llm-applications
6215 строк · 511.9 Кб
1{2"config": {3"experiment_name": "num-chunks-13",4"chunk_size": 700,5"chunk_overlap": 50,6"num_chunks": 13,7"embedding_model_name": "thenlper/gte-base",8"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1",9"temperature": 0.0,10"max_context_length": 32768,11"system_content": "Answer the query using the context provided. Be succinct.",12"assistant_content": "",13"docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",14"experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",15"references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",16"num_samples": 17717},18"results": [19{20"question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",21"sources": [22"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",23"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",24"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",25"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",26"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",27"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches",28"https://docs.ray.io/en/master/data/working-with-tensors.html#transforming-tensor-data",29"https://docs.ray.io/en/master/data/working-with-tensors.html#batches-of-variable-shape-tensors",30"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",31"https://docs.ray.io/en/master/data/working-with-tensors.html#batches-of-fixed-shape-tensors",32"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.take_batch.html#ray-data-dataset-take-batch",33"https://docs.ray.io/en/master/data/working-with-pytorch.html#pytorch-dataloader",34"https://docs.ray.io/en/master/data/overview.html#ray-data-overview"35],36"document_ids": [376994,386965,396790,406786,419180,426979,436777,446775,4514727,466774,477824,486985,49684150],51"answer": " When using `map_batches`, ensure that the output batch can fit into memory by setting a small enough batch size. You can configure the batch format to be either NumPy ndarrays or pandas DataFrames by specifying `batch_format` in `map_batches()`. If you're encountering issues with type conversions, make sure your function returns the correct format. For example, if you want to increase the brightness of an image batch represented as NumPy ndarrays, you can do:\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\nAlso, remember that `map_batches` uses tasks by default, so you don't need to specify a `map_batches` function with the `tasks.execute` call.",52"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"53},54{55"question": "How does autoscaling work in a Ray Serve application?",56"sources": [57"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",58"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",59"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",60"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",61"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",62"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",63"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",64"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",65"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#load-metrics-are-based-on-application-semantics",66"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",67"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",68"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",69"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters"70],71"document_ids": [7213471,735559,746451,755953,765955,775969,7814726,7913447,805971,8113448,826453,835975,84596385],86"answer": " Ray Serve's autoscaling feature automatically adjusts the number of replicas in a deployment based on its load. The Ray autoscaler, which runs on the head node, increases or decreases the number of worker nodes based on the resource demands of the Ray workload. It reacts to task and actor resource requests, not application metrics or physical resource utilization. Autoscaling can be configured using the Ray cluster launcher or on Kubernetes. It's recommended to start with non-autoscaling clusters if you're new to Ray. The Ray Serve Autoscaler is an application-level autoscaler that sits on top of the Ray Autoscaler, requesting replica actors based on demand. The autoscaler considers logical resource requests for scaling and adds nodes to satisfy resource demands in the queue. It also removes nodes after they become idle. The Ray Autoscaler complements the Kubernetes Cluster Autoscaler when using Ray on Kubernetes.",87"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"88},89{90"question": "how do I get the address of a ray node",91"sources": [92"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",93"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",94"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",95"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",96"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",97"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",98"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",99"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",100"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",101"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",102"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",103"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-1-execute-a-ray-job-in-the-head-pod",104"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node"105],106"document_ids": [1074305,1086367,1094817,1105587,1114824,1126708,1136465,1146537,115493,1166605,1176839,1185728,119754120],121"answer": " To get the address of a Ray node, you can look at the output of the `ray.nodes()` function, which provides information about the nodes in the Ray cluster. The address of a Ray node can be obtained by combining the `NodeManagerAddress` and `MetricsExportPort` of a node.\n\nFor example, if the output of `ray.nodes()` contains the following information:\n```json\n{\n 'NodeID': '2691a0c1aed6f45e262b2372baf58871734332d7',\n 'Alive': True,\n 'NodeManagerAddress': '192.168.1.82',\n 'NodeManagerHostname': 'host-MBP.attlocal.net',\n 'NodeManagerPort': 58472,\n 'ObjectManagerPort': 52383,\n 'ObjectStoreSocketName': '/tmp/ray/session_2020-08-04_11-00-17_114725_17883/sockets/plasma_store',\n 'RayletSocketName': '/tmp/ray/session_2020-08-04_11-00-17_114725_17883/sockets/raylet',\n 'MetricsExportPort': 64860,\n 'alive': True,\n 'Resources': {'CPU': 16.0, 'memory': 100.0, 'object_store_memory': 34.0, 'node:192.168.1.82': 1.0}\n}\n```\nThen the address of the Ray node would be `192.168.1.82:64860`.",122"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"123},124{125"question": "Does Ray support NCCL?",126"sources": [127"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",128"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",129"https://docs.ray.io/en/master/installation.html#installing-ray",130"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray",131"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",132"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",133"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",134"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#the-ray-version",135"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#developing-ray-serve-python-scripts-on-a-raycluster",136"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice",137"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",138"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",139"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#image"140],141"document_ids": [14214403,1434469,14459,14514541,146284,1476597,1486208,1495922,1505979,1515780,1529668,1536287,1545931155],156"answer": " Yes, Ray supports NCCL (NVIDIA Collective Communications Library) as one of the optional high-performance communication backends in its collective communication library. It enables more efficient out-of-band communication between Ray actor and task processes on both distributed CPUs and GPUs.",157"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"158},159{160"question": "Is Ray integrated with DeepSpeed?",161"sources": [162"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",163"https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",164"https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",165"https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",166"https://docs.ray.io/en/master/tune/api/integration.html#external-library-integrations-for-ray-tune",167"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",168"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.html#ray-train-lightning-raydeepspeedstrategy",169"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#gpu-profiling",170"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.html#ray-train-lightning-raydeepspeedstrategy",171"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",172"https://docs.ray.io/en/master/ray-overview/index.html#overview",173"https://docs.ray.io/en/master/train/examples/deepspeed/deepspeed_example.html#deepspeed-zero-3-distributed-training-example-with-ray-train",174"https://docs.ray.io/en/master/train/deepspeed.html#training-with-deepspeed"175],176"document_ids": [1779668,1788356,1798101,1808135,1811306,1828881,1838780,184645,1858784,1869373,187363,1889540,1898059190],191"answer": " Yes, Ray is integrated with DeepSpeed through the Ray Train module. Specifically, DeepSpeed can be used with Ray Train for distributed training, as shown in the examples provided in the context. Additionally, there is a RayTrainWorker warning in the context indicating that the NCCL backend in DeepSpeed is not yet implemented in Ray.",192"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"193},194{195"question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",196"sources": [197"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",198"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",199"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",200"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",201"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example",202"https://docs.ray.io/en/master/ray-core/walkthrough.html#running-a-task",203"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",204"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",205"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",206"https://docs.ray.io/en/master/ray-core/actors/task-orders.html#asynchronous-or-threaded-actor",207"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",208"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",209"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle"210],211"document_ids": [2124550,2134595,2144564,2154208,2165197,2174703,2184886,2194567,2205236,2214519,2225248,2234553,22414277225],226"answer": " If you use `await` to wait for a Ray future like `await x.remote()`, it will convert the Ray future into an asyncio future using `asyncio.wrap_future()`. This allows you to await on Ray futures in existing concurrent applications that use asyncio. However, it's important to note that you cannot use `await` directly with remote tasks, only with Ray actors. Also, remember to delay calling `ray.get()` until you have spawned all remote functions, to allow them to run in parallel in the background.",227"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"228},229{230"question": "How would you compare Spark, Ray, Dask?",231"sources": [232"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",233"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",234"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",235"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",236"https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",237"https://docs.ray.io/en/master/ray-overview/ray-libraries.html#the-ray-ecosystem",238"https://docs.ray.io/en/master/ray-more-libs/index.html#more-ray-ml-libraries",239"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",240"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#callbacks",241"https://docs.ray.io/en/master/data/api/api.html#ray-data-api",242"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",243"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",244"https://docs.ray.io/en/master/ray-overview/ray-libraries.html#the-ray-ecosystem"245],246"document_ids": [24714472,24814484,2496859,2506861,25114653,252216,25314673,25414506,25514514,2567102,25714476,25814479,259210260],261"answer": " Spark, Ray, and Dask are all distributed computing frameworks in Python, each with its own strengths. Apache Spark is a mature and feature-rich framework for large-scale data processing, particularly excelling in ETL (Extract, Transform, Load) tasks and SQL queries. Dask is designed for parallel analytics and scientific computing workloads, providing big data collections that mimic NumPy and Pandas APIs, making it easier to scale up existing code. Ray is a high-performance distributed execution system, geared towards production, with a focus on general-purpose computing, including machine learning and AI workloads.\n\nDask can be integrated with Ray to leverage Ray's fast and fault-tolerant task execution system while using Dask's familiar APIs. This combination allows you to build data analyses using Dask's collections and execute them on a Ray cluster. However, Dask-on-Ray is not expected to achieve the same performance as using Ray directly.\n\nIn terms of batch processing workloads, Ray Data handles many of the same tasks as Apache Spark but with a streaming paradigm that is better suited for GPU workloads for deep learning inference. For a more detailed performance comparison between Ray Data and Apache Spark, you can refer to the Offline Batch Inference: Comparing Ray, Apache Spark, and SageMaker article.\n\nRayDP (Spark on Ray) combines Spark and Ray clusters, making it easy to do large-scale data processing using the PySpark API and seamlessly use that data to train models using TensorFlow and PyTorch. This can be useful when you want to use Spark for data processing and Ray for model training in the same application without having two different clusters.\n\nIn summary, Spark, Ray, and Dask all have their unique features and use cases. Dask can be integrated with Ray for a seamless experience when working with familiar NumPy and Pandas APIs, while Ray Data offers better performance for GPU workloads compared to Apache Spark. RayDP allows you to combine Spark and Ray clusters for large-scale data processing and model training.",262"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"263},264{265"question": "why would ray overload a node w/ more task that the resources allow ?",266"sources": [267"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",268"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",269"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case",270"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",271"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",272"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",273"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",274"https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks",275"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",276"https://docs.ray.io/en/master/ray-core/miscellaneous.html#miscellaneous-topics",277"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",278"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#custom-resources",279"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides"280],281"document_ids": [2825231,2835230,2845232,2855115,2865104,2875108,2885112,2894199,290563,2914283,2926537,2935106,2944676295],296"answer": " Ray's scheduler limits the number of concurrently running tasks based on the available resources, such as CPUs and memory, to prevent overloading a node and causing issues like out-of-memory errors. If a node is overloaded with more tasks than its resources allow, it may be necessary to reduce the number of concurrent tasks or increase the resources available to each task. This can be done by specifying resource requirements for each task or actor, such as `ray::Task(MyFunction).SetResource(\"CPU\", 1.0).SetResource(\"GPU\", 1.0).SetResource(\"special_hardware\", 1.0).Remote();` in C++. It is important to note that resource requirements are logical and do not impose limits on actual physical resource usage. It is the user's responsibility to ensure that tasks or actors use no more resources than specified via resource requirements.",297"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"298},299{300"question": "when should I use Ray Client?",301"sources": [302"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",303"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",304"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",305"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",306"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",307"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",308"https://docs.ray.io/en/master/ray-air/getting-started.html#pick-and-choose-your-own-libraries",309"https://docs.ray.io/en/master/ray-core/advanced-topics.html#advanced-topics",310"https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure",311"https://docs.ray.io/en/master/ray-overview/use-cases.html#ml-platform",312"https://docs.ray.io/en/master/ray-air/getting-started.html#design-principles",313"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3",314"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime"315],316"document_ids": [3176597,3186589,3196583,3205636,3216593,3226591,3239815,3244196,3259809,326152,3279813,328534,3294186330],331"answer": " You should use Ray Client when you want to connect an interactive Python shell to a remote cluster. It is suitable for interactive development on machine learning projects, but it is not recommended for ML workloads like Ray Tune or Ray Train. Use the command `ray.init(\"ray://<head_node_host>:10001\")` to connect your shell to the cluster. Note that Ray Client requires the pip package `ray[client]`.",332"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"333},334{335"question": "how to scatter actors across the cluster?",336"sources": [337"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",338"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",339"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",340"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",341"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",342"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",343"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",344"https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",345"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",346"https://docs.ray.io/en/master/ray-observability/reference/api.html#list-apis",347"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",348"https://docs.ray.io/en/master/ray-core/api/utility.html#utility",349"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference"350],351"document_ids": [3525145,3535195,3545441,3557978,35613515,3574680,35813516,359659,360673,361660,3627976,3634516,3646903365],366"answer": " To scatter actors across the cluster, you can create multiple actors with the `scheduling_strategy` set to \"SPREAD\". Here's an example:\n\n```python\n@ray.remote(num_cpus=1, scheduling_strategy=\"SPREAD\")\nclass SpreadActor:\n pass\n\nactors = [SpreadActor.remote() for _ in range(10)]\n```\n\nThis will create 10 actors and spread them across the available nodes in the cluster. The \"SPREAD\" strategy tries to distribute tasks or actors evenly among the nodes in the cluster.",367"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"368},369{370"question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",371"sources": [372"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",373"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",374"https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",375"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",376"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",377"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",378"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",379"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",380"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",381"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#running-on-a-cluster",382"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",383"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",384"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster"385],386"document_ids": [3875681,3884816,38914683,3906591,39113822,3924817,3936599,3946605,3954264,396578,3974266,3986163,3996484400],401"answer": " The connection refused error you're encountering when running `ray debug` on your remote Ray cluster could be due to a few reasons:\n\n1. The dashboard, dashboard agent, or GCS (Global Control Store) may not have started up yet. These components may take a few seconds to start after the head Pod is ready.\n2. You might be trying to connect to the Ray cluster too soon after starting it. It's recommended to wait for 1 minute before attempting to connect again.\n3. The environment variables or Python packages on your remote cluster might not match your local machine, causing connection issues.\n\nTo troubleshoot, you can:\n\n1. Wait for 1 minute and then try running `ray debug` again.\n2. Verify that the environment variables and Python packages on your remote cluster are correctly configured and match your local machine.\n3. Check the Ray cluster logs for any error messages that might indicate the cause of the connection issue.\n\nFor more information on setting up and connecting to a remote Ray cluster, refer to the documentation: <https://docs.ray.io/en/master/cluster/getting-started.html>",402"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"403},404{405"question": "How does Ray AIR set up the model to communicate gradient updates across machines?",406"sources": [407"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",408"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",409"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",410"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",411"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",412"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",413"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",414"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary",415"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#how-it-works",416"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#asynchronous-parameter-server-training",417"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",418"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",419"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a"420],421"document_ids": [4229845,42310019,4249820,425156,426374,427337,428183,4299867,43010150,4315338,4329887,43310047,43410078435],436"answer": " Ray AIR sets up the model to communicate gradient updates across machines using PyTorch DDP. The model is initialized inside a function called `trainer_init_per_worker`, which is passed to the `TransformersTrainer` and runs on every Ray worker. This allows for data-parallel training by sharding the training data and communicating gradient updates across machines.",437"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"438},439{440"question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",441"sources": [442"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",443"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",444"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",445"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",446"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",447"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",448"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice",449"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",450"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",451"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",452"https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",453"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",454"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve"455],456"document_ids": [45713980,45813622,45913626,46013596,46113598,46213614,4635739,4645785,46513619,4666597,467145,46813597,4692774470],471"answer": " Ray Serve is a scalable and framework-agnostic model serving library that allows you to serve various types of models and business logic. It is open-source and can be deployed on your infrastructure, providing scalability and flexibility. Unlike Modal or Seldon, Ray Serve primarily focuses on model serving and lacks some ML platform functionality like model lifecycle management and visualization. However, Ray Serve offers features like model composition, autoscaling, and integration with business logic, making it a powerful tool for building production-ready ML applications. Additionally, Ray Serve is not tied to any specific framework or cloud provider, allowing you to avoid lock-in.",472"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"473},474{475"question": "How do I deploy an LLM workload on top of Ray Serve?",476"sources": [477"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",478"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",479"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",480"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",481"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",482"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",483"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",484"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",485"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",486"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",487"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",488"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",489"https://docs.ray.io/en/master/cluster/vms/user-guides/community/lsf.html#deploying-on-lsf"490],491"document_ids": [49213618,49314255,49413416,49513731,49613732,49710395,49813962,4995636,50013471,50113622,50213930,50313980,5046489505],506"answer": " To deploy a large language model (LLM) workload on Ray Serve, follow these steps:\n\n1. Write your LLM application as a single Python module, including all necessary steps like prompt preprocessing, vector database lookups, LLM API calls, and response validation.\n2. Use the `@serve.deployment` decorator on a Python class (or function for simple use cases) to define a deployment.\n3. Bind the deployment with optional arguments to the constructor.\n4. Configure your deployment to scale out and assign replica resources such as CPUs and GPUs.\n5. Deploy your Ray Serve application to production on a Ray cluster using the Ray Serve CLI and a config file.\n\nFor more details, refer to the provided context, especially the sections on 'Deployments', 'Configuring a Deployment', 'Ray Serve Autoscaling', and 'Deploy on VM'.",507"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"508},509{510"question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",511"sources": [512"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#fine-tuning-the-model-with-ray-train-a-name-train-a",513"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-size",514"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#task-based-batch-prediction",515"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",516"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#initialize-generation-pipeline",517"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#observe-and-debug-placement-groups",518"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",519"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",520"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#compute-instances",521"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",522"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-optimize-xgboost-memory-usage",523"https://docs.ray.io/en/master/train/benchmarks.html#gpu-image-training",524"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training"525],526"document_ids": [52710449,5286792,5295463,530855,5319486,5325084,533853,53410749,5359339,5365070,5377990,5388199,5393280540],541"answer": " The exact amount of memory required for setting the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d depends on various factors such as batch size, available GPU memory, and the specific training and inference tasks. However, considering the information provided, it is recommended to use a GPU with at least 16 GB of memory and to leverage DeepSpeed to optimize memory usage. The example uses DeepSpeed ZeRO Stage 3 to load large models without running out of memory.\n\nTo give you an idea, the usage example provided shows that the system has 2.00GiB of object\\_store\\_memory. However, this might not be sufficient for training the \u201cEleutherAI/gpt-j-6B\u201d model. You should ensure that your GPU has enough memory to hold the model and the dataset. Additionally, you may need to adjust the batch size to fit the model into the GPU memory.\n\nIn summary, to train the \u201cEleutherAI/gpt-j-6B\u201d model, use a GPU with at least 16 GB of memory and DeepSpeed to optimize memory usage. Adjust the batch size according to your GPU memory to prevent out-of-memory errors.",542"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"543},544{545"question": "How do I log the results from multiple distributed workers into a single tensorboard?",546"sources": [547"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",548"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",549"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",550"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",551"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",552"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-concurrent-tune-trials",553"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#batch-prediction-with-gpus",554"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#logging-and-outputs-in-tune",555"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-tune-results-on-a-single-node",556"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",557"https://docs.ray.io/en/master/ray-core/examples/plot_example-a3c.html#benchmarks-and-visualization",558"https://docs.ray.io/en/master/data/user-guide.html#user-guides",559"https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#how-to-obtain-and-aggregate-results-from-different-workers"560],561"document_ids": [5622411,5632354,56410680,5659682,5669660,5672380,5685471,5692351,5702383,571231,5725454,5736835,5749264575],576"answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use TensorBoardX to output TensorBoard files during the training process. After running the experiment, you can visualize the results by specifying the output directory of your results using the command `tensorboard --logdir=<output_directory>`. If you're running Ray on a remote multi-user cluster, you can make sure TensorBoard is able to write to the tmp directory by setting the TMPDIR environment variable.\n\nIn a distributed setup with multiple workers, TensorBoard will show the aggregated results from all the workers. However, note that by default, Ray Tune logs results for TensorBoard, CSV, and JSON formats, and if you need to log something lower level like model weights or gradients, you can use Trainable Logging.\n\nAdditionally, if you want to calculate optimization metrics besides accuracy and loss, such as recall, precision, Fbeta, etc., you can use third-party libraries or distributed primitives of your machine learning framework to report metrics from multiple workers. Ray Train natively supports TorchMetrics for distributed, scalable PyTorch models.",577"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"578},579{580"question": "how do you config SyncConfig for a Ray AIR job?",581"sources": [582"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",583"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",584"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",585"https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration",586"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",587"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.html#ray-train-runconfig",588"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.syncer.html#ray-train-syncconfig-syncer",589"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_artifacts.html#ray-train-syncconfig-sync-artifacts",590"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningConfigBuilder.checkpointing.html#ray-train-lightning-lightningconfigbuilder-checkpointing",591"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_period.html#ray-train-syncconfig-sync-period",592"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_timeout.html#ray-train-syncconfig-sync-timeout",593"https://docs.ray.io/en/master/tune/api/syncing.html#syncing-in-tune-train-syncconfig",594"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed"595],596"document_ids": [59710910,5988441,5991443,6001138,6011782,6028426,6031678,6042116,6058252,6061761,6071852,6081137,60910904610],611"answer": " To configure `SyncConfig` for a Ray AIR job, you can create a `RunConfig` object and set the `sync_config` parameter to a `SyncConfig` object. Here's an example:\n```python\nfrom ray import train\n\nsync_config = train.SyncConfig(\n syncer='ray.train._internal.syncer.dummy.DummySyncer',\n sync_period=300,\n sync_timeout=1800,\n sync_artifacts=False,\n sync_artifacts_on_checkpoint=True,\n sync_on_checkpoint=False,\n)\n\nrun_config = train.RunConfig(\n name='my_job',\n storage_path='/path/to/storage',\n callbacks=[...],\n stop={...},\n failure_config={...},\n checkpoint_config={...},\n progress_reporter=...,\n verbose=...,\n log_to_file=...,\n local_dir=...,\n sync_config=sync_config,\n)\n```\nIn this example, we create a `SyncConfig` object with some optional parameters set. You can adjust these parameters as needed for your use case. Then, we pass the `SyncConfig` object to the `RunConfig` constructor as the `sync_config` parameter.\n\nNote that synchronization of 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.",612"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"613},614{615"question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",616"sources": [617"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",618"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",619"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",620"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",621"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",622"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",623"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-logs",624"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",625"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",626"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-1-check-kuberay-operators-logs-for-errors",627"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-component-logs",628"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",629"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr"630],631"document_ids": [632415,633681,634444,6355764,636445,6376443,6386576,6394426,6405860,6415663,642515,6435880,644410645],646"answer": " To quickly narrow down the root cause of a failed Ray job, you can access all the logs and follow these steps:\n\n1. Use `ray job logs <job_id>` to get the logs of the specific job. You can also specify options like `--address`, `-f`, and `--verify` if needed.\n2. If the Ray Job is submitted by the Job API, the Job logs are available from the Dashboard with the format `job-driver-<job_submission_id>.log`.\n3. Task and Actor logs (Worker logs) are accessible from the Task and Actor table view. Click the \u201cLog\u201d button to see the `stdout` and `stderr` logs containing the output emitted from Tasks and Actors.\n4. Use the provided search functionality in the Logs view to help you find specific log messages.\n5. If you execute the Driver directly on the Head Node of the Ray Cluster or run with Ray Client, the Driver logs are not accessible from the Dashboard. In this case, view the terminal or Jupyter Notebook output to view the Driver logs.\n\nRemember that Ray does not provide a native storage solution for log data, so users need to manage the lifecycle of the logs by themselves.",647"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"648},649{650"question": "How do I specify how many GPUs a serve deployment needs?",651"sources": [652"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",653"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",654"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",655"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#resource-management-cpus-gpus",656"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#set-up-autoscaling-and-resource-allocation",657"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",658"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",659"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",660"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",661"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",662"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",663"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",664"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema"665],666"document_ids": [66713458,66813575,66913456,67013735,67113443,67214121,67314124,67413731,67514299,67614183,67714221,67814137,67914024680],681"answer": " To specify the number of GPUs a Serve deployment needs, you can use the `ray_actor_options` parameter in the `@serve.deployment` decorator and set the `\"num_gpus\"` key to the desired number of GPUs. This can be a fraction if you want to pack multiple replicas together on a machine with fewer GPUs than replicas. Here is an example:\n```\n@serve.deployment(ray_actor_options={\"num_gpus\": 1.5})\ndef func_2(*args):\n return do_something_with_my_gpu()\n```\nIn this example, each replica of the deployment will be allocated 1.5 GPUs. The same can be done to multiplex over CPUs, using the `\"num_cpus\"` key. These resource parameters are optional, so you can omit them if you don't need to specify resource requirements.",682"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"683},684{685"question": "One of my worker nodes keeps dying on using TensorflowTrainer with around 1500 workers, I observe SIGTERM has been received to the died node's raylet. How can I debug this?",686"sources": [687"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",688"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",689"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",690"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",691"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",692"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",693"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",694"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",695"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",696"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",697"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",698"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",699"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed"700],701"document_ids": [7025129,70310812,7049410,70510826,7069409,7079406,7089407,7099408,71010819,71110832,71210868,71310806,71410809715],716"answer": " The worker node might be dying due to memory pressure, which causes Out-Of-Memory (OOM) errors. To debug, you can check the raylet logs for more information about the workers killed on the node using the command: `ray logs raylet.out -ip 172.31.183.172`. Additionally, you can consider adjusting settings to reduce memory consumption and add `get_accelerator().empty_cache()` calls in your training loop to ensure that all ranks flush their caches at the same time. The high number of pytorch allocator cache flushes also indicates high memory pressure.",717"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"718},719{720"question": "what are the possible reasons for nodes dying in a cluster?",721"sources": [722"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",723"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",724"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",725"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",726"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",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/serve/production-guide/fault-tolerance.html#worker-node-failure",729"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#cluster-setting",730"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",731"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",732"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",733"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#worker-node-failure",734"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy"735],736"document_ids": [7375639,7384387,73913898,7404391,7414393,7429644,74313889,7449337,74513869,74613894,747554,7484389,7495129750],751"answer": " Based on the provided context, there are several possible reasons for nodes dying in a cluster:\n\n1. Overloading the head node with too many simultaneous connections, which can be caused by autoscaling or user action.\n2. Head node failure, which results in the failure of the entire Ray cluster.\n3. Raylet failure, which marks the corresponding node as dead and treats it as a new raylet/node to the Ray cluster.\n4. Worker node failure, which results in the failure of all running tasks and actors on that node and the loss of objects owned by worker processes of this node.\n5. Memory pressure or running low on memory, which can cause tasks to be killed due to OOM (Out of Memory) errors.\n\nTo tolerate these failures, Ray cluster needs to make GCS fault-tolerant so that when a new head node is started, all the cluster-level data is still available. Similarly, when a worker node fails, the actors running on it also fail, and Serve detects that the actors have failed and attempts to respawn the actors on the remaining, healthy nodes. KubeRay detects that the node itself has failed, so it attempts to restart the worker pod on another running node and brings up a new healthy node to replace it. Once the node comes up, if the pod is still pending, it can be restarted on that node. Similarly, Serve can also respawn any pending actors on that node as well.",752"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"753},754{755"question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",756"sources": [757"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",758"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",759"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",760"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",761"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",762"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",763"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",764"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#programmatic-cluster-scaling",765"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",766"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",767"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#deploy-a-ray-cluster",768"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",769"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher"770],771"document_ids": [7726451,7735953,7745559,7756455,7765919,7775975,7785963,7796714,7805955,781854,7826175,7832400,7846293785],786"answer": " To programmatically set the target size of a Ray remote cluster immediately without scaling up through the autoscaler, you can use the `AutoscalingCluster` class from `ray.cluster_utils` and specify the desired number of worker nodes using the `min_workers` and `max_workers` parameters.\n\nHere's an example of how to create a cluster with 3 worker nodes:\n\n```python\nfrom ray.cluster_utils import AutoscalingCluster\n\ncluster = AutoscalingCluster(\n head_resources={\"CPU\": 2},\n worker_node_types={\n \"cpu_node\": {\n \"resources\": {\"CPU\": 4},\n \"node_config\": {},\n \"min_workers\": 3,\n \"max_workers\": 3,\n }\n },\n)\n```\n\nNote that this will still create an autoscaling cluster, but with a fixed number of worker nodes. If you want to create a non-autoscaling cluster, you can set `min_workers` and `max_workers` to the same value and use the `RayCluster` class instead.\n\nAlso, keep in mind that manually setting the cluster size may not be the most cost-effective or efficient option, as autoscaling can help reduce workload costs and improve resource utilization. It's recommended to start with non-autoscaling clusters if you're new to Ray and only use manual scaling when necessary.",787"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"788},789{790"question": "how do you disable async iter_batches with Ray Dataset?",791"sources": [792"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",793"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",794"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",795"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",796"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches",797"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",798"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowTrainer.html#ray-train-tensorflow-tensorflowtrainer",799"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",800"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",801"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#prefetching-batches",802"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",803"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_torch_batches.html#ray-data-dataiterator-iter-torch-batches",804"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches"805],806"document_ids": [8077188,8087543,8097379,8108478,8117811,8127189,8138540,8146994,8156947,8169218,8177380,8187678,8196895820],821"answer": " To disable async iter\\_batches with Ray Dataset, you can set the `prefetch_batches` parameter to 1 in the `iter_batches()` or `iter_torch_batches()` method. This will execute the iterations synchronously, without prefetching additional batches in the background. Here is an example:\n```python\nfor batch in ray.data.range(1000000).iter_batches(prefetch_batches=1):\n # Process the batch\n pass\n```\nKeep in mind that this might reduce the performance of your training loop if it is bottlenecked on cross-node data transfer or last-mile preprocessing.",822"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"823},824{825"question": "what is the different between a batch and a block, for ray datasets?",826"sources": [827"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",828"https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",829"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",830"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",831"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",832"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",833"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",834"https://docs.ray.io/en/master/data/loading-data.html#performance-considerations",835"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches",836"https://docs.ray.io/en/master/data/working-with-tensors.html#batches-of-variable-shape-tensors",837"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#batch-training-with-ray-core",838"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#batch-prediction-with-ray-core",839"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"840],841"document_ids": [84214728,8436919,8446965,8456994,84614578,8476851,8489180,8496771,8506979,8516775,8525274,8535455,8547875855],856"answer": " A batch and a block are related concepts in the context of Ray Datasets, but they differ in terms of abstraction level and usage.\n\nA block is an internal abstraction used by Ray Data to partition rows of data for parallel processing. A Dataset consists of a collection of blocks, and each block contains a disjoint subset of rows. Blocks are loaded and transformed in parallel, and their size should be managed to avoid out-of-memory situations.\n\nA batch, on the other hand, is a user-facing object representing data from multiple rows. Batches are the result of processing data in parallel using Ray Datasets. Users can inspect batches and change their format, like NumPy ndarrays or pandas DataFrames.\n\nIn summary, blocks are an internal concept used by Ray Data for parallel processing, while batches are user-facing objects representing data from multiple rows after parallel processing.",857"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"858},859{860"question": "How to setup the development environments for ray project?",861"sources": [862"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",863"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",864"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher",865"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",866"https://docs.ray.io/en/master/ray-core/key-concepts.html#environment-dependencies",867"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",868"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",869"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",870"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-from-source",871"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",872"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#environment-dependencies",873"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",874"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment"875],876"document_ids": [877910,8784251,8794250,8806595,8814203,882271,8836354,8846427,885877,88613850,8874247,8886394,8899342890],891"answer": " To set up the development environment for the Ray project, you can follow these steps:\n\n1. Checkout the Ray repository and build Ray from source.\n2. Use the Ray Cluster launcher to prepare a single environment across the cluster before starting the Ray runtime. You can build all your files and dependencies into a container image and specify this in your Cluster YAML Configuration.\n3. Alternatively, you can install dependencies dynamically while Ray is running using runtime environments. A runtime environment describes the dependencies your Ray application needs to run, including files, packages, environment variables, and more.\n4. When Ray executes tasks and actors on remote machines, their environment dependencies must be available. You can prepare these dependencies on the cluster in advance using the Ray Cluster Launcher or use Ray\u2019s runtime environments to install them on the fly.\n5. If you're developing Ray, you can create a Docker image that has your Ray repo pre-cloned and replace the pip installs with a git checkout <your_sha> (and possibly a recompile).\n\nFor more information, you can refer to the User Guide for Environment Dependencies.",892"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"893},894{895"question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",896"sources": [897"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#rewriting-trajectories",898"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",899"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",900"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",901"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.__init__.html#ray-rllib-evaluation-rollout-worker-rolloutworker-init",902"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.AsyncSampler.html#ray-rllib-evaluation-sampler-asyncsampler",903"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.SyncSampler.html#ray-rllib-evaluation-sampler-syncsampler",904"https://docs.ray.io/en/master/rllib/key-concepts.html#policy-evaluation",905"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",906"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.foreach_worker.html#ray-rllib-evaluation-worker-set-workerset-foreach-worker",907"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.par_iter_slice_batch.html#ray-rllib-evaluation-rollout-worker-rolloutworker-par-iter-slice-batch",908"https://docs.ray.io/en/master/ray-core/actors/task-orders.html#synchronous-single-threaded-actor",909"https://docs.ray.io/en/master/rllib/package_ref/evaluation.html#sampling-the-environment-or-offline-data"910],911"document_ids": [91210444,91312707,91414759,91512708,91613196,91712341,91812797,91911422,92013127,92112597,92212917,9234509,92410808925],926"answer": " To debug why Ray RLlib Rollout workers are deadlocking when using the sample API, you can follow these steps:\n\n1. **Check your configuration**: Ensure that the number of workers, environment setup, and other configurations are correct. Incorrect configurations might lead to deadlocks or other issues.\n\n2. **Implement logging and debugging information**: Add print statements or use a logging library to print information about the state of the workers, environments, and policies during rollouts. This can help you identify which part of the code is causing the deadlock.\n\n3. **Use Ray's built-in tools for debugging**: Ray provides several tools for debugging, such as the Ray dashboard and the `ray profiler`. These tools can help you identify performance bottlenecks, resource contention, and other issues that might be causing deadlocks.\n\n4. **Inspect the Ray dashboard**: The Ray dashboard provides a visual representation of the Ray cluster, including information about tasks, actors, and resources. By examining the dashboard, you can identify if there are any issues with task execution, resource allocation, or other aspects of the Ray cluster that might be causing deadlocks.\n\n5. **Implement error handling and retry mechanisms**: Implement error handling and retry mechanisms in your code to ensure that deadlocks or other issues do not cause the entire system to fail. This can help you identify which tasks are failing and why, and allow you to retry those tasks to see if the issue has been resolved.\n\n6. **Use Ray's `WorkerSet.foreach_worker_async` or `WorkerSet.foreach_worker` methods**: These methods allow you to call a function on each worker in the WorkerSet, either asynchronously or synchronously. By using these methods, you can debug each worker individually and identify which worker is causing the deadlock.\n\n7. **Check the `par_iter_slice_batch` method in `RolloutWorker`**: This method batches the parallel iteration slices. If there is an issue with the batching or slicing, it might cause a deadlock.\n\n8. **Check the synchronous, single-threaded actor execution**: Ray executes tasks from the same submitter in a synchronous, single-threaded manner. If there is an issue with task submission or execution order, it might cause a deadlock.\n\n9. **Check the Counter example**: The provided Counter example demonstrates how to create a simple Ray actor that can be used to debug synchronous, single-threaded actor execution. By examining this example, you can identify if there are any issues with task submission or execution order in your code.\n\n10. **Check the environment and offline data sampling**: Data ingest via environment rollouts or offline files is done in RLlib by RolloutWorker instances. If there is an issue with the environment or offline data sampling, it might cause a deadlock.",927"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"928},929{930"question": "how do I join two ray datasets?",931"sources": [932"https://docs.ray.io/en/master/data/preprocessors.html#overview",933"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",934"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",935"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",936"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",937"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy.html#ray-data-from-numpy",938"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",939"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#load-and-preprocess-datasets",940"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",941"https://docs.ray.io/en/master/data/api/doc/ray.data.from_tf.html#ray-data-from-tf",942"https://docs.ray.io/en/master/data/preprocessors.html#numerical-data",943"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",944"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas"945],946"document_ids": [9476845,9489180,9497255,95010436,95110014,9527467,9537289,9549348,95510013,9567287,9576867,9587045,9597818960],961"answer": " To join two Ray datasets, you can use the `union` function provided by the Ray Dataset API. Here's an example:\n```python\ndataset1 = ... # your first dataset\ndataset2 = ... # your second dataset\n\n# Join the two datasets\ndataset_joined = dataset1.union(dataset2)\n```\nThe `union` function concatenates the datasets row-wise, preserving the order of the blocks in the original datasets and the relative ordering between them. Note that this operation will trigger the execution of any lazy transformations performed on the dataset.\n\nKeep in mind that the `union` function does not perform a SQL-like join (e.g., inner join, left join, etc.) but simply concatenates the datasets. If you need to perform a SQL-like join, you would need to implement it manually, for example, by filtering and merging the datasets based on a common column.",962"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"963},964{965"question": "Is there a way to retrieve an object ref from its id?",966"sources": [967"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",968"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",969"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle",970"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",971"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait",972"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",973"https://docs.ray.io/en/master/tune/api/doc/ray.tune.with_parameters.html#ray-tune-with-parameters",974"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",975"https://docs.ray.io/en/master/ray-core/objects.html#objects",976"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",977"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails",978"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",979"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory"980],981"document_ids": [9824982,9834984,98414276,98513987,9864750,9874898,9881599,9894885,9904630,9914884,9926652,9935016,9945039995],996"answer": " Yes, there are a few ways to retrieve an object ref from its id in Ray. One way is to use the `ray.get()` function, which blocks until the object corresponding to the object ref is available in the local object store. If the object ref is not in the local object store, it will be shipped from an object store that has it. Another way is to use the `ray.put()` function, which returns an object ref that can be used to retrieve the object later. Additionally, object refs can be returned by remote function calls.",997"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"998},999{1000"question": "how to create model Checkpoint from the model in memory?",1001"sources": [1002"https://docs.ray.io/en/master/tune/examples/includes/pbt_convnet_function_example.html#pbt-convnet-example",1003"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",1004"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",1005"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#check-the-training-results-and-checkpoints",1006"https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example",1007"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",1008"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#enabling-checkpointing-to-retrieve-the-model",1009"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",1010"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningPredictor.from_checkpoint.html#ray-train-lightning-lightningpredictor-from-checkpoint",1011"https://docs.ray.io/en/master/tune/examples/includes/pbt_memnn_example.html#memory-nn-example",1012"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint",1013"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningCheckpoint.get_model.html#ray-train-lightning-lightningcheckpoint-get-model",1014"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#class-api-checkpointing"1015],1016"document_ids": [10174104,10188798,10199037,10209331,10214073,10228893,10239857,10249036,10258445,10263977,10279066,10288820,102922201030],1031"answer": " To create a model checkpoint from a model in memory, you can use the `TensorflowCheckpoint.from_model` class method provided by the TensorFlow library. Here's an example:\n```python\nimport tensorflow as tf\nfrom ray.train.tensorflow import TensorflowCheckpoint\n\n# Create the model\nmodel = tf.keras.applications.resnet.ResNet101()\n\n# Create the 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:\n```python\ncheckpoint.to_directory(\"my_model\")\n```\nAlternatively, if you have a pre-trained model in memory, you can create a checkpoint from its state dictionary using the `from_state_dict` method:\n```python\nfrom ray.train.checkpoint import Checkpoint\n\n# Assume `state_dict` is the state dictionary of a pre-trained model\ncheckpoint = Checkpoint.from_state_dict(state_dict)\n\n# Save the checkpoint to disk\ncheckpoint.to_directory(\"my_model\")\n```\nNote that the `from_state_dict` method is not specific to TensorFlow and can be used with any framework that supports the `torch.nn.StateDict` interface, such as PyTorch.",1032"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1033},1034{1035"question": "what is Deployment in Ray Serve?",1036"sources": [1037"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",1038"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",1039"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1040"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",1041"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",1042"https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",1043"https://docs.ray.io/en/master/serve/tutorials/java.html#converting-to-a-ray-serve-deployment",1044"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#whats-next",1045"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1046"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",1047"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",1048"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#serving-tuned-models-with-ray-serve",1049"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling"1050],1051"document_ids": [105214255,105313416,105414732,105513731,105610395,105713766,105814555,105913828,106013736,106113930,106214558,10632841,1064134711065],1066"answer": " A Deployment in Ray Serve is a central concept that contains business logic or an ML model to handle incoming requests. It consists of a number of replicas, which are 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. You can define a deployment using the `@serve.deployment` decorator on a Python class, and then bind it with optional arguments to the constructor. Deployments can be combined into a directed acyclic graph (DAG) to handle requests, enabling model composition.",1067"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1068},1069{1070"question": "What is user config in Ray Serve? how do I use it?",1071"sources": [1072"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1073"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",1074"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",1075"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serves-recovery-procedures",1076"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",1077"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",1078"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#dynamically-changing-parameters-without-restarting-your-replicas-user-config",1079"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",1080"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-2-serveconfigv2-is-incorrect",1081"https://docs.ray.io/en/master/serve/api/index.html#serve-config",1082"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",1083"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",1084"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-6-runtime-env"1085],1086"document_ids": [10876597,108813980,108913731,109013883,109113962,10925636,109313565,10945802,10955672,109614009,109714255,109813562,109956841100],1101"answer": " User config in Ray Serve is a way to configure the deployments. You can use it by specifying the configuration in the Serve config file or in the decorator when creating a deployment. The user\\_config can include settings like threshold, numReplicas, rayActorOptions, etc.\n\nHere is an example of how to use user config in a YAML file:\n```yaml\ndeployments:\n - name: Model\n user_config:\n threshold: 1.5\n numReplicas: 6\n rayActorOptions:\n numCpus: 0\n```\nIn this example, the deployment named \"Model\" has a user\\_config that sets the threshold to 1.5, numReplicas to 6, and numCpus to 0.\n\nNote that user\\_config can also be updated by updating the Serve config file with a new user\\_config and reapplying it to the Ray cluster.\n\nAlso, keep in mind that ray\\_actor\\_options counts as a single setting, and the entire ray\\_actor\\_options dictionary in the config file overrides the entire ray\\_actor\\_options dictionary from the graph code. If there are individual options within ray\\_actor\\_options (e.g. runtime\\_env, num\\_gpus, memory) that are set in the code but not in the config, Serve still won\u2019t use the code settings if the config has a ray\\_actor\\_options dictionary. It treats these missing options as though the user never set them and uses defaults instead.",1102"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1103},1104{1105"question": "What is the difference between PACK and SPREAD strategy?",1106"sources": [1107"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",1108"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",1109"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1110"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",1111"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1112"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1113"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1114"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.placement_strategy.html#ray-train-scalingconfig-placement-strategy",1115"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",1116"https://docs.ray.io/en/master/train/getting-started-pytorch-lightning.html#configuring-distributed-strategy",1117"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-groups",1118"https://docs.ray.io/en/master/ray-core/api/scheduling.html#scheduling-strategy",1119"https://docs.ray.io/en/master/ray-core/key-concepts.html#placement-groups"1120],1121"document_ids": [11225145,112311365,112411115,11251872,11265078,11275077,11285075,11298773,11304789,11318400,11325046,11334446,113442021135],1136"answer": " In the context provided, \"PACK\" and \"SPREAD\" are strategies used in Ray for resource management and task scheduling.\n\n1. PACK: This strategy tries to pack the tasks or actors onto as few nodes as possible. It's useful when you want to maximize the locality of resources.\n\n2. SPREAD: This strategy attempts to place tasks or actors across distinct nodes as evenly as possible. It's useful when you want to distribute the workload uniformly across multiple nodes.\n\nThese strategies can be applied to placement groups, which allow users to reserve groups of resources across multiple nodes for scheduling Ray tasks and actors. The \"SPREAD\" strategy can be used to spread the placement groups across distinct nodes, while the \"PACK\" strategy can be used to pack the placement groups into as few nodes as possible.",1137"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1138},1139{1140"question": "What\u2019s the best way to run ray across multiple machines?",1141"sources": [1142"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",1143"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",1144"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",1145"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",1146"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",1147"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1148"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1149"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#monte-carlo-estimation-of",1150"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#network-overhead-in-ray-tune",1151"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",1152"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",1153"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",1154"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework"1155],1156"document_ids": [11575636,115813615,11594186,1160372,11616208,11624207,11636597,11645385,11652386,116614710,11674681,11685600,11693691170],1171"answer": " The best way to run Ray across multiple machines is to start a Ray cluster. You can do this explicitly via the command line or the cluster launcher. A Ray cluster consists of a head node and worker nodes, which can be fixed-size or autoscale according to the resources requested by applications running on the cluster. This allows for workload isolation for multiple Ray users.",1172"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1173},1174{1175"question": "how do I specify ScalingConfig for a Tuner run?",1176"sources": [1177"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",1178"https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",1179"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",1180"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#configuring-scale-and-gpus",1181"https://docs.ray.io/en/master/train/api/doc/ray.train.trainer.BaseTrainer.html#ray-train-trainer-basetrainer",1182"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#on-a-single-node-cluster",1183"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",1184"https://docs.ray.io/en/master/tune/api/suggestion.html#saving-and-restoring-tune-search-algorithms",1185"https://docs.ray.io/en/master/tune/key-concepts.html#tune-trials",1186"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",1187"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#reporting-metrics-with-tune",1188"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-on-trial-failures",1189"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#more-resources"1190],1191"document_ids": [11921826,11931303,11949126,11959251,11968519,11972343,11981825,11991279,1200967,12012400,12022292,12032208,120492561205],1206"answer": " To specify the `ScalingConfig` for a Tuner run, you can include it in the `param_space` dictionary when creating the `Tuner` object. Here's an example:\n```python\nfrom ray.tune. ScalingConfig import ScalingConfig\n\nparam_space = {\n \"scaling_config\": ScalingConfig(\n num_workers=tune.grid_search([2, 4]),\n resources_per_worker={\"CPU\": tune.grid_search([1, 2])},\n use_gpu=True, # specify GPU usage here\n ),\n # other parameters\n}\n\ntuner = Tuner(\n trainable=trainer,\n param_space=param_space,\n run_config=RunConfig(\n name=\"my-experiment-1\",\n storage_path=\"~/my_results\",\n )\n)\n\ntuner.fit()\n```\nIn this example, the `ScalingConfig` is included in the `param_space` dictionary and can be modified as needed. The `use_gpu` parameter can be set to `True` to use GPUs for the training process.",1207"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1208},1209{1210"question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",1211"sources": [1212"https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only",1213"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1214"https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",1215"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1216"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",1217"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",1218"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1219"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#preprocessing",1220"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",1221"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",1222"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",1223"https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",1224"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes"1225],1226"document_ids": [12274944,12287012,12294942,12307005,12314225,12324407,12336887,12348030,12358164,123610140,12377047,12386773,123949451240],1241"answer": " To utilize the 'zero-copy' feature that Ray provides for numpy, you should ensure that the function you're using with Ray doesn't mutate the input numpy arrays. If the function needs to modify the data, manually copy the array at the destination. When using `map_batches()` with numpy arrays, set `zero_copy_batch=True` to improve performance and decrease memory utilization. Here's an example:\n\n```python\nds = ray.data.from_numpy(np.ones((1, 100)))\n\ndef add_dog_years(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n batch[\"age_in_dog_years\"] = 7 * batch[\"age\"]\n return batch\n\nds = ds.map_batches(add_dog_years, batch_format=\"numpy\", zero_copy_batch=True)\n```\n\nIf your function mutates the input, you'll need to create a copy of the numpy array before modifying it to avoid \"assignment destination is read-only\" errors.",1242"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1243},1244{1245"question": "if there are O(millions) of keys that all have state, is it ok to spin up 1=1 actors? Or would it be advised to create \u2018key pools\u2019 where an actor can hold 1=many keys?",1246"sources": [1247"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",1248"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_random_access_dataset.html#ray-data-dataset-to-random-access-dataset",1249"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",1250"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1251"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",1252"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",1253"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1254"https://docs.ray.io/en/master/data/examples/random-access.html#architecture",1255"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1256"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.submit.html#ray-util-actorpool-submit",1257"https://docs.ray.io/en/master/ray-observability/reference/api.html#state-apis-schema",1258"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#key-concepts",1259"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-concurrency-for-actors"1260],1261"document_ids": [12625441,12637308,12647976,12655192,12665231,12677978,12684678,12698070,12704878,12714784,1272663,1273478,127445491275],1276"answer": " It is not advised to spin up one actor for each key due to the large number of actors that would need to be created. Instead, it would be more efficient to create \"key pools\" where an actor can hold many keys, as this would reduce the overhead of creating and managing numerous actors. This approach aligns with the recommendation to limit the number of concurrently running actors on each node to the number of CPUs or GPUs available.",1277"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1278},1279{1280"question": "How to find the best checkpoint from the trial directory?",1281"sources": [1282"https://docs.ray.io/en/master/train/api/doc/ray.train.Result.html#ray-train-result",1283"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",1284"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1285"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",1286"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",1287"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis",1288"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1289"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.save_checkpoint.html#ray-rllib-algorithms-algorithm-algorithm-save-checkpoint",1290"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_trial_checkpoints_paths.html#ray-tune-experimentanalysis-get-trial-checkpoints-paths",1291"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",1292"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1293"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis",1294"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.skopt.SkOptSearch.html#ray-tune-search-skopt-skoptsearch"1295],1296"document_ids": [12978371,12981645,12991431,13002188,13013402,13021474,130311635,130411423,13051692,13062348,13071430,13081472,130917221310],1311"answer": " To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` method provided by Ray Tune. This method 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 method:\n```\nfrom ray.tune.experiment import ExperimentAnalysis\n\nanalysis = ExperimentAnalysis(path)\nbest_checkpoint = analysis.get_best_checkpoint(metric=\"your_metric\", mode=\"your_mode\")\n```\nAlternatively, you can use the `get_best_checkpoint` method of the `Result` object, which is returned by the `get_best_trial` method. This method also allows you to specify a metric and a mode to determine the best checkpoint.\n\nHere is an example of how to use this method:\n```\nfrom ray.tune.experiment import ExperimentAnalysis\n\nanalysis = ExperimentAnalysis(path)\nbest_result = analysis.get_best_trial(metric=\"your_metric\", mode=\"your_mode\")\nbest_checkpoint = best_result.checkpoint\n```\nNote that the `get_best_checkpoint` method returns `None` if there is no valid checkpoint associated with the metric.",1312"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1313},1314{1315"question": "what are the advantage and disadvantage of using singleton Actor ?",1316"sources": [1317"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1318"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",1319"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",1320"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",1321"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1322"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",1323"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1324"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1325"https://docs.ray.io/en/master/ray-core/actors/async_api.html#threaded-actors",1326"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1327"https://docs.ray.io/en/master/ray-core/patterns/index.html#design-patterns-anti-patterns",1328"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",1329"https://docs.ray.io/en/master/ray-observability/getting-started.html#task-and-actor-breakdown"1330],1331"document_ids": [13325192,13334200,13344455,133514708,13364371,133711813,13384678,133914723,13404586,13415191,13425244,13435195,13444231345],1346"answer": " Advantages of using a singleton Actor in Ray:\n\n1. Stateful worker: Actors provide a stateful service, allowing methods to access and mutate the state of the worker.\n2. Resource support: Actors support CPU, GPU, and custom resource requirements.\n3. Fault tolerance: Ray handles actor fault tolerance in case of actor process death or owner death.\n4. Useful for synchronization: Actors can be used to synchronize other tasks and actors when multiple tasks need to wait on some condition or synchronize across tasks and actors on a cluster.\n\nDisadvantages of using a singleton Actor in Ray:\n\n1. Overhead for simple tasks: If you don't need the stateful part of an actor, you're mostly better off using tasks, as actors come with additional overhead.\n2. Performance issues with global variables: Using global variables to share state between tasks and actors is an anti-pattern and can harm performance.\n3. Threaded concurrency limitations: When using asyncio for actors, there's a limitation of executing only 1 task at a time, which can hurt performance if not handled properly.\n\nIn summary, using a singleton Actor in Ray can be beneficial for stateful services, fault tolerance, and synchronization across tasks and actors. However, it's essential to consider the additional overhead and potential performance issues when using global variables or asyncio for tasks.",1347"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1348},1349{1350"question": "what are the advantages of using a named actor?",1351"sources": [1352"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",1353"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",1354"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1355"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1356"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1357"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1358"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1359"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1360"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1361"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1362"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",1363"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",1364"https://docs.ray.io/en/master/ray-more-libs/actors.html#more-about-ray-actors"1365],1366"document_ids": [136714708,136811813,13694371,13704678,13714880,13724631,13734633,13744637,13754719,13764871,13774200,13784649,1379147091380],1381"answer": " Named actors in Ray provide a way to retrieve an actor from any job in the Ray cluster using a unique name within a namespace. 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. Named actors will still be garbage-collected if no handles to them exist.",1382"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1383},1384{1385"question": "How do I read a text file stored on S3 using Ray Data?",1386"sources": [1387"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1388"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",1389"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1390"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1391"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",1392"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1393"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",1394"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",1395"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",1396"https://docs.ray.io/en/master/data/working-with-text.html#saving-text",1397"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-1-reading-the-dataset-from-s3",1398"https://docs.ray.io/en/master/data/loading-data.html#reading-files-from-cloud-storage",1399"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text"1400],1401"document_ids": [14027565,14037909,14046993,14057496,14066732,14077783,14087910,14096779,14107955,14117004,14128013,14136743,141475641415],1416"answer": " To read a text file stored on S3 using Ray Data, you can use the `read_text()` function and provide the S3 path to the text file as an argument. Here's an example:\n```python\nimport ray\n\nds = ray.data.read_text(\"s3://<bucket>/<folder>/<filename>.txt\")\n```\nReplace `<bucket>`, `<folder>`, and `<filename>` with the appropriate names for your S3 bucket, folder, and text file.\n\nIf the S3 bucket is private, you need to set up authentication by creating a `pyarrow.fs.S3FileSystem` object with your AWS credentials and passing it as the `filesystem` argument to `read_text()`. Here's an example:\n```python\nimport pyarrow.fs\n\nfs = pyarrow.fs.S3FileSystem(\n access_key=\"<your_access_key>\",\n secret_key=\"<your_secret_key>\",\n session_token=\"<your_session_token>\"\n)\n\nds = ray.data.read_text(\"s3://<bucket>/<folder>/<filename>.txt\", filesystem=fs)\n```\nReplace `<your_access_key>`, `<your_secret_key>`, and `<your_session_token>` with your actual AWS credentials.",1417"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1418},1419{1420"question": "how do I get the IP of the head node for my Ray cluster?",1421"sources": [1422"https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",1423"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",1424"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",1425"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",1426"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",1427"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",1428"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",1429"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#solution-1-container-command-recommended",1430"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",1431"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1432"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",1433"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",1434"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-5-collect-head-node-metrics-with-a-servicemonitor"1435],1436"document_ids": [14375631,14386537,14396365,14406367,1441493,14425587,14435413,14445847,14455557,14466465,14476708,14485530,144960671450],1451"answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command, which is used to return the head node IP of a Ray cluster. The command requires the cluster configuration file as an argument.\n\nAlternatively, if you have already started the Ray cluster, you can use the `ray list nodes` command to get the node ID/node IP of the head node and then use the `ray logs` command with the node ID to view the logs of the head node. The logs will contain the IP address of the head node.\n\nYou can also use the `ray nodes()` function in a Python script to get information about the nodes in the Ray cluster, including the IP addresses of the nodes.\n\nOnce you have the IP address of the head node, you can use it to access the Ray dashboard or to run commands on the head node.",1452"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1453},1454{1455"question": "How to write a map function that returns a list of object for `map_batches`?",1456"sources": [1457"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1458"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1459"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1460"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1461"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1462"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1463"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",1464"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1465"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",1466"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",1467"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1468"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches",1469"https://docs.ray.io/en/master/rllib/rllib-concepts.html#how-to-customize-policies"1470],1471"document_ids": [14726994,14737019,14747800,14757979,14768218,14776997,14787738,14797640,14805350,14816786,14827008,14836895,1484115041485],1486"answer": " To write a map function that returns a list of objects for `map_batches`, you can define a function that takes a batch as input and yields the desired objects in the list. Here's an example:\n```python\ndef map_fn(batch):\n for i in range(len(batch)):\n yield {\"id\": batch[\"id\"][i] * 2}\n```\nThen you can use this function with `map_batches` like this:\n```python\nds.map_batches(map_fn, batch_size=10)\n```\nThis will apply the `map_fn` function to each batch of data in the dataset, yielding a list of objects for each batch. The `batch_size` argument specifies the number of rows in each batch.\n\nNote that if your function returns large objects, you should yield outputs in chunks to avoid creating large intermediate objects.",1487"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1488},1489{1490"question": "How do I set a maximum episode length when training with Rllib?",1491"sources": [1492"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",1493"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.reporting.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-reporting",1494"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",1495"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",1496"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",1497"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.replay_buffers.replay_buffer.StorageUnit.EPISODES.html#ray-rllib-utils-replay-buffers-replay-buffer-storageunit-episodes",1498"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#distributed-prioritized-experience-replay-ape-x",1499"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner.Learner.register_optimizer.html#ray-rllib-core-learner-learner-learner-register-optimizer",1500"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",1501"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",1502"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.reporting.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-reporting",1503"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers",1504"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.rollouts.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-rollouts"1505],1506"document_ids": [150710410,150813177,150911046,151010411,151111069,151212765,151312048,151413077,151511058,151611043,151713176,151810918,1519126311520],1521"answer": " In RLlib, you can set a maximum episode length by using the `rollout_fragment_length` configuration parameter in your algorithm's configuration. This parameter determines the minimum number of timesteps that will be covered in the rollout. If you set `complete_episodes` to `True`, the rollout will always contain full episodes and will stop when the episode length reaches `rollout_fragment_length` or greater.\n\nHere's an example of how to set `rollout_fragment_length` and `complete_episodes` in your algorithm's configuration:\n```yaml\nconfig = {\n \"rollout_fragment_length\": 100,\n \"complete_episodes\": true,\n # other configuration parameters...\n}\n```\nThis configuration will ensure that each rollout contains at least 100 timesteps, and if `complete_episodes` is set to `True`, the rollout will always contain full episodes and will stop when the episode length reaches 100 timesteps or greater.",1522"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1523},1524{1525"question": "how do I make a Ray Tune trial retry on failures?",1526"sources": [1527"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1528"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1529"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#summary",1530"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",1531"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_recover.html#ray-tune-callback-on-trial-recover",1532"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1533"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune",1534"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-on-trial-failures",1535"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_error.html#ray-tune-callback-on-trial-error",1536"https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.aim.AimLoggerCallback.on_trial_recover.html#ray-tune-logger-aim-aimloggercallback-on-trial-recover",1537"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1538"https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.TBXLoggerCallback.on_trial_recover.html#ray-tune-logger-tbxloggercallback-on-trial-recover",1539"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner"1540],1541"document_ids": [15422326,15432267,15442268,15454505,15462152,15471114,154811734,15492207,15501980,15511762,15522266,15532046,155418271555],1556"answer": " To make a Ray Tune trial retry on failures, you can use the `FailureConfig` class in the `run_config` argument of the `Tuner` object. Here is an example:\n\n```python\nfrom ray import tune\n\ntuner = tune.Tuner(\n train_model,\n param_space=trial_space,\n run_config=tune.RunConfig(\n storage_path=os.path.expanduser(\"~/ray_results\"),\n name=\"trial_fault_tolerance\",\n failure_config=tune.FailureConfig(max_failures=3),\n ),\n)\n\ntuner.fit()\n```\n\nIn this example, the `max_failures` argument of `FailureConfig` specifies the maximum number of times a trial can fail before it is permanently stopped. You can set this value to the number of retries you want for the trial.\n\nWhen a trial encounters a runtime error, the above configuration will reschedule that trial up to `max_failures` times. Similarly, if a node failure occurs for a node that a trial is running on, this configuration will reschedule all trials that lived on that node up to `max_failures` times.",1557"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1558},1559{1560"question": "For the supervised actor pattern, can we keep the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor, to allow the worker actor to remain even on Supervisor / Driver failure?",1561"sources": [1562"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",1563"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1564"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",1565"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1566"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1567"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1568"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#what-happens-in-tuner-fit",1569"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",1570"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#how-to-write-fault-tolerant-ray-applications",1571"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.html#ray-rllib-algorithms-algorithm-algorithm",1572"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",1573"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1574"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-checkpointing"1575],1576"document_ids": [15775208,15784493,15794455,15805191,15814654,15824678,15832235,15845195,15854429,158611741,15874848,15885192,158944851590],1591"answer": " No, keeping the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor does not prevent the worker actor from being terminated on Supervisor/Driver failure. This is because of actor reference counting, where if the supervisor dies, the worker actors are automatically terminated. However, you can make the worker actor persistent even after the driver process exits by specifying its lifetime as \"detached\" when creating the actor.",1592"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1593},1594{1595"question": "How do I read a large text file in S3 with Ray?",1596"sources": [1597"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1598"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1599"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",1600"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1601"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",1602"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",1603"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1604"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",1605"https://docs.ray.io/en/master/data/loading-data.html#reading-files-from-cloud-storage",1606"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",1607"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",1608"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-1-reading-the-dataset-from-s3",1609"https://docs.ray.io/en/master/data/loading-data.html#handling-compressed-files"1610],1611"document_ids": [16127565,16137496,16147909,16156993,16166991,16174966,16187783,16196732,16206743,16217868,16227955,16238013,162467471625],1626"answer": " To read a large text file in S3 with Ray, you can use the `read_text` function and provide the S3 path to the file as an argument. Here's an example:\n```python\nimport ray\n\ns3_path = \"s3://bucket/path/to/your/text/file.txt\"\nds = ray.data.read_text(s3_path)\n```\nIf you want to read a directory of text files in S3, you can use the `read_text_files` function instead:\n```python\nimport ray\n\ns3_path = \"s3://bucket/path/to/your/text/files\"\nds = ray.data.read_text_files(s3_path)\n```\nNote that if you are reading from a private S3 bucket, you will need to authenticate with your AWS credentials. You can do this by setting the appropriate environment variables or using the `boto3` library to create an S3 client.",1627"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1628},1629{1630"question": "how do I get a ray dataset from pandas",1631"sources": [1632"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1633"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1634"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",1635"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",1636"https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-datasets",1637"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_pandas.html#ray-data-dataset-to-pandas",1638"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",1639"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas_refs.html#ray-data-from-pandas-refs",1640"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",1641"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1642"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1643"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1644"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows"1645],1646"document_ids": [16479180,16486750,16497818,16506752,16516972,16527302,16537865,16547419,165510140,16566749,16576879,165810017,165969771660],1661"answer": " To create a Ray Dataset from a pandas DataFrame, you can use the `ray.data.from_pandas()` function. Here's an example:\n\n```python\nimport pandas as pd\nimport ray\n\ndf = pd.DataFrame({\n \"food\": [\"spam\", \"ham\", \"eggs\"],\n \"price\": [9.34, 5.37, 0.94]\n})\n\nds = ray.data.from_pandas(df)\nprint(ds)\n```\n\nThis will create a Ray Dataset `ds` from the pandas DataFrame `df`. The `ray.data.from_pandas()` function can also take a list of pandas DataFrames to create a single Ray Dataset.\n\n```python\nds = ray.data.from_pandas([df, df])\nprint(ds)\n```\n\nThis will create a Ray Dataset `ds` from a list of two identical pandas DataFrames `df`.",1662"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1663},1664{1665"question": "can you give me an example of using `ray.data.map` ?",1666"sources": [1667"https://docs.ray.io/en/master/data/api/from_other_data_libs.html#api-guide-for-users-from-other-data-libraries",1668"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1669"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",1670"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1671"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",1672"https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",1673"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1674"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1675"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1676"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1677"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",1678"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",1679"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data"1680],1681"document_ids": [16827039,16836994,16845433,1685221,16865352,16876773,168810014,16896839,16907796,16919180,16925346,16937360,169468471695],1696"answer": " Sure, here's an example of using `ray.data.map`:\n```python\nfrom typing import Dict\nimport numpy as np\nimport ray\n\n# Create a Ray Dataset\nds = ray.data.read_csv(\"s3://anonymous@ray-example-data/iris.csv\")\n\n# Define a map function\ndef square_root(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n batch[\"sepal_length\"] = np.sqrt(batch[\"sepal_length\"])\n return batch\n\n# Apply the map function to the Ray Dataset\nds = ds.map(square_root)\n```\nIn this example, we create a Ray Dataset `ds` by reading a CSV file from an S3 bucket. We then define a `square_root` function that takes a batch of data (represented as a dictionary of NumPy arrays) and applies the square root function to the `sepal_length` feature. Finally, we apply the `square_root` function to the Ray Dataset using the `map` method.",1697"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1698},1699{1700"question": "can you give me an example of using `ray.data.map` , with a callable class as input?",1701"sources": [1702"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1703"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",1704"https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",1705"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1706"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1707"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1708"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1709"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",1710"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",1711"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",1712"https://docs.ray.io/en/master/data/api/doc/ray.data.DataContext.html#ray-data-datacontext",1713"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1714"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air"1715],1716"document_ids": [17177796,17185433,17196782,17206749,17219180,1722221,172310014,17245350,17255414,17265352,17277158,17287274,1729101401730],1731"answer": " Sure, here's an example of using `ray.data.map` with a callable class as input:\n```python\nimport ray\nfrom typing import Dict\n\nclass MyMapper:\n def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:\n data[\"new_col\"] = data[\"col1\"] + data[\"col2\"]\n return data\n\n# Create a Ray Dataset\nds = ray.data.from_items([\n {\"col1\": 1, \"col2\": 2},\n {\"col1\": 3, \"col2\": 4},\n {\"col1\": 5, \"col2\": 6},\n])\n\n# Use the MyMapper class to map over the Dataset\nds_mapped = ds.map(MyMapper())\n\n# Print the resulting Dataset\nprint(ds_mapped.take(3))\n```\nIn this example, we define a callable class `MyMapper` that takes a dictionary as input and adds a new key-value pair to it. We then create a Ray Dataset `ds` from a list of dictionaries. Finally, we use the `map` method to apply the `MyMapper` class to each row of the Dataset, resulting in a new Dataset `ds_mapped` with an additional column.",1732"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1733},1734{1735"question": "How to set memory limit for each trial in Ray Tuner?",1736"sources": [1737"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",1738"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#summary",1739"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",1740"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1741"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#scalability-and-overhead-benchmarks-for-ray-tune",1742"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#memory",1743"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",1744"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.max_concurrent_trials.html#ray-tune-tuneconfig-max-concurrent-trials",1745"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",1746"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1747"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",1748"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",1749"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#how-do-i-configure-the-memory-monitor"1750],1751"document_ids": [17521025,17532268,17542406,17552325,17562376,17575938,17586934,17591939,17602257,17611114,17622396,17635109,176451211765],1766"answer": " To set the memory limit for each trial in Ray Tuner, you can override the autodetected memory value by setting the desired memory value in bytes under `rayStartParams.memory`. Here's an example:\n\n```python\nfrom ray import tune\n\n# Set memory limit to 4GB\ntune.run(my_trainable, config={\"memory\": \"4GB\"})\n```\n\nThis will limit the memory usage of each trial to 4GB. Note that the value of `memory` must be supplied as a string.\n\nAdditionally, you can configure the resources allocated per trial to control the parallelism. For example, if you want to allocate 1GB of memory per trial, you can do the following:\n\n```python\ntrain_model = tune.with_resources(train_model, {\"cpu\": 1, \"memory\": \"1GB\"})\n```\n\nThis will ensure that each trial is allocated 1GB of memory and 1 CPU. Note that the values of all Ray start parameters, including memory, must be supplied as strings.",1767"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1768},1769{1770"question": "how do I get the actor id of an actor",1771"sources": [1772"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_actor_id.html#ray-runtime-context-runtimecontext-get-actor-id",1773"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",1774"https://docs.ray.io/en/master/ray-observability/getting-started.html#actor-detail-page",1775"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1776"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor",1777"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1778"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",1779"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",1780"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",1781"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1782"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",1783"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1784"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor"1785],1786"document_ids": [17874787,17884931,1789436,1790475,1791690,1792477,179314318,179414202,1795750,1796476,17974241,17984631,179948711800],1801"answer": " To get the actor ID of the current actor in a Ray application, you can use the `RuntimeContext.get_actor_id()` function or the deprecated `RuntimeContext.actor_id` property. These functions return the current actor id in hex format if there is an actor id; otherwise, they return None.\n\nHere is an example of how to use `RuntimeContext.get_actor_id()` to get the actor id:\n\n```python\nfrom ray.runtime_context import RuntimeContext\n\nactor_id = RuntimeContext.get_actor_id()\nprint(actor_id)\n```\n\nAlternatively, you can use the `ray.util.state.get_actor()` function to get an actor by its id. This function returns the actor state if the actor is found; otherwise, it returns None.\n\nHere is an example of how to use `ray.util.state.get_actor()` to get the actor state by its id:\n\n```python\nfrom ray.util.state import get_actor\n\nactor_state = get_actor(id=\"<ACTOR_ID>\")\nprint(actor_state)\n```\n\nNote that the `RuntimeContext.actor_id` property is deprecated and may be removed in future Ray releases. It is recommended to use `RuntimeContext.get_actor_id()` instead.",1802"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1803},1804{1805"question": "can ray.init() can check if ray is all-ready initiated ?",1806"sources": [1807"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",1808"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1809"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",1810"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1811"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1812"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.__init__.html#ray-data-preprocessor-preprocessor-init",1813"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1814"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1815"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",1816"https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.Reader.__init__.html#ray-data-datasource-reader-init",1817"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.__init__.html#ray-train-huggingface-transformers-raytrainreportcallback-init",1818"https://docs.ray.io/en/master/ray-core/advanced-topics.html#advanced-topics",1819"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ProgressReporter.__init__.html#ray-tune-progressreporter-init"1820],1821"document_ids": [18224785,18234811,18244194,18254190,18264189,18277833,18284188,18294204,18306595,18317821,18328464,18334196,183419431835],1836"answer": " No, `ray.init()` is not used to check if Ray is already initiated. Instead, you should use `ray.is_initialized()` to check if Ray has been initialized in the current process. This function returns a boolean value - True if Ray has been initialized, and False otherwise.",1837"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1838},1839{1840"question": "What does the `compute=actor` argument do within `ray.data.map_batches` ?",1841"sources": [1842"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",1843"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1844"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1845"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.filter.html#ray-data-dataset-filter",1846"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",1847"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1848"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1849"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.drop_columns.html#ray-data-dataset-drop-columns",1850"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1851"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.select_columns.html#ray-data-dataset-select-columns",1852"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1853"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1854"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks"1855],1856"document_ids": [18576947,18588218,18596787,18607840,186110144,18626994,18637979,18647423,18657003,18667531,18678044,18687798,186967861870],1871"answer": " The `compute=actor` argument within `ray.data.map_batches` specifies that the user-defined function (UDF) should be executed as a long-running Ray actor instead of a short-running Ray task. This can be useful for UDFs that are computationally expensive or need to maintain state between different batches of data. The `ActorPoolStrategy` can be used to configure the number of parallel actors.",1872"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1873},1874{1875"question": "how do I use wandb logger with accelerateTrainer?",1876"sources": [1877"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",1878"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1879"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",1880"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#setup-wandb",1881"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",1882"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1883"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1884"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1885"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1886"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1887"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1888"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",1889"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example"1890],1891"document_ids": [18921132,18932432,18948470,18952469,18962431,18979658,18982463,18992464,19002434,19012433,19022453,19039136,190424351905],1906"answer": " To use Weights & Biases (WandB) logger with AccelerateTrainer, you can follow these steps:\n\n1. Install WandB: `pip install wandb`\n2. Import the necessary libraries, including WandB and AccelerateTrainer.\n3. Initialize WandB with your API key and project name.\n4. Create a PyTorch Lightning module (if you haven't already) and configure your training function to use AccelerateTrainer.\n5. In your training function, use WandB to log metrics and artifacts as needed.\n\nHere's an example of how you might use WandB with AccelerateTrainer:\n\n```python\nimport os\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nfrom transformers import AutoModel, AutoTokenizer\nfrom pytorch_lightning import LightningModule, Trainer\nfrom accelerate import Accelerator\n\n# Initialize WandB\nos.environ[\"WANDB_API_KEY\"] = \"your_api_key\"\nwandb.init(project=\"your_project_name\")\n\n# Define your PyTorch Lightning module\nclass YourModel(LightningModule):\n # Your model definition here\n\n# Initialize AccelerateTrainer\naccelerator = Accelerator()\ntrainer = Trainer(\n accelerator=accelerator,\n # Other training parameters here\n)\n\n# Train your model\nmodel = YourModel()\ntrainer.fit(model, DataLoader(...))\n\n# Log metrics and artifacts to WandB\nwandb.log({\"loss\": loss, \"accuracy\": accuracy})\nwandb.log({\"artifact\": artifact})\n```\n\nNote that this is just a basic example, and you may need to modify it to fit your specific use case.",1907"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1908},1909{1910"question": "What will be implicitly put into object store?",1911"sources": [1912"https://docs.ray.io/en/master/ray-core/objects/serialization.html#plasma-store",1913"https://docs.ray.io/en/master/ray-core/objects.html#objects",1914"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1915"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1916"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1917"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",1918"https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",1919"https://docs.ray.io/en/master/data/data-internals.html#object-store-memory",1920"https://docs.ray.io/en/master/ray-core/patterns/pass-large-arg-by-value.html#anti-pattern-passing-the-same-large-argument-by-value-repeatedly-harms-performance",1921"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1922"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1923"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",1924"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"1925],1926"document_ids": [19274941,19284634,19295013,19305014,193114748,19325028,19334775,19346967,19355240,193614747,19374819,193814122,1939141381940],1941"answer": " In the context provided, data that will be implicitly put into the object store includes:\n\n1. Results of remote functions: When a remote function is called, its return value is automatically put into the object store of the node where the function was executed.\n2. Objects put into the object store using APIs: In Ray, you can explicitly put objects into the object store using APIs such as `ray.put()` in Python, `Ray.put()` in Java, and `ray::Put()` in C++.\n\nIt is important to note that data in the object store is implicitly local to the node where it was put, and it does not get automatically broadcasted to other nodes. Objects in the object store are immutable and can be replicated in multiple object stores without needing to synchronize the copies.",1942"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1943},1944{1945"question": "How do I kill or cancel a ray task that I already started?",1946"sources": [1947"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1948"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",1949"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1950"https://docs.ray.io/en/master/ray-core/api/core.html#tasks",1951"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",1952"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1953"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",1954"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",1955"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1956"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#helpful-ray-commands",1957"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1958"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",1959"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"1960],1961"document_ids": [19624430,19634498,19644894,19654689,196614703,19674361,196811804,19694859,19704897,19715525,19724357,197311801,1974147011975],1976"answer": " To kill or cancel a Ray task that you already started, you can call `ray.cancel()` on the ObjectRef returned by the task. Here is an example:\n\n```python\n@ray.remote\ndef blocking_operation():\n time.sleep(10e6)\n\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n```\n\nBy default, this will send a `KeyboardInterrupt` to the task's worker if it is mid-execution. Passing `force=True` to `ray.cancel()` will force-exit the worker. Note that currently, Ray will not automatically retry tasks that have been cancelled.\n\nIf you want to cancel an actor task, you can also call `ray.cancel()` on the ObjectRef returned by the actor task. If you want to cancel all child and actor tasks recursively, you can pass the `recursive=True` argument to `ray.cancel()`.\n\nNote that cancellation in Ray is not guaranteed and is done on a best-effort basis. If the cancellation request doesn't get through to the executor, the task might not be cancelled. You can check if a task was successfully cancelled using `ray.get(actor_task_ref)`.",1977"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1978},1979{1980"question": "how to send extra arguments in dataset.map_batches function?",1981"sources": [1982"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1983"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1984"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1985"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1986"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches",1987"https://docs.ray.io/en/master/data/working-with-pytorch.html#pytorch-dataloader",1988"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",1989"https://docs.ray.io/en/master/data/api/dataset.html#basic-transformations",1990"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",1991"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1992"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",1993"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1994"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map"1995],1996"document_ids": [19976994,19986997,19998218,20007979,20016895,20026985,20037738,20047065,20056915,20067800,20076786,20087019,200976402010],2011"answer": " To send extra arguments in the `dataset.map_batches` function, you can use the `fn_kwargs` parameter. This parameter allows you to pass keyword arguments to the user-defined function (UDF) that you use in the `map_batches` function. Here's an example:\n```python\nds.map_batches(my_function, fn_kwargs={'arg1': 'value1', 'arg2': 'value2'})\n```\nIn this example, `my_function` is the UDF that you want to apply to the dataset, and `arg1` and `arg2` are the extra arguments that you want to pass to the function. The values of these arguments are `'value1'` and `'value2'`, respectively.\n\nYou can also use the `fn_args` parameter to pass extra positional arguments to the UDF. Here's an example:\n```python\nds.map_batches(my_function, fn_args=('value1', 'value2'))\n```\nIn this example, `my_function` is the UDF that you want to apply to the dataset, and `value1` and `value2` are the extra positional arguments that you want to pass to the function.\n\nNote that the `fn_args` and `fn_kwargs` parameters are optional, and you can use one or both of them depending on your use case.",2012"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2013},2014{2015"question": "where does ray GCS store the history of jobs run on a kuberay cluster? What type of database and format does it use for this?",2016"sources": [2017"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",2018"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",2019"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",2020"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/experimental.html#gcs-fault-tolerance",2021"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",2022"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-overview",2023"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance",2024"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",2025"https://docs.ray.io/en/master/ray-observability/getting-started.html#jobs-view",2026"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",2027"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",2028"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",2029"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#ray-job-submission"2030],2031"document_ids": [203213871,20335754,20345729,20355916,203613870,20376634,20385830,20396148,2040421,20415647,20425763,20435817,204458342045],2046"answer": " Ray GCS (Global Control Store) stores the history of jobs run on a KubeRay cluster in an external Redis database when enabling GCS fault tolerance. This allows the Ray cluster to prevent crashing if the head node goes down and recover its state from the external Redis instance.",2047"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2048},2049{2050"question": "How to resolve ValueError: The actor ImplicitFunc is too large?",2051"sources": [2052"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#move-the-data-loader-to-the-training-function",2053"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler",2054"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",2055"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2056"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2057"https://docs.ray.io/en/master/data/api/doc/ray.data.ActorPoolStrategy.html#ray-data-actorpoolstrategy",2058"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2059"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",2060"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",2061"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2062"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",2063"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2064"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor"2065],2066"document_ids": [20679858,20681895,20696150,20706908,20718367,20727733,20736787,20745468,207510366,20766899,20775469,20786911,207946252080],2081"answer": " The ValueError \"The actor ImplicitFunc is too large\" is likely caused by a large object being implicitly captured in the actor's scope. To resolve this issue, you can use `ray.put()` to put large objects in the Ray object store and then pass them by object ID to the actor. Additionally, you may want to consider reducing the size of the actor or increasing the amount of object store memory available to Ray.\n\nIn the context provided, it is recommended to move the data loader to the training function and to use `ray.put()` to put large objects in the Ray object store. It is also suggested to use an autoscaling actor pool for the inference stage and to set the `max_tasks_in_flight_per_actor` parameter to 2 or greater to increase opportunities for pipelining task dependency prefetching with computation and avoid actor startup delays.\n\nHere is an example of how you can modify your code to use `ray.put()` to put a large model object in the Ray object store and then pass it to the `TorchPredictor` actor:\n\n```python\n# Put the model object in the Ray object store\nmodel_ref = ray.put(model)\n\n# Create the TorchPredictor actor and pass the model object ID as an argument\ntorchtorch_predictor_actor = TorchPredictor.remote(model_ref)\n\n# Use the TorchPredictor actor to make predictions\npredictions = ds.map_batches(\n TorchPredictor,\n num_gpus=1,\n batch_size=1,\n compute=ray.data.ActorPoolStrategy(size=2, max_tasks_in_flight_per_actor=2),\n f=torchtorch_predictor_actor\n)\n```\n\nThis will put the `model` object in the Ray object store and then pass its object ID (`model_ref`) to the `TorchPredictor` actor. The actor can then retrieve the model object from the object store using the object ID. This can help to reduce the size of the actor and avoid the ValueError.",2082"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2083},2084{2085"question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",2086"sources": [2087"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2088"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2089"https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",2090"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",2091"https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others",2092"https://docs.ray.io/en/master/train/getting-started-pytorch.html#getting-started-with-pytorch",2093"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowTrainer.html#ray-train-tensorflow-tensorflowtrainer",2094"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",2095"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",2096"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",2097"https://docs.ray.io/en/master/ray-overview/index.html#overview",2098"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve",2099"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#define-your-training-function"2100],2101"document_ids": [2102228,21036887,210414653,21059675,210614326,21078308,21088542,21099183,21109845,21119820,2112363,21132775,211495292115],2116"answer": " To distribute training for your custom Keras neural net in Ray on Databricks, you can follow these steps:\n\n1. Install the required packages: `!pip install -U \"ray[train]\" tensorflow`\n2. Set up your dataset and model using TensorFlow and Keras.\n3. Create a Ray Dataset from your dataset.\n4. Define a custom training function using your Keras model and the Ray Dataset.\n5. Use the `TorchTrainer` from Ray Train to set up Multi-worker training with your custom training function.\n\nHere's a sample code snippet based on the context:\n\n```python\nimport tensorflow as tf\nimport ray\nfrom ray.train.tensorflow import TensorflowTrainer\n\n# Set up your dataset and model\n# ...\n\n# Create a Ray Dataset\nds = ray.data.from_numpy(data_array)\n\n# Define a custom training function\ndef train_func(model, dataset):\n # Convert the Ray Dataset to a TensorFlow dataset\n tf_dataset = dataset.to_tf()\n\n # Set up the model and compile it\n model.compile(\n optimizer=\"Adam\",\n loss=\"mean_squared_error\",\n metrics=[\"mse\"]\n )\n\n # Train the model\n model.fit(tf_dataset)\n\n# Use the TorchTrainer to set up Multi-worker training\ntrainer = TensorflowTrainer(\n train_func,\n scaling_config=ray.train.ScalingConfig(num_workers=4, use_gpu=use_gpu)\n)\n\n# Fit the model\nresult = trainer.fit()\n```\n\nReplace `data_array` with your actual dataset and adjust the `num_workers` and `use_gpu` parameters according to your needs.",2117"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2118},2119{2120"question": "how to use ray.put and ray,get?",2121"sources": [2122"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",2123"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2124"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2125"https://docs.ray.io/en/master/ray-core/api/core.html#objects",2126"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",2127"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2128"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",2129"https://docs.ray.io/en/master/ray-core/patterns/pass-large-arg-by-value.html#anti-pattern-passing-the-same-large-argument-by-value-repeatedly-harms-performance",2130"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",2131"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-tasks",2132"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2133"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",2134"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples"2135],2136"document_ids": [21375443,21384811,21396597,21404693,21415030,21424209,21434264,21445240,21455035,21465423,21474849,21484205,214953802150],2151"answer": " To use `ray.put()`, you store a value in Ray's object store and get a reference to it. Here's an example:\n\n```python\nvalue_ref = ray.put(value)\n```\n\nTo use `ray.get()`, you get values from the object store using the reference. Here's an example:\n\n```python\nstored_value = ray.get(value_ref)\n```\n\nWhen using tasks or actors, you can pass the object reference as an argument and use `ray.get()` to retrieve the value when needed. Remember that `ray.get()` is a blocking operation, so it will wait for the result if it's not yet available.\n\n`ray.wait()` is used to wait for a list of object references to be ready, returning a list of IDs that are ready and a list of IDs that are not.\n\nTo avoid repeatedly passing large arguments to remote task or method calls, use `ray.put()` to create a copy of the object in the object store and pass the object store reference instead.",2152"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2153},2154{2155"question": "how do I use Ray Data to pre process many files?",2156"sources": [2157"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",2158"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-data",2159"https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",2160"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#code-example",2161"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",2162"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2163"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2164"https://docs.ray.io/en/master/data/data.html#ray-data-scalable-datasets-for-ml",2165"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",2166"https://docs.ray.io/en/master/data/user-guide.html#user-guides",2167"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#preprocessing",2168"https://docs.ray.io/en/master/data/preprocessors.html#built-in-preprocessors",2169"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray"2170],2171"document_ids": [21729178,21739189,21746866,21755233,21769180,21776841,21786839,21796988,21809222,21816834,21828030,21836855,21841422185],2186"answer": " To preprocess many files using Ray Data, you can follow these general steps:\n\n1. Initialize Ray and create a Ray Dataset from your files. Ray Data supports various data sources and formats.\n2. Preprocess your Ray Dataset using operations like `map_batches()` to transform data before training.\n3. If your preprocessed Dataset is small enough to fit in Ray object store memory, materialize it in Ray's built-in object store using `materialize()`. This will avoid re-running preprocessing operations when iterating over the dataset repeatedly.\n\nHere's a simple code example:\n\n```python\nimport ray\n\nray.init()\n\n# Create a Ray Dataset from files or other sources\ntrain_dataset = ray.data.read_csv('path/to/csv_files/*.csv')\n\n# Preprocess your Ray Dataset\ndef preprocess_batch(batch):\n # Add your preprocessing code here\n return preprocessed_batch\n\ntrain_dataset = train_dataset.map_batches(preprocess_batch)\n\n# Materialize the preprocessed dataset if it's small enough\nif train_dataset.count() < (0.3 * ray.cluster_resources()['memory_total_mb']):\n train_dataset = train_dataset.materialize()\n```\n\nFor more specific preprocessing operations, Ray Data provides built-in preprocessors you can use. Check the documentation for more information on these preprocessors and how to use them.",2187"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2188},2189{2190"question": "can\u2019t pickle SSLContext objects",2191"sources": [2192"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",2193"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",2194"https://docs.ray.io/en/master/ray-core/objects/serialization.html#customized-serialization",2195"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2196"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2197"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2198"https://docs.ray.io/en/master/installation.html#installed-python-dependencies",2199"https://docs.ray.io/en/master/ray-overview/installation.html#installed-python-dependencies",2200"https://docs.ray.io/en/master/ray-more-libs/installation.html#installed-python-dependencies",2201"https://docs.ray.io/en/master/ray-core/objects/serialization.html#known-issues",2202"https://docs.ray.io/en/master/installation.html#installed-python-dependencies",2203"https://docs.ray.io/en/master/ray-more-libs/installation.html#installed-python-dependencies",2204"https://docs.ray.io/en/master/ray-overview/installation.html#installed-python-dependencies"2205],2206"document_ids": [22074939,22084945,22094947,22104957,22114953,22124952,2213106,2214331,221514643,22164958,221794,221814619,22193192220],2221"answer": " Based on the provided context, Ray uses a customized Pickle protocol version 5 backport for serialization, which has wider object compatibility compared to the default Pickle protocol. However, it is still not possible to pickle `SSLContext` objects. To identify non-serializable objects, you can use the `ray.util.inspect_serializability` function.",2222"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2223},2224{2225"question": "How do I install CRDs in Kuberay?",2226"sources": [2227"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",2228"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",2229"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",2230"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",2231"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#getting-started-with-kuberay",2232"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",2233"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",2234"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#installing-the-kuberay-operator",2235"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource",2236"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-2-create-a-raycluster-cr",2237"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/kubeflow.html#step-3-install-kuberay-operator",2238"https://docs.ray.io/en/master/cluster/kubernetes/examples/mobilenet-rayservice.html#step-2-install-kuberay-operator",2239"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites"2240],2241"document_ids": [22425720,22436103,22445647,22455817,22465646,22475658,22486155,224913933,22505722,22515981,22526092,22536120,225457522255],2256"answer": " To install Custom Resource Definitions (CRDs) in Kuberay, follow these steps:\n\n1. Add the Kuberay Helm chart repository:\n ```\n helm repo add kuberay https://ray-project.github.io/kuberay-helm/\n ```\n\n2. Install both CRDs and the KubeRay operator v0.6.0:\n ```\n helm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n ```\n\nThe CRDs will be installed as part of the KubeRay operator installation.",2257"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2258},2259{2260"question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",2261"sources": [2262"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",2263"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",2264"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",2265"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",2266"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2267"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2268"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2269"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2270"https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",2271"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",2272"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",2273"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",2274"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format"2275],2276"document_ids": [2277140,227814578,22798038,22808161,22816788,22827003,22838346,22846847,22856727,22867971,22879180,22887973,228967902290],2291"answer": " The function for Ray data batch inference has to be named as `__call__()` because it is a required method name for a callable object in Python, which is used to apply a function to each batch of data in Ray Data's `map_batches()` method. This allows for parallel processing of data batches during batch inference.",2292"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2293},2294{2295"question": "How to disconnnect ray client?",2296"sources": [2297"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2298"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",2299"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",2300"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",2301"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-5-change-the-ray-image-from-rayproject-ray-ray-version-to-rayproject-ray-ml-ray-version",2302"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",2303"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",2304"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",2305"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",2306"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#starting-a-connection-on-older-ray-versions",2307"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",2308"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#developing-ray-serve-python-scripts-on-a-raycluster",2309"https://docs.ray.io/en/master/cluster/vms/getting-started.html#deleting-a-ray-cluster"2310],2311"document_ids": [23126597,23136613,23146611,23156605,23165985,23176587,23186599,23196583,232014483,23216616,23226591,23235979,232462072325],2326"answer": " To disconnect a Ray client, you can use the `disconnect()` method on the Ray client object. Here is an example:\n\n```\ncli1.disconnect()\ncli2.disconnect()\n```\n\nThis will close the connection to the Ray cluster. Note that object references can only be used by the client from which they were obtained, so you will not be able to use any objects or actor references held by the server on behalf of the client after disconnecting. If the client disconnects unexpectedly, the client will attempt to reconnect to the server for a certain period of time before giving up. You can increase this time by setting the `RAY_CLIENT_RECONNECT_GRACE_PERIOD` environment variable.",2327"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2328},2329{2330"question": "how to submit job with python with local files?",2331"sources": [2332"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2333"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2334"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2335"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2336"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm-launch.html#slurm-launch-py",2337"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job",2338"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",2339"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#dependency-management",2340"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2341"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",2342"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",2343"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#submit-the-workload",2344"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#submit-the-workload"2345],2346"document_ids": [23476460,23486516,23496467,23506518,23516575,23524255,23536510,23546530,23556522,23566557,23576635,23586179,235964162360],2361"answer": " To submit a job with Python using local files, you can use the Ray Jobs API. Here's a step-by-step guide:\n\n1. Install Ray: Follow the instructions in the [Ray documentation](https://docs.ray.io/en/latest/installation.html) to install Ray on your local machine.\n\n2. Create a Python script: Write your Python code in a script, e.g., `script.py`.\n\n3. Create a runtime environment: Define a runtime environment that specifies the working directory and any required dependencies. For local clusters, the working directory can be specified as a local directory path.\n\n4. Submit the job: Use the `ray job submit` command to submit the job. Make sure to specify the path to the working directory in the `--working-dir` argument.\n\nHere's an example:\n\n```bash\n# runtime_env.json\n{\n \"working_dir\": \"./\"\n}\n\n# Submit the job\nray job submit --working-dir your_working_directory --runtime-env-json=runtime_env.json -- python script.py\n```\n\nReplace `your_working_directory` with the path to the directory containing `script.py` and `runtime_env.json`.\n\nIf you're using a remote cluster, replace the local working directory path with the remote path, e.g., an S3 bucket or Git repository.\n\nFor more information, refer to the [Ray Jobs documentation](https://docs.ray.io/en/latest/jobs.html).",2362"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2363},2364{2365"question": "How do I do inference from a model trained by Ray tune.fit()?",2366"sources": [2367"https://docs.ray.io/en/master/data/batch_inference.html#using-models-from-ray-train",2368"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#putting-it-together",2369"https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",2370"https://docs.ray.io/en/master/tune/examples/bayesopt_example.html#running-tune-experiments-with-bayesopt",2371"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve",2372"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2373"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve",2374"https://docs.ray.io/en/master/tune/examples/tune-sklearn.html#using-bayesian-optimization",2375"https://docs.ray.io/en/master/tune/faq.html#how-can-i-pass-further-parameter-values-to-my-trainable",2376"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#tuning-the-configuration-parameters",2377"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2378"https://docs.ray.io/en/master/tune/examples/bayesopt_example.html#running-tune-experiments-with-bayesopt",2379"https://docs.ray.io/en/master/tune/examples/includes/bayesopt_example.html#bayesopt-example"2380],2381"document_ids": [23826927,23833223,2384986,23853093,23862774,23876879,23882775,23892645,23901033,23912669,23926887,23933089,239440952395],2396"answer": " To do inference from a model trained by Ray tune.fit(), you can use the Checkpoint returned by Ray Train to make predictions on new data. Here's a general outline of how to do this:\n\n1. Train a model with Ray Train and get the Checkpoint:\n```python\nfrom ray.train import Checkpoint\n\ncheckpoint = train.fit(...)\n```\n2. Use the Checkpoint to make predictions:\n```python\nimport torch\n\n# Load the model from the Checkpoint\nmodel = checkpoint.to_torch_model()\n\n# Prepare your data for inference\ndata = ...\n\n# Make predictions\nwith torch.no_grad():\n outputs = model(data)\n```\n\nRemember to replace the ellipsis (`...`) with your actual code for loading the data and making predictions. The exact code will depend on the model and data you are using.",2397"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2398},2399{2400"question": "is there a way to load and run inference without using pytorch or tensorflow directly?",2401"sources": [2402"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2403"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2404"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2405"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-2-inference-on-a-single-batch",2406"https://docs.ray.io/en/master/data/user-guide.html#user-guides",2407"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2408"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2409"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",2410"https://docs.ray.io/en/master/data/batch_inference.html#optimizing-expensive-cpu-preprocessing",2411"https://docs.ray.io/en/master/rllib/rllib-torch2x.html#some-meta-level-comments",2412"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",2413"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",2414"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"2415],2416"document_ids": [24176959,24186963,24196899,24208021,24216836,24226847,24236889,24248161,24256920,242611825,24277971,24288036,242968872430],2431"answer": " Yes, it is possible to load and run inference without using PyTorch or TensorFlow directly by using the PyTorch model within a custom predictor class, as demonstrated in the provided context. This approach allows for scalable offline batch inference using Ray Datasets and can also utilize GPUs for inference.",2432"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2433},2434{2435"question": "what does ray do",2436"sources": [2437"https://docs.ray.io/en/master/data/data.html#learn-more",2438"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2439"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",2440"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2441"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2442"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",2443"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2444"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment",2445"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice",2446"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",2447"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2448"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",2449"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"2450],2451"document_ids": [24526995,24534811,24545556,24556597,24565380,24575656,245813980,24591386,24605780,24619668,2462245,24634469,24644752465],2466"answer": " Ray is a universal array API, distributed execution engine, and a collection of libraries for machine learning and AI. It allows you to parallelize and scale Python code easily. You can use Ray's core API by initiating a Ray cluster with 'ray.init()', and then use Ray's various libraries like Ray Jobs, Ray Serve, Ray Tune, and RLlib for specific use-cases. Additionally, Ray can be integrated with Kubernetes, and it provides a client API, CLI, and RAY service for managing Ray applications. To learn more, you can refer to the Ray blogs, tutorials, examples, and documentation. The 'ray get actors <ACTOR_ID>' command is used to get information about Ray actors.",2467"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2468},2469{2470"question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",2471"sources": [2472"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",2473"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",2474"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus",2475"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",2476"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",2477"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2478"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2479"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",2480"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",2481"https://docs.ray.io/en/master/tune/api/sklearn.html#tunegridsearchcv",2482"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#more-resources",2483"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2484"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus"2485],2486"document_ids": [248713457,24884999,24892738,24905116,24911025,249211083,249311103,249411353,24953222,24961219,24979255,24984851,249932212500],2501"answer": " If you specify a fractional GPU in the resource spec, and your task uses more resources than allocated, it could lead to undefined behavior or errors. It is the user's responsibility to ensure that multiple tasks can share GPUs and there is enough memory to do so. Ray does not automatically handle this for you.",2502"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2503},2504{2505"question": "how to pickle a variable defined in actor\u2019s init method",2506"sources": [2507"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",2508"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2509"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",2510"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",2511"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",2512"https://docs.ray.io/en/master/ray-core/patterns/concurrent-operations-async-actor.html#example-use-case",2513"https://docs.ray.io/en/master/ray-core/patterns/return-ray-put.html#code-example",2514"https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",2515"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#memory-profiling-ray-tasks-and-actors",2516"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",2517"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",2518"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",2519"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles"2520],2521"document_ids": [25225194,25235192,25244241,2525637,25264664,25275162,25285181,25294406,2530568,2531520,25324351,253311795,2534146982535],2536"answer": " To pickle a variable defined in an actor's `__init__` method, you can't directly pickle the actor object itself. However, you can pass the actor handle to other tasks or actor methods and use it there.\n\nIn the provided context, `global_var_actor` is an actor handle that is passed to the `Actor` class during instantiation. This handle can then be used in other methods of the `Actor` class, such as `f()`.\n\nHere's an example of how to pass the actor handle to another remote function:\n\n```python\n@ray.remote\ndef use_actor(actor_handle):\n return actor_handle.f.remote()\n\n# Create the actor\nactor = Actor.remote(global_var_actor)\n\n# Use the actor in another remote function\nresult = use_actor.remote(actor)\n\n# Get the result\nprint(ray.get(result))\n```\n\nIn this example, `use_actor()` is a remote function that takes an actor handle as an argument and calls the `f()` method on that actor. The `actor` handle is then passed to `use_actor()` when it is called remotely.\n\nNote that you can't directly pickle the `actor` object, but you can pass its handle around and use it in other remote functions or actor methods.",2537"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2538},2539{2540"question": "how do I do an all_reduce operation among a list of actors",2541"sources": [2542"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2543"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",2544"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",2545"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",2546"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2547"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",2548"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",2549"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",2550"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map.html#ray-util-actorpool-map",2551"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner_group.LearnerGroup.update.html#ray-rllib-core-learner-learner-group-learnergroup-update",2552"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner_group.LearnerGroup.async_update.html#ray-rllib-core-learner-learner-group-learnergroup-async-update",2553"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#collective-communication",2554"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map_unordered.html#ray-util-actorpool-map-unordered"2555],2556"document_ids": [25576787,2558673,2559788,25605441,25618369,2562641,256314448,25645468,25654602,256611383,256712803,256814426,256949372570],2571"answer": " To perform an all\\_reduce operation among a list of actors using Ray, you can use the `ray.util.collective.allreduce` function. Here's an example:\n\n1. First, create a list of actors.\n2. Then, create a collective group using `ray.util.collective.create_collective_group`.\n3. Perform the all\\_reduce operation using `ray.util.collective.allreduce`.\n\nHere's some example code:\n\n```python\nimport ray\nfrom ray.util.collective import allreduce, create_collective_group\n\n# Create a list of actors\nactors = [BatchPredictor.remote(model_ref) for _ in range(num_actors)]\n\n# Create a collective group\ngroup = create_collective_group(actors, world_size=num_actors, ranks=list(range(num_actors)), backend='nccl', group_name='default')\n\n# Perform the all_reduce operation\nresult = allreduce(input_data, op=allreduce.SUM, group=group)\n```\n\nIn this example, `input_data` is the data you want to perform the all\\_reduce operation on. The `op` parameter specifies the reduction operation (in this case, summation). The `group` parameter specifies the collective group to use for the operation. The result of the all\\_reduce operation will be stored in the `result` variable.",2572"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2573},2574{2575"question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",2576"sources": [2577"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",2578"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",2579"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",2580"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",2581"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#bundles",2582"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",2583"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",2584"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",2585"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",2586"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",2587"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",2588"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2589"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy"2590],2591"document_ids": [25925063,25934790,25944789,25955051,25965050,25975055,25985060,259914262,260014133,26015054,260211365,260311115,260450762605],2606"answer": " If you specify a bundle with `{\"CPU\":0}` in the PlacementGroup, it will not be able to create the PlacementGroup. This is because the current cluster only has 2 CPUs left, and a bundle with 1 CPU is required. Additionally, creating a bundle with 0 CPUs is not allowed. Even if you specify a bundle with only GPU resources, the PlacementGroup will still not be created if there are not enough available GPUs in the cluster.",2607"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2608},2609{2610"question": "How to cancel job from UI?",2611"sources": [2612"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",2613"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",2614"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",2615"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",2616"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2617"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobStatus.STOPPED.html#ray-job-submission-jobstatus-stopped",2618"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",2619"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#interacting-with-long-running-jobs",2620"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2621"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2622"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",2623"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.stop_job.html#ray-job-submission-jobsubmissionclient-stop-job",2624"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks"2625],2626"document_ids": [26274430,26286478,26299791,26304498,26314894,26326690,26336710,26346528,26354359,263611802,263714702,26386686,263943572640],2641"answer": " To cancel a job from the UI, you can use the `ray job stop` command followed by the job ID. For example:\n\n```\n$ ray job stop raysubmit_tUAuCKubPAEXh6CW\n```\n\nThis will request the job to exit asynchronously. If you want to cancel a workflow, you can use `ray.workflow.cancel` and pass the workflow ID as an argument.\n\nIf you want to cancel a task, you can call `ray.cancel` on the returned Object Ref. For example:\n\n```\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n```\n\nThis will attempt to cancel the task, and if it is successful, invoking `ray.get(obj_ref)` will produce a `TaskCancelledError`. Note that the cancellation behavior depends on the task's current state. For unscheduled tasks, Ray attempts to cancel the scheduling. For running actor tasks, Ray offers no mechanism for interruption. For running async actor tasks, Ray seeks to cancel the associated `asyncio.Task`.",2642"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2643},2644{2645"question": "how do I get my project files on the cluster when using Ray Serve? My workflow is to call `serve deploy config.yaml --address <remote dashboard agent address>`",2646"sources": [2647"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",2648"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",2649"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2650"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#development-workflow-with-serve-run",2651"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2652"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",2653"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",2654"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",2655"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",2656"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup",2657"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",2658"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2659"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#reference-your-cloudwatch-json-config-files-in-your-cluster-config-yaml"2660],2661"document_ids": [266213738,266313732,26646484,266513696,26666465,266713962,266813824,26696514,267013734,26716458,26724262,267313852,267463532675],2676"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. When deploying to a remote cluster, you can use the `serve deploy` command with the `--address` option to specify the remote Ray cluster\u2019s dashboard agent address, which should be in the format of `[RAY_CLUSTER_URI]:[DASHBOARD_AGENT_PORT]`.\n\nFor example, if you have a `config.yaml` file and a local directory called `project` that contains your project files, you can use the following command to deploy your application to a remote cluster:\n\n`serve deploy config.yaml -a [RAY_CLUSTER_URI]:[DASHBOARD_AGENT_PORT] --working-dir /path/to/project`\n\nMake sure to replace `[RAY_CLUSTER_URI]` and `[DASHBOARD_AGENT_PORT]` with the actual URI and port of your remote Ray cluster, and replace `/path/to/project` with the actual path to your `project` directory on your local machine.",2677"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2678},2679{2680"question": "how do i install ray nightly wheel",2681"sources": [2682"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",2683"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2684"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2685"https://docs.ray.io/en/master/installation.html#from-wheels",2686"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",2687"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",2688"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2689"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2690"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2691"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2692"https://docs.ray.io/en/master/installation.html#installed-python-dependencies",2693"https://docs.ray.io/en/master/ray-overview/installation.html#installed-python-dependencies",2694"https://docs.ray.io/en/master/ray-more-libs/installation.html#installed-python-dependencies"2695],2696"document_ids": [269714549,269863,2699288,270061,2701286,270214545,27036597,2704271,27056427,27066354,270796,2708321,2709146232710],2711"answer": " To install the Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use pip to install the nightly wheel with support for the dashboard and cluster launcher by using the command `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`, where `LINK_TO_WHEEL.whl` is the link to the nightly wheel file. You can find the appropriate link for your system and Python version in the context provided.",2712"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2713},2714{2715"question": "how do i install the latest ray nightly wheel?",2716"sources": [2717"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2718"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2719"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",2720"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",2721"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",2722"https://docs.ray.io/en/master/installation.html#from-wheels",2723"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2724"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#install-the-latest-ray-release",2725"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2726"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2727"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2728"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2729"https://docs.ray.io/en/master/installation.html#installed-python-dependencies"2730],2731"document_ids": [2732288,273363,273414549,273514545,2736286,273761,27386597,27395820,2740271,27416354,27426427,27436396,2744962745],2746"answer": " To install the latest Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use pip to install the nightly wheel with support for the dashboard and cluster launcher by using the following command: `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`, where `LINK_TO_WHEEL.whl` is the link to the nightly wheel file.\n\nNote that the specific link to the nightly wheel file will depend on your operating system and Python version. You can find the appropriate link in the context provided, which includes links to various nightly wheels for different configurations of Linux and Python versions.",2747"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2748},2749{2750"question": "how can I write unit tests for Ray code?",2751"sources": [2752"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",2753"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",2754"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",2755"https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",2756"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2757"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",2758"https://docs.ray.io/en/master/ray-contribute/debugging.html#debugging-for-ray-developers",2759"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",2760"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#understanding-ci-test-jobs",2761"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#library-development",2762"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",2763"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",2764"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code"2765],2766"document_ids": [27675397,27685395,27699668,27704403,27715380,277214558,2773944,27745757,2775928,27764270,2777246,27785414,277966052780],2781"answer": " To write unit tests for Ray code, you can follow these tips:\n\n1. Fix the resource quantity with `ray.init(num_cpus=...)`.\n2. Share the Ray cluster across tests if possible.\n3. Create a mini-cluster with `ray.cluster_utils.Cluster`.\n4. Be careful when running tests in parallel.\n\nHere's an example of how to set up a test case with a shared Ray cluster:\n\n```python\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n def setUp(self):\n ray.init(num_cpus=4, num_gpus=0)\n\n def test_my_function(self):\n # Your test code here\n pass\n\n def tearDown(self):\n ray.shutdown()\n```\n\nMake sure to call `ray.shutdown()` in the `tearDown` method to release resources after each test.\n\nIf you're working with Java and Ray, you can use TestNG for testing:\n\n```java\nimport io.ray.api.ObjectRef;\nimport io.ray.api.Ray;\nimport org.testng.Assert;\n\npublic class JavaCallPythonDemo {\n\n public static void main(String[] args) {\n Ray.init();\n\n // Your test code here\n\n Ray.shutdown();\n }\n}\n```\n\nIn this example, call `Ray.shutdown()` after your tests to release resources.",2782"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2783},2784{2785"question": "How I stop Ray from spamming lots of Info updates on stdout?",2786"sources": [2787"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",2788"https://docs.ray.io/en/master/cluster/cli.html#ray-up",2789"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",2790"https://docs.ray.io/en/master/cluster/cli.html#ray-start",2791"https://docs.ray.io/en/master/cluster/cli.html#ray-stop",2792"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",2793"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",2794"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#disable-logging-to-the-driver",2795"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2796"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",2797"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2798"https://docs.ray.io/en/master/ray-overview/installation.html#launch-ray-in-docker",2799"https://docs.ray.io/en/master/installation.html#launch-ray-in-docker"2800],2801"document_ids": [2802410,28035616,280413929,28055611,28065613,28075880,2808524,2809523,28104811,28114469,281213517,2813312,2814872815],2816"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 redirect Ray logs to stderr by setting the environment variable `RAY_LOG_TO_STDERR=1` on all Ray nodes. However, be aware that there are known issues with this feature and it may break features like Worker log redirection to Driver.",2817"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2818},2819{2820"question": "how to deploy stable diffusion 2.1 with Ray Serve?",2821"sources": [2822"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",2823"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",2824"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#serve-a-stablediffusion-text-to-image-model-on-kubernetes",2825"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-7-submit-a-request-to-the-ray-serve-application",2826"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",2827"https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",2828"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",2829"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",2830"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2831"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",2832"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",2833"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",2834"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"2835],2836"document_ids": [28376127,283814398,28396124,28405989,284114255,284213766,28435636,28445671,284513980,284610138,284713622,284813734,2849136242850],2851"answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install Ray and Ray Serve: `pip install \"ray[serve]\"`\n2. Prepare your Stable Diffusion script (e.g., `stable_diffusion.py`)\n3. Download the RayService configuration for Stable Diffusion: `curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml`\n4. Modify the configuration file to point to your `stable_diffusion.py` script\n5. Deploy the configuration to your Ray cluster: `serve deploy ray-service.stable-diffusion.yaml`\n\nEnsure that your script works locally before deploying it to a RayService.",2852"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2853},2854{2855"question": "what is actor_handle?",2856"sources": [2857"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",2858"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",2859"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",2860"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.current_actor.html#ray-runtime-context-runtimecontext-current-actor",2861"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",2862"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",2863"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",2864"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",2865"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2866"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#terminating-actors",2867"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",2868"https://docs.ray.io/en/master/ray-core/actors/task-orders.html#actor-task-execution-order",2869"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ObjectState.html#ray-util-state-common-objectstate"2870],2871"document_ids": [287211795,287314698,28744351,28754874,287614699,287711797,28784353,28794200,28804606,28814598,28824664,28834507,28847862885],2886"answer": " Actor handle is a reference to an actor object, which can be passed into other tasks and used to call remote functions or methods on that actor. It allows for communication and interaction with the actor and its stateful worker. The context shows examples of using actor handles in Python and Java.",2887"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2888},2889{2890"question": "how to kill a r detached actors?",2891"sources": [2892"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2893"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",2894"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2895"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2896"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",2897"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",2898"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2899"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2900"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",2901"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",2902"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2903"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2904"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes"2905],2906"document_ids": [29074859,29084658,29094601,29104848,29114504,29124502,29134615,29144606,29154493,29164455,29174604,29184622,291946542920],2921"answer": " To kill a detached Ray actor, you can use the `ray.kill()` function and provide the actor handle as an argument. Here is an example:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nactor_handle = Actor.remote()\n\n# Kill the actor\nray.kill(actor_handle)\n```\n\nDetached actors are not automatically garbage-collected by Ray, so you need to manually destroy them once you are sure that they are no longer needed.\n\nIf you want to kill the actor but let pending tasks finish, you can call `actor.__ray_terminate__.remote()` instead. Any atexit handlers installed in the actor will be run in this case.\n\nNote that if you kill a detached actor, subsequent calls to get its handle via `ray.get_actor()` will fail.",2922"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2923},2924{2925"question": "How to force upgrade the pip package in the runtime environment if an old version exists?",2926"sources": [2927"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2928"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2929"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2930"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",2931"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",2932"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",2933"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",2934"https://docs.ray.io/en/master/ray-contribute/development.html#prepare-the-python-environment",2935"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",2936"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2937"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2938"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",2939"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging"2940],2941"document_ids": [29424624,29434292,29444380,29454260,29464267,29474268,294813848,2949882,295010393,29514288,29524382,2953884,295443742955],2956"answer": " To force upgrade the pip package in the runtime environment if an old version exists, you can specify the pip version in the runtime environment as shown in the example:\n```python\nRuntimeEnv(\n pip={\"packages\":[\"tensorflow\", \"requests\"], \"pip_check\": False,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"}\n)\n```\nIn this example, the pip version is set to `22.0.2` for Python version `3.8.11`. This will ensure that pip is upgraded to the specified version during runtime. Note that the conda and pip keys of runtime\\_env cannot both be specified at the same time, so you should choose one or the other.\n\nIf you want to upgrade pip itself, you can do so by running `python -m pip install --upgrade pip` in your virtual environment. This will upgrade pip to the latest version.",2957"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2958},2959{2960"question": "How do I do global shuffle with Ray?",2961"sources": [2962"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2963"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",2964"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2965"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2966"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2967"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2968"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2969"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",2970"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Experiment.html#ray-tune-experiment",2971"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",2972"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2973"https://docs.ray.io/en/master/data/performance-tips.html#optimizing-shuffles",2974"https://docs.ray.io/en/master/ray-core/cross-language.html#cross-language-programming"2975],2976"document_ids": [29776826,29786928,29796827,29806825,29819206,29826597,29834811,29847395,29851386,29866795,29879209,29886820,298943902990],2991"answer": " To do a global shuffle with Ray, you can use the `random_shuffle()` function on a Ray Data dataset. Here is an example:\n```\nimport ray\n\n# Create a Ray Data dataset\nds = ray.data.range(1000)\n\n# Do a global shuffle of all rows in this dataset\nds = ds.random_shuffle()\n\n# To use the push-based shuffle, set the environment variable RAY_DATA_PUSH_BASED_SHUFFLE=1\n# and set the DataContext.use_push_based_shuffle flag to True\nctx = ray.data.DataContext.get_current()\nctx.use_push_based_shuffle = True\n```\nNote that shuffles materialize Datasets in memory, so they can be expensive in terms of memory and computation. For an in-depth guide on shuffle performance, see the [Performance Tips and Tuning](https://docs.ray.io/en/latest/data/performance.html) page.\n\nAlso, keep in mind that there are two approaches to random shuffling in Ray Data: shuffling data blocks and local shuffling on each training worker, and full global shuffle. The former is less expensive and may suffice for most cases, while the latter is more expensive and will fully decorrelate row iteration order from the original dataset order.\n\nYou can specify the shuffle implementation during program execution by setting the `RAY_DATA_SHUFFLE_IMPL` environment variable to either `'auto'`, `'block'`, or `'global'`. For example:\n```\n# Use the block-based shuffle implementation\n$ RAY_DATA_SHUFFLE_IMPL=block python my_script.py\n```\nFor more information, see the [Shuffling rows](https://docs.ray.io/en/latest/data/shuffle.html#shuffling-rows) page.",2992"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2993},2994{2995"question": "How to find namespace of an Actor?",2996"sources": [2997"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",2998"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",2999"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3000"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",3001"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace",3002"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3003"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",3004"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",3005"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3006"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3007"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3008"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3009"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"3010],3011"document_ids": [30124719,30134871,30144709,30154880,30164751,301714746,30184726,30194699,30204640,30214711,3022474,30234631,302446373025],3026"answer": " To find the namespace of an Actor in Ray, you can use the `ray.get_actor()` function, which returns an `ActorHandle` to the actor. The `ray.get_actor()` function takes a `name` and an optional `namespace` parameter. If the namespace is not specified, the current namespace is used.\n\nHere is an example of how to use the `ray.get_actor()` function to find the namespace of an Actor:\n```\nimport ray\n\n# Get a handle to the actor\nactor_handle = ray.get_actor(\"actor_name\")\n\n# Get the namespace of the actor\nactor_namespace = actor_handle.namespace\nprint(actor_namespace)\n```\nAlternatively, you can specify the namespace when calling `ray.get_actor()`:\n```\nimport ray\n\n# Get a handle to the actor in the specified namespace\nactor_handle = ray.get_actor(\"actor_name\", namespace=\"my_namespace\")\n\n# The namespace is not needed in this case, as it is already specified\nprint(actor_handle.namespace) # prints \"my_namespace\"\n```\nNote that actors are only accessible within their namespaces, and that the namespace must be specified when creating the actor. If no namespace is specified, Ray will place the actor in an anonymous namespace.",3027"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3028},3029{3030"question": "How does Ray work with async.io ?",3031"sources": [3032"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3033"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",3034"https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks",3035"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",3036"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",3037"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",3038"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",3039"https://docs.ray.io/en/master/ray-core/actors/task-orders.html#synchronous-single-threaded-actor",3040"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",3041"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",3042"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",3043"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3044"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary"3045],3046"document_ids": [30474208,30484550,30494199,30506597,30514553,30524572,30534578,30544509,30555443,30565236,30576589,30584811,305954423060],3061"answer": " Ray's invocation of remote operations is asynchronous, returning a promise/future that can be used to launch multiple operations in parallel. To integrate with asyncio, Ray provides the `asyncio.run(async_get())` function to run the event loop and process the results asynchronously. This allows you to use Ray alongside popular async frameworks like aiohttp, aioredis, etc. However, it's important to note that in async actors, only one task can be running at any point in time, and there will be only one thread in AsyncActor. The `ray.get()` function should be used carefully to avoid blocking the execution of the event loop.",3062"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3063},3064{3065"question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",3066"sources": [3067"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",3068"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",3069"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-tasks",3070"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",3071"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",3072"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#stepping-between-ray-tasks",3073"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3074"https://docs.ray.io/en/master/ray-core/patterns/ray-get-too-many-objects.html#anti-pattern-fetching-too-many-objects-at-once-with-ray-get-causes-failure",3075"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",3076"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3077"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",3078"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#non-blocking-calls",3079"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger"3080],3081"document_ids": [308214432,30835235,30845423,30855236,30865219,3087581,30884209,30895169,309013672,30914214,30924645,30935428,30945733095],3096"answer": " To debug a hanging `ray.get()` call, there are a few things to consider. First, `ray.get()` is a blocking call, which means it waits until the requested result is available. If you call `ray.get()` in a loop, the loop will not continue to run until the call to `ray.get()` is resolved. This can harm parallelism, as you wait for the previous function call to finish before spawning the next call in the next iteration of the loop.\n\nTo avoid this, you can separate the call to `ray.get()` from the call to the remote functions. This way, all remote functions are spawned before you wait for the results, allowing them to 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.\n\nAnother anti-pattern to avoid is calling `ray.get()` unnecessarily for intermediate steps. If you don't need to manipulate the object, you probably don't need to call `ray.get()` on it. 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.\n\nIf you're still having trouble, you can use Ray's built-in debugger to set breakpoints in your Ray tasks and actors and inspect variables in that context, step within that task or actor, move up or down the stack, and more. However, keep in mind that the Ray Debugger is an experimental feature and is currently unstable.",3097"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3098},3099{3100"question": "can you show me an example of ray.actor.exit_actor()",3101"sources": [3102"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3103"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3104"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3105"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3106"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3107"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3108"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",3109"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",3110"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",3111"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3112"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3113"https://docs.ray.io/en/master/ray-core/actors.html#more-about-ray-actors",3114"https://docs.ray.io/en/master/rllib/actors.html#more-about-ray-actors"3115],3116"document_ids": [31174615,31184601,31194618,31204622,31214606,31224504,312311811,31244369,312514707,31264859,31274658,31284373,3129118153130],3131"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 is an example:\n\n```python\n@ray.remote\nclass Actor:\n def exit(self):\n ray.actor.exit_actor()\n\nactor = Actor.remote()\nactor.exit.remote()\n```\n\nHowever, it is generally not necessary to manually terminate actors, as they are automatically garbage collected. The object ref resulting from the task can be waited on to wait for the actor to exit.\n\nTo forcefully terminate an actor, you can use the `ray.kill()` function. This should be reserved for cases where an actor is unexpectedly hanging or leaking resources. Here is an example:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nactor_handle = Actor.remote()\n\nray.kill(actor_handle)\n```\n\nThis will not go through the normal Python sys.exit teardown logic, so any exit handlers installed in the actor using `atexit` will not be called.\n\nNote that garbage collection for actors has not been implemented yet, so this is currently the only way to terminate an actor gracefully. The object ref resulting from the task can be waited on to wait for the actor to exit.",3132"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3133},3134{3135"question": "how to add log inside actor?",3136"sources": [3137"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",3138"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",3139"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",3140"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#add-metadata-to-structured-logs",3141"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",3142"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3143"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",3144"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3145"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3146"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",3147"https://docs.ray.io/en/master/ray-observability/getting-started.html#actors-view",3148"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",3149"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"3150],3151"document_ids": [3152414,3153540,3154682,3155537,3156446,3157477,3158520,315914295,316014286,316114106,3162434,316314202,3164143183165],3166"answer": " To add log inside an actor, you can use the logging library in Python. Here is an example:\n\n1. Import the logging library.\n2. Get the logger by calling `logging.getLogger(__name__)`.\n3. Use the logger to log messages with different levels, such as `logger.info(msg)`, `logger.error(msg)`, etc.\n\nHere is an example of how to use this in an actor:\n\n```python\ndef log(self, msg):\n logger = logging.getLogger(__name__)\n logger.info(msg)\n\nactor = Actor.remote()\nray.get(actor.log.remote(\"A log message for an actor.\"))\n```\n\nBy default, all stdout and stderr of Tasks and Actors are redirected to the Driver output. If you want to disable this feature, you can refer to the documentation on Configuring Logging.\n\nNote that if a task is from a concurrent actor (i.e. an async actor or a threaded actor), the log of the tasks are expected to be interleaved. In this case, you can use `ray logs actor --id` to get the entire actor log.\n\nYou can also add metadata to structured logs by fetching the metadata of Jobs, Tasks or Actors with Ray's `ray.runtime_context.get_runtime_context` API. For example, to get the job ID, you can use the following code:\n\n```python\nimport ray\n\n# Initiate a driver.\nray.init()\n\njob_id = ray.get_runtime_context().get_job_id\n```\n\nFor more information, you can refer to the documentation on Add metadata to structured logs.",3167"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3168},3169{3170"question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",3171"sources": [3172"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt",3173"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",3174"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",3175"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",3176"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",3177"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#next-steps",3178"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#next-steps",3179"https://docs.ray.io/en/master/rllib/rllib-offline.html#input-api",3180"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",3181"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",3182"https://docs.ray.io/en/master/data/examples/batch_training.html#run-batch-training-using-map-groups",3183"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",3184"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"3185],3186"document_ids": [318710923,31887002,31895292,31906853,31917001,319210285,319310137,319411349,31958003,31968357,31977890,319814576,319968813200],3201"answer": " Sure, I can provide a script to do batch inference with GPT-2 on text data from an S3 bucket using Ray Train and Hugging Face's Transformers library. Here's an example script:\n```python\nimport os\nimport ray\nfrom transformers import pipeline, AutoTokenizer, GPT2ForSequenceClassification\n\n# Download the fine-tuned model checkpoint from S3\nos.system(f\"aws s3 cp s3://your-bucket/checkpoint/ . --recursive\")\n\n# Load the fine-tuned model and tokenizer\nmodel = GPT2ForSequenceClassification.from_pretrained(\"checkpoint\")\ntokenizer = AutoTokenizer.from_pretrained(\"checkpoint\")\n\n# Create a pipeline for generating text\ngenerate_text = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n\n# Read text data from S3 in batches\nray.init()\nds = ray.data.read_text(\"s3://your-bucket/data/*.txt\")\n\n# Map the batches to the generate_text function\nresults = ds.map_batches(lambda batch: generate_text(batch), batch_size=4)\n\n# Save the results to S3\nresults.write_text(\"s3://your-bucket/results/\")\n```\nThis script assumes that you have already fine-tuned a GPT-2 model and saved the checkpoint to an S3 bucket. It uses Ray Train to read the text data from S3 in batches and applies the `generate_text` function to each batch using `map_batches`. Finally, it saves the results to S3 using `write_text`.\n\nNote that this script uses `GPT2ForSequenceClassification` as an example, but you can replace it with any fine-tuned GPT-2 model that is compatible with the `pipeline` function. Also, make sure to replace `your-bucket` with the name of your S3 bucket.",3202"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3203},3204{3205"question": "How do I enable Ray debug logs?",3206"sources": [3207"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",3208"https://docs.ray.io/en/master/ray-contribute/debugging.html#debugging-for-ray-developers",3209"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",3210"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3211"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3212"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#debugger-commands",3213"https://docs.ray.io/en/master/ray-observability/key-concepts.html#debugger",3214"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#running-on-a-cluster",3215"https://docs.ray.io/en/master/ray-observability/getting-started.html#ray-dashboard",3216"https://docs.ray.io/en/master/ray-contribute/debugging.html#backend-logging",3217"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",3218"https://docs.ray.io/en/master/cluster/cli.html#ray-start",3219"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"3220],3221"document_ids": [3222510,3223944,3224573,3225680,32264372,3227579,3228405,3229578,3230416,3231947,32326597,32335611,3234135173235],3236"answer": " To enable Ray debug logs, you can set the `RAY_BACKEND_LOG_LEVEL` environment variable to `debug` before starting Ray. For example:\n```\nexport RAY_BACKEND_LOG_LEVEL=debug\nray start\n```\nThis will print detailed information about events like task execution and object transfers between nodes to the `raylet.err` file.\n\nIf you want to print the full runtime\\_env setup log messages to the driver, you can enable runtime\\_env debugging log streaming by setting an environment variable `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1` on each node before starting Ray.\n\nFor more information, you can refer to the \"Backend logging\" section in the provided context.",3237"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3238},3239{3240"question": "How do I list the current Ray actors from python?",3241"sources": [3242"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3243"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",3244"https://docs.ray.io/en/master/ray-core/actors/actor-utils.html#actor-pool",3245"https://docs.ray.io/en/master/ray-observability/key-concepts.html#ray-states",3246"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3247"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",3248"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.html#ray-util-actorpool",3249"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3250"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",3251"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",3252"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",3253"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",3254"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources"3255],3256"document_ids": [3257475,3258488,32594543,3260402,3261477,3262673,32634742,3264495,326513911,3266636,32674369,326811811,3269147073270],3271"answer": " To list the current Ray actors from Python, you can use the following command:\n```python\nfrom ray.util.state import list_actors\nlist_actors(filters=[(\"state\", \"=\", \"ALIVE\")])\n```\nThis will list all the Ray actors in the cluster that are currently alive.",3272"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3273},3274{3275"question": "I want to kill the replica actor from Python. how do I do it?",3276"sources": [3277"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3278"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",3279"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",3280"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3281"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3282"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#terminating-actors",3283"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3284"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3285"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",3286"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",3287"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",3288"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",3289"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor"3290],3291"document_ids": [32924848,329313912,32944502,32954615,32964859,32974598,32984601,32994622,330013916,330113905,33024896,330313911,330445043305],3306"answer": " To kill a replica actor from Python, you can delete all actor handles referring to it, which will cause them to complete any outstanding work and then shut down. If you want to kill the actor immediately, you can use the `ray.kill(actor)` function. Here is an example:\n\n```\nimport ray\n\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\n\nThis will kill the actor and prevent it from being restarted. If you want to allow the actor to be restarted, you can pass `no_restart=False` to the `ray.kill()` function.\n\nNote: Actors are automatically garbage collected, so you generally should not need to manually terminate them. You can wait for the actor to exit by calling `ray.get()` on the object ref resulting from the task that created the actor. This will raise a `RayActorError` if the actor has already exited.",3307"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3308},3309{3310"question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",3311"sources": [3312"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3313"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",3314"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",3315"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3316"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",3317"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",3318"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3319"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-workload-scheduling",3320"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",3321"https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",3322"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3323"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",3324"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map"3325],3326"document_ids": [33274994,33286548,33294582,33304997,33315000,33326541,333314767,33346017,33354999,33364470,33374996,33386546,333977993340],3341"answer": " To specify that a task should run on a V100 GPU type, you can use the `@ray.remote(num_gpus=1)` decorator when defining the task function. This tells Ray to schedule the task to a node that has a free GPU and assign the GPU to the task. The specific GPU type is not specified in this decorator, so it's up to you to ensure that the node where the task is running has a V100 GPU available.\n\nHere is an example of how you can define a task that runs on a V100 GPU:\n```\n@ray.remote(num_gpus=1)\ndef my_task():\n # Your task code here\n ...\n```\nNote that the `num_gpus` argument in the `@ray.remote` decorator only specifies the number of GPUs to reserve for the task, it does not guarantee that a specific type of GPU will be used. To ensure that your task runs on a V100 GPU, you will need to make sure that the node where the task is running has a V100 GPU available.\n\nAlso note that the actual GPU utilization is not controlled or limited by Ray, it is up to the task to make use of the GPU. This is typically done through an external library like TensorFlow.\n\nYou can check the GPU IDs assigned to a task or actor by calling `ray.get_gpu_ids()` inside the task or actor.\n```\n@ray.remote(num_gpus=1)\nclass GPUActor:\n def ping(self):\n print(\"ray.get_gpu_ids(): {}\".format(ray.get_gpu_ids()))\n```\nTypically, it is not necessary to call `ray.get_gpu_ids()` because Ray will automatically set the `CUDA_VISIBLE_DEVICES` environment variable, which most ML frameworks will respect for purposes of GPU assignment.",3342"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3343},3344{3345"question": "How do I get started?",3346"sources": [3347"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",3348"https://docs.ray.io/en/master/cluster/vms/index.html#learn-more",3349"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",3350"https://docs.ray.io/en/master/serve/index.html#learn-more",3351"https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started",3352"https://docs.ray.io/en/master/train/train.html#get-started",3353"https://docs.ray.io/en/master/ray-overview/getting-started.html#getting-started",3354"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#what-s-next",3355"https://docs.ray.io/en/master/ray-core/cluster/index.html#what-s-next",3356"https://docs.ray.io/en/master/cluster/getting-started.html#what-s-next",3357"https://docs.ray.io/en/master/cluster/vms/getting-started.html#requirements",3358"https://docs.ray.io/en/master/workflows/basics.html#getting-started",3359"https://docs.ray.io/en/master/cluster/vms/getting-started.html#about-the-demo"3360],3361"document_ids": [33626335,33636210,33644702,336513629,33666189,33678125,3368220,336914712,33704684,33715602,33726191,33739624,337461903375],3376"answer": " To get started with Ray, you should first install it using pip by running `pip install -U ray`. Then, you can import and initialize Ray in your Python script with `import ray` and `ray.init()`. If you're using a recent version of Ray (1.5 or later), `ray.init()` is automatically called on the first use of a Ray remote API.\n\nIf you want to get started with Ray on cloud VMs, you can refer to the Ray docs, which provide all the information you need to start running Ray workloads on VMs. The docs include tutorials on how to start a Ray cluster, deploy Ray applications in the cloud, and try example Ray workloads. You can also find user guides and API references for configuring cloud clusters and using Ray's remote APIs.\n\nIf you're interested in using Ray for distributed training, you can check out the Ray Train documentation, which includes tutorials on how to use Ray Train with PyTorch, PyTorch Lightning, and Hugging Face Transformers.\n\nFor more information on getting started with Ray, you can refer to the \"Getting Started\" sections of the Ray and Ray Serve documentation, which provide quick start tutorials and overviews of the key concepts and main ways of interacting with a Ray cluster. You can also find guides on how to run Ray on Kubernetes, on a cloud provider, and on an existing Ray cluster.",3377"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3378},3379{3380"question": "How to specify python version in runtime_env?",3381"sources": [3382"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3383"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",3384"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",3385"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3386"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",3387"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3388"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3389"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3390"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",3391"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",3392"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3393"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#tuning-the-configuration-parameters",3394"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv"3395],3396"document_ids": [33974624,33984253,33994466,34004610,340113856,34024292,34034380,34044621,34054826,340614217,34074386,34082677,340946173410],3411"answer": " To specify the Python version in the runtime environment, you can use the `pip_version` parameter in the `RuntimeEnv` configuration. Here is an example:\n\n```python\nfrom ray.runtime_env import RuntimeEnv\n\nruntime_env = RuntimeEnv(\n pip=[\"emoji\"],\n pip_version=\"==3.8.11\"\n)\n```\n\nIn this example, the runtime environment will use Python version 3.8.11 and install the `emoji` package using pip. Note that you cannot specify both `conda` and `pip` dependencies at the same time in the same `RuntimeEnv` configuration. If you need to use both, you should create a conda environment with the required pip dependencies and reference it using the `conda` parameter.",3412"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3413},3414{3415"question": "how to create a Actor in a namespace?",3416"sources": [3417"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",3418"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3419"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3420"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",3421"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3422"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",3423"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",3424"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3425"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace",3426"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",3427"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3428"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3429"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"3430],3431"document_ids": [34324719,34334709,34344640,34354871,34364637,34374880,34384726,34394631,34404751,34414699,34424633,34434711,3444147463445],3446"answer": " To create an actor in a specific namespace, you can use the `options` method to specify the `name` and `namespace` parameters when creating the actor. Here's an example in Python:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nctx = ray.init(\"ray://localhost:10001\")\n\n# Create an actor with a specified namespace.\nActor.options(name=\"my_actor\", namespace=\"actor_namespace\", lifetime=\"detached\").remote()\n```\nNote that the namespace must be specified when you first connect to the cluster, as shown in the context. Also, named actors are only accessible within their namespaces.",3447"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3448},3449{3450"question": "Can I specify multiple working directories?",3451"sources": [3452"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",3453"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3454"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",3455"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3456"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",3457"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-jobs-cli-api-reference",3458"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3459"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",3460"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",3461"https://docs.ray.io/en/master/ray-core/configure.html#driver-options",3462"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",3463"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3464"https://docs.ray.io/en/master/tune/faq.html#how-can-i-run-multiple-ray-tune-jobs-on-the-same-cluster-at-the-same-time-multi-tenancy"3465],3466"document_ids": [34671775,34681790,34694328,34704282,34712371,34726555,34734284,34746561,347513674,34764620,34777265,34786627,347910703480],3481"answer": " No, you cannot specify multiple working directories in Ray. However, you can specify a single working directory that can contain multiple files and directories. The working directory can be a local directory or a remote URI to a zip file. Relative paths should be used for read-only purposes, and you must ensure that the directory is synced on all nodes if running on multiple machines. Additionally, all workers on the same node now share the same working directory.",3482"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3483},3484{3485"question": "what if I set num_cpus=0 for tasks",3486"sources": [3487"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",3488"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",3489"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3490"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#configuring-parallelism-with-omp-num-threads",3491"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3492"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#trainer-resources",3493"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",3494"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",3495"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",3496"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",3497"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3498"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",3499"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.num_cpus_per_worker.html#ray-train-scalingconfig-num-cpus-per-worker"3500],3501"document_ids": [35025113,35035936,35046511,350513461,35064850,35079259,35086703,35094877,3510623,35117639,35124851,35136717,351489983515],3516"answer": " If you set `num_cpus=0` for tasks, it means that no CPU resources will be reserved for those tasks. However, it's important to note that tasks may still require some CPU resources to run, even if they don't explicitly request them. Additionally, setting `num_cpus=0` for tasks may prevent them from being scheduled on the Ray head node, as tasks requiring CPU resources are not scheduled to the Ray head node by default. It's generally recommended to explicitly set `num_cpus` for tasks and actors to avoid any surprises and ensure that they have the necessary resources to run.",3517"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3518},3519{3520"question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",3521"sources": [3522"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",3523"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",3524"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",3525"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",3526"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",3527"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",3528"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#overview",3529"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",3530"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",3531"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",3532"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",3533"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",3534"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview"3535],3536"document_ids": [35375818,35385953,35395975,35405963,35415752,35425958,35435956,35445969,35455972,35465694,354713926,35485656,354956573550],3551"answer": " Yes, it is possible to have Ray on Kubernetes without using KubeRay and still enable autoscaling. This can be achieved by using the Kubernetes Cluster Autoscaler, which can provision or remove nodes based on the resource demands of the Ray Autoscaler. The Ray Autoscaler automatically scales the number of Ray pods in the cluster based on the resources required by tasks, actors, or placement groups. To enable Ray autoscaling in Kubernetes, you would need to configure the RayCluster custom resource (CR) config with the necessary parameters. However, this method may not be as seamless as using KubeRay, which provides built-in support for Ray autoscaling.",3552"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3553},3554{3555"question": "how to manually configure and manage Ray cluster on Kubernetes",3556"sources": [3557"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",3558"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",3559"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",3560"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",3561"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide",3562"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",3563"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",3564"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",3565"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",3566"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",3567"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",3568"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#what-does-the-rayservice-provide",3569"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#deploy-a-ray-cluster"3570],3571"document_ids": [35725717,35735917,35745918,35755526,35765783,35775754,35785953,35795642,35805958,35815657,35825636,35835738,358461753585],3586"answer": " To manually configure and manage a Ray cluster on Kubernetes, follow these steps:\n\n1. Install `kubectl` and Helm, and prepare a Kubernetes cluster.\n2. Deploy a KubeRay operator.\n3. Create a RayCluster custom resource (CR) using a configuration file (e.g., `raycluster.yaml`) with the desired Ray cluster specifications, such as `rayVersion` and `enableInTreeAutoscaling`.\n4. Apply the RayCluster CR using `kubectl apply -f raycluster.yaml`.\n5. For Ray Dashboard configuration, refer to the specific guide based on your cluster launch method (KubeRay, etc.).\n6. Use `kubectl` to manage and interact with the Ray cluster, including creating, updating, and deleting the cluster and its applications.\n\nFor autoscaling, consider setting up an autoscaling node group or pool and applying the `xgboost-benchmark-autoscaler.yaml` config. This will create one Ray head pod, and the Ray autoscaler will trigger creation of Ray worker pods as needed. Kubernetes autoscaling will then create nodes to place the Ray pods.\n\nRefer to the 'RayCluster Configuration' and 'KubeRay Autoscaling' user guides for more details.",3587"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3588},3589{3590"question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",3591"sources": [3592"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",3593"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",3594"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",3595"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",3596"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3597"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options",3598"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",3599"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",3600"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3601"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",3602"https://docs.ray.io/en/master/serve/api/doc/ray.serve.shutdown.html#ray-serve-shutdown",3603"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",3604"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up"3605],3606"document_ids": [360714512,36085130,36095129,36104393,3611558,361214504,361313448,36144387,3615557,361614483,361713989,36185127,361962163620],3621"answer": " Shutting down a raylet will not directly kill tasks and workers on that node. However, it may lead to worker killing due to memory pressure, as the raylet is responsible for managing resources on the node. If you want to disable worker killing, you can set the environment variable `RAY_memory_monitor_refresh_ms` to zero when starting Ray.",3622"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3623},3624{3625"question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",3626"sources": [3627"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",3628"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",3629"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",3630"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#what-s-the-out-of-memory-error",3631"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management",3632"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",3633"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#failures-due-to-memory-issues",3634"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3635"https://docs.ray.io/en/master/serve/monitoring.html#profiling-memory",3636"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",3637"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3638"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",3639"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#reduce-parallelism"3640],3641"document_ids": [3642548,3643547,3644559,3645549,36465010,36475118,3648606,3649557,365013549,3651598,3652551,3653562,36545643655],3656"answer": " To debug out-of-memory issues in Ray, you can follow the steps below:\n\n1. Familiarize yourself with Ray's Memory Management model.\n2. View the documentation on how to detect out-of-memory errors.\n3. Find the tasks and actors that have high memory usage by viewing the per task and actor memory usage graph or using the CLI command `htop`.\n4. Refer to the documentation on how to address the out-of-memory issue: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>.\n5. If you notice a memory leak on a Serve actor, use `memray` to debug.\n\nThe documentation to look for would be \"Debugging Out of Memory\" and \"Out-Of-Memory Prevention\" in the Ray Memory Management model.",3657"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3658},3659{3660"question": "How to use callback in Trainer?",3661"sources": [3662"https://docs.ray.io/en/master/train/examples/transformers/transformers_torch_trainer_basic.html#ray-train-basic-example-for-huggingface-transformers",3663"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_end.html#ray-train-lightning-raytrainreportcallback-on-init-end",3664"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_start.html#ray-train-lightning-raytrainreportcallback-on-init-start",3665"https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune",3666"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_batch_end.html#ray-train-lightning-raytrainreportcallback-on-batch-end",3667"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.html#ray-train-huggingface-transformers-raytrainreportcallback",3668"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.on_init_end.html#ray-train-huggingface-transformers-raytrainreportcallback-on-init-end",3669"https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",3670"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",3671"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.html#ray-tune-callback",3672"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.on_train_begin.html#ray-train-huggingface-transformers-raytrainreportcallback-on-train-begin",3673"https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#monitoring-and-logging-metrics",3674"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.on_step_begin.html#ray-train-huggingface-transformers-raytrainreportcallback-on-step-begin"3675],3676"document_ids": [36779704,36788531,36798760,36802390,36818206,36828635,36838572,36849934,36858057,36861405,36878536,36889262,368983993690],3691"answer": " To use a callback in the Trainer from the context provided, you can add it to the Trainer object like this: `trainer.add_callback(YourCallback())`.\n\nIn the given context, `RayTrainReportCallback()` is added as a callback to the Trainer. This callback reports metrics and checkpoints to Ray Train.\n\nIf you want to create a custom callback, you can create a class that inherits from `pytorch_lightning.callbacks.Callback` and implement the methods you need. For example, the following code creates a custom callback that prints the training duration after each epoch:\n\n```python\nclass PrintTrainingDurationCallback(pl.callbacks.Callback):\n def on_train_epoch_end(self, trainer, pl_module):\n print(f\"Training duration for epoch {trainer.current_epoch}: {trainer.logged_metrics['training_time']}s\")\n\ntrainer = Trainer(\n # ...\n callbacks=[PrintTrainingDurationCallback()],\n # ...\n)\n```\n\nThe available hooks for callbacks in PyTorch Lightning are listed in the documentation: <https://pytorch-lightning.readthedocs.io/en/stable/common/callback.html#callback-hooks>.",3692"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3693},3694{3695"question": "How to provide current working directory to ray?",3696"sources": [3697"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3698"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3699"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",3700"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",3701"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",3702"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3703"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3704"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",3705"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",3706"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",3707"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3708"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3709"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup"3710],3711"document_ids": [37126627,37136625,37146597,37155757,37162371,3717511,37184531,37196595,37206484,37216512,37226462,37231790,372464563725],3726"answer": " To provide the current working directory to Ray, you can specify the `working_dir` in the runtime environment when calling `ray.init()`. This will upload the working directory on the laptop to the Ray cluster. Here's an example:\n```python\nray.init(\n runtime_env={\"working_dir\": \"path/to/working/directory\"}\n)\n```\nAfter that, you can use relative paths in your remote task definitions, and they will work on both your laptop and the cluster without any code changes. The current working directory of both functional and class trainables is set to the corresponding trial directory once it\u2019s been launched as a remote Ray actor.",3727"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3728},3729{3730"question": "how to create an actor instance with parameter?",3731"sources": [3732"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",3733"https://docs.ray.io/en/master/ray-core/cross-language.html#python-calling-java",3734"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",3735"https://docs.ray.io/en/master/rllib/actors.html#actors",3736"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3737"https://docs.ray.io/en/master/ray-core/actors.html#actors",3738"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",3739"https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",3740"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",3741"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3742"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3743"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",3744"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles"3745],3746"document_ids": [37475391,37484398,37495192,375011774,375114687,37524329,37534664,37544704,37554200,37564633,375714723,375811795,375943513760],3761"answer": " To create an actor instance with a parameter, you can call the `ActorClass.remote()` method with the argument(s) 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 in Python:\n\n```python\n# Define the Counter actor.\n@ray.remote\nclass Counter:\n def __init__(self, value=0):\n self.value = value\n\n def increment(self):\n self.value += 1\n return self.value\n\n# Create a Counter actor with an initial value of 5.\ncounter = Counter.remote(5)\n\n# Call the increment method on the actor.\nnew_value = ray.get(counter.increment.remote())\nprint(new_value) # Output: 6\n```\n\nIn this example, the `Counter` actor takes an optional `value` parameter in its constructor. When creating an instance of the actor, you can pass this parameter as shown above.",3762"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3763},3764{3765"question": "how to push a custom module to ray which is using by Actor ?",3766"sources": [3767"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3768"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3769"https://docs.ray.io/en/master/ray-core/actors.html#actors",3770"https://docs.ray.io/en/master/rllib/actors.html#actors",3771"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",3772"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#library-development",3773"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.push.html#ray-util-actorpool-push",3774"https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",3775"https://docs.ray.io/en/master/ray-core/actors.html#actors",3776"https://docs.ray.io/en/master/rllib/actors.html#actors",3777"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3778"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",3779"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources"3780],3781"document_ids": [37824844,378314685,37844327,378511772,37864200,37874270,37884758,37894704,37904333,379111778,379214689,37935443,3794147073795],3796"answer": " To push a custom module to Ray for use with an Actor, you can use the `py_modules` field in the `ray.init()` function. Here's an example:\n\nSuppose you have a custom module named `my_module` that you want to use with a Ray Actor. You can initialize Ray and specify the `py_modules` field like this:\n```python\nimport ray\nimport my_module\n\nray.init(\"ray://123.456.7.89:10001\", runtime_env={\"py_modules\": [my_module]})\n```\nThen, you can define your Actor class and use the `@ray.remote` decorator to indicate that instances of the class will be actors. Here's an example:\n```python\n@ray.remote\nclass MyActor:\n def __init__(self):\n self.value = 0\n\n def increment(self):\n self.value += 1\n return self.value\n\n def get_value(self):\n return self.value\n```\nNote that you don't need to import `my_module` inside the `increment` and `get_value` methods because it has already been imported in the `ray.init()` function.\n\nFinally, you can create an instance of the `MyActor` class and call its methods using the `.remote()` method:\n```python\nmy_actor = MyActor.remote()\nresult = my_actor.increment.remote()\nvalue = ray.get(result)\n```\nThis will create a new Ray worker process for the `MyActor` class and schedule methods of the actor on that specific worker, allowing it to access and mutate the state of that worker.",3797"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3798},3799{3800"question": "how to print ray working directory?",3801"sources": [3802"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",3803"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3804"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#print",3805"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",3806"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",3807"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",3808"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",3809"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3810"https://docs.ray.io/en/master/ray-contribute/docs.html#building-the-ray-documentation",3811"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-3-run-the-pytorch-image-training-benchmark",3812"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",3813"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",3814"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"3815],3816"document_ids": [38175757,3818511,3819600,38205732,38216597,3822415,38236445,38244388,3825802,38266165,38275380,38285861,382964623830],3831"answer": " To print the working directory in Ray, you can use the following code:\n```python\nimport ray\nprint(ray.global_state.get_runtime_context().get_working_directory())\n```\nHowever, if you want to print the logging directory of Ray, you can use the following code:\n```python\nimport ray\nprint(ray.worker.global_worker.log_dir)\n```\nThis will print the default logging directory of Ray, which is `/tmp/ray/session_*/logs`. To change the logging directory, you can specify it when calling `ray.init()` or `ray start`.",3832"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3833},3834{3835"question": "why I can not see log.info in ray log?",3836"sources": [3837"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3838"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",3839"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",3840"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",3841"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",3842"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",3843"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",3844"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",3845"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3846"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3847"https://docs.ray.io/en/master/ray-observability/key-concepts.html#application-logs",3848"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",3849"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence"3850],3851"document_ids": [3852680,3853535,38546445,3855444,3856530,3857415,3858445,38595861,386013517,3861511,3862408,3863510,386464433865],3866"answer": " The reason you can't see `log.info` in Ray logs might be because of the logging level configuration. By default, Ray's logging level is set to `logging.INFO`. However, it is possible that the logging level has been changed to a higher level (like `logging.WARNING` or `logging.ERROR`) in your configuration, which would cause `log.info` messages to not be displayed.\n\nTo modify the Ray logger and ensure that `log.info` messages are visible, you can use the following code:\n\n```python\nimport logging\n\nlogger = logging.getLogger(\"ray\")\nlogger.setLevel(logging.INFO) # Set the logging level to INFO\n```\n\nThis will set the logging level for the Ray logger to `logging.INFO`, ensuring that `log.info` messages are displayed in the Ray logs.",3867"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3868},3869{3870"question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",3871"sources": [3872"https://docs.ray.io/en/master/data/examples/batch_training.html#filtering-a-dataset-on-read-a-class-anchor-id-filter-ds-a",3873"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",3874"https://docs.ray.io/en/master/data/user-guide.html#user-guides",3875"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",3876"https://docs.ray.io/en/master/data/saving-data.html#saving-data",3877"https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-dolly-v2-7b-with-ray-train-pytorch-lightning-and-fsdp",3878"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",3879"https://docs.ray.io/en/master/data/data-internals.html#reading-files",3880"https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",3881"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#define-a-custom-datasource",3882"https://docs.ray.io/en/master/data/loading-data.html#loading-data",3883"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",3884"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data"3885],3886"document_ids": [38877870,38887360,38896834,38906839,38916798,38929911,38939180,38946923,38956773,389610224,38976738,38987865,389967793900],3901"answer": " No, when using Ray Dataset to read a file, the order of the data is not guaranteed to be preserved. Ray Data uses Ray tasks to read files in parallel, and the order of the data blocks produced by these tasks may not match the order of the original files. Additionally, if you apply any transformations to the dataset, the order of the data may be further shuffled. If you need to preserve the order of the data, you may need to implement a custom datasource or apply a sorting operation after reading the data.",3902"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3903},3904{3905"question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",3906"sources": [3907"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",3908"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3909"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",3910"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",3911"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3912"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3913"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3914"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayTaskError.html#ray-exceptions-raytaskerror",3915"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",3916"https://docs.ray.io/en/master/rllib/actors.html#fault-tolerance",3917"https://docs.ray.io/en/master/ray-core/actors.html#fault-tolerance",3918"https://docs.ray.io/en/master/ray-more-libs/actors.html#fault-tolerance",3919"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks"3920],3921"document_ids": [39224505,39234414,39244423,39254460,39264453,39274854,39284405,39294779,39304462,393111808,39324365,393314705,393444223935],3936"answer": " This means that if an exception is thrown by the user's code during the execution of a task, Ray will not automatically retry the task by default. The user can manually configure Ray to retry tasks upon application-level errors by setting the `retry_exceptions` argument to `True` in the `ray.remote()` or `.options()` methods.",3937"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3938},3939{3940"question": "how do I specify the log directory when starting Ray?",3941"sources": [3942"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",3943"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3944"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",3945"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",3946"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",3947"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3948"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#mounting-the-ray-log-directory",3949"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",3950"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3951"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3952"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",3953"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",3954"https://docs.ray.io/en/master/ray-observability/key-concepts.html#driver-logs"3955],3956"document_ids": [39576445,3958511,39595861,3960510,39615880,39624531,39635871,3964530,3965680,39664811,3967535,3968415,39694093970],3971"answer": " To specify the log directory when starting Ray, you can use the `--logs-dir` option with the `ray start` command or the `logging_dir` parameter with `ray.init()`. Here's an example:\n\nFor `ray start`:\n```bash\nray start --logs-dir /path/to/your/log/directory\n```\n\nFor `ray.init()`:\n```python\nimport ray\n\nray.init(logging_dir=\"/path/to/your/log/directory\")\n```\n\nThis will change the default log directory to the path you specified. Note that this will also change the temp directory to the same path, as Ray uses the same directory for both logs and temp files by default. If you want to keep the temp directory separate, you should specify a different temp directory using the `--temp-dir` option or the `_temp_dir` argument in `ray.init()`.",3972"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3973},3974{3975"question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",3976"sources": [3977"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3978"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-3-create-a-mini-cluster-with-ray-cluster-utils-cluster",3979"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",3980"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#walkthrough-using-ray-with-slurm",3981"https://docs.ray.io/en/master/cluster/vms/index.html#overview",3982"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3983"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3984"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3985"https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters",3986"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",3987"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3988"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",3989"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#running-a-basic-example"3990],3991"document_ids": [39926400,39935401,39945555,39956531,39966209,39976380,39986503,39996509,40004313,40016170,40026203,40036365,400464954005],4006"answer": " To launch a Ray cluster with 10 nodes without setting the minimum number of workers as 10, you can use the `ray.cluster_utils.Cluster` utility to create a mini-cluster. Here's an example of how to do this:\n\n```python\nfrom ray.cluster_utils import Cluster\n\n# Start a head node with 10 CPUs\ncluster = Cluster(initialize_head=True, head_node_args={\"num_cpus\": 10})\n\n# Launch 9 additional worker nodes\nfor _ in range(9):\n cluster.add_worker()\n\n# Wait for the cluster to be ready\ncluster.wait_for_cluster()\n\n# Connect to the cluster\nray.init(address=\"auto\")\n\n# Run your Ray program here\n```\n\nNote that this will launch a cluster with a single head node and 9 worker nodes, for a total of 10 nodes. The minimum number of workers is still 0, but the autoscaler will not add or remove nodes since the maximum number of workers is set to 9.\n\nAlso note that this is a local mini-cluster, not a cloud-based cluster. If you want to launch a cloud-based cluster with 10 nodes, you will need to use a cloud provider's API or command-line tools to launch the nodes and configure them to join the Ray cluster. The `ray.cluster_utils.Cluster` utility can still be used to manage the cluster once it is up and running.",4007"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4008},4009{4010"question": "how to use ray api to scale up a cluster",4011"sources": [4012"https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",4013"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-ray-settings",4014"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",4015"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",4016"https://docs.ray.io/en/master/ray-overview/index.html#overview",4017"https://docs.ray.io/en/master/cluster/vms/index.html#overview",4018"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#scaling-with-ray-data",4019"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/gcp.html#start-ray-with-the-ray-cluster-launcher",4020"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",4021"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",4022"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-3-create-a-mini-cluster-with-ray-cluster-utils-cluster",4023"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",4024"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling"4025],4026"document_ids": [40275604,40284321,40296451,40305953,4031364,40326209,40338155,40346388,40358028,40366846,40375401,40386455,403955594040],4041"answer": " To scale up a Ray cluster, you can use the Ray Cluster Management API, specifically the `ray.autoscaler.sdk.request_resources()` function. This function allows you to programmatically request additional resources for your cluster.\n\nAdditionally, you can use the Ray autoscaler, which is a process that runs on the head node and automatically scales the number of worker nodes based on resource demand. The autoscaler can be configured using the Ray cluster launcher or on Kubernetes.\n\nIt is recommended to start with non-autoscaling clusters if you're new to Ray and to tune several parameters to run a large cluster.\n\nNote that there is an ongoing project to improve Ray's scalability and stability, and you are encouraged to share your thoughts and use cases.",4042"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4043},4044{4045"question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",4046"sources": [4047"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4048"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4049"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",4050"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",4051"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",4052"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents",4053"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",4054"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4055"https://docs.ray.io/en/master/cluster/vms/index.html#overview",4056"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#run-ray-up-cloudwatch-basic-yaml-to-start-your-ray-cluster",4057"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#deploy-a-ray-cluster",4058"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",4059"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher"4060],4061"document_ids": [40626199,4063273,40646629,40655503,40666304,40676397,40686287,40696203,40706209,40716344,40726407,40736104,407462934075],4076"answer": " To specify a subnet in the Ray cloud launcher 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: minimal\n\nprovider:\n type: aws\n region: us-west-2\n subnet_id: subnet-01234567890123456\n```\n\nReplace `subnet-01234567890123456` with the ID of the subnet you want to use.\n\nNote: Make sure to install boto3 (`pip install boto3`) and configure your AWS credentials in `~/.aws/credentials` as described in the Automatic Cluster Setup page.",4077"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4078},4079{4080"question": "where I can find HTTP server error code log for Ray serve",4081"sources": [4082"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4083"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4084"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",4085"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",4086"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",4087"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",4088"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",4089"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",4090"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4091"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",4092"https://docs.ray.io/en/master/ray-observability/getting-started.html#ray-dashboard",4093"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",4094"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#whats-next"4095],4096"document_ids": [409713980,409813517,40995802,410014255,41015747,410213514,41036183,41046422,41056622,410613991,4107416,410813541,4109138284110],4111"answer": " HTTP server error code logs for Ray Serve can be found in the Ray dashboard, which you can access at port 8265 at your cluster's URI. The dashboard provides a Serve page that shows logs for your Serve controller, deployment replicas, and proxies. Alternatively, logs may also be found on disk on each node at `/tmp/ray/session_latest/logs/serve/`.\n\nAdditionally, Ray provides metrics related to non-200 HTTP responses returned by each deployment, which can be accessed through Ray's metric system. These metrics include `ray_serve_num_deployment_http_error_requests`, which records the number of non-200 HTTP responses returned by each deployment.",4112"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4113},4114{4115"question": "I am running ray cluster on amazon and I have troubles displaying the dashboard. When a I tunnel the dashboard port from the headnode to my machine, the dashboard opens, and then it disappears (internal refresh fails). Is it a known problem? What am I doing wrong?",4116"sources": [4117"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",4118"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",4119"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",4120"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",4121"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",4122"https://docs.ray.io/en/master/ray-observability/getting-started.html#ray-dashboard",4123"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",4124"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/observability.html#ray-dashboard",4125"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",4126"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",4127"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",4128"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",4129"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#dashboard-host"4130],4131"document_ids": [41325530,41335528,41346223,41355526,413613740,4137416,4138418,41395851,414013514,41415543,4142417,41436514,414459354145],4146"answer": " Yes, it's a known issue when accessing the Ray Dashboard from outside the head node. The dashboard port may not be publicly exposed, and you need additional setup to access it. This can be done through port forwarding.\n\nWhen you start a remote Ray cluster, the dashboard launches on the head node, but it might not be publicly exposed. To view the dashboard from outside the head node, you need to configure the dashboard for external access.\n\nYou can customize the port for the Ray Dashboard by passing the `--dashboard-port` argument with `ray start` in the command line or passing the `dashboard_port` keyword argument in your call to `ray.init()`.\n\nIn your case, it seems like the internal refresh is failing, which might be due to network restrictions or firewall settings. Make sure that the port you're using for the dashboard is open and accessible from your machine.\n\nAlso, consider setting up authentication for the Ray Dashboard if you haven't already, as it is not recommended to expose the dashboard publicly without proper authentication.\n\nLastly, when using the Ray Dashboard, it is highly recommended to also set up Prometheus and Grafana for critical features such as Metrics View.",4147"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4148},4149{4150"question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",4151"sources": [4152"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",4153"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",4154"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",4155"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4156"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",4157"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4158"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",4159"https://docs.ray.io/en/master/cluster/key-concepts.html#worker-node",4160"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",4161"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",4162"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",4163"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",4164"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"4165],4166"document_ids": [41675557,41685555,41696299,4170256,41716509,41726380,41736227,41745558,41756403,41764387,4177563,41786459,417964004180],4181"answer": " In the Ray cluster launcher YAML, `max_workers` includes only worker nodes, not the head node. This is because the head node is designated for cluster management processes, while worker nodes serve to run user code in Ray tasks and actors. The head node can still run tasks and actors, but this is not recommended in large-scale clusters as it can lead to out-of-memory issues due to the memory-demanding system components it runs.",4182"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4183},4184{4185"question": "How to update files in working directory ?",4186"sources": [4187"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",4188"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",4189"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",4190"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",4191"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4192"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",4193"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4194"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4195"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4196"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4197"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4198"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4199"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart"4200],4201"document_ids": [42021775,42031790,42042369,42058894,42066625,42072371,42081067,42091066,42101068,42116423,42126343,42136387,42142694215],4216"answer": " To update files in the working directory, you can use the `with open(filepath, \"w\")` context manager to write to a file, where `filepath` is a string specifying the path to the file. When using Tune, the current working directory of the trainable is set to the corresponding trial directory, so you can use relative paths to write to files. If you need to write to a specific directory, you can use the `to_directory([path])` function to write checkpoint data to the specified directory. Additionally, if you're running Ray on multiple machines, you'll need to make sure that the directory is synced on all nodes.\n\nHere's an example of how you can write to a file in the current working directory:\n```\nwith open(\"./artifact.txt\", \"w\") as f:\n f.write(\"Artifact Data\")\n```\nAnd here's an example of how you can write to a specific directory:\n```\nimport ray\n\nray.put(\"some_data\", to_directory=\"/path/to/directory\")\n```\nYou can also use the `to_directory` function to write checkpoint data to a specific directory.\n```\nray.train.checkpoint.save_checkpoint(checkpoint_path=\"/path/to/directory\", ...)\n```\nIt's important to note that the working directory is shared between workers, so you should use the `ray.train.get_context().get_trial_dir()` API to get the path for saving trial-specific outputs.\n\nAlso, you can use `cluster_synced_files` and `file_mounts` to sync files and directories from the head node to the worker nodes.\n```\nray.init(runtime_env={\"cluster_synced_files\": [\"/path/to/file\"]})\n```\nYou can also use `file_mounts` to mount a directory from the head node to the worker nodes.\n```\nray.init(runtime_env={\"file_mounts\": [(\"/path/to/directory\", \"/mount/path\")]})\n```\nYou can also use `rsync_exclude` to exclude certain files or directories when syncing or mounting.\n```\nray.init(runtime_env={\"file_mounts\": [(\"/path/to/directory\", \"/mount/path\")], \"rsync_exclude\": [\"**/.git\"]})\n```\nYou can also disable the behavior of changing the working directory by setting the `RAY_CHDIR_TO_TRIAL_DIR=0` environment variable. This allows access to files in the original working directory, but 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.",4217"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4218},4219{4220"question": "How I can update working directory file when ray allready initiated ?",4221"sources": [4222"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4223"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4224"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",4225"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",4226"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",4227"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",4228"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",4229"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4230"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4231"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",4232"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4233"https://docs.ray.io/en/master/cluster/cli.html#ray-submit",4234"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"4235],4236"document_ids": [42376625,42384531,42392371,42401790,42416595,42421775,42435757,42446627,42454811,42464263,4247511,42485624,424964704250],4251"answer": " To update the working directory file when Ray is already initiated, you can use the `ray.put()` and `ray.get()` functions to store and retrieve data in the object store, which is shared across all nodes in the Ray cluster. This allows you to access and update data from any worker or task in the cluster.\n\nAlternatively, you can use the `ray.train.get_context().get_trial_dir()` function to get the path to the trial directory, and use that as the working directory for saving any outputs. This will ensure that each trial has its own separate working directory, and avoid issues with multiple workers accidentally writing to the same location.\n\nNote that the current working directory of both functional and class trainables is set to the corresponding trial directory once it\u2019s been launched as a remote Ray actor. Therefore, you should use the trial directory as the path to save any outputs, rather than the original working directory.\n\nYou can also disable the behavior of changing the working directory to the trial directory by setting the `RAY_CHDIR_TO_TRIAL_DIR` environment variable to `0`. This will allow access to files in the original working directory, but 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\nFinally, you can use the `ray submit` command to upload and run a script on the specified Ray cluster, and make the contents of the directory passed as `--working-dir` available to the Ray job by downloading the directory to all nodes in your cluster.",4252"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4253},4254{4255"question": "how can I force ray head node to use custom pem file to ssh worker node?",4256"sources": [4257"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4258"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",4259"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4260"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4261"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4262"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4263"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4264"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",4265"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",4266"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4267"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",4268"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",4269"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#minimal-configuration"4270],4271"document_ids": [42726385,42736205,42746406,42756356,4276272,4277261,42786329,42796367,42806538,42816431,42826537,42836607,428463174285],4286"answer": " To force the Ray head node to use a custom PEM file for SSHing into the worker node, you can specify the `ssh_private_key` field in the `auth` section of the configuration file (e.g., `config.yaml`). Here's an example:\n\n```yaml\nauth:\n ssh_user: ubuntu\n ssh_private_key: /path/to/your/custom/key.pem\n```\n\nMake sure to replace `/path/to/your/custom/key.pem` with the actual path to your custom PEM file. Also, ensure that the specified private key matches the public key that is specified in the worker node's authorized keys.",4287"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4288},4289{4290"question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",4291"sources": [4292"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",4293"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",4294"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#running-out-of-file-descriptors-too-may-open-files",4295"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",4296"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-operating-system-settings",4297"https://docs.ray.io/en/master/ray-core/configure.html#head-node",4298"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",4299"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",4300"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",4301"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",4302"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",4303"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",4304"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-resources-object-store-memory"4305],4306"document_ids": [430714740,43084375,4309603,4310563,43114315,43124556,43136475,431413870,431513928,431613902,43174391,4318562,431963094320],4321"answer": " The GCS (Global Control Service) is a centralized metadata server that runs on the head node of a Ray cluster, managing node membership and actor directory, as well as providing cluster-level operations like actor, placement groups, and node management. It stores all data in-memory, which means its failure can cause the entire Ray cluster to fail. To make the GCS fault tolerant, HA Redis is required.\n\nThe head node has more memory-demanding system components such as GCS or the dashboard, and the driver runs from a head node by default. If the head node has the same memory capacity as worker nodes and if you execute the same number of Tasks and Actors from a head node, it can easily have out-of-memory problems. In this case, you can specify --num-cpus=0 when starting a head node to prevent running any Tasks and Actors on the head node.\n\nYour GCS server might be taking up so much memory on the head node due to more than 1024 connections to the component, which is common for the head node GCS process because it is a centralized component that many other components in Ray communicate with. You can adjust the max file descriptors limit per process via the ulimit command, such as ulimit -n 65536, to handle more workers and nodes.\n\nAdditionally, because all nodes and workers connect to the GCS, many network connections will be created, and the operating system has to support that number of connections. The head node needs to open several more ports, including the GCS server port (default: 6379), Ray Client Server listening port (default: 10001), non-primary Redis shards ports (default: random values), the gRPC port used by the dashboard (default: random value), and the dashboard port (default: 8265) if --include-dashboard is true. Make sure the head node has sufficient bandwidth, as the most heavily stressed resource on the head node is outbound bandwidth.",4322"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4323},4324{4325"question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",4326"sources": [4327"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",4328"https://docs.ray.io/en/master/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",4329"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#troubleshooting",4330"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-for-using-tune-with-spot-instances-aws",4331"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource",4332"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",4333"https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters",4334"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#solution-1-container-command-recommended",4335"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",4336"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-status",4337"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",4338"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",4339"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"4340],4341"document_ids": [4342467,43435692,43442428,43452421,43465724,4347852,43484313,43495847,43506365,43516184,435210267,435313899,435456914355],4356"answer": " If you're experiencing nodes in a \"pending\" state for a long time when starting a cluster with `ray up`, it might be due to insufficient resources in your Kubernetes cluster. The KubeRay operator may keep restarting the RayCluster, leading to a loop of restarts.\n\nTo debug this issue, you can:\n\n1. Check the Kubernetes cluster resources and ensure they are set high enough.\n2. Analyze error messages using `kubectl describe pod <pod_name>` if the pods are stuck in the \"Pending\" state.\n3. Examine the RayCluster logs with `kubectl logs <head_pod_name>` to look for any relevant error messages.\n4. Use `ray status` to view detailed cluster status and look for any warnings or errors related to resource allocation.\n\nIf you're using KubeRay v0.6.1 or earlier, ensure that the Kubernetes cluster has sufficient resources to accommodate the RayCluster, as the KubeRay operator does not have a clear plan to handle situations where the Kubernetes cluster runs out of resources.",4357"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4358},4359{4360"question": "how to install Ray 2.5.1 from github or wheel?",4361"sources": [4362"https://docs.ray.io/en/master/installation.html#from-wheels",4363"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",4364"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",4365"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",4366"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",4367"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",4368"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray-on-arch-linux",4369"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray-on-arch-linux",4370"https://docs.ray.io/en/master/installation.html#installing-ray-on-arch-linux",4371"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",4372"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",4373"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",4374"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started"4375],4376"document_ids": [437761,437814545,4379286,438063,4381288,438214549,4383301,438414583,438576,438666,4387291,438814556,438947024390],4391"answer": " To install Ray 2.5.1, you need to install the wheel for that specific commit. First, find the corresponding wheel URL for your OS and Python version from the Ray wheels documentation: <https://docs.ray.io/en/latest/installation.html#installing-from-a-specific-commit>.\n\nFor example, if you are using Linux, Python 3.8, and want the `ray[default]` components, the wheel URL would be:\n\n<https://s3-us-west-2.amazonaws.com/ray-wheels/master/a3e0086c5e8e1b1e8e086c5e8e1b1e8e0/ray-2.5.1-cp38-cp38-linux_x86_64.whl>\n\nThen, use the following command to install the wheel:\n\n```bash\npip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/a3e0086c5e8e1b1e8e086c5e8e1b1e8e0/ray-2.5.1-cp38-cp38-linux_x86_64.whl\n```\n\nReplace the URL with the correct wheel URL for your environment.",4392"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4393},4394{4395"question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",4396"sources": [4397"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",4398"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-component-logs",4399"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4400"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4401"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",4402"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4403"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4404"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#worker-setup-commands",4405"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",4406"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",4407"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",4408"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job",4409"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"4410],4411"document_ids": [4412542,4413518,44144632,44154610,441613199,44176356,4418272,44196259,442010344,442110265,44229138,44234254,442443004425],4426"answer": " To use `worker_setup_hook` in a runtime environment to set up on worker node creation, you can define a callable function and pass it to the `worker_process_setup_hook` parameter when initializing the Ray cluster. Here's an example:\n\n```python\ndef logging_setup_func():\n # Your setup code here\n pass\n\nray.init(runtime_env={\"worker_process_setup_hook\": logging_setup_func})\n```\n\nAlternatively, you can create a runtime environment configuration file (e.g., `my_runtime_env.yaml`) and specify the `worker_process_setup_hook` in that file:\n\n```yaml\n# my_runtime_env.yaml\nimage: anyscale/ray-ml:nightly-py38-cpu\nworker_path: /root/python/ray/workers/default_worker.py\nrun_options:\n - --cap-drop SYS_ADMIN\n - --log-level=debug\nworker_process_setup_hook:\n - module_name: my_module\n function_name: logging_setup_func\n```\n\nThen, load the runtime environment from the configuration file and initialize Ray:\n\n```python\nimport ray\nfrom ray.runtime_env import RuntimeEnv\n\n@ray.remote\nclass MyActor:\n pass\n\nray.init(runtime_env=RuntimeEnv(\"my_runtime_env.yaml\"))\nmy_actor = MyActor.remote()\n```\n\nThe `logging_setup_func` will be called on each worker node after the worker starts and before tasks and actors are scheduled.",4427"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4428},4429{4430"question": "how to use Ray dataset on aws",4431"sources": [4432"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",4433"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",4434"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",4435"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",4436"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset",4437"https://docs.ray.io/en/master/rllib/rllib-offline.html#ray-data-integration",4438"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",4439"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",4440"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-1-reading-the-dataset-from-s3",4441"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",4442"https://docs.ray.io/en/master/data/working-with-pytorch.html#custom-pytorch-datasets",4443"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",4444"https://docs.ray.io/en/master/data/overview.html#ray-data-overview"4445],4446"document_ids": [44476732,44487959,44496846,44507865,44518159,445211324,4453221,445414731,44558017,44566879,44576974,44587868,445968394460],4461"answer": " To use Ray dataset on AWS, you can read data from an S3 bucket using the `ray.data.read_csv` or `ray.data.read_parquet` functions. Here's an example of how to read a CSV file from S3:\n```python\nimport ray\n\nds = ray.data.read_csv(\"s3://anonymous@air-example-data/iris.csv\")\nds.show(limit=1)\n```\nNote that Ray Data requires schemas for all datasets in Ray 2.5, so standalone Python objects are no longer supported. Also, the default batch format is fixed to NumPy. To revert to legacy behavior temporarily, set the environment variable `RAY_DATA_STRICT_MODE=0` on all cluster processes.\n\nYou can use Ray's built-in dashboard to understand and debug Ray applications. To learn more about Ray Data, you can refer to the Ray Data documentation and the Ray AI Runtime Libraries Quickstart guide.\n\nWhen using Ray Data for offline inference, you can follow these basic steps:\n\n1. Load your data into a Ray Dataset.\n2. Define a Python class to load the pre-trained model.\n3. Transform your dataset using the pre-trained model.\n4. Get the final predictions.\n\nFor custom PyTorch Datasets, you can migrate to Ray Data by converting the logic in `__getitem__` to Ray Data read and transform operations. Any logic for reading data from cloud storage and disk can be replaced by one of the Ray Data read\\_* APIs, and any transformation logic can be applied as a map call on the Dataset.",4462"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4463},4464{4465"question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",4466"sources": [4467"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4468"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",4469"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",4470"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",4471"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",4472"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-data",4473"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",4474"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",4475"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",4476"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",4477"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",4478"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",4479"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing"4480],4481"document_ids": [44826994,44836928,44847395,44856841,44866965,44876890,44887328,44899206,44909207,44917543,44926902,44937738,449481684495],4496"answer": " To avoid shuffling of your dataset during `ray.data.map_batches()`, you should avoid using shuffling operations like `random_shuffle()`, `sort()`, or `groupby()` before `map_batches()`. These operations shuffle data in a map-reduce style, which can be slow and may not be what you want if you only need to apply a map function to your dataset.\n\nIf you require shuffling during training, consider using local shuffling with `local_shuffle_buffer_size` instead of a full global shuffle with `random_shuffle()`. This approach is less expensive and provides sufficient randomness for most cases.",4497"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4498},4499{4500"question": "Is the order of the input data preserved after a map_batches operation?",4501"sources": [4502"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4503"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",4504"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",4505"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",4506"https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms",4507"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4508"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",4509"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",4510"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4511"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4512"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",4513"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",4514"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data"4515],4516"document_ids": [45176994,45187738,45196965,45208218,45216813,45227019,45236915,45247640,45257005,45266997,45277800,45287544,452979794530],4531"answer": " Yes, the order of the input data is preserved after a `map_batches` operation in Ray Data. This is evident in the provided context where the dataset is first sorted by \"id\" after a `map_batches` operation. However, if the dataset is shuffled before or after the `map_batches` operation, the order will not be preserved.",4532"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4533},4534{4535"question": "ray serve returns generic internal service error when there is an internal failure, how do I get it to emit more detailed errors or logs?",4536"sources": [4537"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",4538"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",4539"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#common-issues",4540"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4541"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",4542"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4543"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",4544"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",4545"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#rayservice-troubleshooting",4546"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",4547"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",4548"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice",4549"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#debugging-performance-issues"4550],4551"document_ids": [45525671,45534423,45545670,455513980,4556595,455713517,45584426,45595802,45605661,45615747,45624505,45635780,4564137534565],4566"answer": " To get more detailed errors or logs when Ray Serve returns a generic internal service error, you can:\n\n1. Test your Ray Serve script locally or in a RayCluster before deploying it to a RayService. Refer to rayserve-dev-doc.md for more details.\n2. Check the Ray Serve logs in the Ray dashboard. You can forward the dashboard port to localhost and check the Serve page in the Ray dashboard. By default, logs are emitted from actors both to stderr and on disk on each node at /tmp/ray/session\\_latest/logs/serve/.\n3. Enable debug mode in your Ray Serve deployment configuration to get more detailed logs.\n4. Handle errors and retry failed code in your Ray Serve script to recover from application-level failures.\n5. Check the RayService troubleshooting guide for tips on debugging issues with Ray Serve scripts or Ray Serve configurations.\n\nNote that Ray provides mechanisms to automatically recover from internal system-level failures like node failures.",4567"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4568},4569{4570"question": "how do i track an uncaught exception in ray serve",4571"sources": [4572"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",4573"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4574"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception",4575"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayTaskError.html#ray-exceptions-raytaskerror",4576"https://docs.ray.io/en/master/ray-observability/reference/api.html#state-apis-exceptions",4577"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",4578"https://docs.ray.io/en/master/ray-observability/key-concepts.html#exceptions",4579"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",4580"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",4581"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.LocalRayletDiedError.html#ray-exceptions-localrayletdiederror",4582"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4583"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",4584"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"4585],4586"document_ids": [45874560,458813980,4589778,45904779,4591665,45925747,4593404,45945802,45954506,45964858,45974453,45985671,459948114600],4601"answer": " To track an uncaught exception in Ray Serve, you can check the Ray Serve page in the Ray dashboard at the address <http://localhost:8265/#/serve>. This can be accessed by forwarding the dashboard port to localhost using the command `kubectl port-forward svc/rayservice-sample-head-svc --address 0.0.0.0 8265:8265`. If there is an uncaught exception in Ray Serve, it will be displayed in the dashboard. Additionally, you can check the RayServe script for any errors and test it locally or in a RayCluster before deploying it to a RayService. Ray Serve exceptions, such as `RayTaskError` and `RayActorError`, can be found in the `ray.exceptions` module.",4602"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4603},4604{4605"question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",4606"sources": [4607"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4608"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",4609"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4610"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",4611"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",4612"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",4613"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4614"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4615"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-hangs.html#view-stack-traces-in-ray-dashboard",4616"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",4617"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3",4618"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#check-out-your-ray-cluster-s-logs-metrics-and-dashboard-in-the-cloudwatch-console",4619"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"4620],4621"document_ids": [462213517,4623680,4624445,4625415,4626535,46274372,46286622,4629444,4630571,46315860,4632533,46336346,4634135244635],4636"answer": " To view the logs emitted by 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. You can view the logs in the terminal or Jupyter Notebook output where the driver script is running.\n2. By default, logs are also emitted on disk on each node at `/tmp/ray/session_latest/logs/serve/`. This includes both system-level logs from the Serve controller and proxy as well as access logs and custom user logs produced from within deployment replicas.\n\nNote that if you execute the driver script directly on the head node of the Ray cluster or run it with Ray Client, the driver logs are not accessible from the Dashboard. In this case, you should view the logs in the terminal or Jupyter Notebook output where the driver script is running.\n\nAdditionally, you can use the Ray logging CLI to access the logs from the cluster. However, this feature is currently in alpha and requires a full installation of Ray using `pip install \"ray[default]\"`.\n\nFinally, if you want to control the logs written to STDOUT or files on disk, you can use the standard Python logger and set up custom logging for your Ray application. You can find a detailed overview of logging in Ray in the Ray Logging documentation.",4637"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4638},4639{4640"question": "where can I see logs for a failed ray serve deployment",4641"sources": [4642"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",4643"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",4644"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4645"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",4646"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4647"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#inspect-deeper",4648"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4649"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",4650"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",4651"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#whats-next",4652"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",4653"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4654"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications"4655],4656"document_ids": [465713960,465814255,465913523,466013514,466113517,466213694,46636622,466413442,4665415,466613828,46675860,4668444,466958024670],4671"answer": " To view logs for a failed Ray Serve deployment, you can check the following places:\n\n1. Kuberay operator logs: Run `kubectl logs -f <kuberay-operator-pod-name>` to see information about the Serve application deployment event and health checks.\n2. Controller log and deployment log: Located under `/tmp/ray/session_latest/logs/serve/` in both the head node pod and worker node pod. These logs contain information about specific deployment failure reasons and autoscaling events.\n3. Ray Dashboard: Access the Ray dashboard at port 8265 at your cluster's URI to view logs for your Serve controller, deployment replicas, and proxies.\n4. Ray client logs: Found at `/tmp/ray/session_latest/logs` on the head node.\n\nFor future reference, Ray Serve supports log rotation of these logs through setting the environment variables `RAY_ROTATION_MAX_BYTES` and `RAY_ROTATION_BACKUP_COUNT`. To silence the replica-level logs or otherwise configure logging, configure the \"ray.serve\" logger inside the deployment constructor.",4672"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4673},4674{4675"question": "How to take a subset of a Ray Dataset?",4676"sources": [4677"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",4678"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",4679"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",4680"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",4681"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",4682"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",4683"https://docs.ray.io/en/master/data/api/doc/ray.data.from_tf.html#ray-data-from-tf",4684"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",4685"https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",4686"https://docs.ray.io/en/master/data/api/doc/ray.data.range_tensor.html#ray-data-range-tensor",4687"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",4688"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",4689"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_tf.html#ray-data-dataset-to-tf"4690],4691"document_ids": [46929180,469310014,469410436,46957274,46966839,469710017,46987287,469914578,47006773,47017228,47027865,47036977,470475804705],4706"answer": " To take a subset of a Ray Dataset, you can use the `take` method which returns the first n rows of the dataset. Here's an example:\n```python\nsubset_size = 50\nsubset_dataset = train_dataset.take(subset_size)\n```\nAlternatively, you can use the `filter` method to create a subset based on a condition. Here's an example:\n```python\ndef is_even(batch):\n return batch[\"x\"] % 2 == 0\n\nsubset_dataset = train_dataset.filter(is_even)\n```\nThis will return a new Ray Dataset containing only the batches where the value of \"x\" is even.",4707"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4708},4709{4710"question": "How do I load all checkpoints from trials of a Tune experiment launched with `tune.run`? I ran my initial experiment with cloud checkpointing, so I\u2019d need to download all the checkpoints to analyze them.",4711"sources": [4712"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",4713"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",4714"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",4715"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",4716"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis",4717"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#tunecontroller",4718"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints",4719"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-out-of-tune-using-checkpoints-other-artifacts",4720"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",4721"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing",4722"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",4723"https://docs.ray.io/en/master/tune/tutorials/overview.html#tune-feature-guides",4724"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#configuring-tune-with-cloud-storage-aws-s3-google-cloud-storage"4725],4726"document_ids": [47272226,47282439,47292347,47302253,47311473,47322243,47332212,47342299,47352255,47362217,47372348,47382270,473923394740],4741"answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `get_all_configs` method from the `ExperimentAnalysis` class. This method returns a list of all configurations, including the checkpoint directories. You can then use these directories to load the checkpoints.\n\nHere's an example of how you can do this:\n\n```python\nfrom ray import tune\n\n# Run your Tune experiment\ntune.run(my_trainable, name=\"my_exp\", local_dir=\"~/tune_results\")\n\n# Create an ExperimentAnalysis object\nanalysis = tune.ExperimentAnalysis(experiment_checkpoint_path=\"~/tune_results/my_exp/state.json\")\n\n# Get a list of all configurations (including checkpoint directories)\nconfigs = analysis.get_all_configs()\n\n# Load each checkpoint\nfor config in configs:\n checkpoint_dir = config['checkpoint_dir']\n load_checkpoint(checkpoint_dir)\n```\n\nIn this example, `load_checkpoint` is a placeholder function that you can replace with your own function for loading checkpoints.\n\nNote that if you used cloud checkpointing, you'll need to download the checkpoints to your local machine before you can analyze them. You can do this by copying the checkpoint files from the cloud storage to your local machine using a tool like `aws s3 cp` or `gsutil cp`.",4742"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4743},4744{4745"question": "How can I kill a \"detached\" Actor ?",4746"sources": [4747"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",4748"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",4749"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",4750"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",4751"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",4752"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",4753"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",4754"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",4755"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",4756"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",4757"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",4758"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",4759"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.pop_idle.html#ray-util-actorpool-pop-idle"4760],4761"document_ids": [47624658,47634859,47644502,47654493,47664455,47674504,47684848,47694601,47704606,47714615,47724654,47734604,477447804775],4776"answer": " To kill a detached Actor in Ray using C++, you can call the `Kill()` method on the actor handle. In Python, you can use the `ray.kill(actor_handle)` function to terminate the actor forcefully. Detached actors are not garbage-collected, so you must manually destroy them once they are no longer needed. After calling `ray.kill`, subsequent calls to get the actor's handle will fail, and the actor's name may be reused.",4777"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4778},4779{4780"question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",4781"sources": [4782"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",4783"https://docs.ray.io/en/master/rllib/rllib-env.html#configuring-environments",4784"https://docs.ray.io/en/master/rllib-env.html#configuring-environments",4785"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#environment-variables-are-not-passed-from-the-driver-process-to-worker-processes",4786"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",4787"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4788"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",4789"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4790"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",4791"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",4792"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4793"https://docs.ray.io/en/master/serve/api/index.html#serve-run",4794"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"4795],4796"document_ids": [47976595,479810502,47993,4800609,48019343,48024296,48031111,48044811,480513850,48066061,48074617,480814017,480948124810],4811"answer": " To set environment variables in `ray.init()`, you can use the `runtime_env` parameter with the `env_vars` field. Here's an example:\n\n```python\nray.init(\n runtime_env={\n \"env_vars\": {\n \"foo\": \"foo\"\n }\n }\n)\n```\n\nIn this example, the environment variable `foo` is set to the value `\"foo\"`. Note that if the environment variable already exists, its value will not be overridden. Instead, the existing value will be used.",4812"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4813},4814{4815"question": "What is the rest api for getting the head node id?",4816"sources": [4817"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",4818"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",4819"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",4820"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_node_id.html#ray-runtime-context-runtimecontext-get-node-id",4821"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4822"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails",4823"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.node_id.html#ray-runtime-context-runtimecontext-node-id",4824"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",4825"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",4826"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4827"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",4828"https://docs.ray.io/en/master/serve/api/index.html#v1-rest-api-single-application",4829"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails"4830],4831"document_ids": [48326535,4833721,4834754,48354909,4836493,48376652,48384725,48394307,484014202,484114318,484214081,484314035,4844141054845],4846"answer": " Based on the provided context, there isn't a direct REST API for getting the head node ID in Ray. However, you can obtain the head node IP address using the script in the context, and then use the `ray.util.state.get_node` function to get the node by id.\n\nAlternatively, you can use the `ray runtime context node_id` command to get the current node ID for this worker or driver, which should be the head node if you are running this command on the head node.\n\nHere's an example of how to use `ray.util.state.get_node` to get the node by id:\n```python\nimport ray\n\nhead_node_id = <head_node_id> # replace with the actual head node id\nnode = ray.util.state.get_node(head_node_id)\nprint(node.node_ip)\n```\nAnd here's an example of how to use `ray runtime context node_id` to get the current node ID:\n```python\nimport ray\n\nhead_node_id = ray.runtime_context.RuntimeContext.node_id\nprint(head_node_id)\n```",4847"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4848},4849{4850"question": "how to rerun a canceled ray task",4851"sources": [4852"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",4853"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",4854"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",4855"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",4856"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",4857"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",4858"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",4859"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",4860"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",4861"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",4862"https://docs.ray.io/en/master/ray-core/api/core.html#tasks",4863"https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#recovering-from-data-loss",4864"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks"4865],4866"document_ids": [48674897,48684498,486911802,48704359,487114702,48724430,48734894,487414703,48754361,487611804,48774689,48784441,4879118014880],4881"answer": " You cannot rerun a canceled Ray task as cancelled tasks aren't retried in Ray. When a task is cancelled, it raises a TaskCancelledError if the task has been scheduled or interrupted. Only async actor tasks can be interrupted in Ray. Unscheduled tasks, if not scheduled yet, can be cancelled, and invoking ray.get(actor\\_task\\_ref) will produce a TaskCancelledError. For running actor tasks, Ray offers no mechanism for interruption. If a task is hanging, you can cancel the task by calling ray.cancel on an ObjectRef returned by the task. This will send a KeyboardInterrupt to the task's worker if it is mid-execution. Note that currently, Ray will not automatically retry tasks that have been cancelled.",4882"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4883},4884{4885"question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",4886"sources": [4887"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",4888"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",4889"https://docs.ray.io/en/master/ray-core/actors/async_api.html#setting-concurrency-in-async-actors",4890"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#pattern-using-ray-wait-to-limit-the-number-of-pending-tasks",4891"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#dividing-data-into-batches-and-spawning-a-ray-task-for-each-batch-to-be-ran-in-parallel",4892"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ConcurrencyLimiter.html#ray-tune-search-concurrencylimiter",4893"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",4894"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.set_max_concurrency.html#ray-tune-search-optuna-optunasearch-set-max-concurrency",4895"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.skopt.SkOptSearch.set_max_concurrency.html#ray-tune-search-skopt-skoptsearch-set-max-concurrency",4896"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.html#ray-tune-search-optuna-optunasearch",4897"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.set_max_concurrency.html#ray-tune-search-searcher-set-max-concurrency",4898"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",4899"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"4900],4901"document_ids": [49027738,49031787,49044581,49055246,49065289,49071464,49081543,49091610,49101547,49111867,49121712,49131434,491413404915],4916"answer": " To set the maximum number of parallel concurrent scheduled tasks in `map_batches`, you cannot do it directly in the function. However, you can control the concurrency of Ray by setting the `max_concurrency` parameter in the actor options when defining a Ray actor. By default, 1000 tasks can be running concurrently. If you want to limit the number of tasks that can run concurrently, you can modify the `max_concurrency` parameter accordingly. However, note that Ray automatically decides task parallelism based on resource availability, so adjusting how many tasks can run concurrently by modifying each task's resource requirements is recommended instead.",4917"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4918},4919{4920"question": "How do I get the number of cpus from ray cluster?",4921"sources": [4922"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",4923"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",4924"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",4925"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",4926"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",4927"https://docs.ray.io/en/master/data/performance-tips.html#tuning-read-parallelism",4928"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",4929"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-3-create-a-mini-cluster-with-ray-cluster-utils-cluster",4930"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",4931"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#using-ray-cluster-launcher",4932"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-1-fixing-the-resource-quantity-with-ray-init-num-cpus",4933"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",4934"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#system-metrics"4935],4936"document_ids": [49375936,49385693,493910003,4940614,49415413,49426815,49436370,49445401,49455731,49466287,49475396,49484206,49496514950],4951"answer": " To get the number of CPUs from a Ray cluster, you can check the Ray dashboard which displays information such as the number of CPU cores available. Alternatively, you can use the `ray.cluster_resources()` function to get the resources of the Ray cluster programmatically. If you want to get the number of CPUs from the Ray cluster specification, you can look for the `num-cpus` field in the configuration. For example, in the provided context, the Ray head pod has 4 physical CPUs (`The cluster has 1 head Pod with 4 physical CPUs`).",4952"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4953},4954{4955"question": "How to use the exclude option to the runtime_env",4956"sources": [4957"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",4958"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",4959"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",4960"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4961"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",4962"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.RuntimeEnvState.html#ray-util-state-common-runtimeenvstate",4963"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",4964"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",4965"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",4966"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4967"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",4968"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",4969"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments"4970],4971"document_ids": [49724466,497313856,49744826,49754610,49764764,4977752,49786665,49794342,49804308,49814617,498214217,49834310,498442534985],4986"answer": " To use the `exclude` option with the runtime environment in Ray, you can use the `RuntimeEnvConfig` class and set the `_validate` parameter to `False` when creating a `RuntimeEnv` object. However, there is no direct `exclude` option in the runtime environment configuration. If you want to exclude certain packages or variables, you can specify them in the `pip` or `env_vars` fields and then remove them from the `RuntimeEnv` object after creation.\n\nHere's an example:\n\n```python\nfrom ray.runtime_env import RuntimeEnv, RuntimeEnvConfig\n\n# Specify the runtime environment\nruntime_env_config = RuntimeEnvConfig()\nruntime_env = RuntimeEnv(\n py_modules=[\"mypackage\"],\n working_dir=\"/path/to/working/dir\",\n pip=[\"package1\", \"package2\"],\n env_vars={\"VAR1\": \"value1\", \"VAR2\": \"value2\"},\n config=runtime_env_config,\n _validate=False,\n)\n\n# Exclude certain packages or variables\nruntime_env.pop(\"pip\", [\"package1\", \"package2\"]) # Exclude package1 and package2\nruntime_env.pop(\"env_vars\", {\"VAR1\": \"value1\", \"VAR2\": \"value2\"}) # Exclude VAR1 and VAR2\n\n# Use the modified runtime environment\nray.init(runtime_env=runtime_env)\n```\n\nNote that the `exclude` option is not directly supported, and you'll need to manually remove the packages or variables you want to exclude. Also, be aware that modifying the `runtime_env` object after creation may have unintended consequences, so use this approach with caution.",4987"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4988},4989{4990"question": "show a map batch example with batch_format",4991"sources": [4992"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4993"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",4994"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",4995"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",4996"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",4997"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",4998"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",4999"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",5000"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches",5001"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",5002"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",5003"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches",5004"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.StandardScaler.preferred_batch_format.html#ray-data-preprocessors-standardscaler-preferred-batch-format"5005],5006"document_ids": [50076994,500814727,50097019,50106786,501110365,50126790,50138218,50147800,50156895,50167002,50177979,50186979,501974505020],5021"answer": " Here's an example of using `map_batches` with `batch_format` in Ray Data:\n\n```python\nfrom typing import Dict\nimport numpy as np\nimport ray\n\ndef increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n return batch\n\nds = (\n ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .map_batches(increase_brightness, batch_format=\"numpy\")\n)\n```\n\nIn this example, we read image data and increase the brightness of each image in the dataset. The `batch_format` is set to \"numpy\", which means that the batches will be represented as dictionaries of numpy arrays.",5022"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5023},5024{5025"question": "how to find local ray address",5026"sources": [5027"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5028"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",5029"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",5030"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",5031"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",5032"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",5033"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",5034"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",5035"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",5036"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",5037"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5038"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",5039"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"5040],5041"document_ids": [50424817,50436422,50446183,50456484,50466465,50479668,50485413,50496708,505010001,50514264,50524818,50536839,505448155055],5056"answer": " To find the local Ray address, you can look for the address of the Ray dashboard. When you start a local Ray cluster, the output will indicate the address of the Ray dashboard. For example, \"View the Ray dashboard at http://127.0.0.1:8265\". Alternatively, you can find the address by checking the environment variable RAY\\_ADDRESS or the address of the latest cluster started in /tmp/ray/ray\\_current\\_cluster. If no address is provided, Ray will try to find an existing Ray instance to connect to, starting with the RAY\\_ADDRESS environment variable.",5057"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5058},5059{5060"question": "How to start ray cluster on multiple node via CLI?",5061"sources": [5062"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-3-create-a-mini-cluster-with-ray-cluster-utils-cluster",5063"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",5064"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",5065"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",5066"https://docs.ray.io/en/master/cluster/vms/index.html#overview",5067"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",5068"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",5069"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-up",5070"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",5071"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",5072"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",5073"https://docs.ray.io/en/master/ray-core/configure.html#java-applications",5074"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up"5075],5076"document_ids": [50775401,50786203,50796599,50805636,50816209,50826364,50835605,5084862,50855528,50866365,50875111,50884600,508941935090],5091"answer": " To start a Ray cluster on multiple nodes via CLI, you can use the `ray up` command with a configuration file that specifies the head node and worker nodes. Here are the general steps:\n\n1. Set up your Ray cluster, either locally or remotely, as described in the context.\n2. Create a configuration file (e.g., `config.yaml`) that specifies the head node and worker nodes. Here is an example:\n```yaml\ncluster_name: my_cluster\nprovider:\n type: aws\n region: us-west-2\n availability_zone: us-west-2a\n instance_type: m5.large\n head_instance_count: 1\n worker_instance_count: 2\n # additional configuration options for the cloud provider\nhead_group:\n instance_type: m5.large\n instance_count: 1\n # additional configuration options for the head group\nworker_groups:\n - instance_type: m5.large\n instance_count: 2\n # additional configuration options for the worker group\n```\n3. Run the `ray up` command with the configuration file:\n```\nray up -y config.yaml\n```\nThis will start a Ray cluster with a head node and two worker nodes on AWS.\n\nNote that the `ray up` command supports various configuration options and cloud providers. You can find more information in the Ray documentation.",5092"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5093},5094{5095"question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",5096"sources": [5097"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",5098"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",5099"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",5100"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",5101"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",5102"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",5103"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",5104"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",5105"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",5106"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",5107"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",5108"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#network-overhead-in-ray-tune",5109"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible"5110],5111"document_ids": [51125068,51136934,511410283,51159898,5116622,5117614,5118625,511910674,51209397,51214226,512210609,51232386,512453995125],5126"answer": " The Ray tuner shows \"running\" but CPU usage is almost 0% because the Ray actors and trials are using up the available CPUs, leaving none for the Ray Data to run on. This can cause the Ray Data job to hang. To ensure that there are always available CPUs for Ray Data execution, you can limit the number of concurrent Tune trials with the max\\_concurrent\\_trials Tune option. Additionally, network communication overhead in Ray Tune on a distributed setup can also lead to a slowdown in performance.",5127"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5128},5129{5130"question": "should the Ray head node and all workers have the same object store memory size allocated?",5131"sources": [5132"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",5133"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",5134"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",5135"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",5136"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",5137"https://docs.ray.io/en/master/data/data-internals.html#memory-management",5138"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",5139"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization",5140"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",5141"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",5142"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#head-node-commands",5143"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",5144"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#optional-optimizing-for-runtime-over-memory-with-centralized-data-loading"5145],5146"document_ids": [5147563,51485557,51495013,51505014,51515109,51526960,5153560,51544938,51555555,51566592,51576600,51584387,515953065160],5161"answer": " Yes, it is recommended to allocate a smaller object store memory size to the Ray head node compared to the worker nodes, due to the head node running additional system processes that are memory-demanding. This can help prevent out-of-memory issues on the head node. The exact memory configuration will depend on the specific requirements of your application.",5162"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5163},5164{5165"question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",5166"sources": [5167"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",5168"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",5169"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",5170"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5171"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.ProxyLocation.html#ray-serve-config-proxylocation",5172"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5173"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.ProxyLocation.EveryNode.html#ray-serve-config-proxylocation-everynode",5174"https://docs.ray.io/en/master/serve/develop-and-deploy.html#build-serve-config-files-for-production-deployment",5175"https://docs.ray.io/en/master/serve/api/index.html#serve-start",5176"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5177"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",5178"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.ProxyLocation.HeadOnly.html#ray-serve-config-proxylocation-headonly",5179"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema"5180],5181"document_ids": [518214075,518314081,518413958,518514010,518614279,518714288,518814117,518913439,519014025,519114287,519213991,519314163,5194140085195],5196"answer": " In Ray Serve, you can specify whether to set up an HTTP proxy on each node or just the head node using the `ProxyLocation` configuration. The options are:\n\n- `Disabled`: don't run proxies at all\n- `HeadOnly`: only run a single proxy on the head node\n- `EveryNode`: run a proxy on every node in the cluster that has at least one replica actor (this is the default)\n\nYou can configure this in the Serve config or by using the `--proxy-location` option with the `serve start` command.",5197"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5198},5199{5200"question": "Want to embed Grafana into the Ray Dashboard, given that I am using KubeRay\n\nGiven the context that Prometheus and Grafana are not running on my Head node, and that I am using KubeRay, how should I be setting the following variables?\n\u2022 `RAY_GRAFANA_HOST`\n\u2022 `RAY_PROMETHEUS_HOST`\nAnd is there a way to set them more intelligently, given that head node IP is changing every time we reconfigure our cluster?",5201"sources": [5202"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",5203"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-11-embed-grafana-panels-in-ray-dashboard",5204"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",5205"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",5206"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",5207"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-grafana-host-location",5208"https://docs.ray.io/en/master/cluster/metrics.html#simplist-setting-up-grafana-with-ray-provided-configurations",5209"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",5210"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",5211"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-2-install-kubernetes-prometheus-stack-via-helm-chart",5212"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#grafana-dashboards-are-not-embedded-in-the-ray-dashboard",5213"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",5214"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana"5215],5216"document_ids": [52175544,52186086,52195543,52205545,52216061,52225547,52235592,5224419,52255526,52266056,52275550,52286085,522960835230],5231"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\nTo make these settings more intelligent and avoid hardcoding the IP of the head node, which changes every time you reconfigure your cluster, you can use the service name and namespace instead. For example:\n\n* `RAY_PROMETHEUS_HOST=http://prometheus-kube-prometheus-prometheus.prometheus-system.svc:9090`\n* `RAY_GRAFANA_HOST=http://prometheus-grafana.prometheus-system.svc:80`\n\nNote that these values are based on the default configuration of the kube-prometheus-stack chart, and may need to be adjusted based on your specific setup.\n\nRegarding `RAY_GRAFANA_IFRAME_HOST`, if it is not set, Ray Dashboard uses the value of `RAY_GRAFANA_HOST`. Therefore, you can either set it to the same value as `RAY_GRAFANA_HOST`, or leave it unset.\n\nFinally, make sure that the head node of the cluster is able to access Prometheus and Grafana, and that the browser of the dashboard user is able to access Grafana.",5232"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5233},5234{5235"question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",5236"sources": [5237"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#experiment-results-kuberay-v0-6-0",5238"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-1-create-a-new-kubernetes-cluster",5239"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",5240"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",5241"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",5242"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",5243"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",5244"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",5245"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",5246"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",5247"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#worker-init-container",5248"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",5249"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-vs-horizontal-pod-autoscaler"5250],5251"document_ids": [52525779,52535772,52545975,525513902,52566170,52575972,52585953,525913957,526013956,52615973,52625708,52635963,526459705265],5266"answer": " The GCS (Global Control Service) in KubeRay determines which Kubernetes pod to kill when using KubeRay autoscaling by having fine-grained control over scale-down. It is able to select precisely which pods to scale down, unlike the Horizontal Pod Autoscaler which can only decrease a replica count without much control over which pods are deleted. This is motivated by the need to accommodate the statefulness of Ray applications, where downscaling a random pod could be dangerous.",5267"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5268},5269{5270"question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",5271"sources": [5272"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5273"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",5274"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5275"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",5276"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",5277"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s",5278"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5279"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",5280"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5281"https://docs.ray.io/en/master/serve/api/index.html#serve-config",5282"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",5283"https://docs.ray.io/en/master/serve/api/index.html#put-api-serve-applications",5284"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s"5285],5286"document_ids": [528713854,528813762,528913852,52904300,529113991,52928655,529314288,529413832,529514010,529614009,529713552,529814056,529984815300],5301"answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you can include it in the `http_options` field as follows:\n```yaml\nhttp_options:\n request_timeout_s: <value_in_seconds>\n\napplications:\n- name: <application_name>\n route_prefix: <route_prefix>\n import_path: <import_path>\n runtime_env: <runtime_env>\n deployments:\n - name: <deployment_name>\n num_replicas: <num_replicas>\n ...\n```\nReplace `<value_in_seconds>` with the desired timeout value in seconds, and update the other fields as necessary for your specific use case.\n\nKeep in mind that this config is global to your Ray cluster and cannot be updated during runtime. By default, there is no request timeout, and the Serve HTTP proxy retries up to 10 times when a response is not received due to failures.",5302"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5303},5304{5305"question": "How do I make the GPU available on my M1 laptop to ray?",5306"sources": [5307"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#gpu-support",5308"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",5309"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",5310"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray",5311"https://docs.ray.io/en/master/installation.html#installing-ray",5312"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",5313"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",5314"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",5315"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#gpu-profiling",5316"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#starting-ray-nodes-with-gpus",5317"https://docs.ray.io/en/master/ray-overview/installation.html#m1-mac-apple-silicon-support",5318"https://docs.ray.io/en/master/installation.html#m1-mac-apple-silicon-support",5319"https://docs.ray.io/en/master/ray-more-libs/installation.html#m1-mac-apple-silicon-support"5320],5321"document_ids": [53224991,53235105,5324284,532514541,532659,53276597,53284995,53293222,5330645,53314993,5332298,533373,5334145775335],5336"answer": " To make the GPU available on your M1 laptop for Ray, you need to ensure that Ray is installed correctly on your M1 Mac. Ray officially supports Apple silicon (M1) hardware. Here are the steps to install Ray on your M1 Mac:\n\n1. Install miniforge by running the following commands in your terminal:\n```bash\nwget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh\nbash Miniforge3-MacOSX-arm64.sh\nrm Miniforge3-MacOSX-arm64.sh # Cleanup.\n```\n2. Ensure you're using the miniforge environment by activating it in your terminal:\n```bash\nsource ~/.bash_profile\nconda activate\n```\n3. Install Ray as you normally would by running the following command:\n```\npip install ray\n```\n\nOnce Ray is installed, you can use the `num_gpus` parameter in the `@ray.remote` decorator to specify the number of GPUs required for a particular task. For example, the following code specifies that the `use_gpu` function requires 1 GPU:\n```python\n@ray.remote(num_gpus=1)\ndef use_gpu():\n print(\"ray.get_gpu_ids(): {}\".format(ray.get_gpu_ids()))\n print(\"CUDA_VISIBLE_DEVICES: {}\".format(os.environ[\"CUDA_VISIBLE_DEVICES\"]))\n```\nNote that Ray automatically sets the `CUDA_VISIBLE_DEVICES` environment variable to make the specified GPUs visible to Ray. If you want to limit the GPUs that are visible to Ray, you can use the `CUDA_VISIBLE_DEVICES` environment variable to specify the GPUs that Ray should see. For example, the following command starts Ray and limits it to using GPUs 1 and 3:\n```bash\nCUDA_VISIBLE_DEVICES=1,3 ray start --head --num-gpus=2\n```\nHowever, it's important to note that Ray does not automatically handle GPU sharing or memory management for multiple trials. If you want to use multiple GPUs per trial, you should check out LightningTrainer. Additionally, Ray does not provide native integration with GPU profiling tools. You can try running GPU profilers like Pytorch Profiler without Ray to identify any issues.",5337"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5338},5339{5340"question": "How can I add a timeout for the Ray job?",5341"sources": [5342"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",5343"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s",5344"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",5345"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",5346"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s",5347"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",5348"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",5349"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.GetTimeoutError.html#ray-exceptions-gettimeouterror",5350"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#callbacks",5351"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5352"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",5353"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",5354"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources"5355],5356"document_ids": [53575757,53588655,535913762,53604639,53618481,53626472,536314512,53644796,536514516,536613854,53675236,53684645,536950565370],5371"answer": " To add a timeout for a Ray job, you can use the `timeout_s` field in the relevant configuration for the job. For example, if you're running a Horovod job with Ray, you can set the `HorovodConfig.timeout_s` field to specify the timeout in seconds.\n\nHere's an example of how to set the timeout for a Horovod job:\n```python\nfrom ray.train.horovod import HorovodConfig\n\nHorovodConfig.timeout_s = 300 # Set timeout to 300 seconds\n```\nAlternatively, you can also set a timeout for a specific `get` call to wait for a result from a remote function. You can do this by wrapping the `get` call in a try-except block and catching the `GetTimeoutError` exception.\n\nHere's an example of how to set a timeout for a `get` call:\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```\nIn this example, the `get` call will timeout after 4 seconds and raise a `GetTimeoutError` exception. The exception is caught and handled by printing a message.",5372"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5373},5374{5375"question": "how do I set custom /tmp directory for remote cluster?",5376"sources": [5377"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",5378"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",5379"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",5380"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#cluster-setting",5381"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",5382"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",5383"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",5384"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#common-tune-commands",5385"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",5386"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",5387"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",5388"https://docs.ray.io/en/master/cluster/cli.html#ray-start",5389"https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources"5390],5391"document_ids": [5392872,53936482,53946517,53959337,53966465,539713824,539813738,53992426,54004274,540113822,54026484,54035610,540445215405],5406"answer": " To set a custom /tmp directory for a remote cluster in Ray, you can use the `ray_temp_root_dir` configuration option when starting the cluster. This option allows you to specify a local disk path where the ray temporary data will be stored. The cluster will create a subdirectory beneath this path for the temporary files of the cluster.\n\nFor example, if you want to set the temporary directory to `/custom/tmp`, you can start the cluster with the following command:\n\n```\nray start --head --ray-temp-root-dir=/custom/tmp\n```\n\nThis will create a subdirectory called `ray-<head_port>-<random_suffix>` beneath `/custom/tmp` where the temporary files for the cluster will be stored.\n\nNote that this option only works when the `--head` flag is specified, and it is currently not possible to set a local directory per-task or per-actor. If the local directory contains a `.gitignore` file, the files and paths specified there will not be uploaded to the cluster. You can disable this by setting the environment variable `RAY_RUNTIME_ENV_IGNORE_GITIGNORE=1` on the machine doing the uploading.\n\nI hope this helps! Let me know if you have any other questions.",5407"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5408},5409{5410"question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",5411"sources": [5412"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",5413"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",5414"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",5415"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",5416"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",5417"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",5418"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",5419"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",5420"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5421"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",5422"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",5423"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",5424"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads"5425],5426"document_ids": [54274960,54284531,5429511,54304959,5431872,54326517,54336445,54345861,54354823,54364528,54374961,54381775,543966275440],5441"answer": " Yes, if you set the `object_spilling_config` parameter in `ray.init()`, Ray will use the specified directory for object spilling instead of the default `/tmp`. Here's an example of how to set the spilling directory to `/my/spill/directory`:\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/spill/directory\"}}\n )\n }\n )\n)\n```\nNote that this will only change the spilling directory for the current Ray session. If you want to change the default spilling directory for all Ray sessions, you can set the `RAY_OBJECT_SPILL_DIR` environment variable before starting Ray. For example:\n```bash\nexport RAY_OBJECT_SPILL_DIR=/my/spill/directory\nray start ...\n```",5442"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5443},5444{5445"question": "can you give me an example for *`--runtime-env-json`*",5446"sources": [5447"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",5448"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",5449"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",5450"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",5451"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",5452"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5453"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",5454"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5455"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails",5456"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5457"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",5458"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5459"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails"5460],5461"document_ids": [54624342,54634253,54644466,54654310,546613826,546714022,546814120,546914217,54706655,547113856,54724272,547314219,5474140905475],5476"answer": " The `--runtime-env-json` option is used to specify a runtime environment for a Ray job or task using a JSON object. The JSON object can include fields such as `working_dir`, `pip`, and `env_vars` to specify the working directory, Python packages, and environment variables for the job or task.\n\nHere is an example of using `--runtime-env-json` to specify a runtime environment:\n\n```\nserve run --address=ray://<head-node-ip-address>:10001 --runtime-env-json='{\n \"env_vars\": {\"MY_ENV_VAR\": \"my-value\"},\n \"working_dir\": \"./project/src\",\n \"pip\": [\"requests\", \"chess\"]\n}' local_dev:graph\n```\n\nThis example sets the `MY_ENV_VAR` environment variable, specifies the working directory as `./project/src`, and installs the `requests` and `chess` Python packages.\n\nYou can also specify the runtime environment using a YAML file or a Python dictionary. For more information, see the Ray documentation on Runtime Environments.",5477"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5478},5479{5480"question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",5481"sources": [5482"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-autoscaler",5483"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5484"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5485"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5486"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5487"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5488"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5489"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#configuring-the-autoscaler-sidecar-container",5490"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",5491"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",5492"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5493"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5494"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema"5495],5496"document_ids": [54976479,549813450,549914303,550014225,550114187,550214032,550313449,55045967,55056457,55066319,550714034,550814188,5509142265510],5511"answer": " The context does not provide a specific value for `maxConcurrentReplicas` when autoscaling configuration is specified. It is recommended to tune parameters such as `min_replicas`, `max_replicas`, and `upscaling_speed` for autoscaling configurations. The `maxConcurrentReplicas` parameter seems to be unrelated to the autoscaling configuration, as it is concerned with the maximum number of pending queries in a single replica.",5512"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5513},5514{5515"question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",5516"sources": [5517"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5518"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5519"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5520"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5521"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5522"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",5523"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5524"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5525"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5526"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",5527"https://docs.ray.io/en/master/serve/api/doc/ray.serve.multiplexed.html#ray-serve-multiplexed",5528"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#using-async-methods",5529"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters"5530],5531"document_ids": [553213451,553314187,553414225,553514303,553614032,553713561,553814224,553914186,554014302,554114030,554214253,554313758,5544134505545],5546"answer": " The value of `max_concurrent_queries` is not directly derived from `target_num_ongoing_requests_per_replica`. `max_concurrent_queries` is the maximum number of pending queries in a single replica, with a default value of 1 and a minimum exclusive value of 0. It can be set in the configuration or passed into the deployment's reconfigure method.",5547"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5548},5549{5550"question": "what is a `smoothing_factor`",5551"sources": [5552"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5553"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",5554"https://docs.ray.io/en/master/tune/examples/includes/pb2_example.html#pb2-example",5555"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",5556"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",5557"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.AsyncHyperBandScheduler.html#ray-tune-schedulers-asynchyperbandscheduler",5558"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#learning-rate-eta",5559"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",5560"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.rollouts.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-rollouts",5561"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-rollout-workers",5562"https://docs.ray.io/en/master/tune/api/doc/ray.tune.stopper.ExperimentPlateauStopper.html#ray-tune-stopper-experimentplateaustopper",5563"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#slateq",5564"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg"5565],5566"document_ids": [556713455,556813454,55693900,55701925,55711920,55721735,55732665,55741924,557512634,557610928,55771460,557812225,5579121255580],5581"answer": " A `smoothing_factor` is a multiplicative factor that speeds up or slows down the pace of upscaling or downscaling decisions made by an autoscaler in response to changes in traffic volume. A higher value for the `smoothing_factor` will result in a more rapid response to changes, while a lower value will result in a more gradual response. This parameter can be adjusted to control the sensitivity of the autoscaler to changes in traffic volume.",5582"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5583},5584{5585"question": "What is the reason actors change their state to unhealthy?",5586"sources": [5587"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",5588"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",5589"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",5590"https://docs.ray.io/en/master/serve/api/index.html#get-api-serve-deployments-status",5591"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",5592"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",5593"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",5594"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",5595"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5596"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5597"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5598"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",5599"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema"5600],5601"document_ids": [560211813,56034371,560414708,560514047,560613836,5607747,560812122,56094455,561014294,561114190,561214306,561314228,5614140385615],5616"answer": " Actors in the context of Ray, a distributed computing framework, can change their state to \"UNHEALTHY\" due to various reasons such as an error in the deployment's constructor, a crashed replica, or a general system or machine error. This information is provided in the context through the definition of Actor States and their meaning.",5617"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5618},5619{5620"question": "How do I access logs for a dead node?",5621"sources": [5622"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",5623"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",5624"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",5625"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",5626"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",5627"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",5628"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",5629"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",5630"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",5631"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",5632"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",5633"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",5634"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"5635],5636"document_ids": [5637492,5638493,56396443,5640680,56415129,5642512,5643740,5644662,5645739,5646681,564713894,56485860,56494945650],5651"answer": " Based on the provided context, you cannot access the logs from a dead node. However, you can access the logs of a particular node while it's alive. To do this, you can use the `ray logs` command with the `--node-ip` flag followed by the IP address of the node. For example:\n```css\nray logs raylet.out --node-ip <NODE_IP> --follow\n```\nOr, you can use the `ray logs cluster` command with the `--node-ip` flag followed by the IP address of the node:\n```css\nray logs cluster raylet.out --node-ip <NODE_IP> --follow\n```\nNote that you can find the node IP using `ray list nodes` or `ray.nodes()`.\n\nIf you need to access the logs after the node is dead, you should store and persist the logs as they may get lost whenever the node is stopped or terminated. The context provides instructions on how to process and export logs for clusters on VMs and KubeRay Clusters.",5652"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5653},5654{5655"question": "What are the reasons for a node to change it\u2019s status to dead?",5656"sources": [5657"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",5658"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",5659"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",5660"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",5661"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.PlacementGroupState.html#ray-util-state-common-placementgroupstate",5662"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",5663"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",5664"https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#handling-failures-and-node-preemption",5665"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#worker-node-failure",5666"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",5667"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",5668"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",5669"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate"5670],5671"document_ids": [56724393,567313869,56744625,56754307,5676724,5677721,5678554,56799228,56804389,56815129,56824609,568313894,56847205685],5686"answer": " A node in a Ray cluster can change its status to \"dead\" for several reasons, such as:\n1. Raylet process failure: If the raylet process fails, the corresponding node will be marked as dead.\n2. Worker node failure: When a worker node fails, all the running tasks and actors will fail, and the node will be detected as dead by KubeRay.\n3. Low memory: A node can be killed due to low memory, causing the tasks and actors running on it to fail.\n4. OOM (Out-Of-Memory) errors: Workers can be killed due to memory pressure, leading to the failure of tasks and actors on the node.\n5. Node preemption: In some cases, nodes can be preempted, leading to the failure of tasks and actors running on them.\n\nTo investigate the cause of a node's death, one can check the logs of the raylet process or use the Ray state API to get more information about the node's state and the death cause of dead actors.",5687"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5688},5689{5690"question": "What are the reasons for spikes in node CPU utilization",5691"sources": [5692"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",5693"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",5694"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",5695"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5696"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5697"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5698"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5699"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5700"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#system-metrics",5701"https://docs.ray.io/en/master/data/examples/random-access.html#performance",5702"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5703"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",5704"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training"5705],5706"document_ids": [5707555,5708554,5709468,57103273,57113249,57123269,57133287,57143300,5715654,57168078,57173341,57183277,571933145720],5721"answer": " The reasons for spikes in node CPU utilization, in this context, include:\n\n1. High memory usage: The node's memory usage was at 96.22% (27.71GB / 28.80GB) when the task was running, which exceeded the memory usage threshold of 0.95. This could have led to the Ray framework killing the worker to free up memory, causing spikes in CPU usage.\n2. Running low on memory: The task was killed due to the node running low on memory. This could have been the result of other tasks or processes consuming a significant amount of memory.\n3. Resources requested: Although there were no specific resource demands mentioned in the context, the task requested 1.0/16 CPUs, 0/0 GPUs, 0.0/4.72 GiB heap, and 0.0/2.0 GiB objects. If other tasks or processes were requesting a large number of resources, this could have contributed to the spikes in CPU utilization.\n\nHowever, the context does not provide detailed information about CPU usage or specific reasons for the spikes in CPU utilization. To better understand the causes, you may need to analyze additional logs, metrics, or system information.",5722"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5723},5724{5725"question": "What AWS machine type is recommended to deploy a RayService on EKS?",5726"sources": [5727"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",5728"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",5729"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-eks-cluster-aws",5730"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploying-a-serve-application",5731"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",5732"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/aws-eks-gpu-cluster.html#step-1-create-a-kubernetes-cluster-on-amazon-eks",5733"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#what-does-the-rayservice-provide",5734"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",5735"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#getting-the-status-of-the-application",5736"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#whats-a-rayservice",5737"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",5738"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#prerequisites",5739"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#whats-a-rayservice"5740],5741"document_ids": [574213931,574313930,57445886,574513938,574613962,57475889,57485738,57496477,575013945,57515782,575213624,57536102,575457375755],5756"answer": " The recommended machine type for deploying a RayService on EKS is a cluster with networking characteristics at least as good as an r5dn.16xlarge on AWS EC2. This is due to the heavy networking load and the GCS and dashboard processes. For Ray clusters deployed using KubeRay, you should set the number of CPUs to 0 on the head node.",5757"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5758},5759{5760"question": "Is there a way to configure the session name generated by ray?",5761"sources": [5762"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",5763"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",5764"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5765"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",5766"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",5767"https://docs.ray.io/en/master/installation.html#launch-ray-in-docker",5768"https://docs.ray.io/en/master/ray-overview/installation.html#launch-ray-in-docker",5769"https://docs.ray.io/en/master/ray-more-libs/installation.html#launch-ray-in-docker",5770"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",5771"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",5772"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",5773"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",5774"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment"5775],5776"document_ids": [57774528,57786597,57794811,57801782,5781511,578287,5783312,578414605,57856593,57866595,57876445,57886208,578922575790],5791"answer": " Yes, you can configure the session name generated by Ray by specifying the `temp_dir` or `log_directory` argument when calling `ray.init()`. The session name will be based on the directory path. For example:\n\n```python\nray.init(temp_dir=\"my_session_dir\")\n```\n\nAlternatively, you can specify a custom session name using the `session_dir` argument:\n\n```python\nray.init(session_dir=\"my_session_name\")\n```\n\nThis will create a new session directory at `/tmp/ray/my_session_name`. Note that the `session_dir` argument takes precedence over the `temp_dir` argument.",5792"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5793},5794{5795"question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",5796"sources": [5797"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",5798"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",5799"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",5800"https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",5801"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli",5802"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",5803"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",5804"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#python-sdk-api-reference",5805"https://docs.ray.io/en/master/data/examples/batch_training.html#walkthrough",5806"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-hangs.html#view-stack-traces-in-ray-dashboard",5807"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#visualizing-tasks-with-ray-timeline",5808"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup",5809"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard"5810],5811"document_ids": [58126640,58136510,58146516,5815275,58166454,58176484,58186462,58196438,58207863,5821571,5822626,58236456,58244175825],5826"answer": " No, the Python SDK does not provide a direct method to get a link to the Ray dashboard for a given job. The link to the Ray dashboard is determined by the Ray cluster's address and port, which you can find in the environment variable `RAY_ADDRESS` or in the output of `ray.init()`.\n\nYou can access the Ray dashboard through the default URL, which is `http://localhost:8265` for a local Ray cluster. If you're using a remote Ray cluster, you'll need to replace `localhost` with the cluster's address and use the same port.",5827"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5828},5829{5830"question": "What may possible cause the node where this task was running crashed unexpectedly. This can happen if: (1) the instance where the node was running failed, (2) raylet crashes unexpectedly (OOM, preempted node, etc).",5831"sources": [5832"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",5833"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",5834"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",5835"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",5836"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",5837"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",5838"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",5839"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",5840"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",5841"https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#recovering-from-owner-failure",5842"https://docs.ray.io/en/master/ray-observability/key-concepts.html#exceptions",5843"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",5844"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance"5845],5846"document_ids": [58475129,58484456,5849554,58505130,58514393,58524387,58534453,5854597,5855557,58564445,5857404,585814742,585944235860],5861"answer": " The node where the task was running crashed unexpectedly potentially due to:\n\n1. The instance where the node was running failed.\n2. Raylet process crashed unexpectedly, possibly due to Out-Of-Memory (OOM) error or a preempted node.\n3. The task was killed due to the node running low on memory.\n4. Unintended system failures, such as processes crashing or nodes failing.\n\nRefer 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>.",5862"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5863},5864{5865"question": "Do you know how to resolve (gcs_server) gcs_health_check_manager.cc:108: Health check failed for node? I observed that the node is still up and running.",5866"sources": [5867"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",5868"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",5869"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",5870"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",5871"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",5872"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",5873"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",5874"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",5875"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",5876"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#replica-health-checking",5877"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",5878"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",5879"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance"5880],5881"document_ids": [58824375,58835709,58845638,58856372,588613899,588713890,588813895,588913836,589013902,589113865,58924391,589314264,5894138705895],5896"answer": " The error message you're seeing, \"Health check failed for node,\" is likely related to the Global Control Service (GCS) in Ray. The GCS is a critical component of Ray that manages cluster-level metadata and provides node management. If the GCS fails, then functions like actor creation, deletion, and resource management will not be available.\n\nTo make the GCS fault tolerant, you need to use HA Redis. This will allow the GCS to recover and resume regular functions when it restarts. During the recovery period, some functions will not be available, but the node should still be up and running.\n\nBased on the context provided, some possible causes for this error include:\n\n* The GCS server process has failed in the head Pod. You can check the log directory /tmp/ray/session_latest/logs/ in the head Pod for errors related to the GCS server.\n* The ray executable is not included in the $PATH for the image, so the init container will fail to run ray health-check.\n* The CLUSTER_DOMAIN environment variable is not set correctly.\n\nTo troubleshoot this issue, you can try the following steps:\n\n1. Check the GCS server logs for any errors.\n2. Verify that the ray executable is included in the $PATH for the image.\n3. Check that the CLUSTER_DOMAIN environment variable is set correctly.\n4. If you are using HA Redis, make sure it is properly configured and running.\n5. If the issue persists, you may need to restart the Ray cluster or the affected node.",5897"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5898},5899{5900"question": "Do you need the DAGDriver to deploy a serve application using RayServe?",5901"sources": [5902"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",5903"https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph",5904"https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",5905"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-the-applications",5906"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",5907"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploying-a-serve-application",5908"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",5909"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5910"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",5911"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",5912"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5913"https://docs.ray.io/en/master/serve/production-guide/docker.html#adding-your-serve-application-to-the-docker-image",5914"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm"5915],5916"document_ids": [591713660,591813426,591913502,592013690,592113680,592213937,592313674,592413859,592513738,592613684,592713862,592813924,5929137345930],5931"answer": " No, you don't need the DAGDriver to deploy a RayServe application. The DAGDriver is used to route HTTP requests through your call graph and handle HTTP parsing. However, it is not a requirement for deploying a RayServe application. You can deploy a RayServe application using the `serve deploy` command with a configuration file. The configuration file will include details about the deployments, such as the name, number of replicas, and user-defined configurations.",5932"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5933},5934{5935"question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",5936"sources": [5937"https://docs.ray.io/en/master/workflows/basics.html#sub-task-results",5938"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",5939"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",5940"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",5941"https://docs.ray.io/en/master/serve/tutorials/batch.html#define-the-deployment",5942"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#defining-an-application-builder",5943"https://docs.ray.io/en/master/serve/index.html#quickstart",5944"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",5945"https://docs.ray.io/en/master/serve/api/index.html#serve-run",5946"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",5947"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",5948"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Application.html#ray-serve-application",5949"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#imports"5950],5951"document_ids": [59529636,595313844,595413850,59555678,595614580,595713797,595813599,59595789,596014019,596113661,596213680,596313979,596427885965],5966"answer": " To provide an import path for a simple RayServe deployment, you need to import Ray and RayServe first:\n\n```python\nimport ray\nfrom ray import serve\n```\n\nThen, create a deployment with `@serve.deployment`. Here's a simple \"Hello World\" example:\n\n```python\n@serve.deployment\nclass HelloWorld:\n def __init__(self, message: str):\n self._message = message\n\n def __call__(self, request):\n return self._message\n```\n\nFinally, use the `serve.run()` function to deploy the application locally:\n\n```python\napp = HelloWorld.bind(message=\"Hello world!\")\nserve.run(app)\n```\n\nThe import path for this deployment would be the name of the Python file (without the .py extension) followed by the name of the class annotated with `@serve.deployment`. In this case, the import path is `HelloWorld`.",5967"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5968},5969{5970"question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",5971"sources": [5972"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",5973"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",5974"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-2-submit-a-ray-job-to-the-raycluster-via-ray-job-submission-sdk",5975"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",5976"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",5977"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",5978"https://docs.ray.io/en/master/cluster/kubernetes/index.html#about-kuberay",5979"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",5980"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",5981"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",5982"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",5983"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-is-a-rayjob",5984"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds"5985],5986"document_ids": [59875751,59885760,59895729,59905762,59915763,59925754,59935660,59946144,59955757,59966162,59975642,59985753,599956476000],6001"answer": " Yes, the context includes an example of using RayJob in Kuberay. Here are the steps:\n\n1. Download the `ray_v1alpha1_rayjob.yaml` configuration file using the following command:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml\n```\n2. Create a RayJob by applying the configuration file using the following command:\n```bash\nkubectl apply -f ray_v1alpha1_rayjob.yaml\n```\n3. Check the status of the RayJob using the following command:\n```bash\nkubectl get rayjobs.ray.io rayjob-sample -o json | jq '.status.jobStatus'\n```\nThe output should be `\"SUCCEEDED\"` if the job has finished successfully.\n\nThe `ray_v1alpha1_rayjob.yaml` configuration file defines a RayJob that creates a RayCluster and submits a Ray job to it. The Ray job is defined in the `entrypoint` field of the RayJob custom resource. In this example, the `entrypoint` is `python /home/ray/samples/sample_code.py`, which is a Python script stored in a Kubernetes ConfigMap mounted to the head Pod of the RayCluster.\n\nNote that RayJob support in Kuberay v0.x is in alpha.",6002"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6003},6004{6005"question": "Does Ray metrics have to be exported via an actor?",6006"sources": [6007"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",6008"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",6009"https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",6010"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",6011"https://docs.ray.io/en/master/serve/monitoring.html#exporting-metrics-into-arize",6012"https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",6013"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",6014"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",6015"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",6016"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",6017"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",6018"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",6019"https://docs.ray.io/en/master/cluster/metrics.html#scraping-metrics"6020],6021"document_ids": [6022503,60235573,6024438,6025656,602613550,6027403,602814723,602914707,603011811,60314369,60324200,60335586,603455816035],6036"answer": " No, Ray metrics do not have to be exported via an actor. Ray can automatically export default metrics, including system and application metrics, when you use certain installation commands that include the Dashboard component. These metrics can be viewed in the Ray Dashboard's Metrics view. Additionally, you can manually configure Prometheus to read metrics from a static list of endpoints or use the Dashboard agent process for scraping metrics.",6037"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6038},6039{6040"question": "how can I avoid objects not getting spilled?",6041"sources": [6042"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",6043"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",6044"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",6045"https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",6046"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6047"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6048"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#out-of-core-data-processing",6049"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#stats",6050"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#cluster-mode",6051"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6052"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6053"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6054"https://docs.ray.io/en/master/data/data-internals.html#object-store-memory"6055],6056"document_ids": [60574959,60586473,605914748,60604674,60614960,60624963,606314487,60644969,60654968,60664962,60674966,60684961,606969676070],6071"answer": " To avoid objects not getting spilled, you can:\n\n1. Use NVMe SSDs or other high-performance storage for object spilling.\n2. Configure the directory where objects are spilled to by setting the \"directory\\_path\" parameter in the \"object\\_spilling\\_config\" when initializing Ray.\n3. Specify multiple directories for spilling to spread the IO load and disk space usage across multiple physical devices.\n4. Adjust the local\\_fs\\_capacity\\_threshold to prevent running out of disk space.\n5. Increase the buffer size (> 1MB) to reduce IO requests during spilling.\n6. Use an SSD instead of an HDD when using object spilling for memory-intensive workloads.\n7. If using remote storage, specify a large buffer size and increase the number of IO workers for parallelism.\n\nAdditionally, it is recommended to use locality scheduling to reduce the need to transfer objects between nodes in the cluster.",6072"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6073},6074{6075"question": "what\u2019s ray core",6076"sources": [6077"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",6078"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",6079"https://docs.ray.io/en/master/data/data.html#learn-more",6080"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",6081"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice",6082"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",6083"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",6084"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",6085"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",6086"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",6087"https://docs.ray.io/en/master/workflows/comparison.html#comparison-between-ray-core-apis-and-workflows",6088"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",6089"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#rayservice-quickstart"6090],6091"document_ids": [60924469,60934701,60946995,60955656,60965780,6097371,60984811,609913980,6100372,61019668,61029696,61036208,610457356105],6106"answer": " Ray Core is a distributed computing system that provides primitives like tasks, actors, and objects for building and scaling distributed applications. It is the foundation of Ray's AI libraries and third-party integrations, and can run on various environments including laptops, clusters, clouds, and Kubernetes. Ray Workflows is built on top of Ray Core and offers a mostly consistent subset of its API while providing durability. Ray Core can be used for custom applications, and it has a growing ecosystem of community integrations.",6107"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6108},6109{6110"question": "Does ray support cron job",6111"sources": [6112"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",6113"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",6114"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",6115"https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",6116"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",6117"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",6118"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",6119"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",6120"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",6121"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-jobs",6122"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#why-are-changes-to-the-raycluster-or-rayjob-cr-not-taking-effect",6123"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",6124"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment"6125],6126"document_ids": [61275757,612813980,6129410,61309574,6131415,61326208,613313517,61346635,6135541,61365560,61375716,61386597,6139142556140],6141"answer": " No, Ray does not directly support cron jobs. However, you can use an external job scheduler that interacts with your Ray cluster to trigger workflow runs, including cron jobs. Ray Workflows does not have a built-in job scheduler. Ray applications are usually run as Ray Jobs, and Worker logs of Ray Jobs are always captured in the Ray logging directory.",6142"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6143},6144{6145"question": "can you give me the dependencies list for api read_images?",6146"sources": [6147"https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",6148"https://docs.ray.io/en/master/installation.html#docker-source-images",6149"https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",6150"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",6151"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning",6152"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",6153"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-3-1-the-ray-image-does-not-include-the-required-dependencies",6154"https://docs.ray.io/en/master/data/examples/ocr_example.html#walkthrough",6155"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-3-2-examples-for-troubleshooting-dependency-issues",6156"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",6157"https://docs.ray.io/en/master/data/working-with-images.html#reading-images",6158"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#define-a-custom-datasource",6159"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages"6160],6161"document_ids": [616214593,616381,6164306,616510238,61666011,61677361,61685673,61697906,61705675,617114090,61726720,617310220,617442676175],6176"answer": " The `read_images` function doesn't have a specific dependencies list as it's a custom function. However, to use it in a Ray environment, you need to have the Ray cluster with the required dependencies installed.\n\nBased on the context, you can use the `rayproject/ray-ml` Docker image, which includes Ray, Python, and many additional ML libraries. If you require specific dependencies, you can build your own Ray image with those dependencies or use runtime environments to specify the required dependencies.\n\nFor the `read_images` function, you need to have the following dependencies:\n\n1. Ray\n2. Numpy\n3. Pillow (PIL)\n4. Requests\n\nYou can ensure these dependencies are installed in your Ray environment by using the appropriate Docker image or runtime environment configuration.",6177"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6178},6179{6180"question": "how do I kill a specific serve replica",6181"sources": [6182"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",6183"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",6184"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",6185"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",6186"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",6187"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",6188"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",6189"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",6190"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#next-steps",6191"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",6192"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",6193"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",6194"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#replica-health-checking"6195],6196"document_ids": [619713912,619813905,619913911,620013901,620113903,620213820,620313908,620413448,620513961,620613862,620713915,620813818,6209138656210],6211"answer": " To kill a specific serve replica, you can follow these steps:\n\n1. Get the list of actors by running `ray list actors` in the Python interpreter.\n2. Identify the NAME of the ServeReplica you want to kill from the list.\n3. Get a handle to the replica by running `replica_handle = ray.get_actor(\"NAME\", namespace=\"serve\")`.\n4. Kill the replica by running `ray.kill(replica_handle, no_restart=True)`.\n\nFor example, if you want to kill the replica with NAME \"SERVE\\_REPLICA::SleepyPid#PHvdJW\", you can run:\n```python\n>>> import ray\n>>> replica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#PHvdJW\", namespace=\"serve\")\n>>> ray.kill(replica_handle, no_restart=True)\n```",6212"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6213}6214]6215}6216