llm-applications
3383 строки · 286.5 Кб
1{2"config": {3"experiment_name": "with-context",4"chunk_size": 300,5"chunk_overlap": 50,6"num_chunks": 5,7"embedding_model_name": "thenlper/gte-base",8"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1",9"temperature": 0.0,10"max_context_length": 32768,11"system_content": "Answer the query using the context provided. Be succinct.",12"assistant_content": "",13"docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",14"experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",15"references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",16"num_samples": 17717},18"results": [19{20"question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",21"sources": [22"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.take_batch.html#ray-data-dataset-take-batch",23"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",24"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",25"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",26"https://docs.ray.io/en/master/data/examples/batch_training.html#batch-training-with-ray-data"27],28"document_ids": [2917000,3014846,3114991,3215609,331708034],35"answer": " When struggling with Ray Data type conversions in `map_batches`, consider specifying the `batch_format` argument to configure the batch type as NumPy arrays or pandas DataFrames. By default, Ray Data represents batches as dicts of NumPy ndarrays. However, be aware that large block size can lead to potential out-of-memory situations. To avoid these issues, ensure that no single item in your Ray Data is too large, and always call `ds.map_batches()` with a small enough batch size so that the output batch can comfortably fit into memory. You can return either format from your function in `map_batches`.",36"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"37},38{39"question": "How does autoscaling work in a Ray Serve application?",40"sources": [41"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",42"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",43"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",44"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",45"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling"46],47"document_ids": [4829168,4929218,5029868,5112385,521425853],54"answer": " Ray Serve's autoscaling feature automatically adjusts the number of replicas in a deployment based on its current load. The Ray autoscaler, which runs on the head node, is responsible for this process. It scales the cluster up or down based on resource demand to efficiently accommodate user traffic and improve performance of LLM applications. You can configure the Ray autoscaler using the Ray cluster launcher. Ray Serve also supports batching and streaming responses, and it can integrate with any model optimization technique.",55"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"56},57{58"question": "how do I get the address of a ray node",59"sources": [60"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",61"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",62"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",63"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",64"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs"65],66"document_ids": [6713820,688044,6914678,7014622,7185172],73"answer": " To get the address of a Ray node, first start Ray on the head node with the command `ray start --address=<head-node-address:port>`, replacing `<head-node-address:port>` with the value printed by the command on the head node. Then, initialize Ray using `ray.init()` and print the addresses of the nodes using `print(ray.nodes())`. The address of each node is listed under the 'NodeManagerAddress' field.",74"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"75},76{77"question": "Does Ray support NCCL?",78"sources": [79"https://docs.ray.io/en/master/ray-core/actors/out-of-band-communication.html#wrapping-library-processes",80"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning",81"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",82"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#image",83"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib"84],85"document_ids": [869231,8713250,881152,8913058,903136891],92"answer": " Yes, Ray does support NCCL. It is mentioned as one of the high-performance communication backends for Ray's collective communication library, which operates on both distributed CPUs and GPUs.",93"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"94},95{96"question": "Is Ray integrated with DeepSpeed?",97"sources": [98"https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",99"https://docs.ray.io/en/master/train/api/api.html#pytorch-lightning",100"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",101"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",102"https://docs.ray.io/en/master/workflows/basics.html#ray-integration"103],104"document_ids": [10517642,10618130,10720305,10824233,10920553110],111"answer": " Yes, Ray is integrated with DeepSpeed through the RayDeepSpeedStrategy, which is a subclass of DeepSpeedStrategy that ensures compatibility with Ray orchestration. You can use DeepSpeed with Ray Train by specifying the DeepSpeed configuration in the TrainingArguments object, particularly when using the \ud83e\udd17 Transformers and Ray integration.",112"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"113},114{115"question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",116"sources": [117"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",118"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",119"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",120"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",121"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks"122],123"document_ids": [1249501,12530068,12632075,12726760,1288374129],130"answer": " Using `asyncio`'s `await` to wait for a Ray future like `await x.remote()` is not supported and will likely fail. Instead, you should wrap the async function with a synchronous wrapper to run the task. Ray provides synchronous (`ray.wait([ref])`) and async (`await asyncio.wait([ref])`) APIs for waiting on futures, which can be used interchangeably. However, keep in mind that asyncio task cancellation is not guaranteed in Ray, and the task won't be interrupted in the middle of execution if you don't `await` within the async function.",131"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"132},133{134"question": "How would you compare Spark, Ray, Dask?",135"sources": [136"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",137"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",138"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",139"https://docs.ray.io/en/master/ray-overview/ray-libraries.html#the-ray-ecosystem",140"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries"141],142"document_ids": [14314396,14431534,14515286,146555,14714778148],149"answer": " Spark, Ray, and Dask are all parallel computing libraries, but they have some differences in terms of use cases and capabilities.\n\nSpark is a general-purpose cluster computing system that can handle batch processing, real-time data streaming, and machine learning. It has a large and active community, and it supports a wide range of programming languages, including Python, Scala, Java, and R.\n\nRay is a high-performance distributed computing system that is designed to handle complex workloads that require low latency and high throughput. It has a flexible and modular architecture that allows it to scale seamlessly from a single machine to a large cluster. Ray can be used to accelerate machine learning workloads, and it has built-in support for popular deep learning frameworks such as TensorFlow and PyTorch.\n\nDask is a Python library that provides parallel and distributed computing capabilities for analytics and scientific computing workloads. It is designed to be used with Pandas and NumPy, and it provides big data collections that mimic the APIs of those libraries. Dask is well-suited for tasks that require parallel processing of large datasets, such as data cleaning, transformation, and analysis.\n\nRay and Dask can interoperate, allowing users to leverage the strengths of both systems. For example, RayDP (\"Spark on Ray\") enables the use of Spark inside a Ray program, allowing users to read input data using Spark, process it using Spark SQL, DataFrame, or Pandas (via Koalas) API, extract and transform features using Spark MLLib, and use RayDP Estimator API for distributed training.\n\nIn terms of performance, a detailed comparison between Ray Data and Apache Spark can be found in the Offline Batch Inference report. The report compares the two systems in terms of throughput, latency, and resource utilization for a specific use case. However, the best choice of system will depend on the specific requirements of the use case, such as the size and complexity of the data, the desired performance characteristics, and the available resources.",150"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"151},152{153"question": "why would ray overload a node w/ more task that the resources allow ?",154"sources": [155"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",156"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#task-based-batch-prediction",157"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",158"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",159"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#sbatch-directives"160],161"document_ids": [16211646,16312109,16411352,16511368,16614445167],168"answer": " Ray will not intentionally overload a node with more tasks than its resources allow. Ray ensures that the total resources required by all concurrent tasks and actors on a node do not exceed the node's total resources. Overloading the node is the user's responsibility, and Ray does not provide CPU isolation for tasks or actors. To specify a task or actor's resource requirements, you can use Ray's resource specification feature, which ensures that the task or actor will only run on a node if there are enough required logical resources available.",169"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"170},171{172"question": "when should I use Ray Client?",173"sources": [174"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",175"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",176"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",177"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",178"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"179],180"document_ids": [18114309,18214292,18314298,18414285,18532168186],187"answer": " You should use Ray Client for developing interactively in a local Python shell when you require a connection to a remote Ray cluster. However, be aware of its limitations such as the need for a stable connection and its potential unsuitability for long-running workloads or ML projects using Ray Tune or Ray Train. Consider using Ray Jobs API for ML projects instead.",188"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"189},190{191"question": "how to scatter actors across the cluster?",192"sources": [193"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",194"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",195"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",196"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",197"https://docs.ray.io/en/master/ray-observability/reference/api.html#list-apis"198],199"document_ids": [2001835,20111455,20212195,20317160,2041760205],206"answer": " To scatter actors across the cluster in Ray, you can use the following code:\n```makefile\nactors = [SpreadActor.options(scheduling_strategy=\"SPREAD\").remote() for _ in range(10)]\n```\nThis creates 10 instances of the `SpreadActor` actor and deploys them across the cluster using the \"SPREAD\" scheduling strategy, which ensures that the actors are evenly distributed across the available nodes.\n\nYou can also list the actors in the cluster using the `ray.util.state.list_actors()` function, which returns a list of all the actors currently running in the cluster.",207"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"208},209{210"question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",211"sources": [212"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",213"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#deploying-a-static-ray-cluster",214"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",215"https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",216"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-7-failed-to-get-serve-application-statuses"217],218"document_ids": [21930587,22012887,22114295,22231990,22312639224],225"answer": " The connection refused error you're encountering when running `ray debug` on your remote Ray cluster suggests that your client is unable to connect to the Ray cluster. This could be due to a few reasons:\n\n1. The address you're using to connect to the Ray cluster is incorrect. Make sure you're using the correct `<head-node-ip-address>` and port number (10001) when specifying the address in the `--address` argument or the `ray_address` variable.\n2. The Ray cluster is not running or is not reachable from your current location. Check the status of your Ray cluster and ensure that it's up and running.\n3. There may be a network issue preventing your client from connecting to the Ray cluster. Check your network settings and firewall rules to ensure that there are no blocks preventing the connection.\n\nYou can try connecting to the Ray cluster using `ray.init(\"ray://<head_node_host>:10001\")` or setting the `RAY_ADDRESS` environment variable to the cluster's IP address and port. Once connected, you should be able to run `ray debug` without encountering the connection refused error.",226"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"227},228{229"question": "How does Ray AIR set up the model to communicate gradient updates across machines?",230"sources": [231"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",232"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",233"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tune-a-transformers-model",234"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",235"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary"236],237"document_ids": [23821321,23924217,24021715,24121269,24221368243],244"answer": " Ray AIR sets up the model to communicate gradient updates across machines by using data-parallel training and sharding the training data. This is achieved through the use of Ray Train abstractions, which can be integrated seamlessly with existing PyTorch training code.",245"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"246},247{248"question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",249"sources": [250"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",251"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",252"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests",253"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",254"https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments"255],256"document_ids": [25729807,25830712,25930489,26029730,26129104262],263"answer": " You may want to use Ray Serve instead of Modal or Seldon because it supports multi-model composition with a programmable API, making it easier to call different models as if they were function calls. Ray Serve also allows for flexible model composition and scaling by enabling multiple independent deployments to call into each other. This can be more convenient than managing containers directly.",264"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"265},266{267"question": "How do I deploy an LLM workload on top of Ray Serve?",268"sources": [269"https://docs.ray.io/en/master/serve/develop-and-deploy.html#deploy-ray-serve-in-production",270"https://docs.ray.io/en/master/serve/tutorials/gradio-integration.html#parallelize-using-ray-serve",271"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",272"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",273"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm"274],275"document_ids": [27629149,27731598,27831071,2791411,28030279281],282"answer": " To deploy an LLM (large language model) workload on top of Ray Serve, you can follow these steps:\n\n1. First, deploy Ray Serve in production, either on-premise or on cloud platforms like AWS, GCP, or Azure. You can use Ray cluster managers to run Ray on existing Kubernetes, YARN, or Slurm clusters.\n2. Alternatively, you can deploy Ray Serve on a VM (Virtual Machine) using the Ray Serve CLI. The `serve deploy` command takes in a config file path and deploys it to a Ray cluster over HTTP.\n3. In the config file, define your LLM workload as a Ray Serve deployment, specifying necessary details such as the model architecture, input/output processing, and any required pre-/post-processing steps.\n4. Once the deployment is created, Ray Serve will handle incoming requests and manage the resources needed for your LLM workload.\n\nFor more information, refer to the Ray Serve documentation on deploying workloads in production: <https://docs.ray.io/en/latest/serve/production.html>",283"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"284},285{286"question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",287"sources": [288"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",289"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#task-based-batch-prediction",290"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-size",291"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",292"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-a-model-on-a-text-classification-task"293],294"document_ids": [29523845,29612107,29715009,29824225,29921755300],301"answer": " Based on the context provided, the model \"EleutherAI/gpt-j-6B\" is a relatively large model and may require a GPU with 16 or more GBs of memory for serving. The exact amount of memory needed will depend on the model and the batch size used. To fit the model on smaller GPU types, you can use DeepSpeed to offload and partition optimizer and parameter states, reducing GRAM usage. You can also control the parallelism by specifying the amount of memory needed for each task using the `memory` option when launching tasks with `make_prediction.options()`.",302"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"303},304{305"question": "How do I log the results from multiple distributed workers into a single tensorboard?",306"sources": [307"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",308"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",309"https://docs.ray.io/en/master/tune/getting-started.html#early-stopping-with-adaptive-successive-halving-ashascheduler",310"https://docs.ray.io/en/master/ray-core/examples/plot_example-a3c.html#benchmarks-and-visualization",311"https://docs.ray.io/en/master/tune/index.html#why-choose-tune"312],313"document_ids": [3145140,3155018,3162298,31712279,3182398319],320"answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use the `--logdir` flag to specify the output directory of your results. This directory should be accessible to all workers, such as a shared network drive or cloud storage. When starting TensorBoard, point it to this directory using the `--logdir` flag. For example:\n\n```\nray exec tune-default.yaml 'tensorboard --logdir=gs://my-bucket/ray_results/ --port 6006' --port-forward 6006\n```\n\nThis will start TensorBoard and display the results from all workers in a single view.\n\nAlternatively, you can use a tool like MLflow to log and centralize your results from multiple distributed workers. MLflow supports logging results to TensorBoard, as well as other popular visualization tools.",321"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"322},323{324"question": "how do you config SyncConfig for a Ray AIR job?",325"sources": [326"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",327"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",328"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",329"https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration",330"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed"331],332"document_ids": [3333184,33417616,33525579,3362566,33725570338],339"answer": " To configure `SyncConfig` for a Ray AIR job, you can create a `RunConfig` object and set the `sync_config` field to a new `SyncConfig` object. Here's an example:\n```\nfrom ray.train.run_config import RunConfig\nfrom ray.train.sync_config import SyncConfig\n\nrun_config = RunConfig(\n sync_config=SyncConfig(\n upload_dir=None, # specify the directory to upload files to\n syncer=None # specify the syncer to use\n )\n)\n```\nIf you want to avoid errors/warnings and continue running with syncing explicitly turned off, you can set `RunConfig(SyncConfig(syncer=None))`. To re-enable the head node syncing behavior, you can set the environment variable `RAY_AIR_REENABLE_DEPRECATED_SYNC_TO_HEAD_NODE=1`.\n\nKeep in mind that Ray AIR no longer supports the synchronization of checkpoints and other artifacts from worker nodes to the head node. This means that the checkpoints and artifacts saved by trials scheduled on worker nodes will not be accessible during the run.\n\nFor more information, you can refer to the documentation for `SyncConfig` and `RunConfig` in the Ray library.",340"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"341},342{343"question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",344"sources": [345"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",346"https://docs.ray.io/en/master/ray-contribute/debugging.html#backend-logging",347"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",348"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",349"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs"350],351"document_ids": [35213002,3532177,35412589,3551809,3561761357],358"answer": " To quickly narrow down the root cause of a failed Ray job, follow these steps:\n\n1. Set the log level to debug: `export RAY_BACKEND_LOG_LEVEL=debug` and start Ray with `ray start`. This will print debug-level logs to the `raylet.err` file.\n2. Check the logs of Ray Pods using `ray logs`. You can get logs based on the filename or resource identifiers like actor.\n3. If you have submitted the Ray job via Jobs API, find the captured Driver logs using the Dashboard UI, CLI (using the `ray job logs` command), or the Python SDK (using `JobSubmissionClient.get_logs()` or `JobSubmissionClient.tail_job_logs()`).\n\nRemember to check the `raylet.err` file and the relevant logs for any error messages or indications of what went wrong with the Ray job.",359"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"360},361{362"question": "How do I specify how many GPUs a serve deployment needs?",363"sources": [364"https://docs.ray.io/en/master/serve/develop-and-deploy.html#convert-a-model-into-a-ray-serve-application",365"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",366"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",367"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",368"https://docs.ray.io/en/master/serve/develop-and-deploy.html#convert-a-model-into-a-ray-serve-application"369],370"document_ids": [37129135,37229191,37329600,37429187,37529134376],377"answer": " To specify the number of GPUs a Serve deployment needs, you can use the `ray_actor_options` parameter with the `num_gpus` property in the `@serve.deployment` decorator. This property takes a float representing the logical number of GPUs each replica should reserve. For example, to create a deployment where each replica uses 0.5 GPUs, you can do the following:\n```python\n@serve.deployment(ray_actor_options={\"num_gpus\": 0.5})\ndef func(*args):\n ...\n```\nThis will allocate 0.5 GPUs for each replica of the deployment. You can adjust this value based on your specific requirements.",378"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"379},380{381"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?",382"sources": [383"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#tune-hyperparameters-with-ray-air-a-name-predict-a",384"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",385"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",386"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",387"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a"388],389"document_ids": [39022630,39121042,39222122,3931210,39422060395],396"answer": " Based on the information you provided, it seems that one of your worker nodes is running out of memory and receiving a SIGTERM signal as a result. This is indicated by the message \"10 Workers killed due to memory pressure (OOM)\" in the raylet logs.\n\nTo debug this issue, you may want to consider the following steps:\n\n1. Check the memory usage of the worker nodes to see if any of them are consistently running out of memory. You can do this by checking the output of the `ray memory` command.\n2. If you find that one or more worker nodes are consistently running out of memory, you may need to adjust the memory configuration for those nodes. You can do this by setting the `--memory` flag when launching the Ray cluster, or by using the `ray.worker.global_worker.set_memory_limit()` function in your application code.\n3. You may also want to consider using a profiling tool, such as `ray memory-summary`, to get a more detailed view of memory usage in your application. This can help you identify any memory leaks or other issues that may be causing the worker nodes to run out of memory.\n4. It's also worth checking the logs for the TensorFlowTrainer to see if there are any errors or warnings that may be related to the worker node failures. The messages you provided suggest that there may be some issues with the TensorFlow build that could be contributing to the problem.\n\nI hope this helps! Let me know if you have any other questions.",397"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"398},399{400"question": "what are the possible reasons for nodes dying in a cluster?",401"sources": [402"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",403"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",404"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",405"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",406"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure"407],408"document_ids": [40911386,4108507,41130148,41230346,41330315414],415"answer": " Based on the provided context, possible reasons for nodes dying in a cluster include:\n\n1. Crashing of the head node, which can stall the dashboard and other control processes, causing the entire Ray cluster to become unusable.\n2. Simulating a head node failure by killing the head pod or the head node.\n\nIn a Ray cluster running on Kubernetes, the RayService controller health-checks the Ray cluster and restarts it if the head node crashes, but this introduces some downtime. It's quicker to simulate failures for debugging by killing pods rather than nodes.",416"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"417},418{419"question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",420"sources": [421"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",422"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",423"https://docs.ray.io/en/master/cluster/running-applications/index.html#application-guide",424"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",425"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling"426],427"document_ids": [42814258,42913136,43013970,43113137,43214270433],434"answer": " To programmatically set the size of a Ray remote cluster to a target size immediately, you can use the `ray.autoscaler.sdk.request_resources` function. This function allows you to request a specific amount of resources, which will trigger the autoscaler to adjust the number of nodes in the cluster as needed to meet the requested resource level.\n\nHowever, it's important to note that the Ray autoscaler typically scales a cluster up and down based on resource demand, so requesting a specific resource level may not have the desired effect if the current demand in the cluster is not sufficient to meet the requested level.\n\nIf you want to set a fixed size for your Ray cluster without using the autoscaler, you may want to consider using a non-autoscaling cluster. The Ray documentation recommends starting with non-autoscaling clusters if you're new to Ray.",435"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"436},437{438"question": "how do you disable async iter_batches with Ray Dataset?",439"sources": [440"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",441"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches",442"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",443"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",444"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_torch_batches.html#ray-data-dataiterator-iter-torch-batches"445],446"document_ids": [44716000,44816975,44915484,45016412,45116732452],453"answer": " To disable async (asynchronous) behavior when using `iter_batches` with Ray Dataset, you can set the `async_bufsize` argument to `0`. Here's an example:\n```python\ndataset = ray.data.read_parquet('s3://my-bucket/data/*.parquet')\niterator = dataset.iter_batches(batch_size=1024, async_bufsize=0)\n```\nIn this example, `async_bufsize=0` disables async behavior and the iterator will return batches synchronously. Note that this argument is available for all the methods mentioned in the context, including `iter_tf_batches`, `iter_torch_batches`, and the iterators in `ray.data.DataIterator`.",454"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"455},456{457"question": "what is the different between a batch and a block, for ray datasets?",458"sources": [459"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",460"https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",461"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",462"https://docs.ray.io/en/master/data/examples/batch_training.html#batch-training-with-ray-data",463"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"464],465"document_ids": [46632164,46715417,46815609,46917080,47014846471],472"answer": " A batch and a block are related concepts in Ray Datasets, but they differ in their abstraction levels and usage. A block is an internal abstraction that represents a data processing unit, while a batch is a user-facing object. Specifically:\n\n1. Block (#A): It is an internal abstraction that represents a data processing unit. A Dataset consists of a collection of blocks, which are partitioned rows that allow for parallel operations.\n2. Batch: It is a user-facing object that is created by calling `ds.map_batches()`. The batch size should be small enough to fit into memory and avoid out-of-memory situations.\n\nIn summary, a block is an internal data processing unit, while a batch is a user-facing object created for parallel processing with a specified batch size.",473"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"474},475{476"question": "How to setup the development environments for ray project?",477"sources": [478"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning",479"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",480"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",481"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",482"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher"483],484"document_ids": [48513250,4862100,4879156,48820303,4899133490],491"answer": " To set up the development environment for the Ray project, you can follow these steps:\n\n1. Checkout the Ray repository and build Ray from source to make changes easily.\n2. Alternatively, you can install dependencies dynamically while Ray is running. A runtime environment describes the dependencies your Ray application needs to run, including files, packages, environment variables, and more.\n3. To ensure that Ray workers have access to all necessary packages, you can define a runtime environment. If you have already included these dependencies in your Docker image or installed them on each node, you can ignore the runtime\\_env argument.\n4. Another way to set up dependencies is to prepare a single environment across the cluster before starting the Ray runtime using the Ray Cluster launcher.",492"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"493},494{495"question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",496"sources": [497"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",498"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",499"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",500"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",501"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#code-example"502],503"document_ids": [50426908,50532237,50626909,50728617,50811621509],510"answer": " To debug why Ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`, you can:\n\n1. Check if there are any issues with the `env_creator` function or the environment itself.\n2. Ensure that the `validate_env` function is not causing any issues.\n3. Verify that the `config` is correctly set up.\n4. Make sure that the `worker_index` and `num_workers` are correctly specified.\n5. Avoid using `ray.get()` in a loop, as it can harm parallelism.\n6. Use Ray's built-in debugging tools, such as the dashboard and profiling, to identify any potential bottlenecks or issues.\n7. Check the Ray logs for any error messages or warnings related to the rollout workers.\n8. If the issue persists, consider creating a minimal, reproducible example and filing a bug report in the Ray GitHub repository.",511"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"512},513{514"question": "how do I join two ray datasets?",515"sources": [516"https://docs.ray.io/en/master/data/preprocessors.html#overview",517"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-preprocess-data-for-training",518"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",519"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#gpt-j-6b-fine-tuning-with-ray-train-and-deepspeed",520"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a"521],522"document_ids": [52314799,52417173,52521803,52624096,52717130528],529"answer": " To join two Ray datasets, you can use the `join()` method provided by Ray Datasets. Here's an example of how to join `dataset1` and `dataset2`:\n```python\ndataset1 = dataset1.join(dataset2, how='inner', on=None)\n```\nIn this example, `how` is the type of join to perform (e.g., 'inner', 'outer', 'left', 'right'), and `on` is the column(s) to join on (if None, it defaults to joining on all columns with the same name in both datasets).\n\nNote that this is a general example and might need to be adapted to your specific use case. Make sure to check the Ray Datasets documentation for more information on the `join()` method and its options.",530"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"531},532{533"question": "Is there a way to retrieve an object ref from its id?",534"sources": [535"https://docs.ray.io/en/master/ray-core/objects.html#objects",536"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",537"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",538"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner.Learner.get_param_ref.html#ray-rllib-core-learner-learner-learner-get-param-ref",539"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive"540],541"document_ids": [54210384,54310815,54410805,54522724,54630065547],548"answer": " Yes, you can retrieve an object ref from its ID using the Ray ObjectRef in Ray, a distributed computing framework. An object ref is returned by remote function calls and can be used to refer to a remote object. You can use the synchronous Ray Core API `ray.get(ref)` or the async API `await ref` to retrieve the object referenced by the ref. Additionally, you can use `ray.wait([ref])` or the async API to wait for the result to be available without retrieving it.",549"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"550},551{552"question": "how to create model Checkpoint from the model in memory?",553"sources": [554"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",555"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",556"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#saving-and-loading-xgboost-and-lightgbm-checkpoints",557"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",558"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchTrainer.html#ray-train-torch-torchtrainer"559],560"document_ids": [56119435,56219018,56317135,56419184,56518405566],567"answer": " To create a model checkpoint from a model in memory for Keras, you can use the `from_model` function from the checkpoint class, like so: `checkpoint = Checkpoint.from_model(model)`. For PyTorch models, you can use `torch.save({\"model_state_dict\": base_model.state_dict()}, PATH)` to save the model and load it back into memory using `model = MyModel(...)` followed by `model.load_state_dict(torch.load(PATH)[\"model_state_dict\"])`. For XGBoost and LightGBM, checkpoint loading can be done using their respective `get_model` static methods.",568"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"569},570{571"question": "what is Deployment in Ray Serve?",572"sources": [573"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",574"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",575"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",576"https://docs.ray.io/en/master/serve/develop-and-deploy.html#deploy-ray-serve-in-production",577"https://docs.ray.io/en/master/serve/tutorials/gradio-integration.html#parallelize-using-ray-serve"578],579"document_ids": [58031071,58129096,58232175,58329149,58431598585],586"answer": " A Deployment in Ray Serve is a fundamental concept that contains business logic or machine learning models to handle incoming requests. It's a group of actors that can manage traffic and is defined as a single class with various options, such as the number of \"replicas\" to deploy across a Ray cluster. Deployments can be scaled up to run in production environments, handling incoming requests efficiently.",587"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"588},589{590"question": "What is user config in Ray Serve? how do I use it?",591"sources": [592"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",593"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#rayservice",594"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#defining-an-application-builder",595"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",596"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide"597],598"document_ids": [59914309,60012686,60129768,60230606,60312804604],605"answer": " User config in Ray Serve refers to the configuration that Ray Serve uses to deploy an application, represented by `serveConfigV2`. To use it, you can pass the parameters to the constructor of your deployments using the `.bind()` method. This allows you to configure deployments using ordinary Python code. Alternatively, you can update the Ray Serve config in the RayService CR config and use `kubectl apply` to update the applications. Troubleshooting may be challenging, but you can refer to the rayservice-troubleshooting documentation for more details.",606"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"607},608{609"question": "What is the difference between PACK and SPREAD strategy?",610"sources": [611"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",612"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",613"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",614"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",615"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread"616],617"document_ids": [61822608,61924484,6208950,6214026,62211454623],624"answer": " The \"PACK\" strategy bundles tasks into as few nodes as possible, while the \"SPREAD\" strategy distributes bundles across distinct nodes as evenly as possible. The \"PACK\" strategy tries to minimize the number of nodes used, whereas the \"SPREAD\" strategy aims for an even distribution of tasks or actors across the available nodes.",625"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"626},627{628"question": "What\u2019s the best way to run ray across multiple machines?",629"sources": [630"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",631"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",632"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",633"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-2-avoid-tiny-tasks",634"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#running-jobs-interactively"635],636"document_ids": [63712543,63815247,6398314,6408813,64114393642],643"answer": " The best way to run Ray across multiple machines is to start a Ray cluster for each user, as it ensures workload isolation. You can do this by initiating the Ray runtime on multiple servers, which consists of several services and processes for communication, data transfer, and scheduling. When developing Ray programs, ensure tasks take at least a few milliseconds to run, and consider using the Ray Client for running scripts from your local machine while maintaining a connection to the cluster (expert users only).",644"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"645},646{647"question": "how do I specify ScalingConfig for a Tuner run?",648"sources": [649"https://docs.ray.io/en/master/tune/examples/tune-pytorch-lightning.html#training-with-gpus",650"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",651"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",652"https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",653"https://docs.ray.io/en/master/tune/examples/includes/mlflow_ptl_example.html#mlflow-pytorch-lightning-example"654],655"document_ids": [6568482,65719193,65819615,6593015,6607326661],662"answer": " To specify the `ScalingConfig` for a Tuner run in the provided context, you can include it in the `run_config` argument of the `Tuner` constructor. The `run_config` argument is used to configure the execution of the training run, and it accepts an `air.RunConfig` object which can contain the `scaling_config`. Here's an example:\n\n```python\nfrom ray import tune\n\ntuner = tune.Tuner(\n tune.with_resources(trainable, resources={\"cpu\": 1, \"gpu\": gpus_per_trial}),\n tune_config=tune.TuneConfig(\n metric=\"loss\",\n mode=\"min\",\n num_samples=num_samples,\n ),\n run_config=tune.RunConfig(\n scaling_config={\n # Your ScalingConfig here\n }\n )\n)\n```\n\nReplace the comment with your desired `ScalingConfig` configuration.",663"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"664},665{666"question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",667"sources": [668"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_numpy_refs.html#ray-data-dataset-to-numpy-refs",669"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",670"https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",671"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",672"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes"673],674"document_ids": [67516602,67614867,67710575,67810398,67910600680],681"answer": " To utilize the 'zero-copy' feature Ray provides for numpy, you should first convert your numpy data to Arrow format using `Dataset.to_arrow()` or access the internal block references using `Dataset.get_internal_block_refs()`. When using functions like `map_batches()`, set `zero_copy_batch=True` if the function does not mutate its input to improve performance and decrease memory utilization.\n\nWhen working with numpy arrays in Ray, if the object is a numpy array or a collection of numpy arrays, the `get` call is zero-copy and returns arrays backed by shared object store memory. This allows for zero-copy reads among Ray workers on the same node.",682"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"683},684{685"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?",686"sources": [687"https://docs.ray.io/en/master/data/examples/random-access.html#performance",688"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.push.html#ray-util-actorpool-push",689"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",690"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.state_key.html#ray-train-lightning-raytrainreportcallback-state-key",691"https://docs.ray.io/en/master/tune/api/doc/ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback.state_key.html#ray-tune-integration-pytorch-lightning-tunereportcheckpointcallback-state-key"692],693"document_ids": [69417958,69511288,69617158,69719038,6984471699],700"answer": " Given the context, it is not advised to spin up 1 actor per key due to the potential for uneven load distribution. Instead, creating \"key pools\" where an actor can hold many keys would be a better approach to ensure a more even distribution of load across the key space. This is in line with the recommendation to start more than one actor per node for multi-GPU training, where each actor is assigned a portion of the resources. Additionally, providing a unique state key is important for maintaining the state of multiple instances of a callback, as mentioned in the context.",701"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"702},703{704"question": "How to find the best checkpoint from the trial directory?",705"sources": [706"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",707"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",708"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",709"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",710"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint"711],712"document_ids": [7133164,7147393,7153587,7163165,7174633718],719"answer": " To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` method provided by Ray Tune's `ExperimentAnalysis`. This method returns the path for the last checkpoint of the trial. You can specify the metric and mode to determine the best trial. If you want to get the checkpoint path of the best trial of the entire experiment, you can use the `best_checkpoint` property.\n\nHere's an example usage:\n```python\nfrom ray import tune\n\n# Assuming you have an ExperimentAnalysis object `analysis`\nbest_checkpoint = analysis.get_best_checkpoint(return_path=True)\n# or\nbest_checkpoint = analysis.best_checkpoint\n```\nThe `best_checkpoint` variable will contain the path to the best checkpoint, which you can then use to load the model or inspect it using Tensorboard.",720"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"721},722{723"question": "what are the advantage and disadvantage of using singleton Actor ?",724"sources": [725"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",726"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#how-to-improve-the-gpu-utilization-rate",727"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.reset_config.html#ray-tune-trainable-reset-config",728"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.reset_config.html#ray-rllib-algorithms-algorithm-algorithm-reset-config",729"https://docs.ray.io/en/master/ray-core/patterns/concurrent-operations-async-actor.html#pattern-using-asyncio-to-run-actor-methods-concurrently"730],731"document_ids": [73211557,73312158,7344572,73524449,73611486737],738"answer": " Advantage of using a singleton actor includes:\n\n* More efficient actor-based approach that reuses model initialization for many tasks, allowing the node to spend more time on the actual workload.\n* Each actor instance has its own copy of class variables, reducing the risk of variable conflicts.\n\nDisadvantage of using a singleton actor includes:\n\n* Limited concurrency, as only one task can be processed at a time. However, this can be mitigated by using threaded actors instead.",739"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"740},741{742"question": "what are the advantages of using a named actor?",743"sources": [744"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",745"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",746"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",747"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",748"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources"749],750"document_ids": [7519625,75210479,7539706,7549637,75532094756],757"answer": " The advantages of using a named actor are:\n1. Unique name: An actor can be given a unique name within their namespace, allowing you to retrieve the actor from any job in the Ray cluster.\n2. Accessibility: Named actors can be retrieved even if you cannot directly pass the actor handle to the task that needs it.\n3. Persistence: Named actors can be retrieved as long as the original driver is still running, ensuring their persistence in the cluster.\n\nNamed actors can be useful when you need to access a specific actor instance across different tasks or jobs in the Ray cluster, and when you want to ensure the actor's persistence even if it's not directly passed around between tasks. However, if you don't need the stateful part of an actor, tasks may be a better option for simplicity and performance.",758"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"759},760{761"question": "How do I read a text file stored on S3 using Ray Data?",762"sources": [763"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",764"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-1-reading-the-dataset-from-s3",765"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",766"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-1-reading-the-dataset-from-s3",767"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text"768],769"document_ids": [77017658,77117818,77217654,77317820,77416454775],776"answer": " To read a text file stored on S3 using Ray Data, you can use the `ray.data.read_text` function and provide the S3 URI of the text file. Here is an example:\n\n```\ns3_uri = \"s3://anonymous@air-example-data-2/mytextfile.txt\"\nds = ray.data.read_text(s3_uri)\n```\n\nThis will return a Ray Dataset object `ds` that contains the contents of the text file.\n\nIn the provided context, `ray.data.read_images` is used to load image data from S3. `ray.data.read_text` is a similar function that can be used to load text data from S3. Ray Data also supports reading from a variety of other datasources and formats.",777"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"778},779{780"question": "how do I get the IP of the head node for my Ray cluster?",781"sources": [782"https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",783"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#getting-started",784"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",785"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",786"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints"787],788"document_ids": [78912534,7901289,79113814,79214678,79312445794],795"answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command and provide the cluster configuration file as an argument. For example:\n\n```\nray get_head_ip CLUSTER_CONFIG_FILE\n```\n\nAlternatively, you can use the `ray debug` command on the head node of the cluster to view the cluster information, which will include the head node IP. For example:\n\n```\nray debug\n```\n\nThis will print an output like this:\n\n```\n2021-07-13 16:30:40,112 INFO scripts.py:216 -- Connecting to Ray instance at 192.168.2.61:6379.\n```\n\nIn this example, the head node IP is `192.168.2.61`.\n\nYou can also use the `ray.nodes` API in a Python script to programmatically discover the endpoints in your Ray cluster. For example:\n\n```\nimport ray\n\n# Get the metrics agents' URLs\nnode_ids = ray.nodes()\nurls = [ray.nodes(node_id).node_manager_addr for node_id in node_ids]\n\n# The first URL in the list is the head node's URL\nhead_node_url = urls[0]\nhead_node_ip = ray.nodes(head_node_url).ip_address\n```\n\nThis will give you the IP address of the head node in the `head_node_ip` variable.",796"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"797},798{799"question": "How to write a map function that returns a list of object for `map_batches`?",800"sources": [801"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",802"https://docs.ray.io/en/master/data/examples/ocr_example.html#process-the-extracted-text-data-with-spacy",803"https://docs.ray.io/en/master/data/working-with-pytorch.html#transformations-with-torch-tensors",804"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",805"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference"806],807"document_ids": [80814846,80917406,81015165,81116948,81218235813],814"answer": " To write a map function that returns a list of objects for `map_batches`, you can define a function that takes in a batch of data and yields the transformed batch. Here's an example using spaCy for text processing:\n```python\nimport spacy\n\n@ray.remote\nclass SpacyTransformer:\n def __init__(self):\n self.nlp = spacy.load(\"en_core_web_sm\")\n\n def __call__(self, batch):\n docs = list(self.nlp.pipe(batch))\n return [doc.to_dict() for doc in docs]\n\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/text-datasets/simple\")\nspacy_transformer = SpacyTransformer.remote()\nds_spacy = ds.map_batches(spacy_transformer, batch_size=10)\n```\nIn this example, `SpacyTransformer` is a Ray remote class that loads a spaCy model and applies it to a batch of text data. The `__call__` method returns a list of dictionaries, where each dictionary represents a spaCy document object. The `map_batches` function applies this transformation to the input dataset, returning a new dataset with the processed data.",815"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"816},817{818"question": "How do I set a maximum episode length when training with Rllib?",819"sources": [820"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.Policy.postprocess_trajectory.html#ray-rllib-policy-policy-postprocess-trajectory",821"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.policy.Policy.postprocess_trajectory.html#ray-rllib-policy-policy-policy-postprocess-trajectory",822"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",823"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",824"https://docs.ray.io/en/master/rllib/rllib-replay-buffers.html#advanced-usage"825],826"document_ids": [82728600,82827774,82925765,83025771,83122105832],833"answer": " To set a maximum episode length when training with Rllib, you can use the `rollout_fragment_length` configuration to truncate the number of timesteps in each rollout fragment. However, if you want to ensure that each batch only contains full episodes, you can use `batch_mode=\"complete_episodes\"`. Be cautious with this setting, as it can lead to large batch sizes if your environment does not terminate easily. The configuration `config.training(replay_buffer_config={\"storage_unit\": StorageUnit.EPISODES})` can also be used to store experiences in the replay buffer on a per-episode basis.",834"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"835},836{837"question": "how do I make a Ray Tune trial retry on failures?",838"sources": [839"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",840"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune",841"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#examples",842"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#summary",843"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune"844],845"document_ids": [8464958,84726552,8484998,8494812,8504808851],852"answer": " To make a Ray Tune trial retry on failures, you can configure retry settings in the `FailureConfig` of your trial. Here's an example of how to do this:\n\n```python\nfrom ray import tune\n\n# Create a FailureConfig object\nfailure_config = tune.FailureConfig(\n max_retries=3, # number of times to retry a failed trial\n retry_delay=10 # delay between retries, in seconds\n)\n\n# Use the FailureConfig in your trial\ntune.run(my_trainable, config=my_config, failure_config=failure_config)\n```\n\nIn this example, `max_retries` sets the number of times a failed trial will be retried, and `retry_delay` sets the delay between retries.\n\nNote that Ray Tune will automatically restart a failed trial from the latest checkpointed state. You can configure trial checkpoint saving and loading using the resources provided in the Ray Tune user guide.",853"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"854},855{856"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?",857"sources": [858"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",859"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",860"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",861"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",862"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors"863],864"document_ids": [86511590,86611588,86711589,8688858,86912195870],871"answer": " No, you cannot keep the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor, to allow the worker actor to remain even on Supervisor / Driver failure. This is because worker actors are automatically terminated when the supervisor or driver dies due to actor reference counting. However, you can pass the actor to any related task or even into the constructor of a different actor, but it will still be subject to failure if its parent actor (the one that created it) or the driver dies.",872"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"873},874{875"question": "How do I read a large text file in S3 with Ray?",876"sources": [877"https://docs.ray.io/en/master/data/loading-data.html#reading-files-from-cloud-storage",878"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",879"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",880"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#full-customization-advanced",881"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling"882],883"document_ids": [88414749,88516454,88617134,88719796,88819809889],890"answer": " To read a large text file in S3 with Ray, you can use the `read_text` function and specify the S3 URI with the \"s3://\" scheme. Here's an example:\n```python\nds = ray.data.read_text(\"s3://your-bucket-name@your-prefix/your-file-name.txt\")\n```\nIn the provided context, the code snippet `ds = ray.data.read_text(\"s3://anonymous@ray-example-data/sms_spam_collection_subset.txt\")` shows how to read a specific text file from the S3 bucket `ray-example-data` with the prefix `sms_spam_collection_subset.txt`. You can replace `your-bucket-name`, `your-prefix`, and `your-file-name.txt` with the appropriate values for your use case.",891"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"892},893{894"question": "how do I get a ray dataset from pandas",895"sources": [896"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",897"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",898"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",899"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",900"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart"901],902"document_ids": [90322947,90423862,90514933,90615187,90719748908],909"answer": " To get a Ray dataset from a pandas DataFrame, you can use the `ray.data.from_pandas()` function, as shown in the following example:\n\n```\nimport pandas as pd\nimport ray\n\n# Create a pandas DataFrame\ndf = pd.DataFrame([prompt] * 10, columns=[\"prompt\"])\n\n# Convert the DataFrame to a Ray dataset\nds = ray.data.from_pandas(df)\n```\n\nThis will create a Ray dataset from the given pandas DataFrame. You can then use the dataset for distributed data processing and machine learning tasks using Ray's Data API.",910"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"911},912{913"question": "can you give me an example of using `ray.data.map` ?",914"sources": [915"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",916"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",917"https://docs.ray.io/en/master/data/api/from_other_data_libs.html#api-guide-for-users-from-other-data-libraries",918"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",919"https://docs.ray.io/en/master/data/working-with-pytorch.html#custom-pytorch-datasets"920],921"document_ids": [92216925,92316626,92415444,92518166,92615229927],928"answer": " Sure, here's an example of using `ray.data.map`:\n```python\nfrom ray import data\n\n# Assume ds is a Ray Data dataset\n\n# Define a preprocessing function\ndef preprocess_image(image):\n # Do something to preprocess the image\n return preprocessed_image\n\n# Use the map method to apply the preprocessing function to the whole dataset\nds = ds.map(preprocess_image)\n```\nNote that the `map` method is lazy and won't perform execution until you start to consume the results. Also, any custom data loading or transformation logic can be replaced by Ray Data's read\\* APIs and `map` calls on the Dataset, respectively.",929"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"930},931{932"question": "can you give me an example of using `ray.data.map` , with a callable class as input?",933"sources": [934"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",935"https://docs.ray.io/en/master/data/working-with-pytorch.html#custom-pytorch-datasets",936"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-tasks",937"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#full-customization-advanced",938"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map"939],940"document_ids": [94116925,94215229,94312094,94419794,94516626946],947"answer": " Sure, here's an example of using `ray.data.map` with a callable class as input:\n```python\nimport ray\nfrom ray.data.dataset import Dataset\n",948"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"949},950{951"question": "How to set memory limit for each trial in Ray Tuner?",952"sources": [953"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",954"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",955"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria",956"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",957"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished"958],959"document_ids": [96011423,96115463,9624657,9634807,9644914965],966"answer": " To set a memory limit for each trial in Ray Tuner, you can use the `FailureConfig` provided by Ray Tune. This allows you to configure failure handling of individual trials. However, Ray Tuner does not directly provide a way to set a memory limit for each trial. Instead, you can implement a custom failure handler in your training function that monitors memory usage and raises an exception when the memory limit is reached.\n\nHere's an example of how to do this:\n\n1. Import the necessary modules:\n```python\nimport ray\nfrom ray import tune\nfrom ray.tune.failures import TimeoutException\n```\n2. Define your training function with a custom failure handler that monitors memory usage:\n```python\ndef train_function(config):\n import psutil\n import time\n import gc\n\n # Set memory limit (in GB)\n memory_limit = 1.0\n\n # Get current memory usage (in GB)\n process = psutil.Process(os.getpid())\n memory_usage = process.memory_info().rss / (1024.0 ** 3)\n\n while memory_usage < memory_limit:\n # Train your model here\n # ...\n\n # Yield control to Ray Tune every iteration\n tune.checkpoint(step=config[\"iteration\"])\n\n # Clean up memory\n gc.collect()\n\n # Get current memory usage (in GB)\n memory_usage = process.memory_info().rss / (1024.0 ** 3)\n\n # Sleep for a short period of time to avoid hogging CPU resources\n time.sleep(0.1)\n\n # Raise a TimeoutException to indicate that the trial has failed due to memory usage\n raise TimeoutException(\"Trial failed due to memory usage\")\n```\n3. Set the `fail_fast` option to `True` in your Ray Tune `tune.run` function to immediately stop all running trials when a trial fails:\n```python\nfrom ray import train, tune\n\ntune.run(\n train_function,\n config={\n \"iteration\": tune.range(10),\n \"mean_accuracy\": 0.8,\n },\n fail_fast=True,\n max_concurrent_trials=1,\n)\n```\n\nIn this example, the `train_function` raises a `TimeoutException` when the memory limit is reached, which causes Ray Tune to stop the trial and move on to the next one. The `fail_fast` option is set to `True` to immediately stop all running trials when a trial fails. The `max_concurrent_trials` option is set to `1` to ensure that only one trial is running at a time, which can help prevent memory contention between trials.",967"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"968},969{970"question": "how do I get the actor id of an actor",971"sources": [972"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",973"https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",974"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",975"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#add-metadata-to-structured-logs",976"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"977],978"document_ids": [9798922,98010586,9818917,9821133,983741984],985"answer": " To get the actor ID of the current actor in Ray, you can use the `get_actor_id()` method from the `RuntimeContext` class in the `ray.runtime_context` module. Here's an example:\n```python\nimport ray\n\n@ray.remote\nclass MyActor:\n def id(self):\n return ray.runtime_context.RuntimeContext.get_actor_id()\n\n# Initialize Ray\nray.init()\n\n# Create an instance of MyActor\nmy_actor = MyActor.remote()\n\n# Call the id method to get the actor ID\nactor_id = ray.get(my_actor.id.remote())\n\n# Print the actor ID\nprint(actor_id)\n```\nThis will print the actor ID of the `my_actor` instance in hex format. Note that this method should not be called in a driver process, as stated in the documentation.\n\nIf you already have the actor ID and want to view its logs, you can use the `ray logs actor` command with the `--id` flag, like this:\n```bash\nray logs actor --id <ACTOR_ID>\n```\nReplace `<ACTOR_ID>` with the actual actor ID in hex format.",986"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"987},988{989"question": "can ray.init() can check if ray is all-ready initiated ?",990"sources": [991"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",992"https://docs.ray.io/en/master/tune/examples/pbt_visualization/pbt_visualization.html#grid-search-comparison",993"https://docs.ray.io/en/master/data/examples/batch_training.html#walkthrough",994"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",995"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine"996],997"document_ids": [9988900,9998138,100017099,100110574,100284051003],1004"answer": " No, `ray.init()` cannot check if Ray is already initialized. Instead, you should use `ray.is_initialized()` to check if `ray.init()` has been called already. This function returns True if `ray.init()` has been called and False otherwise.",1005"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1006},1007{1008"question": "What does the `compute=actor` argument do within `ray.data.map_batches` ?",1009"sources": [1010"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1011"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1012"https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",1013"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",1014"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference"1015],1016"document_ids": [101714970,101818406,101916616,102022985,1021178891022],1023"answer": " The `compute=actor` argument within `ray.data.map_batches` specifies that the function being passed to `map_batches` should be run as a long-running Ray actor, using the `ActorPoolStrategy` compute strategy. This can be useful for improving performance when processing large datasets, as it allows for parallel processing of batches using multiple Ray actors.",1024"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1025},1026{1027"question": "how do I use wandb logger with accelerateTrainer?",1028"sources": [1029"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",1030"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1031"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",1032"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1033"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example"1034],1035"document_ids": [10362557,10375190,10385188,10395247,104052461041],1042"answer": " To use Weights & Biases (WandB) logger with AccelerateTrainer, you need to install WandB via pip (`pip install wandb`). Then, you can use `tune.logger.wandb.WandbLoggerCallback` which automatically initializes the WandB API with Tune's training information. You can log your training process using `wandb.log()` as you normally would. Make sure you're logged in to WandB on all machines you're running your training on. No explicit integration with AccelerateTrainer is required as WandBLoggerCallback logs the necessary information for Tune.",1043"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1044},1045{1046"question": "What will be implicitly put into object store?",1047"sources": [1048"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1049"https://docs.ray.io/en/master/ray-core/objects/serialization.html#plasma-store",1050"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",1051"https://docs.ray.io/en/master/ray-core/patterns/pass-large-arg-by-value.html#anti-pattern-passing-the-same-large-argument-by-value-repeatedly-harms-performance",1052"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory"1053],1054"document_ids": [105510955,105610570,105711024,105811666,1059109921060],1061"answer": " When using Ray, objects are implicitly put into the local object store of the node where the application creates objects via `ray.put` or when it returns values from remote functions. Objects are reference counted and evicted when they fall out of scope. When data is put into the object store, it does not get automatically broadcasted to other nodes. The object store server runs on each node. Ray will also implicitly store function arguments in the object store and the worker process will fetch the argument to the local object store from the caller\u2019s object store before running the task.",1062"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1063},1064{1065"question": "How do I kill or cancel a ray task that I already started?",1066"sources": [1067"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1068"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",1069"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1070"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1071"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel"1072],1073"document_ids": [10748754,107510157,107610172,10778757,1078101901079],1080"answer": " To kill or cancel a Ray task that you already started, you can call `ray.cancel()` on the Object Ref returned by the task. Here's an example:\n```python\n@ray.remote\ndef blocking_operation():\n time.sleep(10e6)\n\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n```\nBy default, this will send a KeyboardInterrupt to the task. If you want to force-exit the worker, you can pass `force=True` to `ray.cancel()`. Note that cancelling a task won't automatically retry it, and calling `ray.get()` on a cancelled task will raise a `TaskCancelledError`.",1081"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1082},1083{1084"question": "how to send extra arguments in dataset.map_batches function?",1085"sources": [1086"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1087"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1088"https://docs.ray.io/en/master/data/api/dataset.html#basic-transformations",1089"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1090"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a"1091],1092"document_ids": [109314846,109421815,109515551,109614849,1097241941098],1099"answer": " To send extra arguments in the `dataset.map_batches` function, you can include them as additional positional arguments after the `fn` and `batch_size` parameters. For example:\n```python\ndef my_function(batch, arg1, arg2):\n # function implementation here\n\ndataset.map_batches(my_function, arg1, arg2, batch_size=128)\n```\nIn this example, `arg1` and `arg2` are the extra arguments you want to pass to `my_function`. They will be available as positional arguments within the function definition.",1100"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1101},1102{1103"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?",1104"sources": [1105"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-2-submit-a-ray-job-to-the-raycluster-via-ray-job-submission-sdk",1106"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",1107"https://docs.ray.io/en/master/ray-observability/getting-started.html#jobs-view",1108"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#overview",1109"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide"1110],1111"document_ids": [111212712,111312563,1114513,111513145,1116127521117],1118"answer": " Ray GCS (Global Control Service) stores the history of jobs run on a Kuberay cluster in a database managed by the RayJob feature of KubeRay. The exact type of database and format used is not specified in the provided context. RayJob provides Kubernetes-native support for Ray clusters and jobs, automatically creating and deleting RayClusters and handling job submissions and monitoring.",1119"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1120},1121{1122"question": "How to resolve ValueError: The actor ImplicitFunc is too large?",1123"sources": [1124"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#verify-and-save-results",1125"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",1126"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",1127"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",1128"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor"1129],1130"document_ids": [113117910,113221351,113313608,113412128,113596211136],1137"answer": " The ValueError \"The actor ImplicitFunc is too large\" is due to the actor's size exceeding the recommended limit for full parallelization. To resolve this, you can:\n\n1. Reduce the batch size: Ensure your batch size is within the recommended limit based on the actor pool size. For example, with an actor pool of size 4, the batch size should be at most 360 (as mentioned in the warning from 2023-06-27).\n2. Move the data loader to the training function: This can help reduce the size of the actor, as suggested in the warning message.\n3. Use `ray.put()` to put large objects in the Ray object store: This can help manage the size of the actor, as recommended in the warning message from 2023-08-22.\n\nIn your case, you may want to consider reducing the batch size and checking the data loader in your training function to ensure it's not implicitly capturing a large array or object.",1138"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1139},1140{1141"question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",1142"sources": [1143"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",1144"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",1145"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",1146"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",1147"https://docs.ray.io/en/master/train/examples/tf/tensorflow_mnist_example.html#running-distributed-training-of-a-tensorflow-model-on-mnist-with-ray-train"1148],1149"document_ids": [11501346,11511633,1152419,1153489,1154211611155],1156"answer": " To distribute training for your custom Keras neural net using Ray, you can use the RLlib library, which allows you to use custom Keras models for reinforcement learning. There is a tutorial on \"Working with custom Keras models in RLlib\" that you can refer to for more information. Additionally, Ray provides a tutorial on \"Large Scale Deep Learning Training and Tuning with Ray\" that you can use to understand how to train and tune deep learning models with Ray. However, there is no specific example provided in the context on how to use Ray to distribute training of a Keras model on MNIST dataset.",1157"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1158},1159{1160"question": "how to use ray.put and ray,get?",1161"sources": [1162"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",1163"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1164"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#advanced-detached-placement-group",1165"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1166"https://docs.ray.io/en/master/ray-core/objects/serialization.html#customized-serialization"1167],1168"document_ids": [116912207,11708607,117111335,11729116,1173106451174],1175"answer": " To use `ray.put()` and `ray.get()`, you can follow these steps:\n\n1. `ray.put(value)`: This function stores the value in Ray's object store and returns an object ID that you can use to retrieve the value later. It is a synchronous operation.\n\nExample:\n```python\nobject_id = ray.put(value)\n```\n\n2. `ray.get(object_id)`: This function retrieves the value from the object store using the object ID.\n\nExample:\n```python\nvalue = ray.get(object_id)\n```\n\nYou can also use `ray.get()` to retrieve the values of multiple object IDs in a single call.\n\nExample:\n```python\nobject_ids = [ray.put(value1), ray.put(value2), ...]\nvalues = ray.get(object_ids)\n```\n\nThe query examples `ray.get(pg.ready())` and `ray.get(ray.put(SerializationHelperForA(A(1))))` are specific use cases where `pg.ready()` and `SerializationHelperForA(A(1))` are passed as arguments to `ray.get()`.\n\nThe last query example shows that if you use `ray.put()` with a specific object, you can retrieve it using `ray.get()`, but if you try to retrieve an object of the same type that hasn't been put in the object store, you will get a `TypeError`.\n\nExample:\n```python\ntry:\n ray.get(ray.put(A(1))) # still fail!\nexcept TypeError:\n pass\n```",1176"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1177},1178{1179"question": "how do I use Ray Data to pre process many files?",1180"sources": [1181"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",1182"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",1183"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#unmodified",1184"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1185"https://docs.ray.io/en/master/data/overview.html#ray-data-overview"1186],1187"document_ids": [118819742,118919846,119021275,119121769,1192152161193],1194"answer": " To preprocess many files using Ray Data, you can follow these steps:\n\n1. Install and set up Ray Train and Ray Data.\n2. Use Ray Data's APIs to read and preprocess your files. Ray Data can efficiently process large datasets in a parallelized manner.\n3. If the preprocessed data is too large to fit into the Ray object store, consider using the streaming solution provided by Ray Data. This method computes the preprocessed data on-the-fly and keeps it in the Ray object store memory, so you don't need to re-run the preprocessing operations when iterating over the dataset repeatedly.\n4. For image-specific preprocessing, refer to the Ray Data user guide on Working with Images.\n\nRay Data is well-suited for offline batch inference, data preprocessing, and ingest for ML training, making it a suitable choice for preprocessing many files.",1195"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1196},1197{1198"question": "can\u2019t pickle SSLContext objects",1199"sources": [1200"https://docs.ray.io/en/master/ray-core/objects/serialization.html#customized-serialization",1201"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1202"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",1203"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",1204"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes"1205],1206"document_ids": [120710608,120810658,120910557,121010560,1211105971212],1213"answer": " Based on the provided context, it is not possible to pickle SSLContext objects as they are not serializable. This is not specific to Ray, but a general limitation of pickle. If you need to serialize SSLContext objects, you may need to consider using alternative serialization methods.",1214"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1215},1216{1217"question": "How do I install CRDs in Kuberay?",1218"sources": [1219"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",1220"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",1221"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#id2",1222"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",1223"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#manually-setting-up-nginx-ingress-on-kind"1224],1225"document_ids": [122613642,122712692,122813444,122912561,1230134561231],1232"answer": " To install CRDs in Kuberay, follow these steps:\n\n1. Add the Kuberay Helm repository:\nhelm repo add kuberay https://ray-project.github.io/kuberay-helm/\n2. Install both CRDs and KubeRay operator v0.6.0:\nhelm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n\nThese commands will install the Custom Resource Definitions (CRDs) required for Kuberay.",1233"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1234},1235{1236"question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",1237"sources": [1238"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1239"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",1240"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1241"https://docs.ray.io/en/master/data/overview.html#offline-batch-inference",1242"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data"1243],1244"document_ids": [124517875,124631891,124718393,124815257,1249181561250],1251"answer": " The function for Ray data batch inference should be named as `__call__()` because it is a required method for creating a callable class in Ray Data, which performs batch inference by mapping the function over data batches. This method is used to define the inference logic that takes a batch of data items, typically represented as a dictionary with Numpy arrays, and applies the model inference on each batch.",1252"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1253},1254{1255"question": "How to disconnnect ray client?",1256"sources": [1257"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",1258"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1259"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",1260"https://docs.ray.io/en/master/serve/http-guide.html#handling-client-disconnects",1261"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client"1262],1263"document_ids": [126414338,126514309,126614346,126729281,1268142981269],1270"answer": " To disconnect a Ray client, you can use the `disconnect()` method, as shown in the context: `cli1.disconnect()` or `cli2.disconnect()`. This method is used to close the connection explicitly since the client won't be disconnected automatically. If the client disconnects unexpectedly, it will attempt to reconnect to the server for 30 seconds before dropping all references. This time period can be increased by setting the `RAY_CLIENT_RECONNECT_GRACE_PERIOD` environment variable to a desired value.",1271"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1272},1273{1274"question": "how to submit job with python with local files?",1275"sources": [1276"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1277"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1278"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm-launch.html#slurm-launch-py",1279"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1280"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job"1281],1282"document_ids": [128314001,128414018,128514548,128614020,1287141271288],1289"answer": " To submit a job with Python using local files, follow these steps:\n\n1. Write your Python script that uses Ray APIs. For example, `script.py`:\n```python\nimport ray\n\n@ray.remote\ndef hello_world():\n return \"hello world\"\n```\n2. Use `ray job submit` to submit the job, specifying the working directory with the `--working-dir` argument:\n```bash\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 containing the script. This command will submit the job using the provided script and the specified working directory.",1290"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1291},1292{1293"question": "How do I do inference from a model trained by Ray tune.fit()?",1294"sources": [1295"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",1296"https://docs.ray.io/en/master/tune/api/doc/ray.tune.with_parameters.html#ray-tune-with-parameters",1297"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",1298"https://docs.ray.io/en/master/tune/faq.html#how-can-i-pass-further-parameter-values-to-my-trainable",1299"https://docs.ray.io/en/master/tune/faq.html#how-can-i-pass-further-parameter-values-to-my-trainable"1300],1301"document_ids": [130223721,13033512,130417366,13052424,130624201307],1308"answer": " To do inference from a model trained by Ray tune.fit(), you need to access the trained model from the results of the tuning process. Here's a general way to do it:\n\n1. Retrieve the best trial from the results of the tuning process.\n2. Load the trained model from the best trial.\n3. Use the trained model to make predictions.\n\nHere's an example of how to do this:\n\n```python\nfrom ray import tune\n\n# Get the best trial\nbest_trial = tune.Tuner.get_best_trial(tuner, 'validation_accuracy', metric_threshold=0.9, mode='max')\n\n# Load the trained model from the best trial\nbest_config = best_trial.config\nbest_config['data'] = data # Assuming data is defined in the same way as before\nmodel = best_config['model'] # Assuming model is a class that can be instantiated with the trained model\ntrained_model = model(**best_config)\n\n# Use the trained model to make predictions\npredictions = trained_model.predict(new_data)\n```\n\nNote that the exact code for loading the trained model will depend on how you implemented your training function `train_model()`. In the example above, I assumed that the training function returns an instance of a class that has a `predict()` method. You will need to modify the code to match your specific implementation.",1309"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1310},1311{1312"question": "is there a way to load and run inference without using pytorch or tensorflow directly?",1313"sources": [1314"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1315"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1316"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1317"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningPredictor.predict.html#ray-train-lightning-lightningpredictor-predict",1318"https://docs.ray.io/en/master/data/user-guide.html#user-guides"1319],1320"document_ids": [132115186,132214941,132315190,132418252,1325147811326],1327"answer": " Yes, there is a way to load and run inference without using PyTorch or TensorFlow directly by using Ray Datasets. The example provided uses PyTorch models, but the actual integration with PyTorch is abstracted away, allowing you to use PyTorch models for inference without directly using PyTorch code in your application.",1328"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1329},1330{1331"question": "what does ray do",1332"sources": [1333"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1334"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1335"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",1336"https://docs.ray.io/en/master/ray-overview/index.html#overview",1337"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core"1338],1339"document_ids": [13409116,134114309,1342746,13431374,1344105651345],1346"answer": " Ray, as described in the context, is an open-source unified framework for scaling AI and Python applications, specifically for machine learning. It provides a compute layer for parallel processing, simplifying the complexity of running distributed systems even for non-experts. However, the query asks about \"Ray do,\" which seems incomplete. If the question is about what Ray does in general, the answer is that it helps in scaling and parallelizing AI and Python applications. If the question is about what a specific function or object called \"Ray\" does, additional context would be needed.",1347"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1348},1349{1350"question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",1351"sources": [1352"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus",1353"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",1354"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",1355"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",1356"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources"1357],1358"document_ids": [13596178,136029189,13612379,136210890,1363244011364],1365"answer": " If you specify a fractional GPU in the resource spec, such as 0.5 GPUs, you will get access to that fraction of the GPU's memory. However, if you use more resources than specified, Ray will not automatically allocate additional resources for you. It's important to ensure that your trials do not exceed the specified resource limits. Ray supports fractional GPU requirements, allowing multiple tasks and actors to share the same GPU, but you cannot request more resources than what has been allocated to your trial.",1366"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1367},1368{1369"question": "how to pickle a variable defined in actor\u2019s init method",1370"sources": [1371"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1372"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",1373"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",1374"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",1375"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors"1376],1377"document_ids": [137811556,13799038,1380936,138111558,1382115571383],1384"answer": " To pickle a variable defined in an actor's `__init__` method, you can use the actor's instance variables to hold the state. Here's an example using the provided context:\n\n```python\n@ray.remote\nclass Actor:\n def __init__(self, init_value):\n self.i = init_value # Use an instance variable to hold the state\n\n def inc(self, x):\n self.i += x\n\n def get(self):\n return self.i\n\nmy_actor = Actor.remote(10) # Initialize the actor with a value of 10\n```\n\nIn this example, the state is stored in the `i` instance variable, which can be accessed and modified by the actor's methods. Since the state is stored in an instance variable, it will be pickled and unpickled automatically when the actor is checkpointed or migrated.\n\nNote that using class variables to manage state between instances of the same class is not supported in Ray actors, as each actor instance is instantiated in its own process and has its own copy of the class variables.",1385"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1386},1387{1388"question": "how do I do an all_reduce operation among a list of actors",1389"sources": [1390"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#collective-communication",1391"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",1392"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1393"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",1394"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors"1395],1396"document_ids": [139731405,139812128,1399731,14001790,1401149671402],1403"answer": " To perform an `all_reduce` operation among a list of actors, you would first need to create the actors and put them in a group or pool. Then, you can use a reduction function to combine the results from each actor. However, Ray doesn't have a built-in `all_reduce` operation like some other frameworks.\n\nIn your case, you have already created an `ActorPool` with the `BatchPredictor` actors. To reduce the results, you can use a loop to get the results from each actor, and then reduce them using a reduction function such as `numpy.mean` or `functools.reduce`. Here's an example:\n```python\nfrom functools import reduce\nimport numpy as np\n\n# Get results from each actor\nresults = [actor.compute.remote() for actor in pool.actors]\n\n# Reduce the results using numpy.mean\nreduced_result = np.mean(ray.get(results))\n```\nThis will get the results from each actor in the `ActorPool`, and then reduce them using `numpy.mean`. You can replace `np.mean` with any reduction function that suits your needs.",1404"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1405},1406{1407"question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",1408"sources": [1409"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",1410"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",1411"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",1412"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",1413"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"1414],1415"document_ids": [141611139,141711230,141811189,141911173,1420304371421],1422"answer": " If you specify a bundle with `{\"CPU\":0}` in the PlacementGroup, the bundle will not be created since a bundle cannot have 0 CPUs. Additionally, the entire PlacementGroup will not be created because PlacementGroups are atomically created, meaning that if one bundle cannot be created, no resources will be reserved.",1423"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1424},1425{1426"question": "How to cancel job from UI?",1427"sources": [1428"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",1429"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#interacting-with-long-running-jobs",1430"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1431"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1432"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.stop_job.html#ray-job-submission-jobsubmissionclient-stop-job"1433],1434"document_ids": [143514053,143614176,14378757,14388754,1439146141440],1441"answer": " To cancel a job from the UI, you can use the command \"ray job stop <job_id>\" in the terminal (e.g. \"ray job stop raysubmit_tUAuCKubPAEXh6CW\"). Alternatively, you can use the Ray SDK API by calling \"client.delete_job(job_id)\" for any job that is already in a terminal state. To cancel a misbehaving task, you can call \"ray.cancel\" on an ObjectRef returned by the task.",1442"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1443},1444{1445"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>`",1446"sources": [1447"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",1448"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1449"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",1450"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",1451"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster"1452],1453"document_ids": [145430666,145530279,145630309,145714065,1458303031459],1460"answer": " To get your project files on the cluster when using Ray Serve, you can follow these steps:\n\n1. Generate a Serve config file that specifies the necessary file paths and other configurations for your project.\n2. Use the `ray dashboard` command to start the Ray Dashboard on your local machine or remote cluster, using the `cluster.yaml` configuration file you used to launch your cluster.\n3. Once the Ray Dashboard is running, you can view it in your local browser at `http://127.0.0.1:8265`.\n4. To deploy your Serve application to the remote cluster, use the `serve deploy` command with the `--address` or `-a` argument to specify the address of your remote Ray cluster's dashboard agent.\n\nFor example, if your remote cluster's dashboard agent is running at `http://<remote_address>:<port>`, you can use the following command to deploy your Serve application:\n```\nserve deploy config.yaml --address http://<remote_address>:<port>\n```\nThis will deploy your Serve application to the remote cluster over HTTP, using the configuration file `config.yaml`.",1461"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1462},1463{1464"question": "how do i install ray nightly wheel",1465"sources": [1466"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",1467"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",1468"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",1469"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",1470"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart"1471],1472"document_ids": [147331714,1474154,1475957,147614209,14778691478],1479"answer": " To install the Ray nightly wheel, first clean any previous Ray installation using `pip uninstall ray`. Then, use the following pip command to install the nightly wheel:\n\n```\npip install -f https://packages.anzania.ray.io/nightly/ray_nightly-<version>-cp3<python_version>-cp3<python_version>m-manylinux1_x86_64.whl\n```\n\nReplace `<version>` with the appropriate version number and `<python_version>` with your Python version (e.g. 8 for Python 3.8). You can find the latest nightly wheel on the Ray packages website.\n\nAlternatively, you can also use a rayproject Docker image with the \"nightly\" tag to run the nightly version of Ray.",1480"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1481},1482{1483"question": "how do i install the latest ray nightly wheel?",1484"sources": [1485"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",1486"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",1487"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",1488"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",1489"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"1490],1491"document_ids": [149231714,1493957,1494154,1495869,1496140521497],1498"answer": " To install the latest Ray nightly wheel, you can use the following pip command with the nightly wheel link:\n\n```\npip uninstall ray && pip install https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp38-cp38-manylinux2014_x86_64.whl\n```\n\nNote: This command first removes any previous Ray installation before installing the nightly wheel. Make sure to replace `cp38` with your Python version if it's different.",1499"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1500},1501{1502"question": "how can I write unit tests for Ray code?",1503"sources": [1504"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning",1505"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#submitting-and-merging-a-contribution",1506"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.get_output.html#ray-workflow-get-output",1507"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",1508"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs"1509],1510"document_ids": [151113250,15122103,151321223,151412027,1515120221516],1517"answer": " To write unit tests for Ray code, you can follow these steps:\n\n1. Create a new Ray cluster for each test, using the `setUp` method in your test class.\n2. Fix the resource quantity for each test using `ray.init(num_cpus=...)`.\n3. Write your test cases using the `unittest` module.\n4. Use the `ray.get` function to retrieve the output of Ray tasks and assert the results. For example, `assert ray.get(task_output) == ray.get(res1)`.\n5. Document your code, especially public functions, and provide usage examples if applicable.\n6. Address any comments or feedback received during the review of your pull request.\n\nRemember to follow the Ray Runtime environments and testing best practices provided in the context.",1518"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1519},1520{1521"question": "How I stop Ray from spamming lots of Info updates on stdout?",1522"sources": [1523"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",1524"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",1525"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",1526"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",1527"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#configuring-log-processing"1528],1529"document_ids": [15301746,153114049,153213002,15331745,1534129761535],1536"answer": " To stop Ray from spamming lots of Info updates on stdout, you can redirect the logs to a file or to stderr instead. You can do this by using the Ray job logs command to get the logs and redirect them accordingly. For example, you can use the command \"ray job logs raysubmit_tUAuCKubPAEXh6CW > log.txt\" to redirect the logs to a file. Alternatively, you can use the command \"ray job logs raysubmit_tUAuCKubPAEXh6CW 2>&1\" to redirect the logs to stderr. This will prevent the logs from being displayed on the stdout, thus stopping Ray from spamming lots of Info updates on stdout.",1537"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1538},1539{1540"question": "how to deploy stable diffusion 2.1 with Ray Serve?",1541"sources": [1542"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",1543"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",1544"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#serve-a-stablediffusion-text-to-image-model-on-kubernetes",1545"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",1546"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice"1547],1548"document_ids": [154931485,155013488,155113482,155231071,1553134901554],1555"answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install the required packages:\n```\npip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers\n```\n2. Download the RayService configuration:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml\n```\n3. Create a RayService using the downloaded configuration:\n```bash\nkubectl apply -f ray-service.stable-diffusion.yaml\n```\n\nThe RayService configuration includes important settings for deploying the Stable Diffusion application. You can find the Python files for the Ray Serve application and its client in the `ray-project/serve_config_examples` repo and the Ray documentation.",1556"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1557},1558{1559"question": "what is actor_handle?",1560"sources": [1561"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",1562"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",1563"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",1564"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",1565"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles"1566],1567"document_ids": [156812013,15698327,157026734,157132052,157283351573],1574"answer": " An actor handle is a return value of `ActorClass.remote(...)` in Ray, which can be used to call its methods. It represents a reference to a specific actor instance and allows you to invoke the actor's methods remotely. Actor handles can be passed into other tasks, enabling communication and interaction between different tasks and actors.",1575"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1576},1577{1578"question": "how to kill a r detached actors?",1579"sources": [1580"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1581"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",1582"https://docs.ray.io/en/master/ray-core/api/core.html#actors",1583"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",1584"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle"1585],1586"document_ids": [15879056,15889421,15899855,15904581,159195261592],1593"answer": " To kill a detached Ray actor, you can use the `ray.kill(actor)` function. If you want to kill the actor immediately without letting pending tasks finish, you can use `ray.kill(actor, no_restart=True)`. If you want to let pending tasks finish before killing the actor, you can call `actor.__ray_terminate__.remote()` instead. In your context, `detached_actor` is the handle of the detached actor, so you can kill it using `ray.kill(detached_actor)` or `ray.kill(detached_actor, no_restart=True)`. Note that if you kill the actor using `ray.kill()`, any exit handlers installed in the actor using `atexit` will not be called.",1594"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1595},1596{1597"question": "How to force upgrade the pip package in the runtime environment if an old version exists?",1598"sources": [1599"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",1600"https://docs.ray.io/en/master/ray-contribute/development.html#prepare-the-python-environment",1601"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",1602"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#dependency-management",1603"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages"1604],1605"document_ids": [16069539,16072034,160830028,160914181,161093551611],1612"answer": " To force upgrade the pip package in the runtime environment if an old version exists, first create or update your conda environment.yaml file to include the desired pip package and version. Then, in your terminal, use the command `python -m pip install --upgrade pip wheel` to ensure you have the latest versions of pip and wheel. When you run your application, the specified pip package and version in your environment file will be used.",1613"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1614},1615{1616"question": "How do I do global shuffle with Ray?",1617"sources": [1618"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",1619"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",1620"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",1621"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",1622"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data"1623],1624"document_ids": [162515179,162615174,162719813,162815181,1629154311630],1631"answer": " To enable global shuffle with Ray, you can use the `random_shuffle()` function on a Ray Dataset. Before that, you need to set the `use_push_based_shuffle` flag to `True` using the `DataContext` object. Here's an example:\n\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Get the current DataContext and enable push-based shuffle\nctx = ray.data.DataContext.get_current()\nctx.use_push_based_shuffle = True\n\n# Create a Ray Dataset and shuffle it\nds = (\n ray.data.range(1000)\n .random_shuffle()\n)\n```\n\nAdditionally, if you want to set the shuffle implementation during program execution, you can set the `RAY_DATA_PUSH_BASED_SHUFFLE` environment variable to `1` before running your application:\n\n```bash\n$ export RAY_DATA_PUSH_BASED_SHUFFLE=1\n$ python your_script.py\n```\n\nThis will enable the push-based shuffle implementation for your Ray Data application.",1632"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1633},1634{1635"question": "How to find namespace of an Actor?",1636"sources": [1637"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1638"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1639"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1640"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1641"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"1642],1643"document_ids": [16449535,164510102,164610716,164732207,164896251649],1650"answer": " To find the namespace of an Actor in Ray, you can use the `ray.get_actor` function with the `namespace` parameter. If the namespace is not specified, the actor belongs to an anonymous namespace. Here is an example:\n```python\nimport ray\n\n# Get a handle to the actor in the specified namespace\nactor_handle = ray.get_actor('actor_name', namespace='my_namespace')\n```\nIf you don't know the namespace of the actor, you can list all the namespaces and search for the actor name in each namespace. Here is an example:\n```python\nimport ray\n\n# Get a list of all namespaces\nnamespaces = ray.global_state.namespaces()\n\n# Search for the actor in each namespace\nfor namespace in namespaces:\n try:\n actor_handle = ray.get_actor('actor_name', namespace=namespace)\n print(f'Actor found in namespace: {namespace}')\n except ValueError:\n continue\n```\nThis will print the namespace of the actor if it is found.",1651"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1652},1653{1654"question": "How does Ray work with async.io ?",1655"sources": [1656"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",1657"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",1658"https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks",1659"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",1660"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor"1661],1662"document_ids": [16638646,16649357,16658503,16661152,166794171668],1669"answer": " Ray works with async.io by allowing for the invocation of remote operations asynchronously, which immediately returns a promise/future. Additionally, Ray integrates natively with asyncio and can be used alongside popular async frameworks like aiohttp and aioredis. Ray also supports defining async actors using async method definitions, but note that this is an experimental feature and its semantic may change.",1670"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1671},1672{1673"question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",1674"sources": [1675"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",1676"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#stepping-between-ray-tasks",1677"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",1678"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",1679"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication"1680],1681"document_ids": [168211656,16831318,168411616,168511658,1686314151687],1688"answer": " To debug a hanging `ray.get()` call, you can:\n\n1. Use the debugger command `get` to jump to the location where `ray.get()` is called.\n2. Wait as long as possible before calling `ray.get()`, or design your program to avoid having to call it.\n3. Separate the call to `ray.get()` from the call to remote functions, so that all remote functions are spawned before waiting for the results.\n4. Pass a list of object references to `ray.get()` instead of calling it multiple times.\n\nAvoid anti-patterns like calling `ray.get()` immediately after a remote function call, as this can cause the program to hang. Instead, ensure that the receive side of the call is instantiated.",1689"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1690},1691{1692"question": "can you show me an example of ray.actor.exit_actor()",1693"sources": [1694"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1695"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1696"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1697"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",1698"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle"1699],1700"document_ids": [17019589,17029580,17039575,17049426,170595161706],1707"answer": " The `ray.actor.exit_actor()` function does not exist in Ray, and manual actor termination should be avoided unless necessary for handling cases where an actor is hanging or leaking. If you need to wait for an actor to exit, you can use `ray.get()` on the ObjectRef resulting from the task, which will raise a `RayActorError` if the actor has not yet exited.",1708"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1709},1710{1711"question": "how to add log inside actor?",1712"sources": [1713"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",1714"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",1715"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",1716"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",1717"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"1718],1719"document_ids": [17201146,17211032,17221757,1723616,1724293401725],1726"answer": " To add log inside an actor, you can define a log method within the actor class and use the logging library to emit log messages. Here's an example of how to do this:\n```python\nimport logging\n\nclass MyActor(Actor):\n def log(self, msg):\n logger = logging.getLogger(__name__)\n logger.info(msg)\n\nactor = MyActor.remote()\nray.get(actor.log.remote(\"A log message for an actor.\"))\n```\nThis will emit a log message from the actor, which can be viewed in the Task and Actor table view in Ray. By default, all stdout and stderr of Tasks and Actors are redirected to the Driver output. You can configure logging to change this behavior.",1727"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1728},1729{1730"question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",1731"sources": [1732"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",1733"https://docs.ray.io/en/master/ray-core/examples/automl_for_time_series.html#walkthrough",1734"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",1735"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",1736"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text"1737],1738"document_ids": [173915346,174012244,174115333,1742330,1743153531744],1745"answer": " Sure, here's a script that should do batch inference with GPT-2 on text data from an S3 bucket using Ray:\n```python\nimport ray\nfrom ray import data_plane\nimport boto3\nimport io\n\n# Set up Ray\nray.init()\n\n# Set up S3 client\ns3 = boto3.client('s3')\n\n# Define GPT-2 inference function\ndef gpt2_inference(text):\n # Load GPT-2 model\n model = pipeline('text-generation')\n\n # Perform inference on text\n result = model(text, max_length=50, num_return_sequences=1)\n\n # Extract inferred text\n inferred_text = result[0]['generated_text']\n\n # Return inferred text\n return inferred_text\n\n# Define function to read text data from S3 bucket\ndef read_s3_data(s3_path):\n # Get object from S3 bucket\n obj = s3.get_object(Bucket='your-bucket-name', Key=s3_path)\n\n # Read text data from object\n data = obj['Body'].read().decode('utf-8')\n\n # Return text data\n return data\n\n# Define function to preprocess data for batch inference\ndef preprocess_data(data):\n # Preprocess data as needed for GPT-2 inference\n preprocessed_data = data\n\n # Return preprocessed data\n return preprocessed_data\n\n# Define function to perform batch inference with GPT-2\n@data_plane.function\ndef batch_inference(data):\n # Preprocess data\n preprocessed_data = preprocess_data(data)\n\n # Perform GPT-2 inference on preprocessed data\n inferred_data = ray.put(list(map(gpt2_inference, preprocessed_data)))\n\n # Return inferred data\n return inferred_data\n\n# Define S3 path and read data\ns3_path = 'your-s3-path'\ndata = read_s3_data(s3_path)\n\n# Preprocess data\npreprocessed_data = preprocess_data(data)\n\n# Create Ray Dataset from preprocessed data\nds = ray.data.from_items(preprocessed_data)\n\n# Perform batch inference with GPT-2\ninferred_ds = ds.map_batches(batch_inference, batch_size=100)\n\n# Extract inferred text from Ray Dataset\ninferred_text = [x.collect() for x in inferred_ds]\n\n# Print inferred text\nprint(inferred_text)\n```\nNote that you will need to replace `your-bucket-name` and `your-s3-path` with the appropriate values for your S3 bucket and data. Also, you may need to modify the `preprocess_data` function to preprocess the data as needed for GPT-2 inference.",1746"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1747},1748{1749"question": "How do I enable Ray debug logs?",1750"sources": [1751"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",1752"https://docs.ray.io/en/master/ray-observability/reference/cli.html#state",1753"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",1754"https://docs.ray.io/en/master/ray-contribute/debugging.html#backend-logging",1755"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging"1756],1757"document_ids": [17589872,17591776,1760958,17612177,176213431763],1764"answer": " To enable Ray debug logs, you can set the environment variable `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1` on each node before starting Ray. This is the default behavior for `ray start` and `ray.init()`. For more detailed debugging, you can check the dashboard log at `<RAY_LOG_DIR>/dashboard.log`, which is usually `/tmp/ray/session_latest/logs/dashboard.log`. Additionally, you can configure logging by setting the `RAY_BACKEND_LOG_LEVEL` environment variable to `debug` before starting Ray, which will print any `RAY_LOG(DEBUG)` lines in the source code to the `raylet.err` file.",1765"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1766},1767{1768"question": "How do I list the current Ray actors from python?",1769"sources": [1770"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1771"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1772"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",1773"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1774"https://docs.ray.io/en/master/rllib/actors.html#actors"1775],1776"document_ids": [1777731,1778725,1779790,1780739,1781266931782],1783"answer": " To list the current Ray actors from Python, you can use the `list_actors()` function from the `ray.util.state` module. Here is an example:\n\n```python\nfrom ray.util.state import list_actors\nprint(list_actors())\n```\n\nThis will print a list of all the current Ray actors.\n\nAlternatively, you can use the following command in the Ray CLI:\n\n```\nray list actors\n```\n\nThis is also recommended if you are not developing internally with the Ray SDK.",1784"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1785},1786{1787"question": "I want to kill the replica actor from Python. how do I do it?",1788"sources": [1789"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",1790"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",1791"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1792"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1793"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle"1794],1795"document_ids": [179630442,179730401,17989056,17999570,180095261801],1802"answer": " To kill a replica actor from Python, you can use the `ray.kill()` function and pass in the actor handle that you obtained using `ray.get_actor()`. Here is an example:\n```\nimport ray\n\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nNote that you will need to use the correct name for the replica actor when calling `ray.get_actor()`. You can check the list of active actors using the `ray list actors` command.\n\nAdditionally, it is worth noting that manually terminating an actor from within the actor itself is also possible. This can be done using the `ray.actor.exit_actor()` function within an actor method. However, this approach will not call any exit handlers installed in the actor using `atexit`.",1803"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1804},1805{1806"question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",1807"sources": [1808"https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",1809"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",1810"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",1811"https://docs.ray.io/en/master/ray-core/walkthrough.html#next-steps",1812"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors"1813],1814"document_ids": [181510089,181614202,181711376,181810621,1819108631820],1821"answer": " To specify that a remote function should run on a V100 GPU type, you can use the `SetResource` function in C++ to set the \"GPU\" resource requirement to 1.0 and the \"GPU_instance\\_type\" requirement to \"V100\". Here is an example:\n\n```\nray::Task(MyFunction).SetResource(\"CPU\", 1.0).SetResource(\"GPU\", 1.0).SetResource(\"GPU_instance_type\", \"V100\").Remote();\n```\n\nThis will tell Ray to schedule the task to a node that has a free GPU of type V100. Note that the `num_gpus` argument in the `@ray.remote` decorator only specifies the minimum number of GPUs required, and does not guarantee the type of GPU that will be used.",1822"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1823},1824{1825"question": "How do I get started?",1826"sources": [1827"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",1828"https://docs.ray.io/en/master/rllib/rllib-offline.html#getting-started",1829"https://docs.ray.io/en/master/ray-contribute/docs.html#contributing-to-the-ray-documentation",1830"https://docs.ray.io/en/master/serve/index.html#learn-more",1831"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started"1832],1833"document_ids": [183413686,183526193,18361860,183729910,1838105711839],1840"answer": " To get started with Ray Serve, follow these steps:\n\n1. Install Ray using `pip install -U ray`.\n2. Import and initialize Ray by adding `import ray` to your code.\n3. Check out the 'Getting Started' and 'Key Concepts' sections, or head over to the Examples to begin building your Ray Serve applications.\n4. Review the quick start tutorials for deploying a single model locally and converting an existing model into a Ray Serve deployment.",1841"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1842},1843{1844"question": "How to specify python version in runtime_env?",1845"sources": [1846"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",1847"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",1848"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",1849"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",1850"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"1851],1852"document_ids": [18538602,18549189,18559409,18569376,1857146461858],1859"answer": " To specify the Python version in the runtime environment, you can add the \"python\" key to the \"conda\" dependencies in the \"runtime\\_env\" dictionary. Here is an example:\n```bash\nruntime_env = RuntimeEnv(\n runtime_env={\n \"working_dir\": \"./\",\n \"conda\": {\n \"dependencies\": [\n \"python=3.8.11\", # specify Python version here\n \"pytorch\",\n \"pip\",\n {\"pip\": [\"requests\", \"ray[serve]\"]}\n ]\n }\n }\n)\n```\nAlternatively, you can also specify the Python version in the `pip_version` field for a specific pip package, like this:\n```python\nruntime_env = RuntimeEnv(\n pip={\n \"packages\": [\n \"emoji\",\n \"tensorflow==2.4.0;python_version=='3.8.11'\", # specify Python version here\n ],\n \"pip_check\": False,\n }\n)\n```\nNote that the `python_version` value should be a string in the format `'major.minor.patch'`.",1860"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1861},1862{1863"question": "how to create a Actor in a namespace?",1864"sources": [1865"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1866"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1867"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1868"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1869"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"1870],1871"document_ids": [187210716,187310726,187410102,18759535,1876322071877],1878"answer": " To create an actor in a specified namespace using Ray and Python, you can use the `setName()` method and specify the namespace in the method call when creating the actor. Here's an example:\n```python\nimport ray\n\n# Initialize Ray with the correct address\nray.init(address=\"localhost:10001\")\n\n# Create an actor with a specified namespace\nactor_handle = ray.remote(Actor).setName(\"my_actor\", \"actor_namespace\").remote()\n```\nIn this example, `Actor` is the class of the actor you want to create, and `\"my_actor\"` is the name of the actor. The second argument, `\"actor_namespace\"`, specifies the namespace for the actor.\n\nYou can retrieve the actor later using `ray.get_actor()` method, as long as the actor is still alive.\n```python\nactor = ray.get_actor(\"my_actor\", namespace=\"actor_namespace\")\n```\nNote that if you don't specify a namespace when creating an actor, Ray will place it in an anonymous namespace by default.",1879"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1880},1881{1882"question": "Can I specify multiple working directories?",1883"sources": [1884"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",1885"https://docs.ray.io/en/master/ray-core/configure.html#driver-options",1886"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",1887"https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#how-do-i-create-an-algorithm-checkpoint",1888"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable"1889],1890"document_ids": [18912579,189210369,18939415,189425928,189538381896],1897"answer": " Yes, you can specify multiple working directories in Ray by setting the `working_dir` parameter to a list of directories or a colon-separated string of directories. However, each worker process will change its working directory to the trial directory, which is guaranteed to be separate for each worker to avoid conflicts when saving trial-specific outputs. This behavior can be disabled by setting the `RAY_CHDIR_TO_TRIAL_DIR` environment variable to 0.",1898"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1899},1900{1901"question": "what if I set num_cpus=0 for tasks",1902"sources": [1903"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1904"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#configuring-parallelism-with-omp-num-threads",1905"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",1906"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",1907"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"1908],1909"document_ids": [19108629,191129197,191213076,191311371,191492011915],1916"answer": " If you set `num_cpus=0` for tasks, it will prevent Ray workloads with non-zero CPU requirements from being scheduled on that task. This is because each task requests by default one CPU, and setting `num_cpus=0` will not allow any CPU resources for that task. It is recommended to always explicitly set `num_cpus` for actors and tasks to avoid any surprises.",1917"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1918},1919{1920"question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",1921"sources": [1922"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",1923"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",1924"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",1925"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",1926"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"1927],1928"document_ids": [192913136,193012878,193113182,193213157,1933298961934],1935"answer": " Yes, it is possible to have Ray on K8s without using Kuberay and still enable autoscaling by using the Kubernetes Cluster Autoscaler. The Ray Autoscaler and the Kubernetes Cluster Autoscaler complement each other, with the Ray Autoscaler deciding when to create a Ray pod and the Kubernetes Cluster Autoscaler provisioning a Kubernetes node for the pod to be placed. However, if you don't have the necessary Kubernetes admin permissions for deploying KubeRay, you can still deploy a static Ray cluster to Kubernetes, but it won't have the built-in autoscaling feature that KubeRay provides.",1936"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1937},1938{1939"question": "how to manually configure and manage Ray cluster on Kubernetes",1940"sources": [1941"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",1942"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",1943"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",1944"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-aks-microsoft-azure",1945"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#ray-job-submission"1946],1947"document_ids": [194813804,194912689,195012992,195113025,1952129101953],1954"answer": " To manually configure and manage a Ray cluster on Kubernetes, follow these steps:\n\n1. Refer to the 'Manually Set up a Ray Cluster' and 'RayCluster Configuration' guides for understanding the key aspects of Ray cluster configuration on Kubernetes.\n2. Identify the Ray head node. The static Ray cluster configuration file sets up a Kubernetes service that targets the Ray head pod. This service allows you to interact with Ray clusters without directly executing commands in the Ray container.\n3. To deploy Ray on Kubernetes, you will need to configure pools of Kubernetes nodes. Find guidance on this in the documentation and quickstart guides.\n\nNote that successfully managing a Ray cluster on Kubernetes requires a good understanding of Kubernetes node configuration and management.",1955"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1956},1957{1958"question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",1959"sources": [1960"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#what-is-the-memory-monitor",1961"https://docs.ray.io/en/master/ray-core/cross-language.html#cross-language-data-serialization",1962"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",1963"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",1964"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors"1965],1966"document_ids": [196711390,19689973,196913592,197031629,197112101972],1973"answer": " Yes, shutting down a raylet will cause tasks and workers on that node to be killed, as the raylet is responsible for managing and killing tasks or actors if the system experiences memory pressure. Shutting down the raylet with `ray.shutdown()` will stop all processes on that node.",1974"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1975},1976{1977"question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",1978"sources": [1979"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",1980"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",1981"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",1982"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1983"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage"1984],1985"document_ids": [19861172,19871170,198811387,198910520,199012241991],1992"answer": " To debug out-of-memory issues in Ray, first familiarize yourself with the Ray Memory Management model. Then, view the sections on \"How to Detect Out-of-Memory Errors\" and \"Find per Task and Actor Memory Usage\" to locate the source of the memory leak. The \"Memory Monitor\" section provides information on how to enable and configure it for detecting and resolving memory issues. The command \"ray memory\" can also be used for debugging. For any questions or issues, refer to the documentation on \"Out-Of-Memory Prevention\" and \"Using the Memory Monitor\".",1993"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1994},1995{1996"question": "How to use callback in Trainer?",1997"sources": [1998"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",1999"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",2000"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",2001"https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#monitoring-and-logging-metrics",2002"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_load_checkpoint.html#ray-train-lightning-raytrainreportcallback-on-load-checkpoint"2003],2004"document_ids": [200519702,200624321,200717390,200819945,2009195862010],2011"answer": " To use a callback in the Trainer from the transformers library, you can add a callback instance to the trainer using the `add_callback()` method. For example:\n\n```python\nfrom transformers import Trainer, TrainingArguments\n\n# Create a training arguments object\nargs = TrainingArguments(...)\n\n# Create a Trainer instance\ntrainer = Trainer(args, ...)\n\n# Add a callback to the trainer\ntrainer.add_callback(MyRayTrainReportCallback())\n\n# Prepare the trainer (if necessary)\ntrainer = prepare_trainer(trainer)\n\n# Run training\ntrainer.train()\n```\n\nIn this example, `MyRayTrainReportCallback` is a custom callback class that you would need to define. The `on_train_epoch_end()` method in the callback class will be called at the end of each training epoch. You can define other methods as well to customize the behavior of the callback at different stages of training.\n\nThe `trainer` parameter of the callback methods is an instance of the `Trainer` class, and the `pl_module` parameter is an instance of the `LightningModule` class (if you are using PyTorch Lightning). The `callback_state` parameter is a dictionary that contains the state of the callback returned by the `on_save_checkpoint()` method, if it is defined.",2012"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2013},2014{2015"question": "How to provide current working directory to ray?",2016"sources": [2017"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2018"https://docs.ray.io/en/master/ray-contribute/docs.html#building-the-ray-documentation",2019"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2020"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2021"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging"2022],2023"document_ids": [202414362,20251863,2026964,202714360,2028102232029],2030"answer": " To provide the current working directory to Ray, you can specify the `working_dir` in the runtime environment when calling `ray.init()`. This will upload the working directory on the laptop to the Ray temp directory on the cluster. Here's an example:\n```python\nimport ray\n\nray.init(working_dir=os.getcwd())\n```\nBy default, Ray uses `/tmp/ray` as the temp directory on Linux and macOS. If you need to change the temp directory, you can provide the `_temp_dir` argument to `ray.init()`. Note that there is not currently a stable way to change the root temporary directory when calling `ray.init()`.",2031"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2032},2033{2034"question": "how to create an actor instance with parameter?",2035"sources": [2036"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",2037"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2038"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2039"https://docs.ray.io/en/master/ray-core/cross-language.html#python-calling-java",2040"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors"2041],2042"document_ids": [204310447,20449637,204511557,20469934,2047115562048],2049"answer": " To create an actor instance with a parameter, you can pass the parameter to the actor constructor. The exact syntax for this depends on the programming language you're using. Here are some examples in Python and Java:\n\nPython:\n```python\nobj = ray.put(2)\nactor_handle = Actor.remote(obj) # by-value\n```\nJava:\n```java\nActorHandle<Counter> counter = Ray.actor(Counter::new).setName(\"some_name\").remote();\n```\nIn both examples, we're creating an actor instance and passing a parameter to the constructor. In the Python example, we're passing an object (`obj`) to the `Actor` constructor. In the Java example, we're creating a new instance of the `Counter` class and passing it to the `Ray.actor()` method.\n\nNote that each actor instance is instantiated in its own process, so each actor will have its own copy of the class variables. If you need to manage global state between instances of the same class, you can use an actor's instance variables to hold the state and pass the actor handle to places where the state needs to be modified or accessed.",2050"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2051},2052{2053"question": "how to push a custom module to ray which is using by Actor ?",2054"sources": [2055"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2056"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",2057"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2058"https://docs.ray.io/en/master/ray-core/walkthrough.html#next-steps",2059"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors"2060],2061"document_ids": [206218415,206330021,20649235,206510624,206685182067],2068"answer": " To push a custom module to Ray for use with an Actor, follow these steps:\n\n1. Ensure you have installed Ray's Runtime Environments feature by running `pip install \"ray[default]\"`.\n2. Define your custom module or actor class. For example, you can define a remote actor class as follows:\n```python\nimport ray\n\n@ray.remote\nclass MyCustomActor:\n def method1(self):\n # Your code here\n\n def method2(self):\n # Your code here\n```\n3. When deploying the actor, pass the runtime environment in via `ray_actor_options` in your deployment. Here's an example:\n```python\nray.init(runtime_env={\"type\": \"pip\", \"packages\": [\"my-custom-module\"]})\n\nmy_actor = MyCustomActor.remote()\n```\nIn this example, `my-custom-module` is the name of your custom module that you want to use with the actor.\n\nNote: Make sure to replace `\"my-custom-module\"` with the actual name of your custom module.",2069"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2070},2071{2072"question": "how to print ray working directory?",2073"sources": [2074"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",2075"https://docs.ray.io/en/master/ray-contribute/docs.html#building-the-ray-documentation",2076"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",2077"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2078"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging"2079],2080"document_ids": [208112756,20821863,20832035,2084964,208598922086],2087"answer": " To print the working directory of Ray, you can use the following command in your Python script:\n```python\nimport os\nprint(os.getcwd())\n```\nAlternatively, if you want to see the directory that Ray is using for its temporary files, you can use the following command:\n```python\nimport ray\nprint(ray.global_state.get_temp_dir())\n```\nBased on the context provided, the Ray working directory is `/Users/user/ray/python/ray`.",2088"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2089},2090{2091"question": "why I can not see log.info in ray log?",2092"sources": [2093"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2094"https://docs.ray.io/en/master/ray-observability/reference/cli.html#state",2095"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",2096"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",2097"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"2098],2099"document_ids": [21001807,21011776,21021464,2103865,21048482105],2106"answer": " Based on the information provided, it seems like you're having trouble viewing `log.info` messages in your Ray logs. The context suggests that the default logs for Ray can be accessed using the Ray CLI or by checking the dashboard log at `/tmp/ray/session_latest/logs/dashboard.log`.\n\nHowever, if you want to view `log.info` messages specifically, you may need to configure Ray to log at a higher level of verbosity. You can do this by setting the log level to `debug` using the following command:\n```python\nimport ray\nray.init(logging_level=ray.logging_levels.DEBUG)\n```\nOnce you've done this, you should be able to see `log.info` messages in your Ray logs. If you're still having trouble, you can try using the `get_log` function from `ray.util.state` to retrieve the logs directly. Here's an example:\n```python\nfrom ray.util.state import get_log\n\nlog_data = get_log()\nprint(log_data)\n```\nThis will print out the logs for the current Ray session, which should include any `log.info` messages that have been emitted.",2107"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2108},2109{2110"question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",2111"sources": [2112"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",2113"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",2114"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2115"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",2116"https://docs.ray.io/en/master/data/transforming-data.html#repartitioning-data"2117],2118"document_ids": [211919818,212019819,212119808,212215922,2123150312124],2125"answer": " No, using Ray Datasets to read a file does not guarantee the preservation of data order by default. However, you can enable deterministic execution in Ray Datasets to preserve the order of the data. Set the 'preserve\\_order' flag in the DataContext as shown in the context:\n\n```python\nctx = ray.data.DataContext.get_current()\nctx.execution_options.preserve_order = True\n```\n\nAfter setting the 'preserve\\_order' flag, the order of the data will be preserved when reading a file with Ray Datasets.",2126"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2127},2128{2129"question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",2130"sources": [2131"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",2132"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",2133"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",2134"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",2135"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions"2136],2137"document_ids": [21388688,213910166,214010523,21418895,214280312143],2144"answer": " This means that if any exception or error is thrown by the user's code within a task in Ray, Ray will not automatically retry that task by default. However, Ray does provide options to customize fault tolerance, such as setting the number of retries and specifying which exceptions to retry, for tasks that fail due to system issues or particular application-level failures. Additionally, Ray offers at-least-once execution semantics for actor tasks, which will automatically retry tasks if the actor is unreachable.",2145"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2146},2147{2148"question": "how do I specify the log directory when starting Ray?",2149"sources": [2150"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2151"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",2152"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2153"https://docs.ray.io/en/master/ray-observability/reference/cli.html#state",2154"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory"2155],2156"document_ids": [2157964,215814248,2159961,21601776,2161129652162],2163"answer": " To specify the log directory when starting Ray, you can use the `--log-directory` or `--temp-directory` option with the `ray start` or `ray.init()` commands. For example:\n\n```\nray start --log-directory /path/to/log/directory\n```\n\nor\n\n```\nray.init(log_directory=\"/path/to/log/directory\")\n```\n\nThis will change the default log directory from `/tmp/ray/session_*/logs` to the specified path.",2164"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2165},2166{2167"question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",2168"sources": [2169"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",2170"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",2171"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",2172"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#walkthrough-using-ray-with-slurm",2173"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#launching-an-on-premise-cluster"2174],2175"document_ids": [217613915,217713554,21785149,217914441,2180138012181],2182"answer": " To launch a Ray cluster with 10 nodes without setting the minimum worker nodes to 10, you can use the Ray cluster launcher and manually set up the cluster. After installing Ray on each node, start the Ray processes on 9 worker nodes and connect them to the head node by providing the head node address. The Ray address for the head node would typically be in the format of localhost:6379. Make sure you have SSH access to all the nodes in the cluster.",2183"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2184},2185{2186"question": "how to use ray api to scale up a cluster",2187"sources": [2188"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",2189"https://docs.ray.io/en/master/cluster/running-applications/index.html#application-guide",2190"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-ray-settings",2191"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2192"https://docs.ray.io/en/master/ray-references/api.html#api-references"2193],2194"document_ids": [219514689,219613970,21978141,219815247,2199321462200],2201"answer": " To scale up a Ray cluster programmatically, use the `ray.autoscaler.sdk.request_resources()` call within your Ray program. This will immediately request the desired resources from the cluster, bypassing normal upscaling speed constraints. Note that there are other parameters that may need to be tuned for running a large cluster with Ray.",2202"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2203},2204{2205"question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",2206"sources": [2207"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",2208"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2209"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",2210"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",2211"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager"2212],2213"document_ids": [221412269,2215883,221613501,221713588,2218146432219],2220"answer": " To specify a subnet in the deployment file for Ray cloud launcher on AWS, you can include the subnet information in the cluster.yaml configuration file under the \"provider\" section. Here's an example:\n\n```yaml\nprovider:\n type: aws\n region: us-west-2\n availability_zone: us-west-2a\n subnet_id: subnet-0abcd1234efgh5678 # specify your subnet ID here\n ...\n```\n\nMake sure to replace `subnet-0abcd1234efgh5678` with your actual subnet ID. After updating the configuration file, you can launch the cluster as described in the context: `ray submit cluster.yaml example.py --start`.",2221"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2222},2223{2224"question": "where I can find HTTP server error code log for Ray serve",2225"sources": [2226"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2227"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",2228"https://docs.ray.io/en/master/serve/tutorials/gradio-integration.html#parallelize-using-ray-serve",2229"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2230"https://docs.ray.io/en/master/serve/tutorials/streaming.html#batch-requests-and-stream-the-output-for-each"2231],2232"document_ids": [223330712,223412590,223531598,223629346,2237320592238],2239"answer": " To find the HTTP server error code log for Ray Serve, you can check the Ray Serve logs directly by accessing the log files on the pods. These logs contain system level logs from the Serve controller and HTTP proxy, as well as access logs and user-level logs. You can use the Ray Serve logging and Ray logging resources for more details. Additionally, you can use the logging library in your code to get a logger for Ray Serve, like so: `logger = logging.getLogger(\"ray.serve\")`. Any warnings or errors that occur during request handling will be logged through this logger.",2240"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2241},2242{2243"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?",2244"sources": [2245"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",2246"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",2247"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",2248"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",2249"https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart"2250],2251"document_ids": [2252503,225312334,225412328,225512336,22568882257],2258"answer": " Yes, it's a known issue that the Ray Dashboard may not be publicly exposed when running a remote Ray Cluster, causing it to disappear after opening. To resolve this, you need to set up port forwarding from the head node to your local machine.\n\nYou can customize the Ray Dashboard port by starting the cluster explicitly with the CLI and passing the `--dashboard-port` argument with `ray start` in the command line.\n\nFor example:\n\n`$ ray dashboard -p <your_preferred_port> <cluster_config_file>`\n\nRefer to the documentation on configuring the dashboard for more information on viewing the Ray Dashboard from outside the head node.",2259"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2260},2261{2262"question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",2263"sources": [2264"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2265"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",2266"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2267"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",2268"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis"2269],2270"document_ids": [2271765,227213915,227314178,227413667,2275144062276],2277"answer": " In the Ray cluster launcher YAML, `max_workers` does not include the head node, it only includes the worker nodes. This is specified in the context where it's mentioned that the maximum number of worker nodes to launch in addition to the head node is set to 2. Also, the `num_worker_nodes` argument in Ray cluster represents how many ray worker nodes to start for the ray cluster, which further supports that the head node is not included in the worker node count.",2278"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2279},2280{2281"question": "How to update files in working directory ?",2282"sources": [2283"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2284"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#loading-data-in-a-tune-trainable",2285"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",2286"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",2287"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable"2288],2289"document_ids": [22909415,22914862,22925045,22933870,229450512295],2296"answer": " To update files in the working directory, you can write to a file directly in the working directory because it is set to the trial directory. You don't need to worry about multiple workers saving to the same location. Here's an example:\n```python\nwith open(f\"./artifact_{step}.txt\", \"w\") as f:\n # write to the file\n```\nOr, you can use `ray.train.get_context().get_trial_dir()` to get the path to save any outputs, since all workers on the same node now share the same working directory.\n```python\nwith open(f\"{ray.train.get_context().get_trial_dir()}/artifact_{step}.txt\", \"w\") as f:\n # write to the file\n```\nJust replace `step` with your desired iteration or step number.",2297"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2298},2299{2300"question": "How I can update working directory file when ray allready initiated ?",2301"sources": [2302"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2303"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2304"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",2305"https://docs.ray.io/en/master/ray-contribute/docs.html#building-the-ray-documentation",2306"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory"2307],2308"document_ids": [230914360,231014362,231110223,23121863,23139642314],2315"answer": " Unfortunately, there is no stable way to change the working directory once Ray has already been initiated. However, when you initialize Ray, it uploads the working directory from your laptop to the cluster. Relative paths in your remote tasks and actors will work on both your laptop and the cluster without any code changes.\n\nIf you need to change the temporary directory when calling `ray.init()`, you can provide the `_temp_dir` argument. Ray uses `/tmp/ray` as the default temp directory for Linux and macOS. To change the temp and logging directory, specify it when you call `ray start` or `ray.init()`.",2316"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2317},2318{2319"question": "how can I force ray head node to use custom pem file to ssh worker node?",2320"sources": [2321"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",2322"https://docs.ray.io/en/master/cluster/faq.html#what-is-the-difference-between-node-ip-address-and-address",2323"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",2324"https://docs.ray.io/en/master/cluster/vms/user-guides/community/lsf.html#deploying-on-lsf",2325"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes"2326],2327"document_ids": [232814326,232912545,233013532,233114366,2332138162333],2334"answer": " To force the Ray head node to use a custom PEM file to SSH to the worker node, you can modify the `ray start --head` command to include the `--redis-address` and `--object-store-address` flags with the head node's IP address and port, and use an SSH command with the custom PEM file to start the worker node.\n\nFor example:\n\nOn the head node:\n```css\nray start --head --redis-address xx.xx.xx.xx:6379 --object-store-address xx.xx.xx.xx:12379\n```\nOn the worker node:\n```bash\nssh -i /path/to/custom/pem.file user@xx.xx.xx.xx -p nnnn ray start --address xx.xx.xx.xx:nnnn --redis-address xx.xx.xx.xx:6379 --object-store-address xx.xx.xx.xx:12379\n```\nReplace `xx.xx.xx.xx` and `nnnn` with the appropriate IP address and port for your cluster.",2335"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2336},2337{2338"question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",2339"sources": [2340"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",2341"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",2342"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2343"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",2344"https://docs.ray.io/en/master/ray-core/configure.html#head-node"2345],2346"document_ids": [234730575,23488433,234932195,23501241,2351102562352],2353"answer": " The GCS server, or Global Control Service, is a metadata server that manages cluster-level metadata and provides cluster-level operations in a Ray cluster, running on the head node. The head node also has other memory-demanding system components and the driver running by default, which can cause high memory usage. The GCS server in this case is using 2Gi of memory, as specified in the resource limits.",2354"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2355},2356{2357"question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",2358"sources": [2359"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#updating-the-raycluster-config",2360"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",2361"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",2362"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#troubleshooting",2363"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource"2364],2365"document_ids": [236630652,236713814,23685149,23695182,2370127032371],2372"answer": " If some nodes are pending for a long time when starting a Ray cluster with \"ray up\", you can try the following steps to debug:\n\n1. Check the Ray cluster status with \"rayClusterStatus\" command to see if the pending cluster is healthy and became the active cluster.\n2. Ensure that your firewall settings and network configuration are correct, and that the Ray address is accessible from all nodes.\n3. If the Ray cluster is already started, you should not need to run anything on the worker nodes.\n4. If the pods are stuck in the Pending state, you can check for errors via \"kubectl describe pod\" command and ensure that your Docker resource limits are set high enough.\n5. You can also try restarting the Ray cluster with \"ray up CLUSTER.YAML --restart-only\" command to see if it resolves the issue.",2373"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2374},2375{2376"question": "how to install Ray 2.5.1 from github or wheel?",2377"sources": [2378"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-from-source",2379"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#monte-carlo-estimation-of",2380"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2381"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2382"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies"2383],2384"document_ids": [23852019,238611998,2387962,2388156,2389317182390],2391"answer": " To install Ray 2.5.1, first check if there is a pre-built wheel available for your platform and Python version at <https://github.com/ray-project/ray/releases/tag/v2.5.1>. If so, you can install it using `pip install -U LINK_TO_WHEEL.whl`. If not, you will need to build Ray from source using the instructions at <https://ray-project.github.io/install.html#building-ray-from-source>. Note that building from source may require additional dependencies and can take some time.",2392"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2393},2394{2395"question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",2396"sources": [2397"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2398"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2399"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",2400"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",2401"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-component-logs"2402],2403"document_ids": [24048551,24058632,24065138,240719643,240810202409],2410"answer": " To use `worker_setup_hook` in a runtime environment for setting up worker nodes, you can specify it in the configuration for `ray up` command. Here's an example:\n\n```yaml\nworker_setup_hook: \"path_to_your_setup_hook_script\"\n```\n\nThe `worker_setup_hook` is an experimental feature that allows you to specify a hook that gets executed on each worker node at the beginning of the worker process setup. The hook can be a Python callable or a string specifying the path to a Python script.\n\nIn your Python script, you can define the necessary setup steps, such as installing packages or setting environment variables. For example, you can define a function `setup_hook()` and specify its path in the `worker_setup_hook` configuration.\n\nNote that the `worker_setup_hook` is only executed on worker nodes, not on the head node. If you need to perform setup steps on the head node, you can use the `setup_commands` configuration instead.\n\nAlso, keep in mind that the `worker_setup_hook` is an experimental feature, so it may change in future versions of Ray.",2411"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2412},2413{2414"question": "how to use Ray dataset on aws",2415"sources": [2416"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2417"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#starting-with-pytorch-data",2418"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",2419"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-1-reading-the-dataset-from-s3",2420"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3"2421],2422"document_ids": [242315240,242419778,242514725,242617818,2427176542428],2429"answer": " To use Ray dataset on AWS, you can follow these steps:\n\n1. Start a Ray cluster on AWS using the Ray cluster launcher.\n2. Once the cluster is up and running, you can use Ray Data to load data from various sources, including S3.\n3. To load data from S3, you can use the `ray.data.read_images` function, which supports reading images from S3.\n4. Ray Data also supports a wide variety of other formats, including CSV, PyTorch's `torch.utils.data.Dataset`, and HuggingFace's `datasets.Dataset`.\n5. You can create datasets from on-disk files, Python objects, and cloud storage services like S3. Ray Data can read from any filesystem supported by Arrow.\n\nHere's an example of how to load the validation set from S3 using Ray Data:\n```python\nimport ray\n\nvalidation_dataset = ray.data.read_images('s3://my-bucket/validation/*.jpg')\n```\nNote that you will need to replace `'s3://my-bucket/validation/*.jpg'` with the actual path to your validation dataset in S3.",2430"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2431},2432{2433"question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",2434"sources": [2435"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2436"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2437"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",2438"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",2439"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches"2440],2441"document_ids": [244214846,244319808,244416824,244515431,2446160002447],2448"answer": " To avoid your dataset from shuffling during a `ray.data.map_batches()`, you can randomize each block of your dataset only once using `randomize_block_order()` and then enable local shuffling during training by specifying a `local_shuffle_buffer_size` to `iter_batches()` or `iter_torch_batches()`. This way, you ensure that the overall order of the data is maintained during the `map_batches()` transformation while still allowing for some randomness during training.",2449"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2450},2451{2452"question": "Is the order of the input data preserved after a map_batches operation?",2453"sources": [2454"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2455"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",2456"https://docs.ray.io/en/master/data/examples/ocr_example.html#process-the-extracted-text-data-with-spacy",2457"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2458"https://docs.ray.io/en/master/data/data-internals.html#execution-memory"2459],2460"document_ids": [246114846,246224194,246317406,246414912,2465156092466],2467"answer": " Yes, the order of the input data is preserved after a `map_batches` operation in Ray Data. The function is applied to batches of data, and the output maintains the same order as the input. However, be mindful of memory constraints when choosing the batch size to avoid out-of-memory situations.",2468"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2469},2470{2471"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?",2472"sources": [2473"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#rayservice",2474"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",2475"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",2476"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2477"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"2478],2479"document_ids": [248012686,24819999,248212602,248330712,2484293462485],2486"answer": " To get more detailed errors or logs when Ray Serve returns a generic internal service error, you can:\n\n1. Check the Ray Serve script for any incorrect code configurations (Issue 1).\n2. Test the script locally or in a RayCluster before deploying it to a RayService.\n3. Use the Ray Serve API to configure logging, for example by getting the logger for \"ray.serve\" and using it to log more detailed information.\n4. Refer to the Ray service troubleshooting guide for more details on troubleshooting Ray Serve configurations (serveConfigV2) and external system failures.",2487"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2488},2489{2490"question": "how do i track an uncaught exception in ray serve",2491"sources": [2492"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",2493"https://docs.ray.io/en/master/serve/api/doc/ray.serve.get_deployment_handle.html#ray-serve-get-deployment-handle",2494"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2495"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#streaming-batched-requests",2496"https://docs.ray.io/en/master/ray-contribute/debugging.html#backend-logging"2497],2498"document_ids": [24998379,250031099,250130712,250230489,250321772504],2505"answer": " To track an uncaught exception in Ray Serve, you can use the Ray Serve API and configure the logging level to debug. This will print any Ray log lines, including exceptions, to the `raylet.err` file. Here's an example of how to start Ray with the debug logging level:\n\n```\nexport RAY_BACKEND_LOG_LEVEL=debug\nray start\n```\n\nYou can then check the `raylet.err` file for any exceptions that occur during the execution of your Ray Serve application.\n\nAdditionally, you can implement error handling in your Ray Serve application to catch and handle exceptions as needed. For example, you can use a try-except block to catch and log exceptions:\n\n```python\nfrom ray import serve\n\n@serve.deployment\nclass MyApp:\n def __init__(self):\n self.counter = 0\n\n def handle_request(self, request):\n try:\n self.counter += 1\n # do some processing\n return {\"result\": self.counter}\n except Exception as e:\n # log the exception\n import logging\n logging.error(f\"Uncaught exception: {e}\")\n raise\n```\n\nThis will catch any exceptions that occur during the processing of a request and log them before re-raising them.",2506"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2507},2508{2509"question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",2510"sources": [2511"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2512"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2513"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2514"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2515"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs"2516],2517"document_ids": [25181807,2519613,252029331,252129329,252217632523],2524"answer": " To view logs emitted by your Ray Serve endpoint in the Ray cluster, you should use the Python logger with the name \"ray.serve\". In development, these logs are streamed to the driver Ray program, so you can view them in the terminal or Jupyter Notebook output where you started the Ray cluster or connected via Ray Client.\n\nHowever, if you execute the driver directly on the head node or run it with Ray Client, the driver logs won't be accessible from the Ray dashboard. In this case, you should check the terminal or Jupyter Notebook output for the logs.\n\nAdditionally, note that log APIs are currently in alpha stage and require a full installation of Ray using `pip install \"ray[default]\"`.",2525"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2526},2527{2528"question": "where can I see logs for a failed ray serve deployment",2529"sources": [2530"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",2531"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",2532"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",2533"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",2534"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"2535],2536"document_ids": [253730663,253812590,253931071,254029405,2541293442542],2543"answer": " You can see logs for a failed Ray serve deployment in the controller log and deployment log, which are located under /tmp/ray/session\\_latest/logs/serve/ in both the head node pod and worker node pod. You can also find a copy of these logs at /tmp/ray/session\\_latest/logs/serve/ and parse them with a logging stack such as ELK or Loki to search by deployment or replica. Additionally, you can check failed requests through the Ray metrics monitoring infrastructure, which are exposed in Prometheus format on each node.",2544"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2545},2546{2547"question": "How to take a subset of a Ray Dataset?",2548"sources": [2549"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",2550"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",2551"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2552"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2553"https://docs.ray.io/en/master/data/examples/batch_training.html#batch-training-with-ray-data"2554],2555"document_ids": [255619748,255717130,255814933,255915187,2560170802561],2562"answer": " To take a subset of a Ray Dataset, you can use the `take` or `map_partitions` functions. Here's an example using `take`:\n\n```python\nsubset_size = 50\nsubset_indices = ray.data.range_index(stop=len(train_dataset))[:subset_size]\nsubset_dataset = train_dataset.take(subset_indices)\n```\n\nIn this example, `subset_size` is the number of elements you want in your subset, and `subset_indices` is a Ray Dataset containing the indices of the elements you want to include in the subset. The `take` function then creates a new Ray Dataset containing only the elements at the specified indices.\n\nNote that this example uses the `range_index` function to generate a Ray Dataset containing a range of indices, and then takes the first `subset_size` elements to create the subset. You can modify this to suit your needs, for example by generating a custom set of indices or using `map_partitions` to filter the dataset based on some condition.",2563"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2564},2565{2566"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.",2567"sources": [2568"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",2569"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",2570"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",2571"https://docs.ray.io/en/master/tune/tutorials/overview.html#tune-feature-guides",2572"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing"2573],2574"document_ids": [25754721,25765004,25775007,25784815,257946982580],2581"answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `tune.run` function's `checkpoint_at_end` argument to ensure that all trials save their final checkpoint. After the experiment has finished, you can then manually load each trial's checkpoint using the `train.get_checkpoint()` method.\n\nSince you mentioned that you used cloud checkpointing, you would need to download the checkpoints from the cloud storage to your local machine before loading them. The location of the checkpoints for each trial depends on your specific setup, but the context suggests that they may be saved in a directory structure like `/tmp/mypath/my-tune-exp/<trial_name>/checkpoint_<step>`.\n\nHere's an example of how you can load all the checkpoints from a Tune experiment:\n```python\nimport os\nimport tune\n\n# Define the trainable function that loads the checkpoint\ndef my_trainable_from_checkpoint(checkpoint_dir):\n # Load the model or state from the checkpoint directory\n # ...\n pass\n\n# Restore the experiment from the cloud storage (you would need to implement this part)\nrestored_results = restore_experiment_from_cloud('my-tune-exp')\n\n# Iterate over all trials and load their final checkpoints\nfor trial_name in restored_results.trials:\n trial_dir = os.path.join('/tmp/mypath/my-tune-exp', trial_name)\n checkpoint_dir = tune.utils.find_trial_checkpoint(trial_dir)\n if checkpoint_dir:\n my_trainable_from_checkpoint(checkpoint_dir)\n```\nNote that you would need to replace the `restore_experiment_from_cloud` function with the actual method of restoring the experiment from the cloud storage. The `find_trial_checkpoint` function is a utility function provided by Tune to find the latest checkpoint directory for a trial.",2582"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2583},2584{2585"question": "How can I kill a \"detached\" Actor ?",2586"sources": [2587"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.html#ray-job-config-jobconfig",2588"https://docs.ray.io/en/master/ray-core/api/core.html#actors",2589"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2590"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",2591"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill"2592],2593"document_ids": [259411182,25959855,25969304,25979056,259894212599],2600"answer": " To kill a \"detached\" Actor, you can use the `ray.kill()` function, passing the Actor as an argument. Here's an example:\n```python\nray.kill(detached_actor)\n```\nThis will kill the Actor immediately. If you want to allow pending tasks to finish before killing the Actor, you can call `__ray_terminate__().remote()` method on the Actor object instead:\n```python\ndetached_actor.__ray_terminate__.remote()\n```\nThis will queue a termination task and run any installed atexit handlers before terminating the Actor.",2601"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2602},2603{2604"question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",2605"sources": [2606"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",2607"https://docs.ray.io/en/master/rllib-env.html#configuring-environments",2608"https://docs.ray.io/en/master/rllib/rllib-env.html#configuring-environments",2609"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",2610"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv"2611],2612"document_ids": [26131435,26146,261523046,26161429,261785422618],2619"answer": " To set environment variables when initializing Ray, you can use the `runtime_env` argument in the `ray.init()` function. Specifically, you can set the environment variables using the `env_vars` key in the `runtime_env` dictionary. Here's an example:\n```python\nray.init(runtime_env={\"env_vars\": {\"foo\": \"foo\"}})\n```\nIn this example, the environment variable `foo` is set to the value `\"foo\"`. You can then access this environment variable in your Ray tasks and actors using the `os.environ` dictionary.\n\nNote that any environment variables set in this way will only be available to the Ray workers, and not to the main Ray process. If you need to set environment variables for the main process, you should use the `os.environ` dictionary directly.",2620"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2621},2622{2623"question": "What is the rest api for getting the head node id?",2624"sources": [2625"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",2626"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",2627"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",2628"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2629"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate"2630],2631"document_ids": [26321048,263314459,263414456,263532196,263610522637],2638"answer": " Based on the context provided, there isn't a specific REST API endpoint for getting the head node ID directly. However, you can get the information about all nodes, including the head node, by using a REST API endpoint that provides node details.\n\nYou can use the following API endpoint to get the node details:\n\n`GET /api/nodes`\n\nThis will return a list of nodes in the Ray cluster. The head node can be identified by checking the `is_head_node` field for each node. Here's an example of how to get the ID of the head node using `jq`:\n\n```bash\nCLUSTER_URL=\"http://localhost:6060\" # Replace with your Ray cluster URL if needed\nHEAD_NODE_ID=$(curl -s \"$CLUSTER_URL/api/nodes\" | jq -r '.[] | select(.is_head_node == true) | .node_id')\necho \"Head node ID: $HEAD_NODE_ID\"\n```\n\nThis code snippet sends a GET request to the `/api/nodes` endpoint, filters the nodes with `is_head_node` set to `true`, and extracts the `node_id` field for the head node.",2639"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2640},2641{2642"question": "how to rerun a canceled ray task",2643"sources": [2644"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2645"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2646"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",2647"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2648"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks"2649],2650"document_ids": [26518364,265226754,265332072,265410178,2655101572656],2657"answer": " To rerun a canceled Ray task, you can simply call the same task again using the `remote` function and store the new object reference. Here's an example:\n\n```python\n@ray.remote\ndef my_task():\n # Your task code here\n pass\n\nobj_ref = my_task.remote() # Initial task creation\nray.cancel(obj_ref) # Cancel the task\n\n# Rerun the task by creating a new object reference\nnew_obj_ref = my_task.remote()\n```\n\nNote that canceling a task in Ray doesn't mean it can be retried or resumed from the same state. Canceling a task will stop its execution immediately, and if you want to rerun the task, you have to create a new object reference and execute the task again.",2658"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2659},2660{2661"question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",2662"sources": [2663"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ConcurrencyLimiter.html#ray-tune-search-concurrencylimiter",2664"https://docs.ray.io/en/master/rllib/key-concepts.html#key-subconcepts",2665"https://docs.ray.io/en/master/tune/examples/tune-sklearn.html#walkthrough",2666"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.nevergrad.NevergradSearch.set_max_concurrency.html#ray-tune-search-nevergrad-nevergradsearch-set-max-concurrency",2667"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"2668],2669"document_ids": [26703230,267126525,26725772,26733111,267431702675],2676"answer": " To set the maximum parallel concurrent scheduled tasks in `map_batches`, you can use the `max_concurrent` parameter. This parameter determines the maximum number of tasks that can run concurrently. Here is an example:\n```\nmap_batches(func=your_function, max_concurrent=your_number)\n```\nIn the context provided, `max_concurrent` is used in several places to limit the number of concurrent tasks. For example, in the `synchronous_parallel_sample` function, `max_concurrent` is passed to the `ConcurrencyLimiter` to limit the number of concurrent samples. Similarly, in the description of the `max_concurrent` parameter for `map_batches`, it is stated that this parameter \"determines the maximum number of concurrently running tasks.\"\n\nIt is worth noting that the `max_concurrent` parameter is not specific to `map_batches`, but is a more general concept used in parallel processing to limit the number of tasks that can run at the same time.\n\nI hope this helps! Let me know if you have any other questions.",2677"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2678},2679{2680"question": "How do I get the number of cpus from ray cluster?",2681"sources": [2682"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",2683"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-1-fixing-the-resource-quantity-with-ray-init-num-cpus",2684"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-8-a-loop-of-restarting-the-raycluster-occurs-when-the-kubernetes-cluster-runs-out-of-resources-kuberay-v0-6-1-or-earlier",2685"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",2686"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources"2687],2688"document_ids": [268913074,269012024,269112644,269232085,269383972694],2695"answer": " To get the number of CPUs in a Ray cluster, you can use the `ray.cluster_resources()` function, which returns a dictionary of the cluster's resources. The number of CPUs is represented as `\"CPU\"` in the dictionary. For example, you can get the number of CPUs in the cluster by calling `ray.cluster_resources()[\"CPU\"]`.\n\nAlternatively, if you want to get the number of CPUs for a specific Ray node, you can use the `ray.nodes()` function, which returns a list of nodes in the cluster. Each node has a `resources` field that contains a dictionary of the node's resources. You can get the number of CPUs for a specific node by calling `ray.nodes()[i].resources[\"CPU\"]`, where `i` is the index of the node in the list.\n\nNote that the number of CPUs in the cluster or node may be autodetected or manually specified, depending on the configuration of the Ray cluster. If the number of CPUs has been manually specified, it may not reflect the actual number of physical CPUs on the machine.",2696"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2697},2698{2699"question": "How to use the exclude option to the runtime_env",2700"sources": [2701"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",2702"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",2703"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2704"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job",2705"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"2706],2707"document_ids": [27089189,27098093,271030067,27119232,271293152713],2714"answer": " To use the \"exclude\" option with the runtime environment in Ray, you can create a `RuntimeEnv` object and specify the packages to exclude in the `pip` argument as a list of strings. Here's an example:\n```python\nruntime_env = RuntimeEnv(\n pip=[\"emoji\", \"--exclude=package_to_exclude\"],\n env_vars={\"TF_WARNINGS\": \"none\"}\n)\n```\nIn this example, the `pip` argument includes the package \"emoji\" and the option `--exclude` to exclude \"package\\_to\\_exclude\". You can replace \"package\\_to\\_exclude\" with the name of the package you want to exclude.\n\nNote that the `exclude` option may not be available in all contexts or configurations of Ray. If you encounter any issues, refer to the Ray documentation for your specific use case.",2715"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2716},2717{2718"question": "show a map batch example with batch_format",2719"sources": [2720"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",2721"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.to_batch_format.html#ray-data-block-blockaccessor-to-batch-format",2722"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2723"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2724"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data"2725],2726"document_ids": [272722999,272816021,272932161,273014846,2731177602732],2733"answer": " Sure, here's an example of using `map_batches` with `batch_format`:\n```python\nimport ray\nfrom typing import List, Dict\n\ndef process_batch(batch: List[Dict]) -> List[Dict]:\n outputs = model.predict_batch(batch)\n",2734"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2735},2736{2737"question": "how to find local ray address",2738"sources": [2739"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2740"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",2741"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",2742"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",2743"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"2744],2745"document_ids": [274614012,274713948,274813825,274913820,275091882751],2752"answer": " To find the local Ray address, follow these steps:\n\n1. Check the `RAY_ADDRESS` environment variable. If it's not set, proceed to step 2.\n2. Look for the address of the latest cluster started in `/tmp/ray/ray_current_cluster`. If this is also empty, start a new local Ray instance.\n3. If the provided address is \"auto\", follow the process to start a new local Ray instance and check the address printed by the command.\n\nTo start a local Ray instance, use the command `ray start --address=<head-node-address:port>`, replacing `head-node-address:port` with the value printed by the command on the head node.\n\nAdditionally, you can access the Ray Dashboard by viewing `localhost:8265` in your browser.",2753"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2754},2755{2756"question": "How to start ray cluster on multiple node via CLI?",2757"sources": [2758"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",2759"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#launching-an-on-premise-cluster",2760"https://docs.ray.io/en/master/ray-core/configure.html#configuring-ray",2761"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-3-create-a-mini-cluster-with-ray-cluster-utils-cluster",2762"https://docs.ray.io/en/master/ray-core/configure.html#java-applications"2763],2764"document_ids": [276513013,276613801,276710176,276812035,2769103442770],2771"answer": " To start a Ray cluster on multiple nodes via the command-line interface (CLI), follow these steps:\n\n1. Install Ray on all nodes.\n2. Set up password-less SSH access to all nodes.\n3. Use the `cluster-launcher` to start the Ray cluster on multiple nodes:\n\n```bash\ncluster-launcher start --head --node-ip-addresses <head_node_ip> --redis-address <head_node_ip>:6379 --object-manager-port 6379 --autoscaling-config @path/to/autoscaling_config.yaml\n```\n\n4. On other nodes, join the Ray cluster:\n\n```bash\ncluster-launcher start --address <head_node_ip>:6379 --object-manager-port 6379\n```\n\n5. In your Python or Java application, use `ray.init()` or `Ray.init()` to connect to the cluster.\n\nNote: Multi-node Ray clusters on Windows are experimental and may have issues.",2772"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2773},2774{2775"question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",2776"sources": [2777"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",2778"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",2779"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",2780"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",2781"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements"2782],2783"document_ids": [278411216,278515458,27861506,278725173,2788113692789],2790"answer": " Based on the information provided, your Ray tuner shows \"running\" but with almost 0% CPU usage because after a Ray actor is created, it occupies 0 CPU for running by default, even though it requires 1 CPU for scheduling. This behavior might be due to the configuration of Ray Data and Tune, which tries to fully utilize cluster CPUs, potentially preventing Ray Data from scheduling tasks and reducing performance. The RayTrainWorker configuration also shows that zero-enabled and zero-force-ds-cpu-optimizer are set to True, which could further contribute to the low CPU usage.",2791"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2792},2793{2794"question": "should the Ray head node and all workers have the same object store memory size allocated?",2795"sources": [2796"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",2797"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",2798"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#head-node-commands",2799"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",2800"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node"2801],2802"document_ids": [28031233,28041241,280514596,280610957,2807123822808],2809"answer": " Yes, it is recommended to allocate the same object store memory size to the Ray head node and all workers. However, keep in mind that the Ray head node has additional memory-demanding system components, so you may need to allocate more memory to the head node to accommodate these components. The object store memory size can be controlled using the `--object-store-memory` flag when starting Ray. By default, Ray reserves 30% of available memory for the object store, so you may need to adjust this value based on your application's needs.",2810"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2811},2812{2813"question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",2814"sources": [2815"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",2816"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2817"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",2818"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#building-the-call-graph-methodnodes-and-functionnodes",2819"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.ProxyLocation.EveryNode.html#ray-serve-config-proxylocation-everynode"2820],2821"document_ids": [282230658,282331141,282429829,282529408,2826303382827],2828"answer": " In Ray Serve, you can specify whether to set up an HTTP proxy on each node or just the head node using the `ProxyLocation` configuration in the Serve config. The options are `HeadOnly` to run a proxy only on the head node, and `EveryNode` to run a proxy on every node in the cluster that has at least one replica actor (this is the default). You can change the port for HTTP proxy traffic using the `http_options` in the Serve config. The API for this configuration is stable across Ray releases.",2829"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2830},2831{2832"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?",2833"sources": [2834"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-11-embed-grafana-panels-in-ray-dashboard",2835"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",2836"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",2837"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",2838"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard"2839],2840"document_ids": [284113400,284212362,284312358,284412357,2845123612846],2847"answer": " To answer your query, you should set `RAY_GRAFANA_HOST` and `RAY_PROMETHEUS_HOST` to the addresses that the KubeRay head node can use to access Grafana and Prometheus respectively. Since the head node IP is changing every time you reconfigure your cluster, it would be more intelligent to set these variables to the IP addresses or hostnames of the machines where Grafana and Prometheus are running.\n\nFor example, if Grafana is hosted on a machine with the IP address 55.66.77.88 and Prometheus is hosted on a machine with the IP address 11.22.33.44, you should set the values as follows:\n\n* `RAY_GRAFANA_HOST=http://55.66.77.88:3000`\n* `RAY_PROMETHEUS_HOST=http://11.22.33.44:9090`\n\nMake sure to configure these settings using the `RAY_GRAFANA_HOST`, `RAY_PROMETHEUS_HOST`, `RAY_PROMETHEUS_NAME`, and `RAY_GRAFANA_IFRAME_HOST` environment variables when you start the Ray clusters.\n\nNote that if you use a different Prometheus server for each Ray cluster and use the same Grafana server for all clusters, you should set the `RAY_PROMETHEUS_NAME` environment variable to different values for each Ray cluster and add these datasources in Grafana.",2848"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2849},2850{2851"question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",2852"sources": [2853"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",2854"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",2855"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",2856"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",2857"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"2858],2859"document_ids": [286013177,286130654,286230655,286313183,2864303742865],2866"answer": " When using KubeRay autoscaling in a GCS (Generic Cluster Autoscaler for Kubernetes) environment, the KubeRay operator determines which Kubernetes pod to kill when scaling down. This is because the GCS only decreases the replica count during autoscaling, without control over which pods are deleted. The KubeRay operator, however, can make informed decisions on which pod to delete based on the application's needs, ensuring safer downscaling for Ray applications.",2867"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2868},2869{2870"question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",2871"sources": [2872"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2873"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",2874"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2875"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-summary-tasks",2876"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema"2877],2878"document_ids": [287930051,288029667,288130039,28821779,2883298532884],2885"answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you can include it in the following way:\n```yaml\nhttp_options:\n request_timeout_s: <value_in_seconds>\n```\nFor example, if you want to set the request timeout to 60 seconds, you can do:\n```yaml\nhttp_options:\n request_timeout_s: 60\n```\nThis config is global to your Ray cluster, and it cannot be updated during runtime.",2886"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2887},2888{2889"question": "How do I make the GPU available on my M1 laptop to ray?",2890"sources": [2891"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",2892"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#gpu-support",2893"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#requesting-gpus",2894"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpus-and-ray",2895"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources"2896],2897"document_ids": [28987489,289910853,290013267,290113260,2902141942903],2904"answer": " To make the GPU available for Ray on your M1 laptop, you need to specify the number of GPUs for Ray to use when starting the entrypoint. If you don't specify the number of GPUs, they will still be available to the entrypoint script, but Ray will not provide isolation in terms of visible devices. Ray natively supports GPU as a pre-defined resource type, and you can make a direct request to the autoscaler to scale up GPU resources. However, using multiple GPUs per trial with Ray requires using LightningTrainer.",2905"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2906},2907{2908"question": "How can I add a timeout for the Ray job?",2909"sources": [2910"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",2911"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s",2912"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",2913"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s",2914"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#callbacks"2915],2916"document_ids": [291712756,291818777,291929667,292018357,2921316352922],2923"answer": " To add a timeout for a Ray job, you can use the `HorovodConfig.timeout_s` or `TorchConfig.timeout_s` attributes when setting up your Ray training configurations. These attributes allow you to specify the timeout in seconds for the respective frameworks.\n\nHere's an example of how to use `HorovodConfig.timeout_s`:\n\n```python\nfrom ray.train.horovod import HorovodConfig\n\nhorovod_config = HorovodConfig(timeout_s=<your_timeout_value>)\n```\n\nAnd here's an example of how to use `TorchConfig.timeout_s`:\n\n```python\nfrom ray.train.torch import TorchConfig\n\ntorch_config = TorchConfig(timeout_s=<your_timeout_value>)\n```\n\nReplace `<your_timeout_value>` with the number of seconds you want to set as the timeout for your Ray job.\n\nNote that the provided context also includes an example of a custom timer callback (`MyTimerCallback`) that can be used to implement custom time tracking and handling for Ray tasks. However, this is not a direct way to set a timeout for a Ray job.",2924"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2925},2926{2927"question": "how do I set custom /tmp directory for remote cluster?",2928"sources": [2929"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",2930"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",2931"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",2932"https://docs.ray.io/en/master/cluster/cli.html#ray-start",2933"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster"2934],2935"document_ids": [293630301,293730591,29382011,293912498,2940140592941],2942"answer": " To set a custom `/tmp` directory for a remote cluster, you can use the `RAY_TEMPDIR` environment variable. Before connecting to the remote cluster, run the following command on your local machine, replacing `<head-node-ip-address>` with the IP address of your remote cluster head node:\n\n```bash\nRAY_TEMPDIR=\"/ray-mount\"\n```\n\nThis environment variable sets the temporary directory for the cluster files to be created in the `/ray-mount` directory, which should be accessible by the host, the outer container, and the inner container. In the inner container, you can control the directory name.\n\nKeep in mind that this method is not explicitly mentioned in the context for setting a custom `/tmp` directory for a remote cluster, but it is a reasonable assumption based on the information provided for local clusters and the `RAY_TEMPDIR` environment variable.",2943"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2944},2945{2946"question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",2947"sources": [2948"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2949"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",2950"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",2951"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",2952"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory"2953],2954"document_ids": [2955964,295610686,29572011,295810223,29599672960],2961"answer": " Yes, if you set --temp-dir to a different directory than /tmp, Ray object spill will occur in the custom directory. By default, Ray uses the /tmp/ray directory for Linux and macOS as the temporary directory, and objects are spilled to a subdirectory within this temp directory. However, you can configure the temp and logging directory by specifying it when you call ray start or ray.init(). To change the temporary directory for the cluster files, you can use the RAY\\_TEMPDIR environment variable. This directory should be accessible by the host, the outer container, and the inner container. In the inner container, you can control the directory name. If you need to change the root temporary directory when calling ray.init(), you can provide the \\_temp\\_dir argument to ray.init().",2962"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2963},2964{2965"question": "can you give me an example for *`--runtime-env-json`*",2966"sources": [2967"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2968"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",2969"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",2970"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",2971"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"2972],2973"document_ids": [297431254,297529990,297630903,29779189,297894092979],2980"answer": " The `--runtime-env-json` flag in Ray allows you to specify a runtime environment in JSON format. Here's an example:\n\n```json\n{\n \"pip\": [\"emoji\"],\n \"env_vars\": {\"TF_WARNINGS\": \"none\"}\n}\n```\n\nThis JSON object can be used with the `--runtime-env-json` flag to set up a runtime environment with the specified pip packages and environment variables.\n\nThe Ray documentation also mentions that there are two primary scopes for which you can specify a runtime environment: per-job and per-task/actor within a job. The example above can be used for either of those scopes.",2981"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2982},2983{2984"question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",2985"sources": [2986"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",2987"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",2988"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",2989"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",2990"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"2991],2992"document_ids": [299330411,299429491,299529169,299630505,2997304142998],2999"answer": " Based on the provided context, `maxConcurrentReplicas` is not a specified parameter in the autoscaling configuration in Ray Serve. It is not mentioned in the list of configurable parameters for autoscaling. Therefore, it's not possible to define a value for `maxConcurrentReplicas` in this context.",3000"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3001},3002{3003"question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",3004"sources": [3005"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",3006"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3007"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",3008"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",3009"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters"3010],3011"document_ids": [301229489,301329173,301429508,301530405,3016291783017],3018"answer": " When `target_num_ongoing_requests_per_replica` is specified, the value of `max_concurrent_queries` can be adjusted based on performance tuning needs, which defaults to 100. It is the maximum number of queries that can be sent to a replica without receiving a response. Note that `target_num_ongoing_requests_per_replica` is just a target value used for autoscaling and the real number of ongoing requests can be higher.",3019"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3020},3021{3022"question": "what is a `smoothing_factor`",3023"sources": [3024"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3025"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3026"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3027"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",3028"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining"3029],3030"document_ids": [303129183,303229181,303329184,30344125,303541333036],3037"answer": " A `smoothing_factor` is a multiplicative factor that adjusts the speed of scaling decisions in a system. A downscale smoothing factor slows down the pace of downscaling decisions, while an upscale smoothing factor speeds up the pace of upscaling decisions. It can be adjusted to make the system more or less sensitive to changes in traffic volume.",3038"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3039},3040{3041"question": "What is the reason actors change their state to unhealthy?",3042"sources": [3043"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",3044"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",3045"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",3046"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",3047"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"3048],3049"document_ids": [305029939,30518423,305226782,305332094,30547323055],3056"answer": " Actors in this context refer to a system for managing stateful entities in a distributed computing environment. An actor's state can become \"UNHEALTHY\" due to various reasons, such as errors in the deployment's constructor, a crashed replica, or general system or machine issues. This state is undesirable, especially if the stateful aspect of an actor is not necessary for your use case. In such cases, it is recommended to use tasks instead, which are generally more lightweight and less prone to such issues.",3057"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3058},3059{3060"question": "How do I access logs for a dead node?",3061"sources": [3062"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3063"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3064"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3065"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",3066"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy"3067],3068"document_ids": [30691808,3070823,30711212,307214244,3073114183074],3075"answer": " Based on the provided context, you cannot access logs for a dead node directly through the API. However, if workers were killed on that node, you can use the command `ray logs raylet.out -ip <node_ip>` to see more information about those workers. Additionally, enabling log persistence can help in troubleshooting issues related to dead nodes.",3076"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3077},3078{3079"question": "What are the reasons for a node to change it\u2019s status to dead?",3080"sources": [3081"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",3082"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",3083"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",3084"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",3085"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate"3086],3087"document_ids": [30888507,308930315,3090696,30911562,309215543093],3094"answer": " Based on the provided context, a node in this context could change its status to 'dead' if the actor running on that node is unable to recover or is permanently unresponsive. This is different from pod failures, which can be simulated and recovered more quickly. The node's status could be monitored to check for any failures or pending nodes, which would be indicated in the status output.",3095"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3096},3097{3098"question": "What are the reasons for spikes in node CPU utilization",3099"sources": [3100"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",3101"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3102"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",3103"https://docs.ray.io/en/master/ray-core/examples/web-crawler.html#speed-up-your-web-crawler-by-parallelizing-it-with-ray",3104"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention"3105],3106"document_ids": [310712549,31081199,310911385,311012051,3111113863112],3113"answer": " The reasons for spikes in node CPU utilization can be:\n\n1. Overloading the head node with too many simultaneous connections.\n2. Application tasks or actors consuming a large amount of heap space, causing the node to run out of memory (OOM).\n3. High CPU times for user, system, or total processes.\n\nThese issues can lead to metrics stalling, disruption of application processes, and in severe cases, make the cluster unusable. Additionally, in the provided context, running out of memory on the node where the task was running also contributed to high CPU utilization.",3114"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3115},3116{3117"question": "What AWS machine type is recommended to deploy a RayService on EKS?",3118"sources": [3119"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",3120"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",3121"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",3122"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup",3123"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework"3124],3125"document_ids": [312630588,312730665,312830590,312913999,313014113131],3132"answer": " The recommended way to deploy a RayService on AWS EKS is to use the KubeRay RayService custom resource, which handles important production requirements such as health checking, status reporting, failure recovery, and upgrades. It is recommended to use the RayService controller provided as part of KubeRay for managing the RayService.",3133"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3134},3135{3136"question": "Is there a way to configure the session name generated by ray?",3137"sources": [3138"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3139"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3140"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3141"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",3142"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments"3143],3144"document_ids": [314510213,3146964,314710215,314814309,3149143023150],3151"answer": " Yes, you can configure the session name generated by Ray by specifying the `namespace` argument when calling `ray.init()`. This will change the format of the session name to `session_{namespace}_{timestamp}_{pid}`. Note that the session directory will still be placed under the root temporary path, which defaults to `/tmp/ray`.",3152"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3153},3154{3155"question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",3156"sources": [3157"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",3158"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",3159"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli",3160"https://docs.ray.io/en/master/data/examples/batch_training.html#walkthrough",3161"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#visualizing-tasks-with-ray-timeline"3162],3163"document_ids": [316414398,316514117,316613991,316717104,316815323169],3170"answer": " Yes, you can use the Python SDK to interact with Ray jobs, including getting a link to the Ray dashboard. However, the SDK does not provide a direct method to get the dashboard link. You can use the SDK to submit and manage jobs, and then use the provided dashboard URL in the Ray cluster's information to access the dashboard.\n\nHere's an example of getting the Ray cluster resources and dashboard URL using the Python SDK:\n\n```python\nimport ray\n\n# Connect to the Ray cluster\nray.init(address=\"auto\")\n\n# Get Ray cluster resources\nprint(ray.cluster_resources())\n\n# Extract the dashboard URL from the Ray cluster information\ndashboard_url = ray.services.get_dashboard_url()\nprint(\"Dashboard URL:\", dashboard_url)\n```\n\nThis will print the Ray cluster resources and the dashboard URL, which you can use to access the Ray dashboard.",3171"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3172},3173{3174"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).",3175"sources": [3176"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",3177"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3178"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",3179"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3180"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance"3181],3182"document_ids": [318311417,31841210,318511385,31861190,318785073188],3189"answer": " The node where the task was running may have crashed unexpectedly due to:\n\n1. The instance where the node was running failing.\n2. Raylet crashing unexpectedly, possibly due to memory pressure (OOM), as indicated by worker processes being killed due to running out of memory.\n3. The worker being crashed unexpectedly due to SIGSEGV or other unexpected errors.\n\nNote that in this case, there is evidence of worker processes being killed due to memory pressure (OOM) in the provided context.",3190"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3191},3192{3193"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.",3194"sources": [3195"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",3196"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#worker-init-container",3197"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",3198"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",3199"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"3200],3201"document_ids": [320212546,320312674,320430153,32058433,32068373207],3208"answer": " Based on the context provided, the error message you're seeing suggests that the worker node is having trouble connecting to the Global Control Service (GCS) server. Here are some steps you can take to troubleshoot this issue:\n\n1. Check if the GCS server is running and accessible at the specified address. You can do this by running the command `ray logs gcs_server.out --node-id <NODE_ID>` to view the GCS server logs for the node in question. Look for any error messages or indications of why the worker node might be unable to connect.\n2. Make sure that the GCS server has been started successfully with a matching version as the worker node. If the GCS server is not running or has a different version than the worker node, this could cause connection issues.\n3. If you're using a customized init container, ensure that it's properly checking the GCS server status and handling any errors or exceptions that might occur.\n4. If the GCS server is temporarily unavailable or recovering from a failure, worker nodes may still be able to handle traffic for Serve applications, but new actors or worker nodes cannot be created, and existing ones cannot recover from failures. In this case, you may need to wait for the GCS server to recover before the cluster returns to normal behavior.\n\nI hope this helps you resolve the issue! Let me know if you have any further questions.",3209"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3210},3211{3212"question": "Do you need the DAGDriver to deploy a serve application using RayServe?",3213"sources": [3214"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",3215"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",3216"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#building-the-call-graph-methodnodes-and-functionnodes",3217"https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",3218"https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph"3219],3220"document_ids": [322129438,322230088,322329426,322429291,3225291173226],3227"answer": " Yes, the DAGDriver is needed to deploy a serve application using RayServe, as mentioned in the \"Get Started\" and \"Serve Deployment Graph DAGDriver\" sections. It is used to route incoming requests through your deployments based on the call graph that you've written.",3228"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3229},3230{3231"question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",3232"sources": [3233"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",3234"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",3235"https://docs.ray.io/en/master/serve/api/doc/ray.serve.get_multiplexed_model_id.html#ray-serve-get-multiplexed-model-id",3236"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",3237"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive"3238],3239"document_ids": [324030449,324130017,324229690,324329716,3244300573245],3246"answer": " The import path you need to provide for a simple RayServe deployment is:\n\n`import ray`\n`from ray import serve`",3247"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3248},3249{3250"question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",3251"sources": [3252"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",3253"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",3254"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",3255"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",3256"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples"3257],3258"document_ids": [325912749,326012563,32611372,32621679,32635103264],3265"answer": " Yes, the context mentions a \"RayJob Batch Inference Example on Kubernetes with Ray.\" However, please note that RayJob support in KubeRay v0.x is in the alpha stage.",3266"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3267},3268{3269"question": "Does Ray metrics have to be exported via an actor?",3270"sources": [3271"https://docs.ray.io/en/master/ray-core/walkthrough.html#next-steps",3272"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",3273"https://docs.ray.io/en/master/serve/monitoring.html#exporting-metrics-into-arize",3274"https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",3275"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics"3276],3277"document_ids": [327810624,327912413,328029475,3281577,32829123283],3284"answer": " No, Ray metrics do not have to be exported via an actor. Ray exports metrics by default, and the Dashboard agent process is responsible for aggregating and reporting them to endpoints for Prometheus to scrape. Additionally, Ray Serve can export metrics into other observability platforms. Exporting metrics through an Actor is just one example, not a requirement.",3285"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3286},3287{3288"question": "how can I avoid objects not getting spilled?",3289"sources": [3290"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",3291"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3292"https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",3293"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#out-of-core-data-processing",3294"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node"3295],3296"document_ids": [329710684,329832212,329910471,330031575,3301106863302],3303"answer": " To avoid objects from getting spilled in Ray, you can increase the capacity of the object store, for example by expanding the local filesystem's storage or changing the temp\\_folder to a larger disk. Alternatively, you can disable object spilling in Ray 1.3+ by setting the environment variable `RAY_object_spilling_config='{\\ \"spill\\_enabled\\\": false}'` before starting Ray. However, keep in mind that disabling object spilling may cause out-of-memory errors for memory-intensive distributed applications.",3304"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3305},3306{3307"question": "what\u2019s ray core",3308"sources": [3309"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",3310"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",3311"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",3312"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",3313"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core"3314],3315"document_ids": [331610565,3317746,33188099,33191393,3320105693321],3322"answer": " Ray Core is an open-source, general purpose, distributed computing library for Python, which enables ML engineers and developers to scale Python applications and accelerate machine learning workloads. It provides a set of core primitives such as tasks, actors, and objects for building and scaling distributed applications. With Ray Core, you can easily turn your functions and classes into distributed components.",3323"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3324},3325{3326"question": "Does ray support cron job",3327"sources": [3328"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",3329"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",3330"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",3331"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3332"https://docs.ray.io/en/master/ray-observability/getting-started.html#jobs-view"3333],3334"document_ids": [33351153,33361759,333712756,333830712,33395153340],3341"answer": " Based on the provided context, Ray does not directly support cron jobs. However, Ray Jobs can be used to run Ray workloads that use Ray APIs, and these jobs can be submitted to Ray clusters via the Ray Job API. If you want to schedule recurring tasks, you may need to use a separate tool or system that supports cron jobs and can interact with Ray, such as a cron daemon that triggers Ray job submissions.",3342"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3343},3344{3345"question": "can you give me the dependencies list for api read_images?",3346"sources": [3347"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",3348"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3349"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning",3350"https://docs.ray.io/en/master/ray-core/key-concepts.html#environment-dependencies",3351"https://docs.ray.io/en/master/installation.html#installed-python-dependencies"3352],3353"document_ids": [335430609,33559544,335613249,33578580,33582073359],3360"answer": " Based on the context provided, the dependencies for the API \"read\\_images\" can be included in a Dockerfile or an environment file such as \"environment.yml\" or \"pytorch\\_p36.\" The dependencies should be listed in a format similar to: [\"pytorch\", \"torchvision\", \"pip\", {\"pip\": [\"pendulum\"]}]. Additionally, you can build a Docker image using one of the official Ray Docker images as a base to add custom dependencies. You can also refer to the User Guide for Environment Dependencies for more information.",3361"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3362},3363{3364"question": "how do I kill a specific serve replica",3365"sources": [3366"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",3367"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",3368"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",3369"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#scaling-horizontally-with-num-replicas",3370"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure"3371],3372"document_ids": [337330442,337430401,337530418,337629158,3377303803378],3379"answer": " To kill a specific serve replica, you can follow these steps:\n\n1. Get a handle to the serve replica using `ray.get_actor()` with the appropriate NAME and namespace.\n2. Kill the replica using `ray.kill()` and set `no_restart=True`.\n\nHere is an example:\n```python\nimport ray\n\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nNote that you can get the NAME of the replica from the output of `ray list actors` command.\n\nIf you are running KubeRay, you can exec into a Ray pod before running these commands.\n\nAlso, you can simulate replica failures by manually killing deployment replicas using the `ray summary actors` command.",3380"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3381}3382]3383}3384