llm-applications
4091 строка · 274.7 Кб
1{2"config": {3"experiment_name": "gte-large-fine-tuned",4"chunk_size": 500,5"chunk_overlap": 50,6"num_chunks": 7,7"embedding_model_name": "/efs/shared_storage/goku/gte-large-ft",8"llm": "gpt-3.5-turbo",9"temperature": 0.0,10"max_context_length": 4096,11"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/ray-references/glossary.html#ray-glossary",25"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",26"https://docs.ray.io/en/master/data/key-concepts.html#transforming-data",27"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",28"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing"29],30"document_ids": [319477,329110,3319788,349323,359035,369104,371078138],39"answer": "When using Ray Data's `map_batches` function, you can configure the batch format by specifying the `batch_format` parameter. You can choose between \"numpy\" and \"pandas\" formats. By default, Ray Data represents batches as dicts of NumPy ndarrays. To learn more about batch formats and how to configure them, refer to the documentation on configuring batch formats. Additionally, be cautious of the default `batch_size` of 4096, as it may be too large for datasets with large rows.",40"llm": "gpt-3.5-turbo"41},42{43"question": "How does autoscaling work in a Ray Serve application?",44"sources": [45"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",46"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",47"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",48"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",49"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",50"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",51"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling"52],53"document_ids": [5418089,558548,5618057,577537,5818060,598021,601805861],62"answer": "In a Ray Serve application, autoscaling works by automatically increasing or decreasing the number of replicas based on the deployment's load. The Ray Serve autoscaler asks Ray to start a number of replica actors based on the request demand. If there aren't enough available CPUs, it requests more Ray nodes from the underlying cloud provider. The Ray autoscaler runs on the head node and increases the number of worker nodes when the resource demands exceed the current capacity. It removes idle worker nodes from the cluster. Ray Serve autoscaling allows the minimum number of replicas to be 0, and the scale-up occurs when traffic is sent. When scaling down, Ray Serve tries to terminate replica actors in a way that results in the most nodes having no Ray actors or tasks running on them, and the Ray autoscaler removes those nodes.",63"llm": "gpt-3.5-turbo"64},65{66"question": "how do I get the address of a ray node",67"sources": [68"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",69"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",70"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",71"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.streaming_split.html#ray-data-dataset-streaming-split",72"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",73"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",74"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information"75],76"document_ids": [771002,789005,793192,809906,817330,825912,83591084],85"answer": "To get the address of a Ray node, you can use the `ray.nodes()` function. This will return a list of nodes in the cluster, including their addresses.",86"llm": "gpt-3.5-turbo"87},88{89"question": "Does Ray support NCCL?",90"sources": [91"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",92"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",93"https://docs.ray.io/en/master/ray-air/getting-started.html#design-principles",94"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",95"https://docs.ray.io/en/master/installation.html#installing-ray",96"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",97"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray"98],99"document_ids": [1006330,10119327,10213131,103359,10490,105400,10619535107],108"answer": "Yes, Ray supports NCCL as one of its optional high-performance communication backends for collective communication between distributed CPUs or GPUs.",109"llm": "gpt-3.5-turbo"110},111{112"question": "Is Ray integrated with DeepSpeed?",113"sources": [114"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",115"https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",116"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.deepspeed_engine.html#ray-train-lightning-raydeepspeedstrategy-deepspeed-engine",117"https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",118"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",119"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#fine-tuning-the-model-with-ray-train-a-name-train-a",120"https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate"121],122"document_ids": [12312446,12410988,12511983,12611161,12711842,12813825,12911034130],131"answer": "Yes, Ray is integrated with DeepSpeed. The context mentions that many deep learning frameworks, including Transformers, Accelerate, and more, have integrated with DeepSpeed, and you can run all these combinations in Ray Train.",132"llm": "gpt-3.5-turbo"133},134{135"question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",136"sources": [137"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",138"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",139"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",140"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example",141"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",142"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",143"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors"144],145"document_ids": [1466222,1476249,1486220,1497023,1506224,1517090,1526218153],154"answer": "If you use AsyncIO's `await` to wait for a Ray future like `await x.remote()`, you can await on Ray futures in existing concurrent applications. This allows you to wait for the completion of the future before proceeding with the rest of your code.",155"llm": "gpt-3.5-turbo"156},157{158"question": "How would you compare Spark, Ray, Dask?",159"sources": [160"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",161"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",162"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",163"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",164"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",165"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",166"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference"167],168"document_ids": [16919421,17019441,17119425,17219443,1738587,1749131,1759176176],177"answer": "Dask is a Python parallel computing library for scaling analytics and scientific computing workloads. It provides big data collections that mimic the APIs of NumPy and Pandas, allowing operations on larger-than-memory data to be run on a multi-machine cluster. Dask can be used with Ray, a fast and fault-tolerant distributed task execution system, to execute data analyses using familiar NumPy and Pandas APIs. Dask-on-Ray allows the entire Dask ecosystem to be executed on top of Ray, but it may not achieve the same performance as using Ray directly. Ray Data interoperates with distributed data processing frameworks like Dask and Spark, allowing conversion of datasets to distributed DataFrames. Ray Data handles similar batch processing workloads as Apache Spark, but with a streaming paradigm better suited for GPU workloads for deep learning inference.",178"llm": "gpt-3.5-turbo"179},180{181"question": "why would ray overload a node w/ more task that the resources allow ?",182"sources": [183"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",184"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",185"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",186"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#pattern-using-ray-wait-to-limit-the-number-of-pending-tasks",187"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",188"https://docs.ray.io/en/master/ray-core/tasks.html#scheduling",189"https://docs.ray.io/en/master/ray-core/scheduling/index.html#locality-aware-scheduling"190],191"document_ids": [1927067,1936908,1946913,1957088,1966898,1976030,1986965199],200"answer": "Ray may overload a node with more tasks than the resources allow if the tasks or actors use more than their proportionate share of memory. This can cause issues like Out of Memory (OOM). To prevent this, Ray allows specifying the logical resource requirements for tasks or actors. By increasing the amount of resources requested by tasks or actors, the number of concurrently running tasks or actors on each node can be reduced, ensuring that the sum of the resource requirements does not exceed the node's total resources.",201"llm": "gpt-3.5-turbo"202},203{204"question": "when should I use Ray Client?",205"sources": [206"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",207"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",208"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",209"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",210"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",211"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",212"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests"213],214"document_ids": [2158885,2168882,2178879,2188876,21919791,2208883,22118418222],223"answer": "You should use Ray Client when you want to connect an interactive Python shell to a remote Ray cluster for interactive development. However, it is not recommended for ML workloads. If you have a long-running workload, it is better to use Ray Jobs instead.",224"llm": "gpt-3.5-turbo"225},226{227"question": "how to scatter actors across the cluster?",228"sources": [229"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",230"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",231"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",232"https://docs.ray.io/en/master/data/batch_inference.html#handling-cpu-out-of-memory-failures",233"https://docs.ray.io/en/master/ray-core/scheduling/index.html#default",234"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",235"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources"236],237"document_ids": [2386956,23910810,2407367,2419327,2426954,2437021,24419759245],246"answer": "To scatter actors across the cluster, you can use the \"SPREAD\" scheduling strategy. An example of how to do this is by using the `@ray.remote(scheduling_strategy=\"SPREAD\")` decorator for the function or class that defines the actor. You can then create multiple instances of the actor using the `options(scheduling_strategy=\"SPREAD\")` method.",247"llm": "gpt-3.5-turbo"248},249{250"question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",251"sources": [252"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",253"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",254"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",255"https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",256"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",257"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",258"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster"259],260"document_ids": [2617690,2626518,2638894,26419699,2658878,2665792,26718440268],269"answer": "The connection refused error occurs when the Ray cluster is not ready or the necessary components such as the Dashboard, Dashboard Agent, and GCS have not fully started up. It is important to wait a few seconds after the head Pod is ready before using `ray debug`.",270"llm": "gpt-3.5-turbo"271},272{273"question": "How does Ray AIR set up the model to communicate gradient updates across machines?",274"sources": [275"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",276"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#asynchronous-parameter-server-training",277"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",278"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#fine-tuning-dreambooth-with-ray-train",279"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",280"https://docs.ray.io/en/master/ray-core/examples/plot_pong_example.html#running",281"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a"282],283"document_ids": [28413170,2857222,28613139,28713602,28813228,2897120,29013428291],292"answer": "The model in Ray AIR is set up to communicate gradient updates across machines by using a parameter server and workers. The workers asynchronously compute the gradients and send them to the parameter server. Once the parameter server applies the new gradient, it sends back a copy of the current weights to the worker, which then updates the weights and repeats the process.",293"llm": "gpt-3.5-turbo"294},295{296"question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",297"sources": [298"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests",299"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",300"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",301"https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",302"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",303"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",304"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air"305],306"document_ids": [30718418,30818775,30918282,31018021,31119150,31218265,31313788314],315"answer": "You would use Ray Serve instead of Modal or Seldon because Ray Serve supports multi-model composition using a programmable API, allowing calls to different models to look like function calls. It also supports flexible scaling and resource allocation across different machines in a cluster. Using containers alone may be limiting and hard to work with for these purposes.",316"llm": "gpt-3.5-turbo"317},318{319"question": "How do I deploy an LLM workload on top of Ray Serve?",320"sources": [321"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",322"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",323"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",324"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",325"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",326"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",327"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests"328],329"document_ids": [33019150,33118377,332517,33318291,3347637,33518292,33618418337],338"answer": "To deploy an LLM workload on top of Ray Serve, you can use the Ray Serve CLI. The `serve deploy` command takes a config file path and deploys it to a Ray cluster over HTTP. You can deploy to a local, single-node cluster or a remote, multi-node cluster. Ray Serve supports any arbitrary Python code, allowing you to write all the steps of your LLM application as a single Python module. This enables rapid development, easy testing, and efficient autoscaling of each application step to accommodate user traffic. Ray Serve also has features for batching, model optimization, and streaming responses.",339"llm": "gpt-3.5-turbo"340},341{342"question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",343"sources": [344"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",345"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",346"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",347"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",348"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",349"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",350"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails"351],352"document_ids": [35318991,35419051,35519211,35619106,35718968,35810849,35918932360],361"answer": "The context does not provide information about the memory size needed for setting the `model_id` to \"EleutherAI/gpt-j-6B\".",362"llm": "gpt-3.5-turbo"363},364{365"question": "How do I log the results from multiple distributed workers into a single tensorboard?",366"sources": [367"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",368"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#adding-checkpoints-to-the-pytorch-lightning-module",369"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",370"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",371"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",372"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",373"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms"374],375"document_ids": [37614598,3774032,37812746,3793111,380325,381324,3823188383],384"answer": "To log the results from multiple distributed workers into a single TensorBoard, you can specify the output directory of your results when running the training process with TensorBoard. For example, you can use the command \"tensorboard --logdir=~/ray_results\" to log the results.",385"llm": "gpt-3.5-turbo"386},387{388"question": "how do you config SyncConfig for a Ray AIR job?",389"sources": [390"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",391"https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration",392"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",393"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",394"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",395"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.html#ray-train-runconfig",396"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer"397],398"document_ids": [39914228,4001536,40111301,4021924,40314225,40411280,40511842406],407"answer": "To configure SyncConfig for a Ray AIR job, you can use the `ray.train.SyncConfig` class. It takes parameters such as `upload_dir`, `syncer`, `sync_period`, `sync_timeout`, `sync_artifacts`, `sync_artifacts_on_checkpoint`, and `sync_on_checkpoint`. However, please note that the synchronization of checkpoints and artifacts from worker nodes to the head node is no longer supported in Ray AIR.",408"llm": "gpt-3.5-turbo"409},410{411"question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",412"sources": [413"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",414"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",415"https://docs.ray.io/en/master/ray-observability/getting-started.html#job-profiling",416"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",417"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",418"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",419"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job"420],421"document_ids": [4227782,423579,424590,4258817,4268908,4278283,4287784429],430"answer": "To quickly narrow down the root cause of a failed Ray job, you can check the output of the Ray job using the command \"kubectl logs -l=job-name=rayjob-sample\". Additionally, you can access the worker logs in the Ray logging directory, and for driver logs, you can use the Dashboard UI, CLI (using the \"ray job logs\" command), or the Python SDK (JobSubmissionClient.get_logs() or JobSubmissionClient.tail_job_logs()). You can also profile Ray jobs by clicking on the \"Stack Trace\" or \"CPU Flame Graph\" actions.",431"llm": "gpt-3.5-turbo"432},433{434"question": "How do I specify how many GPUs a serve deployment needs?",435"sources": [436"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",437"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",438"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",439"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",440"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",441"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#resources",442"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-scale-out-training"443],444"document_ids": [44518072,44618239,44710690,44818068,44913789,4507989,45110806452],453"answer": "To specify how many GPUs a serve deployment needs, you can use the `ray_actor_options` parameter with the `num_gpus` argument. For example, `@serve.deployment(ray_actor_options={\"num_gpus\": 0.5})` will allocate 0.5 GPUs to each replica of the deployment.",454"llm": "gpt-3.5-turbo"455},456{457"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?",458"sources": [459"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",460"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",461"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",462"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",463"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",464"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",465"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors"466],467"document_ids": [4686934,469755,47012683,47112669,47213859,47312421,474756475],476"answer": "To debug the issue of worker nodes dying due to memory pressure, you can check the logs for more information about the workers killed on each node. Use the command `ray logs raylet.out -ip <node IP>` to view the logs for the specific node. Additionally, you can refer to the documentation on how to address the out of memory issue and consider provisioning more memory on the node or reducing task parallelism by requesting more CPUs per task.",477"llm": "gpt-3.5-turbo"478},479{480"question": "what are the possible reasons for nodes dying in a cluster?",481"sources": [482"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",483"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",484"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",485"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",486"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",487"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",488"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#helpful-ray-commands"489],490"document_ids": [49118672,4926025,493750,4946031,4956029,4966934,4977496498],499"answer": "Possible reasons for nodes dying in a cluster include head node failure, node fault tolerance, raylet failure, and tasks being killed due to low memory.",500"llm": "gpt-3.5-turbo"501},502{503"question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",504"sources": [505"https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",506"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",507"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",508"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",509"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",510"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",511"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#vertical-pod-autoscaler"512],513"document_ids": [5147596,5159013,5168548,51718752,5188021,5198028,5208051521],522"answer": "To programmatically scale a Ray remote cluster to a target size immediately without using the autoscaler, you can use the `request_resources()` function from the Ray autoscaler SDK. This function allows you to command the autoscaler to scale the cluster up to a desired size. By calling `request_resources()`, the cluster will immediately attempt to scale to accommodate the requested resources, bypassing normal upscaling speed constraints.",523"llm": "gpt-3.5-turbo"524},525{526"question": "how do you disable async iter_batches with Ray Dataset?",527"sources": [528"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",529"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",530"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",531"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",532"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",533"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.streaming_split.html#ray-data-dataset-streaming-split",534"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer"535],536"document_ids": [5379862,5389614,53910074,5409477,54119788,5429903,54311349544],545"answer": "To disable async iter_batches with Ray Dataset, you can set the `async_iter` parameter to `False` when calling the `iter_batches()` method.",546"llm": "gpt-3.5-turbo"547},548{549"question": "what is the different between a batch and a block, for ray datasets?",550"sources": [551"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",552"https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",553"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",554"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",555"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",556"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#batch-training-with-ray-core",557"https://docs.ray.io/en/master/data/data-internals.html#execution-memory"558],559"document_ids": [56019789,5619211,5629477,5639862,56419788,5657125,5669241567],568"answer": "In the context of Ray datasets, a batch refers to a user-facing object that represents a collection of data points used to compute and apply one gradient update to the model weights during model training. On the other hand, a block is an internal abstraction that represents a processing unit of data within a dataset. Ray Data partitions rows into distributed data blocks to perform operations in parallel. Unlike a batch, which is a user-facing object, a block is used internally.",569"llm": "gpt-3.5-turbo"570},571{572"question": "How to setup the development environments for ray project?",573"sources": [574"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",575"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",576"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",577"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-from-source",578"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",579"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#development-workflow",580"https://docs.ray.io/en/master/ray-contribute/development.html#preparing-to-build-ray-on-macos"581],582"document_ids": [5831236,58418608,58513632,5861189,5875774,58818427,5891206590],591"answer": "To set up the development environment for the Ray project, follow these steps:\n\n1. Clone the Ray repository and navigate to the example directory.\n2. Install the dependencies by running \"pip install -Ur dreambooth/requirements.txt\".\n3. Set up the necessary directories and environment variables.\n4. Build Ray from source using the provided instructions for your operating system.\n5. Install additional dependencies for development if needed.\n6. Optionally, build the documentation.\n7. Ensure that you have a full installation of Ray using \"pip install 'ray[default]'\" for the Runtime Environments feature.\n8. Follow the recommended development workflow for Ray Serve applications.",592"llm": "gpt-3.5-turbo"593},594{595"question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",596"sources": [597"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",598"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",599"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",600"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",601"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",602"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",603"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"604],605"document_ids": [60619831,607863,60817717,60917069,61017413,61112700,61217117613],614"answer": "To debug why Ray rollout workers are deadlocking, you can use cProfile's output to check if ray.get() is being called multiple times. Additionally, make sure that you have initialized multiple Sleeper actors to enable parallelization.",615"llm": "gpt-3.5-turbo"616},617{618"question": "how do I join two ray datasets?",619"sources": [620"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#load-and-preprocess-datasets",621"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",622"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#starting-with-pytorch-data",623"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",624"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",625"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",626"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"627],628"document_ids": [62912455,63010714,63112236,63212228,6339160,6349131,6359295636],637"answer": "To join two Ray datasets, you can use the `concatenate_datasets` function. In the provided context, the code snippet demonstrates how to join the `hf_dataset_curated` and `hf_dataset_mined` datasets using `concatenate_datasets`. The resulting merged dataset is then converted into a Ray dataset using `ray.data.from_huggingface`.",638"llm": "gpt-3.5-turbo"639},640{641"question": "Is there a way to retrieve an object ref from its id?",642"sources": [643"https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",644"https://docs.ray.io/en/master/ray-core/objects.html#objects",645"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",646"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",647"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait",648"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",649"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get"650],651"document_ids": [6526474,6536131,6546732,6556613,6566443,65718334,6586610659],660"answer": "Yes, you can retrieve an object ref from its id using the `ray.get()` method from the Ray Core API. This method blocks until the object corresponding to the object ref is available in the local object store.",661"llm": "gpt-3.5-turbo"662},663{664"question": "how to create model Checkpoint from the model in memory?",665"sources": [666"https://docs.ray.io/en/master/ray-air/examples/batch_tuning.html#load-a-model-from-checkpoint-and-perform-batch-prediction-a-class-anchor-id-load-checkpoint-a",667"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",668"https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example",669"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint",670"https://docs.ray.io/en/master/tune/examples/includes/xgboost_dynamic_resources_example.html#xgboost-dynamic-resources-example",671"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningCheckpoint.get_model.html#ray-train-lightning-lightningcheckpoint-get-model",672"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint"673],674"document_ids": [67513749,67611850,6775523,67812069,6795367,68011764,68111859682],683"answer": "To create a model Checkpoint from a model in memory, you can use the `Checkpoint.from_model()` method. This method creates a Checkpoint object that stores a Torch model.",684"llm": "gpt-3.5-turbo"685},686{687"question": "what is Deployment in Ray Serve?",688"sources": [689"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",690"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",691"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",692"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",693"https://docs.ray.io/en/master/serve/tutorials/java.html#converting-to-a-ray-serve-deployment",694"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment",695"https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments"696],697"document_ids": [69819150,69918017,70019794,70118377,70219560,70319007,70418021705],706"answer": "Deployment in Ray Serve is a central concept that contains business logic or an ML model to handle incoming requests. It can be scaled up to run across a Ray cluster and consists of a number of replicas, which are individual copies of the class or function started in separate Ray Actors. The number of replicas can be scaled up or down to match the incoming request load. Deployments are defined as a single class with options such as the number of replicas, and requests to a deployment are load balanced across its replicas.",707"llm": "gpt-3.5-turbo"708},709{710"question": "What is user config in Ray Serve? how do I use it?",711"sources": [712"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",713"https://docs.ray.io/en/master/serve/api/index.html#serve-config",714"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",715"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#dynamically-changing-parameters-without-restarting-your-replicas-user-config",716"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#adding-deleting-and-updating-applications",717"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",718"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"719],720"document_ids": [72118590,72218789,72318725,72418226,72518355,72618775,7278057728],729"answer": "User config in Ray Serve is a configuration that can be set when creating a deployment in Ray Serve. It allows users to customize the behavior of their deployments. The user_config is passed as an argument to the deployment's __init__ method and can also be updated by modifying the Serve config file and reapplying it to the Ray cluster. To use user config, you can set it in the decorator or the Serve config file when creating a deployment.",730"llm": "gpt-3.5-turbo"731},732{733"question": "What is the difference between PACK and SPREAD strategy?",734"sources": [735"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",736"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",737"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",738"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",739"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",740"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",741"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy"742],743"document_ids": [74415506,74515077,7466490,7476861,7482486,7496860,7506856751],752"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.",753"llm": "gpt-3.5-turbo"754},755{756"question": "What\u2019s the best way to run ray across multiple machines?",757"sources": [758"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",759"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",760"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",761"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",762"https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure",763"https://docs.ray.io/en/master/ray-core/configure.html#code-search-path",764"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray"765],766"document_ids": [7677637,76819762,7696309,7707590,77113125,7726117,7735679774],775"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 autoscale according to the resources requested by applications running on the cluster. Each user should have their own Ray cluster to isolate their workloads.",776"llm": "gpt-3.5-turbo"777},778{779"question": "how do I specify ScalingConfig for a Tuner run?",780"sources": [781"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",782"https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",783"https://docs.ray.io/en/master/tune/examples/tune-pytorch-lightning.html#training-with-gpus",784"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",785"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.__init__.html#ray-tune-tuner-init",786"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",787"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#tuning-the-configuration-parameters"788],789"document_ids": [79012143,7911828,7924284,79312144,7942659,7953181,7963558797],798"answer": "To specify ScalingConfig for a Tuner run, you need to define a ScalingConfig object with the desired parameters. In the given context, the example code shows how to define a ScalingConfig object with the number of workers, GPU usage, and resources per worker. This ScalingConfig object can then be passed into the Tuner constructor along with other configurations like TuneConfig and RunConfig.",799"llm": "gpt-3.5-turbo"800},801{802"question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",803"sources": [804"https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",805"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_numpy_refs.html#ray-data-dataset-to-numpy-refs",806"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",807"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_numpy_refs.html#ray-data-dataset-to-numpy-refs",808"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",809"https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#object-fault-tolerance",810"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"811],812"document_ids": [8136677,81410213,81510012,81610214,8176681,8186087,8199295820],821"answer": "To utilize the 'zero-copy' feature that Ray provides for numpy, you can use the `to_numpy_refs()` method of the Ray Dataset object. This method converts the Dataset into a distributed set of NumPy ndarrays or a dictionary of NumPy ndarrays. However, note that this function induces a copy of the data. For zero-copy access to the underlying data, you can consider using `to_arrow()` or `get_internal_block_refs()` methods instead.",822"llm": "gpt-3.5-turbo"823},824{825"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?",826"sources": [827"https://docs.ray.io/en/master/ray-core/patterns/concurrent-operations-async-actor.html#pattern-using-asyncio-to-run-actor-methods-concurrently",828"https://docs.ray.io/en/master/data/examples/random-access.html#architecture",829"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",830"https://docs.ray.io/en/master/data/examples/random-access.html#performance",831"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",832"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",833"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use"834],835"document_ids": [8366976,83710705,8387068,83910708,84010980,8417367,84210810843],844"answer": "It would be advised to create 'key pools' where an actor can hold 1=many keys rather than spinning up 1=1 actors for each key.",845"llm": "gpt-3.5-turbo"846},847{848"question": "How to find the best checkpoint from the trial directory?",849"sources": [850"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",851"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",852"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",853"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",854"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",855"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",856"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis"857],858"document_ids": [8592195,8602884,8611910,8621909,8634917,8644920,8651966866],867"answer": "To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint()` function from `ray.tune.ExperimentAnalysis`. This function takes a trial and optional metric and mode parameters, and returns the path of the best persistent checkpoint associated with the trial. If you didn't pass the metric and mode parameters to `tune.run()`, you can use the `best_checkpoint` property or the `get_last_checkpoint()` function instead.",868"llm": "gpt-3.5-turbo"869},870{871"question": "what are the advantage and disadvantage of using singleton Actor ?",872"sources": [873"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#how-to-improve-the-gpu-utilization-rate",874"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",875"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",876"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",877"https://docs.ray.io/en/master/ray-core/patterns/concurrent-operations-async-actor.html#pattern-using-asyncio-to-run-actor-methods-concurrently",878"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",879"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#default-concurrency-group"880],881"document_ids": [8827423,88319760,88415751,8856007,8866976,8877017,8886305889],890"answer": "The advantage of using a singleton actor is that it allows for efficient reuse of model initialization for multiple tasks, resulting in more time spent on the actual workload. However, if you don't need the stateful part of an actor, it is generally better to use tasks.",891"llm": "gpt-3.5-turbo"892},893{894"question": "what are the advantages of using a named actor?",895"sources": [896"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",897"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#how-to-improve-the-gpu-utilization-rate",898"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",899"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",900"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",901"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",902"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources"903],904"document_ids": [9056277,9067423,9076594,9086287,90919760,9106007,91115751912],913"answer": "The advantages of using a named actor are:\n\n1. Easy retrieval: A named actor can be retrieved from any job in the Ray cluster, even if you cannot directly pass the actor handle to the task that needs it or if it was launched by another driver.\n\n2. Garbage collection: The named actor will still be garbage-collected if no handles to it exist.\n\n3. Efficient reuse: Actor-based approach allows for efficient reuse of model initialization for many tasks, resulting in more time spent on the actual workload.\n\n4. Flexibility in scheduling: Using named actors allows for more flexible scheduling of tasks, enabling both the needed actors and other defined tasks to run concurrently.\n\n5. Stateful functionality: If you require the stateful part of an actor, using a named actor is beneficial. However, if you don't need the stateful part, using tasks may be a better option.",914"llm": "gpt-3.5-turbo"915},916{917"question": "How do I read a text file stored on S3 using Ray Data?",918"sources": [919"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",920"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",921"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",922"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",923"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",924"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",925"https://docs.ray.io/en/master/data/api/doc/ray.data.read_json.html#ray-data-read-json"926],927"document_ids": [92810107,9299376,93012254,93110518,93210582,9339651,9349784935],936"answer": "To read a text file stored on S3 using Ray Data, you can use the `ray.data.read_text()` function. Here's an example:\n\n```python\nimport ray\n\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\nds.show(3)\n```\n\nThis will create a dataset (`ds`) with rows representing each line of text in the file.",937"llm": "gpt-3.5-turbo"938},939{940"question": "how do I get the IP of the head node for my Ray cluster?",941"sources": [942"https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",943"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",944"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",945"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",946"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",947"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",948"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes"949],950"document_ids": [9517630,9528689,9533192,954675,9557574,9568809,9578691958],959"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\" with the cluster configuration file. Alternatively, you can start the head node using the command \"ray start --head --port=6379\" and the Ray cluster address will be printed out. Another option is to use the \"ray.nodes\" API in a Python script to programmatically discover the endpoints of the nodes in your Ray cluster.",960"llm": "gpt-3.5-turbo"961},962{963"question": "How to write a map function that returns a list of object for `map_batches`?",964"sources": [965"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",966"https://docs.ray.io/en/master/data/api/doc/ray.data.range_tensor.html#ray-data-range-tensor",967"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",968"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",969"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",970"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",971"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a"972],973"document_ids": [97410647,9759666,9769477,9779478,97810347,9799489,98013822981],982"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 processes it to generate a list of objects. Here's an example:\n\n```python\ndef process_batch(batch):\n # Process the batch and generate a list of objects\n objects = []\n for output in batch:\n obj = {\n \"score\": output[0][\"score\"],\n \"label\": output[0][\"label\"]\n }\n objects.append(obj)\n return objects\n```\n\nYou can then use this function with the `map_batches` API to apply it to the dataset:\n\n```python\nprocessed_dataset = dataset.map_batches(process_batch)\n```\n\nThis will apply the `process_batch` function to each batch of data in the dataset and return a new dataset containing the list of objects generated for each batch.",983"llm": "gpt-3.5-turbo"984},985{986"question": "How do I set a maximum episode length when training with Rllib?",987"sources": [988"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",989"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",990"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",991"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",992"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",993"https://docs.ray.io/en/master/rllib/rllib-replay-buffers.html#advanced-usage",994"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner.Learner.register_optimizer.html#ray-rllib-core-learner-learner-learner-register-optimizer"995],996"document_ids": [99714206,99815531,99917694,100015009,100114354,100214036,1003175691004],1005"answer": "To set a maximum episode length when training with Rllib, you can use the parameter \"evaluation_duration\" and set it to the desired number of timesteps.",1006"llm": "gpt-3.5-turbo"1007},1008{1009"question": "how do I make a Ray Tune trial retry on failures?",1010"sources": [1011"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1012"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1013"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune",1014"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1015"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",1016"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-on-trial-failures",1017"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.MedianStoppingRule.html#ray-tune-schedulers-medianstoppingrule"1018],1019"document_ids": [10203074,10212988,102215693,10231507,10242555,10252908,102623081027],1028"answer": "To make a Ray Tune trial retry on failures, you can enable automatic retry by configuring the FailureConfig. Ray Tune provides fault tolerance and recovery at the experiment trial level. If a trial fails, Ray Tune will automatically restart it from the latest checkpointed state.",1029"llm": "gpt-3.5-turbo"1030},1031{1032"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?",1033"sources": [1034"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",1035"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",1036"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",1037"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1038"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1039"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",1040"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#how-to-write-fault-tolerant-ray-applications"1041],1042"document_ids": [10437037,10447036,10456106,10466158,10476290,10487021,104959641050],1051"answer": "No, if the supervisor dies or the driver fails, the worker actors will be automatically terminated due to actor reference counting.",1052"llm": "gpt-3.5-turbo"1053},1054{1055"question": "How do I read a large text file in S3 with Ray?",1056"sources": [1057"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1058"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1059"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",1060"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",1061"https://docs.ray.io/en/master/data/loading-data.html#handling-compressed-files",1062"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",1063"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text"1064],1065"document_ids": [106610107,10679376,106810518,106912254,10709053,107110582,107293751073],1074"answer": "To read a large text file in S3 with Ray, you can use the `ray.data.read_text()` function. Here is an example:\n\n```python\nimport ray\n\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\n```\n\nThis will create a dataset from the lines stored in the text file.",1075"llm": "gpt-3.5-turbo"1076},1077{1078"question": "how do I get a ray dataset from pandas",1079"sources": [1080"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-pandas",1081"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1082"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1083"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",1084"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",1085"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-rows",1086"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a"1087],1088"document_ids": [10899128,10909295,10919055,10929247,10939841,10949192,1095134261096],1097"answer": "To get a Ray dataset from pandas, you can use the `ray.data.from_items()` function and pass in a list of dictionaries, where each dictionary represents a row of data.",1098"llm": "gpt-3.5-turbo"1099},1100{1101"question": "can you give me an example of using `ray.data.map` ?",1102"sources": [1103"https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",1104"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#preprocessing",1105"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1106"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1107"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_arrow_refs.html#ray-data-dataset-to-arrow-refs",1108"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1109"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"1110],1111"document_ids": [111211050,111310680,11149300,11159055,111610124,11179731,111892951119],1120"answer": "An example of using `ray.data.map` is shown in the context. In this example, a Ray Dataset `ds` is created from in-memory Numpy arrays. The `map` method is then used to preprocess the images in the dataset. The `map` method is lazy, meaning it won't perform execution until the results are consumed. The transformed dataset is assigned to `transformed_ds`.",1121"llm": "gpt-3.5-turbo"1122},1123{1124"question": "can you give me an example of using `ray.data.map` , with a callable class as input?",1125"sources": [1126"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1127"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1128"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",1129"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1130"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1131"https://docs.ray.io/en/master/tune/api/doc/ray.tune.with_parameters.html#ray-tune-with-parameters",1132"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_arrow_refs.html#ray-data-dataset-to-arrow-refs"1133],1134"document_ids": [113510416,11369055,113713816,113813421,11399300,11402141,1141101241142],1143"answer": "Here is an example of using `ray.data.map` with a callable class as input:\n\n```python\nimport ray.data\n\nclass MyCallableClass:\n def __call__(self, row):\n # Perform some transformation on the row\n return row\n\n# Create a Ray Dataset from Python objects\nds = ray.data.from_items([\n {\"food\": \"spam\", \"price\": 9.34},\n {\"food\": \"ham\", \"price\": 5.37},\n {\"food\": \"eggs\", \"price\": 0.94}\n])\n\n# Apply the callable class to each row of the dataset\nmapped_ds = ds.map(MyCallableClass())\n\nprint(mapped_ds)\n```\n\nThis example creates a Ray Dataset from a list of dictionaries and applies the `MyCallableClass` to each row of the dataset using the `map` function. The `MyCallableClass` is a callable class that performs some transformation on each row. The resulting mapped dataset is then printed.",1144"llm": "gpt-3.5-turbo"1145},1146{1147"question": "How to set memory limit for each trial in Ray Tuner?",1148"sources": [1149"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1150"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",1151"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",1152"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",1153"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",1154"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1155"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria"1156],1157"document_ids": [11583073,11593165,11603180,11613170,11626937,11631507,116428951165],1166"answer": "To set the memory limit for each trial in Ray Tuner, you can use the `tune.with_resources` function and specify the memory resource request using a dictionary, ScalingConfig, or PlacementGroupFactory object. For example, if you want to set a memory limit of 4GB for each trial, you can use `tune.with_resources(trainable, {\"memory\": 4})`.",1167"llm": "gpt-3.5-turbo"1168},1169{1170"question": "how do I get the actor id of an actor",1171"sources": [1172"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",1173"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",1174"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor",1175"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#add-metadata-to-structured-logs",1176"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1177"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1178"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"1179],1180"document_ids": [11816487,11826664,1183928,1184730,1185655,1186654,11876521188],1189"answer": "To get the actor ID of an actor, you can use the `ray.runtime_context.RuntimeContext.get_actor_id()` method.",1190"llm": "gpt-3.5-turbo"1191},1192{1193"question": "can ray.init() can check if ray is all-ready initiated ?",1194"sources": [1195"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",1196"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1197"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",1198"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1199"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1200"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",1201"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users"1202],1203"document_ids": [12046485,12056514,12065681,12075684,12085682,12097052,121057021211],1212"answer": "Yes, the function `ray.is_initialized()` can be used to check if Ray has already been initiated. It returns `True` if `ray.init()` has been called and `False` otherwise.",1213"llm": "gpt-3.5-turbo"1214},1215{1216"question": "What does the `compute=actor` argument do within `ray.data.map_batches` ?",1217"sources": [1218"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1219"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1220"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",1221"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1222"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1223"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1224"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"1225],1226"document_ids": [12279105,122810966,122913763,12309109,123110688,123210648,123393031234],1235"answer": "The `compute=actor` argument within `ray.data.map_batches` is used to specify the ActorPoolStrategy with the number of parallel actors when using a class-based user-defined function (UDF).",1236"llm": "gpt-3.5-turbo"1237},1238{1239"question": "how do I use wandb logger with accelerateTrainer?",1240"sources": [1241"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1242"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",1243"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1244"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",1245"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1246"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1247"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration"1248],1249"document_ids": [12503218,12513215,12523262,12533217,125412742,125512746,125615301257],1258"answer": "To use the Wandb logger with accelerateTrainer, you can use the WandbLoggerCallback and the setup_wandb function. Make sure you are logged in to wandb on all machines you are running your training on. Import the necessary libraries and use the Wandb API like you normally would, such as using wandb.log() to log your training process.",1259"llm": "gpt-3.5-turbo"1260},1261{1262"question": "What will be implicitly put into object store?",1263"sources": [1264"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1265"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1266"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1267"https://docs.ray.io/en/master/ray-core/objects.html#objects",1268"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1269"https://docs.ray.io/en/master/data/data-internals.html#object-store-memory",1270"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema"1271],1272"document_ids": [12736768,12746770,127519815,12766132,127719816,12789242,1279188911280],1281"answer": "Objects created using `ray.put` and the return values from remote functions will be implicitly put into the object store.",1282"llm": "gpt-3.5-turbo"1283},1284{1285"question": "How do I kill or cancel a ray task that I already started?",1286"sources": [1287"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1288"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",1289"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1290"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1291"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",1292"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1293"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"1294],1295"document_ids": [12966081,12976028,12986620,12996624,130015745,13015994,1302197541303],1304"answer": "To kill or cancel a Ray task that you have already started, you can call `ray.cancel()` on the returned ObjectRef of the task. By default, this will send a KeyboardInterrupt to the task's worker if it is mid-execution. If you pass `force=True` to `ray.cancel()`, it will force-exit the worker. Note that Ray will not automatically retry tasks that have been cancelled.",1305"llm": "gpt-3.5-turbo"1306},1307{1308"question": "how to send extra arguments in dataset.map_batches function?",1309"sources": [1310"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1311"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1312"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#data-loading",1313"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-execution-statistics",1314"https://docs.ray.io/en/master/data/api/doc/ray.data.range_tensor.html#ray-data-range-tensor",1315"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1316"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches"1317],1318"document_ids": [13199477,13209478,132113613,13229252,13239666,132410647,132598621326],1327"answer": "To send extra arguments in the `map_batches` function of the `ray.data.Dataset`, you can pass them as keyword arguments (`**kwargs`) to the `map_batches` function. For example:\n\n```python\ndataset.map_batches(fn, **kwargs)\n```\n\nYou can include the extra arguments in the `fn_kwargs` parameter when calling the `map_batches` function. For example:\n\n```python\ndataset.map_batches(fn, fn_kwargs={\"arg1\": value1, \"arg2\": value2})\n```\n\nThese extra arguments can then be accessed within the `fn` function.",1328"llm": "gpt-3.5-turbo"1329},1330{1331"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?",1332"sources": [1333"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",1334"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",1335"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance",1336"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",1337"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",1338"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",1339"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance"1340],1341"document_ids": [13428269,13437650,13447867,13458266,134618634,13477907,1348186351349],1350"answer": "Ray GCS stores the history of jobs run on a KubeRay cluster in its internal key-value store. However, to ensure fault tolerance and high availability, Ray can also write this metadata to an external Redis database. The format of the database is key-value.",1351"llm": "gpt-3.5-turbo"1352},1353{1354"question": "How to resolve ValueError: The actor ImplicitFunc is too large?",1355"sources": [1356"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",1357"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",1358"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1359"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1360"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#verify-and-save-results",1361"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.submit.html#ray-util-actorpool-submit",1362"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map_unordered.html#ray-util-actorpool-map-unordered"1363],1364"document_ids": [13658272,136613187,136710966,1368656,136910699,13706483,137166711372],1373"answer": "To resolve the ValueError: The actor ImplicitFunc is too large, you can try reducing the batch size to a value that is at most 5.",1374"llm": "gpt-3.5-turbo"1375},1376{1377"question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",1378"sources": [1379"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",1380"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",1381"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#data-loading-and-preprocessing",1382"https://docs.ray.io/en/master/train/examples/pytorch/torch_fashion_mnist_example.html#running-distributed-training-of-a-pytorch-model-on-fashion-mnist-with-ray-train",1383"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#how-it-works",1384"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1385"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#updating-your-training-function"1386],1387"document_ids": [138813170,138913139,139011115,139112840,139213606,1393320,1394111071395],1396"answer": "To distribute training for your custom neural net written using Keras in Databricks with Ray, you need to make a few changes to your code:\n\n1. Import Ray Train and Ray AIR Session:\n ```python\n import ray.train as train\n ```\n\n2. Prepare your data loader for distributed training:\n ```python\n train_dataloader = ray.train.torch.prepare_data_loader(train_dataloader)\n test_dataloader = ray.train.torch.prepare_data_loader(test_dataloader)\n ```\n\n3. Create your model:\n ```python\n model = NeuralNetwork()\n ```\n\n4. Set up your trainer and start training:\n ```python\n trainer = TorchTrainer(train_func_distributed, scaling_config=ScalingConfig(num_workers=4, use_gpu=use_gpu))\n results = trainer.fit()\n ```\n\nNote that this example assumes you are using PyTorch. If you are using TensorFlow, you may need to convert your Ray Data dataset shard to a TensorFlow dataset in your training function.",1397"llm": "gpt-3.5-turbo"1398},1399{1400"question": "how to use ray.put and ray,get?",1401"sources": [1402"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1403"https://docs.ray.io/en/master/ray-core/api/core.html#objects",1404"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",1405"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",1406"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",1407"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",1408"https://docs.ray.io/en/master/ray-core/examples/automl_for_time_series.html#walkthrough"1409],1410"document_ids": [14115703,14126317,14136567,14147052,14157370,14167092,141774421418],1419"answer": "To use `ray.put()`, you can store an object in the object store and it will return its ID. This ID can be used to pass the object as an argument to any remote function or method call.\n\nTo use `ray.get()`, you can retrieve an object or a list of objects from the object ID or list of object IDs. This is a synchronous operation.\n\nIt is recommended to use `ray.put()` to create a copy of the object in the object store instead of repeatedly passing in large arguments to remote task or method calls.\n\nYou can wait as long as possible before calling `ray.get()`, or design your program to avoid calling `ray.get()` at all.\n\nThe Ray API also includes other calls, but `ray.put()` and `ray.get()` are powerful and commonly used.",1420"llm": "gpt-3.5-turbo"1421},1422{1423"question": "how do I use Ray Data to pre process many files?",1424"sources": [1425"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",1426"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#data-exploration-and-cleaning",1427"https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",1428"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",1429"https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors",1430"https://docs.ray.io/en/master/train/user-guides.html#ray-train-user-guides",1431"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset"1432],1433"document_ids": [143412217,143510930,14369178,14379164,14389266,143910758,1440122751441],1442"answer": "To preprocess many files using Ray Data, you can incorporate Ray Data into your Ray Train script and customize your data ingestion pipeline. Ray Data is designed for deep learning applications and allows for efficient streaming of large datasets. You can use Ray Data preprocessors for tabular data and existing Ray Data operations for unstructured data. If your preprocessed dataset can fit in Ray object store memory, you can call the materialize() method to compute and store the preprocessed dataset in the object store, reducing the need to re-run preprocessing operations.",1443"llm": "gpt-3.5-turbo"1444},1445{1446"question": "can\u2019t pickle SSLContext objects",1447"sources": [1448"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1449"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1450"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1451"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1452"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",1453"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#tls-verification",1454"https://docs.ray.io/en/master/ray-core/objects/serialization.html#customized-serialization"1455],1456"document_ids": [14576694,14586693,14596692,14606697,14616674,14628854,146366831464],1465"answer": "The context provided does not contain information about pickling SSLContext objects.",1466"llm": "gpt-3.5-turbo"1467},1468{1469"question": "How do I install CRDs in Kuberay?",1470"sources": [1471"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",1472"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#manually-setting-up-nginx-ingress-on-kind",1473"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",1474"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#id2",1475"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",1476"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",1477"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay"1478],1479"document_ids": [14807733,14818228,14828212,14838222,14847649,14858281,148678511487],1488"answer": "To install CRDs in KubeRay, follow these steps:\n\n1. Add the KubeRay Helm chart repository:\n ```\n helm repo add kuberay https://ray-project.github.io/kuberay-helm/\n ```\n\n2. Install both CRDs and the KubeRay operator (version 0.6.0):\n ```\n helm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n ```",1489"llm": "gpt-3.5-turbo"1490},1491{1492"question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",1493"sources": [1494"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",1495"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1496"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",1497"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.sample_batch.SampleBatch.concat.html#ray-rllib-policy-sample-batch-samplebatch-concat",1498"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.window.html#ray-data-dataset-window",1499"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",1500"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment"1501],1502"document_ids": [1503195,150410962,1505196,150617108,15079964,150810771,1509184091510],1511"answer": "The function for Ray data batch inference is named as `__call__()` because it is a special method in Python classes that allows instances of the class to be called as if they were functions. In this case, the `__call__()` method is used to perform batch inference on a model.",1512"llm": "gpt-3.5-turbo"1513},1514{1515"question": "How to disconnnect ray client?",1516"sources": [1517"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1518"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",1519"https://docs.ray.io/en/master/ray-core/cross-language.html#cross-language-data-serialization",1520"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",1521"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",1522"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",1523"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client"1524],1525"document_ids": [15268885,15278897,15285942,152919493,15308901,153118438,153288821533],1534"answer": "To disconnect the Ray client, you can call the `disconnect()` method explicitly.",1535"llm": "gpt-3.5-turbo"1536},1537{1538"question": "how to submit job with python with local files?",1539"sources": [1540"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1541"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job",1542"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",1543"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1544"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job",1545"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",1546"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job"1547],1548"document_ids": [15498793,15508989,15518832,15528797,15535781,15548837,155588341556],1557"answer": "To submit a job with Python using local files, you can use the Ray APIs. Here is an example script:\n\n```python\nimport ray\n\n@ray.remote\ndef hello_world():\n return \"hello world\"\n```\n\nTo submit the job, you can use the `ray job submit` command. Make sure to specify the path to the working directory using the `--working-dir` argument. Here is an example command:\n\n```\n$ ray job submit --working-dir your_working_directory -- python script.py\n```\n\nReplace `your_working_directory` with the path to your working directory.",1558"llm": "gpt-3.5-turbo"1559},1560{1561"question": "How do I do inference from a model trained by Ray tune.fit()?",1562"sources": [1563"https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",1564"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#putting-it-together",1565"https://docs.ray.io/en/master/tune/examples/ml-frameworks.html#examples-using-ray-tune-with-ml-frameworks",1566"https://docs.ray.io/en/master/tune/examples/index.html#examples-using-ray-tune-with-ml-frameworks",1567"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-out-of-ray-tune",1568"https://docs.ray.io/en/master/ray-air/examples/batch_tuning.html#run-batch-training-on-ray-tune-a-class-anchor-id-run-tune-search-a",1569"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning"1570],1571"document_ids": [15721336,15734000,15743731,15755015,15763021,157713726,15781671579],1580"answer": "To do inference from a model trained by Ray tune.fit(), you need to load the trained model and use it to make predictions on new data.",1581"llm": "gpt-3.5-turbo"1582},1583{1584"question": "is there a way to load and run inference without using pytorch or tensorflow directly?",1585"sources": [1586"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1587"https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",1588"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",1589"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",1590"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1591"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1592"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch"1593],1594"document_ids": [15959354,15969026,159710771,159810718,15999356,16009302,160193551602],1603"answer": "Yes, you can load and run inference without using PyTorch or TensorFlow directly by using Ray Datasets. Ray Datasets allow you to perform scalable offline batch inference with Torch models by mapping a pre-trained model over your data. You can create a Ray Dataset from various sources and file formats, and perform inference by preprocessing the data and running the model inference.",1604"llm": "gpt-3.5-turbo"1605},1606{1607"question": "what does ray do",1608"sources": [1609"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1610"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",1611"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",1612"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1613"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",1614"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",1615"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart"1616],1617"document_ids": [16186514,16197283,16208350,16219160,16227658,16232420,16243591625],1626"answer": "Ray is a software platform that offers tutorials, examples, cloud VM support, data overview, Kubernetes integration, and a tuner module called ray.tune.Tuner.",1627"llm": "gpt-3.5-turbo"1628},1629{1630"question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",1631"sources": [1632"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",1633"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",1634"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1635"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus",1636"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",1637"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",1638"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources"1639],1640"document_ids": [16411394,164218070,164315051,16443638,16456753,164615497,1647150691648],1649"answer": "If you specify a fractional GPU in the resource spec, you can allocate a fraction of the GPU memory to a task or actor. However, you need to ensure that your model still fits within the allocated fractional memory.",1650"llm": "gpt-3.5-turbo"1651},1652{1653"question": "how to pickle a variable defined in actor\u2019s init method",1654"sources": [1655"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1656"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",1657"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",1658"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",1659"https://docs.ray.io/en/master/rllib/actors.html#actors",1660"https://docs.ray.io/en/master/ray-core/actors.html#actors",1661"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"1662],1663"document_ids": [16647017,16655761,16667020,166719703,166815721,16695943,167062781671],1672"answer": "To pickle a variable defined in an actor's `__init__` method, you can use the actor's instance variables to hold the global state. Pass the actor handle to places where the state needs to be modified or accessed. Note that using class variables to manage state between instances of the same class is not supported. Each actor instance is instantiated in its own process, so each actor will have its own copy of the class variables.",1673"llm": "gpt-3.5-turbo"1674},1675{1676"question": "how do I do an all_reduce operation among a list of actors",1677"sources": [1678"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",1679"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",1680"https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",1681"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1682"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1683"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",1684"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list"1685],1686"document_ids": [168719362,16881071,1689886,1690652,16919105,16927367,16939071694],1695"answer": "To perform an all_reduce operation among a list of actors, you can use the `col.allreduce()` function. In the given context, the `do_allreduce()` method calls `col.allreduce(self.buffer)` to perform the all_reduce operation. The `buffer` variable is the data that will be reduced.",1696"llm": "gpt-3.5-turbo"1697},1698{1699"question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",1700"sources": [1701"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",1702"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",1703"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",1704"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",1705"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",1706"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1707"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy"1708],1709"document_ids": [17106840,17116839,17126834,17136491,17146821,17156857,171668581717],1718"answer": "If we specify a bundle with `{\"CPU\":0}` in the PlacementGroup, it means that we are reserving a bundle with 0 CPUs. This bundle will not consume any CPU resources and will not be created in the cluster.",1719"llm": "gpt-3.5-turbo"1720},1721{1722"question": "How to cancel job from UI?",1723"sources": [1724"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1725"https://docs.ray.io/en/master/workflows/comparison.html#trip-booking",1726"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",1727"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",1728"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",1729"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1730"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.delete_job.html#ray-job-submission-jobsubmissionclient-delete-job"1731],1732"document_ids": [17336081,173413052,173513099,17368806,17376028,17386620,173989561740],1741"answer": "To cancel a job from the UI, you can call the `ray.cancel()` function on the ObjectRef returned by the task. By default, this will send a KeyboardInterrupt to the task's worker. If you want to force-exit the worker, you can pass `force=True` to `ray.cancel()`.",1742"llm": "gpt-3.5-turbo"1743},1744{1745"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>`",1746"sources": [1747"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",1748"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",1749"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",1750"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",1751"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",1752"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#development-workflow-with-serve-run",1753"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm"1754],1755"document_ids": [175618383,175718384,175818442,175918791,17608810,176118351,1762183771763],1764"answer": "To get your project files on the cluster when using Ray Serve, you can use the `serve deploy` command with the `--address` option. Specify the remote Ray cluster's dashboard agent address in the format `[RAY_CLUSTER_URI]:[DASHBOARD_AGENT_PORT]`. For example:\n\n```\nserve deploy config_file.yaml -a http://127.0.0.1:52365\n```\n\nAlternatively, you can use the `serve run` command with the `--address` and `--working-dir` options to connect to the remote cluster via Ray Client, upload the specified working directory, and run your Serve application. The working directory should contain `local_dev.py` so that it can be uploaded and imported by Ray Serve. For example:\n\n```\nserve run --address=ray://<head-node-ip-address>:10001 --working-dir=\"./project/src\" local_dev:graph\n```\n\nAfter deploying, you can use `serve config` to fetch the current configurations and `serve status` to check the status of the application and deployments.",1765"llm": "gpt-3.5-turbo"1766},1767{1768"question": "how do i install ray nightly wheel",1769"sources": [1770"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",1771"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",1772"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",1773"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",1774"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",1775"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",1776"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only"1777],1778"document_ids": [177995,178019543,1781405,178219553,1783100,1784410,178511981786],1787"answer": "To install the nightly Ray wheel, use the following pip command:\n\n```\npip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"\n```\n\nReplace `LINK_TO_WHEEL` with the actual link to the wheel file.",1788"llm": "gpt-3.5-turbo"1789},1790{1791"question": "how do i install the latest ray nightly wheel?",1792"sources": [1793"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",1794"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",1795"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",1796"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",1797"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",1798"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",1799"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit"1800],1801"document_ids": [180219543,180395,1804405,18051198,180619553,1807100,18084101809],1810"answer": "To install the latest Ray nightly wheel, use the following pip command:\n\npip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"\n\nReplace \"LINK_TO_WHEEL\" with the actual link to the nightly Ray wheel you want to install.",1811"llm": "gpt-3.5-turbo"1812},1813{1814"question": "how can I write unit tests for Ray code?",1815"sources": [1816"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",1817"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1818"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",1819"https://docs.ray.io/en/master/ray-core/cross-language.html#cross-language-data-serialization",1820"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#remove-placement-groups-free-reserved-resources",1821"https://docs.ray.io/en/master/tune/api/doc/ray.tune.utils.diagnose_serialization.html#ray-tune-utils-diagnose-serialization",1822"https://docs.ray.io/en/master/ray-core/cross-language.html#python-calling-java"1823],1824"document_ids": [18257308,18266514,18277305,18285940,18296865,18302559,183159191832],1833"answer": "To write unit tests for Ray code, you can follow these tips:\n\n1. Start a new Ray cluster for each test.\n2. Use `ray.init(num_cpus=4, num_gpus=0)` in the `setUp` method of your test class.\n3. Fix the resource quantity with `ray.init(num_cpus=...)` to ensure consistent testing.\n4. If possible, share the Ray cluster across tests.\n5. Create a mini-cluster with `ray.cluster_utils.Cluster` for testing.\n6. Be cautious when running tests in parallel.\n\nAdditionally, you can use assertions like `Assert.assertEquals(res.get(), o)` to validate the results of Ray tasks.\n\nNote: The provided context contains various snippets related to Ray code and testing practices.",1834"llm": "gpt-3.5-turbo"1835},1836{1837"question": "How I stop Ray from spamming lots of Info updates on stdout?",1838"sources": [1839"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",1840"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#disable-logging-to-the-driver",1841"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",1842"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",1843"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",1844"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",1845"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests"1846],1847"document_ids": [18488804,1849714,1850715,18515709,18527785,1853575,1854184381855],1856"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()`.",1857"llm": "gpt-3.5-turbo"1858},1859{1860"question": "how to deploy stable diffusion 2.1 with Ray Serve?",1861"sources": [1862"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",1863"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",1864"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#serve-a-stablediffusion-text-to-image-model-on-kubernetes",1865"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",1866"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",1867"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",1868"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-7-submit-a-request-to-the-ray-serve-application"1869],1870"document_ids": [18718242,187219367,18738239,187419150,187519375,18767676,187780671878],1879"answer": "To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install the necessary dependencies: pip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers.\n\n2. Download the Ray Serve configuration file: curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml.\n\n3. Save the Serve code to a file named stable_diffusion.py.\n\n4. Use the @serve.deployment decorator to define the deployment configuration for StableDiffusionV2 class.\n\n5. Test your Ray Serve script locally or in a RayCluster before deploying it to a RayService.\n\n6. Prepare a sample image file and update the image path in mobilenet_req.py.\n\n7. Send a request to the Ray Serve application using python3 mobilenet_req.py.",1880"llm": "gpt-3.5-turbo"1881},1882{1883"question": "what is actor_handle?",1884"sources": [1885"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",1886"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",1887"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",1888"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",1889"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1890"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1891"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill"1892],1893"document_ids": [18946581,189519737,189615736,18975975,18986594,18996281,190065821901],1902"answer": "An actor handle is a reference to an actor that allows for communication and interaction with the actor. It can be passed into other tasks or remote functions to perform operations on the actor.",1903"llm": "gpt-3.5-turbo"1904},1905{1906"question": "how to kill a r detached actors?",1907"sources": [1908"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1909"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",1910"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",1911"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1912"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",1913"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",1914"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle"1915],1916"document_ids": [19176293,19186566,19196256,19206160,19216580,19222847,192362611924],1925"answer": "To kill a detached actor in C++, you can use `ray.kill(actor)`.\n\nTo kill an actor immediately in Python, you can call `ray.kill(actor)`.\n\nTo kill an actor in Java, you can use `actorHandle.kill()`.\n\nDetached actors in Ray will continue to be automatically restarted until the maximum restarts is exceeded, the actor is destroyed, or the Ray cluster is destroyed.\n\nYou can kill an actor forcefully using `ray.kill(actor)`. This will interrupt any running tasks on the actor and atexit handlers will not be run.\n\nIn Ray Tune, you can kill a Ray actor by calling `ray.kill(actor)` or removing all references to it and waiting for garbage collection.\n\nYou can use the `ray list actors --detail` command to see the death cause of dead actors.",1926"llm": "gpt-3.5-turbo"1927},1928{1929"question": "How to force upgrade the pip package in the runtime environment if an old version exists?",1930"sources": [1931"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",1932"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",1933"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",1934"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",1935"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",1936"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",1937"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"1938],1939"document_ids": [19406396,19415798,19425895,19435905,19445903,19455897,194658161947],1948"answer": "To force upgrade the pip package in the runtime environment, you can specify the desired version in the `pip_version` parameter of the `RuntimeEnv` function. For example, you can set `pip_version` to \"==22.0.2\" to upgrade to that specific version.",1949"llm": "gpt-3.5-turbo"1950},1951{1952"question": "How do I do global shuffle with Ray?",1953"sources": [1954"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",1955"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",1956"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",1957"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",1958"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",1959"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",1960"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle"1961],1962"document_ids": [196312253,19649116,19659152,19669151,19679215,19689883,196991481970],1971"answer": "To perform a global shuffle with Ray, you can use the `random_shuffle()` method provided by the `ray.data.Dataset` class. This method randomly shuffles the rows of the dataset. However, note that this method can be slow. For better performance, you can try iterating over batches with shuffling. Additionally, Ray Data provides an alternative shuffle implementation called push-based shuffle, which is designed for improving large-scale performance. You can try this out if your dataset has more than 1000 blocks or is larger than 1 TB in size.",1972"llm": "gpt-3.5-turbo"1973},1974{1975"question": "How to find namespace of an Actor?",1976"sources": [1977"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1978"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1979"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1980"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",1981"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1982"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace",1983"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces"1984],1985"document_ids": [19866595,19876252,19886277,19896259,19906283,19916444,199262341993],1994"answer": "To find the namespace of an Actor, you can specify the namespace while creating the actor. The created actor will belong to the specified namespace. Alternatively, you can retrieve the actor from any job in the Ray cluster using its unique name within the namespace. If a namespace is not specified, the job will be placed in an anonymous namespace and will not have access to actors in other namespaces.",1995"llm": "gpt-3.5-turbo"1996},1997{1998"question": "How does Ray work with async.io ?",1999"sources": [2000"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2001"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",2002"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",2003"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",2004"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",2005"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2006"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks"2007],2008"document_ids": [20095707,20106216,20116220,20126228,20136232,20145992,2015157442016],2017"answer": "Ray works with async.io by allowing the invocation of every remote operation to be asynchronous. This means that operations immediately return a promise/future, which is an identifier of the operation's result. To get the actual results, the programmer needs to call ray.get() on the IDs of the results. Ray natively integrates with asyncio, allowing it to be used alongside popular async frameworks like aiohttp and aioredis. Ray also supports async actors, where async method definitions are automatically detected. However, running blocking ray.get or ray.wait inside async actor methods is not allowed, as it will block the execution of the event loop. Ray seeks to cancel associated asyncio.Tasks for async actor tasks, aligning with asyncio task cancellation standards.",2018"llm": "gpt-3.5-turbo"2019},2020{2021"question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",2022"sources": [2023"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",2024"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2025"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",2026"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2027"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2028"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",2029"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism"2030],2031"document_ids": [20327052,20335719,20347074,20355706,20365709,20376137,203870762039],2040"answer": "To debug a hanging `ray.get()` call, you can follow these steps:\n\n1. Ensure that you are calling `ray.get()` as late as possible in your program, as calling it eagerly can hurt parallelism.\n2. Avoid calling `ray.get()` in a loop, as it is a blocking call and will prevent the loop from continuing until the call is resolved.\n3. Separate the call to `ray.get()` from the call to the remote functions. This allows all remote functions to be spawned before waiting for the results, enabling parallel execution.\n4. Instead of calling `ray.get()` one by one, pass a list of object references to `ray.get()` to wait for all tasks to finish simultaneously.",2041"llm": "gpt-3.5-turbo"2042},2043{2044"question": "can you show me an example of ray.actor.exit_actor()",2045"sources": [2046"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2047"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2048"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2049"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2050"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",2051"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",2052"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources"2053],2054"document_ids": [20556254,20566267,20576256,20586272,20596168,206019759,206160042062],2063"answer": "The example of `ray.actor.exit_actor()` can be found in the second context.",2064"llm": "gpt-3.5-turbo"2065},2066{2067"question": "how to add log inside actor?",2068"sources": [2069"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",2070"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",2071"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2072"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2073"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2074"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",2075"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"2076],2077"document_ids": [2078733,2079578,208019191,2081621,2082652,2083918,20846572085],2086"answer": "To add a log inside an actor, you can use the `logging` module in Python. Here's an example:\n\n```python\nimport logging\n\nclass Actor:\n def __init__(self):\n logging.basicConfig(level=logging.INFO)\n\n def log(self, msg):\n logger = logging.getLogger(__name__)\n logger.info(msg)\n```\n\nYou can then call the `log` method on the actor object to add logs:\n\n```python\nactor = Actor.remote()\nray.get(actor.log.remote(\"A log message for an actor.\"))\n```\n\nThe logs can be accessed from the Task and Actor table view in the Ray dashboard.",2087"llm": "gpt-3.5-turbo"2088},2089{2090"question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",2091"sources": [2092"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",2093"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2094"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#next-steps",2095"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#next-steps",2096"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",2097"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",2098"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training"2099],2100"document_ids": [2101197,21029383,210313588,210413698,210513755,2106196,210771542108],2109"answer": "Yes, you can write a script to do batch inference with GPT-2 on text data from an S3 bucket. The provided context includes an example script that demonstrates how to perform batch inference using GPT-2 and Ray Data. The script reads text data from an S3 bucket, maps the batches to a TextClassifier actor, and returns the predictions. You can adapt this example to suit your specific needs.",2110"llm": "gpt-3.5-turbo"2111},2112{2113"question": "How do I enable Ray debug logs?",2114"sources": [2115"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",2116"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2117"https://docs.ray.io/en/master/ray-observability/user-guides/index.html#user-guides",2118"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",2119"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",2120"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#debugger-commands",2121"https://docs.ray.io/en/master/cluster/cli.html#ray-start"2122],2123"document_ids": [2124698,2125916,2126740,212712684,212813879,2129787,213076052131],2132"answer": "To enable Ray debug logs, you can configure the logging system by following the instructions in the \"Configuring Logging\" guide provided.",2133"llm": "gpt-3.5-turbo"2134},2135{2136"question": "How do I list the current Ray actors from python?",2137"sources": [2138"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",2139"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",2140"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",2141"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",2142"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2143"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",2144"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"2145],2146"document_ids": [2147670,214819759,214915750,21506004,2151650,2152907,21536562154],2155"answer": "To list the current Ray actors from Python, you can use the following code:\n\n```python\nfrom ray.util.state import list_actors\n\nactors = list_actors(filters=[(\"state\", \"=\", \"ALIVE\")])\nprint(actors)\n```\n\nAlternatively, you can use the CLI command `ray list actors -f state=ALIVE` or the Python SDK command `ray.list_actors(filters=[(\"state\", \"=\", \"ALIVE\")])`.",2156"llm": "gpt-3.5-turbo"2157},2158{2159"question": "I want to kill the replica actor from Python. how do I do it?",2160"sources": [2161"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",2162"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2163"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2164"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2165"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2166"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2167"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill"2168],2169"document_ids": [217018690,21716566,21726256,21736580,21746267,21756261,217665822177],2178"answer": "To kill the replica actor from Python, you can use the `ray.kill(actor_handle, no_restart=True)` function. This will forcefully kill the actor and interrupt any running tasks on it.",2179"llm": "gpt-3.5-turbo"2180},2181{2182"question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",2183"sources": [2184"https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",2185"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",2186"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",2187"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",2188"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#accelerator-types",2189"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",2190"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus"2191],2192"document_ids": [21935999,21946912,21956910,21966746,21976761,21988851,2199180712200],2201"answer": "To specify that you want a task to run on a V100 GPU type, you can use the `@ray.remote(num_gpus=1, accelerator_type=NVIDIA_TESLA_V100)` decorator in Python.",2202"llm": "gpt-3.5-turbo"2203},2204{2205"question": "How do I get started?",2206"sources": [2207"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",2208"https://docs.ray.io/en/master/serve/index.html#learn-more",2209"https://docs.ray.io/en/master/ray-overview/getting-started.html#getting-started",2210"https://docs.ray.io/en/master/ray-core/examples/plot_example-a3c.html#worker-code-walkthrough",2211"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",2212"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",2213"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started"2214],2215"document_ids": [22168641,221718301,2218309,22197383,22201477,2221178,222262132223],2224"answer": "To get started, you can follow these steps:\n\n1. Check out the \"Getting Started\" and \"Key Concepts\" sections.\n2. Explore the examples provided to build your Ray Serve applications.\n3. Start with the quick start tutorials for deploying a single model locally and converting an existing model into a Ray Serve deployment.\n4. If you want to scale ML workloads, use the \"Ray Libraries Quickstart\". For scaling general Python applications, use the \"Ray Core Quickstart\". For deploying to the cloud, use the \"Ray Clusters Quickstart\". For debugging and monitoring applications, use the \"Debugging and Monitoring Quickstart\".\n5. If you want to tune a PyTorch model effectively, check out the \"Get Started with Tune\" tutorial.\n6. Install Ray via pip install -U ray.\n7. Import and initialize Ray using the code: import ray; ray.init()",2225"llm": "gpt-3.5-turbo"2226},2227{2228"question": "How to specify python version in runtime_env?",2229"sources": [2230"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2231"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",2232"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2233"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2234"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",2235"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2236"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"2237],2238"document_ids": [22395821,22405798,22415804,22426396,22436329,22445895,2245186182246],2247"answer": "To specify the Python version in the runtime_env, you can use the \"pip\" field in the conda environment.yaml file. Specify the desired Python version using the \"pip_version\" parameter, for example: \"pip_version\": \"==3.8.11\".",2248"llm": "gpt-3.5-turbo"2249},2250{2251"question": "how to create a Actor in a namespace?",2252"sources": [2253"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",2254"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",2255"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",2256"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2257"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2258"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2259"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces"2260],2261"document_ids": [22626252,22636595,22646255,22656240,22666233,22676277,226862412269],2270"answer": "To create an Actor in a namespace, you can use the `setName` method while creating the actor. Specify the desired namespace as an argument to the `setName` method.",2271"llm": "gpt-3.5-turbo"2272},2273{2274"question": "Can I specify multiple working directories?",2275"sources": [2276"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2277"https://docs.ray.io/en/master/serve/api/index.html#serve-run",2278"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2279"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2280"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",2281"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",2282"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris"2283],2284"document_ids": [22855805,228618794,22875813,22885811,22892363,22902383,229158462292],2293"answer": "Yes, you can specify multiple working directories. The `working_dir` parameter allows you to specify the working directory for the Ray workers. It can be a local existing directory, a local existing zipped file, or a URI to a remotely-stored zip file. You can also use the `--working-dir` or `-d` option to specify the directory containing files that your application(s) will run in.",2294"llm": "gpt-3.5-turbo"2295},2296{2297"question": "what if I set num_cpus=0 for tasks",2298"sources": [2299"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2300"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",2301"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",2302"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case",2303"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",2304"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",2305"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources"2306],2307"document_ids": [23086569,23096898,231019447,23117070,23128565,23136002,2314157492315],2316"answer": "Setting `num_cpus=0` for tasks means that the tasks will not be scheduled on a head node. It is recommended to always explicitly set `num_cpus` for actors to avoid any surprises. Ray does not provide CPU isolation for tasks or actors, so it is the responsibility of the user to ensure that tasks or actors do not use more resources than specified.",2317"llm": "gpt-3.5-turbo"2318},2319{2320"question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",2321"sources": [2322"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",2323"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#overview",2324"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",2325"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",2326"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",2327"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",2328"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling"2329],2330"document_ids": [23318021,23328024,23337852,23348033,233518752,23367767,2337187512338],2339"answer": "Yes, it is possible to have Ray on Kubernetes without using KubeRay. However, if you choose to deploy a static Ray cluster without KubeRay, you will not have the built-in autoscaling feature that KubeRay provides. If you want to enable autoscaling, it is recommended to use KubeRay or enable Kubernetes autoscaling in your cluster.",2340"llm": "gpt-3.5-turbo"2341},2342{2343"question": "how to manually configure and manage Ray cluster on Kubernetes",2344"sources": [2345"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",2346"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",2347"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",2348"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",2349"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",2350"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents",2351"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#deploying-a-static-ray-cluster"2352],2353"document_ids": [23547730,23557977,23567658,23577978,23587643,23598730,236078602361],2362"answer": "To manually configure and manage a Ray cluster on Kubernetes, you can follow these steps:\n\n1. Read the RayCluster Quickstart and RayCluster Configuration guides to understand the process.\n2. Deploy a Ray cluster by creating a RayCluster custom resource using the command: `kubectl apply -f raycluster.yaml`.\n3. Configure the Ray cluster by setting the Redis IP address and password in the config file.\n4. Once the Ray cluster is deployed, you can view the pods for the head node and worker nodes using the command: `kubectl get pods`.",2363"llm": "gpt-3.5-turbo"2364},2365{2366"question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",2367"sources": [2368"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",2369"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",2370"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",2371"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",2372"https://docs.ray.io/en/master/ray-core/cross-language.html#cross-language-data-serialization",2373"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",2374"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options"2375],2376"document_ids": [23778361,23786934,2379755,23806031,23815942,23826025,2383194832384],2385"answer": "Yes, if a raylet is shutdown, the tasks and workers on that node will also get killed.",2386"llm": "gpt-3.5-turbo"2387},2388{2389"question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",2390"sources": [2391"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",2392"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",2393"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",2394"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",2395"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management",2396"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",2397"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides"2398],2399"document_ids": [2400741,24016918,2402742,2403758,24046765,2405754,240661932407],2408"answer": "To debug out-of-memory issues, you can refer to the \"Debugging Out of Memory\" section in the documentation. Additionally, you can find information on how to find per Task and Actor memory usage to locate the source of the memory leak. The documentation also provides details on memory management in Ray. For specific instructions on addressing the out-of-memory issue, you can refer to the documentation link provided: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html.",2409"llm": "gpt-3.5-turbo"2410},2411{2412"question": "How to use callback in Trainer?",2413"sources": [2414"https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#monitoring-and-logging-metrics",2415"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",2416"https://docs.ray.io/en/master/train/examples/transformers/transformers_torch_trainer_basic.html#ray-train-basic-example-for-huggingface-transformers",2417"https://docs.ray.io/en/master/train/getting-started-transformers.html#quickstart",2418"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",2419"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.html#ray-tune-callback",2420"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints"2421],2422"document_ids": [242312329,242410918,242512772,242610884,242712188,24281875,2429121972430],2431"answer": "To use a callback in the Trainer class, you can follow these steps:\n\n1. Create a custom callback class that inherits from the `Callback` class or `TrainerCallback` class.\n2. Implement the desired callback method, such as `on_train_epoch_end` or `on_trial_result`, depending on your use case.\n3. Inside the callback method, you can access the trainer object and retrieve the necessary metrics using `trainer.callback_metrics`.\n4. Optionally, you can modify or add additional metrics to the `metrics` dictionary.\n5. Use the callback to report the metrics or perform any other desired actions.\n\nExample usage:\n```python\nfrom transformers import Trainer, TrainerCallback\n\nclass MyTrainReportCallback(TrainerCallback):\n def on_train_epoch_end(self, trainer, pl_module):\n metrics = trainer.callback_metrics\n metrics = {k: v.item() for k, v in metrics.items()}\n # Add customized metrics if needed\n metrics[\"custom_metric\"] = 123\n # Report metrics or perform other actions\n\ntrainer = Trainer(\n model=model,\n args=args,\n train_dataset=train_dataset,\n eval_dataset=eval_dataset,\n)\n\n# Add the callback to the trainer\ntrainer.add_callback(MyTrainReportCallback())\n\n# Train the model\ntrainer.train()\n```\n\nNote: The specific implementation of the callback may vary depending on the use case and the desired actions to be performed.",2432"llm": "gpt-3.5-turbo"2433},2434{2435"question": "How to provide current working directory to ray?",2436"sources": [2437"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2438"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2439"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2440"https://docs.ray.io/en/master/serve/api/index.html#serve-run",2441"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2442"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2443"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files"2444],2445"document_ids": [24468909,24475805,24488910,244918794,24508797,24518800,245257902453],2454"answer": "To provide the current working directory to Ray, you can use the `--working-dir` argument when submitting the job using `ray job submit`. This argument should specify the path to the working directory. Ray will then upload the contents of the working directory to the cluster. Additionally, you can specify the working directory for Ray workers using the `working_dir` parameter in `ray.init()`.",2455"llm": "gpt-3.5-turbo"2456},2457{2458"question": "how to create an actor instance with parameter?",2459"sources": [2460"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",2461"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2462"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2463"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2464"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",2465"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2466"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments"2467],2468"document_ids": [24696599,24706278,24717017,24726233,24737299,24746282,247561632476],2477"answer": "To create an actor instance with parameters, you can use the `options` method of the actor class. Pass the desired parameters as keyword arguments to the `options` method when creating the actor instance using the `remote` method.",2478"llm": "gpt-3.5-turbo"2479},2480{2481"question": "how to push a custom module to ray which is using by Actor ?",2482"sources": [2483"https://docs.ray.io/en/master/ray-core/api/core.html#actors",2484"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2485"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2486"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#initialization",2487"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",2488"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",2489"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources"2490],2491"document_ids": [24926316,24936282,24946233,249519341,249619759,24976004,2498157502499],2500"answer": "To push a custom module to Ray that is used by an Actor, you can define the Actor class with the `@ray.remote` decorator and use the `remote()` method to instantiate the Actor. You can also configure and override the actor instantiation parameters using the `options()` method.",2501"llm": "gpt-3.5-turbo"2502},2503{2504"question": "how to print ray working directory?",2505"sources": [2506"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2507"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2508"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2509"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2510"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2511"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2512"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#submit-the-workload"2513],2514"document_ids": [25155805,25168794,25178910,25188909,2519699,25208800,252183142522],2523"answer": "To print the Ray working directory, you can use the command `print(ray.worker._global_node.get_session_dir())`.",2524"llm": "gpt-3.5-turbo"2525},2526{2527"question": "why I can not see log.info in ray log?",2528"sources": [2529"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2530"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",2531"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2532"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",2533"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",2534"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2535"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs"2536],2537"document_ids": [2538619,2539996,2540916,254113879,254212684,2543699,254489082545],2546"answer": "The provided context does not mention anything about the visibility of `log.info` in Ray logs.",2547"llm": "gpt-3.5-turbo"2548},2549{2550"question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",2551"sources": [2552"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",2553"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2554"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",2555"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",2556"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",2557"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",2558"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#define-a-custom-datasource"2559],2560"document_ids": [256110397,25629160,25639647,256412258,256512228,256610012,2567136532568],2569"answer": "Yes, when using Ray Dataset to read a file, you can ensure that the order of the data is preserved by setting the preserve_order flag in the DataContext to True.",2570"llm": "gpt-3.5-turbo"2571},2572{2573"question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",2574"sources": [2575"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",2576"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",2577"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",2578"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",2579"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",2580"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",2581"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance"2582],2583"document_ids": [25846032,25856059,25866112,25876035,25886051,25896323,259059562591],2592"answer": "\"Ray will not retry tasks upon exceptions thrown by application code\" means that if an exception is thrown by the application code during the execution of a task, Ray will not automatically retry that task.",2593"llm": "gpt-3.5-turbo"2594},2595{2596"question": "how do I specify the log directory when starting Ray?",2597"sources": [2598"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2599"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",2600"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",2601"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",2602"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2603"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2604"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#mounting-the-ray-log-directory"2605],2606"document_ids": [2607699,26088545,26097909,26107930,2611916,261219192,261379202614],2615"answer": "To specify the log directory when starting Ray, you can change the temp and logging directory by specifying it when you call `ray start` or `ray.init()`. By default, Ray writes logs to files in the directory `/tmp/ray/session_*/logs` on each Ray node's or pod's file system. If you prefer to redirect logs to stderr of the host pods instead, you can set the environment variable `RAY_LOG_TO_STDERR=1` on all Ray nodes.",2616"llm": "gpt-3.5-turbo"2617},2618{2619"question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",2620"sources": [2621"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2622"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2623"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",2624"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",2625"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",2626"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",2627"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources"2628],2629"document_ids": [26308491,26318502,26328591,26338601,26348345,26353192,2636197582637],2638"answer": "To launch a Ray cluster with 10 nodes, you need to modify the configuration file `config.yaml` to set the `max_workers` parameter to 10. Then, use the Ray CLI to start the cluster by running the command `ray up -y config.yaml`.",2639"llm": "gpt-3.5-turbo"2640},2641{2642"question": "how to use ray api to scale up a cluster",2643"sources": [2644"https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",2645"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",2646"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",2647"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",2648"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",2649"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",2650"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart"2651],2652"document_ids": [26537596,26546309,265519762,26567590,26579013,26588548,265980282660],2661"answer": "To scale up a Ray cluster using the Ray API, 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 by specifying the number of CPUs or other resource requirements. The cluster will immediately attempt to scale to accommodate the requested resources.",2662"llm": "gpt-3.5-turbo"2663},2664{2665"question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",2666"sources": [2667"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",2668"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2669"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",2670"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",2671"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",2672"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",2673"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents"2674],2675"document_ids": [26768338,2677387,26788345,26798610,26808213,26817464,268287302683],2684"answer": "To specify a subnet in the deployment file for launching a cluster using Ray Cloud Launcher in AWS, you need to edit the cluster configuration file (e.g., cluster.yaml) and include the subnet information in the YAML file.",2685"llm": "gpt-3.5-turbo"2686},2687{2688"question": "where I can find HTTP server error code log for Ray serve",2689"sources": [2690"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2691"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests",2692"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2693"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",2694"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",2695"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",2696"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect"2697],2698"document_ids": [269918775,270018418,270118161,270218152,27038908,27047761,270576762706],2707"answer": "The HTTP server error code log for Ray Serve can be found at /tmp/ray/session_latest/logs on the head node.",2708"llm": "gpt-3.5-turbo"2709},2710{2711"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?",2712"sources": [2713"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",2714"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",2715"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",2716"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",2717"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2718"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",2719"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard"2720],2721"document_ids": [27227502,27237501,272418385,27258371,27268808,27277499,272874972729],2730"answer": "The issue you are experiencing with the Ray Dashboard disappearing after tunneling the port may be due to the dashboard port not being publicly exposed. You need to set up additional configuration to access the Ray Dashboard from outside the head node. Make sure to follow the instructions provided in the context to properly configure and manage the Ray Dashboard.",2731"llm": "gpt-3.5-turbo"2732},2733{2734"question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",2735"sources": [2736"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",2737"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",2738"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",2739"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",2740"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2741"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",2742"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters"2743],2744"document_ids": [27458601,27468454,27478551,27488377,27498491,27507534,275185532752],2753"answer": "In the Ray cluster launcher YAML, `max_workers` does not include the head node.",2754"llm": "gpt-3.5-turbo"2755},2756{2757"question": "How to update files in working directory ?",2758"sources": [2759"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2760"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2761"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",2762"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",2763"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",2764"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#loading-data-in-a-tune-trainable",2765"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function"2766],2767"document_ids": [27688909,27695805,27702383,27712363,27728358,27733020,277414522775],2776"answer": "To update files in the working directory, you can use the \"ray up\" command if you are working with a cluster configuration. If you are using the Ray client, you can upload the working directory to the specified location using ray.init().",2777"llm": "gpt-3.5-turbo"2778},2779{2780"question": "How I can update working directory file when ray allready initiated ?",2781"sources": [2782"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2783"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2784"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",2785"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2786"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",2787"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",2788"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"2789],2790"document_ids": [27918909,27928800,27935790,27945805,27958358,27968803,279765142798],2799"answer": "To update the working directory file after Ray has already been initiated, you can use the `ray.init()` function with the `runtime_env` parameter. Specify the new working directory path in the `runtime_env` dictionary. Ray will download the updated directory to all nodes in the cluster.",2800"llm": "gpt-3.5-turbo"2801},2802{2803"question": "how can I force ray head node to use custom pem file to ssh worker node?",2804"sources": [2805"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2806"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2807"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2808"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2809"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",2810"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2811"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding"2812],2813"document_ids": [28148505,2815370,28168474,28178518,28188347,28198513,282088922821],2822"answer": "To force the Ray head node to use a custom PEM file to SSH into the worker node, you need to set the \"ssh_private_key\" parameter in the Ray configuration file (config.yaml) to the path of your custom PEM file.",2823"llm": "gpt-3.5-turbo"2824},2825{2826"question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",2827"sources": [2828"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",2829"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",2830"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2831"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",2832"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",2833"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",2834"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-operating-system-settings"2835],2836"document_ids": [28376011,28388563,283919806,284018715,2841764,284218088,284359252844],2845"answer": "The GCS server manages cluster-level metadata and provides cluster-level operations. By default, the GCS is not fault-tolerant and stores data in-memory. If the GCS fails, the entire Ray cluster fails. To make the GCS fault-tolerant, HA Redis is required. The GCS server on the head node can consume a significant amount of memory due to additional system processes running on it. It is recommended to have a head node with at least 8 CPUs and 32 GB of memory, although the actual hardware specification depends on the workload and cluster size.",2846"llm": "gpt-3.5-turbo"2847},2848{2849"question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",2850"sources": [2851"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",2852"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",2853"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",2854"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",2855"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",2856"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",2857"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment"2858],2859"document_ids": [28601154,28617640,286219487,286312710,286413247,286513684,286631922867],2868"answer": "If there are nodes pending for a long time when starting a cluster with Ray up, you can debug this by checking the log for any failures or routing issues. One possible cause could be overloading the head node with too many simultaneous connections. To resolve this, start the worker nodes more slowly. Additionally, you can use the command \"ray status\" to view detailed cluster status and check if there are any resource constraints or actors claiming all the cluster resources.",2869"llm": "gpt-3.5-turbo"2870},2871{2872"question": "how to install Ray 2.5.1 from github or wheel?",2873"sources": [2874"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",2875"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",2876"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",2877"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",2878"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2879"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2880"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit"2881],2882"document_ids": [288319553,2884100,2885410,288619543,288795,2888405,2889992890],2891"answer": "To install Ray 2.5.1 from GitHub or a wheel, you can use the following command:\n\npip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/{COMMIT_HASH}/ray-{RAY_VERSION}-{PYTHON_VERSION}-{PYTHON_VERSION}-{OS_VERSION}.whl\n\nFor example, to install Ray 3.0.0.dev0 for Python 3.9 on MacOS from commit 4f2ec46c3adb6ba9f412f09a9732f436c4a5d0c9, you can use the following command:\n\npip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/4f2ec46c3adb6ba9f412f09a9732f436c4a5d0c9/ray-3.0.0.dev0-cp39-cp39-macosx_10_15_x86_64.whl",2892"llm": "gpt-3.5-turbo"2893},2894{2895"question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",2896"sources": [2897"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2898"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",2899"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",2900"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",2901"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#set-up-ray-a-name-setup-a",2902"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#worker-setup-commands",2903"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms"2904],2905"document_ids": [29066400,2907736,290812445,290914111,291013805,29118415,291231862913],2914"answer": "To use `worker_setup_hook` in a runtime environment to set up worker nodes, you need to define a callable function that will be called after workers start and before Tasks and Actors are scheduled. In the provided example, the `logging_setup_func()` function is used as the setup hook to apply a new logging configuration to all worker processes within a job. You can pass this function to the `runtime_env` parameter when initializing Ray, like this:\n\n```python\nimport logging\nimport warnings\nimport ray\n\ndef logging_setup_func():\n logger = logging.getLogger(\"ray\")\n logger.setLevel(logging.DEBUG)\n warnings.simplefilter(\"always\")\n\nray.init(runtime_env={\"worker_process_setup_hook\": logging_setup_func})\n```\n\nMake sure to define the `logging_setup_func()` function before calling `ray.init()`.",2915"llm": "gpt-3.5-turbo"2916},2917{2918"question": "how to use Ray dataset on aws",2919"sources": [2920"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2921"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",2922"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",2923"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",2924"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#starting-with-pytorch-data",2925"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchTrainer.html#ray-train-torch-torchtrainer",2926"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes"2927],2928"document_ids": [29299165,29309034,293110714,293212228,293312236,293411371,293591312936],2937"answer": "To use Ray dataset on AWS, you can start a Ray cluster on AWS using the Ray cluster launcher. Ray Data supports various ML frameworks such as PyTorch, HuggingFace, and Tensorflow. It also supports different data formats including CSV, Parquet, and raw images. You can create datasets from on-disk files, Python objects, or cloud storage services like S3 using Ray Data. Ray Data can read from any filesystem supported by Arrow. To convert a Ray dataset to a distributed DataFrame, you can use frameworks like Dask or Spark.",2938"llm": "gpt-3.5-turbo"2939},2940{2941"question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",2942"sources": [2943"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2944"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",2945"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",2946"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2947"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",2948"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",2949"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing"2950],2951"document_ids": [29529477,29539862,29549883,295512253,29569116,295713426,2958107812959],2960"answer": "To avoid shuffling your dataset during `ray.data.map_batches`, you can use the `random_shuffle` method before calling `map_batches`. This will randomize the order of the rows in your dataset. However, note that `random_shuffle` can be slow, so it is recommended to iterate over batches with shuffling for better performance.",2961"llm": "gpt-3.5-turbo"2962},2963{2964"question": "Is the order of the input data preserved after a map_batches operation?",2965"sources": [2966"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",2967"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2968"https://docs.ray.io/en/master/data/api/doc/ray.data.range_tensor.html#ray-data-range-tensor",2969"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",2970"https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms",2971"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2972"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map"2973],2974"document_ids": [297510209,29769477,29779666,297810647,29799135,29809489,2981104222982],2983"answer": "Yes, the order of the input data is preserved after a map_batches operation.",2984"llm": "gpt-3.5-turbo"2985},2986{2987"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?",2988"sources": [2989"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",2990"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",2991"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",2992"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2993"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",2994"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2995"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance"2996],2997"document_ids": [29985956,29996035,300018086,300118156,30027676,300318775,300459543005],3006"answer": "To get more detailed errors or logs from Ray Serve, you can leverage Ray logging. Ray Serve uses Python's standard logging module with a logger named \"ray.serve\". By default, logs are emitted from actors both to stderr and on disk on each node at /tmp/ray/session_latest/logs/serve/. This includes both system-level logs from the Serve controller and proxy as well as access logs and custom user logs produced from within deployment replicas.",3007"llm": "gpt-3.5-turbo"3008},3009{3010"question": "how do i track an uncaught exception in ray serve",3011"sources": [3012"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3013"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",3014"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayTaskError.html#ray-exceptions-raytaskerror",3015"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",3016"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",3017"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests",3018"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures"3019],3020"document_ids": [302118775,30226367,30236478,30247761,302518152,302618418,302760353028],3029"answer": "To track an uncaught exception in Ray Serve, you can catch application-level failures using the RayTaskError exception. Ray surfaces application-level failures as Python-level exceptions and wraps the original exception in a RayTaskError. You can access the Serve page in the Ray dashboard at http://localhost:8265 to view important information about your application.",3030"llm": "gpt-3.5-turbo"3031},3032{3033"question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",3034"sources": [3035"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3036"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3037"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3038"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",3039"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3040"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3041"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver"3042],3043"document_ids": [304418156,3045916,304618157,3047580,304818165,30495881,30507093051],3052"answer": "You can view logs emitted by your Ray Serve endpoint in the Ray cluster by checking the logs located at /tmp/ray/session_latest/logs/serve/ on each node. These logs include system-level logs from the Serve controller and proxy, as well as access logs and custom user logs.",3053"llm": "gpt-3.5-turbo"3054},3055{3056"question": "where can I see logs for a failed ray serve deployment",3057"sources": [3058"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",3059"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3060"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3061"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",3062"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#inspect-deeper",3063"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",3064"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment"3065],3066"document_ids": [306718756,306818163,306918161,30708908,307118350,307218051,3073191503074],3075"answer": "You can check the controller log and deployment log for a failed Ray Serve deployment. These logs are located under /tmp/ray/session_latest/logs/serve/ in both the head node pod and worker node pod. The logs contain information about specific deployment failure reasons and autoscaling events.",3076"llm": "gpt-3.5-turbo"3077},3078{3079"question": "How to take a subset of a Ray Dataset?",3080"sources": [3081"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",3082"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",3083"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",3084"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",3085"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",3086"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#splitting-datasets",3087"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer"3088],3089"document_ids": [309010714,30919295,309213426,30939160,309412228,309512241,3096115113097],3098"answer": "To take a subset of a Ray Dataset, you can use the `map` function with the `batch_format` parameter set to \"pandas\". Additionally, you can customize which datasets are split by passing a DataConfig to the Trainer constructor.",3099"llm": "gpt-3.5-turbo"3100},3101{3102"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.",3103"sources": [3104"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",3105"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing",3106"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints",3107"https://docs.ray.io/en/master/tune/tutorials/overview.html#tune-feature-guides",3108"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",3109"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",3110"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#tunecontroller"3111],3112"document_ids": [31132935,31142923,31152915,31162993,31172968,31183106,311929563120],3121"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's latest checkpoint whenever Tune restores a trial. You can download all the checkpoints to analyze them.",3122"llm": "gpt-3.5-turbo"3123},3124{3125"question": "How can I kill a \"detached\" Actor ?",3126"sources": [3127"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3128"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3129"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3130"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3131"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3132"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",3133"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes"3134],3135"document_ids": [31366293,31376580,31386267,31396168,31406256,31416160,314262903143],3144"answer": "To kill a \"detached\" actor in Ray, you can use the `ray.kill` function. This function forcefully terminates the actor and interrupts any running tasks on it. It does not run any atexit handlers installed in the actor. You can call `ray.kill(actor_handle)` to kill the actor.",3145"llm": "gpt-3.5-turbo"3146},3147{3148"question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",3149"sources": [3150"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#environment-variables-are-not-passed-from-the-driver-process-to-worker-processes",3151"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3152"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#environment-variables-are-not-passed-from-the-driver-process-to-worker-processes",3153"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3154"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",3155"https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#set-up-ray-cluster",3156"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air"3157],3158"document_ids": [3159822,31606388,3161823,31626514,316318443,316413271,3165137873166],3167"answer": "To set environment variables in `ray.init()`, you can use the `runtime_env` parameter. For example, you can set the environment variable `FOO` to `\"bar\"` by calling `ray.init(runtime_env={\"env_vars\": {\"FOO\": \"bar\"}})`.",3168"llm": "gpt-3.5-turbo"3169},3170{3171"question": "What is the rest api for getting the head node id?",3172"sources": [3173"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",3174"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.node_id.html#ray-runtime-context-runtimecontext-node-id",3175"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3176"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",3177"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",3178"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3179"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node"3180],3181"document_ids": [3182974,31836413,318419256,318519075,318618949,31878528,318810263189],3190"answer": "The REST API for getting the head node ID is deprecated and may be removed in future Ray releases. It is recommended to use the `get_node_id()` function instead.",3191"llm": "gpt-3.5-turbo"3192},3193{3194"question": "how to rerun a canceled ray task",3195"sources": [3196"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",3197"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",3198"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",3199"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",3200"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",3201"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",3202"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks"3203],3204"document_ids": [32056081,32066028,320719750,320815743,32095990,32105994,3211157453212],3213"answer": "To rerun a canceled Ray task, you need to call `ray.cancel()` on the ObjectRef of the task that was canceled. By default, this will send a KeyboardInterrupt to the task's worker if it is mid-execution. If you want to force-exit the worker, you can pass `force=True` to `ray.cancel()`. Note that Ray does not automatically retry tasks that have been canceled.",3214"llm": "gpt-3.5-turbo"3215},3216{3217"question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",3218"sources": [3219"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3220"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ConcurrencyLimiter.html#ray-tune-search-concurrencylimiter",3221"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.hebo.HEBOSearch.html#ray-tune-search-hebo-hebosearch",3222"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.bohb.TuneBOHB.html#ray-tune-search-bohb-tunebohb",3223"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",3224"https://docs.ray.io/en/master/rllib/rllib-concepts.html#building-policies-in-tensorflow",3225"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.dragonfly.DragonflySearch.set_max_concurrency.html#ray-tune-search-dragonfly-dragonflysearch-set-max-concurrency"3226],3227"document_ids": [32289485,32291953,32302671,32312686,323213664,323315004,323419153235],3236"answer": "To set the maximum number of concurrent scheduled tasks in `map_batches`, you can use the `max_concurrent` parameter. Set the value of `max_concurrent` to the desired number of concurrent tasks you want to allow.",3237"llm": "gpt-3.5-turbo"3238},3239{3240"question": "How do I get the number of cpus from ray cluster?",3241"sources": [3242"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",3243"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",3244"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",3245"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",3246"https://docs.ray.io/en/master/cluster/vms/getting-started.html#create-a-basic-python-application",3247"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-6-profile-using-ray-dashboard",3248"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core"3249],3250"document_ids": [325113406,325219758,325315749,32546002,32558336,32568143,325773303258],3259"answer": "To get the number of CPUs from a Ray cluster, you can use the `ray.cluster_resources()[\"CPU\"]` command.",3260"llm": "gpt-3.5-turbo"3261},3262{3263"question": "How to use the exclude option to the runtime_env",3264"sources": [3265"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",3266"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",3267"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3268"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",3269"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",3270"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",3271"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages"3272],3273"document_ids": [327418444,32756329,32766388,32776535,327818618,32796534,328057983281],3282"answer": "To use the exclude option to the runtime_env, you can specify the configuration options for a runtime environment using the ray.runtime_env.RuntimeEnvConfig() class. The exclude option allows you to exclude certain dependencies or packages from the runtime environment.",3283"llm": "gpt-3.5-turbo"3284},3285{3286"question": "show a map batch example with batch_format",3287"sources": [3288"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3289"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3290"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3291"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",3292"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#data-loading",3293"https://docs.ray.io/en/master/data/api/doc/ray.data.range_tensor.html#ray-data-range-tensor",3294"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map"3295],3296"document_ids": [32979477,329819788,32999485,330010647,330113613,33029666,3303102093304],3305"answer": "Here is an example of using the `map_batches` method with the `batch_format` parameter:\n\n```python\nimport ray\ndataset = ray.data.range(10)\nnext(iter(dataset.iter_batches(batch_format=\"numpy\", batch_size=5)))\n# Output: {'id': array([0, 1, 2, 3, 4])}\n\nnext(iter(dataset.iter_batches(batch_format=\"pandas\", batch_size=5)))\n# Output:\n# id\n# 0 0\n# 1 1\n# 2 2\n# 3 3\n# 4 4\n```\n\nIn this example, the `iter_batches` method is used to iterate over the dataset in batches. The `batch_format` parameter is set to \"numpy\" and \"pandas\" to specify the format of the batches.",3306"llm": "gpt-3.5-turbo"3307},3308{3309"question": "how to find local ray address",3310"sources": [3311"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",3312"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3313"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",3314"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",3315"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",3316"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",3317"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime"3318],3319"document_ids": [33203192,33216520,33229005,332318152,33248765,33258318,332656813327],3328"answer": "To find the local Ray address, you can try the following methods:\n\n1. Check if the Ray cluster is already started. In that case, you should not need to run anything on the worker nodes. The commonly used Ray address is localhost:6379.\n\n2. If a concrete address is provided, try to connect to it. Concrete addresses can be prefixed with \"ray://\" to connect to a remote cluster. For example, passing in the address \"ray://123.45.67.89:50005\" will connect to the cluster at the given address.\n\n3. If no address is provided, try to find an existing Ray instance. If unspecified, it will try to connect to a running local Ray cluster.\n\n4. You can access the Ray Dashboard by going to http://localhost:8265 in your browser. The dashboard provides important information about your application.\n\nNote: The RAY_ADDRESS environment variable can override the address specified in ray.init().",3329"llm": "gpt-3.5-turbo"3330},3331{3332"question": "How to start ray cluster on multiple node via CLI?",3333"sources": [3334"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3335"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",3336"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",3337"https://docs.ray.io/en/master/ray-core/configure.html#configuring-ray",3338"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-via-the-cli-ray-start",3339"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3340"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments"3341],3342"document_ids": [33438345,334419487,33458591,33466038,33475688,33486391,3349184543350],3351"answer": "To start a Ray cluster on multiple nodes via CLI, you need to follow these steps:\n\n1. Run the command \"ray start\" on each machine to start the Ray cluster services.\n2. In your Python code, use \"ray.init()\" to connect to the cluster services.\n3. Optionally, you can also connect other nodes to the head node by calling \"ray start\" on those nodes.\n4. Use the Ray CLI command \"ray up -y config.yaml\" to start the Ray cluster on the cloud, creating a head node and worker nodes.",3352"llm": "gpt-3.5-turbo"3353},3354{3355"question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",3356"sources": [3357"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",3358"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",3359"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",3360"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",3361"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",3362"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-1-fixing-the-resource-quantity-with-ray-init-num-cpus",3363"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm"3364],3365"document_ids": [33666848,33679219,3368839,336912526,33703170,33717307,3372132513373],3374"answer": "The Ray tuner shows \"running\" because it is scheduled and created, but it occupies almost 0% CPU usage. This could be due to the default behavior of Ray Tune, which tries to fully utilize cluster CPUs, potentially preventing Ray Data from scheduling tasks. To ensure CPU resources are available for Ray Data execution, limit the number of concurrent Tune trials with the max_concurrent_trials option.",3375"llm": "gpt-3.5-turbo"3376},3377{3378"question": "should the Ray head node and all workers have the same object store memory size allocated?",3379"sources": [3380"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3381"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",3382"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",3383"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",3384"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",3385"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",3386"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts"3387],3388"document_ids": [33898607,33908750,33918727,3392761,3393764,33947534,339567683396],3397"answer": "No, the Ray head node and workers do not necessarily need to have the same object store memory size allocated. The object store memory available to each worker node is capped by a formula that takes into account the available shared memory and the number of tasks per worker. The object store memory available to the head node is also capped by the available shared memory. The head node may require more memory for system components such as GCS or the dashboard, and running tasks and actors on the head node can lead to out-of-memory problems. It is recommended to avoid running tasks and actors on the head node in large-scale clusters.",3398"llm": "gpt-3.5-turbo"3399},3400{3401"question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",3402"sources": [3403"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",3404"https://docs.ray.io/en/master/serve/architecture.html#how-does-serve-ensure-horizontal-scalability-and-availability",3405"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",3406"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",3407"https://docs.ray.io/en/master/serve/api/index.html#serve-start",3408"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",3409"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments"3410],3411"document_ids": [341218833,341318095,341418837,341518916,341618799,341718753,3418184553419],3420"answer": "To specify whether to set up an HTTP proxy on each node or just the head node in Ray Serve, you can use the `proxy_location` field in the `http_options` configuration. Set the value to \"EveryNode\" to start one HTTP server per node or \"HeadOnly\" to start one HTTP server on the head node. The default behavior is \"HeadOnly\".",3421"llm": "gpt-3.5-turbo"3422},3423{3424"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?",3425"sources": [3426"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",3427"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",3428"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",3429"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-grafana-host-location",3430"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",3431"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",3432"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-prometheus-host-location"3433],3434"document_ids": [34357518,34368159,34377519,34387523,34397517,34407520,344175223442],3443"answer": "To embed Grafana into the Ray Dashboard when using KubeRay, you should set the following variables:\n\n- `RAY_GRAFANA_HOST`: Set this to an address that the head node can use to access Grafana for health checks on the backend.\n- `RAY_PROMETHEUS_HOST`: Set this to an address that the head node can use to access Prometheus.\n\nTo set these variables more intelligently, considering that the head node IP changes when reconfiguring the cluster, you can use the following approach:\n\n- Set `RAY_GRAFANA_HOST` to the IP address of the head node and the port where Grafana is hosted, for example: `RAY_GRAFANA_HOST=http://<head_node_ip>:<grafana_port>`.\n- Set `RAY_PROMETHEUS_HOST` to the IP address of the head node and the port where Prometheus is hosted, for example: `RAY_PROMETHEUS_HOST=http://<head_node_ip>:<prometheus_port>`.\n\nMake sure to properly configure these environment variables when starting the Ray Clusters.",3444"llm": "gpt-3.5-turbo"3445},3446{3447"question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",3448"sources": [3449"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",3450"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",3451"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",3452"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",3453"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",3454"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",3455"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down"3456],3457"document_ids": [345818751,345918750,34608302,34618021,34628046,34638049,346480443465],3466"answer": "The GCS (Google Cloud Scheduler) does not determine which Kubernetes pod to kill when using KubeRay autoscaling. The Ray autoscaler, which is a Ray cluster process running as a sidecar container in the Ray head pod, automatically scales the cluster up and down based on resource demand. It adjusts the number of nodes (Ray pods) in the cluster based on the resources required by tasks, actors, or placement groups. The Ray autoscaler has more fine-grained control over scale-down than the Horizontal Pod Autoscaler, as it can select precisely which pods to scale down.",3467"llm": "gpt-3.5-turbo"3468},3469{3470"question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",3471"sources": [3472"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",3473"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",3474"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",3475"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",3476"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-summary-objects",3477"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-summary-tasks",3478"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"3479],3480"document_ids": [348118616,348218543,348318916,348418844,3485905,3486899,3487186133488],3489"answer": "To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you need to specify the desired timeout value for the `request_timeout_s` field in the `http_options` field of the Serve config. This config is global to your Ray cluster and cannot be updated during runtime.",3490"llm": "gpt-3.5-turbo"3491},3492{3493"question": "How do I make the GPU available on my M1 laptop to ray?",3494"sources": [3495"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",3496"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3497"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.filter.html#ray-data-dataset-filter",3498"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.add_column.html#ray-data-dataset-add-column",3499"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-autoscaling",3500"https://docs.ray.io/en/master/ray-overview/installation.html#m1-mac-apple-silicon-support",3501"https://docs.ray.io/en/master/installation.html#m1-mac-apple-silicon-support"3502],3503"document_ids": [35043998,35056746,350610479,350710435,35088103,3509417,35101073511],3512"answer": "To make the GPU available on your M1 laptop to Ray, you can specify the resource requirements using the `@ray.remote(num_gpus=1)` decorator. Ray will then schedule the task or actor to a node with available GPU resources and set the `CUDA_VISIBLE_DEVICES` environment variable before running the code.",3513"llm": "gpt-3.5-turbo"3514},3515{3516"question": "How can I add a timeout for the Ray job?",3517"sources": [3518"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",3519"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",3520"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",3521"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",3522"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-stop",3523"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",3524"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job"3525],3526"document_ids": [35278802,352818543,35297772,35308817,35318867,35328803,353377853534],3535"answer": "To add a timeout for a Ray job, you can use the `--no-wait` flag when submitting the job. This allows the client to not wait for the job to finish. You can then use other CLI commands, such as `ray job status`, to check on the job's status.",3536"llm": "gpt-3.5-turbo"3537},3538{3539"question": "how do I set custom /tmp directory for remote cluster?",3540"sources": [3541"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3542"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",3543"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",3544"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher",3545"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#cluster-domain",3546"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#solution-2-poststart-hook",3547"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster"3548],3549"document_ids": [35505807,35518809,355218441,35535773,35547726,35557894,3556183833557],3558"answer": "To set a custom /tmp directory for a remote cluster, you can adjust the CLUSTER_DOMAIN environment variable in the KubeRay operator or modify the Helm chart.",3559"llm": "gpt-3.5-turbo"3560},3561{3562"question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",3563"sources": [3564"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",3565"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",3566"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3567"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3568"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",3569"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3570"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment"3571],3572"document_ids": [35736701,35746700,3575700,3576699,35776769,35786056,357911833580],3581"answer": "Yes, if you set the `--temp-dir` flag to a different directory than `/tmp`, Ray objects will spill to the custom directory specified.",3582"llm": "gpt-3.5-turbo"3583},3584{3585"question": "can you give me an example for *`--runtime-env-json`*",3586"sources": [3587"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",3588"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",3589"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",3590"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",3591"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",3592"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",3593"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails"3594],3595"document_ids": [35965854,359718443,35985870,35995857,36008859,360118910,3602190693603],3604"answer": "An example for the `--runtime-env-json` argument is:\n\n`--runtime-env-json='{\"env_vars\": {\"MY_ENV_VAR\": \"my-value\"}, \"working_dir\": \"./project/src\", \"pip\": [\"requests\", \"chess\"]}'`",3605"llm": "gpt-3.5-turbo"3606},3607{3608"question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",3609"sources": [3610"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",3611"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3612"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",3613"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",3614"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",3615"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",3616"https://docs.ray.io/en/master/ray-overview/index.html#overview"3617],3618"document_ids": [36198034,362018059,362118897,362218938,362318207,36248097,36255123626],3627"answer": "The value of `maxConcurrentReplicas` should be specified in the autoscaling configuration.",3628"llm": "gpt-3.5-turbo"3629},3630{3631"question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",3632"sources": [3633"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",3634"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",3635"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3636"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#using-async-methods",3637"https://docs.ray.io/en/master/serve/architecture.html#lifetime-of-a-request",3638"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",3639"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"3640],3641"document_ids": [364218999,364318207,364418062,364518541,364618084,364718091,3648189773649],3650"answer": "The value of `max_concurrent_queries` should be set to a number that represents the maximum number of pending queries in a single replica. The default value is 100, but it can be increased for performance tuning.",3651"llm": "gpt-3.5-turbo"3652},3653{3654"question": "what is a `smoothing_factor`",3655"sources": [3656"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3657"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3658"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#twin-delayed-ddpg-td3",3659"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg",3660"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",3661"https://docs.ray.io/en/master/rllib/package_ref/utils.html#tensorflow-utilities",3662"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#behavior-cloning-bc-derived-from-marwil-implementation"3663],3664"document_ids": [366518066,366618067,366716061,366816045,36692553,367014417,3671157833672],3673"answer": "A `smoothing_factor` is a multiplicative factor used to speed up or slow down each upscaling or downscaling decision in an autoscaling algorithm. It can be adjusted to control the pace of resource scaling based on traffic volume.",3674"llm": "gpt-3.5-turbo"3675},3676{3677"question": "What is the reason actors change their state to unhealthy?",3678"sources": [3679"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",3680"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",3681"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",3682"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",3683"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3684"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3685"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate"3686],3687"document_ids": [368818594,36891011,369018723,369118724,3692655,3693652,369410123695],3696"answer": "Actors change their state to unhealthy due to various reasons such as errors in the deployment's constructor, crashed replicas, or general system or machine errors.",3697"llm": "gpt-3.5-turbo"3698},3699{3700"question": "How do I access logs for a dead node?",3701"sources": [3702"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3703"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3704"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",3705"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",3706"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3707"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",3708"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log"3709],3710"document_ids": [3711674,3712675,3713890,3714934,3715752,3716728,371710023718],3719"answer": "You cannot access logs from a dead node.",3720"llm": "gpt-3.5-turbo"3721},3722{3723"question": "What are the reasons for a node to change it\u2019s status to dead?",3724"sources": [3725"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",3726"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",3727"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3728"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",3729"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3730"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",3731"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#rescheduling-bundles-on-a-dead-node"3732],3733"document_ids": [37346031,37351154,3736750,373718632,37386263,37391018,374068903741],3742"answer": "A node can change its status to dead for the following reasons:\n\n1. Raylet failure: When a raylet process fails, the corresponding node is marked as dead and treated as a node failure.\n2. Task killed due to low memory: If a node is running low on memory, a task may be killed, resulting in the node being marked as dead.\n3. Worker node failure: If a worker node fails, the actors running on it also fail. The node is detected as failed, and attempts are made to restart the worker pod on another node.\n4. Actor death: An actor may be killed using the `ray.kill` command, resulting in the node being marked as dead.\n5. Rescheduling bundles on a dead node: If a node containing bundles of a placement group dies, the bundles are rescheduled on different nodes by the Global Control Store (GCS).",3743"llm": "gpt-3.5-turbo"3744},3745{3746"question": "What are the reasons for spikes in node CPU utilization",3747"sources": [3748"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",3749"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3750"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",3751"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",3752"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-1-create-a-new-kubernetes-cluster",3753"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3754"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#adding-cpu-only-nodes-to-your-cluster"3755],3756"document_ids": [37578564,3758750,37591157,3760644,37617793,3762751,3763122833764],3765"answer": "The reasons for spikes in node CPU utilization can include insufficient outbound bandwidth on the head node, low memory on the node causing tasks to be killed, and the need for more CPU cores to parallelize preprocessing tasks.",3766"llm": "gpt-3.5-turbo"3767},3768{3769"question": "What AWS machine type is recommended to deploy a RayService on EKS?",3770"sources": [3771"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-aks-microsoft-azure",3772"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",3773"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",3774"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",3775"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",3776"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",3777"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"3778],3779"document_ids": [37807938,378118758,378218719,37838831,378418298,3785517,3786182963787],3788"answer": "The recommended machine type to deploy a RayService on EKS (Amazon Elastic Kubernetes Service) is not mentioned in the provided context.",3789"llm": "gpt-3.5-turbo"3790},3791{3792"question": "Is there a way to configure the session name generated by ray?",3793"sources": [3794"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3795"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3796"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3797"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3798"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3799"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",3800"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments"3801],3802"document_ids": [38036054,38046056,3805700,38066514,380719840,38086017,380988833810],3811"answer": "Yes, you can configure the session name generated by Ray. By default, the session name is in the format \"session_{timestamp}_{pid}\". You can 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()\".",3812"llm": "gpt-3.5-turbo"3813},3814{3815"question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",3816"sources": [3817"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",3818"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#python-sdk-api-reference",3819"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli",3820"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",3821"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",3822"https://docs.ray.io/en/master/cluster/running-applications/job-submission/rest.html#ray-jobs-rest-api",3823"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#observe-and-debug-placement-groups"3824],3825"document_ids": [38268828,38278782,38288790,38298915,38308920,38318823,383268683833],3834"answer": "Yes, you can use the Python SDK to get a link to the Ray dashboard for a given job.",3835"llm": "gpt-3.5-turbo"3836},3837{3838"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).",3839"sources": [3840"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",3841"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3842"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3843"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.LocalRayletDiedError.html#ray-exceptions-localrayletdiederror",3844"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.TaskUnschedulableError.html#ray-exceptions-taskunschedulableerror",3845"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",3846"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.WorkerCrashedError.html#ray-exceptions-workercrashederror"3847],3848"document_ids": [38496934,3850755,3851750,38526579,38536540,38546031,385566353856],3857"answer": "Possible causes for the node where the task was running to crash unexpectedly include: (1) failure of the instance where the node was running, (2) raylet crashing unexpectedly due to memory pressure (OOM), preempted node, or other reasons.",3858"llm": "gpt-3.5-turbo"3859},3860{3861"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.",3862"sources": [3863"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",3864"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",3865"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",3866"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",3867"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",3868"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",3869"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance"3870],3871"document_ids": [38727639,38738696,387418636,38756011,38766029,38771154,3878186353879],3880"answer": "The error message \"gcs_health_check_manager.cc:108: Health check failed for node\" indicates that there is a failure in the health check for a node in the GCS (Global Control Service). This could be due to various reasons such as the head node being inaccessible, a different version of Ray running at the specified address, an incorrect address, or firewall settings preventing access. To troubleshoot, you can use tools like nmap or nc to check if each port can be reached from the node. To make the GCS fault tolerant and prevent the entire Ray cluster from failing, you can enable GCS fault tolerance by adding an external Redis server and modifying the RayService Kubernetes object.",3881"llm": "gpt-3.5-turbo"3882},3883{3884"question": "Do you need the DAGDriver to deploy a serve application using RayServe?",3885"sources": [3886"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",3887"https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph",3888"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#building-the-call-graph-methodnodes-and-functionnodes",3889"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",3890"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",3891"https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",3892"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment"3893],3894"document_ids": [389518473,389618029,389718469,389818474,38997815,390018135,3901191503902],3903"answer": "Yes, you need the DAGDriver to deploy a serve application using RayServe. The DAGDriver routes HTTP requests through your call graph and is used as the ingress deployment in the deployment graph structure provided by Ray Serve.",3904"llm": "gpt-3.5-turbo"3905},3906{3907"question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",3908"sources": [3909"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",3910"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3911"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",3912"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",3913"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",3914"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests",3915"https://docs.ray.io/en/master/serve/production-guide/index.html#working-example-fruitstand-application"3916],3917"document_ids": [391818608,391919242,392019068,392118909,39227687,392318418,3924187613925],3926"answer": "The import path that needs to be provided for a simple RayServe deployment is \"module.submodule_1...submodule_n.dag_node\".",3927"llm": "gpt-3.5-turbo"3928},3929{3930"question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",3931"sources": [3932"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",3933"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",3934"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",3935"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",3936"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",3937"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",3938"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview"3939],3940"document_ids": [39417766,39427650,39437775,39448263,39457767,39467780,394776613948],3949"answer": "Yes, there are documents/examples showing the usage of RayJob in KubeRay. You can find a quickstart guide and example output in the provided context.",3950"llm": "gpt-3.5-turbo"3951},3952{3953"question": "Does Ray metrics have to be exported via an actor?",3954"sources": [3955"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",3956"https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",3957"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",3958"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",3959"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",3960"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",3961"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources"3962],3963"document_ids": [3964689,3965611,3966690,3967882,39687556,396919759,397060043971],3972"answer": "Yes, Ray metrics can be exported via an actor using the ray.util.metrics API.",3973"llm": "gpt-3.5-turbo"3974},3975{3976"question": "how can I avoid objects not getting spilled?",3977"sources": [3978"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",3979"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",3980"https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",3981"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3982"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",3983"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",3984"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node"3985],3986"document_ids": [39876704,39886700,39896173,399019816,39918562,39926710,399367013994],3995"answer": "To avoid objects from getting spilled, you can follow these recommendations:\n\n1. Use an SSD instead of an HDD for better performance.\n2. Specify a large buffer size (> 1MB) to reduce IO requests during spilling.\n3. Use NVMe SSDs or other high-performance storage if possible.\n4. Configure the directory where objects are spilled to, if needed.",3996"llm": "gpt-3.5-turbo"3997},3998{3999"question": "what\u2019s ray core",4000"sources": [4001"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",4002"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",4003"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",4004"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",4005"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",4006"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#want-to-learn-more",4007"https://docs.ray.io/en/master/workflows/basics.html#ray-integration"4008],4009"document_ids": [4010359,40116330,4012515,40136211,4014521,40157372,4016129584017],4018"answer": "Ray Core is an open-source, Python, general-purpose, distributed computing library that allows ML engineers and Python developers to scale Python applications and accelerate machine learning workloads. It provides core primitives for building and scaling distributed applications and enables the easy development of scalable, distributed systems that can run on various platforms such as laptops, clusters, clouds, or Kubernetes. Ray Core is the foundation for Ray AI libraries and third-party integrations.",4019"llm": "gpt-3.5-turbo"4020},4021{4022"question": "Does ray support cron job",4023"sources": [4024"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",4025"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4026"https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",4027"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",4028"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",4029"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",4030"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes"4031],4032"document_ids": [40338915,403418775,403512885,40366330,40378917,403819150,403976584040],4041"answer": "No, Ray does not have built-in support for cron jobs. However, you can use an external job scheduler to interact with your Ray cluster and trigger workflow runs.",4042"llm": "gpt-3.5-turbo"4043},4044{4045"question": "can you give me the dependencies list for api read_images?",4046"sources": [4047"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4048"https://docs.ray.io/en/master/data/working-with-images.html#reading-images",4049"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4050"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset",4051"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset",4052"https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",4053"https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images"4054],4055"document_ids": [40569839,40579019,40589838,405910764,406010765,406119583,40624264063],4064"answer": "The dependencies for the `read_images` API are not explicitly mentioned in the provided context.",4065"llm": "gpt-3.5-turbo"4066},4067{4068"question": "how do I kill a specific serve replica",4069"sources": [4070"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",4071"https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-deployments",4072"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",4073"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",4074"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",4075"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",4076"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests"4077],4078"document_ids": [407918690,408018814,408118439,408218686,408318058,408418682,4085184384086],4087"answer": "To kill a specific Serve replica, you can use the `ray.kill()` function with the replica handle as the argument. Set the `no_restart` parameter to `True` to prevent the replica from being restarted.",4088"llm": "gpt-3.5-turbo"4089}4090]4091}4092