llm-applications
4799 строк · 349.4 Кб
1{2"config": {3"experiment_name": "gpt-4",4"chunk_size": 700,5"chunk_overlap": 50,6"num_chunks": 9,7"embedding_model_name": "thenlper/gte-large",8"llm": "gpt-4",9"temperature": 0.0,10"max_context_length": 8192,11"system_content": "Answer the query using the context provided. Be succinct.",12"assistant_content": "",13"docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",14"experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",15"references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",16"num_samples": 17717},18"results": [19{20"question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",21"sources": [22"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",23"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",24"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",25"https://docs.ray.io/en/master/data/key-concepts.html#transforming-data",26"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",27"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",28"https://docs.ray.io/en/master/data/working-with-tensors.html#transforming-tensor-data",29"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.MultiHotEncoder.transform_batch.html#ray-data-preprocessors-multihotencoder-transform-batch",30"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.transform_batch.html#ray-data-preprocessor-preprocessor-transform-batch"31],32"document_ids": [337081,346790,356786,366733,3714727,388046,396777,407421,41724142],43"answer": "You can specify the batch format in the `map_batches()` function. By default, Ray Data represents batches as dicts of NumPy ndarrays, but you can also configure it to use pandas DataFrames. You can do this by setting the `batch_format` parameter in `map_batches()`. For example, `.map_batches(increase_brightness, batch_format=\"numpy\")` or `.map_batches(increase_brightness, batch_format=\"pandas\")`. Make sure your function returns the same format as specified in `batch_format`.",44"llm": "gpt-4"45},46{47"question": "How does autoscaling work in a Ray Serve application?",48"sources": [49"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",50"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",51"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",52"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",53"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",54"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",55"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#load-metrics-are-based-on-application-semantics",56"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",57"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling"58],59"document_ids": [6013471,616363,625559,6313448,645969,655953,665971,6714726,681344769],70"answer": "Autoscaling in a Ray Serve application works by automatically increasing or decreasing the number of deployment's replicas based on its load. The Ray autoscaler, a process that runs on the head node, adjusts the number of nodes in the cluster based on the resources required by tasks, actors, or placement groups. When the resource demands exceed the current capacity of the cluster, the autoscaler increases the number of worker nodes. Conversely, when worker nodes sit idle, the autoscaler removes them from the cluster. The Ray Serve autoscaler is an application-level autoscaler that requests Ray to start a number of replica actors based on the request demand. If there aren't enough available CPUs to place these actors, the Ray Autoscaler requests more Ray nodes from the underlying cloud provider.",71"llm": "gpt-4"72},73{74"question": "how do I get the address of a ray node",75"sources": [76"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",77"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",78"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",79"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",80"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",81"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",82"https://docs.ray.io/en/master/ray-core/api/doc/ray.nodes.html#ray-nodes",83"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",84"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints"85],86"document_ids": [87739,884817,89754,904446,916552,92493,934769,946502,95558796],97"answer": "To get the address of a Ray node, you can use the `ray.util.state.get_node(id: str, address: Optional[str] = None, timeout: int = 30, _explain: bool = False)` function. You need to provide the id of the node as a parameter. If the address is not specified, it will be resolved automatically from an initialized ray. Alternatively, you can use the `ray.nodes()` function to get a list of the nodes in the cluster, which includes their addresses. If you are using a Ray Cluster started on VMs or Kubernetes, you can set the RAY_ADDRESS environment variable to the Ray Dashboard address.",98"llm": "gpt-4"99},100{101"question": "Does Ray support NCCL?",102"sources": [103"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",104"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",105"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",106"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray",107"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",108"https://docs.ray.io/en/master/installation.html#installing-ray",109"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",110"https://docs.ray.io/en/master/ray-overview/index.html#overview",111"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api"112],113"document_ids": [1144662,11514391,1164811,11714537,118284,11959,1206835,121364,12213974123],124"answer": "Yes, Ray does support NCCL. It uses NCCL and GLOO as the optional high-performance communication backends in its collective communication library.",125"llm": "gpt-4"126},127{128"question": "Is Ray integrated with DeepSpeed?",129"sources": [130"https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",131"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.deepspeed_engine.html#ray-train-lightning-raydeepspeedstrategy-deepspeed-engine",132"https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",133"https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",134"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",135"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.strategy_name.html#ray-train-lightning-raydeepspeedstrategy-strategy-name",136"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#fine-tune-vicuna-13b-with-lightning-and-deepspeed",137"https://docs.ray.io/en/master/train/deepspeed.html#training-with-deepspeed",138"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning"139],140"document_ids": [1418232,1428996,1438331,1448249,1458881,1468578,1479334,1488207,1499393150],151"answer": "Yes, Ray is integrated with DeepSpeed. You can run DeepSpeed with other frameworks in Ray Train. Examples of such integrations include Accelerate, Transformers, and Lightning.",152"llm": "gpt-4"153},154{155"question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",156"sources": [157"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",158"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example",159"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",160"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",161"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",162"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",163"https://docs.ray.io/en/master/ray-core/walkthrough.html#running-a-task",164"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",165"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures"166],167"document_ids": [1684600,1695197,1704611,1714599,1725248,1734598,1744485,1754603,1764601177],178"answer": "If you use AsyncIO's await to wait for a Ray future like `await x.remote()`, it will allow you to asynchronously wait for the result of the remote task. This is because Ray's ObjectRefs can be translated to asyncio.Futures, making it possible to use async/await syntax with Ray futures.",179"llm": "gpt-4"180},181{182"question": "How would you compare Spark, Ray, Dask?",183"sources": [184"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",185"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",186"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",187"https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",188"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",189"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",190"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",191"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",192"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray"193],194"document_ids": [19514465,19614479,1976847,19814647,19914502,2006846,20114467,2026809,20314469204],205"answer": "Dask is a Python parallel computing library that scales analytics and scientific computing workloads. It provides big data collections that mimic the APIs of NumPy and Pandas libraries, allowing operations on larger-than-memory data to be run on a multi-machine cluster. Ray provides a scheduler for Dask, allowing data analyses using Dask\u2019s collections to be executed on a Ray cluster. However, Dask-on-Ray is an ongoing project and may not achieve the same performance as using Ray directly.\n\nRay Data handles many of the same batch processing workloads as Apache Spark, but with a streaming paradigm that is better suited for GPU workloads for deep learning inference. RayDP combines Spark and Ray clusters, making it easy to do large scale data processing using the PySpark API and use that data to train models using TensorFlow and PyTorch.\n\nIn summary, Dask is ideal for scaling analytics and scientific computing workloads, Ray is suitable for executing tasks on a fast, fault-tolerant distributed task execution system, and Spark is useful for large scale data processing.",206"llm": "gpt-4"207},208{209"question": "why would ray overload a node w/ more task that the resources allow ?",210"sources": [211"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",212"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",213"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",214"https://docs.ray.io/en/master/ray-core/tasks.html#scheduling",215"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",216"https://docs.ray.io/en/master/ray-core/scheduling/index.html#locality-aware-scheduling",217"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",218"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",219"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case"220],221"document_ids": [2225231,2235112,2245104,2254376,2265230,2275152,2286504,2295115,2305232231],232"answer": "Ray would overload a node with more tasks than the resources allow if the tasks or actors use more resources than specified via resource requirements. This is because Ray's resource requirements are logical and do not impose limits on actual physical resource usage. It's the user's responsibility to ensure tasks or actors do not use more resources than specified. If tasks or actors use more than their proportionate share of memory, it may overload a node and cause issues like Out Of Memory (OOM).",233"llm": "gpt-4"234},235{236"question": "when should I use Ray Client?",237"sources": [238"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",239"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",240"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",241"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",242"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",243"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",244"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",245"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",246"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve"247],248"document_ids": [2496615,2506611,2516608,2526612,2536613,2544811,25513974,2564204,25713442258],259"answer": "You should use Ray Client when you want to connect an interactive Python shell to a remote cluster for interactive work. However, it may not work as expected for ML workloads like Ray Tune or Ray Train, in which case the Ray Jobs API is recommended. Ray Client requires a stable connection to the remote cluster and will terminate the workload if the connection is lost for more than 30 seconds. For long running workloads, it is recommended to use Ray Jobs instead.",260"llm": "gpt-4"261},262{263"question": "how to scatter actors across the cluster?",264"sources": [265"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",266"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",267"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",268"https://docs.ray.io/en/master/serve/architecture.html#high-level-view",269"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",270"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",271"https://docs.ray.io/en/master/ray-core/scheduling/index.html#default",272"https://docs.ray.io/en/master/ray-core/api/utility.html#utility",273"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference"274],275"document_ids": [2765145,2771027,2785195,27913466,2805391,2816787,2825143,2834677,2848119285],286"answer": "To scatter actors across the cluster in Ray, you can use the \"SPREAD\" scheduling strategy. This can be done by using the `options` method with the `scheduling_strategy` parameter set to \"SPREAD\" when creating the actor. Here is an example:\n\n```python\n@ray.remote(num_cpus=1)\nclass SpreadActor:\n pass\n\n# Spread actors across the cluster.\nactors = [SpreadActor.options(scheduling_strategy=\"SPREAD\").remote() for _ in range(10)]\n```\n\nThis will try to spread the actors among available nodes in the cluster.",287"llm": "gpt-4"288},289{290"question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",291"sources": [292"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",293"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",294"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",295"https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",296"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",297"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",298"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#connect-to-the-cluster",299"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",300"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"301],302"document_ids": [3035681,3044815,3056610,30614678,3074266,3086619,3096526,31013718,3116551312],313"answer": "The \"connection refused\" error when you do `ray debug` on a remote ray cluster could be due to several reasons. One possibility is that the Dashboard, Dashboard Agent, and GCS may take a few seconds to start up after the head Pod is ready. Therefore, the request may fail a few times initially before the necessary components are fully operational. If you continue to encounter this issue after waiting for 1 minute, it\u2019s possible that the dashboard or dashboard agent may have failed to start. Another possibility is that you might not be connecting to the cluster correctly. Make sure you are using the correct address and port when initializing Ray. For example, use `ray.init(address=\"ray://<head_node_host>:10001\")` to connect to a remote cluster.",314"llm": "gpt-4"315},316{317"question": "How does Ray AIR set up the model to communicate gradient updates across machines?",318"sources": [319"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",320"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#asynchronous-parameter-server-training",321"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",322"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",323"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",324"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",325"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",326"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",327"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#defining-the-parameter-server"328],329"document_ids": [3309845,3315338,3329820,333374,334156,335337,336183,33710019,3385329339],340"answer": "Ray AIR sets up the model to communicate gradient updates across machines by using a parameter server. Workers asynchronously compute the gradients given their current weights and send these gradients to the parameter server as soon as they are ready. When the parameter server finishes applying the new gradient, it sends back a copy of the current weights to the worker. The worker then updates the weights and repeats the process.",341"llm": "gpt-4"342},343{344"question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",345"sources": [346"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",347"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",348"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",349"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",350"https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",351"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",352"https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",353"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",354"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice"355],356"document_ids": [35713621,35813974,35913614,36013597,36113420,36210282,363144,3645785,3655739366],367"answer": "Ray Serve is a scalable model serving library that is not tied to any specific machine learning library or framework. This makes it a flexible choice as it can serve everything from deep learning models built with frameworks like PyTorch, Tensorflow, and Keras, to Scikit-Learn models, to arbitrary Python business logic. It is particularly well suited for model composition and many model serving, enabling you to build a complex inference service consisting of multiple ML models and business logic all in Python code. \n\nWhile containers can be used for model serving, Ray Serve provides several features and performance optimizations for serving Large Language Models such as response streaming, dynamic request batching, multi-node/multi-GPU serving, etc. It also supports complex model deployment patterns requiring the orchestration of multiple Ray actors, where different actors provide inference for different models. \n\nCompared to Modal or Seldon, Ray Serve lacks the functionality for managing the lifecycle of your models and visualizing their performance. However, it brings the scalability and reliability of hosted offerings to your own infrastructure and can be deployed to all major public clouds, K8s, as well as on bare-metal, on-premise machines.",368"llm": "gpt-4"369},370{371"question": "How do I deploy an LLM workload on top of Ray Serve?",372"sources": [373"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",374"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",375"https://docs.ray.io/en/master/cluster/vms/user-guides/community/lsf.html#deploying-on-lsf",376"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",377"https://docs.ray.io/en/master/serve/api/index.html#serve",378"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",379"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",380"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",381"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air"382],383"document_ids": [38413618,38513677,3866382,3875636,38813983,38913598,39013416,39113647,39210282393],394"answer": "To deploy an LLM workload on top of Ray Serve, you need to write all the steps of your LLM application as a single Python module. This can include prompt preprocessing, vector database lookups, LLM API calls, and response validation. You can then deploy your application to production using the Ray Serve CLI. The command 'serve deploy' takes in a config file path and deploys that file to a Ray cluster over HTTP. This can be a local, single-node cluster or a remote, multi-node cluster started with the Ray Cluster Launcher. You can also define a deployment using the @serve.deployment decorator on a Python class or function, and then bind the deployment with optional arguments to the constructor.",395"llm": "gpt-4"396},397{398"question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",399"sources": [400"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",401"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",402"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",403"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",404"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",405"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",406"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",407"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",408"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air"409],410"document_ids": [4114878,41214121,41314137,41414299,41514183,41614062,41714221,41810280,41910260420],421"answer": "You should have at least one GPU with 16 or more GBs of memory to set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d.",422"llm": "gpt-4"423},424{425"question": "How do I log the results from multiple distributed workers into a single tensorboard?",426"sources": [427"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",428"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",429"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",430"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#example-logging-to-mlflow-and-tensorboard",431"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#quickstart",432"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",433"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",434"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-tune-results-on-a-single-node",435"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-concurrent-tune-trials"436],437"document_ids": [4389544,43910927,4402354,4419144,4428286,443231,4442411,4452383,4462380447],448"answer": "To log results from multiple distributed workers into a single TensorBoard, you can use the `tf.distribute.MultiWorkerMirroredStrategy()` in TensorFlow. This strategy mirrors the model's variables across all the workers. During training, Tune automatically outputs TensorBoard files. After running an experiment, you can visualize your experiment with TensorBoard by specifying the output directory of your results using the command `tensorboard --logdir=~/ray_results/my_experiment`. If you're running Ray on a remote multi-user cluster, make sure TensorBoard is able to write to the tmp directory by exporting the TMPDIR.",449"llm": "gpt-4"450},451{452"question": "how do you config SyncConfig for a Ray AIR job?",453"sources": [454"https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration",455"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",456"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",457"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningConfigBuilder.checkpointing.html#ray-train-lightning-lightningconfigbuilder-checkpointing",458"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",459"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",460"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_timeout.html#ray-train-syncconfig-sync-timeout",461"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",462"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.checkpoint_config.html#ray-train-runconfig-checkpoint-config"463],464"document_ids": [4651138,46610466,4678441,4688371,4691443,47010464,4711852,4721782,4739098474],475"answer": "To configure SyncConfig for a Ray AIR job, you need to use the `ray.train.SyncConfig` class. You can specify parameters such as `upload_dir`, `syncer`, `sync_period`, `sync_timeout`, `sync_artifacts`, `sync_artifacts_on_checkpoint`, and `sync_on_checkpoint`. For example:\n\n```python\nsync_config = ray.train.SyncConfig(upload_dir='your_upload_dir', syncer='your_syncer', sync_period=300, sync_timeout=1800, sync_artifacts=False, sync_artifacts_on_checkpoint=True, sync_on_checkpoint='DEPRECATED')\n```\n\nThen, you can set this `sync_config` to the `RunConfig`:\n\n```python\nrun_config = ray.train.RunConfig(sync_config=sync_config)\n```\n\nPlease note that the `upload_dir` and `syncer` parameters are deprecated. If you want to turn off syncing, you can set `syncer=None` in `SyncConfig`. To re-enable the head node syncing behavior, set the environment variable `RAY_AIR_REENABLE_DEPRECATED_SYNC_TO_HEAD_NODE=1`.",476"llm": "gpt-4"477},478{479"question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",480"sources": [481"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",482"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",483"https://docs.ray.io/en/master/ray-observability/getting-started.html#job-profiling",484"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",485"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",486"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",487"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",488"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/observability.html#method-3-check-logs-of-ray-pods",489"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods"490],491"document_ids": [4925764,493444,494422,495415,49613523,4976631,4986157,4995855,5005665501],502"answer": "To quickly narrow down the root cause of a failed Ray job, you can follow these steps:\n\n1. Check the output of the Ray job using the command `kubectl logs -l=job-name=rayjob-sample`.\n2. Use the Logs view in your Cluster, which lists the Ray logs organized by node and log file name. You can use the search functionality to find specific log messages.\n3. Check the Driver logs. If the Ray Job is submitted by the Job API, the Job logs are available from the Dashboard. The log file follows the format: job-driver-<job_submission_id>.log.\n4. Check the Worker logs of Ray Jobs in the Ray logging directory.\n5. Check the Ray client logs found at /tmp/ray/session_latest/logs on the head node.\n6. Use the command `ray job logs 'raysubmit_xxxxxxxxxxxxxxxx' --address http://127.0.0.1:8265 --follow` to follow the Job's logs.\n7. Access the log files on the Pods directly using the command `kubectl exec -it $RAY_POD -n $YOUR_NAMESPACE -- bash` and check the logs under /tmp/ray/session_latest/logs/.\n8. Check the Ray Serve logs directly by accessing the log files on the pods. These log files contain system level logs from the Serve controller and HTTP proxy as well as access logs and user-level logs.",503"llm": "gpt-4"504},505{506"question": "How do I specify how many GPUs a serve deployment needs?",507"sources": [508"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",509"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",510"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",511"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",512"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#resource-management-cpus-gpus",513"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",514"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",515"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#usage-example",516"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#resources"517],518"document_ids": [51913458,52011062,52113575,52213456,52313778,52410283,52511063,5266420,5275927528],529"answer": "To specify how many GPUs a serve deployment needs, you can use the \"num_gpus\" option in the \"ray_actor_options\" parameter. For example, if each replica of the deployment needs 1 GPU, you can specify it as follows: '@serve.deployment(ray_actor_options={\"num_gpus\": 1})'. You can also specify a fraction of a GPU if you want to pack multiple replicas together on a machine with fewer GPUs than replicas.",530"llm": "gpt-4"531},532{533"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?",534"sources": [535"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",536"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",537"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",538"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",539"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",540"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",541"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",542"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",543"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop"544],545"document_ids": [5465129,5479493,54810333,549557,550555,55110339,5529483,55310456,5549326555],556"answer": "The worker node is dying due to memory pressure or Out Of Memory (OOM) issues. You can debug this by checking the logs of the worker node that died using the command `ray logs raylet.out -ip <IP_ADDRESS>`. You can also check the logs of the worker using `ray logs worker-<WORKER_ID>*out -ip <IP_ADDRESS>`. To address the OOM issue, you can refer to the documentation at https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html.",557"llm": "gpt-4"558},559{560"question": "what are the possible reasons for nodes dying in a cluster?",561"sources": [562"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",563"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",564"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",565"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",566"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",567"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",568"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",569"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",570"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group"571],572"document_ids": [57313898,5744522,5754519,5764524,5779524,578554,5795639,5805129,5814790582],583"answer": "Possible reasons for nodes dying in a cluster include head node failure, raylet process failure, the cluster running low on memory, overloading the head node with too many simultaneous connections, and user action leading to the termination of the cluster.",584"llm": "gpt-4"585},586{587"question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",588"sources": [589"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",590"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",591"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",592"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",593"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",594"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",595"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",596"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",597"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster"598],599"document_ids": [6006363,6016715,6026365,6035953,6049510,6056337,606851,6076457,608854609],610"answer": "To programmatically get a Ray remote cluster to a target size immediately without scaling up through the autoscaler, you can use the `ray.autoscaler.sdk.request_resources()` function. This function allows you to command the autoscaler to scale the cluster up to a desired size. The cluster will immediately attempt to scale to accommodate the requested resources, bypassing normal upscaling speed constraints.",611"llm": "gpt-4"612},613{614"question": "how do you disable async iter_batches with Ray Dataset?",615"sources": [616"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",617"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",618"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",619"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.streaming_split.html#ray-data-dataset-streaming-split",620"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",621"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches",622"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#define-your-training-function",623"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",624"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat"625],626"document_ids": [6277081,6287379,6297188,6307414,6317380,6327811,6339361,6349906,6357328636],637"answer": "The context does not provide information on how to disable async iter_batches with Ray Dataset.",638"llm": "gpt-4"639},640{641"question": "what is the different between a batch and a block, for ray datasets?",642"sources": [643"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",644"https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",645"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",646"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",647"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#batch-training-with-ray-core",648"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.batch_to_block.html#ray-data-block-blockaccessor-batch-to-block",649"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",650"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",651"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-data"652],653"document_ids": [65414728,6556872,65614727,6577081,6585274,6597150,6606895,66114584,6626859663],664"answer": "A block in Ray datasets is a processing unit of data, an internal abstraction that allows operations to be performed in parallel. A dataset consists of a collection of these blocks. On the other hand, a batch is a user-facing object that represents batches of data. It is used in methods like Dataset.iter_batches() and Dataset.map_batches() to specify the batch type. The batch size in the context of model training is the number of data points used to compute and apply one gradient update to the model weights.",665"llm": "gpt-4"666},667{668"question": "How to setup the development environments for ray project?",669"sources": [670"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-from-source",671"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",672"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",673"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#development-workflow",674"https://docs.ray.io/en/master/serve/api/index.html#serve-run",675"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-on-linux-macos-full",676"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",677"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher",678"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments"679],680"document_ids": [681877,682910,68310168,68413709,68513990,686889,6874251,6884250,6896614690],691"answer": "To set up the development environment for the Ray project, follow these steps:\n\n1. Clone the Ray repository and go to the example directory: \n```\ngit clone https://github.com/ray-project/ray.git\ncd doc/source/templates/05_dreambooth_finetuning\n```\n2. Install dependencies: \n```\npip install -Ur dreambooth/requirements.txt\n```\n3. Prepare some directories and environment variables. If running on multiple nodes, change the path to a shared directory.\n4. Build Ray for Python. Make sure to activate any Python virtual (or conda) environment you could be using.\n5. Install NodeJS to build the dashboard. Enter into the project directory and the dashboard client directory, then install the dependencies and build the dashboard:\n```\ncd ray\ncd dashboard/client\nnpm ci\nnpm run build\n```\n6. Move back to the top level Ray directory: \n```\ncd ../..\n```\n7. Set up dependencies either by preparing a single environment across the cluster before starting the Ray runtime or installing them dynamically while Ray is running. For production settings, it is recommended to build any necessary packages into a container image.",692"llm": "gpt-4"693},694{695"question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",696"sources": [697"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#rewriting-trajectories",698"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",699"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",700"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",701"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.set_weights.html#ray-rllib-evaluation-rollout-worker-rolloutworker-set-weights",702"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",703"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",704"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.sample.html#ray-rllib-evaluation-rollout-worker-rolloutworker-sample",705"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model"706],707"document_ids": [70810845,70913196,71012708,71112707,71212965,71310211,714640,71512739,71610218717],718"answer": "To debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`, you can follow these steps:\n\n1. Check the logs: Look for any error messages or warnings in the logs. The logs might give you a clue about what's going wrong. For example, the warning about the Ray cluster not having any available CPUs could indicate a resource allocation issue.\n\n2. Use a profiler: Tools like cProfile can help you understand where your code is spending most of its time. This can help you identify any bottlenecks or areas of your code that are causing the deadlock.\n\n3. Check your resource usage: The warning messages suggest that the Ray cluster does not have any available CPUs. Make sure that your tasks are not using up all the available resources, causing the deadlock.\n\n4. Check your actor usage: If you're using actors, make sure you're not initializing a single actor and trying to run all your tasks on it. This can cause a bottleneck and potentially lead to a deadlock.\n\n5. Check your task submission and retrieval: Make sure you're not waiting for each task to complete before submitting the next one. This can cause your tasks to run in serial, rather than in parallel, and could lead to a deadlock.",719"llm": "gpt-4"720},721{722"question": "how do I join two ray datasets?",723"sources": [724"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",725"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",726"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",727"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",728"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",729"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",730"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#load-and-preprocess-datasets",731"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-ml-libraries",732"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes"733],734"document_ids": [7358006,7366835,7379180,7389188,7397255,7407289,7419348,7426756,7436809744],745"answer": "You can join two Ray datasets using either the `zip` or `union` methods. \n\nThe `zip` method concatenates the columns of the second dataset horizontally with the columns of the first dataset. Duplicate column names are disambiguated with suffixes like \"_1\". Here is an example:\n\n```python\nimport ray\nds1 = ray.data.range(5)\nds2 = ray.data.range(5)\nds1.zip(ds2).take_batch()\n```\n\nThe `union` method concatenates datasets across rows, preserving the order of the blocks in the datasets and the relative ordering between the datasets. Here is an example:\n\n```python\nimport ray\nds1 = ray.data.range(2)\nds2 = ray.data.range(3)\nds1.union(ds2).take_all()\n```\n\nNote that the `union` operation will trigger execution of the lazy transformations performed on the dataset and unioned datasets aren\u2019t lineage-serializable.",746"llm": "gpt-4"747},748{749"question": "Is there a way to retrieve an object ref from its id?",750"sources": [751"https://docs.ray.io/en/master/ray-core/objects.html#objects",752"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",753"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",754"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait",755"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",756"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",757"https://docs.ray.io/en/master/ray-core/api/core.html#objects",758"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",759"https://docs.ray.io/en/master/ray-core/objects.html#objects"760],761"document_ids": [7624413,7635426,7644982,7654750,7664885,76714041,7684651,7695436,7704412771],772"answer": "Yes, you can retrieve an object ref from its id using the `ray.get()` function in Ray. This function blocks until the object corresponding to the object ref is available in the local object store. If the object is not in the local object store, it will be shipped from an object store that has it. If the object_refs argument is a list, then the objects corresponding to each object in the list will be returned.",773"llm": "gpt-4"774},775{776"question": "how to create model Checkpoint from the model in memory?",777"sources": [778"https://docs.ray.io/en/master/tune/examples/includes/pbt_convnet_function_example.html#pbt-convnet-example",779"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",780"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#check-the-training-results-and-checkpoints",781"https://docs.ray.io/en/master/tune/examples/tune-pytorch-cifar.html#the-train-function",782"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",783"https://docs.ray.io/en/master/tune/examples/includes/xgboost_dynamic_resources_example.html#xgboost-dynamic-resources-example",784"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#enabling-checkpointing-to-retrieve-the-model",785"https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example",786"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint"787],788"document_ids": [7894104,7909037,7919331,7923858,7938798,7943964,7959857,7964073,7979066798],799"answer": "To create a model checkpoint from the model in memory, you can use the `from_model` method provided by the `TensorflowCheckpoint` or `Checkpoint` class. Here is an example using TensorFlow:\n\n```python\nmodel = tf.keras.applications.resnet.ResNet101()\ncheckpoint = TensorflowCheckpoint.from_model(model)\n```\n\nAnd here is an example using PyTorch:\n\n```python\ncheckpoint = Checkpoint.from_dict(\n dict(epoch=t, model=model.state_dict())\n)\n```\n\nIn both cases, the `from_model` method creates a checkpoint from the current state of the model in memory.",800"llm": "gpt-4"801},802{803"question": "what is Deployment in Ray Serve?",804"sources": [805"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",806"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",807"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",808"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment",809"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",810"https://docs.ray.io/en/master/serve/tutorials/java.html#converting-to-a-ray-serve-deployment",811"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",812"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",813"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy"814],815"document_ids": [81614255,81713416,81814732,81914150,82013679,82114560,82213677,82313776,82413987825],826"answer": "In Ray Serve, a deployment is the central concept that contains business logic or a Machine Learning (ML) model to handle incoming requests. It can be scaled up to run across a Ray cluster. A deployment consists of a number of replicas, which are individual copies of the class or function that are started in separate Ray Actors (processes). The number of replicas can be scaled up or down, or even autoscaled, to match the incoming request load. Deployments are defined using the @serve.deployment decorator on a Python class or function. They can be composed together into an Application which is then run via serve.run or a config file.",827"llm": "gpt-4"828},829{830"question": "What is user config in Ray Serve? how do I use it?",831"sources": [832"https://docs.ray.io/en/master/serve/api/index.html#serve-config",833"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",834"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",835"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#updating-user-config",836"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",837"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-4-prepare-your-ray-serve-python-scripts-and-run-the-ray-serve-application",838"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#dynamically-changing-parameters-without-restarting-your-replicas-user-config",839"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#adding-deleting-and-updating-applications",840"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"841],842"document_ids": [84313986,84413831,84513974,84613687,84713551,8485983,84913565,85013661,8516615852],853"answer": "User config in Ray Serve is a feature that allows you to issue lightweight updates to your deployments. It is used in conjunction with the reconfigure() method in your deployments. If the user_config is set when the deployment is created, the reconfigure method is called right after the deployment\u2019s __init__ method, and the user_config is passed in as an argument. You can also trigger the reconfigure method by updating your Serve config file with a new user_config and reapplying it to your Ray cluster. For example, in the YAML configuration for your deployment, you might have a section like this:\n\n```yaml\ndeployments:\n - name: Model\n user_config:\n threshold: 1.5\n```\n\nIn this case, the \"threshold\" is a user-configurable parameter.",854"llm": "gpt-4"855},856{857"question": "What is the difference between PACK and SPREAD strategy?",858"sources": [859"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",860"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",861"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",862"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",863"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",864"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",865"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",866"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",867"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.placement_strategy.html#ray-train-scalingconfig-placement-strategy"868],869"document_ids": [8701872,87112053,87211075,8735075,8745078,8755077,8764789,8775145,8788773879],880"answer": "The \"PACK\" strategy packs bundles into as few nodes as possible, while the \"SPREAD\" strategy places bundles across distinct nodes as evenly as possible.",881"llm": "gpt-4"882},883{884"question": "What\u2019s the best way to run ray across multiple machines?",885"sources": [886"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",887"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",888"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",889"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",890"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",891"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",892"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",893"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",894"https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure"895],896"document_ids": [8975636,8984644,89914710,9005600,901372,90213615,9034186,9045634,9059809906],907"answer": "The best way to run Ray across multiple machines is to deploy a Ray cluster. A Ray cluster is a set of worker nodes connected to a common Ray head node. Ray clusters can be fixed-size or they may autoscale according to the resources requested by applications running on the cluster. You can start the Ray runtime implicitly via ray.init(), explicitly via CLI (ray start), or via the cluster launcher (ray up).",908"llm": "gpt-4"909},910{911"question": "how do I specify ScalingConfig for a Tuner run?",912"sources": [913"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",914"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",915"https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",916"https://docs.ray.io/en/master/tune/key-concepts.html#tune-trials",917"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",918"https://docs.ray.io/en/master/tune/examples/pbt_ppo_example.html#example",919"https://docs.ray.io/en/master/tune/examples/nevergrad_example.html#optional-passing-the-hyper-parameter-space-into-the-search-algorithm",920"https://docs.ray.io/en/master/tune/examples/flaml_example.html#incorporating-a-time-budget-to-the-experiment",921"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#more-resources"922],923"document_ids": [9241826,9259126,9261303,927967,9289127,9292741,9302569,9312860,9329256933],934"answer": "The context does not provide information on how to specify ScalingConfig for a Tuner run.",935"llm": "gpt-4"936},937{938"question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",939"sources": [940"https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",941"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_numpy_refs.html#ray-data-dataset-to-numpy-refs",942"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",943"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",944"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",945"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",946"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",947"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy.html#ray-data-from-numpy",948"https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only"949],950"document_ids": [9514942,9527644,9534537,9544945,9554225,9567085,9577102,9587467,9594944960],961"answer": "To utilize the 'zero-copy' feature that Ray provides for numpy, you need to store the numpy array as a read-only object. All Ray workers on the same node can read the numpy array in the object store without copying. Each numpy array object in the worker process holds a pointer to the relevant array held in shared memory. If you need to write to the read-only object, you will first need to copy it into the local process memory. You can also use the Dataset.to_arrow() or Dataset.get_internal_block_refs() methods for zero-copy access to the underlying data. If your function doesn't mutate its input, you can set zero_copy_batch=True to improve performance and decrease memory utilization.",962"llm": "gpt-4"963},964{965"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?",966"sources": [967"https://docs.ray.io/en/master/data/examples/random-access.html#architecture",968"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",969"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",970"https://docs.ray.io/en/master/data/examples/random-access.html#performance",971"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",972"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",973"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",974"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_random_access_dataset.html#ray-data-dataset-to-random-access-dataset",975"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile"976],977"document_ids": [9788000,9798119,9804878,9818002,9828146,9835231,9848101,9857308,986641987],988"answer": "Creating 'key pools' where an actor can hold many keys would be more advisable than spinning up 1 actor per key, especially when dealing with millions of keys. This is because managing a large number of actors can be resource-intensive and may not provide any inherent benefit. The number of actors should be determined based on the workload and cluster setup. For instance, in CPU-only training, there is generally no benefit of running more than one remote actor per node. However, in a multi-GPU training, each GPU should have a separate remote actor.",989"llm": "gpt-4"990},991{992"question": "How to find the best checkpoint from the trial directory?",993"sources": [994"https://docs.ray.io/en/master/train/api/doc/ray.train.Result.html#ray-train-result",995"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",996"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",997"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",998"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",999"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1000"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1001"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_trial_checkpoints_paths.html#ray-tune-experimentanalysis-get-trial-checkpoints-paths",1002"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result"1003],1004"document_ids": [10058414,10061431,10072188,10081645,10093759,10101430,101111460,10121692,101337601014],1015"answer": "To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` method. You need to specify the metric and mode parameters. The metric is the key for checkpoints to order on, and the mode can be either \"min\" or \"max\". This method returns a Checkpoint object, or None if there is no valid checkpoint associated with the metric. If you didn't pass these parameters, you can use `get_best_checkpoint(trial, metric, mode)` instead.",1016"llm": "gpt-4"1017},1018{1019"question": "what are the advantage and disadvantage of using singleton Actor ?",1020"sources": [1021"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1022"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1023"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",1024"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",1025"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#default-concurrency-group",1026"https://docs.ray.io/en/master/ray-core/patterns/index.html#design-patterns-anti-patterns",1027"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",1028"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",1029"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"1030],1031"document_ids": [10325192,10334505,103414708,103511597,10364641,10375244,10384638,10394630,104046241041],1042"answer": "The context does not provide specific advantages or disadvantages of using a singleton Actor.",1043"llm": "gpt-4"1044},1045{1046"question": "what are the advantages of using a named actor?",1047"sources": [1048"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1049"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",1050"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1051"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1052"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1053"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1054"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1055"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",1056"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources"1057],1058"document_ids": [10594623,10604629,10614625,10624871,10634624,10644880,10654505,106614708,1067115971068],1069"answer": "The advantages of using a named actor include:\n\n1. It allows you to retrieve the actor from any job in the Ray cluster, which is 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.\n2. It supports the creation of an actor only if it doesn't exist, with the help of the get_if_exists option.\n3. It supports non-global named actors, meaning the actor name is only valid within the job and cannot be accessed from another job.\n4. It allows you to get a handle to a named actor with the help of the ray.get_actor method.\n5. It allows you to set a globally unique name for the actor, which can be used to retrieve the actor as long as the actor is still alive.",1070"llm": "gpt-4"1071},1072{1073"question": "How do I read a text file stored on S3 using Ray Data?",1074"sources": [1075"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1076"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1077"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1078"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",1079"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1080"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",1081"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1082"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1083"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data"1084],1085"document_ids": [10867565,10877783,10887496,10897261,10906994,10916779,10926996,10937217,109479091095],1096"answer": "To read a text file stored on S3 using Ray Data, you can use the `read_text()` function. Here is an example:\n\n```python\nimport ray\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\n```\n\nThis will create a dataset where each row corresponds to a line of text from the file.",1097"llm": "gpt-4"1098},1099{1100"question": "how do I get the IP of the head node for my Ray cluster?",1101"sources": [1102"https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",1103"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",1104"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",1105"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",1106"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",1107"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",1108"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",1109"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats",1110"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster"1111],1112"document_ids": [11135631,11146501,1115493,11165587,11174446,11186502,11196409,11206416,112166161122],1123"answer": "To get the IP of the head node for your Ray cluster, you can use the command `ray get_head_ip [OPTIONS] CLUSTER_CONFIG_FILE`. If you do not know the IP addresses of the nodes in your Ray Cluster, you can also programmatically discover the endpoints by reading the Ray Cluster information using a Python script and the `ray.nodes` API.",1124"llm": "gpt-4"1125},1126{1127"question": "How to write a map function that returns a list of object for `map_batches`?",1128"sources": [1129"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1130"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1131"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1132"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1133"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1134"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1135"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1136"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",1137"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference"1138],1139"document_ids": [11407082,11417091,11427640,11437081,11447800,11457959,11467086,11477738,114880701149],1150"answer": "To write a map function that returns a list of objects for `map_batches`, you can define a function that takes a batch of data as input and returns a transformed batch. Here is an example:\n\n```python\nfrom typing import Dict\nimport ray\nimport numpy as np\n\ndef map_fn_with_large_output(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n for i in range(3):\n yield {\"large_output\": np.ones((100, 1000))}\n\nds = (\n ray.data.from_items([1])\n .map_batches(map_fn_with_large_output)\n)\n```\n\nIn this example, `map_fn_with_large_output` is a function that takes a batch of data and yields a dictionary with a key \"large_output\" and a value of a numpy array of ones. This function is then passed to the `map_batches` method of a dataset `ds`.",1151"llm": "gpt-4"1152},1153{1154"question": "How do I set a maximum episode length when training with Rllib?",1155"sources": [1156"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",1157"https://docs.ray.io/en/master/rllib/rllib-replay-buffers.html#advanced-usage",1158"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",1159"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1160"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",1161"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",1162"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#evolution-strategies-es",1163"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.train_buffered.html#ray-rllib-algorithms-algorithm-algorithm-train-buffered",1164"https://docs.ray.io/en/master/rllib/rllib-offline.html#getting-started"1165],1166"document_ids": [116710821,116810724,116910510,117011458,117113177,117211048,117311925,117412332,1175105941176],1177"answer": "To set a maximum episode length when training with Rllib, you can use the \"evaluation_duration\" and \"evaluation_duration_unit\" settings. If you want to set the maximum episode length to 10 episodes, you can set \"evaluation_duration\" to 10 and \"evaluation_duration_unit\" to \"episodes\". If you want to set the maximum episode length to 200 time-steps, you can set \"evaluation_duration\" to 200 and \"evaluation_duration_unit\" to \"timesteps\".",1178"llm": "gpt-4"1179},1180{1181"question": "how do I make a Ray Tune trial retry on failures?",1182"sources": [1183"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1184"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1185"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1186"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_error.html#ray-tune-callback-on-trial-error",1187"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_recover.html#ray-tune-callback-on-trial-recover",1188"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",1189"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.MedianStoppingRule.html#ray-tune-schedulers-medianstoppingrule",1190"https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.LoggerCallback.on_trial_recover.html#ray-tune-logger-loggercallback-on-trial-recover",1191"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune"1192],1193"document_ids": [11942326,11951114,11962266,11971980,11982152,11991926,12001728,12011966,1202115541203],1204"answer": "To make a Ray Tune trial retry on failures, you can use the `FailureConfig` to configure failure handling of individual trials. Additionally, you can use the `Callback.on_trial_recover` method which is called after a trial instance has failed but is scheduled for retry. Ray Tune also provides fault tolerance and recovery at the experiment trial level through periodic checkpointing. If a trial fails, Ray Tune will automatically restart it from the latest checkpointed state.",1205"llm": "gpt-4"1206},1207{1208"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?",1209"sources": [1210"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",1211"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",1212"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1213"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1214"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",1215"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#how-to-write-fault-tolerant-ray-applications",1216"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1217"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#advanced-detached-placement-group",1218"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#worker-recovery"1219],1220"document_ids": [12215208,12224561,12234575,12244631,12255195,12264351,12275191,12285095,1229115511230],1231"answer": "No, in the supervised actor pattern, if the supervisor or the driver dies, the worker actors are automatically terminated due to actor reference counting. However, actor lifetimes can be decoupled from the job using detached actors, allowing an actor to persist even after the driver process of the job exits. But this doesn't prevent termination on supervisor failure.",1232"llm": "gpt-4"1233},1234{1235"question": "How do I read a large text file in S3 with Ray?",1236"sources": [1237"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1238"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1239"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1240"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",1241"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1242"https://docs.ray.io/en/master/data/loading-data.html#handling-compressed-files",1243"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1244"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1245"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text"1246],1247"document_ids": [12487565,12497783,12507496,12517261,12526994,12536747,12546996,12557217,125669931257],1258"answer": "To read a large text file in S3 with Ray, you can use the `read_text()` function. Here is an example:\n\n```python\nimport ray\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\n```\n\nThis will create a row for each line of text in the file.",1259"llm": "gpt-4"1260},1261{1262"question": "how do I get a ray dataset from pandas",1263"sources": [1264"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-pandas",1265"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1266"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",1267"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",1268"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1269"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",1270"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1271"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",1272"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_pandas.html#ray-data-dataset-to-pandas"1273],1274"document_ids": [12756806,12766749,12776901,12786752,12799180,12807818,128110017,12827363,128373021284],1285"answer": "To get a Ray dataset from a pandas dataframe, you can use the `ray.data.from_pandas()` function. You can pass either a single pandas dataframe or a list of pandas dataframes to this function. Here is an example:\n\n```python\nimport pandas as pd\nimport ray\n\ndf = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\nray_dataset = ray.data.from_pandas(df)\n```\n\nThis will create a Ray dataset from the pandas dataframe.",1286"llm": "gpt-4"1287},1288{1289"question": "can you give me an example of using `ray.data.map` ?",1290"sources": [1291"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1292"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1293"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1294"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1295"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1296"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",1297"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",1298"https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",1299"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch"1300],1301"document_ids": [13027796,13037274,13049180,13057081,13066835,13078046,13088006,13096773,131079711311],1312"answer": "Sure, here is an example of using `ray.data.map`:\n\n```python\nimport ray\nds = ray.data.range(10000)\nds.map(lambda row: {\"id\": row[\"id\"] * 2}).take(4)\n```\n\nIn this example, a dataset is created with a range of integers from 0 to 9999. Then, the `map` function is used to multiply each \"id\" by 2. The `take(4)` function is used to retrieve the first 4 records from the transformed dataset. The output will be `[{\\'id\\': 0}, {\\'id\\': 2}, {\\'id\\': 4}, {\\'id\\': 6}]`.",1313"llm": "gpt-4"1314},1315{1316"question": "can you give me an example of using `ray.data.map` , with a callable class as input?",1317"sources": [1318"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1319"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",1320"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1321"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1322"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1323"https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",1324"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",1325"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1326"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a"1327],1328"document_ids": [13297796,1330250,13317274,13327636,13336749,13347628,1335247,133610014,1337103031338],1339"answer": "Here is an example of using `ray.data.map` with a callable class as input:\n\n```python\nimport ray\n\nray.init() # Only call this once.\n\nclass MyCallableClass:\n def __init__(self, multiplier):\n self.multiplier = multiplier\n\n def __call__(self, row):\n return {\"id\": row[\"id\"] * self.multiplier}\n\nds = ray.data.range(10000)\n\nmy_callable_instance = MyCallableClass(2)\nds = ds.map(my_callable_instance)\n\nprint(ds.take(4)) # [{'id': 0}, {'id': 2}, {'id': 4}, {'id': 6}]\n```\n\nIn this example, `MyCallableClass` is a callable class that multiplies the 'id' field of each row by a specified multiplier. An instance of `MyCallableClass` is created with a multiplier of 2, and this instance is then passed to `ds.map()`. The result is a new dataset where the 'id' field of each row is twice its original value.",1340"llm": "gpt-4"1341},1342{1343"question": "How to set memory limit for each trial in Ray Tuner?",1344"sources": [1345"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1346"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1347"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",1348"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1349"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",1350"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-trials-individually-with-a-timeout",1351"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria",1352"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",1353"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater"1354],1355"document_ids": [13562325,13571114,13582406,13592266,13602001,13612204,13622198,13631025,136420031365],1366"answer": "To set the memory limit for each trial in Ray Tuner, you can configure the resources allocated per trial. This is done using the `tune.with_resources()` function. For example, to set each trial to use 1 CPU, you would use the following code: `train_model = tune.with_resources(train_model, {\"cpu\": 1})`. However, the context does not provide specific information on setting a memory limit.",1367"llm": "gpt-4"1368},1369{1370"question": "how do I get the actor id of an actor",1371"sources": [1372"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",1373"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",1374"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1375"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor",1376"https://docs.ray.io/en/master/ray-observability/getting-started.html#actor-detail-page",1377"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1378"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",1379"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1380"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"1381],1382"document_ids": [13834787,13844931,1385475,1386690,1387436,1388474,1389750,1390476,13914771392],1393"answer": "To get the actor ID of an actor in Ray, you can use the `get_actor_id()` method from the `RuntimeContext` class. This method returns the current actor ID in hex format. However, this method should not be used in a driver process. If there's no actor ID, it will return None. Note that the `actor_id` property of `RuntimeContext` is deprecated and `get_actor_id()` should be used instead.",1394"llm": "gpt-4"1395},1396{1397"question": "can ray.init() can check if ray is all-ready initiated ?",1398"sources": [1399"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",1400"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1401"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",1402"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1403"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1404"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1405"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#starting-ray",1406"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",1407"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users"1408],1409"document_ids": [14104785,14114811,14124194,14134190,14144188,14154189,14165386,14174186,141842041419],1420"answer": "Yes, the function ray.is_initialized() can be used to check if ray.init() has already been initiated. It returns True if ray.init() has been called and False otherwise.",1421"llm": "gpt-4"1422},1423{1424"question": "What does the `compute=actor` argument do within `ray.data.map_batches` ?",1425"sources": [1426"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1427"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",1428"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1429"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1430"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1431"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",1432"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1433"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map.html#ray-util-actorpool-map",1434"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text"1435],1436"document_ids": [14376787,14386885,14398108,14408070,14417988,14425465,14436981,14444708,144569991446],1447"answer": "The `compute=actor` argument within `ray.data.map_batches` is used to specify an ActorPoolStrategy with the number of parallel actors. This is used when the user-defined function (UDF) is a class, allowing it to run as long-running Ray actors. This strategy fetches the model just once and reuses it for all tasks assigned to the actor, optimizing performance especially when the model size is large.",1448"llm": "gpt-4"1449},1450{1451"question": "how do I use wandb logger with accelerateTrainer?",1452"sources": [1453"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",1454"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1455"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1456"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1457"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",1458"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",1459"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1460"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1461"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries"1462],1463"document_ids": [14648470,14652432,14662463,14679530,14682431,14691132,14702466,14719529,147291361473],1474"answer": "To use wandb logger with accelerateTrainer, you need to follow these steps:\n\n1. Make sure you're logged into wandb on all machines you're running your training on by using `wandb login`.\n\n2. Set up your API keys as environment variables.\n\n3. Set `rank_zero_only.rank = None` to avoid Lightning creating a new experiment run on the driver node.\n\n4. Import the necessary modules:\n```python\nimport pytorch_lightning as pl\nfrom pytorch_lightning.loggers.wandb import WandbLogger\nimport wandb\n```\n\n5. Create a WandbLogger instance:\n```python\nwandb_logger = WandbLogger(\n name=\"demo-run\",\n project=\"demo-project\",\n id=\"unique_id\",\n offline=offline\n)\n```\n\n6. Pass the logger instance to the `pl.Trainer`:\n```python\ntrainer = pl.Trainer(logger=wandb_logger)\n```\n\n7. Run your training with the trainer instance.",1475"llm": "gpt-4"1476},1477{1478"question": "What will be implicitly put into object store?",1479"sources": [1480"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1481"https://docs.ray.io/en/master/ray-core/objects/serialization.html#plasma-store",1482"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1483"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1484"https://docs.ray.io/en/master/data/data-internals.html#object-store-memory",1485"https://docs.ray.io/en/master/ray-core/objects.html#objects",1486"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",1487"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",1488"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"1489],1490"document_ids": [14915013,14924941,14935014,149414748,14956896,14964414,149714094,149814122,1499141381500],1501"answer": "Objects are implicitly put into the object store when your application creates objects in the object store via ray.put and when it returns values from remote functions. This also includes data blocks in the case of Ray Data.",1502"llm": "gpt-4"1503},1504{1505"question": "How do I kill or cancel a ray task that I already started?",1506"sources": [1507"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1508"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",1509"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1510"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",1511"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1512"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",1513"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1514"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",1515"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks"1516],1517"document_ids": [15184550,15194375,15204894,152114702,15224494,152311591,15244497,152514703,1526115921527],1528"answer": "To cancel a Ray task that you've already started, you can call `ray.cancel()` on the ObjectRef returned by the task. For example:\n\n```python\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n```\n\nThen, you can check if the task was successfully cancelled using `ray.get(obj_ref)`. If the task was cancelled, this will raise a `ray.exceptions.TaskCancelledError`.\n\nNote that Ray attempts to cancel tasks on a best-effort basis, so cancellation isn't always guaranteed. Also, Ray does not automatically retry tasks that have been cancelled.",1529"llm": "gpt-4"1530},1531{1532"question": "how to send extra arguments in dataset.map_batches function?",1533"sources": [1534"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1535"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1536"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",1537"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1538"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1539"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1540"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1541"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",1542"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches"1543],1544"document_ids": [15457082,15467081,15478607,15487959,15498070,15507988,15517640,15526956,155368611554],1555"answer": "To send extra arguments in the `dataset.map_batches` function, you can use the `fn_args` and `fn_kwargs` parameters. `fn_args` is an iterable of arguments that will be passed to the function, and `fn_kwargs` is a dictionary of keyword arguments that will be passed to the function.",1556"llm": "gpt-4"1557},1558{1559"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?",1560"sources": [1561"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance",1562"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",1563"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",1564"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",1565"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",1566"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",1567"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/experimental.html#gcs-fault-tolerance",1568"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",1569"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob"1570],1571"document_ids": [15725830,157313871,157413470,157513870,15765642,15775647,15785916,15795763,158061491581],1582"answer": "Ray stores the history of jobs run on a KubeRay cluster in the Global Control Store (GCS), which is an internal key-value store. The GCS can also write this metadata to an external Redis for reliability and high availability. All data in the Controller (routing policies, deployment configurations, etc) is checkpointed to the Ray GCS on the head node. The type of database used is Redis. The format is not explicitly mentioned in the context.",1583"llm": "gpt-4"1584},1585{1586"question": "How to resolve ValueError: The actor ImplicitFunc is too large?",1587"sources": [1588"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",1589"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",1590"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#verify-and-save-results",1591"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1592"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1593"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler",1594"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",1595"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",1596"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction"1597],1598"document_ids": [15996150,16009858,16017992,16028119,16038108,16041895,160510267,16069908,160754651608],1609"answer": "To resolve the ValueError: The actor ImplicitFunc is too large, you should avoid capturing large objects or arrays in the actor's definition. Instead, use `ray.put()` to put large objects in the Ray object store. Also, ensure that the data loader is moved to the training function. This error can also occur if `reuse_actors` is set to True in `tune.TuneConfig()`, so make sure it's set to False.",1610"llm": "gpt-4"1611},1612{1613"question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",1614"sources": [1615"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",1616"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1617"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#distributed-training",1618"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",1619"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1620"https://docs.ray.io/en/master/train/huggingface-accelerate.html#training-with-huggingface-accelerate",1621"https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others",1622"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1623"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#training"1624],1625"document_ids": [16269845,1627226,162810155,1629370,16306940,16318247,163214313,1633228,163455151635],1636"answer": "To distribute training for your custom neural net written using Keras in Databricks using Ray, follow these steps:\n\n1. Import Ray Train and Ray AIR Session:\n```python\nimport ray.train as train\n```\n2. Convert your training function to a distributed multi-worker training function. Use the `ray.train.torch.prepare_model` and `ray.train.torch.prepare_data_loader` utility functions to set up your model and data for distributed training. This automatically wraps the model with DistributedDataParallel and places it on the right device, and adds DistributedSampler to the DataLoaders.\n```python\nfrom ray import train\n\ndef train_func_distributed():\n num_epochs = 3\n batch_size = 64\n\n dataset = get_dataset()\n dataloader = DataLoader(dataset, batch_size=batch_size)\n dataloader = train.torch.prepare_data_loader(dataloader)\n\n model = NeuralNetwork()\n model = train.torch.prepare_model(model)\n```\n3. Load the data shard for each worker with `session.get_dataset_shard(\u201ctrain\u201d)` and iterate over the dataset with `train_dataset.iter_torch_batches()`.\n4. Prepare everything for distributed training using the `accelerator.prepare()` function.\n5. Start training using the `TorchTrainer` class from `ray.train.torch` and the `ScalingConfig` class from `ray.train`.\n```python\nfrom ray.train.torch import TorchTrainer\nfrom ray.train import ScalingConfig\n\ntrainer = TorchTrainer(\n train_func_distributed,\n scaling_config=ScalingConfig(num_workers=4, use_gpu=use_gpu)\n)\n\nresults = trainer.fit()\n```\n6. Finally, initialize Ray and set up Ray distributed actors.\n```python\nray.init(address=args.ray_address)\n\nActor = ray.remote(\n num_cpus=1, num_gpus=int(not args.cpu))(RayDistributedActor)\nworkers = [Actor.remote() for i in range(args.distributed_world_size)]\n```",1637"llm": "gpt-4"1638},1639{1640"question": "how to use ray.put and ray,get?",1641"sources": [1642"https://docs.ray.io/en/master/ray-core/api/core.html#objects",1643"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",1644"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",1645"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1646"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",1647"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",1648"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",1649"https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",1650"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"1651],1652"document_ids": [16534651,16545443,16554849,16564205,16574884,16585219,16594209,16604775,166148111662],1663"answer": "`ray.put(value)` is used to store an object in Ray's object store and it returns an ID for the stored object. This ID can be used to pass the object as an argument to any remote function or method call. \n\n`ray.get(object_refs)` is used to retrieve an object or a list of objects from the object store using the object ID or list of object IDs. \n\nIt's recommended to avoid calling `ray.get()` unnecessarily for intermediate steps and only call it at the end to get the final result. Also, avoid repeatedly passing in large arguments to remote task or method calls, instead, use `ray.put` to create a copy of the object in the object store.",1664"llm": "gpt-4"1665},1666{1667"question": "how do I use Ray Data to pre process many files?",1668"sources": [1669"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",1670"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",1671"https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",1672"https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors",1673"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data",1674"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",1675"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1676"https://docs.ray.io/en/master/train/user-guides.html#ray-train-user-guides",1677"https://docs.ray.io/en/master/data/user-guide.html#user-guides"1678],1679"document_ids": [16809178,16819222,16826849,16836914,16849212,16856838,16866835,16878110,168869111689],1690"answer": "To use Ray Data to preprocess many files, you can use Ray Data operations such as map_batches for unstructured data or Ray Data preprocessors for tabular data. If your preprocessed dataset is small enough to fit in Ray object store memory, you can call materialize() on the preprocessed dataset to pin it in the memory. This avoids re-running preprocessing operations when iterating over the dataset repeatedly. However, if the preprocessed data is too large to fit into Ray object store memory, this approach may decrease performance as data needs to be spilled to and read back from disk. For more details, refer to the Ray Data Key Concepts and the Ray Data Working with Pytorch guide.",1691"llm": "gpt-4"1692},1693{1694"question": "can\u2019t pickle SSLContext objects",1695"sources": [1696"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1697"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1698"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",1699"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1700"https://docs.ray.io/en/master/ray-core/objects/serialization.html#known-issues",1701"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#tls-verification",1702"https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#checkpoints-are-py-version-specific-but-can-be-converted-to-be-version-independent",1703"https://docs.ray.io/en/master/serve/tutorials/java.html#calling-ray-serve-deployment-with-http",1704"https://docs.ray.io/en/master/ray-core/configure.html#step-3-set-the-environment-variables-for-both-ray-head-and-worker-to-enable-tls"1705],1706"document_ids": [17074953,17084952,17094939,17104957,17114958,17126592,171310551,171414578,171544001716],1717"answer": "The context does not provide information on the ability to pickle SSLContext objects.",1718"llm": "gpt-4"1719},1720{1721"question": "How do I install CRDs in Kuberay?",1722"sources": [1723"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",1724"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",1725"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",1726"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",1727"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-2-create-a-raycluster-cr",1728"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-3-install-a-kuberay-operator",1729"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-3-install-a-kuberay-operator",1730"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-2-install-the-kuberay-operator",1731"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-1-install-a-kuberay-cluster"1732],1733"document_ids": [17345720,17356103,17365817,17375647,17385981,17395774,17406057,17416044,174259801743],1744"answer": "To install CRDs in KubeRay, you can use the Helm chart repository. First, add the KubeRay repository with the command `helm repo add kuberay https://ray-project.github.io/kuberay-helm/`. Then, install both CRDs and the KubeRay operator v0.6.0 with the command `helm install kuberay-operator kuberay/kuberay-operator --version 0.6.0`.",1745"llm": "gpt-4"1746},1747{1748"question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",1749"sources": [1750"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1751"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1752"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",1753"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",1754"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",1755"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1756"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",1757"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging",1758"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text"1759],1760"document_ids": [17618107,17626788,176313697,1764140,17656955,17667955,17677380,1768590,176969981770],1771"answer": "The function for Ray data batch inference has to be named `__call__()` because it is a special method in Python classes. It allows the class's instance to be called as a function, not a method. In the context of Ray data batch inference, the `__call__()` method is used to define the actual model inference logic, which will be called for each batch. This makes the class instances behave like functions, which is useful in scenarios like batch inference where the same operation (inference) needs to be applied to multiple inputs (batches).",1772"llm": "gpt-4"1773},1774{1775"question": "How to disconnnect ray client?",1776"sources": [1777"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1778"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",1779"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",1780"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",1781"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",1782"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",1783"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1784"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",1785"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks"1786],1787"document_ids": [17886615,17896626,17906624,179114507,17926608,17934613,17944894,17956612,179643751797],1798"answer": "To disconnect a Ray client, you need to call the disconnect method explicitly. For example, if your client is named 'cli1', you would disconnect it by calling 'cli1.disconnect()'.",1799"llm": "gpt-4"1800},1801{1802"question": "how to submit job with python with local files?",1803"sources": [1804"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1805"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job",1806"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1807"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",1808"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",1809"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",1810"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",1811"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#jobsubmissionclient",1812"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job"1813],1814"document_ids": [18156550,18164255,18176553,18186578,18196579,18206575,18216595,18226540,182365811824],1825"answer": "To submit a job with Python using local files, you can use the Ray Jobs Python SDK. Here are the steps:\n\n1. Import the JobSubmissionClient from ray.job_submission.\n2. Initialize the JobSubmissionClient with the address of your Ray cluster head node and the port used by the Ray Dashboard (8265 by default).\n3. Call the submit_job method on the client, providing the entrypoint shell command to execute and the path to the local directory that contains your Python script in the runtime_env argument.\n\nHere is an example:\n\n```python\nfrom ray.job_submission import JobSubmissionClient\n\n# If using a remote cluster, replace 127.0.0.1 with the head node's IP address.\nclient = JobSubmissionClient(\"http://127.0.0.1:8265\")\njob_id = client.submit_job(\n # Entrypoint shell command to execute\n entrypoint=\"python script.py\",\n # Path to the local directory that contains the script.py file\n runtime_env={\"working_dir\": \"./\"}\n)\nprint(job_id)\n```\n\nIn this example, replace \"script.py\" with the name of your Python script and \"./\" with the path to the directory containing your script.",1826"llm": "gpt-4"1827},1828{1829"question": "How do I do inference from a model trained by Ray tune.fit()?",1830"sources": [1831"https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",1832"https://docs.ray.io/en/master/tune/examples/includes/tune_basic_example.html#tune-basic-example",1833"https://docs.ray.io/en/master/data/batch_inference.html#using-models-from-ray-train",1834"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",1835"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",1836"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#configuring-the-search-space-and-starting-ray-tune",1837"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1838"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",1839"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning"1840],1841"document_ids": [1842986,18434111,18446962,18451080,1846117,18472767,1848234,1849127,185010901851],1852"answer": "The context does not provide specific steps on how to do inference from a model trained by Ray tune.fit(). However, it mentions that models trained with Ray Train can be used for batch inference with Ray Data via the Checkpoint returned by Ray Train. It's likely that a similar process would be followed for models trained with Ray Tune, but the exact steps are not provided in the context.",1853"llm": "gpt-4"1854},1855{1856"question": "is there a way to load and run inference without using pytorch or tensorflow directly?",1857"sources": [1858"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1859"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-2-inference-on-a-single-batch",1860"https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",1861"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",1862"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1863"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1864"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",1865"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",1866"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch"1867],1868"document_ids": [18696978,18707976,18716725,18728010,18736980,18746979,18758043,18766947,187779701878],1879"answer": "Yes, there is a way to load and run inference without using PyTorch or TensorFlow directly. This can be achieved using Ray Datasets, which allows for scalable offline batch inference with Torch models by mapping a pre-trained model over your data. This method involves creating a Ray Dataset from in-memory Numpy arrays, performing inference on a single batch of data using a pre-trained model, and implementing a callable class that sets up and invokes your model. It also supports using GPUs for inference.",1880"llm": "gpt-4"1881},1882{1883"question": "what does ray do",1884"sources": [1885"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1886"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",1887"https://docs.ray.io/en/master/data/data.html#learn-more",1888"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",1889"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",1890"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",1891"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",1892"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",1893"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli"1894],1895"document_ids": [18964811,18975380,18986910,18996208,190014255,19016015,19025656,19035556,190446621905],1906"answer": "The context does not provide specific information on what Ray does.",1907"llm": "gpt-4"1908},1909{1910"question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",1911"sources": [1912"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",1913"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",1914"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",1915"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1916"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",1917"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",1918"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1919"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",1920"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus"1921],1922"document_ids": [192313457,19241025,19254999,192611062,19272966,192812049,192911068,19305116,193127301932],1933"answer": "If you specify a fractional GPU in the resource spec and use more than that, you may run into issues as only a fraction of the GPU memory is allocated for your task. It is the user's responsibility to ensure that multiple trials can share GPUs and there is enough memory to do so. Ray does not automatically handle this for you. If you exceed the allocated GPU memory, your task may fail or cause other tasks sharing the GPU to fail.",1934"llm": "gpt-4"1935},1936{1937"question": "how to pickle a variable defined in actor\u2019s init method",1938"sources": [1939"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1940"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",1941"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",1942"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",1943"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",1944"https://docs.ray.io/en/master/ray-core/actors.html#actors",1945"https://docs.ray.io/en/master/rllib/actors.html#actors",1946"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1947"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs"1948],1949"document_ids": [19505192,19515194,19524435,19534241,195414682,19554469,195611576,19575191,19585201959],1960"answer": "To pickle a variable defined in an actor's init method, you can encapsulate the variable in an actor and pass the actor handle to other tasks and actors. This is because each actor instance is instantiated in its own process, so each actor will have its own copy of the class variables. Here is an example:\n\n```python\n@ray.remote\nclass Actor:\n def __init__(self, global_var_actor):\n self.global_var_actor = global_var_actor\n\n def f(self):\n return ray.get(self.global_var_actor.get_global_var.remote()) + 3\n\nglobal_var_actor = GlobalVarActor.remote()\nactor = Actor.remote(global_var_actor)\nray.get(global_var_actor.set_global_var.remote(4))\nassert ray.get(actor.f.remote()) == 7\n```\n\nIn this example, `global_var_actor` is a variable defined in the actor's init method and is being pickled.",1961"llm": "gpt-4"1962},1963{1964"question": "how do I do an all_reduce operation among a list of actors",1965"sources": [1966"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",1967"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",1968"https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",1969"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1970"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",1971"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#collective-communication",1972"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayFSDPStrategy.reduce.html#ray-train-lightning-rayfsdpstrategy-reduce",1973"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",1974"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile"1975],1976"document_ids": [1977788,197814443,1979659,19806787,1981673,198214415,19838854,19848101,19856411986],1987"answer": "To perform an all_reduce operation among a list of actors in Ray, you can use the `allreduce` method from the `ray.util.collective.collective` module. Here is an example:\n\n```python\n@ray.remote(num_gpus=1)\nclass Worker:\n def __init__(self):\n self.buffer = cupy.ones((10,), dtype=cupy.float32)\n\n def compute(self):\n col.allreduce(self.buffer, \"default\")\n return self.buffer\n\n# Create two actors A and B and create a collective group following the previous example...\nA = Worker.remote()\nB = Worker.remote()\n# Invoke allreduce remotely\nray.get([A.compute.remote(), B.compute.remote()])\n```\n\nIn this example, two actors A and B are created. Each actor has a buffer that is initialized with ones. The `allreduce` operation is performed on the buffer in the `compute` method of each actor. The `allreduce` operation is invoked remotely on both actors. The results are then retrieved using `ray.get`.",1988"llm": "gpt-4"1989},1990{1991"question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",1992"sources": [1993"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",1994"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",1995"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#bundles",1996"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",1997"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",1998"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1999"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",2000"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",2001"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group"2002],2003"document_ids": [20044790,20055063,20065050,20075051,200812053,200911075,20105076,20115060,201247892013],2014"answer": "If you specify a bundle with `{\"CPU\":0}` in the PlacementGroup, it means that the bundle does not require any CPU resources. However, since a bundle must be able to fit on a single node on the Ray cluster, if there are no nodes available that can accommodate this bundle (even though it requires no CPU resources), the entire placement group will not be ready and no resources will be reserved.",2015"llm": "gpt-4"2016},2017{2018"question": "How to cancel job from UI?",2019"sources": [2020"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",2021"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",2022"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",2023"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2024"https://docs.ray.io/en/master/workflows/comparison.html#trip-booking",2025"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",2026"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",2027"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2028"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks"2029],2030"document_ids": [20314550,20324375,20336559,20344894,20359757,20366710,20379791,203811590,203944922040],2041"answer": "To cancel a job from the UI, you can use the command `ray job stop` followed by the job ID. For example: `$ ray job stop raysubmit_tUAuCKubPAEXh6CW`. This will attempt to stop the specified job.",2042"llm": "gpt-4"2043},2044{2045"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>`",2046"sources": [2047"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",2048"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2049"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",2050"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2051"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#development-workflow-with-serve-run",2052"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",2053"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",2054"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",2055"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy"2056],2057"document_ids": [205813680,20596562,206013677,20616552,206213659,206313719,20646577,206513831,2066139872067],2068"answer": "To get your project files on the cluster when using Ray Serve, you can use the `--working-dir` option with the `serve run` command. This command will upload the directory specified by `--working-dir` to the remote cluster. For example, if your project files are in the directory `./project/src`, you can use the command `serve run --address=ray://<head-node-ip-address>:10001 --working-dir=\"./project/src\" local_dev:graph`. Make sure the directory contains the necessary python files so they can be imported by Ray Serve.",2069"llm": "gpt-4"2070},2071{2072"question": "how do i install ray nightly wheel",2073"sources": [2074"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",2075"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2076"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2077"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2078"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",2079"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",2080"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",2081"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",2082"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray"2083],2084"document_ids": [208514545,2086288,208763,20886335,208966,2090291,209114551,2092883,209354122094],2095"answer": "To install the Ray nightly wheel, first uninstall any previous installation of Ray using the command `pip uninstall -y ray`. Then, you can install Ray with support for the dashboard and cluster launcher using the command `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`. Replace `LINK_TO_WHEEL.whl` with the actual link to the wheel you want to install. If you want to install Ray with minimal dependencies, use the command `pip install -U LINK_TO_WHEEL.whl`, again replacing `LINK_TO_WHEEL.whl` with the actual link.",2096"llm": "gpt-4"2097},2098{2099"question": "how do i install the latest ray nightly wheel?",2100"sources": [2101"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2102"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",2103"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2104"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2105"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",2106"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",2107"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",2108"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",2109"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels"2110],2111"document_ids": [2112288,211314545,211463,21156335,2116291,211714551,211866,211914541,21202862121],2122"answer": "To install the latest Ray nightly wheel, first uninstall any previous installation of Ray using the command `pip uninstall -y ray`. Then, install the Ray nightly wheel using the command `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`. Replace `LINK_TO_WHEEL.whl` with the actual link to the nightly wheel you want to install.",2123"llm": "gpt-4"2124},2125{2126"question": "how can I write unit tests for Ray code?",2127"sources": [2128"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",2129"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",2130"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2131"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",2132"https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",2133"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",2134"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2135"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",2136"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users"2137],2138"document_ids": [21395397,21405395,21414811,21424190,21434338,214414561,21455380,21466621,214742042148],2149"answer": "To write unit tests for Ray code, you can use Python's unittest module. Here's a basic example:\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_function(self):\n @ray.remote\n def do_work(x):\n return x ** x\n result = ray.get(do_work.remote(2))\n self.assertEqual(result, 4)\n\n def tearDown(self):\n ray.shutdown()\n```\n\nIn this example, `setUp` method is used to initialize Ray context before each test, `test_function` is a test case where a remote function is defined and called, and `tearDown` is used to shutdown Ray after each test. \n\nRemember to be careful when running tests in parallel due to the nature of parallel programs. You can also create a mini-cluster with `ray.cluster_utils.Cluster` for testing.",2150"llm": "gpt-4"2151},2152{2153"question": "How I stop Ray from spamming lots of Info updates on stdout?",2154"sources": [2155"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",2156"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#disable-logging-to-the-driver",2157"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#distributed-progress-bars-tqdm",2158"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",2159"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2160"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",2161"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",2162"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",2163"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay"2164],2165"document_ids": [21666558,2167523,2168529,2169524,21704209,2171519,2172410,21735128,2174139292175],2176"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`. Here is an example:\n\n```python\nimport ray\n\n# Task and Actor logs are not copied to the driver stdout.\nray.init(log_to_driver=False)\n```",2177"llm": "gpt-4"2178},2179{2180"question": "how to deploy stable diffusion 2.1 with Ray Serve?",2181"sources": [2182"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",2183"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",2184"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#serve-a-stablediffusion-text-to-image-model-on-kubernetes",2185"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-7-submit-a-request-to-the-ray-serve-application",2186"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",2187"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#code-updates",2188"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",2189"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-5-send-a-request-to-the-text-to-image-model",2190"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"2191],2192"document_ids": [21936127,219414409,21956124,21965989,21976128,219813692,21995671,22006130,2201136222202],2203"answer": "To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install RayService by downloading `ray-service.stable-diffusion.yaml` using the command: `curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml`.\n\n2. Create a RayService using the command: `kubectl apply -f ray-service.stable-diffusion.yaml`.\n\n3. Install the necessary packages using the command: `pip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers`.\n\n4. Save the provided Serve code to a file named `stable_diffusion.py`.\n\n5. Forward the serve port of the head Pod on your local machine using the command: `kubectl port-forward --address 0.0.0.0 $HEAD_POD 8000`.\n\n6. Clone the repository on your local machine using the command: `git clone https://github.com/ray-project/serve_config_examples.git`.\n\n7. Download `stable_diffusion_req.py` using the command: `curl -LO https://raw.githubusercontent.com/ray-project/serve_config_examples/master/stable_diffusion/stable_diffusion_req.py`.\n\n8. Set your `prompt` in `stable_diffusion_req.py`.\n\n9. Send a request to the Stable Diffusion model using the command: `python stable_diffusion_req.py`.\n\nRemember to test your Ray Serve script locally or in a RayCluster before deploying it to a RayService.",2204"llm": "gpt-4"2205},2206{2207"question": "what is actor_handle?",2208"sources": [2209"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",2210"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",2211"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",2212"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2213"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2214"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",2215"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",2216"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",2217"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors"2218],2219"document_ids": [222011587,222114698,22224487,22234860,22244615,222511588,22264489,222714699,222842002229],2230"answer": "An actor handle is a reference to an actor that can be passed into other tasks. It allows for the definition of remote functions or actor methods that use the actor handle. This is useful in distributed computing frameworks like Ray, where an actor is a stateful worker or service. Methods of the actor can be scheduled on a specific worker and can access and mutate the state of that worker using the actor handle.",2231"llm": "gpt-4"2232},2233{2234"question": "how to kill a r detached actors?",2235"sources": [2236"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",2237"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2238"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",2239"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2240"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2241"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",2242"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2243"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2244"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance"2245],2246"document_ids": [22474633,22484859,22492157,22504615,22514613,22524579,22534848,22544620,225545612256],2257"answer": "To kill a detached actor in Ray, you can use the `ray.kill` function. This function forcefully terminates the actor, interrupting any running tasks on the actor and causing them to fail immediately. Here is an example of how to use it:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nactor_handle = Actor.remote()\n\nray.kill(actor_handle)\n```\n\nNote that this will not go through the normal Python `sys.exit` teardown logic, so any exit handlers installed in the actor using `atexit` will not be called. Also, if the actor is a detached actor, subsequent calls to get its handle via `ray.get_actor` will fail.",2258"llm": "gpt-4"2259},2260{2261"question": "How to force upgrade the pip package in the runtime environment if an old version exists?",2262"sources": [2263"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2264"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2265"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2266"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",2267"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2268"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",2269"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2270"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",2271"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"2272],2273"document_ids": [22744329,22754717,22764327,22774260,22784330,22794267,22804328,22814268,228242832283],2284"answer": "To force upgrade the pip package in the runtime environment, you can specify the desired version of the package in the \"pip\" field of the RuntimeEnv. For example, if you want to upgrade to pip version 22.0.2, you can use the following code:\n\n```python\nRuntimeEnv(\n pip={\"packages\":[\"tensorflow\", \"requests\"], \"pip_check\": False,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"})\n```\n\nThis will ensure that the specified version of pip is used in the runtime environment, even if an older version already exists.",2285"llm": "gpt-4"2286},2287{2288"question": "How do I do global shuffle with Ray?",2289"sources": [2290"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",2291"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",2292"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2293"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2294"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2295"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2296"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",2297"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",2298"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle"2299],2300"document_ids": [23017395,23026795,23036827,23049209,23056826,23069206,23076876,23086864,230968252310],2311"answer": "To do a global shuffle with Ray, you can use the `random_shuffle()` method on your dataset. Here is an example:\n\n```python\nimport ray\nds = ray.data.range(100)\nds = ds.random_shuffle()\n```\n\nThis will randomly shuffle all rows in your dataset. Note that this method can be slow and for better performance, you can try iterating over batches with shuffling. If you want to use push-based shuffle, you can set the environment variable `RAY_DATA_PUSH_BASED_SHUFFLE=1` when running your application.",2312"llm": "gpt-4"2313},2314{2315"question": "How to find namespace of an Actor?",2316"sources": [2317"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",2318"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2319"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2320"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",2321"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2322"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",2323"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace",2324"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2325"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces"2326],2327"document_ids": [23284521,23294623,23304504,23314871,23324626,23334528,23344751,23354625,233644992337],2338"answer": "To find the namespace of an actor, you can use the `ray.get_actor(name: str, namespace: Optional[str] = None)` function. This function gets a handle to an actor with the given name. The namespace of the actor can be specified, or if it's None, the current namespace will be used. If the named actor does not exist, a ValueError will be raised.",2339"llm": "gpt-4"2340},2341{2342"question": "How does Ray work with async.io ?",2343"sources": [2344"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",2345"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",2346"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",2347"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2348"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",2349"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",2350"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",2351"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",2352"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users"2353],2354"document_ids": [23554598,23564603,23574599,23584208,23595443,23604360,23614636,23624611,236342052364],2365"answer": "Ray natively integrates with asyncio and can be used alongside popular async frameworks like aiohttp, aioredis, etc. It allows you to define async actors and methods, which Ray will automatically detect. The invocation of every remote operation in Ray is asynchronous, immediately returning a promise/future, which is an identifier of the operation\u2019s result. This allows for parallelism as multiple operations can be launched in parallel. To get the actual results, you need to call ray.get() on the IDs of the results. However, asyncio is not supported for remote tasks in Ray. Instead, you can wrap the async function with a wrapper to run the task synchronously.",2366"llm": "gpt-4"2367},2368{2369"question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",2370"sources": [2371"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",2372"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2373"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",2374"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",2375"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",2376"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",2377"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",2378"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#stepping-between-ray-tasks",2379"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get"2380],2381"document_ids": [23825235,23834209,23844416,23855236,23865219,23875169,23884361,2389581,239042142391],2392"answer": "To debug a hanging `ray.get()` call, you can set a timeout to return early from a `get` that's blocking for too long. This can be done using the `GetTimeoutError` from `ray.exceptions`. Here is an example:\n\n```python\nfrom ray.exceptions import GetTimeoutError\n\n@ray.remote\ndef long_running_function():\n time.sleep(8)\n\nobj_ref = long_running_function.remote()\ntry:\n ray.get(obj_ref, timeout=4)\nexcept GetTimeoutError: # You can capture the standard \"TimeoutError\" instead\n print(\"`get` timed out.\")\n```\n\nIn this example, if the `ray.get()` call takes longer than 4 seconds, it will raise a `GetTimeoutError` and print \"`get` timed out.\"\n\nRemember, `ray.get()` is a blocking operation, so it's best to call it as late as possible in your program to maintain parallelism. If possible, design your program to avoid having to call `ray.get()` at all.",2393"llm": "gpt-4"2394},2395{2396"question": "can you show me an example of ray.actor.exit_actor()",2397"sources": [2398"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2399"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2400"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2401"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2402"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",2403"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2404"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2405"https://docs.ray.io/en/master/ray-core/api/core.html#actors",2406"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.AsyncioActorExit.html#ray-exceptions-asyncioactorexit"2407],2408"document_ids": [24094618,24104613,24114620,24124619,24134579,24144859,24154615,24164650,241747812418],2419"answer": "Here is an example of `ray.actor.exit_actor()`:\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\nIn this example, an actor class is defined with a method `exit()`. This method calls `ray.actor.exit_actor()`, which terminates the actor from within one of the actor methods. The actor is then instantiated and the `exit()` method is called remotely, which terminates the actor.",2420"llm": "gpt-4"2421},2422{2423"question": "how to add log inside actor?",2424"sources": [2425"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",2426"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",2427"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",2428"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2429"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",2430"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2431"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2432"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",2433"https://docs.ray.io/en/master/ray-observability/getting-started.html#actors-view"2434],2435"document_ids": [2436414,2437540,2438520,243914324,244014202,244114286,244214295,244314106,24444342445],2446"answer": "To add a log inside an actor, you can use the Python logging module. Here is an example:\n\n```python\nimport logging\nimport ray\n\n@ray.remote\nclass MyActor:\n def log(self, msg):\n logger = logging.getLogger(__name__)\n logger.info(msg)\n\nactor = MyActor.remote()\nray.get(actor.log.remote(\"A log message for an actor.\"))\n```\n\nIn this example, a logger is created inside the `log` method of the `MyActor` class. The `info` method of the logger is then used to log the message. The `log` method of the actor is called remotely using `ray.get(actor.log.remote(\"A log message for an actor.\"))`.",2447"llm": "gpt-4"2448},2449{2450"question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",2451"sources": [2452"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2453"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt",2454"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",2455"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",2456"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2457"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",2458"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#next-steps",2459"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#next-steps",2460"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"2461],2462"document_ids": [24636999,246410470,246510259,246614582,24676998,24686843,246910219,247010137,247169382472],2473"answer": "Yes, you can write a script to do batch inference with GPT-2 on text data from an S3 bucket. Here is an example using Ray Data:\n\n```python\nimport os\nimport ray\nfrom transformers import pipeline\nimport numpy as np\n\nclass TextGenerator:\n def __init__(self):\n self.model = pipeline(\"text-generation\", model=\"gpt2\")\n\n def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, list]:\n predictions = self.model(list(batch[\"text\"]), max_length=20, num_return_sequences=1)\n batch[\"output\"] = [sequences[0][\"generated_text\"] for sequences in predictions]\n return batch\n\n# Sync the S3 bucket data to local storage\nos.system(f\"awsv2 s3 sync s3://your_bucket_path /mnt/local_storage/\")\n\n# Read the text data from local storage and perform batch inference\nds = (\n ray.data.read_text(\"/mnt/local_storage/your_file.txt\")\n .map_batches(TextGenerator, compute=ray.data.ActorPoolStrategy(size=2))\n)\n\nds.show(3)\n```\n\nThis script first syncs the data from the S3 bucket to local storage. Then it reads the text data from local storage and performs batch inference using the GPT-2 model. The results are then displayed.",2474"llm": "gpt-4"2475},2476{2477"question": "How do I enable Ray debug logs?",2478"sources": [2479"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2480"https://docs.ray.io/en/master/ray-observability/user-guides/index.html#user-guides",2481"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",2482"https://docs.ray.io/en/master/cluster/cli.html#ray-start",2483"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#debugger-commands",2484"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-debug",2485"https://docs.ray.io/en/master/ray-contribute/debugging.html#debugging-for-ray-developers",2486"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",2487"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging"2488],2489"document_ids": [2490680,2491546,2492510,24935611,2494579,24954669,2496944,2497573,249843232499],2500"answer": "To enable Ray debug logs, you can set an environment variable RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1 on each node before starting Ray. This can be done using setup_commands in the Ray Cluster configuration file. This will print the full runtime_env setup log messages to the driver (the script that calls ray.init()).",2501"llm": "gpt-4"2502},2503{2504"question": "How do I list the current Ray actors from python?",2505"sources": [2506"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2507"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",2508"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",2509"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2510"https://docs.ray.io/en/master/ray-observability/key-concepts.html#ray-states",2511"https://docs.ray.io/en/master/rllib/actors.html#actors",2512"https://docs.ray.io/en/master/ray-core/actors.html#actors",2513"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",2514"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"2515],2516"document_ids": [2517472,2518788,2519673,2520475,2521402,252211575,25234467,252414680,252546232526],2527"answer": "To list the current Ray actors from Python, you can use the `ray.util.state.list_actors()` function. This function lists actors in the cluster. You can also use the command `ray list actors` to list all actor information from the cluster. If you want to retrieve a specific actor, you can use `ray.get_actor(\"actor_name\")`.",2528"llm": "gpt-4"2529},2530{2531"question": "I want to kill the replica actor from Python. how do I do it?",2532"sources": [2533"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",2534"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2535"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2536"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2537"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2538"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2539"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2540"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",2541"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure"2542],2543"document_ids": [254413912,25454859,25464848,25474620,25484613,25494860,25504618,25514579,2552139162553],2554"answer": "To kill a replica actor from Python, you can use the `ray.kill()` function. Here is an example:\n\n```python\nimport ray\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\n\nThis will forcefully terminate the actor. If you want the actor to finish its pending tasks before termination, you can use `actor.__ray_terminate__.remote()` instead.",2555"llm": "gpt-4"2556},2557{2558"question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",2559"sources": [2560"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",2561"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#accelerator-types",2562"https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",2563"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",2564"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",2565"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",2566"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-workload-scheduling",2567"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",2568"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options"2569],2570"document_ids": [25714994,25725006,25734366,25746590,25754996,25764997,25776017,25784697,257947022580],2581"answer": "To specify that you want a task to run on a V100 GPU type in your remote function declaration, you can use the `accelerator_type` parameter in the `@ray.remote` decorator. Here is an example:\n\n```python\n@ray.remote(num_gpus=1, accelerator_type=NVIDIA_TESLA_V100)\ndef train(data):\n return \"This function was run on a node with a Tesla V100 GPU\"\n```\n\nYou can replace `NVIDIA_TESLA_V100` with the specific GPU type you want to use.",2582"llm": "gpt-4"2583},2584{2585"question": "How do I get started?",2586"sources": [2587"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",2588"https://docs.ray.io/en/master/ray-overview/getting-started.html#getting-started",2589"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-autoscaler-private-fake-multi-node-test-utils-dockercluster",2590"https://docs.ray.io/en/master/serve/index.html#learn-more",2591"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",2592"https://docs.ray.io/en/master/tune/faq.html#how-can-i-get-started-contributing-to-tune",2593"https://docs.ray.io/en/master/serve/advanced-guides/index.html#advanced-guides",2594"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",2595"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-1-create-a-kind-cluster"2596],2597"document_ids": [25986482,2599220,2600864,260113625,26024483,26031049,260413827,26054185,260660432607],2608"answer": "To get started with Ray, you can choose the right guide for your task. If you want to scale ML workloads, use the Ray Libraries Quickstart. For general Python applications, use the Ray Core Quickstart. To deploy to the cloud, use the Ray Clusters Quickstart. For debugging and monitoring applications, use the Debugging and Monitoring Quickstart. You can also check out the Getting Started and Key Concepts sections, or the Examples to start building your Ray Serve applications. To start using Ray Core, install Ray via pip install -U ray, then import and initialize Ray in your Python script with the commands 'import ray' and 'ray.init()'.",2609"llm": "gpt-4"2610},2611{2612"question": "How to specify python version in runtime_env?",2613"sources": [2614"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2615"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2616"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2617"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",2618"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2619"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",2620"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2621"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2622"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv"2623],2624"document_ids": [26254330,26264717,26274329,26284661,262913856,26304253,26314327,26324283,263347122634],2635"answer": "To specify a Python version in runtime_env, you can use the \"pip_version\" field in the \"pip\" dictionary. Here is an example:\n\n```python\nRuntimeEnv(\n pip={\n \"packages\":[\"tensorflow\", \"requests\"], \n \"pip_check\": False,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"\n }\n)\n```\n\nIn this example, the Python version is specified as '3.8.11'.",2636"llm": "gpt-4"2637},2638{2639"question": "how to create a Actor in a namespace?",2640"sources": [2641"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",2642"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2643"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2644"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2645"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2646"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2647"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2648"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2649"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces"2650],2651"document_ids": [26524521,26534626,26544625,26554623,26564513,26574504,26584509,26594499,266045162661],2662"answer": "To create an actor in a specific namespace in Python, you can use the following code:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nray.init(namespace=\"your_namespace\")\n\n# Create an actor with a name in the specified namespace\nActor.options(name=\"actor_name\", lifetime=\"detached\").remote()\n```\n\nIn this code, `ray.init(namespace=\"your_namespace\")` sets the namespace, and `Actor.options(name=\"actor_name\", lifetime=\"detached\").remote()` creates an actor in that namespace. Replace \"your_namespace\" and \"actor_name\" with your desired namespace and actor name.",2663"llm": "gpt-4"2664},2665{2666"question": "Can I specify multiple working directories?",2667"sources": [2668"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",2669"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2670"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",2671"https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.PathPartitionParser.html#ray-data-datasource-pathpartitionparser",2672"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2673"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",2674"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",2675"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",2676"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function"2677],2678"document_ids": [26791775,26804277,26814301,26827297,26834273,268410170,26851790,268610169,268710662688],2689"answer": "No, you cannot specify multiple working directories. The working directory can be set per-job (i.e., in ray.init()), but setting a local directory per-task or per-actor is currently unsupported.",2690"llm": "gpt-4"2691},2692{2693"question": "what if I set num_cpus=0 for tasks",2694"sources": [2695"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2696"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",2697"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",2698"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2699"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",2700"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#code-example",2701"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2702"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",2703"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"2704],2705"document_ids": [270611063,2707623,27085936,27094850,27106716,27115234,27124853,27134877,271467032715],2716"answer": "If you set num_cpus=0 for tasks, it means that the tasks will not reserve any CPU resources. This can be useful in certain scenarios, such as preventing certain workloads with non-zero CPU requirements from being scheduled on a specific node (like the Ray head pod). However, by default, actors cannot get scheduled on a zero-cpu node, but an infinite number of them can run on any non-zero cpu node.",2717"llm": "gpt-4"2718},2719{2720"question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",2721"sources": [2722"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",2723"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",2724"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",2725"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",2726"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",2727"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",2728"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",2729"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",2730"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice"2731],2732"document_ids": [27335953,27345818,27355752,27365969,273713957,27385963,27395656,27405642,274157422742],2743"answer": "Yes, it is possible to deploy a static Ray cluster to Kubernetes without using KubeRay. However, this deployment method does not have the built-in autoscaling feature that KubeRay provides. To fully utilize the resources in your cluster, it is recommended to enable Kubernetes autoscaling.",2744"llm": "gpt-4"2745},2746{2747"question": "how to manually configure and manage Ray cluster on Kubernetes",2748"sources": [2749"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",2750"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",2751"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",2752"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",2753"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",2754"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",2755"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gke-gcs-bucket.html#create-a-minimal-raycluster-yaml-manifest",2756"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",2757"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart"2758],2759"document_ids": [27605717,27615918,27625917,27635656,27645642,27655605,27666034,27675953,276859582769],2770"answer": "To manually configure and manage a Ray cluster on Kubernetes, you need to create a RayCluster custom resource (CR) by applying a yaml file (raycluster.yaml) using the command `kubectl apply -f raycluster.yaml`. This yaml file describes the desired state of the Ray cluster. The KubeRay operator, a custom controller, manages Ray pods to match the RayCluster's spec. You can also configure the Ray autoscaler on Kubernetes, which automatically scales the cluster based on resource demand. This is done by creating an autoscaling RayCluster custom resource using the command `kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/release-0.5/ray-operator/config/samples/ray-cluster.autoscaler.yaml`.",2771"llm": "gpt-4"2772},2773{2774"question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",2775"sources": [2776"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",2777"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",2778"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",2779"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",2780"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options",2781"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",2782"https://docs.ray.io/en/master/serve/api/doc/ray.serve.shutdown.html#ray-serve-shutdown",2783"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",2784"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling"2785],2786"document_ids": [27875129,27884524,27896216,27904519,279114500,27926390,279314043,279414507,2795134482796],2797"answer": "Yes, if a raylet (node) is shut down, the tasks and workers on that node will also get killed. This is because each raylet is associated with a unique id, and if it fails or is shut down, it's treated as a node failure. The node will be marked as dead and any ongoing tasks or workers will be terminated.",2798"llm": "gpt-4"2799},2800{2801"question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",2802"sources": [2803"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",2804"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",2805"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",2806"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management",2807"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#what-s-the-out-of-memory-error",2808"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",2809"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",2810"https://docs.ray.io/en/master/serve/monitoring.html#profiling-memory",2811"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory"2812],2813"document_ids": [2814547,2815548,2816559,28175010,2818549,28195118,2820562,282113549,282250162823],2824"answer": "To debug out of memory issues, you should first familiarize yourself with the Ray Memory Management model. You can detect out-of-memory errors, locate the source of the memory leak, and view per Task and Actor memory usage. If your head node has high memory usage, you can view the Head Node Out-of-Memory Error. If your memory usage is high due to high parallelism, you can view the Reduce Parallelism section. You can also profile per Task and Actor memory usage. The relevant documentation includes sections on Debugging Memory Issues, Debugging Out of Memory, Find per Task and Actor Memory Usage, Memory Management, What\u2019s the Out-of-Memory Error, Out-Of-Memory Prevention, Head node out-of-Memory error, and Debugging using \u2018ray memory\u2019.",2825"llm": "gpt-4"2826},2827{2828"question": "How to use callback in Trainer?",2829"sources": [2830"https://docs.ray.io/en/master/train/examples/transformers/transformers_torch_trainer_basic.html#ray-train-basic-example-for-huggingface-transformers",2831"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",2832"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.html#ray-train-huggingface-transformers-raytrainreportcallback",2833"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",2834"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.html#ray-tune-callback",2835"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_end.html#ray-train-lightning-raytrainreportcallback-on-init-end",2836"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_start.html#ray-train-lightning-raytrainreportcallback-on-init-start",2837"https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",2838"https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune"2839],2840"document_ids": [28419553,28429165,28438635,28448206,28451405,28468531,28478760,28489934,284923902850],2851"answer": "To use a callback in Trainer, you need to create an instance of the Trainer class and then use the `add_callback()` method to add your callback. For example, if you're using the `RayTrainReportCallback`, you would do:\n\n```python\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=small_train_ds,\n eval_dataset=small_eval_ds,\n compute_metrics=compute_metrics,\n)\n\ntrainer.add_callback(RayTrainReportCallback())\n```\n\nYou can also create your own custom callback by subclassing the `TrainerCallback` class and implementing the necessary methods. For example:\n\n```python\nfrom transformers.trainer_callback import TrainerCallback\n\nclass MyTrainReportCallback(TrainerCallback):\n def __init__(self):\n super().__init__()\n self.metrics = {}\n```\n\nThen, you can add this custom callback to the trainer in the same way:\n\n```python\ntrainer.add_callback(MyTrainReportCallback())\n```",2852"llm": "gpt-4"2853},2854{2855"question": "How to provide current working directory to ray?",2856"sources": [2857"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2858"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2859"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",2860"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",2861"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2862"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2863"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2864"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2865"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit"2866],2867"document_ids": [28686632,28696633,28704262,28711790,2872511,28736562,28746551,28756552,287665962877],2878"answer": "To provide the current working directory to Ray, you can specify it in the `runtime_env` parameter when initializing Ray. Here is an example:\n\n```python\nimport os\nimport ray\n\nos.makedirs(\"/tmp/runtime_env_working_dir\", exist_ok=True)\nwith open(\"/tmp/runtime_env_working_dir/hello.txt\", \"w\") as hello_file:\n hello_file.write(\"Hello World!\")\n\n# Specify a runtime environment for the entire Ray job\nray.init(runtime_env={\"working_dir\": \"/tmp/runtime_env_working_dir\"})\n```\n\nIn this example, \"/tmp/runtime_env_working_dir\" is the working directory.",2879"llm": "gpt-4"2880},2881{2882"question": "how to create an actor instance with parameter?",2883"sources": [2884"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",2885"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2886"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",2887"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",2888"https://docs.ray.io/en/master/rllib/actors.html#actors",2889"https://docs.ray.io/en/master/ray-core/actors.html#actors",2890"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",2891"https://docs.ray.io/en/master/ray-core/api/core.html#actors",2892"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments"2893],2894"document_ids": [28954876,28965192,28974629,289814682,289911576,29004469,29015391,29024650,290344352904],2905"answer": "To create an actor instance with a parameter, you can use the `remote()` method with arguments to the constructor. For example, in Python, if you have an actor class `Greeter` that takes a parameter `value`, you can create an instance of it like this: `greeter = Greeter.remote(value)`. The `value` argument will be passed to the `__init__` method of the `Greeter` class.",2906"llm": "gpt-4"2907},2908{2909"question": "how to push a custom module to ray which is using by Actor ?",2910"sources": [2911"https://docs.ray.io/en/master/ray-core/api/core.html#actors",2912"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",2913"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.push.html#ray-util-actorpool-push",2914"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#memory-profiling-ray-tasks-and-actors",2915"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#custom-resources-accelerator-types-and-more",2916"https://docs.ray.io/en/master/rllib/actors.html#actors",2917"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",2918"https://docs.ray.io/en/master/ray-core/actors.html#actors",2919"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema"2920],2921"document_ids": [29224650,29235443,29244758,2925568,292613459,292711575,292814680,29294467,2930142192931],2932"answer": "To push a custom module to Ray which is used by an Actor, you can use the `ActorPool.push(actor)` method. Here is an example:\n\n```python\nimport ray\nfrom ray.util.actor_pool import ActorPool\n\n@ray.remote\nclass Actor:\n def double(self, v):\n return 2 * v\n\na1, a2 = Actor.remote(), Actor.remote()\npool = ActorPool([a1])\npool.push(a2)\n```\n\nIn this example, `Actor` is the custom module, and `a2` is an instance of the Actor. The `push` method adds `a2` to the current list of idle actors in the `ActorPool`.",2933"llm": "gpt-4"2934},2935{2936"question": "how to print ray working directory?",2937"sources": [2938"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2939"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2940"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2941"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#submit-the-workload",2942"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#submit-the-workload",2943"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2944"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",2945"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",2946"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs"2947],2948"document_ids": [29496632,29506551,2951511,29526179,29536528,29546633,29555732,29566157,295765302958],2959"answer": "The Ray working directory is not directly printed. However, it is located in the /tmp/ray/session_latest/runtime_resources/_ray_pkg_<hash of directory contents> directory on the cluster. You can use relative paths in your code to access files in this directory. The absolute path is mainly useful for debugging purposes.",2960"llm": "gpt-4"2961},2962{2963"question": "why I can not see log.info in ray log?",2964"sources": [2965"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2966"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2967"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",2968"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2969"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2970"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",2971"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",2972"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",2973"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"2974],2975"document_ids": [2976444,2977680,29786631,2979445,2980511,2981662,2982530,2983415,29844932985],2986"answer": "The reason you might not be able to see log.info in Ray log could be due to several reasons. One possibility is that the Ray Job was not submitted by the Job API, in which case the Driver logs are not accessible from the Dashboard. Instead, you would need to see the terminal or Jupyter Notebook output to view the Driver logs. Another reason could be that the logging level is not set to logging.INFO. By default, Ray's logger is initialized with a default logging level of logging.INFO. If this level has been changed, it might not capture log.info messages. Lastly, it could be that the logs are being stored in a different directory. By default, Ray log files are stored in a /tmp/ray/session_*/logs directory. If this directory has been changed, you would need to look in the new directory for the logs.",2987"llm": "gpt-4"2988},2989{2990"question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",2991"sources": [2992"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",2993"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",2994"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2995"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",2996"https://docs.ray.io/en/master/data/user-guide.html#user-guides",2997"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",2998"https://docs.ray.io/en/master/data/data-internals.html#reading-files",2999"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",3000"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"3001],3002"document_ids": [30039210,30047362,30056835,30067865,30076911,30087495,30096874,30109188,301178753012],3013"answer": "Yes, you can ensure the order of the data is preserved when using Ray Dataset to read a file. This can be done by enabling deterministic execution in Ray Datasets and setting the preserve_order flag in the DataContext to True.",3014"llm": "gpt-4"3015},3016{3017"question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",3018"sources": [3019"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",3020"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3021"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3022"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",3023"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",3024"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3025"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3026"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",3027"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions"3028],3029"document_ids": [30304377,30314542,30324535,30334527,30344348,30354546,30364657,30374564,303846583039],3040"answer": "\"Ray will *not* retry tasks upon exceptions thrown by application code\" means that if an error or exception occurs within the application code while a task is being executed, Ray will not automatically attempt to re-run the task. However, this behavior can be modified by setting the `retry_exceptions` argument to `True` or by passing a list of exceptions that should trigger a retry. By default, this argument is set to `False`, meaning no retries on application-level exceptions.",3041"llm": "gpt-4"3042},3043{3044"question": "how do I specify the log directory when starting Ray?",3045"sources": [3046"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3047"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",3048"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",3049"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3050"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#mounting-the-ray-log-directory",3051"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",3052"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",3053"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3054"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger"3055],3056"document_ids": [3057511,30586360,30595861,3060680,30615871,3062510,30635880,30644811,30655303066],3067"answer": "To specify the log directory when starting Ray, you need to specify it when you call `ray start` or `ray.init()`. By default, Ray uses `/tmp/ray` as the temp and logging directory. If you want to change this, you can do so when starting Ray.",3068"llm": "gpt-4"3069},3070{3071"question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",3072"sources": [3073"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3074"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3075"https://docs.ray.io/en/master/cluster/cli.html#ray-up",3076"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3077"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3078"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",3079"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",3080"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",3081"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node"3082],3083"document_ids": [30846337,30856328,30865615,30876203,30886392,30896522,3090851,30914193,309265013093],3094"answer": "To launch a Ray cluster with 10 nodes without setting the min worker as 10, you can modify the 'max_workers' parameter in your configuration file to 10. Then, use the 'ray up' command with your configuration file to start the cluster. Here is an example:\n\n```yaml\n# An unique identifier for the head node and workers of this cluster.\ncluster_name: default\n\n# The maximum number of workers nodes to launch in addition to the head\n# node.\nmax_workers: 10\n\n# The autoscaler will scale up the cluster faster with higher upscaling speed.\n# E.g., if the task requires adding more nodes then autoscaler will gradually\n# scale up the cluster in chunks of upscaling_speed*currently_running_nodes.\n# This number should be > 0.\nupscaling_speed: 1.0\n```\n\nThen run the command:\n\n```bash\n$ ray up -y config.yaml\n```\n\nThis will launch a Ray cluster with 10 nodes.",3095"llm": "gpt-4"3096},3097{3098"question": "how to use ray api to scale up a cluster",3099"sources": [3100"https://docs.ray.io/en/master/cluster/vms/index.html#overview",3101"https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",3102"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",3103"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",3104"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",3105"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",3106"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",3107"https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started",3108"https://docs.ray.io/en/master/cluster/cli.html#ray-up"3109],3110"document_ids": [31116209,31125604,311314710,31145600,31154644,31166363,31176715,31186189,311956153120],3121"answer": "To scale up a Ray cluster, you can use the `ray.autoscaler.sdk.request_resources()` function within a Ray program. This function commands the autoscaler to scale the cluster up to a desired size. The cluster will immediately attempt to scale to accommodate the requested resources, bypassing normal upscaling speed constraints. You can specify the number of CPUs and other resources you need. Additionally, you can use the `ray up` command with the `--max-workers` option to override the configured maximum worker node count for the cluster.",3122"llm": "gpt-4"3123},3124{3125"question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",3126"sources": [3127"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3128"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3129"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3130"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",3131"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",3132"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",3133"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents",3134"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",3135"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions"3136],3137"document_ids": [31386199,3139273,31406203,31415503,31426458,31436470,31446519,31456467,314661043147],3148"answer": "To specify a subnet in the deployment file when using Ray cloud launcher to start a cluster in AWS, you need to include the subnet information in the `ray-operator/config/samples/ray-cluster-alb-ingress.yaml` file. Specifically, you need to add the annotation `alb.ingress.kubernetes.io/subnets` and include at least two subnets. Each Availability Zone (e.g., us-west-2a) can only have at most one subnet. In this example, you need to select public subnets (subnets that \"Auto-assign public IPv4 address\" is Yes on AWS dashboard).",3149"llm": "gpt-4"3150},3151{3152"question": "where I can find HTTP server error code log for Ray serve",3153"sources": [3154"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3155"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",3156"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",3157"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",3158"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",3159"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",3160"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",3161"https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally",3162"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"3163],3164"document_ids": [316513974,316613541,31676631,31685671,31695747,31704694,31715802,317213437,3173135173174],3175"answer": "HTTP server error code logs for Ray Serve can be found on each node at /tmp/ray/session_latest/logs/serve/. These logs are emitted from actors both to stderr and on disk. They include system-level logs from the Serve controller and proxy as well as access logs and custom user logs produced from within deployment replicas.",3176"llm": "gpt-4"3177},3178{3179"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?",3180"sources": [3181"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",3182"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",3183"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",3184"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",3185"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",3186"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",3187"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",3188"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",3189"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster"3190],3191"document_ids": [31925530,31935528,3194418,31955526,31966562,31976561,31986223,31995543,3200136813201],3202"answer": "The issue you're experiencing with the Ray dashboard disappearing might be due to a network issue or a problem with the port forwarding setup. The Ray dashboard runs on port 8265 of the head node by default, and this port may not be publicly exposed when you start a remote Ray cluster. You need to set up port forwarding to access the Ray dashboard from outside the head node. If you're using the Ray Cluster Launcher, the port forwarding can be set up automatically using the `ray dashboard` command. If the problem persists, it might be worth checking if the port 8265 (or the one you specified) is available when Ray starts, as the dashboard agent's HTTP server will fail if the port is unavailable.",3203"llm": "gpt-4"3204},3205{3206"question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",3207"sources": [3208"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3209"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",3210"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3211"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3212"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",3213"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",3214"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",3215"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",3216"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"3217],3218"document_ids": [3219256,32206292,32216345,32226392,32236522,32246227,32256366,32266367,322763373228],3229"answer": "The `max_workers` in the Ray cluster launcher YAML refers only to the worker nodes, not including the head node.",3230"llm": "gpt-4"3231},3232{3233"question": "How to update files in working directory ?",3234"sources": [3235"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3236"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",3237"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3238"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",3239"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",3240"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",3241"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",3242"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#outdated-function-definitions",3243"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#synchronizing-files-from-the-cluster-ray-rsync-up-down"3244],3245"document_ids": [32466632,32471775,32481790,32498894,32501068,32516214,32521066,3253619,325462223255],3256"answer": "To update files in the working directory, you can use the `ray up` command again on the existing cluster. This command checks if the local configuration differs from the applied configuration of the cluster, including any changes to synced files specified in the file_mounts section of the config. If there are changes, the new files and config will be uploaded to the cluster. Additionally, you can use `ray rsync_up` to upload files to the cluster head node.",3257"llm": "gpt-4"3258},3259{3260"question": "How I can update working directory file when ray allready initiated ?",3261"sources": [3262"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3263"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3264"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3265"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3266"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3267"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",3268"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",3269"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3270"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory"3271],3272"document_ids": [32736632,32744385,32754811,32766555,32771790,32781775,32794263,32806633,32815113282],3283"answer": "To update the working directory file when Ray is already initiated, you need to change the root temporary directory by passing --temp-dir={your temp path} to ray start. However, there is currently no stable way to change the root temporary directory when calling ray.init(). If you need to, you can provide the _temp_dir argument to ray.init(). Be aware that all workers on the same node share the same working directory, so use ray.train.get_context().get_trial_dir() as the path to save any outputs. The behavior of changing the working directory can be disabled by setting the RAY_CHDIR_TO_TRIAL_DIR=0 environment variable.",3284"llm": "gpt-4"3285},3286{3287"question": "how can I force ray head node to use custom pem file to ssh worker node?",3288"sources": [3289"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3290"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3291"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3292"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3293"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3294"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3295"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",3296"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3297"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"3298],3299"document_ids": [33006330,33016307,3302261,33036340,33046318,3305272,33066205,3307268,330863143309],3310"answer": "To force the Ray head node to use a custom pem file to ssh into the worker node, you need to specify the path to your custom pem file in the 'ssh_private_key' field under the 'auth' section in your configuration. The configuration should look like this:\n\nauth:\n ssh_user: ubuntu\n ssh_private_key: /path/to/your/custom/key.pem\n\nMake sure to also set \"KeyName\" in the head and worker node configurations.",3311"llm": "gpt-4"3312},3313{3314"question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",3315"sources": [3316"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3317"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",3318"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",3319"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",3320"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-operating-system-settings",3321"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",3322"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",3323"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",3324"https://docs.ray.io/en/master/ray-core/configure.html#head-node"3325],3326"document_ids": [332714740,33284508,33296375,3330563,33314457,333213870,33334522,33345011,333543903336],3337"answer": "The GCS (Global Control Service) server is a centralized metadata server for a Ray cluster. It manages node membership and actor directory, and provides cluster-level operations including actor, placement groups, and node management. The GCS server runs on the Ray head node and is also known as the Global Control Store. \n\nYour GCS server might be taking up a lot of memory on the head node because the head node runs extra cluster-level processes like the GCS and API server. Additionally, the head node has more memory-demanding system components such as the GCS or the dashboard. If you execute the same number of Tasks and Actors from a head node as worker nodes, it can easily have out-of-memory problems. To avoid this, you can specify --num-cpus=0 when starting a head node to prevent running any Tasks and Actors on the head node.",3338"llm": "gpt-4"3339},3340{3341"question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",3342"sources": [3343"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",3344"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",3345"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",3346"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",3347"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",3348"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource",3349"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3350"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",3351"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm"3352],3353"document_ids": [3354467,3355852,33566147,33579510,33586215,33595724,3360254,336110208,336298983363],3364"answer": "If your nodes are stuck in the \"pending\" state for a long time, you can debug this by checking for errors using the command `kubectl describe pod raycluster-kuberay-xxxx-xxxxx`. Also, ensure that your Docker resource limits are set high enough. You can also use the `ray status` command to view the detailed status of your cluster. If the issue persists, consider restarting the cluster with `ray up`. If the cluster resources are all claimed by actors, consider creating fewer actors or adding more nodes to the Ray cluster.",3365"llm": "gpt-4"3366},3367{3368"question": "how to install Ray 2.5.1 from github or wheel?",3369"sources": [3370"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",3371"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",3372"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",3373"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3374"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",3375"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",3376"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",3377"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",3378"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels"3379],3380"document_ids": [3381288,338214545,338363,33846335,3385291,338614551,338766,338814541,33892863390],3391"answer": "To install Ray 2.5.1 from a wheel, you can use the pip command. First, uninstall any previous installation of Ray using `pip uninstall -y ray`. Then, install Ray using the command `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`, replacing \"LINK_TO_WHEEL.whl\" with the link to the specific wheel file for Ray 2.5.1. \n\nIf you want to install from a specific commit on GitHub, use the command `pip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/{COMMIT_HASH}/ray-{RAY_VERSION}-{PYTHON_VERSION}-{PYTHON_VERSION}-{OS_VERSION}.whl`, replacing the placeholders with the specific commit hash, Ray version, Python version, and OS version. \n\nPlease note that the exact links to the wheel files or commit hashes are not provided in the context.",3392"llm": "gpt-4"3393},3394{3395"question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",3396"sources": [3397"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",3398"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.__init__.html#ray-rllib-evaluation-worker-set-workerset-init",3399"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3400"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",3401"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3402"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#worker-setup-commands",3403"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3404"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3405"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options"3406],3407"document_ids": [3408542,340912971,34104287,341110762,34124720,34136259,34146202,34154288,341648813417],3418"answer": "To use `worker_process_setup_hook` in a runtime environment to do some setup on worker node creation, you need to pass a callable function to `worker_process_setup_hook` in the `runtime_env` parameter of `ray.init()`. This function will be called after workers start and before tasks and actors are scheduled. Here is an example:\n\n```python\ndef logging_setup_func():\n # Your setup code here\n\nray.init(runtime_env={\"worker_process_setup_hook\": logging_setup_func})\n```\n\nIn this example, `logging_setup_func` is a function that contains the setup code you want to run on each worker node.",3419"llm": "gpt-4"3420},3421{3422"question": "how to use Ray dataset on aws",3423"sources": [3424"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",3425"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",3426"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",3427"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",3428"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",3429"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",3430"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",3431"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",3432"https://docs.ray.io/en/master/data/key-concepts.html#datasets"3433],3434"document_ids": [34356732,34368006,34376839,34389188,34397865,34406935,34416835,34426467,344367313444],3445"answer": "To use Ray dataset on AWS, follow these steps:\n\n1. Install Ray Data using pip: `pip install -U \"ray[data]\"`\n2. Import Ray in your Python script: `import ray`\n3. Load your data into a Ray Dataset from an S3 bucket: `ds = ray.data.read_csv(\"s3://anonymous@air-example-data/iris.csv\")`\n4. You can inspect the data using `ds.show(limit=1)`\n5. If you want to transform your dataset, you can use `ds.map_batches()`\n6. Get the final predictions by either iterating through the output or saving the results.\n\nRemember, you can start a Ray cluster on AWS using the Ray cluster launcher with the AWS Python SDK.",3446"llm": "gpt-4"3447},3448{3449"question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",3450"sources": [3451"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3452"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",3453"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",3454"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",3455"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",3456"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",3457"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",3458"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",3459"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition"3460],3461"document_ids": [34627081,34636864,34647395,34659207,34666795,34678046,346810017,34696876,347073523471],3472"answer": "To avoid shuffling your dataset during a ray.data.map_batches, you can randomize each block of your dataset via randomize_block_order. Then, when iterating over your dataset during training, enable local shuffling by specifying a local_shuffle_buffer_size to iter_batches or iter_torch_batches. This method is more performant because it doesn\u2019t require excessive data movement.",3473"llm": "gpt-4"3474},3475{3476"question": "Is the order of the input data preserved after a map_batches operation?",3477"sources": [3478"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",3479"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3480"https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms",3481"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3482"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",3483"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",3484"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3485"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",3486"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"3487],3488"document_ids": [34897640,34907081,34916813,34927091,34937800,34947738,34957085,34967737,349770883498],3499"answer": "The context does not provide information on whether the order of the input data is preserved after a map_batches operation.",3500"llm": "gpt-4"3501},3502{3503"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?",3504"sources": [3505"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",3506"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",3507"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3508"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",3509"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",3510"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3511"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3512"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",3513"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror"3514],3515"document_ids": [35164349,35174527,351813517,35194348,35205671,35214660,352213974,3523595,352446943525],3526"answer": "To get more detailed errors or logs from Ray Serve, you can leverage Ray logging. Ray Serve uses Python\u2019s standard logging module with a logger named \"ray.serve\". By default, logs are emitted from actors both to stderr and on disk on each node at /tmp/ray/session_latest/logs/serve/. This includes system-level logs from the Serve controller and proxy, as well as access logs and custom user logs produced from within deployment replicas. In development, logs are streamed to the driver Ray program, so it\u2019s convenient to keep the driver running while debugging.",3527"llm": "gpt-4"3528},3529{3530"question": "how do i track an uncaught exception in ray serve",3531"sources": [3532"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3533"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",3534"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",3535"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",3536"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",3537"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception",3538"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3539"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3540"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api"3541],3542"document_ids": [354313974,35444694,35454527,35464674,35475747,3548778,35494658,35504660,355146753552],3553"answer": "To track an uncaught exception in Ray Serve, you can use Ray's mechanism of surfacing application-level failures as Python-level exceptions. When a task on a remote worker or actor fails due to a Python-level exception, Ray wraps the original exception in a RayTaskError and stores this as the task\u2019s return value. This wrapped exception will be thrown to any worker that tries to get the result, either by calling ray.get or if the worker is executing another task that depends on the object. Here is an example:\n\n```python\nimport ray\n\n@ray.remote\ndef f():\n raise Exception(\"the real error\")\n\n@ray.remote\ndef g(x):\n return\n```\n\nIn this example, if function `f` raises an exception, it will be wrapped in a RayTaskError and can be caught when trying to get the result of the function.",3554"llm": "gpt-4"3555},3556{3557"question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",3558"sources": [3559"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3560"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3561"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",3562"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3563"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3564"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",3565"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",3566"https://docs.ray.io/en/master/serve/monitoring.html#json-logging-format",3567"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"3568],3569"document_ids": [357013517,3571680,3572445,35734323,357413524,3575519,3576444,357713525,35784933579],3580"answer": "Logs emitted by your Ray Serve endpoint in the Ray cluster using Python logger can be viewed on each node at /tmp/ray/session_latest/logs/serve/. In development, logs are also streamed to the driver Ray program. You can also access logs from Ray clusters using the Log CLI. If you want to redirect worker logs to the driver, you can do so by default as worker stdout and stderr for tasks and actors stream to the Ray Driver. Additionally, you can enable JSON-formatted logging in the Serve log file by setting the environment variable RAY_SERVE_ENABLE_JSON_LOGGING=1.",3581"llm": "gpt-4"3582},3583{3584"question": "where can I see logs for a failed ray serve deployment",3585"sources": [3586"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3587"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",3588"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",3589"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#inspect-deeper",3590"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",3591"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3592"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",3593"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",3594"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"3595],3596"document_ids": [359713523,35986631,3599444,360013658,360113442,360213517,360313960,360413537,3605135213606],3607"answer": "You can see logs for a failed Ray Serve deployment at /tmp/ray/session_latest/logs/serve/. You can also check the Kuberay operator logs by running kubectl logs -f <kuberay-operator-pod-name>. Additionally, you can view the Ray Serve dashboard at http://localhost:8265/#/serve for more visibility into the applications running on the cluster.",3608"llm": "gpt-4"3609},3610{3611"question": "How to take a subset of a Ray Dataset?",3612"sources": [3613"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",3614"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",3615"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",3616"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",3617"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",3618"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#splitting-datasets",3619"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",3620"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",3621"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray"3622],3623"document_ids": [36249180,36258006,36266835,362710017,36288610,36299198,36309188,36316901,36321433633],3634"answer": "To take a subset of a Ray Dataset, you can use the `take()` method. For example, `rows = ds.take(1)` will take the first row from the dataset. The number inside the parentheses specifies the number of rows to take from the dataset.",3635"llm": "gpt-4"3636},3637{3638"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.",3639"sources": [3640"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",3641"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing",3642"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",3643"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints",3644"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-out-of-tune-using-checkpoints-other-artifacts",3645"https://docs.ray.io/en/master/tune/tutorials/overview.html#tune-feature-guides",3646"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#tunecontroller",3647"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",3648"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage"3649],3650"document_ids": [36512226,36522217,36532253,36542212,36552299,36562270,36572243,36582255,365923483660],3661"answer": "To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `train.get_checkpoint()` function. This function will be populated with a trial\u2019s latest checkpoint whenever Tune restores a trial. If you have used cloud checkpointing, you would need to download all the checkpoints to your local machine for analysis. If your experiment was interrupted, you can resume it using the `tune.Tuner.restore()` function, providing the path to the saved results and checkpoints.",3662"llm": "gpt-4"3663},3664{3665"question": "How can I kill a \"detached\" Actor ?",3666"sources": [3667"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3668"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3669"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3670"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3671"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3672"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3673"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3674"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3675"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance"3676],3677"document_ids": [36784633,36794859,36804631,36814615,36824613,36834579,36844620,36854848,368645613687],3688"answer": "To kill a \"detached\" actor in C++, you can use the `actor_handle.Kill();` method. In Python, you can use `ray.kill(actor_handle)`. These methods will forcefully terminate the actor, interrupting any running tasks and causing them to fail immediately. For detached actors, you need to manually destroy them as they are not automatically garbage-collected by Ray. After killing the actor, its name can be reused.",3689"llm": "gpt-4"3690},3691{3692"question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",3693"sources": [3694"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",3695"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",3696"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3697"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3698"https://docs.ray.io/en/master/serve/api/index.html#serve-run",3699"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",3700"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",3701"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#set-up-ray-a-name-setup-a",3702"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.init_method.html#ray-train-torch-torchconfig-init-method"3703],3704"document_ids": [37056614,37069343,37074811,37084285,370913990,3710542,37114253,371210295,371391183714],3715"answer": "To set environment variables in `ray.init()`, you can use the `runtime_env` parameter with the `env_vars` key. For example, if you want to set the environment variable `foo` to `\"foo\"`, you can do it like this:\n\n```python\nray.init(\n runtime_env={\n \"env_vars\": {\"foo\": \"foo\"}\n }\n)\n```\n\nThis will set the environment variable `foo` to the value `\"foo\"` for the Ray workers.",3716"llm": "gpt-4"3717},3718{3719"question": "What is the rest api for getting the head node id?",3720"sources": [3721"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",3722"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.node_id.html#ray-runtime-context-runtimecontext-node-id",3723"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",3724"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",3725"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",3726"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3727"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",3728"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3729"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails"3730],3731"document_ids": [3732721,37334731,3734754,37354909,37364450,373714324,373814202,3739492,374066573741],3742"answer": "The REST API for getting the head node id is not explicitly mentioned in the provided context. However, you can get the current node ID using the `get_node_id()` method from the `ray.runtime_context.RuntimeContext` class. Please note that this method returns the ID of the current node where your driver, task, or actor runs, not specifically the head node.",3743"llm": "gpt-4"3744},3745{3746"question": "how to rerun a canceled ray task",3747"sources": [3748"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",3749"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",3750"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",3751"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",3752"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",3753"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",3754"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",3755"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",3756"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks"3757],3758"document_ids": [37594897,37604375,37614550,37624494,376314702,376411591,37654497,376614703,3767115923768],3769"answer": "Ray does not automatically retry cancelled tasks. If a task is cancelled, you would need to manually rerun the task. You can do this by calling the remote function again. For example, if your task was initiated with `obj_ref = blocking_operation.remote()`, you can rerun the task by calling this line again.",3770"llm": "gpt-4"3771},3772{3773"question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",3774"sources": [3775"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",3776"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.sigopt.SigOptSearch.html#ray-tune-search-sigopt-sigoptsearch",3777"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.hyperopt.HyperOptSearch.set_max_concurrency.html#ray-tune-search-hyperopt-hyperoptsearch-set-max-concurrency",3778"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",3779"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ConcurrencyLimiter.html#ray-tune-search-concurrencylimiter",3780"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.html#ray-tune-search-optuna-optunasearch",3781"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",3782"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.bayesopt.BayesOptSearch.set_max_concurrency.html#ray-tune-search-bayesopt-bayesoptsearch-set-max-concurrency",3783"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"3784],3785"document_ids": [37867738,37872072,37881654,37891340,37901464,37911867,37921547,37931597,379416103795],3796"answer": "The context does not provide information on how to set the max parallel concurrent scheduled tasks in map_batches.",3797"llm": "gpt-4"3798},3799{3800"question": "How do I get the number of cpus from ray cluster?",3801"sources": [3802"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",3803"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",3804"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-6-profile-using-ray-dashboard",3805"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",3806"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",3807"https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",3808"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",3809"https://docs.ray.io/en/master/data/performance-tips.html#tuning-read-parallelism",3810"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"3811],3812"document_ids": [381310003,38145936,38156049,38166504,38175413,38184380,38196013,38206815,382156933822],3823"answer": "To get the number of CPUs from a Ray cluster, you can use the `ray.cluster_resources()` function in Python. This function will return a dictionary that includes the number of CPUs. For example, the output might look like this: `{'CPU': 12.0}`. This means that there are 12 CPUs available in the cluster.",3824"llm": "gpt-4"3825},3826{3827"question": "How to use the exclude option to the runtime_env",3828"sources": [3829"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",3830"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",3831"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",3832"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3833"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_runtime_env_string.html#ray-runtime-context-runtimecontext-get-runtime-env-string",3834"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-6-runtime-env",3835"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",3836"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",3837"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig"3838],3839"document_ids": [38404661,38414826,384213856,38434712,38444912,38455684,38466665,38474291,384847643849],3850"answer": "The context provided does not contain information on how to use the exclude option with the runtime_env in Ray.",3851"llm": "gpt-4"3852},3853{3854"question": "show a map batch example with batch_format",3855"sources": [3856"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3857"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.sample_batch.SampleBatch.split_by_episode.html#ray-rllib-policy-sample-batch-samplebatch-split-by-episode",3858"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",3859"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3860"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",3861"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",3862"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3863"https://docs.ray.io/en/master/rllib/rllib-offline.html#example-converting-external-experiences-to-batch-format",3864"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format"3865],3866"document_ids": [38677081,386813299,386910266,387014727,38716956,38727640,38737091,387410622,387567903876],3877"answer": "A map batch example with batch_format is shown below:\n\n```python\npreds = (\n ds\n .repartition(100)\n .map_batches(\n PredictCallable,\n batch_size=4,\n fn_constructor_kwargs=dict(model_id=model_id, revision=revision),\n batch_format=\"pandas\",\n compute=ray.data.ActorPoolStrategy(),\n num_gpus=1,\n )\n)\n```\n\nIn this example, the `map_batches` method is used to apply the `PredictCallable` function to the dataset `ds`. The `batch_size` is set to 4, meaning the function will be applied to 4 data points at a time. The `batch_format` is set to \"pandas\", meaning the data will be represented as pandas DataFrames. The `compute` parameter is set to use the `ActorPoolStrategy`, and `num_gpus` is set to 1, indicating the use of one GPU.",3878"llm": "gpt-4"3879},3880{3881"question": "how to find local ray address",3882"sources": [3883"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3884"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3885"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",3886"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",3887"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",3888"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3889"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",3890"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3891"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core"3892],3893"document_ids": [38946552,38954817,38966531,38976183,38984187,38994818,390013988,390113974,390254133903],3904"answer": "To find your local Ray address, you can set the RAY_ADDRESS environment variable by using the command: $ export RAY_ADDRESS=\"http://127.0.0.1:8265\". Alternatively, you can pass the --address=http://127.0.0.1:8265 flag explicitly to each Ray Jobs CLI command, or prepend each command with RAY_ADDRESS=http://127.0.0.1:8265. If no address is provided, the system will check the RAY_ADDRESS OS environment variable, the concrete address passed to ray.init(address=<address>), or the latest Ray instance that was started on the same machine using ray start. If the provided address is \u201clocal\u201d, it will start a new local Ray instance.",3905"llm": "gpt-4"3906},3907{3908"question": "How to start ray cluster on multiple node via CLI?",3909"sources": [3910"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3911"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",3912"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",3913"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",3914"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",3915"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",3916"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",3917"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",3918"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster"3919],3920"document_ids": [39216203,39226616,39235605,39246501,39254193,39266500,39276510,39286622,392962063930],3931"answer": "To start a Ray cluster on multiple nodes via CLI, you first need to set up your Ray cluster. Choose any node to be the head node and run the command `ray start --head --port=6379`. This command will print out the Ray cluster address, which can be passed to `ray start` on other machines to start the worker nodes. To start a Ray cluster on the cloud, you can use the command `ray up -y config.yaml`. This command uses the Ray cluster launcher to start a cluster on the cloud, creating a designated \u201chead node\u201d and worker nodes.",3932"llm": "gpt-4"3933},3934{3935"question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",3936"sources": [3937"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",3938"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",3939"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",3940"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",3941"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",3942"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",3943"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",3944"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",3945"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status"3946],3947"document_ids": [39485068,3949625,395010218,39519397,39526879,3953622,39549901,39551113,39564683957],3958"answer": "The Ray tuner might show \"running\" but CPU usage is almost 0% because after a Ray actor is scheduled and created, it occupies 0 CPU. Also, if you're using Ray Data in conjunction with Ray Tune, Tune tries to fully utilize cluster CPUs which can prevent Ray Data from scheduling tasks, reducing performance or causing workloads to hang. To ensure CPU resources are always available for Ray Data execution, limit the number of concurrent Tune trials with the max_concurrent_trials Tune option.",3959"llm": "gpt-4"3960},3961{3962"question": "should the Ray head node and all workers have the same object store memory size allocated?",3963"sources": [3964"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3965"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",3966"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",3967"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",3968"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",3969"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",3970"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",3971"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",3972"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads"3973],3974"document_ids": [39756394,39765109,3977563,39785013,39796437,39806446,3981855,39825557,3983144833984],3985"answer": "No, the Ray head node and all workers do not need to have the same object store memory size allocated. The Ray head node often has more memory-demanding system components and runs the driver by default. If it has the same memory capacity as worker nodes and executes the same number of tasks, it can easily run into out-of-memory problems. Therefore, it's often recommended to allocate more memory to the head node or avoid running tasks and actors on the head node.",3986"llm": "gpt-4"3987},3988{3989"question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",3990"sources": [3991"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",3992"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",3993"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",3994"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3995"https://docs.ray.io/en/master/serve/api/index.html#serve-start",3996"https://docs.ray.io/en/master/serve/architecture.html#how-does-serve-ensure-horizontal-scalability-and-availability",3997"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",3998"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3999"https://docs.ray.io/en/master/serve/develop-and-deploy.html#build-serve-config-files-for-production-deployment"4000],4001"document_ids": [400214022,400314055,400414025,400514288,400613994,400713476,400813958,400914287,4010134394011],4012"answer": "In Ray Serve, you can specify whether to set up an HTTP proxy on each node or just the head node by using the \"proxy_location\" option. The options are \"HeadOnly\" to start one HTTP server on the head node, \"EveryNode\" to start one HTTP server per node, and \"NoServer\" to disable the HTTP server. The default is \"EveryNode\".",4013"llm": "gpt-4"4014},4015{4016"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?",4017"sources": [4018"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",4019"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",4020"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",4021"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-grafana-host-location",4022"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",4023"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-11-embed-grafana-panels-in-ray-dashboard",4024"https://docs.ray.io/en/master/cluster/metrics.html#simplist-setting-up-grafana-with-ray-provided-configurations",4025"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",4026"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana"4027],4028"document_ids": [40295544,40306061,40315543,40325547,40335545,40346086,40355597,40366084,403760854038],4039"answer": "To set the `RAY_GRAFANA_HOST` and `RAY_PROMETHEUS_HOST` variables, you should use addresses that the head node can use to access Grafana and Prometheus respectively. For example, you can set `RAY_GRAFANA_HOST` to `http://prometheus-grafana.prometheus-system.svc:80` and `RAY_PROMETHEUS_HOST` to `http://prometheus-kube-prometheus-prometheus.prometheus-system.svc:9090`. \n\nIf the head node IP is changing every time you reconfigure your cluster, you might want to consider using a service discovery mechanism or DNS that can dynamically resolve the IP of the head node. This way, you can set the environment variables to the DNS names instead of the IP addresses.",4040"llm": "gpt-4"4041},4042{4043"question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",4044"sources": [4045"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",4046"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",4047"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",4048"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",4049"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",4050"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",4051"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#deploy-a-ray-cluster",4052"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",4053"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"4054],4055"document_ids": [405613957,405713956,40585975,40595953,40605972,40616170,40626175,40635973,4064139024065],4066"answer": "The Ray autoscaler in a KubeRay cluster determines which Kubernetes pod to kill during autoscaling. It has more fine-grained control over scale-down than the Horizontal Pod Autoscaler. It determines the desired scale and selects precisely which pods to scale down. The KubeRay operator then deletes the selected pod. This is safer for a Ray application as downscaling a random pod could be dangerous.",4067"llm": "gpt-4"4068},4069{4070"question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",4071"sources": [4072"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",4073"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",4074"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",4075"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4076"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",4077"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4078"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s",4079"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",4080"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s"4081],4082"document_ids": [408313854,408413795,408514020,40864287,408714044,408814288,40898481,40904765,409186554092],4093"answer": "To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you need to include it in the `http_options` field of the Serve config. The value of `request_timeout_s` should be the number of seconds the HTTP Proxies should wait before terminating an HTTP request. Note that this configuration is global to your Ray cluster and cannot be updated during runtime. Here is an example:\n\n```yaml\nhttp_options:\n request_timeout_s: 10\n```\n\nIn this example, the HTTP Proxies will wait for 10 seconds before terminating an HTTP request.",4094"llm": "gpt-4"4095},4096{4097"question": "How do I make the GPU available on my M1 laptop to ray?",4098"sources": [4099"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",4100"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",4101"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4102"https://docs.ray.io/en/master/installation.html#m1-mac-apple-silicon-support",4103"https://docs.ray.io/en/master/ray-overview/installation.html#m1-mac-apple-silicon-support",4104"https://docs.ray.io/en/master/ray-more-libs/installation.html#m1-mac-apple-silicon-support",4105"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#requesting-gpus",4106"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",4107"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage"4108],4109"document_ids": [41104997,41114995,41124818,411373,4114298,411514571,41166020,41172966,411860124119],4120"answer": "To make the GPU available on your M1 laptop to Ray, you need to specify the number of GPUs in your function definition using the `@ray.remote(num_gpus=1)` decorator. However, Ray does not automatically use the GPU, it is up to the function to make use of it, typically through an external library like TensorFlow. You also need to install the GPU version of TensorFlow for this to work. Additionally, you can request GPU resources directly from the autoscaler using `ray.autoscaler.sdk.request_resources(bundles=[{\"GPU\": 1}] * 2)`. Remember to install Ray on your M1 Mac using miniforge and ensure you're using the miniforge environment.",4121"llm": "gpt-4"4122},4123{4124"question": "How can I add a timeout for the Ray job?",4125"sources": [4126"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",4127"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",4128"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",4129"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",4130"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4131"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.GetTimeoutError.html#ray-exceptions-gettimeouterror",4132"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-stop",4133"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",4134"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management"4135],4136"document_ids": [413713795,41386556,41395757,41404416,41416554,41424796,41436602,41446558,414565694146],4147"answer": "You can add a timeout for a Ray job by using the `ray.get()` function with the `timeout` parameter. Here is an example:\n\n```python\nfrom ray.exceptions import GetTimeoutError\n\n@ray.remote\ndef long_running_function():\n time.sleep(8)\n\nobj_ref = long_running_function.remote()\ntry:\n ray.get(obj_ref, timeout=4)\nexcept GetTimeoutError: # You can capture the standard \"TimeoutError\" instead\n print(\"`get` timed out.\")\n```\n\nIn this example, if the `long_running_function` does not return within 4 seconds, a `GetTimeoutError` is raised.",4148"llm": "gpt-4"4149},4150{4151"question": "how do I set custom /tmp directory for remote cluster?",4152"sources": [4153"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4154"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",4155"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",4156"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",4157"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",4158"https://docs.ray.io/en/master/cluster/cli.html#ray-submit",4159"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.restore.html#ray-rllib-algorithms-algorithm-algorithm-restore",4160"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.restore.html#ray-tune-trainable-restore",4161"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster"4162],4163"document_ids": [41644273,41656561,4166872,416713680,416813718,41695625,417012465,41712081,417262054173],4174"answer": "To set a custom /tmp directory for a remote cluster, you can use the environment variable RAY_TEMPDIR. This variable defines where the temporary directory for the cluster files should be created. This directory needs to be accessible by the host, the outer container, and the inner container. For example, you can set it as RAY_TEMPDIR=\"/ray-mount\".",4175"llm": "gpt-4"4176},4177{4178"question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",4179"sources": [4180"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",4181"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4182"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",4183"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4184"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4185"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",4186"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",4187"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",4188"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable"4189],4190"document_ids": [41914960,41924385,41934959,4194511,41956632,4196872,41976396,41989160,419917754200],4201"answer": "Yes, if you set --temp-dir to a different directory than /tmp, Ray will spill objects to the custom directory you specified.",4202"llm": "gpt-4"4203},4204{4205"question": "can you give me an example for *`--runtime-env-json`*",4206"sources": [4207"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",4208"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",4209"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",4210"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",4211"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",4212"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4213"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4214"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",4215"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv"4216],4217"document_ids": [42184308,42194319,42204253,42214661,422213720,42234712,42244716,422514217,422647134227],4228"answer": "Here is an example of using the `--runtime-env-json` argument:\n\n```bash\nserve run --address=ray://<head-node-ip-address>:10001 --runtime-env-json='{\"env_vars\": {\"MY_ENV_VAR\": \"my-value\"}, \"working_dir\": \"./project/src\", \"pip\": [\"requests\", \"chess\"]}' local_dev:graph\n```\n\nIn this example, the `--runtime-env-json` argument is used to specify environment variables, the working directory, and Python packages for the runtime environment.",4229"llm": "gpt-4"4230},4231{4232"question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",4233"sources": [4234"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4235"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",4236"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",4237"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",4238"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4239"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",4240"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4241"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",4242"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters"4243],4244"document_ids": [424513449,424614225,424714187,424814066,424914303,42505963,425113450,425214141,4253134514254],4255"answer": "The value of `maxConcurrentReplicas` is not explicitly mentioned in the provided context. However, it is mentioned that `max_replicas` (which might be similar) has a default value of 1. This is the maximum number of replicas for the deployment, and Ray Serve Autoscaling will rely on the Ray Autoscaler to scale up more nodes when the currently available cluster resources are not enough to support more replicas.",4256"llm": "gpt-4"4257},4258{4259"question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",4260"sources": [4261"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4262"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4263"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",4264"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",4265"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",4266"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",4267"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4268"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4269"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails"4270],4271"document_ids": [427213451,427314303,427414187,427514225,427614066,427713561,427813450,427914302,4280141864281],4282"answer": "The value of `max_concurrent_queries` should be set based on the expected number of pending queries in a single replica. It doesn't directly depend on the value of `target_num_ongoing_requests_per_replica`. However, it should be configured considering the overall load and performance requirements. If not specified, it uses a default value of 1.",4283"llm": "gpt-4"4284},4285{4286"question": "what is a `smoothing_factor`",4287"sources": [4288"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4289"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4290"https://docs.ray.io/en/master/tune/examples/includes/pb2_example.html#pb2-example",4291"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",4292"https://docs.ray.io/en/master/rllib/package_ref/utils.html#tensorflow-utilities",4293"https://docs.ray.io/en/master/rllib/package_ref/utils.html#numpy-utilities",4294"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.numpy.fc.html#ray-rllib-utils-numpy-fc",4295"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#twin-delayed-ddpg-td3",4296"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg"4297],4298"document_ids": [429913454,430013455,43013991,43021920,430311114,430411120,430513324,430611867,4307118494308],4309"answer": "A smoothing factor, such as upscale_smoothing_factor or downscale_smoothing_factor, is a multiplicative factor used to speed up or slow down each upscaling or downscaling decision respectively. It can be adjusted to control the response of the autoscaling algorithm to changes in traffic volume. For example, a higher upscale_smoothing_factor can help scale up resources quickly during high traffic volume periods.",4310"llm": "gpt-4"4311},4312{4313"question": "What is the reason actors change their state to unhealthy?",4314"sources": [4315"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",4316"https://docs.ray.io/en/master/serve/api/index.html#get-api-serve-deployments-status",4317"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4318"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",4319"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",4320"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",4321"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",4322"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",4323"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#other-possible-scenarios-that-trigger-a-new-raycluster-preparation"4324],4325"document_ids": [432613836,432714006,432814294,432914708,43304505,433111597,433213935,4333747,433458144335],4336"answer": "Actors change their state to unhealthy when the deployment has either failed to update, or has updated and has become unhealthy afterwards. This can be due to an error in the deployment\u2019s constructor, a crashed replica, or a general system or machine error. Additionally, if the status of a serve application is DEPLOY_FAILED or UNHEALTHY for a duration exceeding the serviceUnhealthySecondThreshold parameter, the KubeRay operator marks a RayCluster as unhealthy.",4337"llm": "gpt-4"4338},4339{4340"question": "How do I access logs for a dead node?",4341"sources": [4342"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4343"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4344"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",4345"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",4346"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",4347"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4348"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4349"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",4350"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs"4351],4352"document_ids": [4353492,4354493,4355662,4356739,4357740,4358512,43596631,436013894,43616954362],4363"answer": "You cannot access the logs from a dead node in Ray. However, you can access logs from active nodes using the State API or the Python SDK. You can use the `ray.util.state.list_logs()` function to list available log files and `ray.util.state.get_log()` to retrieve a specific log file. You need to provide the node ID or node IP to these functions. You can get the node ID or node IP using `ray list nodes`. If you need to inspect logs after the clusters are stopped or terminated, you need to store and persist the logs.",4364"llm": "gpt-4"4365},4366{4367"question": "What are the reasons for a node to change it\u2019s status to dead?",4368"sources": [4369"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",4370"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4371"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",4372"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#rescheduling-bundles-on-a-dead-node",4373"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",4374"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.PlacementGroupState.html#ray-util-state-common-placementgroupstate",4375"https://docs.ray.io/en/master/ray-core/scheduling/index.html#nodeaffinityschedulingstrategy",4376"https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#handling-failures-and-node-preemption",4377"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#provider-cache-stopped-nodes"4378],4379"document_ids": [43804524,4381554,438213869,43835098,438413898,4385724,43865147,43879228,438862834389],4390"answer": "A node can change its status to dead due to several reasons including raylet process failure, the node running low on memory, worker node failure, and head node failure. Additionally, if the node does not have enough resources or is infeasible to run a task or actor, it can also lead to the node's status changing to dead.",4391"llm": "gpt-4"4392},4393{4394"question": "What are the reasons for spikes in node CPU utilization",4395"sources": [4396"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",4397"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#adding-cpu-only-nodes-to-your-cluster",4398"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4399"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",4400"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",4401"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",4402"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",4403"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4404"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training"4405],4406"document_ids": [4407468,44089227,4409554,44106375,44116364,44123015,44133008,4414555,441530024416],4417"answer": "Spikes in node CPU utilization can be caused by several factors including:\n\n1. Increased preprocessing compute time: Adding more CPU-only nodes can help parallelize preprocessing and reduce CPU utilization.\n2. Insufficient resources: If a user tries to launch an actor, task, or placement group but there are insufficient resources, the request will be queued, potentially leading to increased CPU utilization.\n3. Extra stress on the head node: Ray\u2019s architecture means that the head node has extra stress due to additional system processes running on it like GCS. This can lead to spikes in CPU utilization.\n4. High memory usage: If the node is running low on memory, tasks may be killed, potentially leading to increased CPU utilization as tasks are restarted or moved.",4418"llm": "gpt-4"4419},4420{4421"question": "What AWS machine type is recommended to deploy a RayService on EKS?",4422"sources": [4423"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-aks-microsoft-azure",4424"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",4425"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",4426"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",4427"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",4428"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",4429"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",4430"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",4431"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides"4432],4433"document_ids": [44345887,4435372,443613622,443713962,443813931,44395656,444013930,44416376,444256424443],4444"answer": "The recommended AWS machine type to deploy a RayService on EKS is r5dn.16xlarge.",4445"llm": "gpt-4"4446},4447{4448"question": "Is there a way to configure the session name generated by ray?",4449"sources": [4450"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4451"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4452"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4453"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",4454"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",4455"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4456"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4457"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",4458"https://docs.ray.io/en/master/cluster/cli.html#ray-attach"4459],4460"document_ids": [44614384,44624811,446313974,44646613,44654514,4466511,44674823,44684662,446956284470],4471"answer": "No, the session name generated by Ray cannot be configured. It is automatically generated with the format 'session_{timestamp}_{pid}', where the timestamp follows the '%Y-%m-%d_%H-%M-%S_%f' format and the pid belongs to the startup process.",4472"llm": "gpt-4"4473},4474{4475"question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",4476"sources": [4477"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",4478"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#python-sdk-api-reference",4479"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli",4480"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",4481"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",4482"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.get_job_info.html#ray-job-submission-jobsubmissionclient-get-job-info",4483"https://docs.ray.io/en/master/cluster/running-applications/job-submission/rest.html#ray-jobs-rest-api",4484"https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",4485"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job"4486],4487"document_ids": [44886575,44896539,44906547,44916640,44926637,44936671,44946571,4495275,449665784497],4498"answer": "The Python SDK does not directly provide a link to the Ray dashboard for a given job. However, you can use the Python SDK to get information about a job using the `get_job_info(job_id: str)` method of the `JobSubmissionClient` class. The Ray dashboard can be accessed through the default URL, `http://localhost:8265`.",4499"llm": "gpt-4"4500},4501{4502"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).",4503"sources": [4504"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",4505"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4506"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4507"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.LocalRayletDiedError.html#ray-exceptions-localrayletdiederror",4508"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.TaskUnschedulableError.html#ray-exceptions-taskunschedulableerror",4509"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4510"https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#understanding-objectlosterrors",4511"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.WorkerCrashedError.html#ray-exceptions-workercrashederror",4512"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure"4513],4514"document_ids": [45155129,45164658,4517554,45184858,45194830,4520557,45214559,45224906,452345244524],4525"answer": "The node where the task was running could have crashed unexpectedly due to several reasons: (1) the instance where the node was running failed, (2) the local raylet of the task died, (3) the worker executing the task died unexpectedly, (4) the corresponding placement group was removed, (5) the object store was full, (6) the local disk was full, (7) the object was lost from distributed memory due to node failure or system error, (8) the task was killed due to the node running low on memory, (9) the task could not be scheduled, for example, if the node specified through NodeAffinitySchedulingStrategy is dead, or (10) a raylet process failure occurred.",4526"llm": "gpt-4"4527},4528{4529"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.",4530"sources": [4531"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",4532"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",4533"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",4534"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",4535"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",4536"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",4537"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",4538"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",4539"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"4540],4541"document_ids": [45425638,45436505,454413870,45454508,45464522,454713871,45485709,45494515,4550147404551],4552"answer": "To resolve the issue of a health check failure for a node in the GCS server, you can try the following steps:\n\n1. Check if the worker node can connect to the head node. If it can't, ensure that the Ray GCS with the matching version started successfully at the specified address and there is no firewall setting preventing access.\n\n2. If the head node is inaccessible, check if it's running, if the specified address is correct, or if there are firewall settings preventing access.\n\n3. Use tools like nmap or nc to check whether each port can be reached from a node.\n\n4. Ensure that the GCS is fault tolerant. If it's not, you need to make it fault tolerant using HA Redis. When the GCS restarts, it should load all the data from the Redis instance and resume regular functions.\n\n5. If you're using Kubernetes but not KubeRay, ensure that the GCS is backed by Redis so that it can recover the state by reading from Redis when it restarts.\n\n6. If the raylet fails to reconnect to the GCS for more than 60 seconds, the raylet will exit and the corresponding node fails. You can tune this timeout threshold by the OS environment variable RAY_gcs_rpc_server_reconnect_timeout_s.\n\nRemember, these are general steps and the exact solution might vary depending on the specific circumstances of your setup.",4553"llm": "gpt-4"4554},4555{4556"question": "Do you need the DAGDriver to deploy a serve application using RayServe?",4557"sources": [4558"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",4559"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-the-applications",4560"https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph",4561"https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",4562"https://docs.ray.io/en/master/serve/tutorials/gradio-dag-visualization.html#requirement-driver",4563"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",4564"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",4565"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",4566"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"4567],4568"document_ids": [456913741,457013655,457113426,457213502,457314524,457413859,457513652,457613650,4577147324578],4579"answer": "No, the DAGDriver is not explicitly required to deploy a serve application using RayServe. However, the DAGDriver is useful for routing HTTP requests through your call graph and for visualizing the deployment graph. It can also accept an HTTP adapter to handle HTTP parsing.",4580"llm": "gpt-4"4581},4582{4583"question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",4584"sources": [4585"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",4586"https://docs.ray.io/en/master/workflows/basics.html#sub-task-results",4587"https://docs.ray.io/en/master/serve/api/index.html#serve-run",4588"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",4589"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",4590"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Application.html#ray-serve-application",4591"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#defining-an-application-builder",4592"https://docs.ray.io/en/master/serve/tutorials/batch.html#define-the-deployment",4593"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#walkthrough"4594],4595"document_ids": [459613844,45979670,459813990,45995789,46005678,460114037,460213813,460314586,460480784605],4606"answer": "The import path that you need to provide to a simple RayServe deployment is the path to the variable representing your Ray Serve application within your Python file. For example, if your application is contained in the variable 'app' in the Python file 'python_file', the import path would be 'python_file:app'.",4607"llm": "gpt-4"4608},4609{4610"question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",4611"sources": [4612"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",4613"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",4614"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",4615"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",4616"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",4617"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",4618"https://docs.ray.io/en/master/serve/develop-and-deploy.html#deploy-ray-serve-in-production",4619"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",4620"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status"4621],4622"document_ids": [46235751,46245760,46255752,46266144,46275647,46285658,462913441,463013926,463157634632],4633"answer": "Yes, there are examples showing the usage of RayJob in Kuberay. You can download a sample RayJob YAML file using the command `curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml`. Then, you can create a RayJob using the command `kubectl apply -f ray_v1alpha1_rayjob.yaml`. Another example is the RayJob spec defined in `ray-job.batch-inference.yaml`, which can be downloaded using the command `curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-job.batch-inference.yaml`. This spec contains a spec for the RayCluster that is to be created for the job.",4634"llm": "gpt-4"4635},4636{4637"question": "Does Ray metrics have to be exported via an actor?",4638"sources": [4639"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",4640"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",4641"https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",4642"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",4643"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",4644"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",4645"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",4646"https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",4647"https://docs.ray.io/en/master/cluster/metrics.html#scraping-metrics"4648],4649"document_ids": [4650503,4651656,4652438,46535573,465411596,46554503,465614707,4657403,465855814659],4660"answer": "No, Ray metrics do not have to be exported via an actor. Ray provides an API for defining and exporting custom metrics for application visibility. These metrics can be exported as time series data to Prometheus by default. Ray also runs a metrics agent per node to export system and application metrics, which can be scraped by Prometheus to access the metrics.",4661"llm": "gpt-4"4662},4663{4664"question": "how can I avoid objects not getting spilled?",4665"sources": [4666"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",4667"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",4668"https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",4669"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",4670"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",4671"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",4672"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#out-of-core-data-processing",4673"https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",4674"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node"4675],4676"document_ids": [46776374,46784959,46794447,468014748,46814960,46824963,468314485,46841247,468549624686],4687"answer": "To avoid objects getting spilled in Ray, you can manage your memory usage to ensure the object store does not get full. However, if you need to handle larger datasets, you can configure the object spilling feature to your advantage. You can specify the directory where objects are spilled to, or even specify multiple directories to spread the IO load and disk space usage across multiple physical devices. You can also adjust the disk utilization threshold to prevent running out of disk space. If you are using an HDD, it is recommended to specify a large buffer size to reduce IO requests during spilling.",4688"llm": "gpt-4"4689},4690{4691"question": "what\u2019s ray core",4692"sources": [4693"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",4694"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",4695"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",4696"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4697"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#want-to-learn-more",4698"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",4699"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",4700"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",4701"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api"4702],4703"document_ids": [47044662,47054481,47064673,47074811,47085444,47095656,47106835,4711367,4712139744713],4714"answer": "Ray Core is an open-source, Python, general purpose, distributed computing library that provides a small number of core primitives for building and scaling distributed applications. It allows Python developers and ML engineers to scale Python applications and accelerate machine learning workloads. It enables the conversion of functions and classes into Ray tasks and actors, and facilitates working with Ray objects.",4715"llm": "gpt-4"4716},4717{4718"question": "Does ray support cron job",4719"sources": [4720"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4721"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",4722"https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",4723"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",4724"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4725"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",4726"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-api-server",4727"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",4728"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api"4729],4730"document_ids": [473113974,47324662,47339634,47346637,47354811,47366208,473713474,47385635,473966384740],4741"answer": "No, Ray does not support cron jobs or have a built-in job scheduler. However, it can interact with any external job scheduler for triggering workflow runs.",4742"llm": "gpt-4"4743},4744{4745"question": "can you give me the dependencies list for api read_images?",4746"sources": [4747"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4748"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4749"https://docs.ray.io/en/master/data/working-with-images.html#reading-images",4750"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",4751"https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",4752"https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",4753"https://docs.ray.io/en/master/installation.html#docker-source-images",4754"https://docs.ray.io/en/master/serve/tutorials/object-detection.html#serving-an-object-detection-model",4755"https://docs.ray.io/en/master/serve/http-guide.html#list-of-built-in-adapters"4756],4757"document_ids": [47587361,47597360,47606720,476110191,4762306,476314587,476481,476514472,4766135114767],4768"answer": "The dependencies for the `read_images` API are:\n\n1. `paths`: A string or list of strings specifying the paths to the images.\n2. `filesystem`: An optional pyarrow.fs.FileSystem object.\n3. `parallelism`: An integer specifying the level of parallelism.\n4. `meta_provider`: An optional ray.data.datasource.file_meta_provider.BaseFileMetadataProvider object.\n5. `ray_remote_args`: A dictionary of arguments for the ray remote function.\n6. `arrow_open_file_args`: An optional dictionary of arguments for the arrow open file function.\n7. `partition_filter`: An optional ray.data.datasource.partitioning.PathPartitionFilter object.\n8. `partitioning`: A ray.data.datasource.partitioning.Partitioning object.\n9. `size`: An optional tuple specifying the size of the images.\n10. `mode`: An optional string specifying the mode of the images.\n11. `include_paths`: A boolean indicating whether to include paths in the output.",4769"llm": "gpt-4"4770},4771{4772"question": "how do I kill a specific serve replica",4773"sources": [4774"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",4775"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",4776"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",4777"https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-deployments",4778"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",4779"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",4780"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",4781"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",4782"https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-applications"4783],4784"document_ids": [478513912,478613911,478713717,478814007,478913908,479013448,479113892,479213915,4793140154794],4795"answer": "To kill a specific serve replica, you can use the following Python code:\n\n```python\nimport ray\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\n\nThis code gets a handle to the replica using its name and then kills it. The `no_restart=True` argument ensures that the replica is not restarted after being killed.",4796"llm": "gpt-4"4797}4798]4799}4800