,1897477,18984623,189914324,190014202,19015201902],1903"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 property `RuntimeContext.actor_id`. These methods will return the ID of the actor of the current process in hex format. Note that this should not be used in a driver process.\n\nHere is an example of how to use `get_actor_id()` to get the actor ID:\n```\nfrom ray.runtime_context import RuntimeContext\n\nactor_id = RuntimeContext.get_actor_id()\nprint(actor_id)\n```\nAlternatively, you can use the `RuntimeContext.actor_id` property to get the actor ID:\n```\nfrom ray.runtime_context import RuntimeContext\n\nactor_id = RuntimeContext.actor_id\nprint(actor_id)\n```\nIf you want to get the state of an actor, you can use the `ray.util.state.get_actor()` function. This function takes the actor ID as a parameter and returns the state of the actor.\n\nHere is an example of how to use `get_actor()` to get the state of an actor:\n```\nfrom ray.util.state import get_actor\n\nactor_state = get_actor(id=\"<ACTOR_ID>\")\nprint(actor_state)\n```\nNote that the `get_actor()` function is a developer API and may change across minor Ray releases.\n\nYou can also use the `ray logs actor --id` command to get the logs of an actor.\n\nHere is an example of how to use this command to get the logs of an actor:\n```\nray logs actor --id <ACTOR_ID>\n```",1904"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1905},1906{1907"question": "can ray.init() can check if ray is all-ready initiated ?",1908"sources": [1909"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",1910"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobType.SUBMISSION.html#ray-job-submission-jobtype-submission",1911"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1912"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",1913"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1914"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1915"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1916"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#starting-ray",1917"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",1918"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1919"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",1920"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1921"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.__init__.html#ray-data-preprocessor-preprocessor-init",1922"https://docs.ray.io/en/master/ray-core/configure.html#java-applications"1923],1924"document_ids": [19254785,19266647,19274811,19284194,19294190,19304188,19314189,19325386,19334186,19344204,19355399,19364576,19377833,193844041939],1940"answer": " No, `ray.init()` is not used to check if Ray is already initiated. Instead, you should use `ray.is_initialized()` to check if Ray has been initialized in the current process. This function returns `True` if `ray.init()` has been called and `False` otherwise.",1941"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1942},1943{1944"question": "What does the `compute=actor` argument do within `ray.data.map_batches` ?",1945"sources": [1946"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1947"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#how-does-tune-work",1948"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",1949"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1950"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1951"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1952"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",1953"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1954"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map.html#ray-util-actorpool-map",1955"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",1956"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",1957"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1958"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",1959"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"1960],1961"document_ids": [19626787,19632232,19646885,19658108,19668070,19677988,19685465,19696981,19704708,19716999,19726952,19736941,19746954,197569441976],1977"answer": " The `compute=actor` argument within `ray.data.map_batches` is used to specify that the function provided to `map_batches` should be run as long-running Ray actors, using an `ActorPoolStrategy`. This allows for parallel processing of data batches by multiple actors.",1978"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1979},1980{1981"question": "how do I use wandb logger with accelerateTrainer?",1982"sources": [1983"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",1984"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1985"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1986"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1987"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1988"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",1989"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",1990"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1991"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1992"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",1993"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",1994"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#tune-wandb-api-reference",1995"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1996"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries"1997],1998"document_ids": [19998470,20001006,20012432,20022463,20039530,20042431,20051132,20062466,20079529,20089136,20099137,20102462,20112434,201291382013],2014"answer": " To use WandB logger with AccelerateTrainer in PyTorch Lightning, you can follow these steps:\n\n1. Install WandB: `pip install wandb`\n2. Import the necessary libraries:\n```python\nimport os\nimport pytorch_lightning as pl\nfrom pytorch_lightning.loggers import WandbLogger\n```\n3. Set the WANDB\\_API\\_KEY environment variable on each worker:\n```python\nWANDB_API_KEY = os.environ[\"WANDB_API_KEY\"]\nos.environ[\"WANDB_API_KEY\"] = WANDB_API_KEY\n```\n4. Initialize the WandB logger:\n```python\nwandb_logger = WandBLogger(name=\"your_experiment_name\", project=\"your_project_name\")\n```\n5. Initialize the AccelerateTrainer with the logger:\n```python\ntrainer = pl.Trainers.accelerate.AccelerateTrainer(logger=wandb_logger, **accelerate_args)\n```\nReplace `accelerate_args` with the appropriate arguments for AccelerateTrainer.\n\nNow you can train your model using the `trainer.fit()` method. The WandB logger will automatically log metrics and other information during training.",2015"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2016},2017{2018"question": "What will be implicitly put into object store?",2019"sources": [2020"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",2021"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#move-the-data-loader-to-the-training-function",2022"https://docs.ray.io/en/master/ray-core/objects/serialization.html#plasma-store",2023"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",2024"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2025"https://docs.ray.io/en/master/data/data-internals.html#object-store-memory",2026"https://docs.ray.io/en/master/ray-core/objects.html#objects",2027"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",2028"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",2029"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",2030"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",2031"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",2032"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",2033"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"2034],2035"document_ids": [20365013,20379858,20384941,20395014,204014748,20416896,20424414,204314094,204414122,204514138,204614063,204714222,204814184,2049143002050],2051"answer": " Implicitly, objects created in the object store via `ray.put` and returned values from remote functions will be put into the object store memory. When the object store fills up, objects will be spilled to disk. Objects are reference counted and evicted when they fall out of scope. Each node has its own object store, and data is not automatically broadcasted to other nodes. Objects in the object store can be efficiently shared among many actors and tasks.",2052"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2053},2054{2055"question": "How do I kill or cancel a ray task that I already started?",2056"sources": [2057"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",2058"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2059"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",2060"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2061"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2062"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",2063"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2064"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",2065"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2066"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2067"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2068"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2069"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2070"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"2071],2072"document_ids": [20734550,20741005,20754375,20764894,20774494,207814702,207911591,208014703,20814497,208211592,20834896,208411590,20854492,2086147012087],2088"answer": " To kill or cancel a Ray task that you have already started, you can call `ray.cancel()` on the Object Ref returned by the task. This will send a KeyboardInterrupt to the task's worker if it is currently in mid-execution. If you want to force-exit the worker, you can pass `force=True` to `ray.cancel()`. Note that Ray will not automatically retry tasks that have been cancelled.\n\nHere is an example of how to cancel a Ray task:\n\n```\n@ray.remote\ndef blocking_operation():\n time.sleep(10e6)\n\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n\ntry:\n ray.get(obj_ref)\nexcept ray.exceptions.TaskCancelledError:\n print(\"Object reference was cancelled.\")\n```\n\nThis cancellation approach aligns with the standards presented in asyncio task cancellation. Note that asyncio.Task won\u2019t be interrupted in the middle of execution if you don\u2019t await within the async function. Ray attempts to cancel Tasks on a best-effort basis, meaning cancellation isn\u2019t always guaranteed. You can check if a Task was successfully cancelled using `ray.get(actor_task_ref)`.\n\nFor Actor Tasks, if the specified Task is pending execution, it is cancelled and not executed. If the Task is currently executing, the behavior depends on the execution model of an Actor. If it is a regular Actor or a threaded Actor, the execution isn\u2019t cancelled. Actor Tasks cannot be interrupted because Actors have states. If it is an async Actor, Ray cancels a asyncio.Task. The semantic of cancellation is equivalent to asyncio\u2019s cancellation. If the Task has finished, nothing happens. Only `force=False` is allowed for an Actor Task. Otherwise, it raises ValueError. Use `ray.kill(actor)` instead to kill an Actor.\n\nTo cancel an Actor Task, you can also call `ray.cancel()` on the returned Object Ref.\n\n```\nimport ray\nimport asyncio\nimport time\n\n@ray.remote\nclass Actor:\n async def f(self):\n try:\n await asyncio.sleep(5)\n except asyncio.CancelledError:\n print(\"Actor task canceled.\")\n\nactor = Actor.remote()\nref = actor.f.remote()\n\n# Wait until task is scheduled.\ntime.sleep(1)\nray.cancel(ref)\n```",2089"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2090},2091{2092"question": "how to send extra arguments in dataset.map_batches function?",2093"sources": [2094"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2095"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#pass-arguments-to-applications",2096"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2097"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",2098"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",2099"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",2100"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",2101"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",2102"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",2103"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches",2104"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",2105"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",2106"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",2107"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"2108],2109"document_ids": [21107082,211113810,21127081,21138607,21147959,21158070,21167988,21177640,21186956,21196861,212010017,21217796,21227738,212370912124],2125"answer": " To send extra arguments in the `dataset.map_batches` function, you can use the `fn_kwargs` parameter. This parameter allows you to pass 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 dataset, and `extra_arg` is an extra argument that you want to pass to `my_function`. The `fn_kwargs` parameter is a dictionary that maps the name of the extra argument (`\"extra_arg\"`) to its value (`\"some value\"`). When `my_function` is called by `map_batches`, it will receive `extra_arg` as a keyword argument.\n\nYou can also use `fn_args` to pass extra positional arguments to the UDF. Here's an example:\n```python\nextra_arg = \"some value\"\nds.map_batches(my_function, fn_args=[extra_arg])\n```\nIn this example, `fn_args` is a list that contains the extra argument (`extra_arg`). When `my_function` is called by `map_batches`, it will receive `extra_arg` as a positional argument.\n\nNote that `fn_args` and `fn_kwargs` are optional parameters, and you can use one or both of them depending on your needs.",2126"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2127},2128{2129"question": "where does ray GCS store the history of jobs run on a kuberay cluster? What type of database and format does it use for this?",2130"sources": [2131"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance",2132"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",2133"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",2134"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",2135"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",2136"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",2137"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",2138"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/experimental.html#gcs-fault-tolerance",2139"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",2140"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",2141"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",2142"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",2143"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#step-1-add-external-redis-server",2144"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes"2145],2146"document_ids": [21475830,21485754,214913871,215013470,215113870,21525642,21535647,21545916,21555763,21566149,21575860,21585658,215913872,216056562161],2162"answer": " The history of jobs run on a Kuberay cluster in Ray 2.0 is stored in the Global Control Store (GCS) with fault tolerance provided by an external Redis database. The GCS stores cluster metadata, and if the head node crashes, the metadata is not lost due to the external Redis integration. The type of database used is Redis, and it stores the metadata in a key-value store format.",2163"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2164},2165{2166"question": "How to resolve ValueError: The actor ImplicitFunc is too large?",2167"sources": [2168"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",2169"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",2170"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#move-the-data-loader-to-the-training-function",2171"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#verify-and-save-results",2172"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2173"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2174"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler",2175"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",2176"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",2177"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",2178"https://docs.ray.io/en/master/data/batch_inference.html#handling-cpu-out-of-memory-failures",2179"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2180"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2181"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options"2182],2183"document_ids": [21846150,21855118,21869858,21877992,21888119,21898108,21901895,219110267,21929908,21935465,21946961,2195476,21966954,219748782198],2199"answer": " The ValueError \"The actor ImplicitFunc is too large\" is likely caused by a large model or data object being implicitly captured in the actor's scope. To resolve this issue, you can:\n\n1. Move the data loader to the training function and use `ray.put()` to put large objects in the Ray object store.\n2. Reduce the batch size to ensure full parallelization across the actor pool.\n3. Consider using a smaller model or breaking down the data into smaller chunks.\n4. Increase the object store memory for actors by adjusting the `object_store_memory` configuration.\n\nAdditionally, you may want to enable and configure the memory monitor to detect and resolve memory issues.",2200"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2201},2202{2203"question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",2204"sources": [2205"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",2206"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2207"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2208"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#distributed-training",2209"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",2210"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2211"https://docs.ray.io/en/master/train/huggingface-accelerate.html#training-with-huggingface-accelerate",2212"https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others",2213"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2214"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#training",2215"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",2216"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#define-the-training-loop",2217"https://docs.ray.io/en/master/train/getting-started-pytorch.html#getting-started-with-pytorch",2218"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#how-it-works"2219],2220"document_ids": [22219845,22221006,2223226,222410155,2225370,22266940,22278247,222814313,2229228,22305515,22319820,223210196,22338312,2234101502235],2236"answer": " To distribute training for your custom neural net written using Keras in Databricks, you can use Ray Train and Ray AIR. Here are the steps:\n\n1. Import Ray Train and Ray AIR Session:\n```python\nimport ray.train as train\n```\n2. Convert your training function to a distributed multi-worker training function using `ray.train.torch.prepare_model` and `ray.train.torch.prepare_data_loader` utility functions. This will automatically wrap the model with DistributedDataParallel and place it on the right device, and add DistributedSampler to the DataLoaders.\n```python\nfrom ray import train\n\ndef train_func_distributed():\n num_epochs = 3\n batch_size = 64\n\n dataset = get_dataset()\n dataloader = DataLoader(dataset, batch_size=batch_size)\n dataloader = train.torch.prepare_data_loader(dataloader)\n\n model = NeuralNetwork()\n model = train.torch.prepare_model(model)\n```\n3. Load the data shard for each worker with `session.get_dataset_shard(\"train\")` and iterate over the dataset with `train_dataset.iter_torch_batches()`.\n4. Set up the model to communicate gradient updates across machines and report the results back to Ray Train.\n\nHere is an example of how to set up Multi-worker training with Keras using Ray Train:\n\n1. Install Ray Train and Tensorflow packages:\n```python\npip install -U \"ray[train]\" tensorflow\n```\n2. Set up your dataset and model.\n3. Use `TorchTrainer` to distribute the training:\n```python\nfrom ray.train.torch import TorchTrainer\nfrom ray.train import ScalingConfig\n\ntrainer = TorchTrainer(\n train_func_distributed,\n scaling_config=ScalingConfig(num_workers=4, use_gpu=use_gpu)\n)\n\nresults = trainer.fit()\n```\n\nNote: You can find more information on how to use Ray Train and Ray AIR in the Ray documentation.",2237"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2238},2239{2240"question": "how to use ray.put and ray,get?",2241"sources": [2242"https://docs.ray.io/en/master/ray-core/api/core.html#objects",2243"https://docs.ray.io/en/master/tune/examples/hpo-frameworks.html#tune-hyperparameter-optimization-framework-examples",2244"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",2245"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2246"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",2247"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",2248"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",2249"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2250"https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",2251"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2252"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",2253"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2254"https://docs.ray.io/en/master/ray-core/objects.html#objects",2255"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"2256],2257"document_ids": [22584651,22592429,22605443,22614849,22624205,22634884,22645219,22654209,22664775,22674811,226813646,22695380,22704413,227166152272],2273"answer": " To use `ray.put` and `ray.get`, you need to understand that these functions are used to store and retrieve objects in Ray's object store. `ray.put(value)` is used to store an object in the object store and returns an object reference (`ObjectRef`). This reference can then be used to retrieve the object using `ray.get(object_ref)`.\n\nHere's a simple example:\n\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Put an object in the object store\nobj_ref = ray.put(\"Hello, Ray!\")\n\n# Get the object from the object store\nobj = ray.get(obj_ref)\nprint(obj) # Output: 'Hello, Ray!'\n```\n\nIn this example, we first initialize Ray using `ray.init()`. Then, we store a string \"Hello, Ray!\" in the object store using `ray.put()`, which returns an object reference (`obj_ref`). Finally, we retrieve the object from the object store using `ray.get(obj_ref)` and print it.\n\nNote that `ray.get()` is a blocking operation, meaning it will wait until the object is available in the object store. If you want to wait for the object to be available without retrieving it, you can use `ray.wait()`.\n\nAlso, keep in mind that unnecessary use of `ray.get()` can harm performance, as it requires transferring objects to the worker/node that calls `ray.get()`. It's best practice to wait as long as possible before calling `ray.get()` or even design your program to avoid having to call `ray.get()` at all.",2274"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2275},2276{2277"question": "how do I use Ray Data to pre process many files?",2278"sources": [2279"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",2280"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",2281"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",2282"https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",2283"https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors",2284"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data",2285"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2286"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2287"https://docs.ray.io/en/master/train/user-guides.html#ray-train-user-guides",2288"https://docs.ray.io/en/master/data/user-guide.html#user-guides",2289"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-preprocess-data-for-training",2290"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.__init__.html#ray-data-preprocessor-preprocessor-init",2291"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-data",2292"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text"2293],2294"document_ids": [22959178,22968010,22979222,22986849,22996914,23009212,23016838,23026835,23038110,23046911,23058155,23067833,23079189,230869932309],2310"answer": " To preprocess many files using Ray Data, you can follow these steps:\n\n1. Load the data using Ray Data's `read_files` function, which supports reading data from various file formats.\n2. Preprocess the data using Ray Data's preprocessors, which are a higher-level API for transforming tabular and structured data. These preprocessors can be applied to the dataset before passing it into a Trainer.\n3. Materialize the preprocessed dataset in Ray's built-in object store if it's small enough to fit in memory. This will compute the entire preprocessed dataset and pin it in memory, so that when iterating over the dataset repeatedly, the preprocessing operations do not need to be re-run.\n\nHere's an example of how to use Ray Data preprocessors for preprocessing structured data:\n\n```python\nimport ray\nfrom ray.data.preprocessors import MinMaxScaler\nfrom ray.train.xgboost import XGBoostTrainer\nfrom ray.train import ScalingConfig\n\n# Load data\ntrain_dataset = ray.data.read_files(\"path/to/train/*.csv\")\n\n# Preprocess data\nscaler = MinMaxScaler()\ntrain_dataset = scaler.fit_transform(train_dataset)\n\n# Materialize the preprocessed dataset in Ray's object store\ntrain_dataset = train_dataset.materialize()\n\n# Use the preprocessed dataset with a Trainer\ntrainer = XGBoostTrainer(...)\nscaling_config = ScalingConfig(...)\nray.train.run(trainer, config=scaling_config, dataset=train_dataset)\n```\n\nNote that if the preprocessed data is too large to fit into Ray object store memory, it's not recommended to materialize the dataset, as it will decrease performance due to disk spills. In this case, you can use Ray Data's streaming capabilities to preprocess and ingest the data for ML training pipelines.",2311"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2312},2313{2314"question": "can\u2019t pickle SSLContext objects",2315"sources": [2316"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2317"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",2318"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2319"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",2320"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2321"https://docs.ray.io/en/master/ray-core/objects/serialization.html#known-issues",2322"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#tls-verification",2323"https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#checkpoints-are-py-version-specific-but-can-be-converted-to-be-version-independent",2324"https://docs.ray.io/en/master/serve/tutorials/java.html#calling-ray-serve-deployment-with-http",2325"https://docs.ray.io/en/master/ray-core/configure.html#step-3-set-the-environment-variables-for-both-ray-head-and-worker-to-enable-tls",2326"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",2327"https://docs.ray.io/en/master/ray-more-libs/installation.html#installed-python-dependencies",2328"https://docs.ray.io/en/master/ray-overview/installation.html#installed-python-dependencies",2329"https://docs.ray.io/en/master/installation.html#installed-python-dependencies"2330],2331"document_ids": [23324953,23334939,23344952,23354939,23364957,23374958,23386592,233910551,234014578,23414400,23424260,234314645,2344335,23451102346],2347"answer": " The error message indicates that `SSLContext` objects cannot be pickled, which is causing the serialization to fail. This is a known issue with certain versions of Python's pickle module. To resolve this issue, you can try upgrading to a later version of Python (3.8.2rc1 or 3.9.0 alpha 4 or later) where this issue has been fixed. Alternatively, you can convert the pickle checkpoint to a msgpack one using an RLlib utility function. Additionally, you can enable TLS by setting the appropriate environment variables `RAY_USE_TLS`, `RAY_TLS_SERVER_CERT`, and `RAY_TLS_SERVER_KEY`.",2348"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2349},2350{2351"question": "How do I install CRDs in Kuberay?",2352"sources": [2353"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",2354"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2355"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",2356"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",2357"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",2358"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-2-create-a-raycluster-cr",2359"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-3-install-a-kuberay-operator",2360"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-3-install-a-kuberay-operator",2361"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-2-install-the-kuberay-operator",2362"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-1-install-a-kuberay-cluster",2363"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-1-install-the-kuberay-operator",2364"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/kubeflow.html#step-3-install-kuberay-operator",2365"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-2-install-the-kuberay-operator",2366"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-2-install-the-kuberay-operator"2367],2368"document_ids": [23695720,23701006,23716103,23725817,23735647,23745981,23755774,23766057,23776044,23785980,23796143,23806092,23815741,238257592383],2384"answer": " To install Custom Resource Definitions (CRDs) in Kuberay, you can use the following Helm command:\n\nhelm repo add kuberay https://ray-project.github.io/kuberay-helm/\nhelm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n\nThis command will install both the CRDs and the Kuberay operator. The CRDs are necessary for Kuberay to manage Ray clusters and jobs on Kubernetes.",2385"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2386},2387{2388"question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",2389"sources": [2390"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2391"https://docs.ray.io/en/master/data/batch_inference.html#end-to-end-offline-batch-inference",2392"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2393"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",2394"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",2395"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",2396"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",2397"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",2398"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging",2399"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2400"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",2401"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",2402"https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",2403"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors"2404],2405"document_ids": [24068107,24076934,24086788,240913697,2410140,24116955,24127955,24137380,2414590,24156998,24167328,24177985,24187632,241967892420],2421"answer": " The function for Ray data batch inference is named `__call__()` because it is a special method in Python classes that allows an instance of the class to be called like a function, with parentheses and arguments. In the context of Ray data batch inference, the `__call__()` method is used to define the function that will be applied to each batch of data during the inference process. By naming this method `__call__()`, it can be used as the `fn` argument in the `map_batches()` method of a Ray data object, which applies the function to each batch of data in the object.",2422"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2423},2424{2425"question": "How to disconnnect ray client?",2426"sources": [2427"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2428"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",2429"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",2430"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",2431"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",2432"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",2433"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2434"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2435"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",2436"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",2437"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options",2438"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2439"https://docs.ray.io/en/master/ray-core/api/doc/ray.shutdown.html#ray-shutdown",2440"https://docs.ray.io/en/master/cluster/usage-stats.html#requesting-removal-of-collected-data"2441],2442"document_ids": [24436615,2444161,24456626,24466624,244714507,24486608,24494613,24504894,24516612,24524375,245314500,24544618,24554827,245655692457],2458"answer": " To disconnect a Ray client, you can call the `disconnect()` method on the client object. If the client disconnects unexpectedly, it will attempt to reconnect to the server for a certain period before dropping all references. You can increase this time by setting the environment variable `RAY_CLIENT_RECONNECT_GRACE_PERIOD`.\n\nHere is an example of how to use the Ray client and disconnect it:\n\n```python\nfrom ray import client, put, get\n\n# Connect to the Ray cluster\nray_client = client.RayClient()\n\n# Put some objects into the object store\nobj1 = put(\"obj1\")\nobj2 = put(\"obj2\")\n\n# Get the objects from the object store\nassert get(obj2) == \"obj2\"\ntry:\n get(obj1) # Cross-cluster ops not allowed.\nexcept:\n print(\"Failed to get object which doesn't belong to this cluster\")\nassert \"obj\" == get(obj)\n\n# Disconnect the client\nray_client.disconnect()\n```\n\nNote that the client won't be disconnected automatically, so you need to call `disconnect()` explicitly to close the connection. Also, object references can only be used by the client from which it was obtained.",2459"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2460},2461{2462"question": "how to submit job with python with local files?",2463"sources": [2464"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2465"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",2466"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job",2467"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2468"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2469"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2470"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",2471"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",2472"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#jobsubmissionclient",2473"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2474"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#dependency-management",2475"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",2476"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",2477"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"2478],2479"document_ids": [24806550,24816595,24824255,24836553,24846578,24856579,24866575,24876595,24886540,24896581,24906585,24916567,24925732,249366932494],2495"answer": " To submit a job with Python while including local files, you can use the Ray Jobs API. Here's an example command that demonstrates how to do this:\n\n```bash\nray job submit --address=\"http://<head-node-ip>:8265\" --runtime-env-json='{\"working_dir\": \"/path/to/local/files\", \"pip\": [\"requirement1\", \"requirement2\"]}' -- python my_ray_script.py\n```\n\nIn this command, replace `<head-node-ip>` with the IP address of your Ray cluster head node, and set `/path/to/local/files` to the path of the local files you want to include in the job. Also, replace `requirement1` and `requirement2` with any Python requirements you need to install.\n\nMake sure to initialize the Ray cluster with the necessary files and requirements before submitting the job.",2496"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2497},2498{2499"question": "How do I do inference from a model trained by Ray tune.fit()?",2500"sources": [2501"https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",2502"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",2503"https://docs.ray.io/en/master/tune/examples/includes/tune_basic_example.html#tune-basic-example",2504"https://docs.ray.io/en/master/data/batch_inference.html#using-models-from-ray-train",2505"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",2506"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",2507"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#configuring-the-search-space-and-starting-ray-tune",2508"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2509"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",2510"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",2511"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",2512"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",2513"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",2514"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples"2515],2516"document_ids": [2517986,25188010,25194111,25206962,2521117,25221080,25232767,2524234,2525127,25261090,2527380,2528162,2529189,25303432531],2532"answer": " To do inference from a model trained by Ray tune.fit(), you can use the Checkpoint returned by Ray Train to perform batch inference with Ray Data. Here is a brief example:\n\n1. Train a model with Ray Train:\n```python\nimport ray\nfrom ray.train import ScalingConfig\nfrom ray.train.xgboost import XGBoostTrainer\n\ndataset = ray.data.read_csv(\"s3://anonymous@air-example-data/breast_cancer.csv\")\ntrain_dataset, valid_dataset = dataset.train_test_split(test_size=0.3)\n\ntrainer = XGBoostTrainer(\n scaling_config=ScalingConfig(num_workers=2, use_gpu=True),\n label_column=\"target\",\n train_dataset=train_dataset,\n valid_dataset=valid_dataset,\n ...\n)\n\ncheckpoint = trainer.train()\n```\n2. Use the Checkpoint for batch inference with Ray Data:\n```python\nfrom ray import data_batch\nfrom ray.data.preprocessors import Chain\nfrom ray.train.checkpoint import Checkpoint\n\n# Load your test dataset\ntest_data = ray.data.read_csv(\"path/to/test/data.csv\")\n\n# Preprocess your data if needed\npreprocessor = Chain(\n ...\n)\n\ntest_data = preprocessor.transform(test_data)\n\n# Create a function for inference\n@ray.remote\ndef inference_function(checkpoint: Checkpoint, batch: data_batch.RayDataBatch):\n model = checkpoint.get_model()\n # Perform inference on the batch\n inference_results = model.predict(batch)\n return inference_results\n\n# Apply the inference function in parallel on the test dataset\ninference_results = ray.get([inference_function.remote(checkpoint, batch) for batch in test_data.split()])\n```\n\nThis example demonstrates how to use the Checkpoint returned by Ray Train to perform batch inference with Ray Data. You can adjust the inference_function to fit your specific use case.",2533"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2534},2535{2536"question": "is there a way to load and run inference without using pytorch or tensorflow directly?",2537"sources": [2538"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2539"https://docs.ray.io/en/master/cluster/usage-stats.html#how-to-disable-it",2540"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-2-inference-on-a-single-batch",2541"https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",2542"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",2543"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2544"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2545"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",2546"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2547"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",2548"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2549"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2550"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2551"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data"2552],2553"document_ids": [25546978,25555565,25567976,25576725,25588010,25596980,25606979,25618043,25626947,25637970,25646941,25656940,25668107,256779552568],2569"answer": " Yes, it is possible to load and run inference without using PyTorch or TensorFlow directly by using a pre-trained model and preprocessing the data before passing it to the model. The example provided demonstrates how to do this using Ray Datasets and a PyTorch model for image classification. The data is preprocessed using the `transform_image` function, and then the pre-trained model is used to make predictions on the preprocessed data. This approach allows you to perform inference without having to write the code for defining and training the model directly in PyTorch or TensorFlow.",2570"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2571},2572{2573"question": "what does ray do",2574"sources": [2575"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2576"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2577"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2578"https://docs.ray.io/en/master/data/data.html#learn-more",2579"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",2580"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",2581"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",2582"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",2583"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",2584"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",2585"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2586"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2587"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2588"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary"2589],2590"document_ids": [25914811,25921005,25935380,25946910,25956208,259614255,25976015,25985656,25995556,26004662,26016835,26026615,260313974,260454432605],2606"answer": " Ray is a framework for building and running distributed applications. It allows you to parallelize and distribute computations across multiple CPUs and GPUs, using the object-oriented programming model. The provided context includes various Ray functions and components, such as:\n\n1. `ray.init()`: Initializes a new Ray cluster or connects to an existing one.\n2. `.remote()`: Runs actor methods or tasks on your Ray cluster and is used to instantiate actors.\n3. `ray.put()`: Puts values into Ray's object store.\n4. `ray.get()`: Gets values from the object store.\n5. `ray.wait()`: Returns two lists of object references, one with finished tasks and one with unfinished tasks.\n\nThese functions enable you to create distributed applications by parallelizing tasks, managing resources, and storing data across multiple nodes.",2607"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2608},2609{2610"question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",2611"sources": [2612"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",2613"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",2614"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",2615"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",2616"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2617"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",2618"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2619"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",2620"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",2621"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus",2622"https://docs.ray.io/en/master/data/performance-tips.html#configuring-resources-and-locality",2623"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",2624"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",2625"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources"2626],2627"document_ids": [262813457,26295001,26301025,26314999,263211062,26332966,263411068,263512049,26365116,26372730,26386829,26396590,26406145,264110242642],2643"answer": " If you specify a fractional GPU in the resource spec, you can still use more than that fraction of GPU. However, doing so may impact the performance of other tasks or actors sharing the same GPU. It's the user's responsibility to ensure that multiple trials can share GPUs and there is enough memory to do so. Ray does not automatically handle this for you.",2644"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2645},2646{2647"question": "how to pickle a variable defined in actor\u2019s init method",2648"sources": [2649"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2650"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#disable-the-init-container-injection",2651"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",2652"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",2653"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",2654"https://docs.ray.io/en/master/ray-core/actors.html#actors",2655"https://docs.ray.io/en/master/rllib/actors.html#actors",2656"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",2657"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2658"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",2659"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",2660"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",2661"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",2662"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor"2663],2664"document_ids": [26655192,26665711,26675194,26684435,26694241,26704469,267111576,267214682,26735191,2674520,26754630,26765066,26774878,267842602679],2680"answer": " It is not possible to pickle a variable defined in an actor's `__init__` method because each actor instance has its own copy of the class variables and is instantiated in its own process. Global variables should not be used to share state between tasks and actors. Instead, encapsulate the global variables in an actor and pass the actor handle to other tasks and actors. Pickling may fail if there are compatibility issues between the runtime environments of the actors, such as different versions of a pip package.",2681"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2682},2683{2684"question": "how do I do an all_reduce operation among a list of actors",2685"sources": [2686"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",2687"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2688"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",2689"https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",2690"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2691"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",2692"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#collective-communication",2693"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayFSDPStrategy.reduce.html#ray-train-lightning-rayfsdpstrategy-reduce",2694"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",2695"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",2696"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDDPStrategy.reduce.html#ray-train-lightning-rayddpstrategy-reduce",2697"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.reduce.html#ray-train-lightning-raydeepspeedstrategy-reduce",2698"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2699"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#shuffling-and-reducing-data"2700],2701"document_ids": [2702788,27031006,270414443,2705659,27066787,2707673,270814415,27098854,27108101,2711641,27128424,27139076,27146939,271553532716],2717"answer": " To perform an all\\_reduce operation among a list of actors in Ray, you can use the `ray.util.collective.allreduce` function. Here's an example of how you can use it:\n\n1. First, define a remote function that performs an allreduce on a tensor using the `ray.util.collective.allreduce` function:\n```python\n@ray.remote\ndef allreduce_worker(tensor):\n ray.util.collective.allreduce(tensor, group_name=\"my_group\")\n return tensor\n```\n2. Next, create some worker actors and pass each of them a tensor to allreduce:\n```python\nworkers = [Worker.remote() for i in range(5)]\ntensors = [ray.put(cupy.ones((10,), dtype=cupy.float32)) for _ in range(5)]\n```\n3. Now, you can call the `allreduce_worker` function on each of the worker actors, passing in the tensor for that actor:\n```python\nreduced_tensors = ray.get([allreduce_worker.remote(tensor) for tensor in tensors])\n```\nIn this example, we're creating 5 worker actors and passing each of them a tensor to allreduce. We're using the `ray.put` function to create Ray objects for each tensor, which allows us to pass them between actors. Finally, we're calling the `allreduce_worker` function on each of the worker actors and storing the results in the `reduced_tensors` list.\n\nNote that in this example, we're using a custom group name (\"my\\_group\") to specify which actors should participate in the allreduce operation. You can use any group name that you like, as long as all of the actors that you want to participate in the allreduce are part of the same group.",2718"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2719},2720{2721"question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",2722"sources": [2723"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",2724"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",2725"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",2726"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#bundles",2727"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",2728"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",2729"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2730"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",2731"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",2732"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",2733"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",2734"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",2735"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.PlacementGroup.bundle_count.html#ray-util-placement-group-placementgroup-bundle-count",2736"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources"2737],2738"document_ids": [27394790,27405072,27415063,27425050,27435051,274412053,274511075,27465076,27475060,27484789,27495069,275014262,27514746,275250702753],2754"answer": " Specifying a bundle with `{\"CPU\":0}` in the PlacementGroup will reserve a bundle with 0 CPUs. However, this is not allowed as a bundle must be able to fit on a single node on the Ray cluster and contain at least one CPU. Therefore, creating a PlacementGroup with this bundle will fail.",2755"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2756},2757{2758"question": "How to cancel job from UI?",2759"sources": [2760"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",2761"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",2762"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",2763"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",2764"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2765"https://docs.ray.io/en/master/workflows/comparison.html#trip-booking",2766"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",2767"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",2768"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2769"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",2770"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2771"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobStatus.STOPPED.html#ray-job-submission-jobstatus-stopped",2772"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.stop_job.html#ray-job-submission-jobsubmissionclient-stop-job",2773"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#cleaning-up"2774],2775"document_ids": [27764550,27779791,27784375,27796559,27804894,27819757,27826710,27839791,278411590,278514701,27864492,27876690,27886686,278964522790],2791"answer": " To cancel a job from the UI, you can use the `ray job stop` command, providing the job ID as an argument. This will request the job to exit asynchronously. If you want to delete the job and all its associated data, you can use the `ray delete job` command instead.\n\nIn the context provided, there is no direct reference to canceling a job from a UI, but the `ray job stop` command can be used in a command-line interface, which is a type of UI.\n\nHere is an example of how to use the `ray job stop` command:\n```\n$ ray job stop <job_id>\n```\nReplace `<job_id>` with the ID of the job you want to stop.\n\nTo delete the job and all its associated data, you can use the `ray delete job` command:\n```\n$ ray delete job <job_id>\n```\nAgain, replace `<job_id>` with the ID of the job you want to delete.\n\nNote that these commands are intended to be used in a terminal or command prompt interface, which is a type of UI. They may not be directly applicable to other types of UIs, such as graphical user interfaces (GUIs).",2792"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2793},2794{2795"question": "how do I get my project files on the cluster when using Ray Serve? My workflow is to call `serve deploy config.yaml --address <remote dashboard agent address>`",2796"sources": [2797"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",2798"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2799"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2800"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",2801"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2802"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#development-workflow-with-serve-run",2803"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",2804"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",2805"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",2806"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",2807"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup",2808"https://docs.ray.io/en/master/serve/api/index.html#serve-config",2809"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",2810"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files"2811],2812"document_ids": [281313680,28141006,28156562,281613677,28176552,281813659,281913719,28206577,282113831,282213987,28236549,282413986,282513718,282642622827],2828"answer": " To get your project files on the cluster when using Ray Serve, you can use the `--working-dir` option in the `serve run` command to specify the local directory containing your project files. The directory and its contents will be uploaded to the cluster and imported by Ray Serve. Here's an example command:\n```bash\nserve run --address=ray://<head-node-ip-address>:10001 --working-dir=\"./project/src\" local_dev:graph\n",2829"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2830},2831{2832"question": "how do i install ray nightly wheel",2833"sources": [2834"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",2835"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2836"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2837"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2838"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2839"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",2840"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",2841"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",2842"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",2843"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",2844"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2845"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2846"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2847"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions"2848],2849"document_ids": [285014545,285163,2852288,285363,28546335,285566,2856291,285714551,2858883,28595412,2860271,28616351,28626317,286361032864],2865"answer": " To install the Ray nightly wheel, first cleanly remove any existing Ray installation using `pip uninstall -y ray`. Then, use the `pip install` command with the appropriate link to the nightly wheel. For example, for a Linux system with Python 3.9 and x86\\_64 architecture, you would use:\n```\npip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"\n```\nReplace `LINK_TO_WHEEL.whl` with the actual link to the nightly wheel for your system and Python version. You can find the appropriate link in the context provided, under the \"Daily Releases (Nightlies)\" section.",2866"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2867},2868{2869"question": "how do i install the latest ray nightly wheel?",2870"sources": [2871"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",2872"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2873"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2874"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2875"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2876"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",2877"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",2878"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",2879"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",2880"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",2881"https://docs.ray.io/en/master/installation.html#from-wheels",2882"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",2883"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#install-the-latest-ray-release",2884"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only"2885],2886"document_ids": [288714545,2888271,2889288,289063,28916335,2892291,289314551,289466,2895286,289614541,289761,28985412,28995820,29008832901],2902"answer": " To install the latest Ray nightly wheel, you can follow these steps:\n\n1. Clean removal of previous Ray install:\n```\npip uninstall -y ray\n```\n2. Install Ray with support for the dashboard and cluster launcher using the nightly wheel:\n```bash\npip install -U \"ray[default] @ LINK_TO_NIGHTLY_WHEEL.whl\"\n```\nReplace `LINK_TO_NIGHTLY_WHEEL.whl` with the actual link to the nightly wheel you want to install. You can find the appropriate link for your system and Python version in the context provided.\n\nNote: The provided context contains multiple links for different configurations. Choose the one that matches your system and Python version.",2903"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2904},2905{2906"question": "how can I write unit tests for Ray code?",2907"sources": [2908"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",2909"https://docs.ray.io/en/master/tune/faq.html#how-can-i-develop-and-test-tune-locally",2910"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",2911"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2912"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",2913"https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",2914"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",2915"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2916"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",2917"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",2918"https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution",2919"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",2920"https://docs.ray.io/en/master/ray-core/patterns/return-ray-put.html#code-example",2921"https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally"2922],2923"document_ids": [29245397,29251048,29265395,29274811,29284190,29294338,293014561,29315380,29326621,29334204,29349644,29355757,29365179,2937134352938],2939"answer": " To write unit tests for Ray code, you can follow these tips:\n\n1. Share the Ray cluster across tests if possible to avoid the overhead of creating a new cluster for each test. You can do this by calling `ray.init()` before running any tests and `ray.shutdown()` after all tests have finished.\n2. Fix the resource quantity with `ray.init(num_cpus=...)` to ensure that each test has consistent resources.\n3. Consider creating a mini-cluster with `ray.cluster_utils.Cluster` for more complex tests.\n4. Be careful when running tests in parallel to avoid race conditions.\n\nHere is an example of how to set up a basic test case for Ray:\n\n```python\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n def setUp(self):\n ray.init(num_cpus=4, num_gpus=0)\n\n def tearDown(self):\n ray.shutdown()\n\n def test_my_ray_code(self):\n # Test your Ray code here.\n pass\n```\n\nYou can then run your tests using a test runner like `unittest.main()`.\n\nNote that if you are testing Tune specifically, you can follow the instructions in the Tune documentation for setting up and running tests.",2940"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2941},2942{2943"question": "How I stop Ray from spamming lots of Info updates on stdout?",2944"sources": [2945"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",2946"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2947"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#disable-logging-to-the-driver",2948"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#distributed-progress-bars-tqdm",2949"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",2950"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2951"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",2952"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",2953"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",2954"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",2955"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",2956"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",2957"https://docs.ray.io/en/master/cluster/cli.html#ray-stop",2958"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks"2959],2960"document_ids": [29616558,29621006,2963523,2964529,2965524,29664209,2967519,2968410,29695128,297013929,29715766,2972525,29735613,297445502975],2976"answer": " To stop Ray from spamming lots of Info updates on stdout, you can disable logging to the driver by setting `log_to_driver=False` in `ray.init()`. This will prevent Task and Actor logs from being copied to the driver stdout. Additionally, you can set `RAY_TQDM_PATCH_PRINT=0` to disable the patching of the built-in print function, which may improve performance if there are more than a couple thousand updates per second.\n\nHere's an example of how to disable logging to the driver:\n\n```python\nimport ray\n\nray.init(log_to_driver=False)\n```\n\nAnd here's how to disable the patching of the built-in print function:\n\n```bash\nexport RAY_TQDM_PATCH_PRINT=0\n```",2977"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2978},2979{2980"question": "how to deploy stable diffusion 2.1 with Ray Serve?",2981"sources": [2982"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",2983"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",2984"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",2985"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#serve-a-stablediffusion-text-to-image-model-on-kubernetes",2986"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-7-submit-a-request-to-the-ray-serve-application",2987"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",2988"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#code-updates",2989"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",2990"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-5-send-a-request-to-the-text-to-image-model",2991"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",2992"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",2993"https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",2994"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",2995"https://docs.ray.io/en/master/serve/index.html#why-choose-serve"2996],2997"document_ids": [29986127,299910138,300014409,30016124,30025989,30036128,300413692,30055671,30066130,300713622,300814416,300913797,301013677,3011136143012],3013"answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install the required packages:\n```\npip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers\n```\n\n2. Save the provided Serve code as `stable_diffusion.py`.\n\n3. Download the RayService configuration:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml\n```\n\n4. Create a RayService:\n```bash\nkubectl apply -f ray-service.stable-diffusion.yaml\n```\n\n5. Send a request to the Stable Diffusion model:\n\n- Download `stable_diffusion_req.py`:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/serve_config_examples/master/stable_diffusion/stable_diffusion_req.py\n```\n\n- Set your `prompt` in `stable_diffusion_req.py`.\n\n- Send a request to the Stable Diffusion model:\n```bash\npython stable_diffusion_req.py\n```\n\n6. Check the output image.\n\nNote: Before deploying to a RayService, test your Ray Serve script locally or in a RayCluster. Also, avoid updating your Serve application in production by deploying an entirely new deployment graph. Instead, start a new Ray cluster, deploy the updated code, and switch traffic from the old cluster to the new one.",3014"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3015},3016{3017"question": "what is actor_handle?",3018"sources": [3019"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",3020"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner.FrameworkHyperparameters.what_to_compile.html#ray-rllib-core-learner-learner-frameworkhyperparameters-what-to-compile",3021"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",3022"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",3023"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3024"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3025"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",3026"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",3027"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",3028"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",3029"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3030"https://docs.ray.io/en/master/ray-core/api/core.html#actors",3031"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",3032"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors"3033],3034"document_ids": [303511587,303613155,303714698,30384487,30394860,30404615,304111588,30424489,304314699,30444200,30454623,30464650,30474871,3048146803049],3050"answer": " An actor handle is a reference to a Ray actor, which is a stateful, long-running object in a Ray cluster. Actor handles can be passed into other tasks and used to call remote functions or methods on the actor. They allow for concurrent, distributed execution of code with shared state. Examples of actor handle usage are provided in the context in Python, Java, and C++.",3051"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3052},3053{3054"question": "how to kill a r detached actors?",3055"sources": [3056"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3057"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",3058"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3059"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",3060"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3061"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3062"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3063"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3064"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3065"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",3066"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3067"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3068"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",3069"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure"3070],3071"document_ids": [30724633,30734578,30744859,30752157,30764615,30774613,30784579,30794848,30804620,30814561,30824618,30834631,30848106,308545773086],3087"answer": " To kill a detached actor in Ray, you can use the `ray.kill()` function, passing the actor handle as an argument. This will immediately terminate the actor, causing any current, pending, and future tasks to fail. Here's an example:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n def ping(self):\n return \"hello\"\n\ndetached_actor = Actor.options(name=\"actor\", lifetime=\"detached\").remote()\n\n# ... use the actor ...\n\nray.kill(detached_actor)\n```\nNote that this will not go through the normal teardown logic, so any exit handlers installed in the actor using `std::atexit` or `atexit` will not be called. Also, subsequent calls to get the actor handle via `ray.get_actor()` will fail.\n\nIf you want to terminate the actor gracefully, you can wait for any previously submitted tasks to finish executing and then exit the process gracefully with `sys.exit()`. However, this is not currently implemented in Ray for C++ actors.\n\nFor non-detached actors, Ray will automatically terminate them when all actor handles to them in Python are deleted. This will cause them to complete any outstanding work and then shut down. If you want to kill actors immediately, you can also call `ray.kill(actor)`.",3088"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3089},3090{3091"question": "How to force upgrade the pip package in the runtime environment if an old version exists?",3092"sources": [3093"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3094"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",3095"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3096"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3097"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",3098"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3099"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",3100"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3101"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",3102"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3103"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3104"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",3105"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",3106"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris"3107],3108"document_ids": [31094329,31104260,31114717,31124327,31134260,31144330,31154267,31164328,31174268,31184283,31194719,312010281,31214252,312243103123],3124"answer": " To force upgrade the pip package in the runtime environment if an old version exists, you can specify the pip version in the runtime environment using the \"pip\\_version\" parameter with the \"==\" operator and the desired pip version. For example:\n```python\nRuntimeEnv(\n pip={\"packages\":[\"tensorflow\", \"requests\"], \"pip_check\": False,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"}\n)\n```\nThis will ensure that the specified pip version is installed in the runtime environment, even if an older version already exists. Note that this will not automatically upgrade existing installations of the package in the environment, but will install the specified version alongside any existing installations.",3125"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3126},3127{3128"question": "How do I do global shuffle with Ray?",3129"sources": [3130"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",3131"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3132"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",3133"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",3134"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",3135"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",3136"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",3137"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",3138"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",3139"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",3140"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition",3141"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",3142"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",3143"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility"3144],3145"document_ids": [31467395,31471006,31486795,31496827,31509209,31516826,31529206,31536876,31546864,31556825,31567352,315714502,31586824,315992113160],3161"answer": " To do a global shuffle of all rows in a Ray Dataset, you can use the `random_shuffle()` function. Here's an example:\n```python\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/sms_spam_collection_subset.txt\")\nds = ds.random_shuffle()\n```\nBy default, `random_shuffle()` performs a full global shuffle, which is more expensive but fully decorrelates row iteration order from the original dataset order.\n\nIf you want to avoid bottlenecks, you can consider iterating over batches with shuffling instead of shuffling all rows at once. You can also set the `DataContext.use_push_based_shuffle` flag to use push-based shuffle, which can improve shuffle performance. Here's an example:\n```python\nctx = ray.data.DataContext.get_current()\nctx.use_push_based_shuffle = True\n\nds = ray.data.range(1000)\nds = ds.random_shuffle()\n```\nNote that shuffling data blocks requires less communication but is less random, while full global shuffle is more expensive and provides more randomness. For most cases, shuffling data blocks is sufficient.",3162"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3163},3164{3165"question": "How to find namespace of an Actor?",3166"sources": [3167"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",3168"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",3169"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3170"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3171"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",3172"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3173"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",3174"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace",3175"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3176"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3177"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3178"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",3179"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3180"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"3181],3182"document_ids": [31834521,31844528,31854623,31864504,31874871,31884626,31894528,31904751,31914625,31924499,31934516,31944476,31954509,3196147463197],3198"answer": " To find the namespace of an Actor in Ray, you can use the `ray.get_actor` function, which returns an `ActorHandle` to the actor with the given name. The namespace can be specified as a parameter in this function. If no namespace is specified, Ray will place your job in an anonymous namespace by default.\n\nExample:\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=\"my_actor\", namespace=\"my_namespace\", lifetime=\"detached\").remote()\n\n# Get the actor handle and print the namespace\nactor_handle = ray.get_actor(\"my_actor\", namespace=\"my_namespace\")\nprint(actor_handle.namespace) # Output: my_namespace\n```\nIn this example, the actor is created in the \"my\\_namespace\" namespace. To find the namespace of the actor, you can use `ray.get_actor` and pass the name and namespace as parameters. The function returns an `ActorHandle` to the actor, and you can access the namespace using the `namespace` attribute of the handle.",3199"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3200},3201{3202"question": "How does Ray work with async.io ?",3203"sources": [3204"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",3205"https://docs.ray.io/en/master/ray-core/patterns/pipelining.html#code-example",3206"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",3207"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",3208"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3209"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",3210"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",3211"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",3212"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",3213"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",3214"https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution",3215"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3216"https://docs.ray.io/en/master/ray-core/patterns/concurrent-operations-async-actor.html#pattern-using-asyncio-to-run-actor-methods-concurrently",3217"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api"3218],3219"document_ids": [32204598,32215190,32224603,32234599,32244208,32255443,32264360,32274636,32284611,32294205,32309644,32314811,32325161,3233139743234],3235"answer": " Ray can work with async.io as it natively integrates with it. You can use Ray alongside popular async frameworks like aiohttp, aioredis, etc. In the context provided, an example of an async actor is demonstrated, which uses the async/await syntax and can run multiple instances of the method concurrently in the event loop. Additionally, Ray provides a WorkQueue class that can be used to pull work off of a queue and process it, with or without pipelining to increase CPU utilization.",3236"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3237},3238{3239"question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",3240"sources": [3241"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",3242"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3243"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3244"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",3245"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",3246"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",3247"https://docs.ray.io/en/master/ray-core/patterns/ray-get-too-many-objects.html#anti-pattern-fetching-too-many-objects-at-once-with-ray-get-causes-failure",3248"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",3249"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#stepping-between-ray-tasks",3250"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3251"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",3252"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",3253"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",3254"https://docs.ray.io/en/master/ray-core/patterns/ray-get-too-many-objects.html#anti-pattern-fetching-too-many-objects-at-once-with-ray-get-causes-failure"3255],3256"document_ids": [32575235,32581006,32594209,32604416,32615236,32625219,32635169,32644361,3265581,32664214,326714421,326813646,32694884,327051683271],3272"answer": " To debug a hanging `ray.get()` call that you have reproduced locally, you should avoid calling `ray.get()` in a loop, as it is a blocking call that waits until the requested result is available. This can harm parallelism, as the loop will not continue to run until the call to `ray.get()` is resolved. Instead, you can separate the call to `ray.get()` from the call to the remote functions, so that all remote functions are spawned before waiting for the results and can run in parallel in the background. Additionally, you can pass a list of object references to `ray.get()` instead of calling it one by one to wait for all of the tasks to finish.",3273"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3274},3275{3276"question": "can you show me an example of ray.actor.exit_actor()",3277"sources": [3278"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3279"https://docs.ray.io/en/master/rllib/rllib-examples.html#custom-and-complex-models",3280"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3281"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3282"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3283"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3284"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3285"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3286"https://docs.ray.io/en/master/ray-core/api/core.html#actors",3287"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.AsyncioActorExit.html#ray-exceptions-asyncioactorexit",3288"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",3289"https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",3290"https://docs.ray.io/en/master/ray-core/api/doc/ray.shutdown.html#ray-shutdown",3291"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure"3292],3293"document_ids": [32944618,329510636,32964613,32974620,32984619,32994579,33004859,33014615,33024650,33034781,3304637,33054488,33064827,330745653308],3309"answer": " The `ray.actor.exit_actor()` function is used to manually terminate a Ray actor from within one of the actor methods. This will kill the actor process and release resources associated with the actor. Here's an example of how to use it:\n```python\n@ray.remote\nclass Actor:\n def exit(self):\n ray.actor.exit_actor()\n\nactor = Actor.remote()\nactor.exit.remote()\n```\nNote that manual termination of actors should generally not be necessary, as actors are automatically garbage collected. The object reference resulting from the task can be waited on to wait for the actor to exit.\n\nAlternatively, you can use `ray.kill(actor_handle)` to terminate an actor forcefully. This should be reserved for cases where an actor is unexpectedly hanging or leaking resources, and for detached actors, which must be manually destroyed. Here's an example:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nactor_handle = Actor.remote()\n\nray.kill(actor_handle)\n```\nThis will interrupt any running tasks on the actor and exit the process gracefully with `sys.exit()`. Any exit handlers installed in the actor using `atexit` will not be called.",3310"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3311},3312{3313"question": "how to add log inside actor?",3314"sources": [3315"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",3316"https://docs.ray.io/en/master/train/examples/horovod/horovod_example.html#horovod-distributed-training-example-with-pytorch-ray-train",3317"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",3318"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",3319"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3320"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",3321"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3322"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3323"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",3324"https://docs.ray.io/en/master/ray-observability/getting-started.html#actors-view",3325"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",3326"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3327"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3328"https://docs.ray.io/en/master/rllib/rllib-catalogs.html#write-a-catalog-from-scratch"3329],3330"document_ids": [3331414,33329568,3333540,3334520,333514324,333614202,333714286,333814295,333914106,3340434,3341682,3342477,3343476,3344115393345],3346"answer": " To add logging inside an actor in Ray, you can define a log method within the actor class and use the logging library to add log messages. Here's an example:\n\n```python\nimport logging\n\n@ray.remote\nclass MyActor:\n def __init__(self):\n self.logger = logging.getLogger(__name__)\n\n def log(self, msg):\n self.logger.info(msg)\n\n def some_method(self):\n # some code here\n self.log(\"Log message inside actor's method\")\n```\n\nYou can then call the log method from a remote function:\n\n```python\nactor = MyActor.remote()\nray.get(actor.log.remote(\"A log message for an actor.\"))\n```\n\nKeep in mind that by default, all stdout and stderr of Tasks and Actors are redirected to the Driver output. If you want to disable this feature, you can view the Configuring Logging documentation for more information.",3347"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3348},3349{3350"question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",3351"sources": [3352"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",3353"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",3354"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt",3355"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",3356"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",3357"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",3358"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",3359"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#next-steps",3360"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#next-steps",3361"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",3362"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",3363"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",3364"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",3365"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial"3366],3367"document_ids": [33686999,33697970,337010470,337110259,337214582,33736998,33746843,337510137,337610219,33776938,33785292,33797970,3380140,3381145843382],3383"answer": " Sure, here's a script to perform batch inference with GPT-2 on text data from an S3 bucket using Ray:\n```python\nimport os\nimport ray\nfrom transformers import pipeline\n\n# Download the GPT-2 checkpoint from S3 to local storage\nos.system(f\"aws s3 cp s3://path/to/checkpoint /mnt/local_storage/\")\n",3384"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3385},3386{3387"question": "How do I enable Ray debug logs?",3388"sources": [3389"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3390"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3391"https://docs.ray.io/en/master/ray-observability/user-guides/index.html#user-guides",3392"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",3393"https://docs.ray.io/en/master/cluster/cli.html#ray-start",3394"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#debugger-commands",3395"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-debug",3396"https://docs.ray.io/en/master/ray-contribute/debugging.html#debugging-for-ray-developers",3397"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",3398"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3399"https://docs.ray.io/en/master/ray-observability/index.html#monitoring-and-debugging",3400"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",3401"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#monitoring-with-the-cli-or-sdk",3402"https://docs.ray.io/en/master/ray-observability/key-concepts.html#debugger"3403],3404"document_ids": [3405680,34061006,3407546,3408510,34095611,3410579,34114669,3412944,3413573,34144323,3415450,34166631,3417465,34184053419],3420"answer": " To enable Ray debug logs, you can use the `--log-style` argument with the `ray` command-line interface (CLI) and set it to `'pretty'` or `'record'`. For example:\n\n```\nray start --log-style pretty\n```\n\nThis will output logs with formatting and color, making it easier to read and understand.\n\nAdditionally, you can enable runtime environment (runtime_env) debugging log streaming by setting the `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED` environment variable to `1` on each node before starting Ray. This will print the full runtime\\_env setup log messages to the driver (the script that calls `ray.init()`).\n\nFor more information on configuring Ray's logging system, you can refer to the \"Configuring Logging\" guide in the Ray documentation.",3421"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3422},3423{3424"question": "How do I list the current Ray actors from python?",3425"sources": [3426"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3427"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",3428"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",3429"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",3430"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3431"https://docs.ray.io/en/master/ray-observability/key-concepts.html#ray-states",3432"https://docs.ray.io/en/master/rllib/actors.html#actors",3433"https://docs.ray.io/en/master/ray-core/actors.html#actors",3434"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3435"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3436"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.html#ray-util-actorpool",3437"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#getting-a-summary-of-states-of-entities-by-type",3438"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",3439"https://docs.ray.io/en/master/ray-observability/reference/api.html#list-apis"3440],3441"document_ids": [3442472,34434464,3444788,3445673,3446475,3447402,344811575,34494467,345014680,34514623,34524742,3453480,3454488,34556603456],3457"answer": " To list the current Ray actors from Python, you can use the `list_actors` function from the `ray.util.state` module. Here is an example:\n\n```python\nimport ray\nfrom ray.util.state import list_actors\n\nactors = list_actors()\nfor actor in actors:\n print(actor)\n```\n\nThis will print information about all the actors in the Ray cluster. If you want to filter the actors by state, you can use the `filters` parameter. For example, the following command will list all the actors with state \"ALIVE\":\n\n```python\nactors = list_actors(filters=[(\"state\", \"=\", \"ALIVE\")])\nfor actor in actors:\n print(actor)\n```\n\nYou can find more information about the `list_actors` function and other Ray state APIs in the Ray documentation.",3458"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3459},3460{3461"question": "I want to kill the replica actor from Python. how do I do it?",3462"sources": [3463"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",3464"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3465"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3466"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3467"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3468"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3469"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3470"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3471"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3472"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",3473"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3474"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",3475"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",3476"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure"3477],3478"document_ids": [347913912,34801006,34814859,34824848,34834620,34844613,34854860,34864618,34874579,348813916,34894615,34904577,349113915,3492139053493],3494"answer": " To kill a replica actor from Python, you can use the `ray.kill()` function, passing in the actor handle as the argument. Here is an example:\n```\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nThis will forcefully terminate the actor, interrupting any running tasks and causing them to fail immediately. If you want to let pending tasks finish before killing the actor, you can call `actor.__ray_terminate__.remote()` instead.\n\nNote that if you want to kill the actor gracefully, you can wait for it to exit by calling `ObjectRef::Get` on the object ref resulting from the task. This will wait for the actor to exit and release any resources associated with it.\n\nFor more information, you can refer to the `ray.kill()` documentation and the section on manual termination of actors in the Ray documentation.",3495"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3496},3497{3498"question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",3499"sources": [3500"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3501"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3502"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#accelerator-types",3503"https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",3504"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",3505"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3506"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3507"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-workload-scheduling",3508"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",3509"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",3510"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",3511"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3512"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",3513"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements"3514],3515"document_ids": [35164994,35171006,35185006,35194366,35206590,35214996,35224997,35236017,35244697,35254702,35265114,352714767,352814461,352951153530],3531"answer": " To specify that a task should run on a node with a V100 GPU, you can use the `accelerator_type` parameter in the `@ray.remote` decorator. Here's an example:\n```\n@ray.remote(accelerator_type=NVIDIA_TESLA_V100)\ndef train(data):\n return \"This function was run on a node with a Tesla V100 GPU\"\n\nray.get(train.remote(1))\n```\nThe `NVIDIA_TESLA_V100` constant is defined in `ray.util.accelerators`.\n\nKeep in mind that this will only ensure that the task is scheduled to run on a node with a V100 GPU. It does not guarantee that the task will actually use the GPU. You will need to make sure that your code is written to use the GPU correctly.\n\nAlso, note that this feature is only available in Ray 1.11.0 or later. If you are using an earlier version of Ray, you will need to upgrade.",3532"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3533},3534{3535"question": "How do I get started?",3536"sources": [3537"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",3538"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3539"https://docs.ray.io/en/master/ray-overview/getting-started.html#getting-started",3540"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-autoscaler-private-fake-multi-node-test-utils-dockercluster",3541"https://docs.ray.io/en/master/serve/index.html#learn-more",3542"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",3543"https://docs.ray.io/en/master/tune/faq.html#how-can-i-get-started-contributing-to-tune",3544"https://docs.ray.io/en/master/serve/advanced-guides/index.html#advanced-guides",3545"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",3546"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-1-create-a-kind-cluster",3547"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#getting-started-with-kuberay",3548"https://docs.ray.io/en/master/train/train.html#get-started",3549"https://docs.ray.io/en/master/serve/key-concepts.html#whats-next",3550"https://docs.ray.io/en/master/cluster/vms/index.html#learn-more"3551],3552"document_ids": [35536482,35541006,3555220,3556864,355713625,35584483,35591049,356013827,35614185,35626043,35635646,35648244,356513428,356662103567],3568"answer": " To get started with Ray, you should install it via `pip install -U ray`. Then, you can import and initialize Ray in your code with `import ray` and `ray.init()`. If you're new to Ray Serve, the Quickstart guide is recommended. To contribute to Tune, you can look at issues labeled \"good first issue\" and \"help wanted\" on the Tune Github project board. To get started with distributed training using Ray Train, you can check out the guides for PyTorch, PyTorch Lightning, and Hugging Face Transformers.",3569"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3570},3571{3572"question": "How to specify python version in runtime_env?",3573"sources": [3574"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3575"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",3576"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3577"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3578"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",3579"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",3580"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",3581"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3582"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3583"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3584"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",3585"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.runtime_env.html#ray-job-submission-jobinfo-runtime-env",3586"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.set_runtime_env.html#ray-job-config-jobconfig-set-runtime-env",3587"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris"3588],3589"document_ids": [35904330,35916565,35924717,35934329,35944661,359513856,35964253,35974327,35984283,35994712,36005679,36016665,36024826,360343103604],3605"answer": " To specify the Python version in the runtime environment, you can use the `conda` key in the `runtime_env` dictionary to create a Conda environment with a specific Python version. Here is an example:\n\n```python\nruntime_env = {\n \"conda\": {\n \"channels\": [\"conda-forge\"],\n \"dependencies\": [\n \"python=3.8\",\n \"numpy\",\n \"pandas\",\n ],\n }\n}\n\nray.init(runtime_env=runtime_env)\n```\n\nIn this example, a Conda environment is created with Python 3.8, along with the `numpy` and `pandas` packages. Note that the `conda` key cannot be used with the `pip` key in the same `runtime_env` dictionary. If you need to specify both Conda and pip dependencies, you should create a Conda environment with a `environment.yaml` file that includes both the Conda and pip dependencies, and then use the `conda` key in the `runtime_env` dictionary to reference that Conda environment.\n\nAlternatively, you can use the `container` key in the `runtime_env` dictionary to specify a Docker image with a specific Python version. Here is an example:\n\n```python\nruntime_env = {\n \"container\": {\n \"image\": \"my-docker-image:latest\",\n \"runtime\": \"docker\",\n }\n}\n\nray.init(runtime_env=runtime_env)\n```\n\nIn this example, a Docker container is used with the `my-docker-image:latest` image, which should have a specific Python version and any necessary dependencies installed. Note that the `container` key requires the `runtime` key to be set to `docker`.",3606"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3607},3608{3609"question": "how to create a Actor in a namespace?",3610"sources": [3611"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",3612"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",3613"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3614"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3615"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3616"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3617"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3618"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3619"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3620"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3621"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",3622"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",3623"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",3624"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"3625],3626"document_ids": [36274521,36285438,36294626,36304625,36314623,36324513,36334504,36344509,36354499,36364516,36374528,36384523,36394871,364046273641],3642"answer": " To create an actor in a specific namespace in Ray, you can use the `options` method on the actor class and set the `name` and `namespace` parameters. Here is an example in Python:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\n# Connect to the Ray cluster\nctx = ray.init(address=\"auto\")\n\n# Create an actor with a specified name and namespace\nActor.options(name=\"my_actor\", namespace=\"actor_namespace\", lifetime=\"detached\").remote()\n```\n\nIn this example, the actor `my_actor` is created in the namespace `actor_namespace`. Note that the current job's namespace is not affected by the specified namespace of the actor.\n\nYou can also retrieve the actor later using the `get_actor` function and specifying the namespace:\n\n```python\n# Get the actor handle\nactor_handle = ray.get_actor(\"my_actor\", namespace=\"actor_namespace\")\n```\n\nNamed actors are scoped by namespace, and if no namespace is assigned, they will be placed in an anonymous namespace by default.",3643"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3644},3645{3646"question": "Can I specify multiple working directories?",3647"sources": [3648"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",3649"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3650"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3651"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",3652"https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.PathPartitionParser.html#ray-data-datasource-pathpartitionparser",3653"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3654"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",3655"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3656"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",3657"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",3658"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",3659"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",3660"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3661"https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.PathPartitionParser.html#ray-data-datasource-pathpartitionparser"3662],3663"document_ids": [36641775,36651006,36664277,36674301,36687297,36694273,367010170,36711790,367210169,36731066,36744305,36756597,36766632,367772983678],3679"answer": " No, you cannot specify multiple working directories in Ray Tune. However, you can use relative paths for read-only purposes and make sure the directory is synced on all nodes if running on multiple machines. Additionally, you can use remote URIs to specify local paths or remote zip files as working directories. If you need to access files relative to the original working directory in your Tune training function, you can configure Tune not to change the working directory to the trial directory.",3680"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3681},3682{3683"question": "what if I set num_cpus=0 for tasks",3684"sources": [3685"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",3686"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3687"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",3688"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",3689"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3690"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",3691"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#code-example",3692"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3693"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",3694"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.entrypoint_num_cpus.html#ray-job-submission-jobinfo-entrypoint-num-cpus",3695"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.num_cpus_per_worker.html#ray-train-scalingconfig-num-cpus-per-worker",3696"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",3697"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",3698"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application"3699],3700"document_ids": [370111063,37021005,3703623,37045936,37054850,37066716,37075234,37084853,37094877,37106703,37118998,37125068,37137639,3714135753715],3716"answer": " If you set `num_cpus=0` for tasks in Ray, your policies will be built solely on the CPU, even if GPUs are available on the machine. However, it's important to note that if your tasks are very small or have variable durations, you might not see speedups due to Ray's task overhead and the need to batch tasks together for better performance.\n\nAdditionally, when specifying resources for Ray tasks or actors, you can use the `num_cpus` and `num_gpus` parameters to reserve the desired number of CPUs and GPUs. These parameters can be set as integers or floats, with floats allowing for packing multiple replicas together on a machine with fewer CPUs or GPUs than replicas.\n\nFor example, if you have a Ray actor that requires 1 CPU when it is scheduled, you can set `num_cpus=1` in the actor's configuration. However, after the actor is created, it will occupy 0 CPU by default. You can adjust this behavior using the `resources` parameter to specify custom resource requirements.\n\nIn summary, setting `num_cpus=0` for tasks will build policies solely on the CPU, but it's important to consider task overhead and resource allocation when working with Ray tasks and actors.",3717"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3718},3719{3720"question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",3721"sources": [3722"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",3723"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",3724"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",3725"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",3726"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",3727"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",3728"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",3729"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",3730"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",3731"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",3732"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#overview",3733"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",3734"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",3735"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice"3736],3737"document_ids": [37385953,373913447,37405818,37415752,37425969,374313957,37445963,37455656,37465642,37475742,37485956,37495958,37505975,375157883752],3753"answer": " Yes, it is possible to have Ray on K8s without using KubeRay, but you will not have the built-in autoscaling feature that KubeRay provides. If you are using a managed Kubernetes service like GKE or EKS, you can utilize their native autoscaling features and enable Ray autoscaling within the RayCluster custom resource config. However, this requires Kubernetes admin permissions for deployment. If you don't have the necessary permissions, you can still deploy a static Ray cluster to Kubernetes, but autoscaling will not be available.",3754"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3755},3756{3757"question": "how to manually configure and manage Ray cluster on Kubernetes",3758"sources": [3759"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",3760"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",3761"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",3762"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",3763"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",3764"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",3765"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",3766"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gke-gcs-bucket.html#create-a-minimal-raycluster-yaml-manifest",3767"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",3768"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",3769"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide",3770"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",3771"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",3772"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"3773],3774"document_ids": [37755717,377613959,37775918,37785917,37795656,37805642,37815605,37826034,37835953,37845958,37855784,37866161,37876500,378861633789],3790"answer": " To manually configure and manage a Ray cluster on Kubernetes, you can follow these steps:\n\n1. Install `kubectl` and Helm.\n2. Prepare a Kubernetes cluster.\n3. Deploy a KubeRay operator using the provided YAML manifest.\n4. Create a `RayCluster` custom resource using a YAML manifest that describes the desired state of the Ray cluster. You can include configurations such as `rayVersion`, `enableInTreeAutoscaling`, and more.\n5. To manage the Ray cluster, you can use the `Cluster Management CLI` commands, such as `kubectl get rayclusters` to view the list of Ray clusters, or `kubectl delete raycluster <cluster-name>` to delete a specific Ray cluster.\n6. You can also monitor your Ray application using the Ray Dashboard, and configure and manage the Dashboard as needed.\n7. To set up Prometheus and Grafana for Dashboard, refer to the Ray on Kubernetes documentation.\n8. To manage logs, you can use the Ray Serve logs and configure log persistence on Kubernetes.\n\nFor more detailed instructions, refer to the Ray on Kubernetes documentation and the RayCluster Quickstart guide.",3791"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3792},3793{3794"question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",3795"sources": [3796"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",3797"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",3798"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",3799"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",3800"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",3801"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options",3802"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3803"https://docs.ray.io/en/master/serve/api/doc/ray.serve.shutdown.html#ray-serve-shutdown",3804"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",3805"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",3806"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job",3807"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",3808"https://docs.ray.io/en/master/cluster/key-concepts.html#worker-node",3809"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors"3810],3811"document_ids": [38125129,38134503,38144524,38156216,38164519,381714500,38186390,381914043,382014507,382113448,38226694,38234510,38245558,38255553826],3827"answer": " Shutting down a raylet will not directly kill tasks and workers on that node. However, it may lead to their termination indirectly. For instance, if the raylet is shut down due to memory pressure, as in the example you provided, then Ray may kill tasks and workers to free up resources. Additionally, when a raylet process fails, the corresponding node is marked as dead, which will also result in the termination of tasks and workers on that node.",3828"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3829},3830{3831"question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",3832"sources": [3833"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",3834"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3835"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",3836"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",3837"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management",3838"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#what-s-the-out-of-memory-error",3839"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",3840"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",3841"https://docs.ray.io/en/master/serve/monitoring.html#profiling-memory",3842"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",3843"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3844"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#what-is-the-memory-monitor",3845"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",3846"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#failures-due-to-memory-issues"3847],3848"document_ids": [3849547,38501005,3851548,3852559,38535010,3854549,38555118,3856562,385713549,38585016,3859557,38605119,38615024,38626063863],3864"answer": " To debug out-of-memory issues in Ray, you should first familiarize yourself with the Ray Memory Management model. If you are experiencing out-of-memory problems on your cluster, you can view the per Task and Actor memory usage graph for more details. To locate the source of the memory leak, you can use the CLI command `htop` or view Find per Task and Actor Memory Usage. The memory usage from the per component graph uses RSS - SHR.\n\nRefer to the documentation on how to address the out of memory issue: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>. Ray memory monitor also periodically prints the aggregated out-of-memory killer summary to Ray drivers.\n\nYou can use the `ray memory` command to help track down what ObjectRef references are in scope and may be causing an ObjectStoreFullError. Running `ray memory` from the command line while a Ray application is running will give you a dump of all of the ObjectRef references that are currently held by the driver, actors, and tasks in the cluster.\n\nYou can also use `ray memory --help` to see the full suite of options, including sorting by object size and grouping by stack trace, which can be helpful for tracking down the line of code where a memory leak occurs.",3865"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3866},3867{3868"question": "How to use callback in Trainer?",3869"sources": [3870"https://docs.ray.io/en/master/train/examples/transformers/transformers_torch_trainer_basic.html#ray-train-basic-example-for-huggingface-transformers",3871"https://docs.ray.io/en/master/tune/examples/hpo-frameworks.html#tune-hyperparameter-optimization-framework-examples",3872"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",3873"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.html#ray-train-huggingface-transformers-raytrainreportcallback",3874"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",3875"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.html#ray-tune-callback",3876"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_end.html#ray-train-lightning-raytrainreportcallback-on-init-end",3877"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_start.html#ray-train-lightning-raytrainreportcallback-on-init-start",3878"https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",3879"https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune",3880"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_batch_start.html#ray-train-lightning-raytrainreportcallback-on-batch-start",3881"https://docs.ray.io/en/master/tune/api/callbacks.html#callback-initialization-and-setup",3882"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_pretrain_routine_start.html#ray-train-lightning-raytrainreportcallback-on-pretrain-routine-start",3883"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_batch_end.html#ray-train-lightning-raytrainreportcallback-on-batch-end"3884],3885"document_ids": [38869553,38872429,38889165,38898635,38908206,38911405,38928531,38938760,38949934,38952390,38968999,38971204,38989115,389983563900],3901"answer": " To use a callback in the Trainer class from the Hugging Face Transformers library, you can add a callback object to the Trainer object using the `add_callback` method. For example:\n\n```python\nfrom transformers.trainer_callback import TrainerCallback\n\nclass MyTrainReportCallback(TrainerCallback):\n def __init__(self):\n super().__init__()\n self.metrics = {}\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=small_train_ds,\n eval_dataset=small_eval_ds,\n compute_metrics=compute_metrics,\n)\n\ntrainer.add_callback(MyTrainReportCallback())\n```\n\nThis will add an instance of `MyTrainReportCallback` to the Trainer object, and the callback's methods will be called at various points during training. You can define the behavior of the callback by implementing methods such as `on_train_begin`, `on_train_end`, `on_batch_begin`, `on_batch_end`, etc.\n\nIn the context provided, a callback called `RayTrainReportCallback` is added to the Trainer object using the same `add_callback` method. This callback is used to report metrics and checkpoints to Ray Train.",3902"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3903},3904{3905"question": "How to provide current working directory to ray?",3906"sources": [3907"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3908"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",3909"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3910"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",3911"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3912"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3913"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",3914"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3915"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3916"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",3917"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3918"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",3919"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3920"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli"3921],3922"document_ids": [39236632,39241066,39256633,39264262,39271790,3928511,39296562,39306551,39316552,39326596,39336555,39344264,39354385,393646623937],3938"answer": " To provide the current working directory to Ray, you can specify a runtime environment when calling `ray.init()`. Here's an example:\n```python\nimport os\n\n# Create a working directory and add a file\nos.makedirs(\"/tmp/runtime_env_working_dir\", exist_ok=True)\nwith open(\"/tmp/runtime_env_working_dir/hello.txt\", \"w\") as hello_file:\n hello_file.write(\"Hello World!\")\n\n# Specify a runtime environment for the entire Ray job\nray.init(runtime_env={\"working_dir\": \"/tmp/runtime_env_working_dir\"})\n```\nThis will make the contents of the specified working directory available to the Ray job by downloading the directory to all nodes in your cluster. Note that relative paths in the remote tasks and actors in the code will work on your laptop and on the cluster without any code changes.",3939"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3940},3941{3942"question": "how to create an actor instance with parameter?",3943"sources": [3944"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",3945"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",3946"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",3947"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",3948"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3949"https://docs.ray.io/en/master/ray-core/actors.html#actors",3950"https://docs.ray.io/en/master/rllib/actors.html#actors",3951"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",3952"https://docs.ray.io/en/master/ray-core/api/core.html#actors",3953"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",3954"https://docs.ray.io/en/master/ray-core/api/doc/ray.cross_language.java_actor_class.html#ray-cross-language-java-actor-class",3955"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3956"https://docs.ray.io/en/master/ray-core/actors.html#actors",3957"https://docs.ray.io/en/master/rllib/actors.html#actors"3958],3959"document_ids": [39604876,39615391,39625192,39634629,396414682,39654469,396611576,39675391,39684650,39694435,39704916,397114686,39724472,3973115783974],3975"answer": " To create an actor instance with a parameter, you can call the `ActorClass.remote()` method with arguments to the constructor. This creates and runs the actor on a remote worker process, and the return value is an actor handle that can be used to call its methods.\n\nHere's an example of creating a ProgressActor instance with the parameter `TOTAL_NUM_SAMPLES`:\n\n```python\nfrom ray.actor import ActorClass\n\n# Define the ProgressActor class\nclass ProgressActor(ActorClass):\n def __init__(self, total_num_samples):\n self.total_num_samples = total_num_samples\n self.current_sample = 0\n\n def get_progress(self):\n return self.current_sample / self.total_num_samples\n\n# Set the cluster scale\nNUM_SAMPLING_TASKS = 10\nNUM_SAMPLES_PER_TASK = 10_000_000\nTOTAL_NUM_SAMPLES = NUM_SAMPLING_TASKS * NUM_SAMPLES_PER_TASK\n\n# Create the progress actor with the TOTAL_NUM_SAMPLES parameter\nprogress_actor = ProgressActor.remote(TOTAL_NUM_SAMPLES)\n```\n\nThis creates an instance of the `ProgressActor` class and passes the `TOTAL_NUM_SAMPLES` parameter to its constructor. The `progress_actor` variable now holds an actor handle that can be used to call methods on the `ProgressActor` instance.",3976"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3977},3978{3979"question": "how to push a custom module to ray which is using by Actor ?",3980"sources": [3981"https://docs.ray.io/en/master/ray-core/api/core.html#actors",3982"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",3983"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",3984"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.push.html#ray-util-actorpool-push",3985"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#memory-profiling-ray-tasks-and-actors",3986"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#custom-resources-accelerator-types-and-more",3987"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3988"https://docs.ray.io/en/master/ray-core/actors.html#actors",3989"https://docs.ray.io/en/master/rllib/actors.html#actors",3990"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",3991"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#defining-the-progress-actor",3992"https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",3993"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",3994"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources"3995],3996"document_ids": [39974650,3998169,39995443,40004758,4001568,400213459,400314680,40044467,400511575,400614219,40075387,40084488,400914707,401045034011],4012"answer": " To push a custom module to Ray which is used by an Actor, you can use the `ray.put()` function to put the module into Ray's object store, and then get it back using `ray.get()` when needed. Here is an example:\n\nFirst, put the custom module into the object store:\n```\nray.put(my_custom_module)\n```\nThen, in the Actor class, you can get the custom module using `ray.get()`:\n```\n@ray.remote\nclass MyActor:\n def __init__(self):\n self.custom_module = ray.get(my_custom_module_ref)\n\n def do_something(self):\n # Use the custom module\n result = self.custom_module.some_function()\n return result\n```\nHere, `my_custom_module_ref` is the object reference of the custom module that was put into the object store.\n\nAlternatively, you can also use the `@ray.remote` decorator to turn the custom module into a task, and then call it remotely from the Actor. This way, you don't need to explicitly put the module into the object store and get it back. Here is an example:\n```\n@ray.remote\ndef my_custom_module():\n # Define the custom module here\n def some_function():\n # Implementation of the function\n pass\n return some_function\n\n@ray.remote\nclass MyActor:\n def do_something(self):\n # Call the custom module remotely\n result = ray.get(my_custom_module.remote()())\n return result\n```\nIn this example, `my_custom_module.remote()` creates a remote function that can be called from the Actor, and `my_custom_module.remote()()` calls the remote function to get the result.\n\nNote that in both examples, the custom module needs to be defined before it is used in the Actor. This is because the Actor is a remote object that runs in a separate process, and it cannot access the local variables of the main process. Therefore, the custom module needs to be defined in the main process and then put into the object store or turned into a remote function, so that it can be accessed by the Actor.\n\nI hope this helps! Let me know if you have any questions.",4013"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4014},4015{4016"question": "how to print ray working directory?",4017"sources": [4018"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4019"https://docs.ray.io/en/master/rllib/user-guides.html#rllib-feature-guides",4020"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4021"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4022"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#submit-the-workload",4023"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#submit-the-workload",4024"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4025"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-2-submit-a-ray-job-to-the-raycluster-via-ray-job-submission-sdk",4026"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",4027"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",4028"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",4029"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",4030"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",4031"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files"4032],4033"document_ids": [40346632,403510537,40366551,4037511,40386528,40396179,40406633,40415732,40426157,40436530,40445757,4045519,40466181,404742644048],4049"answer": " To print the working directory in Ray, you can use the following code:\n```python\nimport ray\nprint(ray.global_state.worker.tmp_dir)\n```\nThis will print the temporary directory of the current Ray worker, which is equivalent to the working directory specified in the runtime environment.",4050"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4051},4052{4053"question": "why I can not see log.info in ray log?",4054"sources": [4055"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4056"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",4057"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",4058"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4059"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4060"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4061"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",4062"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",4063"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",4064"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4065"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",4066"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",4067"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",4068"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence"4069],4070"document_ids": [4071444,40721005,4073680,40746631,4075445,4076511,4077662,4078530,4079415,4080493,40816360,4082535,4083477,408463594085],4086"answer": " Based on the provided context, you may not be able to see `log.info` messages in Ray logs for several reasons. Here are some things to check:\n\n1. **Logging level**: Ensure that the logging level is set to `logging.INFO` or lower (e.g., `logging.DEBUG`). By default, Ray's logging level is set to `logging.INFO`. If it is set to a higher level (e.g., `logging.ERROR`), you will not see `log.info` messages.\n2. **Ray logger**: Make sure you are using Ray's logger to emit the log messages. You can get the Ray logger instance using `logger = logging.getLogger(\"ray\")`.\n3. **Driver logs**: 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 are not accessible from the Dashboard. In this case, you can view the Driver logs in the terminal or Jupyter Notebook output.\n4. **Log APIs**: You can use Ray's `list_logs` and `get_log` functions to list and retrieve log files. For example, you can use `list_logs(node_id=<NODE_ID>)` to list logs from a specific node or `get_log(actor_id=<ACTOR_ID>)` to retrieve logs for a specific actor.\n5. **Log directory**: By default, Ray writes logs to files in the directory `/tmp/ray/session_*/logs` on each Ray node's file system. Make sure that the log files are being written to the correct directory and that you have permission to access them.\n\nIf you have checked all of the above and are still unable to see `log.info` messages in Ray logs, please provide more context or details about your setup and the issue you are facing.",4087"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4088},4089{4090"question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",4091"sources": [4092"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",4093"https://docs.ray.io/en/master/data/performance-tips.html#parquet-column-pruning",4094"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4095"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",4096"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",4097"https://docs.ray.io/en/master/data/user-guide.html#user-guides",4098"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",4099"https://docs.ray.io/en/master/data/data-internals.html#reading-files",4100"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",4101"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",4102"https://docs.ray.io/en/master/data/examples/batch_training.html#filtering-a-dataset-on-read-a-class-anchor-id-filter-ds-a",4103"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",4104"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4105"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition"4106],4107"document_ids": [41089210,41096818,41107362,41116835,41127865,41136911,41147495,41156874,41169188,41177875,41187870,41197782,41207360,412173514122],4123"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 the preservation of order during data ingest for reproducibility. To do this, you need to set the `preserve_order` flag in the `DataContext` to `True` as shown in the following code:\n```python\nimport ray\n\nctx = ray.data.DataContext.get_current()\nctx.execution_options.preserve_order = True\n```\nThis will ensure that the order of the data is preserved when reading a file using Ray Dataset.",4124"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4125},4126{4127"question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",4128"sources": [4129"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",4130"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",4131"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",4132"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",4133"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",4134"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",4135"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",4136"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4137"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",4138"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4139"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayTaskError.html#ray-exceptions-raytaskerror",4140"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",4141"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4142"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure"4143],4144"document_ids": [41454377,41464542,41474542,41484535,41494527,41504348,41514546,41524657,41534564,41544658,41554779,41564674,41574660,415845634159],4160"answer": " The sentence \"Ray will not retry tasks upon exceptions thrown by application code\" means that if a task fails due to an exception or error in the application's code, Ray will not automatically retry the task by default. This behavior can be changed by setting the `retry_exceptions` argument to `True` in the `ray.remote()` or `.options()` functions, which will enable retries upon any exception or pass a list of retryable exceptions.",4161"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4162},4163{4164"question": "how do I specify the log directory when starting Ray?",4165"sources": [4166"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4167"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4168"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",4169"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",4170"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",4171"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#mounting-the-ray-log-directory",4172"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",4173"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",4174"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4175"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",4176"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",4177"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4178"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-rotation",4179"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging"4180],4181"document_ids": [4182511,4183511,41846360,41855861,4186680,41875871,4188510,41895880,41904811,4191530,4192542,41934385,4194543,419543844196],4197"answer": " To specify the log directory when starting Ray, you can use the `--temp-dir` or `--logs-dir` option with the `ray start` or `ray.init()` command. Here's an example:\n```css\nray start --temp-dir /path/to/temp/dir --logs-dir /path/to/log/dir\n```\nor\n```css\nray.init(temp_dir='/path/to/temp/dir', logs_dir='/path/to/log/dir')\n```\nThis will set the temporary directory and log directory to the specified paths. Note that the temporary directory is also used as the default log directory.\n\nAlternatively, you can set the `RAY_TMPDIR` and `RAY_LOG_DIR` environment variables to specify the temporary and log directories, respectively. For example:\n```bash\nexport RAY_TMPDIR=/path/to/temp/dir\nexport RAY_LOG_DIR=/path/to/log/dir\nray start\n```\nor\n```bash\nexport RAY_TMPDIR=/path/to/temp/dir\nexport RAY_LOG_DIR=/path/to/log/dir\nray.init()\n```\nThis will set the temporary and log directories to the specified paths for the duration of the Ray session.",4198"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4199},4200{4201"question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",4202"sources": [4203"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4204"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4205"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4206"https://docs.ray.io/en/master/cluster/cli.html#ray-up",4207"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4208"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",4209"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",4210"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",4211"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",4212"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",4213"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",4214"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",4215"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4216"https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters"4217],4218"document_ids": [42196337,4220264,42216328,42225615,42236203,42246392,42256522,4226851,42274193,42286501,422914483,42306411,42316353,423244554233],4234"answer": " To launch a Ray cluster with 10 nodes without setting the minimum worker count as 10, you can adjust the `max_workers` field in the cluster configuration. However, you cannot set it directly to 10 because the context provided has a maximum limit of 2 for `max_workers`. To overcome this, you can use the `ray up` command with the `--min-workers` and `--max-workers` options to override the configured min and max worker node counts for the cluster.\n\nHere's an example of how to create a `config.yaml` file:\n\n```yaml\ncluster_name: my-ray-cluster\nmax_workers: 2\n\ndocker:\n image: \"rayproject/ray-ml:2.0.0\"\n container_name: \"ray_container\"\n\nprovider:\n type: aws\n region: us-west-2\n availability_zone: us-west-2a\n\nauth:\n ssh_user: ubuntu\n\nworker_start_ray_commands:\n - ray stop\n - ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076\n```\n\nThen, run the following command to launch the Ray cluster with 10 nodes:\n\n```bash\nRAY_HEAD_IP=$(aws ec2 describe-instances --filters \"Name=tag:Name,Values=my-ray-cluster-head\" --query \"Reservations[0].Instances[0].PublicIpAddress\" --output text)\n\nray up --min-workers 0 --max-workers 10 config.yaml\n```\n\nThis command sets the minimum worker count to 0 and the maximum worker count to 10, allowing you to launch a Ray cluster with 10 nodes. Note that you need to replace `my-ray-cluster` with your desired cluster name and update the AWS CLI command accordingly to get the correct `RAY_HEAD_IP`.",4235"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4236},4237{4238"question": "how to use ray api to scale up a cluster",4239"sources": [4240"https://docs.ray.io/en/master/cluster/vms/index.html#overview",4241"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",4242"https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",4243"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",4244"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",4245"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",4246"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",4247"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",4248"https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started",4249"https://docs.ray.io/en/master/cluster/cli.html#ray-up",4250"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#best-practices-for-deploying-large-clusters",4251"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",4252"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",4253"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/gcp.html#start-ray-with-the-ray-cluster-launcher"4254],4255"document_ids": [42566209,42576715,42585604,425914710,42605600,42614644,42626363,42636715,42646189,42655615,42666371,4267368,42687980,426965144270],4271"answer": " To use Ray API to scale up a cluster, you can use the `ray.autoscaler.sdk.request_resources()` function within a Ray program to command the autoscaler to scale the cluster up to a desired size. The cluster will immediately attempt to scale to accommodate the requested resources, bypassing normal upscaling speed constraints.\n\nAdditionally, you can use the Ray Cluster Management API to launch and manage Ray clusters on cloud VMs. The Ray cluster launcher can be used to configure the Ray autoscaler and set up autoscaling for your cluster.\n\nYou can also use the `ray up` command to create or update a Ray cluster, and use the `--min-workers` and `--max-workers` options to specify the minimum and maximum number of worker nodes for the cluster.\n\nIt's also important to follow best practices for deploying Ray clusters at large scale, such as using on-demand instances for the head node and preemptible instances for worker nodes.",4272"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4273},4274{4275"question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",4276"sources": [4277"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4278"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4279"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4280"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4281"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",4282"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",4283"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",4284"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents",4285"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",4286"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",4287"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-2-check-ports",4288"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",4289"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#launching-a-cluster-ray-up",4290"https://docs.ray.io/en/master/cluster/vms/index.html#overview"4291],4292"document_ids": [42936199,42946203,4295273,42966203,42975503,42986458,42996470,43006519,43016467,43026104,43036617,43046471,43056212,430662094307],4308"answer": " To specify a subnet in the Ray cluster deployment file for AWS, you can include the `subnet_id` in the `provider` section of the configuration file. Here is an example:\n\n```\ncluster_name: my-cluster\nprovider:\n type: aws\n region: us-west-2\n subnet_id: subnet-01234567890abcdef0\n```\n\nYou can find the `subnet_id` of the subnet you want to use in the AWS Management Console, in the VPC section.\n\nNote that the `subnet_id` should be in the same region as the `region` specified in the configuration file.\n\nAlso, make sure that the security group associated with the subnet allows inbound access to the Ray Client port on the head node. You can configure this in the `provider.security_group` field in the configuration file.\n\nOnce you have created the configuration file, you can use the Ray CLI to start the cluster with the following command:\n\n```\nray up -y config.yaml\n```\n\nReplace `config.yaml` with the path to your configuration file.\n\nYou will also need to have Boto3 installed and configured with your AWS credentials. You can do this by following the instructions in the context you provided.",4309"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4310},4311{4312"question": "where I can find HTTP server error code log for Ray serve",4313"sources": [4314"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4315"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",4316"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",4317"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4318"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",4319"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",4320"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",4321"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",4322"https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally",4323"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4324"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4325"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",4326"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",4327"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions"4328],4329"document_ids": [433013974,433113539,433213541,43336631,43345671,43355747,43364694,43375802,433813437,433913517,434013523,43416183,43426531,4343140254344],4345"answer": " To find the HTTP server error code log for Ray Serve, you can check the logs at `/tmp/ray/session_latest/logs/serve/` on the head node. Specifically, you can look for non-200 HTTP responses in the `ray_serve_num_deployment_http_error_requests` metric or in the Ray Serve access logs. Additionally, you can use the Ray Dashboard to view the status of your Ray Serve applications and check for any errors.",4346"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4347},4348{4349"question": "I am running ray cluster on amazon and I have troubles displaying the dashboard. When a I tunnel the dashboard port from the headnode to my machine, the dashboard opens, and then it disappears (internal refresh fails). Is it a known problem? What am I doing wrong?",4350"sources": [4351"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",4352"https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",4353"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",4354"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",4355"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",4356"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",4357"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",4358"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",4359"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",4360"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",4361"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",4362"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#dashboard-host",4363"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",4364"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard"4365],4366"document_ids": [43675530,436814606,43695528,4370418,43715526,43726562,43736561,43746223,43755543,437613681,43775531,43785935,4379417,4380135144381],4382"answer": " This issue is not explicitly mentioned as a known problem, but there are some suggestions for accessing the Ray Dashboard from outside the head node. One way to do this is by port forwarding the dashboard port (8265 by default) to your local machine. If you are using the Ray Cluster Launcher, you can automatically set up port forwarding using the `ray dashboard` command.\n\nIt is important to note that exposing the Ray Dashboard publicly without proper authentication is not recommended for security reasons. Make sure to configure authentication and secure access to the dashboard.\n\nIf you are still experiencing issues, it might be helpful to check the Ray Dashboard configuration and ensure that it is set up correctly for remote clusters. Additionally, consider setting up Prometheus and Grafana for enhanced dashboard experience and critical features such as Metrics View.\n\nIn summary, you are likely doing nothing wrong, but you may need to configure port forwarding and authentication to access the Ray Dashboard from outside the head node.",4383"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4384},4385{4386"question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",4387"sources": [4388"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4389"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#disabling-the-dashboard",4390"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",4391"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4392"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",4393"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",4394"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",4395"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",4396"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",4397"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4398"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4399"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",4400"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",4401"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart"4402],4403"document_ids": [4404256,44055538,44066292,44076345,44086392,44096522,44106227,44116366,44126367,44136337,44146328,44155557,44165555,44172654418],4419"answer": " In the Ray cluster launcher YAML, `max_workers` includes only worker nodes, not the head node. This is consistent across different types of Ray clusters, including single-node local clusters, VM Cluster Launcher, and KubeRay. The head node is typically managed separately and has its own configuration options.",4420"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4421},4422{4423"question": "How to update files in working directory ?",4424"sources": [4425"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4426"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4427"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",4428"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",4429"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",4430"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4431"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",4432"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4433"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#outdated-function-definitions",4434"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#synchronizing-files-from-the-cluster-ray-rsync-up-down",4435"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4436"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#common-workflow-syncing-git-branches",4437"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4438"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"4439],4440"document_ids": [44416632,44421066,44431775,44441790,44458894,44461068,44476214,44481066,4449619,44506222,44514273,44526225,4453269,445463494455],4456"answer": " To update files in the working directory, you can use the `ray rsync-up` command to upload files to the cluster head node. Here's the syntax:\n```bash\n$ ray rsync_up cluster.yaml '/local/path' '/path/on/cluster'\n```\nThis command will upload the files from the local path to the specified path on the cluster.\n\nAdditionally, if you want to sync changes to directories in `file_mounts` or `cluster_synced_files` continuously, you can set `file_mounts_sync_continuously` to `True` in your configuration.\n\nNote that if you're running a Tune experiment, you can access relative filepaths in your training function by setting the `RAY_CHDIR_TO_TRIAL_DIR` environment variable to `0`, which tells Tune not to change the working directory to the trial directory. This will give you access to paths relative to the original working directory. However, be sure to use `ray.train.get_context().get_trial_dir()` as the path to save any outputs to avoid conflicts between workers.",4457"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4458},4459{4460"question": "How I can update working directory file when ray allready initiated ?",4461"sources": [4462"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4463"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4464"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4465"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4466"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4467"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",4468"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",4469"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",4470"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4471"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4472"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",4473"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",4474"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",4475"https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution"4476],4477"document_ids": [44786632,44791066,44804385,44814811,44826555,44831790,44841775,44854263,44866633,4487511,44886214,44896157,44904262,449196444492],4493"answer": " To update the working directory file when Ray is already initiated, you can use the `ray.put()` and `ray.get()` functions to store and retrieve data in the object store, which is shared across all workers in the cluster. This allows you to modify the data and have the changes reflected across all workers.\n\nAlternatively, you can use the `ray.train.get_context().get_trial_dir()` function to get the trial directory and save any outputs there, ensuring that each worker has its own separate working directory.\n\nIt is important to note that the behavior of changing the working directory can be disabled by setting the `RAY_CHDIR_TO_TRIAL_DIR=0` environment variable, which allows access to files in the original working directory. However, relative paths should be used for read-only purposes, and you must make sure that the directory is synced on all nodes if running on multiple machines.\n\nAdditionally, you can use the `ray.init()` function with the `runtime_env` parameter to specify a working directory for the entire Ray job, which will be uploaded to the cluster when the job is started.\n\nFor example, you can use the following code to create a Ray task with a specified working directory:\n```\n@ray.remote\ndef f():\n # The function will have its working directory changed to its node's\n # local copy of /tmp/runtime_env_working_dir.\n return open(\"hello.txt\").read()\n```\nAnd then use the following code to specify the working directory when initializing Ray:\n```\nray.init(runtime_env={\"working_dir\": \"/tmp/runtime_env_working_dir\"})\n```\nThis will ensure that the `hello.txt` file is available in the working directory of the remote task.",4494"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4495},4496{4497"question": "how can I force ray head node to use custom pem file to ssh worker node?",4498"sources": [4499"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4500"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4501"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4502"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4503"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4504"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4505"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4506"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",4507"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4508"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4509"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",4510"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4511"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4512"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding"4513],4514"document_ids": [45156330,4516261,4517261,45186307,45196340,45206318,4521272,45226205,45236314,4524268,45256502,45266324,45276353,452866204529],4530"answer": " To force the Ray head node to use a custom PEM file to SSH into the worker node, you can specify the `ssh_private_key` field in the `auth` section of the configuration file. For example:\n\n```\nauth:\n ssh_user: ubuntu\n ssh_private_key: /path/to/your/custom/key.pem\n```\n\nThis will tell Ray to use the specified PEM file for SSH authentication when launching worker nodes. Make sure that the specified PEM file is accessible by the user running the Ray head node.\n\nAdditionally, you should also ensure that the corresponding public key (`key.pem.pub`) is added to the authorized keys of the worker node, so that the head node can SSH into the worker node using the specified PEM file.\n\nNote that this configuration only affects the SSH authentication used by Ray to launch worker nodes. If you want to SSH into the head node from a remote client, you will need to set up SSH keys separately for that purpose.",4531"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4532},4533{4534"question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",4535"sources": [4536"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",4537"https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",4538"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",4539"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",4540"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",4541"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-operating-system-settings",4542"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",4543"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",4544"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",4545"https://docs.ray.io/en/master/ray-core/configure.html#head-node",4546"https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",4547"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",4548"https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",4549"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance"4550],4551"document_ids": [455214740,4553937,45544508,45556375,4556563,45574457,455813870,45594522,45605011,45614390,4562940,456313928,4564939,456558304566],4567"answer": " The GCS (Global Control Service) server is a centralized metadata server for a Ray cluster that runs on the Ray head node. It has functions like managing node membership and actor directory. It's possible that the GCS server is taking up so much memory on the head node due to the workload and size of the cluster. To alleviate this, you can configure the head node with sufficient memory and network bandwidth, and also consider tuning operating system settings to support a large number of connections. Additionally, you can use Jemalloc for memory profiling on the GCS server to identify any memory-related issues.\n\nTo set up Jemalloc for memory profiling on the GCS server, you can follow these steps:\n\n1. Install Jemalloc on the head node.\n2. Set the `RAY_JEMALLOC_LIB_PATH` environment variable to the path of the Jemalloc shared library.\n3. Set the `RAY_JEMALLOC_CONF` environment variable to the MALLOC\\_CONF of Jemalloc, for example, `prof:true,lg_prof_interval:33,lg_prof_sample:17,prof_final:true,prof_leak:true`.\n4. Set the `RAY_JEMALLOC_PROFILE` environment variable to `gcs_server` to profile the GCS server.\n5. Start Ray with the above environment variables.\n\nYou can find more information on configuring the head node and using Jemalloc for memory profiling in the provided context.",4568"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4569},4570{4571"question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",4572"sources": [4573"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",4574"https://docs.ray.io/en/master/tune/faq.html#how-can-i-use-tune-with-kubernetes",4575"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",4576"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",4577"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",4578"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",4579"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource",4580"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4581"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",4582"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",4583"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",4584"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",4585"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#troubleshooting",4586"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#solution-1-container-command-recommended"4587],4588"document_ids": [4589467,45901061,4591852,45926147,45939510,45946215,45955724,4596254,459710208,45989898,4599466,460010218,46012428,460258474603],4604"answer": " If some nodes are pending for a long time when starting a cluster with `ray up`, you can debug this by checking the Ray cluster status with the `ray status` command. This command displays the node status, resource usage, and information about pending and failed nodes. Additionally, you can check the Ray logs with `kubectl logs ${RAYCLUSTER_HEAD_POD}` to see if there are any errors or warnings related to the pending nodes.\n\nIf the pending nodes are due to resource constraints, you can try increasing the available resources or reducing the number of actors or Tune trials. If the pending nodes are due to a bad state of the Ray cluster, you can try restarting the cluster with `ray up --restart-only`.\n\nYou can also check the Ray documentation for troubleshooting tips and best practices when running Ray on Kubernetes.",4605"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4606},4607{4608"question": "how to install Ray 2.5.1 from github or wheel?",4609"sources": [4610"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",4611"https://docs.ray.io/en/master/ray-contribute/writing-code-snippets.html#how-to-locally-test-examples",4612"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",4613"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",4614"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4615"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",4616"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",4617"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",4618"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",4619"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",4620"https://docs.ray.io/en/master/installation.html#from-wheels",4621"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",4622"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",4623"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only"4624],4625"document_ids": [4626288,4627849,462814545,462963,46306335,463114551,4632291,463366,4634286,463514541,463661,463710168,46385412,46398834640],4641"answer": " To install Ray 2.5.1 from a GitHub wheel, you need to find the correct wheel for your system and Python version on the Ray releases page on GitHub. However, it seems that Ray 2.5.1 is not available as a wheel for direct installation.\n\nAn alternative approach is to install Ray 2.5.1 from a specific commit by building the Ray package from source. Here are the steps to do so:\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 ray/examples/dreambooth_finetuning\npip install -r requirements.txt\n```\n3. Checkout the commit for Ray 2.5.1:\n```\ngit checkout 4e3d45e36e0e114a2086a1e0a215e28a20c080c5\n```\n4. Build the Ray package:\n```bash\npip install -e .\n```\n\nThis will install Ray 2.5.1 from the specified commit in editable mode.",4642"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4643},4644{4645"question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",4646"sources": [4647"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",4648"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",4649"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.__init__.html#ray-rllib-evaluation-worker-set-workerset-init",4650"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4651"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",4652"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4653"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#worker-setup-commands",4654"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4655"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4656"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",4657"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.__init__.html#ray-rllib-evaluation-rollout-worker-rolloutworker-init",4658"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",4659"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",4660"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"4661],4662"document_ids": [4663542,46641006,466512971,46664287,466710762,46684720,46696259,46706202,46714288,46724881,467313199,467410768,46759342,467642724677],4678"answer": " To use `worker_setup_hook` in a runtime environment to set up on worker node creation, you can specify it in the `runtime_env` configuration when initializing Ray as follows:\n```python\nimport ray\n\ndef logging_setup_func():\n # setup logging here\n pass\n\nray.init(runtime_env={\"worker_process_setup_hook\": logging_setup_func})\n```\nThe `worker_process_setup_hook` is an experimental feature that allows you to specify a setup hook that is called after workers start and before tasks and actors are scheduled. The value has to be a callable when passed to the Job, Task, or Actor. The callable is then exported and this value is converted to a string in the worker setup command.\n\nNote that this feature is experimental, and if you encounter any issues, you should raise them in the Ray GitHub repository.",4679"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4680},4681{4682"question": "how to use Ray dataset on aws",4683"sources": [4684"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",4685"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#ingress",4686"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",4687"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",4688"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",4689"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",4690"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",4691"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",4692"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",4693"https://docs.ray.io/en/master/data/key-concepts.html#datasets",4694"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",4695"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",4696"https://docs.ray.io/en/master/ray-core/using-ray-with-jupyter.html#working-with-jupyter-notebooks-jupyterlab",4697"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images"4698],4699"document_ids": [47006732,47016100,47028006,47036839,47049188,47057865,47066935,47076835,47086467,47096731,47106809,47116779,47124419,471373624714],4715"answer": " To use Ray dataset on AWS, you can read data from an S3 bucket using the `ray.data.read_csv` function. Here's an example:\n```python\nimport ray\n\n# Replace `s3://my-bucket/path/to/data` with the location of your data in S3\nds = ray.data.read_csv(\"s3://my-bucket/path/to/data\")\n```\nThis will create a Ray Dataset that you can use for distributed data processing. Ray Data supports various data sources and formats, including S3.\n\nBefore using the dataset, make sure you have installed Ray Data and have configured your AWS credentials correctly. You can install Ray Data using pip:\n```\npip install -U \"ray[data]\"\n```\nFor AWS credentials, you can use the AWS CLI to configure your credentials:\n```css\naws configure\n```\nFollow the prompts to enter your AWS Access Key ID, Secret Access Key, default region name, and default output format.\n\nFor more information on using Ray Datasets, you can refer to the Ray Data User Guide and Loading Data documentation.",4716"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4717},4718{4719"question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",4720"sources": [4721"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4722"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",4723"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",4724"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",4725"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",4726"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",4727"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",4728"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",4729"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",4730"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition",4731"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-data",4732"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",4733"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4734"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling"4735],4736"document_ids": [47377081,47381006,47396864,47407395,47419207,47426795,47438046,474410017,47456876,47467352,47476859,47487796,47497085,475092094751],4752"answer": " To avoid shuffling of your dataset during `ray.data.map_batches()`, you can set the `local_shuffle_buffer_size` parameter in `iter_batches()` or `iter_torch_batches()` methods. This will enable local shuffling of a subset of rows during iteration, which is more performant than a full global shuffle.\n\nHere's an example:\n\n```python\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\nds = ds.random_shuffle()\n\n# Iterate over batches with local shuffling\nfor batch in ds.iter_batches(batch_size=1024, local_shuffle_buffer_size=100):\n # Process your batch here\n pass\n```\n\nIn this example, `local_shuffle_buffer_size` is set to 100, meaning that a subset of up to 100 rows will be shuffled during each iteration. You can adjust this value based on your specific use case and desired level of randomness.",4753"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4754},4755{4756"question": "Is the order of the input data preserved after a map_batches operation?",4757"sources": [4758"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",4759"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",4760"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4761"https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms",4762"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4763"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",4764"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",4765"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4766"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",4767"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4768"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",4769"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4770"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",4771"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference"4772],4773"document_ids": [47747640,47757289,47767081,47776813,47787091,47797800,47807738,47817085,47827737,47837088,47847988,47857082,478610266,478780704788],4789"answer": " Yes, the order of the input data is preserved after a `map_batches` operation in Ray Dataset. This is because `map_batches` applies a user-defined function to batches of data, and the relative ordering of the batches is maintained.",4790"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4791},4792{4793"question": "ray serve returns generic internal service error when there is an internal failure, how do I get it to emit more detailed errors or logs?",4794"sources": [4795"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",4796"https://docs.ray.io/en/master/serve/tutorials/gradio-integration.html#scaling-your-gradio-app-with-ray-serve",4797"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",4798"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4799"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",4800"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",4801"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4802"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4803"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",4804"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",4805"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4806"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",4807"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#rayservice-troubleshooting",4808"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-2-check-rayservice-cr-status"4809],4810"document_ids": [48114349,481214427,48134527,481413517,48154348,48165671,48174660,481813974,4819595,48204694,48214658,482213469,48235661,482456644825],4826"answer": " To get more detailed errors or logs when Ray Serve returns a generic internal service error due to an internal failure, you can:\n\n1. Check the application-level failures by catching errors, retrying failed code, and handling misbehaving code using task and actor fault tolerance mechanisms provided by Ray. Ray surfaces application-level failures as Python-level exceptions.\n2. Leverage Ray logging to understand system-level behavior and to surface application-level details during runtime. Ray Serve uses Python's standard logging module with a logger named \"ray.serve\". By default, logs are emitted from actors both to stderr and on disk on each node at /tmp/ray/session\\_latest/logs/serve/. In development, logs are streamed to the driver Ray program, so it\u2019s convenient to keep the driver running while debugging.\n3. If you are using KubeRay, you can check the status and events of the RayService CR to see if there are any errors using the command: `kubectl describe rayservice $RAYSERVICE_NAME -n $YOUR_NAMESPACE`.\n\nRefer to the context for more information on fault tolerance, Ray logging, and RayService troubleshooting.",4827"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4828},4829{4830"question": "how do i track an uncaught exception in ray serve",4831"sources": [4832"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4833"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging",4834"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",4835"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",4836"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",4837"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",4838"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception",4839"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4840"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4841"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",4842"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",4843"https://docs.ray.io/en/master/ray-observability/getting-started.html#ray-dashboard",4844"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_placement_group.html#ray-util-state-get-placement-group",4845"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect"4846],4847"document_ids": [484813974,4849585,48504694,48514527,48524674,48535747,4854778,48554658,48564660,48574675,4858696,4859416,4860701,486156714862],4863"answer": " To track an uncaught exception in a Ray Serve application, you can use post-mortem debugging. This involves automatically dropping into the debugger when an error occurs or an exception is thrown. Here's how you can set this up:\n\n1. Install the required dependencies:\n```\npip install \"ray[serve]\" scikit-learn\n```\n2. Create a file called `serve_debugging.py` and add the following code:\n```python\nimport time\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.ensemble import GradientBoostingClassifier\n\nimport ray\nfrom ray import serve\n\nserve.start()\n```\n3. In this example, an uncaught exception is not intentionally thrown. However, if you have a specific function where you expect an uncaught exception, you can use a Python `try`/`except` block to catch and handle the exception as needed.\n4. To verify the status of the Serve application, you can use the Ray dashboard. Forward the dashboard port to localhost and check the Serve page at <http://localhost:8265/#/serve>. This can help you observe the application's performance and troubleshoot issues.\n\nFor more information on Ray exceptions and error handling, refer to the Ray documentation on exceptions.",4864"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4865},4866{4867"question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",4868"sources": [4869"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4870"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4871"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",4872"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4873"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",4874"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4875"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",4876"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4877"https://docs.ray.io/en/master/serve/monitoring.html#json-logging-format",4878"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4879"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4880"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",4881"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",4882"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3"4883],4884"document_ids": [488513517,488613517,4887680,4888445,48894323,489013524,4891519,4892444,489313525,4894493,489513523,4896415,4897411,48985334899],4900"answer": " To view logs for your Ray Serve endpoint in the Ray cluster, you can look in the following locations:\n\n1. In development, logs are streamed to the driver Ray program, so it's convenient to keep the driver running while debugging.\n2. By default, logs are emitted from actors and saved on disk on each node at `/tmp/ray/session_latest/logs/serve/`.\n3. You can also enable runtime environment debugging log streaming by setting the environment variable `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1` on each node before starting Ray.\n4. If you're using the Ray Job API, worker logs of Ray Jobs are always captured in the Ray logging directory, while driver logs are captured only for Ray Jobs submitted via Jobs API.\n\nTo access logs from Ray clusters, you can use the Ray Log CLI, which allows you to access the log from the cluster. Note that only the logs from alive nodes are available through this API.\n\nFor JSON-formatted logging in the Serve log file, set the environment variable `RAY_SERVE_ENABLE_JSON_LOGGING=1`. The logs will then have the following format:\n```json\n{\"levelname\": \"INFO\", \"asctime\": \"2023-07-17 10:34:25,425\", \"deployment\": \"default_api\", \"replica\": \"default_api#bFDOnw\", \"request_id\": \"OGIVJJJPRb\", \"route\": \"/app1\", \"application\": \"default\", \"message\": \"replica.py:664 - Started executing request OGIVJJJPRb\"}\n```\nLastly, Serve supports log rotation of these logs through setting the environment variables `RAY_ROTATION_MAX_BYTES` and `RAY_ROTATION_BACKUP_COUNT`.",4901"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4902},4903{4904"question": "where can I see logs for a failed ray serve deployment",4905"sources": [4906"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4907"https://docs.ray.io/en/master/serve/monitoring.html#filtering-logs-with-loki",4908"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4909"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4910"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#inspect-deeper",4911"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",4912"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4913"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",4914"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",4915"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4916"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",4917"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",4918"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4919"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods"4920],4921"document_ids": [492213523,492313536,49246631,4925444,492613658,492713442,492813517,492913960,493013537,493113521,493213514,493313959,4934445,493556654936],4937"answer": " The logs for a failed Ray serve deployment can be found in the following locations:\n\n1. On the head node, at `/tmp/ray/session_latest/logs/serve/`. These logs include system-level logs from the Serve controller and proxy, as well as access logs and custom user logs produced from within deployment replicas.\n\n2. If you're using Kubernetes, you can check the Kuberay operator logs by running `kubectl logs -f <kuberay-operator-pod-name>`. These logs contain information about the Serve application deployment event and Serve application health checks.\n\n3. You can also check the controller log and deployment log, which are located under `/tmp/ray/session_latest/logs/serve/` in both the head node pod and worker node pod. These logs contain information about specific deployment failure reasons and autoscaling events.\n\n4. To access the logs directly on the pods, you can use the command `kubectl exec -it $RAY_POD -n $YOUR_NAMESPACE -- bash` and then check the logs under `/tmp/ray/session_latest/logs/serve/`.\n\nFor troubleshooting application deployment failures in Serve, you can also check the Kuberay operator logs, which contain information about the Serve application deployment event and Serve application health checks.",4938"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4939},4940{4941"question": "How to take a subset of a Ray Dataset?",4942"sources": [4943"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",4944"https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",4945"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",4946"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",4947"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",4948"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",4949"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#splitting-datasets",4950"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",4951"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",4952"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray",4953"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-ml-libraries",4954"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",4955"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-preprocess-data-for-training",4956"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images"4957],4958"document_ids": [49599180,4960986,49618006,49626835,496310017,49648610,49659198,49669188,49676901,4968143,49696756,497010013,49718155,497273624973],4974"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\n```\nAlternatively, you can use the `map_partitions` method to apply a function to each partition of the dataset and create a new dataset with the results. For example, you can use this method to filter the dataset based on some condition. Here's an example:\n```python\ndef filter_function(batch):\n return batch if batch['x'][0] > 10 else None # return None to filter out the batch\n\nsubset_dataset = train_dataset.map_partitions(filter_function, batch_format=\"pandas\").filter(lambda x: x is not None)\n```\nThis will create a new dataset with only the batches where the first value of the 'x' column is greater than 10.",4975"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4976},4977{4978"question": "How do I load all checkpoints from trials of a Tune experiment launched with `tune.run`? I ran my initial experiment with cloud checkpointing, so I\u2019d need to download all the checkpoints to analyze them.",4979"sources": [4980"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",4981"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",4982"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing",4983"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",4984"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints",4985"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-out-of-tune-using-checkpoints-other-artifacts",4986"https://docs.ray.io/en/master/tune/tutorials/overview.html#tune-feature-guides",4987"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#tunecontroller",4988"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",4989"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",4990"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",4991"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",4992"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",4993"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example"4994],4995"document_ids": [49962226,49971006,49982217,49992253,50002212,50012299,50022270,50032243,50042255,50052348,50062350,50072347,50082310,500924395010],5011"answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `Tuner.restore()` function. This function allows you to restore the experiment from a local path or a cloud storage bucket. You can specify the `resume_errored` option to true if you want to resume an experiment that was interrupted due to an error.\n\nHere is an example of how to use `Tuner.restore()` to restore an experiment from a local path:\n```\ntuner = tune.Tuner.restore(\n os.path.expanduser(\"~/ray_results/tune_fault_tolerance_guide\"),\n trainable=trainable,\n resume_errored=True,\n)\ntuner.fit()\n```\nAnd here is an example of how to restore an experiment from a cloud storage bucket:\n```\ntuner = tune.Tuner.restore(\n \"s3://my-checkpoints-bucket/path/my-tune-exp\",\n trainable=my_trainable,\n resume_errored=True\n)\ntuner.fit()\n```\nYou can also specify the `resume_unfinished` or `restart_errored` options to control how the experiment is restored. For more details, please see the documentation of `Tuner.restore()`.\n\nOnce the experiment is restored, you can access the results and checkpoints by calling `Tuner.get_results()` to receive the `ResultGrid` object, and then proceeding as outlined in the Tune documentation.\n\nNote that if you ran your initial experiment with cloud checkpointing, you will need to download all the checkpoints to your local machine before you can analyze them. You can do this by copying the checkpoint files from the cloud storage bucket to your local machine using a tool like the `aws s3 cp` command or the `gsutil cp` command.",5012"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5013},5014{5015"question": "How can I kill a \"detached\" Actor ?",5016"sources": [5017"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",5018"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",5019"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",5020"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",5021"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",5022"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",5023"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",5024"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",5025"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",5026"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",5027"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",5028"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",5029"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",5030"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure"5031],5032"document_ids": [50334633,50342157,50354859,50364631,50374615,50384613,50394579,50404620,50414848,50424561,50434618,50444614,50452157,504645775047],5048"answer": " To kill a \"detached\" Actor in Ray, you can use the `ray.kill()` function, passing the actor handle as an argument. Here's an example:\n```python\nimport ray\n\n# Create a detached actor\nactor = ray.remote(name=\"actor\", lifetime=\"detached\").remote()\n\n# ... use the actor ...\n\n# Kill the actor\nray.kill(actor)\n```\nNote that you can also kill the actor by removing all references to it and waiting for garbage collection, but this is not recommended for detached actors, as they are not garbage-collected by Ray.\n\nAdditionally, if you are using Ray with RLlib, you can override the `Trainable.cleanup()` method to kill any Ray actors launched in the Trainable when it is stopped. Here's an example:\n```python\nimport ray\nfrom ray.tune.trainable import Trainable\n\nclass MyTrainable(Trainable):\n def setup(self, config):\n self.actor = Actor.options(name=\"actor\", lifetime=\"detached\").remote()\n\n def train(self):\n # ... train ...\n pass\n\n def cleanup(self):\n # Kill the Ray actor launched in the Trainable\n ray.kill(self.actor)\n```\nFinally, if you need to kill a misbehaving actor, you can call `ray.kill()` on any handle to the actor, not just the original handle. If the actor has a maximum number of restarts set, you can also allow Ray to automatically restart the actor by passing `no_restart=False` to `ray.kill()`.",5049"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5050},5051{5052"question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",5053"sources": [5054"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",5055"https://docs.ray.io/en/master/tune/faq.html#how-can-i-continue-training-a-completed-tune-experiment-for-longer-and-with-new-configurations-iterative-experimentation",5056"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",5057"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5058"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",5059"https://docs.ray.io/en/master/serve/api/index.html#serve-run",5060"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",5061"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",5062"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#set-up-ray-a-name-setup-a",5063"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.init_method.html#ray-train-torch-torchconfig-init-method",5064"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",5065"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",5066"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",5067"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv"5068],5069"document_ids": [50706614,50711073,50729343,50734811,50744285,507513990,5076542,50774253,507810295,50799118,508013850,508113684,50826613,508347125084],5085"answer": " To set environment variables in `ray.init()`, you can include the `env_vars` option in the `runtime_env` argument. For example:\n\n```python\nray.init(\n runtime_env={\n \"env_vars\": {\n \"foo\": \"foo\"\n }\n }\n)\n```\n\nThis will set the environment variable `foo` to the value `\"foo\"` in the Ray workers. Note that any existing environment variables with the same name will still be visible to the Ray workers, and you can reference existing environment variables using `${ENV_VAR}` to achieve the appending behavior. If the environment variable doesn't exist, it becomes an empty string.",5086"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5087},5088{5089"question": "What is the rest api for getting the head node id?",5090"sources": [5091"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",5092"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",5093"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.node_id.html#ray-runtime-context-runtimecontext-node-id",5094"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",5095"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_node_id.html#ray-runtime-context-runtimecontext-get-node-id",5096"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",5097"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5098"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5099"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",5100"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails",5101"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",5102"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",5103"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",5104"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions"5105],5106"document_ids": [5107721,51086407,51094731,5110754,51114909,51124450,511314324,511414202,5115492,51166657,5117493,51186407,5119720,5120140225121],5122"answer": " To get the head node ID in Ray, you can use the `get_node_id()` function from the `RuntimeContext` class or the `get_node()` function from the `state` module. Here's how you can use each method:\n\n1. Using `RuntimeContext.get_node_id()`:\n```python\nfrom ray.runtime_context import RuntimeContext\n\nnode_id = RuntimeContext.get_node_id()\nprint(node_id)\n```\nThis method returns the node ID in hex format.\n\n2. Using `state.get_node()`:\n```python\nimport ray\nfrom ray.util.state import get_node\n\nray.init()\nnode = get_node(ray.nodes()[0])\nprint(node.node_id)\n```\nThis method returns a `NodeState` object, and you can access the node ID using the `node_id` attribute.\n\nPlease note that the `RuntimeContext.node_id` property is deprecated and may be removed in future Ray releases. It is recommended to use `RuntimeContext.get_node_id()` instead.\n\nRegarding the REST API for getting the head node ID, Ray does not provide a direct REST API for this purpose. However, you can create a custom REST API using a Ray actor that exposes the node ID through an HTTP endpoint.",5123"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5124},5125{5126"question": "how to rerun a canceled ray task",5127"sources": [5128"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",5129"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",5130"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",5131"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",5132"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",5133"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",5134"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",5135"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",5136"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",5137"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",5138"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",5139"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",5140"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",5141"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"5142],5143"document_ids": [51444897,51455427,51464375,51474550,514814702,51494494,515011591,515114703,51524497,515311592,51544894,51554542,515611590,5157147015158],5159"answer": " You cannot rerun a canceled Ray task as cancelled tasks aren't retried in Ray. Instead, you can create a new object ref for the task and run it again. To cancel a task, you can call `ray.cancel()` on the returned Object Ref. This cancellation approach aligns with the standards presented in asyncio task cancellation. Note that asyncio.Task won\u2019t be interrupted in the middle of execution if you don\u2019t await within the async function. Ray attempts to cancel Tasks on a best-effort basis, meaning cancellation isn\u2019t always guaranteed.",5160"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5161},5162{5163"question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",5164"sources": [5165"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",5166"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",5167"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.sigopt.SigOptSearch.html#ray-tune-search-sigopt-sigoptsearch",5168"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.hyperopt.HyperOptSearch.set_max_concurrency.html#ray-tune-search-hyperopt-hyperoptsearch-set-max-concurrency",5169"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.zoopt.ZOOptSearch.set_max_concurrency.html#ray-tune-search-zoopt-zooptsearch-set-max-concurrency",5170"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ConcurrencyLimiter.html#ray-tune-search-concurrencylimiter",5171"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.html#ray-tune-search-optuna-optunasearch",5172"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.skopt.SkOptSearch.set_max_concurrency.html#ray-tune-search-skopt-skoptsearch-set-max-concurrency",5173"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.bayesopt.BayesOptSearch.set_max_concurrency.html#ray-tune-search-bayesopt-bayesoptsearch-set-max-concurrency",5174"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.set_max_concurrency.html#ray-tune-search-optuna-optunasearch-set-max-concurrency",5175"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ax.AxSearch.set_max_concurrency.html#ray-tune-search-ax-axsearch-set-max-concurrency",5176"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.dragonfly.DragonflySearch.set_max_concurrency.html#ray-tune-search-dragonfly-dragonflysearch-set-max-concurrency",5177"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.set_max_concurrency.html#ray-tune-search-searcher-set-max-concurrency",5178"https://docs.ray.io/en/master/serve/api/doc/ray.serve.batch.html#ray-serve-batch"5179],5180"document_ids": [51817738,51822406,51832072,51841654,51851340,51861464,51871867,51881547,51891597,51901610,51911543,51921434,51931712,5194140875195],5196"answer": " To set the maximum number of parallel concurrent scheduled tasks in `map_batches`, you can use the `max_batch_size` parameter in `BatchedDeployment.bind()`. However, if you're looking to limit the concurrency of trials in Ray Tune, you can set `max_concurrent_trials` in `TuneConfig`. The actual parallelism will be determined by how many trials can fit in the cluster at once. Alternatively, you can use searchers like HyperOptSearch, ZOOptSearch, etc., and set their max concurrency using the `set_max_concurrency` method.",5197"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5198},5199{5200"question": "How do I get the number of cpus from ray cluster?",5201"sources": [5202"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",5203"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",5204"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",5205"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-6-profile-using-ray-dashboard",5206"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",5207"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",5208"https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",5209"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",5210"https://docs.ray.io/en/master/data/performance-tips.html#tuning-read-parallelism",5211"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-8-a-loop-of-restarting-the-raycluster-occurs-when-the-kubernetes-cluster-runs-out-of-resources-kuberay-v0-6-1-or-earlier",5212"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.head_cpus.html#ray-tune-execution-placement-groups-placementgroupfactory-head-cpus",5213"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",5214"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5215"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible"5216],5217"document_ids": [521810003,52196411,52205936,52216049,52226504,52235413,52244380,52256013,52266815,52275693,52282112,5229614,52304818,523153995232],5233"answer": " To get the number of CPUs in a Ray cluster, you can use the `ray.cluster_resources()` function in Python. This function returns a dictionary containing various resources in the cluster, including the number of CPUs, which can be accessed using the key 'CPU'. Here is an example:\n```python\nimport ray\nray.init()\nprint(ray.cluster_resources()['CPU'])\n```\nThis will print the total number of CPUs in the Ray cluster. Note that this function returns the total number of CPUs available to Ray, which may be different from the total number of physical CPUs in the cluster if some CPUs are not allocated to Ray.",5234"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5235},5236{5237"question": "How to use the exclude option to the runtime_env",5238"sources": [5239"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",5240"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.Concatenator.html#ray-data-preprocessors-concatenator",5241"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",5242"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5243"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",5244"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",5245"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-6-runtime-env",5246"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",5247"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",5248"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",5249"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.runtime_env.html#ray-runtime-context-runtimecontext-runtime-env",5250"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",5251"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",5252"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_runtime_envs.html#ray-util-state-list-runtime-envs"5253],5254"document_ids": [52554661,52567445,52574826,525813856,52594712,52604912,52615684,52626665,52634291,52644764,52654770,52664319,52674308,52687075269],5270"answer": " The `exclude` option can be used with `ray.runtime_env.RuntimeEnv` to specify a list of columns to exclude when concatenating. It is a parameter that you can use to customize the runtime environment for a job, task, or actor in Ray. If a column is in both the `include` and `exclude` lists, it will be excluded from concatenation. However, I couldn't find specific documentation on using the `exclude` option with the `runtime_env` in the context you provided. It might be best to refer to the official Ray documentation or seek further clarification on the specific usage of `exclude` with `runtime_env`.",5271"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5272},5273{5274"question": "show a map batch example with batch_format",5275"sources": [5276"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5277"https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",5278"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.sample_batch.SampleBatch.split_by_episode.html#ray-rllib-policy-sample-batch-samplebatch-split-by-episode",5279"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",5280"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",5281"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",5282"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",5283"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5284"https://docs.ray.io/en/master/rllib/rllib-offline.html#example-converting-external-experiences-to-batch-format",5285"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",5286"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5287"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.to_batch_format.html#ray-data-block-blockaccessor-to-batch-format",5288"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.default_batch_format.html#ray-data-dataset-default-batch-format",5289"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference"5290],5291"document_ids": [52927081,52936727,529413299,529510266,529614727,52976956,52987640,52997091,530010622,53016790,53027088,53037384,53047791,530579885306],5307"answer": " Here's an example of using `map_batches()` with `batch_format`:\n\n```python\nfrom typing import Dict\nimport numpy as np\nimport ray\n\ndef increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n return batch\n\nds = (\n ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .map_batches(increase_brightness, batch_format=\"numpy\")\n)\n```\n\nIn this example, we read image data from an S3 bucket, and then apply a function to increase the brightness of each image. The `batch_format` argument specifies that the batches should be represented as dictionaries of NumPy arrays.\n\nThe function `increase_brightness()` takes a batch (which is a dictionary of NumPy arrays) as input, modifies the \"image\" field of the dictionary, and returns the modified batch.\n\nThe resulting `Dataset` `ds` contains the modified image data.",5308"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5309},5310{5311"question": "how to find local ray address",5312"sources": [5313"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",5314"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",5315"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5316"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",5317"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",5318"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",5319"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5320"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",5321"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",5322"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",5323"https://docs.ray.io/en/master/ray-core/configure.html#driver-options",5324"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",5325"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_placement_groups.html#ray-util-state-list-placement-groups",5326"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_objects.html#ray-util-state-list-objects"5327],5328"document_ids": [53296552,53306552,53314817,53326531,53336183,53344187,53354818,533613988,533713974,53385413,53394409,5340739,5341772,53427575343],5344"answer": " To find the local Ray address, you can look for the address of the Ray dashboard, which is displayed in the output when you start a local Ray cluster. For example, if you see a message like \"View the Ray dashboard at <http://127.0.0.1:8265>\" in the output, then the address of the Ray cluster is <http://127.0.0.1:8265>. Alternatively, you can find the Ray address by checking the environment variable RAY\\_ADDRESS, or by querying the Ray state backend using the ray.global\\_state.address() function.\n\nHere are some specific steps you can follow to find the local Ray address:\n\n1. Start a local Ray cluster by running the following commands:\n```\nimport ray\nray.init()\n```\n1. Look for the following message in the output, which will indicate the address of the Ray dashboard:\n```\n... INFO services.py:1263 -- View the Ray dashboard at http://127.0.0.1:8265\n```\n1. Alternatively, you can find the Ray address by checking the environment variable RAY\\_ADDRESS, like this:\n```\nimport os\nray_address = os.environ.get(\"RAY_ADDRESS\", \"auto\")\n```\n1. You can also query the Ray state backend to find the Ray address, like this:\n```\nimport ray\nray_address = ray.global_state.address()\n```\n1. Once you have the Ray address, you can use it to connect to the Ray cluster, for example by setting the RAY\\_ADDRESS environment variable or by passing the --address flag to Ray CLI commands.\n\nFor more information, you can refer to the Ray documentation on connecting to a Ray cluster.",5345"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5346},5347{5348"question": "How to start ray cluster on multiple node via CLI?",5349"sources": [5350"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",5351"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",5352"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",5353"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",5354"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",5355"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",5356"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",5357"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",5358"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",5359"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",5360"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",5361"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",5362"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",5363"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview"5364],5365"document_ids": [53666203,53674186,53686616,53695605,53706501,53714193,53726500,53736510,53746622,53756206,53766509,53774644,53785600,5379147105380],5381"answer": " To start a Ray cluster on multiple nodes via CLI, you can use the `ray up` command with a configuration file (`config.yaml`) that specifies the head node and worker nodes for the cluster. Here is the command you would run:\n\n```\nray up -y config.yaml\n```\n\nThe configuration file should include information about the head node and worker nodes, such as their IP addresses and any necessary authentication details. You can refer to the Ray documentation for more information on how to create a configuration file for the Ray cluster launcher.\n\nBefore running this command, you should make sure that Ray is installed on all of the nodes in the cluster, and that the nodes are able to communicate with each other over the network. You may also need to configure your firewall settings and network configuration to allow the Ray cluster to function properly.\n\nOnce the Ray cluster is up and running, you can connect to it by calling `ray.init()` in your Ray program. This will automatically connect to the Ray cluster running on the default port (10001). If the Ray cluster is running on a different port, you can specify the port number using the `--ray-client-server-port` argument when calling `ray.init()`.\n\nFor more information on managing Ray clusters, you can refer to the Ray documentation on cluster management.",5382"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5383},5384{5385"question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",5386"sources": [5387"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",5388"https://docs.ray.io/en/master/tune/api/trainable.html#advanced-resource-allocation",5389"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",5390"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",5391"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",5392"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",5393"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",5394"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",5395"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",5396"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",5397"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",5398"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",5399"https://docs.ray.io/en/master/ray-air/examples/xgboost_example.html#training-a-model-with-distributed-xgboost",5400"https://docs.ray.io/en/master/tune/faq.html#what-are-all-these-extra-outputs"5401],5402"document_ids": [54035068,54041159,5405625,540610218,54079397,54086879,5409622,54109901,54111113,5412468,54139898,541410384,54159882,541610225417],5418"answer": " The Ray tuner shows \"running\" but CPU usage is almost 0% because the Ray actor or task created by the trainable function is not using the CPU after it is created. This could be due to insufficient resource allocation for the trainable function or other processes using most of the CPUs. It is also possible that the application is waiting for other resources such as GPUs to become available. To resolve this issue, you can try increasing the number of CPU slots reserved for the trainable function or limiting the number of concurrent Tune trials to ensure there are enough free CPUs for Ray Data to run on. Additionally, you can check if other processes are using most of the CPUs and consider adjusting their resource allocation accordingly.",5419"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5420},5421{5422"question": "should the Ray head node and all workers have the same object store memory size allocated?",5423"sources": [5424"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",5425"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#head-node-commands",5426"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",5427"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",5428"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",5429"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",5430"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",5431"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",5432"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",5433"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",5434"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",5435"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",5436"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#head-node-commands",5437"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts"5438],5439"document_ids": [54406394,54416441,54425109,5443563,54445013,54456437,54466446,5447855,54485557,544914483,5450560,54516375,54526441,545350145454],5455"answer": " Yes, it is recommended to have the same object store memory size allocated for both the Ray head node and worker nodes. This is because the head node has more memory-demanding system components and can easily have out-of-memory problems if the same number of tasks and actors are executed from a head node. By default, Ray reserves 30% of available memory for object store memory, but this can be controlled by specifying the `--object-store-memory` flag. It is important to allocate enough memory to the object store to avoid running out of memory during application execution.",5456"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5457},5458{5459"question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",5460"sources": [5461"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",5462"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#launching-an-on-premise-cluster",5463"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5464"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",5465"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5466"https://docs.ray.io/en/master/serve/api/index.html#serve-start",5467"https://docs.ray.io/en/master/serve/architecture.html#how-does-serve-ensure-horizontal-scalability-and-availability",5468"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",5469"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5470"https://docs.ray.io/en/master/serve/develop-and-deploy.html#build-serve-config-files-for-production-deployment",5471"https://docs.ray.io/en/master/serve/architecture.html#high-level-view",5472"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.ProxyLocation.HeadOnly.html#ray-serve-config-proxylocation-headonly",5473"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",5474"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout"5475],5476"document_ids": [547714022,54786499,547914055,548014025,548114288,548213994,548313476,548413958,548514287,548613439,548713465,548814163,548914045,5490137955491],5492"answer": " To specify whether to set up an HTTP proxy on each node or just the head node in Ray Serve, you can use the `http_options` field in the Serve config and set the `proxy-location` property. The options are \"Disabled\", \"HeadOnly\", and \"EveryNode\", with \"EveryNode\" being the default, which starts one HTTP server per node. The `num_cpus` field is deprecated and no longer used.",5493"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5494},5495{5496"question": "Want to embed Grafana into the Ray Dashboard, given that I am using KubeRay\n\nGiven the context that Prometheus and Grafana are not running on my Head node, and that I am using KubeRay, how should I be setting the following variables?\n\u2022 `RAY_GRAFANA_HOST`\n\u2022 `RAY_PROMETHEUS_HOST`\nAnd is there a way to set them more intelligently, given that head node IP is changing every time we reconfigure our cluster?",5497"sources": [5498"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",5499"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",5500"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",5501"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",5502"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-grafana-host-location",5503"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",5504"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-11-embed-grafana-panels-in-ray-dashboard",5505"https://docs.ray.io/en/master/cluster/metrics.html#simplist-setting-up-grafana-with-ray-provided-configurations",5506"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",5507"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",5508"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",5509"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-prometheus-host-location",5510"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#dashboard-host",5511"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster"5512],5513"document_ids": [55145544,55155543,55166061,55175543,55185547,55195545,55206086,55215597,55226084,55236085,55246058,55255546,55265935,552760625528],5529"answer": " Given that Prometheus and Grafana are not running on your Head node and you are using KubeRay, you should set `RAY_PROMETHEUS_HOST` to the address that the head node can use to access Prometheus, and `RAY_GRAFANA_HOST` to the address that the head node can use to access Grafana.\n\nSince the head node's IP address is changing every time you reconfigure the cluster, you can use the Kubernetes service name and port to access Prometheus and Grafana, instead of using the IP address. For example, if Prometheus is exposed as a service named `prometheus` on port `9090`, and Grafana is exposed as a service named `grafana` on port `3000`, you can set the following variables:\n\n- `RAY_PROMETHEUS_HOST=http://prometheus:9090`\n- `RAY_GRAFANA_HOST=http://grafana:3000`\n\nThis way, the values will remain consistent even if the head node's IP address changes.\n\nRegarding `RAY_GRAFANA_IFRAME_HOST`, if you want to embed Grafana visualizations into the Ray Dashboard, you can set it to the same value as `RAY_GRAFANA_HOST`. If you don't need this feature, you can omit this variable.\n\nNote that these environment variables should be set when you start the Ray Clusters.",5530"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5531},5532{5533"question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",5534"sources": [5535"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",5536"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",5537"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",5538"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",5539"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",5540"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",5541"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",5542"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#deploy-a-ray-cluster",5543"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",5544"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",5545"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#experiment-results-kuberay-v0-6-0",5546"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",5547"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-1-create-a-new-kubernetes-cluster",5548"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#worker-init-container"5549],5550"document_ids": [555113957,555213870,555313956,55545975,55555953,55565972,55576170,55586175,55595973,556013902,55615779,55625963,55635772,556457085565],5566"answer": " The Google Kubernetes Engine (GCS) determines which Kubernetes pod to kill when using KubeRay autoscaling based on the resource demand of tasks, actors, or placement groups. The Ray autoscaler, which is a Ray cluster process running as a sidecar container in the Ray head pod, adjusts the number of nodes (Ray pods) in the cluster based on these requirements. The Kubernetes Cluster Autoscaler complements the Ray Autoscaler by provisioning Kubernetes nodes for Ray pods and cleaning up idle nodes when Ray pods are deleted. The Ray Autoscaler has more fine-grained control over scale-down than the Horizontal Pod Autoscaler to accommodate the statefulness of Ray applications.",5567"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5568},5569{5570"question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",5571"sources": [5572"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5573"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",5574"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",5575"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",5576"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",5577"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",5578"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5579"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s",5580"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",5581"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s",5582"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",5583"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5584"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5585"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests"5586],5587"document_ids": [558813854,558913832,559013795,559114020,55924287,559314044,559414288,55958481,55964765,55978655,559814045,559913852,560014055,5601137165602],5603"answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you can include it in the config file as follows:\n```yaml\nhttp_options:\n request_timeout_s: <value_in_seconds>\n```\nFor example:\n```yaml\nhttp_options:\n request_timeout_s: 10\n```\nThis will set the end-to-end timeout for HTTP requests to 10 seconds before terminating and retrying at another replica. The `http_options` field is global to your Ray cluster and cannot be updated during runtime. By default, there is no request timeout.",5604"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5605},5606{5607"question": "How do I make the GPU available on my M1 laptop to ray?",5608"sources": [5609"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",5610"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5611"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",5612"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5613"https://docs.ray.io/en/master/installation.html#m1-mac-apple-silicon-support",5614"https://docs.ray.io/en/master/ray-overview/installation.html#m1-mac-apple-silicon-support",5615"https://docs.ray.io/en/master/ray-more-libs/installation.html#m1-mac-apple-silicon-support",5616"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#requesting-gpus",5617"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",5618"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",5619"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",5620"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gcp-gke-gpu-cluster.html#step-2-create-a-gpu-node-pool",5621"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#starting-ray-nodes-with-gpus",5622"https://docs.ray.io/en/master/tune/api/doc/ray.tune.utils.wait_for_gpu.html#ray-tune-utils-wait-for-gpu"5623],5624"document_ids": [56254997,56261006,56274995,56284818,562973,5630298,563114571,56326020,56332966,56346012,5635417,56365950,56374992,563814875639],5640"answer": " To make the GPU available on your M1 laptop for Ray, you need to install Ray on a machine running Apple Silicon (such as an M1 Mac) using miniforge. Here are the steps:\n\n1. Install miniforge.\n2. Activate the miniforge environment.\n3. Install Ray as you normally would.\n\nTo use the GPU with Ray, you can define a Ray remote function with `num_gpus=1` and use TensorFlow or another external library that supports GPU acceleration. Here's an example:\n```python\n@ray.remote(num_gpus=1)\ndef use_gpu():\n import tensorflow as tf\n # Use TensorFlow to utilize the GPU\n```\nNote that you will need to install the GPU version of TensorFlow for this to work. Also, keep in mind that if you are using fractional GPUs, 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.\n\nTo request GPUs, you can use the `ray.autoscaler.sdk.request_resources` function to scale up GPU resources. Here's an example:\n```python\nimport ray\n\nray.init()\nray.autoscaler.sdk.request_resources(bundles=[{\"GPU\": 1}] * 2)\n```\nAfter the nodes are scaled up, they will persist until the request is explicitly overridden. To remove the resource request, you can use the following program:\n```python\nimport ray\n\nray.init()\nray.autoscaler.sdk.request_resources(bundles=[])\n```\nThe GPU workers can then scale down.\n\nTo configure Ray pods for GPU usage, you need to specify `nvidia.com/gpu` resource limits and requests in the container fields of your RayCluster\u2019s headGroupSpec and/or workerGroupSpecs. Here's a config snippet for a RayCluster workerGroup of up to 5 GPU workers:\n```yaml\ngroupName: gpu-group\nreplicas: 0\nminReplicas: 0\nmaxReplicas: 5\n...\ntemplate:\n spec:\n ...\n containers:\n - name: ray-node\n image: rayproject/ray-ml:2.6.3-gpu\n ...\n resources:\n nvidia.com/gpu: 1 # Optional, included just for documentation.\n cpu: 3\n memory: 50Gi\n limits:\n nvidia.com/gpu: 1 # Required to use GPU.\n cpu: 3\n```\nNote that using Nvidia GPUs requires specifying `nvidia.com/gpu` resource limits and requests in the container fields of your RayCluster\u2019s headGroupSpec and/or workerGroupSpecs.\n\nTo access the dashboard, use `ray[default]`, `ray[air]`, or other installation commands that include the Ray Dashboard component. When you start a single-node Ray Cluster on your laptop, access the dashboard with the URL that Ray prints when it initializes (the default URL is <http://localhost:8265>) or with the context object returned by `ray.init()`.\n\nTo wait for GPU availability, you can use `ray.tune.utils.wait_for_gpu` function. Here's an example:\n```python\nray.tune.utils.wait_for_gpu(gpu_id=0, target_util=0.01, retry=20, delay_s=5, gpu_memory_limit=None)\n```\nThis function checks if a given GPU has freed memory and requires `gputil` to be installed. It blocks until the GPU is completely free if `target_util` is set to 0.",5641"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5642},5643{5644"question": "How can I add a timeout for the Ray job?",5645"sources": [5646"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",5647"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",5648"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",5649"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",5650"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",5651"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",5652"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.GetTimeoutError.html#ray-exceptions-gettimeouterror",5653"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-stop",5654"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",5655"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",5656"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",5657"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s",5658"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s",5659"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"5660],5661"document_ids": [566213795,566313698,56646556,56655757,56664416,56676554,56684796,56696602,56706558,56716569,56724765,56738655,56748481,5675138545676],5677"answer": " To add a timeout for a Ray job, you can use the `timeout` parameter in the `ray.get()` function when retrieving the object reference of the remote function. If the function doesn't complete within the specified timeout, a `GetTimeoutError` will be raised. Here's 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\nAlternatively, if you want to set a timeout for the job itself, you can use the `ray job stop` command with the `--no-wait` flag to periodically check the job's status and stop it if it exceeds a certain runtime. However, this requires additional scripting and is not a built-in feature of Ray.\n\nNote that the `request_timeout_s` field in the `http_options` config is not related to Ray job timeouts, but rather sets the end-to-end timeout for HTTP requests in Ray Serve.",5678"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5679},5680{5681"question": "how do I set custom /tmp directory for remote cluster?",5682"sources": [5683"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",5684"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5685"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",5686"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",5687"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",5688"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",5689"https://docs.ray.io/en/master/cluster/cli.html#ray-submit",5690"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.restore.html#ray-rllib-algorithms-algorithm-algorithm-restore",5691"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.restore.html#ray-tune-trainable-restore",5692"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",5693"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",5694"https://docs.ray.io/en/master/train/user-guides/persistent-storage.html#persistent-storage",5695"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",5696"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster"5697],5698"document_ids": [56994273,57001005,57016561,5702872,570313680,570413718,57055625,570612465,57072081,57086205,570913719,57109150,57119160,571265625713],5714"answer": " To set a custom /tmp directory for a remote cluster in Ray, you can use the `RAY_TEMPDIR` environment variable to define where the temporary directory for the cluster files should be created. This directory should be accessible by the host, the outer container, and the inner container.\n\nHere is an example of how to set the `RAY_TEMPDIR` environment variable for a remote cluster:\n\n```\nRAY_TEMPDIR=\"/ray-mount\"\n```\n\nYou can then use this custom /tmp directory in your Ray application by referring to the `RAY_TEMPDIR` environment variable.\n\nNote: This setting can only be set per-job, i.e., in `ray.init()`. It cannot be set per-task or per-actor.\n\nFor more information about using a remote cluster in Ray, you can refer to the Ray documentation.",5715"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5716},5717{5718"question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",5719"sources": [5720"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",5721"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",5722"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",5723"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",5724"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",5725"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",5726"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",5727"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",5728"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",5729"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",5730"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",5731"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",5732"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",5733"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"5734],5735"document_ids": [57364960,57374960,57384385,57394959,5740511,57416632,5742872,57436396,57449160,57451775,57464384,57474961,57486360,574948235750],5751"answer": " Yes, if you set the `object_spilling_config[\"params\"][\"directory_path\"]` configuration in `ray.init()`, Ray will use the specified directory for object spilling instead of the default `/tmp`.\n\nHere's an example of how to set a custom directory for object spilling:\n\n```python\nimport json\nimport ray\n\nray.init(\n _system_config=json.dumps(\n {\n \"object_spilling_config\": json.dumps(\n {\"type\": \"filesystem\", \"params\": {\"directory_path\": \"/my/custom/spill/directory\"}}\n )\n }\n )\n)\n```\n\nThis will configure Ray to spill objects to the `/my/custom/spill/directory` directory instead of the default `/tmp`.",5752"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5753},5754{5755"question": "can you give me an example for *`--runtime-env-json`*",5756"sources": [5757"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",5758"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",5759"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",5760"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",5761"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",5762"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",5763"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",5764"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",5765"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5766"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",5767"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5768"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",5769"https://docs.ray.io/en/master/serve/api/index.html#serve-run",5770"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"5771],5772"document_ids": [57734308,577411458,57754319,57764253,57774661,577813720,57794712,57804716,578114217,57824713,578314236,57844292,578513990,5786138565787],5788"answer": " The `--runtime-env-json` option is used to specify a JSON-serialized runtime environment dictionary that will be passed to `ray.init()` as the default for deployments. Here's an example:\n\n```json\nserve run --address=ray://<head-node-ip-address>:10001 --runtime-env-json='{\"env_vars\": {\"MY_ENV_VAR\": \"my-value\"}, \"working_dir\": \"./project/src\", \"pip\": [\"requests\", \"chess\"]}' local_dev:graph\n```\n\nIn this example, the runtime environment includes environment variables, a working directory, and pip packages.",5789"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5790},5791{5792"question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",5793"sources": [5794"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5795"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",5796"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5797"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5798"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5799"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5800"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",5801"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5802"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",5803"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5804"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-autoscaler",5805"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",5806"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#configuring-the-autoscaler-sidecar-container",5807"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#programmatic-scaling"5808],5809"document_ids": [581013449,581114141,581214187,581314066,581414225,581514303,58165963,581713450,581814141,581913451,58206377,582114098,58225967,582363705824],5825"answer": " The value of `maxConcurrentReplicas` is not specified in the provided context. It is a parameter for configuring the autoscaling of a deployment, and its value would depend on the specific requirements of the application. The context provides information on various parameters that can be used in autoscaling configurations, but it does not give a specific value for `maxConcurrentReplicas`.",5826"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5827},5828{5829"question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",5830"sources": [5831"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5832"https://docs.ray.io/en/master/rllib/package_ref/learner.html#torchlearner-configurations",5833"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5834"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5835"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5836"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5837"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",5838"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5839"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5840"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5841"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5842"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5843"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.zoopt.ZOOptSearch.set_max_concurrency.html#ray-tune-search-zoopt-zooptsearch-set-max-concurrency",5844"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"5845],5846"document_ids": [584713451,584811140,584914303,585014066,585114225,585214187,585313561,585413450,585514302,585614186,585714065,585814224,58591340,5860141285861],5862"answer": " The value of `max_concurrent_queries` is not directly specified by `target_num_ongoing_requests_per_replica`. `max_concurrent_queries` is an independent configuration that sets the max number of pending queries in a single replica, while `target_num_ongoing_requests_per_replica` is a target value for how many ongoing requests are expected to run concurrently per replica, used by the autoscaler.",5863"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5864},5865{5866"question": "what is a `smoothing_factor`",5867"sources": [5868"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5869"https://docs.ray.io/en/master/tune/faq.html#what-are-hyperparameters",5870"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5871"https://docs.ray.io/en/master/tune/examples/includes/pb2_example.html#pb2-example",5872"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",5873"https://docs.ray.io/en/master/rllib/package_ref/utils.html#tensorflow-utilities",5874"https://docs.ray.io/en/master/rllib/package_ref/utils.html#numpy-utilities",5875"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.numpy.fc.html#ray-rllib-utils-numpy-fc",5876"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#twin-delayed-ddpg-td3",5877"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg",5878"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.AsyncSampler.html#ray-rllib-evaluation-sampler-asyncsampler",5879"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.AsyncHyperBandScheduler.html#ray-tune-schedulers-asynchyperbandscheduler",5880"https://docs.ray.io/en/master/data/examples/batch_training.html#run-batch-training-using-map-groups",5881"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.exploration.gaussian_noise.GaussianNoise.html#ray-rllib-utils-exploration-gaussian-noise-gaussiannoise"5882],5883"document_ids": [588413454,58851007,588613455,58873991,58881920,588911114,589011120,589113324,589211867,589311849,589412379,58951735,58967897,5897126755898],5899"answer": " A `smoothing_factor` is a hyperparameter that acts as a \"gain\" factor to amplify the response of the autoscaling algorithm in a system. It is used to speed up or slow down each upscaling or downscaling decision. For example, a higher `smoothing_factor` can be used to scale up resources quickly during high traffic volume, while a lower `smoothing_factor` can be used to make the application less sensitive to drops in traffic and scale down more conservatively.",5900"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5901},5902{5903"question": "What is the reason actors change their state to unhealthy?",5904"sources": [5905"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",5906"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",5907"https://docs.ray.io/en/master/serve/api/index.html#get-api-serve-deployments-status",5908"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5909"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",5910"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",5911"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",5912"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",5913"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",5914"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#other-possible-scenarios-that-trigger-a-new-raycluster-preparation",5915"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",5916"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.probe_unhealthy_workers.html#ray-rllib-evaluation-worker-set-workerset-probe-unhealthy-workers",5917"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#other-possible-scenarios-that-trigger-a-new-raycluster-preparation",5918"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"5919],5920"document_ids": [592113836,5922656,592314006,592414294,592514708,59264505,592711597,592813935,5929747,59305814,593114131,593212306,59335813,5934143065935],5936"answer": " Actors can become unhealthy in Ray if there's an error in the deployment's constructor, a crashed replica, or a general system or machine error. This information is provided in the context of the Ray framework and its state management for actors. The query about actors changing their state to unhealthy due to a particular reason like bad inputs or actions doesn't seem to be covered in the provided context.",5937"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5938},5939{5940"question": "How do I access logs for a dead node?",5941"sources": [5942"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",5943"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5944"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",5945"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",5946"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",5947"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",5948"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",5949"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",5950"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",5951"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",5952"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",5953"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",5954"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",5955"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused"5956],5957"document_ids": [5958492,59591006,5960493,5961662,5962739,5963740,5964512,59656631,596613894,5967695,5968681,5969445,5970680,597156825972],5973"answer": " Based on the context provided, you cannot access the logs from a dead node directly. However, you can fetch the logs of a particular entity (task, actor, etc.) using the State API. Also, you could get the node ID or node IP from `ray list nodes` and use that to list the logs from a head node.\n\nHere's an example:\n```python\nfrom ray.util.state import list_logs\nlist_logs(node_id=<NODE_ID>)\n```\nAlternatively, you can use the `ray logs` command with the `--node-id` flag to get a particular log file from a node.\n\nFor example:\n```bash\nray logs gcs_server.out --node-id <NODE_ID>\n```\nNote that these methods will only work for logs on alive nodes. If you need to access logs from a dead node, you may need to manually retrieve them from the node's filesystem or from any log storage that was configured for your Ray cluster.",5974"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5975},5976{5977"question": "What are the reasons for a node to change it\u2019s status to dead?",5978"sources": [5979"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",5980"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#configuring-iam-role-and-ec2-instance-profile",5981"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",5982"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",5983"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#rescheduling-bundles-on-a-dead-node",5984"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",5985"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.PlacementGroupState.html#ray-util-state-common-placementgroupstate",5986"https://docs.ray.io/en/master/ray-core/scheduling/index.html#nodeaffinityschedulingstrategy",5987"https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#handling-failures-and-node-preemption",5988"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#provider-cache-stopped-nodes",5989"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",5990"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",5991"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",5992"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure"5993],5994"document_ids": [59954524,59966475,5997554,599813869,59995098,600013898,6001724,60025147,60039228,60046283,60055976,6006467,60074790,6008138946009],6010"answer": " A node in a Ray cluster can change its status to dead for several reasons, including:\n\n1. Raylet failure: If the raylet process fails, the corresponding node will be marked as dead.\n2. Task killed due to low memory: If a worker node is running low on memory and a task is killed as a result, the node may be marked as dead.\n3. Head node failure: If the head node fails, it can be simulated by killing the head pod or the head node.\n4. Node affinity scheduling strategy failure: If a task or actor is scheduled to run on a particular node specified by its node ID, and the specified node doesn't exist or doesn't have the required resources, the task or actor will fail, and the node may be marked as dead.\n5. Provider cache stopped nodes: If nodes are stopped when the cluster scales down, they may be marked as dead.\n\nWhen a node changes its status to dead, Ray attempts to reschedule any pending tasks or actors on remaining, healthy nodes, and KubeRay attempts to restart the worker pod on another running node.",6011"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6012},6013{6014"question": "What are the reasons for spikes in node CPU utilization",6015"sources": [6016"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",6017"https://docs.ray.io/en/master/ray-observability/getting-started.html#view-the-resource-utilization",6018"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#adding-cpu-only-nodes-to-your-cluster",6019"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6020"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",6021"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",6022"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",6023"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",6024"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6025"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",6026"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",6027"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",6028"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",6029"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training"6030],6031"document_ids": [6032468,6033442,60349227,6035554,60366375,60376364,60383015,60393008,6040555,60413002,60423037,60435118,60443029,604530226046],6047"answer": " Spikes in node CPU utilization can be caused by:\n\n1. Increased logical resource demands from tasks and actors.\n2. Physical resource bottlenecks during preprocessing compute time.\n3. System processes running on the head node, like GCS, when deploying a large cluster.\n4. Autoscaling node launch overheads and configuration issues.\n5. High memory usage, which can lead to the operating system killing worker or raylet processes.\n\nIn your provided context, there are no specific reasons mentioned for spikes in CPU utilization. However, you should monitor resource utilization and memory usage to ensure they're within acceptable thresholds.",6048"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6049},6050{6051"question": "What AWS machine type is recommended to deploy a RayService on EKS?",6052"sources": [6053"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-aks-microsoft-azure",6054"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",6055"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",6056"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",6057"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",6058"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",6059"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",6060"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",6061"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",6062"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",6063"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-eks-cluster-aws",6064"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",6065"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#managed-kubernetes-services",6066"https://docs.ray.io/en/master/ray-core/cluster/index.html#where-can-i-deploy-ray-clusters"6067],6068"document_ids": [60695887,607013930,6071372,607213622,607313962,607413931,60755656,607613930,60776376,60785642,60795886,60806458,60815884,608246456083],6084"answer": " The recommended machine type to deploy a RayService on EKS (Amazon Elastic Kubernetes Service) is at least as good as an r5dn.16xlarge in terms of networking characteristics. Additionally, it is recommended to set the quantity of logical CPU resources to 0 on the head node to avoid scheduling additional tasks on it.",6085"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6086},6087{6088"question": "Is there a way to configure the session name generated by ray?",6089"sources": [6090"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",6091"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",6092"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",6093"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",6094"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",6095"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",6096"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",6097"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",6098"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",6099"https://docs.ray.io/en/master/cluster/cli.html#ray-attach",6100"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#developing-ray-serve-python-scripts-on-a-raycluster",6101"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",6102"https://docs.ray.io/en/master/ray-core/configure.html#configuring-ray",6103"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs"6104],6105"document_ids": [61064384,61074384,61084811,610913974,61106613,61114514,6112511,61134823,61144662,61155628,61165979,61176615,61184379,611966316120],6121"answer": " Yes, you can configure the session name generated by Ray by using the `namespace` argument in the `ray.init()` function. This argument allows you to set a custom namespace for the session. However, the session name will still include a timestamp and process ID by default. If you want to change the default format of the session name, you may need to modify the Ray source code or submit a feature request to the Ray team.",6122"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6123},6124{6125"question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",6126"sources": [6127"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",6128"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-2-submit-a-ray-job-to-the-raycluster-via-ray-job-submission-sdk",6129"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#python-sdk-api-reference",6130"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli",6131"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",6132"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",6133"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.get_job_info.html#ray-job-submission-jobsubmissionclient-get-job-info",6134"https://docs.ray.io/en/master/cluster/running-applications/job-submission/rest.html#ray-jobs-rest-api",6135"https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",6136"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",6137"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",6138"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#observe-and-debug-placement-groups",6139"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",6140"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.list_jobs.html#ray-job-submission-jobsubmissionclient-list-jobs"6141],6142"document_ids": [61436575,61445729,61456539,61466547,61476640,61486637,61496671,61506571,6151275,61526578,61536551,61545085,6155415,615666776157],6158"answer": " Yes, you can use the Python SDK to interact with the Ray Dashboard and get information about a job. Specifically, you can use the `ray.job_submission.JobSubmissionClient.get_job_info` method to get the latest status and other information associated with a job. This method takes a job ID as an argument and returns a `JobDetails` object containing information about the job.\n\nTo get a link to the Ray Dashboard for a given job, you can use the `ray.dashboard.modules.job.pydantic_models.JobDetails.driver_url` attribute of the `JobDetails` object returned by `get_job_info`. This attribute contains the URL of the Ray Driver, which you can use to access the Ray Dashboard and view information about the job.\n\nHere is an example of how you can use the Python SDK to get a link to the Ray Dashboard for a given job:\n\n```\nfrom ray.job_submission import JobSubmissionClient\n\n# Initialize the JobSubmissionClient with the address of the Ray cluster head node and the port used by the Ray Dashboard (8265 by default)\nclient = JobSubmissionClient(\"http://127.0.0.1:8265\")\n\n# Submit a job using the JobSubmissionClient\nsubmission_id = client.submit_job(entrypoint=\"sleep 1\")\n\n# Get the JobDetails object for the submitted job\njob_details = client.get_job_info(submission_id)\n\n# Get the URL of the Ray Driver from the JobDetails object\ndriver_url = job_details.driver_url\n\n# The driver_url can be used to access the Ray Dashboard and view information about the job\nprint(driver_url)\n```\n\nNote that the `driver_url` attribute will only be available if the job was submitted via the Ray Jobs API and the Ray Dashboard is running. If the job was not submitted via the Ray Jobs API or the Ray Dashboard is not running, the `driver_url` attribute will be `None`.",6159"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6160},6161{6162"question": "What may possible cause the node where this task was running crashed unexpectedly. This can happen if: (1) the instance where the node was running failed, (2) raylet crashes unexpectedly (OOM, preempted node, etc).",6163"sources": [6164"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",6165"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6166"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",6167"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6168"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.LocalRayletDiedError.html#ray-exceptions-localrayletdiederror",6169"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.TaskUnschedulableError.html#ray-exceptions-taskunschedulableerror",6170"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6171"https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#understanding-objectlosterrors",6172"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.WorkerCrashedError.html#ray-exceptions-workercrashederror",6173"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",6174"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6175"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",6176"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",6177"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance"6178],6179"document_ids": [61805129,6181555,61824658,6183554,61844858,61854830,6186557,61874559,61884906,61894524,6190555,61915118,6192597,619345196194],6195"answer": " The node where the task was running crashed unexpectedly due to the instance where the node was running failing or the raylet crashing unexpectedly, which can be caused by memory pressure (OOM) or other reasons. In this case, the node ran low on memory and Ray had to kill the worker that was running the task.",6196"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6197},6198{6199"question": "Do you know how to resolve (gcs_server) gcs_health_check_manager.cc:108: Health check failed for node? I observed that the node is still up and running.",6200"sources": [6201"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",6202"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",6203"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",6204"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",6205"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",6206"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",6207"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",6208"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",6209"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",6210"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",6211"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#getting-the-status-of-the-application",6212"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",6213"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",6214"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-9-upgrade-from-ray-serves-single-application-api-to-its-multi-application-api-without-downtime"6215],6216"document_ids": [62175638,621813553,62196505,622013870,62214508,62224522,622313871,62245709,62254515,622614740,622713947,622813889,6229467,623057056231],6232"answer": " The error message \"Health check failed for node\" suggests there's an issue with the health check of the worker node in Ray, a distributed computing framework. The node is still running, which means the problem is not a worker node failure but rather a failure in the health check process.\n\nThe context provided offers some insights into troubleshooting Ray issues, particularly those related to connecting to the head node (GCS). The most common cause of such issues is the worker node's inability to access the IP address or port of the head node. To debug this, you can use the 'ip route get' command on the worker node, targeting the head node's IP address.\n\nAnother possible cause for health check failures is a mismatch in Ray versions between the worker node and the head node. Ensure both nodes are running the same Ray version.\n\nLastly, there could be firewall settings preventing access to the head node. You can use tools like 'nmap' or 'nc' to check whether each port can be reached from a node.\n\nIn summary, to resolve the \"Health check failed for node\" issue:\n\n1. Check if the worker node can access the head node's IP address and port.\n2. Ensure both the worker node and the head node are running the same Ray version.\n3. Verify there are no firewall settings preventing access to the head node.",6233"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6234},6235{6236"question": "Do you need the DAGDriver to deploy a serve application using RayServe?",6237"sources": [6238"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",6239"https://docs.ray.io/en/master/serve/production-guide/index.html#working-example-fruitstand-application",6240"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-the-applications",6241"https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph",6242"https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",6243"https://docs.ray.io/en/master/serve/tutorials/gradio-dag-visualization.html#requirement-driver",6244"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",6245"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",6246"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",6247"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",6248"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",6249"https://docs.ray.io/en/master/serve/api/doc/ray.serve.run.html#ray-serve-run",6250"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",6251"https://docs.ray.io/en/master/serve/getting_started.html#getting-started"6252],6253"document_ids": [625413741,625513971,625613655,625713426,625813502,625914524,626013859,626113652,626213650,626313647,626413744,626514238,626613621,6267135676268],6269"answer": " No, you don't need the DAGDriver to deploy a Ray Serve application. The DAGDriver is used to route HTTP requests through your call graph and can be useful when working with deployment graphs, but it is not a requirement for deploying a Ray Serve application. You can deploy an application using the `serve deploy` command and the configuration file for your deployments, as shown in the context.",6270"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6271},6272{6273"question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",6274"sources": [6275"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",6276"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-ray-train-logger-callbacks",6277"https://docs.ray.io/en/master/workflows/basics.html#sub-task-results",6278"https://docs.ray.io/en/master/serve/api/index.html#serve-run",6279"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",6280"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",6281"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Application.html#ray-serve-application",6282"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#defining-an-application-builder",6283"https://docs.ray.io/en/master/serve/tutorials/batch.html#define-the-deployment",6284"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#walkthrough",6285"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",6286"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",6287"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",6288"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-9-create-a-rayservice-yaml-file"6289],6290"document_ids": [629113844,62929141,62939670,629413990,62955789,62965678,629714037,629813813,629914586,63008078,630110281,630210168,630313846,630459946305],6306"answer": " To provide the import path for a simple RayServe deployment, you should use a local or remote YAML file containing a runtime environment that specifies the import path. The import path should include the name of the directory, Python file, and the name of the Ray Serve application variable. For example, \"mobilenet.mobilenet:app\". You can also specify the import path when using the `serve run` CLI command or in a config file. The runtime environment should also include any necessary packages for the Ray workers. You can omit the runtime\\_env argument if you have all the necessary packages installed on each node in your cluster.",6307"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6308},6309{6310"question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",6311"sources": [6312"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",6313"https://docs.ray.io/en/master/ray-contribute/docs.html#what-to-contribute",6314"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",6315"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",6316"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",6317"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",6318"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",6319"https://docs.ray.io/en/master/serve/develop-and-deploy.html#deploy-ray-serve-in-production",6320"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",6321"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",6322"https://docs.ray.io/en/master/cluster/kubernetes/index.html#about-kuberay",6323"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",6324"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",6325"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide"6326],6327"document_ids": [63285751,6329809,63305760,63315752,63326144,63335647,63345658,633513441,633613926,63375763,63385660,63396162,63405742,634157546342],6343"answer": " Yes, the context includes an example of using RayJob in Kuberay. You can download the RayJob configuration file with `curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml` and create a RayJob with `kubectl apply -f ray_v1alpha1_rayjob.yaml`. The RayJob provides Kubernetes-native support for Ray clusters and jobs, allowing you to define a Ray cluster and job in a Kubernetes config and automatically delete the cluster once the job is finished.",6344"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6345},6346{6347"question": "Does Ray metrics have to be exported via an actor?",6348"sources": [6349"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",6350"https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",6351"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",6352"https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",6353"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",6354"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",6355"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",6356"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",6357"https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",6358"https://docs.ray.io/en/master/cluster/metrics.html#scraping-metrics",6359"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",6360"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",6361"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",6362"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors"6363],6364"document_ids": [6365503,6366403,6367656,6368438,63695573,637011596,637114707,63724503,6373403,63745581,637514723,63765443,63774200,6378146806379],6380"answer": " No, Ray metrics do not have to be exported via an actor. While the provided example shows an actor exporting metrics, metrics can also be exported using the `ray.util.metrics` module without an actor. The method of exporting metrics depends on the specific use case and application design.",6381"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6382},6383{6384"question": "how can I avoid objects not getting spilled?",6385"sources": [6386"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",6387"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",6388"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",6389"https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",6390"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",6391"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6392"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6393"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#out-of-core-data-processing",6394"https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",6395"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6396"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#cluster-mode",6397"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#stats",6398"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",6399"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node"6400],6401"document_ids": [64026374,64031006,64044959,64054447,640614748,64074960,64084963,640914485,64101247,64114962,64124968,64134969,64144223,641549616416],6417"answer": " To avoid objects not getting spilled and thus causing performance problems, you can do the following:\n\n1. Use NVMe SSDs or other high-performance storage for storing objects.\n2. Configure the directory where objects are spilled to by using the `object_spilling_config` parameter in the `ray.init()` function.\n3. If using an HDD, specify a large buffer size (> 1MB) to reduce IO requests during spilling.\n4. In multi-node clusters, enable object spilling by specifying the `object_spilling_config` when starting the head node.\n\nAdditionally, to optimize performance, it is recommended to use an SSD instead of an HDD when using object spilling for memory-intensive workloads.",6418"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6419},6420{6421"question": "what\u2019s ray core",6422"sources": [6423"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",6424"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner.LearnerSpec.html#ray-rllib-core-learner-learner-learnerspec",6425"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",6426"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",6427"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",6428"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#want-to-learn-more",6429"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",6430"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",6431"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",6432"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",6433"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",6434"https://docs.ray.io/en/master/ray-core/examples/overview.html#basic-examples",6435"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-for-developers",6436"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework"6437],6438"document_ids": [64394662,644012726,64414481,64424673,64434811,64445444,64455656,64466835,6447367,644813974,6449246,64505383,6451646,64523716453],6454"answer": " Ray Core is a part of Ray, an open-source distributed computing framework. It provides primitives like tasks, actors, and objects for building and scaling distributed applications. It allows you to turn your functions and classes into Ray tasks and actors, and work with Ray objects. Ray Core is the foundation of Ray's unified compute framework, which includes a set of open-source Python libraries for machine learning applications.",6455"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6456},6457{6458"question": "Does ray support cron job",6459"sources": [6460"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",6461"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",6462"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",6463"https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",6464"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",6465"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",6466"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",6467"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-api-server",6468"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",6469"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",6470"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",6471"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide",6472"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",6473"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr"6474],6475"document_ids": [647613974,64775754,64784662,64799634,64806637,64814811,64826208,648313474,64845635,64856638,64865656,64875784,64886530,64894106490],6491"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. Ray Workflows recommends using Kubernetes' job scheduler or any other external job scheduler to schedule recurring workflows. Ray Jobs API provides CLI tools, Python SDK, and a REST API for submitting jobs to a Ray cluster for execution. Ray job logs can be followed using the `ray job logs` command. Worker logs (stdout and stderr) are captured in the Worker stdout and stderr and can be found in the logging directory and file structure.",6492"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6493},6494{6495"question": "can you give me the dependencies list for api read_images?",6496"sources": [6497"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",6498"https://docs.ray.io/en/master/serve/deploy-many-models/model-multiplexing.html#model-multiplexing",6499"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",6500"https://docs.ray.io/en/master/data/working-with-images.html#reading-images",6501"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",6502"https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",6503"https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",6504"https://docs.ray.io/en/master/installation.html#docker-source-images",6505"https://docs.ray.io/en/master/serve/tutorials/object-detection.html#serving-an-object-detection-model",6506"https://docs.ray.io/en/master/serve/http-guide.html#list-of-built-in-adapters",6507"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset",6508"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",6509"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning",6510"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data"6511],6512"document_ids": [65137361,651413666,65157360,65166720,651710191,6518306,651914587,652081,652114472,652213511,65238041,65246779,65256011,652679096527],6528"answer": " The dependencies for the `api read_images` are:\n\n1. `ray` - the Ray library is required to use the `read_images` function.\n2. `pyarrow.fs` (optional) - this library is required if reading images from a private bucket.\n3. `os`, `requests`, `io`, `PIL`, `numpy` - these libraries are used in the example function `read_images` for loading raw images.\n\nNote that the `read_images` function in the example code is a user-defined function that reads images from a specific URL, and it may have additional dependencies based on the implementation. The `ray.data.read_images` function itself does not require any additional dependencies beyond `ray`.",6529"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6530},6531{6532"question": "how do I kill a specific serve replica",6533"sources": [6534"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",6535"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",6536"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",6537"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",6538"https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-deployments",6539"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",6540"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",6541"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",6542"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",6543"https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-applications",6544"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#replica-health-checking",6545"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",6546"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",6547"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"6548],6549"document_ids": [655013912,655113912,655213911,655313717,655414007,655513908,655613448,655713892,655813915,655914015,656013865,656113916,656213905,6563139016564],6565"answer": " To kill a specific serve replica, you can use the following steps:\n\n1. Get the handle to the replica using `ray.get_actor()` function with the NAME of the replica from the output of `ray list actors` command.\n2. Use `ray.kill()` function to kill the replica, passing the replica handle as an argument. You can also specify `no_restart=True` to prevent the replica from automatically restarting.\n\nHere is an example:\n```python\nimport ray\n\n# Get the handle to the replica\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\n\n# Kill the replica\nray.kill(replica_handle, no_restart=True)\n```\nNote: While the replica is restarted, the other replicas can continue processing requests. Eventually, the replica restarts and continues serving requests.",6566"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6567}6568]6569}6570