llm-applications
3383 строки · 285.9 Кб
1{2"config": {3"experiment_name": "chunk-size-300",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, on the node you want to get the address of, initialize Ray with `ray.init()` and print the node information with `print(ray.nodes())`. This will give you a list of dictionaries containing information about the nodes in the Ray cluster, including the `NodeManagerAddress` which is the address of the node.",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 (NVIDIA Collective Communications Library) as one of the high-performance communication backends for its collective communication library.",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 you can mix and match based on your needs. However, keep in mind that asyncio task cancellation is not guaranteed in Ray, and tasks 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 distributed computing systems, each with their own strengths. Spark has a wide range of functionality and a large community, making it a popular choice for big data processing. Ray is a high-performance distributed computing system that can scale down to a single machine, and it has the ability to run Spark applications inside Ray programs. Dask is a Python library for parallel computing that is designed to integrate well with other Python libraries, particularly NumPy and Pandas, and it can be used to scale up data science workloads.\n\nIn terms of performance, a detailed comparison between Ray Data and Apache Spark can be found in the Offline Batch Inference paper. RayDP (\"Spark on Ray\") allows you to use Spark inside a Ray program, which can potentially provide performance benefits by leveraging Ray's advanced scheduling and task execution capabilities.\n\nTo use Spark with Ray, you can convert a Spark DataFrame to a Ray DataFrame using the `ray.data.from_spark()` function, as shown in the example `ds = ray.data.from_spark(df)`. This allows you to use Ray's distributed computing capabilities on data that is stored in a Spark DataFrame.",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 resources. Overloading can occur if the tasks or actors use more resources than specified in their resource requirements. Ray does not provide CPU isolation, so it's essential to specify accurate task or actor resource requirements to prevent overloading.",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": " Based on the information provided, it seems like you're encountering a \"connection refused\" error when trying to use `ray debug` on a remote Ray cluster. This error typically occurs when the specified address or port is not correct, or when the service is not running on the specified address and port.\n\nIn the context of Ray, when connecting to a remote cluster, you need to specify the address of the Ray cluster in the form `ray://<head-node-ip-address>:10001`. It's possible that the `<head-node-ip-address>` or the port number is incorrect in your case, causing the connection to fail.\n\nAnother possibility is that the Ray service is not running or is not reachable on the specified address and port. You can check if the Ray service is running on the remote cluster by trying to connect to it using `ray.init(\"ray://<head_node_host>:10001\")` or by setting the `RAY_ADDRESS` environment variable to the cluster's address and port.\n\nIf none of these solutions work, it's possible that there is a network issue preventing you from connecting to the remote cluster. In this case, you may need to check your network configuration and firewall settings to ensure that traffic is allowed to the remote cluster's address and port.",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, serving function, and any required resources or configurations.\n4. Once the deployment is created, Ray Serve will handle incoming requests and manage the distribution of your LLM workload across the Ray cluster.\n\nFor more information, refer to the Ray Serve documentation on deploying workloads and using the Ray Serve CLI.",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 batch size used for inference. To fit the model on smaller GPU types, you can use DeepSpeed to offload and partition optimizer and parameter states, reducing GPU memory usage. Therefore, the size of memory needed for this model depends on the batch size and the GPU memory, and it may not be possible to fit it on smaller GPU types without using techniques like offloading and partitioning.",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 should use a shared file system (such as NFS) to store the results from all the workers, and then specify the shared directory as the logdir when starting Tensorboard. For example, if you are using Ray, you can start Tensorboard with the command `ray exec tune-default.yaml 'tensorboard --logdir=/shared/results/ --port 6006' --port-forward 6006`, where `/shared/results/` is the shared directory containing the results from all the workers. This will allow Tensorboard to access and display the results from all the workers in a single interface.",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(upload_dir=\"some/directory\")\n)\n```\nThis will configure Ray AIR to sync files to the directory specified in `upload_dir`.\n\nNote that as of the time of this writing, 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. If 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\nFor more information, see the documentation for `ray.train.SyncConfig` and `ray.train.RunConfig.sync_config`.",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`.\n2. Check the `raylet.err` file for any `RAY_LOG(DEBUG)` lines, which will provide detailed information about the issue.\n3. If you have access to Ray Jobs submitted via Jobs API, use the Dashboard UI, CLI, or Python SDK to find the captured Driver logs for more context.\n\nThese steps will help you diagnose and resolve the problem quickly.",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\n```python\n@serve.deployment(ray_actor_options={\"num_gpus\": 0.5})\ndef func_2(*args):\n return do_something_with_my_gpu()\n```\n\nThis allows you to pack multiple replicas together on a machine with fewer GPUs than replicas. Similarly, you can use the `num_cpus` property to reserve CPUs for each replica.",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 of immediately setting the cluster to a target size.\n\nIf you want to set the cluster size to a specific value and prevent the autoscaler from scaling up or down, you may want to consider using a non-autoscaling cluster. The documentation recommends starting with non-autoscaling clusters if you're new to Ray.\n\nIf you're using Kubernetes, you can configure the Ray autoscaler to use a static number of nodes instead of scaling up and down based on resource demand. This will effectively create a non-autoscaling cluster with a fixed number of nodes.",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` parameter to `0`. For example:\n```python\ndataset.iter_batches(batch_size=128, async_bufsize=0)\n```\nThis will use synchronous iteration instead of asynchronous iteration. Note that this may affect the performance of your program.\n\nThe `async_bufsize` parameter is available in all the functions you mentioned in the context:\n\n* `ray.data.Dataset.iter_batches#`\n* `ray.data.Dataset.iter_tf_batches#`\n* `ray.data.DataIterator.iter_batches#`\n* `ray.data.Dataset.iter_torch_batches#`\n* `ray.data.DataIterator.iter_torch_batches#`\n\nSo you can use it in any of these functions to disable async iteration.",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 level of abstraction and usage.\n\nA block is an internal abstraction that represents a processing unit of data. A Dataset consists of a collection of blocks, which are partitioned rows that Ray Data loads and transforms in parallel. Blocks are not user-facing objects.\n\nOn the other hand, a batch is a user-facing object that represents a group of records processed together during batch training. The size of a batch is determined by the user when calling `ds.map_batches()`. It's important to choose a batch size small enough to avoid out-of-memory situations.\n\nIn summary, a block is an internal concept used by Ray Data to manage distributed data processing, while a batch is a user-facing object that represents a group of records processed together during batch training.",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 Ray dashboard and log analysis, to identify any potential issues.\n7. Check if there are any deadlocks caused by resource contention or other synchronization issues.\n8. Consider reducing the number of rollout workers or the complexity of the environment to isolate the issue.\n9. Refer to the Ray documentation and community resources for additional guidance.",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 Data. Here's an example of how to join `dataset1` and `dataset2`:\n```python\n# Generate two simple datasets.\ndataset = ray.data.range(8)\ndataset1, dataset2 = dataset.split(2)\n\n# Perform a full outer join on the 'id' column.\nresult = dataset1.join(dataset2, on='id', how='outer')\n\n# Print the resulting dataset.\nprint(result.take())\n```\nIn this example, we're performing a full outer join on the 'id' column, which means that the result will contain all records from both `dataset1` and `dataset2`, with `null` values in the 'id' column where there's no match. You can adjust the `on` and `how` parameters to perform different types of joins on different columns.",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 ObjectRef 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 ObjectRef. 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:\n```\ncheckpoint = Checkpoint.from_model(model)\n```\nFor PyTorch models, you can use the `torch.save` function to save the model state dictionary to a file, like so:\n```\ntorch.save({\"model_state_dict\": base_model.state_dict()}, \"model.pt\")\n```\nAnd then later load it back using\n```\nbase_model = (model.module if isinstance(model, DistributedDataParallel) else model)\nbase_model.load_state_dict(torch.load(\"model.pt\")[\"model_state_dict\"])\n```\nFor XGBoostTrainer and LightGBMTrainer, you can use the static methods `XGBoostTrainer.get_model` and `LightGBMTrainer.get_model` to load the checkpoints into memory.\n\nIt is important to note that, the checkpoint created with this method needs to be paired with the model when used.",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 group of actors that contain business logic or ML models to handle incoming requests. It's the central concept in Ray Serve and can be scaled up to run across a Ray cluster. Deployments are defined as a single class with options including the number of \"replicas\" of the deployment. These replicas will map to a Ray actor, which is a task or service that performs a specific function in response to messages or events.",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 `.bind()`. 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 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. When using Ray, it's recommended to ensure tasks take at least a few milliseconds to run for optimal performance. Additionally, experts can use the Ray Client to run a script from their local machine while maintaining a connection to the cluster.",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, consider creating 'key pools' where an actor can hold many keys, or starting more than one actor per node for better load balancing and resource utilization. Also, ensure that each callback has a unique state key if it has state and multiple instances need to maintain their state.",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, determined by comparing the last trial results. You can specify the metric and mode for comparison, if needed. Here's an example:\n\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```\n\nAlternatively, you can access the best checkpoint directly using the `best_checkpoint` property:\n\n```python\nbest_checkpoint = analysis.best_checkpoint\n```\n\nThis will give you the best trial's last checkpoint path, which you can then use to load the model or inspect the checkpoint.",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 in scenarios where you need to access a specific actor instance across different tasks or jobs in the Ray cluster. However, if you don't need the stateful part of an actor, tasks are generally a better option.",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, including the head node IP. For example:\n\n```\nimport ray\n\n# Get the node ID of the head node\nhead_node_id = ray.nodes()[0]['NodeManagerAddress']\n\n# Get the IP of the head node\nhead_node_ip = ray.nodes()[0]['RayletAddress'].split(':')[0]\n```\n\nThis will give you the IP of the head node as a string.",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 TextProcessor(object):\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\")\nprocessed_ds = ds.map_batches(TextProcessor.remote)\n```\nIn this example, `TextProcessor` is a class-based UDF that uses spaCy to process a batch of text data. The `__call__` method takes in a batch of text and yields a list of dictionaries representing the processed text. The `map_batches` function is then used to apply this UDF to the dataset.",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 specify the maximum number of steps for each fragment of the rollout. Additionally, you can choose the `batch_mode` configuration as \"truncate_episodes\", which will truncate the episodes at the specified `rollout_fragment_length`. Be careful when choosing \"complete_episodes\" as the batch mode, as it could lead to enormous batch sizes if your environment does not terminate easily. Note that the configuration `config.training(replay_buffer_config={\"storage_unit\": StorageUnit.EPISODES})` is used to configure the replay buffer to store experiences in episodes.",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 the `FailureConfig` in Ray Tune. Here's a code snippet to illustrate this:\n\n```python\nfrom ray import tune\n\n# Create a FailureConfig object\nfail_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# Then, pass this FailureConfig object to your Tune run function\ntune.run(my_trainable, config=my_config, failure_config=fail_config)\n```\n\nIn this example, `max_retries` is set to 3, meaning that if a trial fails, Ray Tune will automatically restart it up to 3 times. The `retry_delay` is set to 10 seconds, indicating the time Ray Tune will wait before retrying a failed trial.\n\nAdditionally, Ray Tune will automatically restart a trial from the latest checkpointed state if it fails. You can configure trial checkpoint saving and loading by referring to the Ray Tune documentation on this topic.",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. This pattern simplifies the driver by managing only a few supervisors and not dealing with failures from worker actors directly.",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\". You can replace \"your-bucket-name@your-prefix/your-file-name.txt\" with the appropriate S3 URI for your text file.",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, passing the DataFrame as an argument. Here's an example:\n```python\nimport pandas as pd\nimport ray\n\n# Create a pandas DataFrame\ndf = pd.DataFrame({\"prompt\": [prompt] * 10})\n\n# Convert the DataFrame to a Ray dataset\nds = ray.data.from_pandas(df)\n```\nIn this example, `prompt` is a placeholder for the actual data you want to use. The resulting `ds` object is a Ray dataset that you can use for distributed data processing.",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\ndef preprocess_data(data):\n # custom preprocessing logic\n return preprocessed_data\n",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 monitor the memory usage within your trial's code and raise a failure condition when the memory usage exceeds your desired limit.\n\nHere's an example of how to implement this:\n\n1. First, start Ray with the memory threshold:\n```bash\nRAY_memory_usage_threshold=0.4 ray start --head\n```\n\n2. Import the necessary modules:\n```python\nimport ray\nfrom ray import tune\nfrom ray.tune.failures import FailureConfig\n```\n\n3. Within your trial's code, monitor memory usage and raise a failure condition when the memory limit is exceeded:\n```python\nimport os\nimport psutil\nimport time\n\ndef check_memory_usage():\n process = psutil.Process(os.getpid())\n mem_info = process.memory_info()\n mem_used = mem_info.rss / (1024 * 1024) # memory usage in MB\n if mem_used > YOUR_MEMORY_LIMIT: # replace YOUR_MEMORY_LIMIT with your desired limit\n raise Exception(\"Memory limit exceeded\")\n\n# Call the check_memory_usage function periodically within your trial's code\nwhile True:\n try:\n check_memory_usage()\n # Your trial's code here\n time.sleep(10) # check memory usage every 10 seconds\n except Exception as e:\n if \"Memory limit exceeded\" in str(e):\n raise tune.FailException(str(e))\n else:\n raise e\n```\n\n4. Limit the number of concurrent Tune trials with the `max_concurrent_trials` Tune option:\n```python\nray.init()\ntune.run(my_trainable, max_concurrent_trials=4) # replace my_trainable with your trainable class\n```\n\nThis way, you can indirectly set a memory limit for each trial in Ray Tuner by monitoring memory usage within your trial's code and raising a failure condition when the memory limit is exceeded.",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()` function from the `RuntimeContext` class in the `ray.runtime_context` module. Here's an example:\n```python\nimport ray\nfrom ray.runtime_context import RuntimeContext\n\n# Initiate a driver.\nray.init()\n\n@ray.remote\nclass actor():\n actor_id = RuntimeContext.get_actor_id()\n```\nThis will give you the actor ID of the current actor in hex format. Note that this method should not be used in a driver process, and it's only applicable to the actor process.\n\nIf you want to get the logs of a specific actor, you can use the `ray logs actor` command with the actor ID, like this:\n```css\nray logs actor --id <ACTOR_ID>\n```\nReplace `<ACTOR_ID>` with the actual actor ID you want to get the logs for.",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 and initialize it with Tune's training information using `tune.logger.wandb.WandbLoggerCallback()`. This logger automatically initializes the WandB API with Tune's training information. You can then use the WandB API as you normally would, such as with `wandb.log()` to log your training process. Make sure you're logged in to WandB on all machines you're running your training on.",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. It remains local to the writer until requested by another task or actor on another node. Additionally, when a task is called, Ray will implicitly store the argument 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` argument. For example:\n```python\ndef my_function(batch, arg1, arg2):\n # process batch of data using arguments arg1 and arg2\n\ndataset.map_batches(my_function, arg1, arg2)\n```\nIn this example, `my_function` is the function that will be applied to each batch of data in the dataset, and `arg1` and `arg2` are the extra arguments that you want to pass to the function. When you call `dataset.map_batches(my_function, arg1, arg2)`, Ray will apply `my_function` to each batch of data in the dataset, passing `arg1` and `arg2` as arguments to the function.\n\nNote that the order of the arguments in the function definition matters - `batch` must be the first argument, followed by any additional arguments that you want to pass to the function.",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 specific type of database and format used for this are not specified in the provided context. RayJob provides Kubernetes-native support for Ray clusters and jobs, allowing you to use a Kubernetes config to define and manage them.",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 issue, 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 specific 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 follow the example of distributing TensorFlow training with Ray Train. Here's a simplified process:\n\n1. Install Ray and Ray Train in your Databricks environment.\n2. Write a script that uses Ray Train's `train_loop_per_worker` function to implement your custom training loop with Keras.\n3. Use Ray's `cluster` launch command to start a Ray cluster with multiple workers.\n4. Submit your training script to the Ray cluster.\n\nFor more information, refer to the 'Large Scale Deep Learning Training and Tuning with Ray' tutorial provided in the context. Note that the example in the tutorial uses TensorFlow, but you can adapt it to work with Keras.",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```\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\nIn the provided context, `ray.get(ray.put(SerializationHelperForA(A(1))))` is an example of using `ray.put()` to store an object in the object store and then using `ray.get()` to retrieve it. However, the context also notes that the serializer only works for a specific object, not all instances of the object, so there will still be a failure when trying to use `ray.get(ray.put(A(1)))`.",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 parallel.\n3. If the preprocessed data is too large to fit into the Ray object store, consider using the streaming solution provided by Ray Data, which re-runs the preprocessing operations only when necessary.\n4. Ray Data is particularly suited for data preprocessing and ingest for ML training, making it an ideal choice for preprocessing many files for machine learning workloads.\n\nFor more details on using Ray Data for images, refer to the Working with Images Ray Data user guide.",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 specifically mentioned in the context, but it is a general limitation of serialization with Ray, which uses a customized Pickle protocol version 5. Some objects, such as locks and SSLContext objects, cannot be pickled due to their inherent non-serializable nature.",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/\n\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 defining a callable class in Ray Data, which performs batch inference by mapping the function over data batches. In this case, the `__call__()` function takes a batch of data items, specifically a dict with a \"transformed\\_image\" key representing the preprocessed images, and applies the inferencing logic to the 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 Ray job system, and the `--working-dir` argument ensures that local files are properly uploaded and accessible during job execution.",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 loaded model to make predictions.\n\nHere's an example of how you might do this:\n\n```python\nfrom ray import tune\n\n# Get the best trial\nbest_trial = tune.Tuner.get_best_trial(tuner, 'metric_to_optimize', 'mode')\n\n# Load the trained model from the best trial\ntrained_model = best_trial.get_state()['model']\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 may vary depending on how you implemented your training function. In the example above, it is assumed that the training function stores the trained model in the trial state using `tune.report()`.",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 inference process is abstracted away from the direct use of PyTorch. Ray Datasets allows you to perform scalable offline batch inference with Torch models by mapping a pre-trained model over your data.",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 provided in the context. If the query is about a specific function or feature of Ray, please clarify for a more precise answer.",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 resource specifications match your computational needs to avoid performance issues.",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 each actor instance is created in its own process, using class variables to manage state between instances is not supported.\n\nTo pass the actor handle to places where the state needs to be modified or accessed, you can use the `remote()` method to call the actor's methods, as shown in the provided context.\n\nRegarding pickling, Ray automatically handles serialization and deserialization of remote objects, including actor instances and their state. Therefore, you don't need to manually pickle the `i` variable.",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. Then, you can use the `ray.get` function to gather the results of the `compute` method on each actor, which would be equivalent to an `all_reduce` operation. Here's an example:\n\n```python\n# Create a list of actors\nnum_actors = 4\nactors = [Worker.remote() for _ in range(num_actors)]\n\n# Create an ActorPool to manage the actors\npool = ActorPool(actors)\n\n# Assume that the `compute` method on the Worker actor performs some computation\n# that can be reduced among all the actors\nresults = ray.get([actor.compute.remote() for actor in actors])\n\n# The `results` list now contains the results of the `all_reduce` operation\n```\n\nNote that this assumes that you have already defined the `Worker` actor and its `compute` method to perform the necessary computation that can be reduced among all the actors.",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, the whole group will not be ready and 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 should first start your Ray cluster and ensure the Ray Dashboard is accessible on your local machine. Then, create a Serve config file (`config.yaml`) that specifies the necessary file paths and deploy it to the cluster using the `serve deploy config.yaml --address <remote dashboard agent address>` command. Replace `<remote dashboard agent address>` with the address of your remote Ray cluster's dashboard agent, such as `http://<cluster-address>:8265`.",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-linux_x86_64.whl\n```\n\nNote that this command first removes any previous Ray installation before installing the nightly wheel. Also, replace `cp38` with your Python version if it's different. The link provided is for a Linux x86_64 system, so adjust the link if you're using a different system.",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 testing best practices, such as starting a new Ray cluster for each test and fixing the resource quantity for each test.",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 stop command with the job ID, followed by the ray job logs command to view the logs in a more controlled manner. For example:\n\n```\nray job stop raysubmit_tUAuCKubPAEXh6CW\nray job logs raysubmit_tUAuCKubPAEXh6CW > log.txt\n```\n\nThis will redirect the logs to a file called `log.txt`, which you can then view at your leisure. Alternatively, you can redirect the logs to stderr by using the `&>` operator, like this:\n\n```\nray job logs raysubmit_tUAuCKubPAEXh6CW &> log.txt\n```\n\nThis will send the logs to stderr, which you can then view using the `tail` command or by redirecting the output to a file.\n\nIt's also worth noting that Ray has special support to improve the visibility of stdout and stderr produced by Worker processes, so that the Ray program appears like a non-distributed program. This is known as \"Worker log redirection to driver\", and it allows you to see the logs from all of the Worker processes in a single, unified stream. This can be useful for debugging and troubleshooting, but it can also lead to a lot of output if you're not expecting it.",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```bash\npip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers\n```\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```\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 model.\n\nFor more information, you can refer to 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, within the environment, use the command `python -m pip install --upgrade pip wheel` to ensure you have the latest pip and wheel. When you run your application with the updated environment, pip will install the specified version of the package, even if it's downgrading from a newer version.\n\nConfirm that your runtime\\_env specifies the pip package and version in the environment.yaml:\n\n```yaml\nruntime_env:\n pip:\n - requests==2.26.0\n```\n\nThen, within the activated conda environment, run:\n\n```bash\npython -m pip install --upgrade pip wheel\n```\n\nThis will ensure that the latest pip is used to install the specified version of the package in the runtime environment.",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`:\n\n```python\nimport ray\n\nctx = ray.data.DataContext.get_current()\nctx.use_push_based_shuffle = True\n\nds = (\n ray.data.range(1000)\n .random_shuffle()\n)\n```\n\nAdditionally, you can enable the push-based shuffle by setting the environment variable `RAY_DATA_PUSH_BASED_SHUFFLE=1` when running your 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 is subject to 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 get debugger command to jump to the location where `ray.get()` is called.\n2. Wait as long as possible before calling `ray.get()` or avoid having to call it altogether.\n3. Separate the call to `ray.get()` from the call to remote functions to allow them to run in parallel.\n4. Pass a list of object references to `ray.get()` instead of calling it multiple times.\n\nAvoid anti-patterns like calling `ray.get()` without instantiating the receive side call.",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 issues like hanging or leaking actors. If you need to wait for an actor to exit, you can use the object ref resulting from the task and call `ray.get()` on it, 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 Ray's remote function to call it. Here's an example using the provided context:\n\n```python\nimport logging\n\ndef log(self, msg):\n logger = logging.getLogger(__name__)\n logger.info(msg)\n\nclass MyActor(Actor):\n def log_message(self):\n ray.get(self.log.remote(\"A log message for an actor.\"))\n\nactor = MyActor.remote()\nray.get(actor.log_message.remote())\n```\n\nThis will add a log message to the actor's logs, which can be accessed from the Task and Actor table view in the Ray dashboard.",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 boto3\nimport ray\nfrom transformers import GPT2Tokenizer, GPT2LMHeadModel\n\n# Initialize Ray\nray.init()\n\n# Define a helper function to read data from S3\ndef read_s3_data(bucket_name, object_name):\n s3 = boto3.client('s3')\n obj = s3.get_object(Bucket=bucket_name, Key=object_name)\n return obj['Body'].read().decode('utf-8')\n\n# Define a function to preprocess the data\ndef preprocess_data(text):\n # Add your preprocessing code here\n return preprocessed_text\n",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 located at `/tmp/ray/session_latest/logs/dashboard.log`.\n\nAdditionally, you can configure the logging level of Ray by exporting the `RAY_BACKEND_LOG_LEVEL` environment variable to the desired log level (e.g., `debug`) before starting Ray. This will print any `RAY_LOG(DEBUG)` lines in the source code to the `raylet.err` file, which you can find in the logging and debugging directory.",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 the actor handle to it. Here's an example from the provided context:\n```python\nimport ray\n\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nThis will kill the replica actor and prevent it from being restarted. Note that this will not call any exit handlers installed in the actor using `atexit`.\n\nIf you need to terminate the actor from within one of its methods, you can use `ray.actor.exit_actor()` as shown in the example:\n```python\n@ray.remote\nclass Actor:\n def exit(self):\n ray.actor.exit_actor()\n```\nThen, you can call the `exit` method on the actor handle to terminate the actor.",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++ Ray API to set the \"GPU\" resource requirement to 1.0 and the \"GPU_MODEL\" resource requirement to \"V100\". Here is an example:\n```c++\nray::Task(MyFunction).SetResource(\"CPU\", 4.0).SetResource(\"GPU\", 1.0).SetResource(\"GPU_MODEL\", \"V10",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` parameter when setting up the `pip` dependencies, like this:\n```python\nruntime_env = RuntimeEnv(\n pip=[\"emoji\"],\n env_vars={\"TF_WARNINGS\": \"none\"},\n runtime_env={\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\" # specify Python version here\n }\n)\n```\nNote that the `python_version` value should match the actual Python version you want to use, and should be enclosed in single quotes.",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, the actor named \"my\\_actor\" is created in the \"actor\\_namespace\" namespace. You can retrieve the actor using `ray.get_actor()` as long as the actor is still alive.\n\nNote that if you don't specify a namespace, Ray will place your job 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 a trial-specific directory within the specified directory 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 specifying `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. This can be achieved by using the Kubernetes Cluster Autoscaler, which provisions Kubernetes nodes when the Ray autoscaler decides to create a new Ray pod. However, deploying a static Ray cluster without KubeRay means you'll miss out on the built-in autoscaling feature that KubeRay provides. To enable Ray autoscaling with KubeRay, you need to set enableInTreeAutoscaling:true in the KubeRay RayCluster custom resource (CR) config, which will automatically configure an autoscaling sidecar container.",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, follow these steps:\n1. Understand Ray's Memory Management model.\n2. Learn how to detect out-of-memory errors.\n3. Locate the source of the memory leak by viewing the per Task and Actor memory usage.\n4. Refer to the documentation on 'Debugging using \u2018ray memory\u2019' and 'Using the Memory Monitor' for more information on how to enable, configure, and use the memory monitor to detect and resolve memory issues.\n\nYou can find the relevant documentation at these links:\n- Debugging Out of Memory: <https://docs.ray.io/en/latest/memory-management.html#debugging-out-of-memory>\n- Memory Management (including Debugging using \u2018ray memory\u2019 and Using the Memory Monitor): <https://docs.ray.io/en/latest/memory-management.html#memory-management>\n- Debugging Memory Issues: <https://docs.ray.io/en/latest/memory-management.html#debugging-memory-issues>\n- Out-Of-Memory Prevention: <https://docs.ray.io/en/latest/memory-management.html#out-of-memory-prevention>",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 is called at the end of each training epoch, and you can define any desired behavior in this method. The `trainer` and `pl_module` arguments provide access to the current Trainer and LightningModule instances, respectively, and the `callback_state` argument provides access to the callback state returned by `on_save_checkpoint()`.\n\nHere's an example of what the `MyRayTrainReportCallback` class might look like:\n\n```python\nclass MyRayTrainReportCallback(Callback):\n def on_train_epoch_end(self, trainer, pl_module):\n metrics = trainer.callback_metrics\n metrics = {k: v.item() for k, v in metrics.items()}\n\n # Report metrics to Ray Train (or any other desired reporting mechanism)\n train.report(metrics=metrics)\n```\n\nNote that the `add_callback()` method can be called multiple times to add multiple callbacks to the trainer.",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()`, but you can specify a different temp directory within the Ray directory structure.",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 context shows examples in both Python and Java:\n\nPython:\n```python\n# Examples of passing objects to actor constructors.\nactor_handle = Actor.remote(obj) # by-value\nactor_handle = Actor.remote([obj]) # by-reference\n```\nJava:\n```java\n// Create an actor with a name and a constructor parameter.\nActorHandle<Counter> counter = Ray.actor(Counter::new, obj).setName(\"some_name\").remote();\n\n...\n\n// Retrieve the actor later somewhere\nOptional<ActorHandle<Counter>> counter = Ray.getActor(\"some_name\");\nAssert.assertTrue(counter.isPresent());\n```\nHere, `Counter::new` is a constructor reference, and `obj` is the parameter passed to the constructor.",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 and its Actor class.\n3. Instantiate the Actor, passing the runtime environment via `ray_actor_options` in your deployment.\n\nHere's an example of how to do this:\n\n```python\n# my_custom_module.py\nimport ray\n\n@ray.remote\nclass MyCustomActor:\n def __init__(self):\n self.data = 0\n\n def increment(self):\n self.data += 1\n return self.data\n\nif __name__ == \"__main__\":\n ray.init()\n my_actor = MyCustomActor.remote()\n print(ray.get(my_actor.increment.remote()))\n```\n\nIn this example, `MyCustomActor` is defined in the `my_custom_module.py` file and is used as an Actor. The `ray.init()` call initializes Ray, and the Actor is instantiated with `MyCustomActor.remote()`. The `increment` method is then called on the Actor.",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 code:\n```python\nimport os\nprint(os.getcwd())\n```\nBased on the context provided, the output of this command will be the Ray working directory, which is `/Users/user/ray/python/ray` in this case. Note that the Ray working directory may be different in your system, and it can be changed using the `ray start` or `ray.init()` command with the `--temp-dir` and `--log-dir` options.",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_id = ... # replace with the ID of the log you want to retrieve\nlog_data = get_log(log_id)\nprint(log_data)\n```\nThis should print out the contents of the log, including any `log.info` messages.",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 first step of the context. This ensures that the order of the data is preserved when reading a file using 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, Ray will not automatically retry that task by default. However, Ray does provide options to customize fault tolerance settings, such as setting the number of retries and specifying which exceptions to retry, but this requires explicit configuration. 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, you can 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 configuration YAML file. Here is an example of how to define the subnet in the YAML file:\n\n```yaml\ncluster_name: my-ray-cluster\n\nprovider:\n type: aws\n region: us-west-2\n availability_zone: us-west-2a\n subnet: subnet-01234567890123456 # replace with your subnet ID\n security_group: my-security-group # replace with your security group name\n\nhead:\n instance_type: m5.xlarge\n node_id: head\n zone: us-west-2a\n\nmin_workers: 0\nmax_workers: 100\nworker_instance_type: m5.xlarge\n```\n\nReplace the `subnet` value with your own subnet ID. You can also specify other parameters such as the availability zone, security group, and instance types as needed. Once you have defined the cluster configuration, you can launch the cluster using the Ray cluster launcher with the `ray submit` command as described in the context.",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 specifically view the error codes in the logs by using the logger \"ray.serve\" (i.e., logging.getLogger(\"ray.serve\")). The warning message you provided suggests that there may be some issues with the current Ray Serve deployment, which could be further investigated in the logs.",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 additional worker nodes. This is clear from the context where it is stated that the cluster includes 1 Ray head node and a certain number of worker nodes, with `max_workers` specifying the number of worker nodes to launch in addition to the head node.",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 in the context provided, you can directly write to a file in the working directory as it is set to the trial directory. 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 the iteration number to name the file:\n```python\nwith open(f\"./artifact_{self.iteration}.txt\", \"w\") as f:\n # Write to the file\n```\nNote that all workers on the same node share the same working directory, so make sure to use `ray.train.get_context().get_trial_dir()` as the path to save any outputs if necessary.",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": " To update the working directory file when Ray is already initiated, you cannot directly change the working directory. However, you can upload a new working directory by stopping Ray, changing the directory on your local machine, and then re-initializing Ray with the new directory.\n\nAlternatively, if you need to access a different directory during the execution of your Ray tasks, you can modify your code to use absolute paths instead of relative paths.\n\nNote that there is no stable way to change the root temporary directory when calling `ray.init()`, but you can provide the `_temp_dir` argument to `ray.init()` if needed.\n\nHere's the relevant information from the context:\n\n* When running `ray.init()`, Ray uploads the working directory to `/tmp/ray/session_latest/runtime_resources/_ray_pkg_<hash of directory contents>` on the cluster.\n* Ray workers are started in the same directory on the cluster.\n* Relative paths in the remote tasks and actors will work on the laptop and on the cluster without any code changes.\n* 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, run:\n```css\nray start --head --redis-address xx.xx.xx.xx:6379 --object-store-address xx.xx.xx.xx:8072 --port nnnn\n",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, especially if you are using different machines for the head and worker nodes.\n3. Verify that the Ray address used is correct, commonly \"localhost:6379\".\n4. If the Ray cluster is already started, you should not need to run anything on the worker nodes.\n5. If your pods are stuck in the Pending state, you can check for errors via \"kubectl describe pod raycluster-kuberay-xxxx-xxxxx\" and ensure that your Docker resource limits are set high enough.\n\nAdditionally, you can try restarting the Ray cluster without running any of the installation commands using the \"ray up CLUSTER.YAML --restart-only\" command.",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 at <https://packages.ray.io/whl/ray/2.5.1.html>. 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.readthedocs.io/en/latest/installation.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 `ray.init()` call in your main application script. Here's an example:\n\n```python\nimport ray\n\ndef worker_setup_hook():\n # Your setup code here\n pass\n\nif __name__ == \"__main__\":\n ray.init(worker_setup_hook=worker_setup_hook, ...)\n # Rest of your code\n```\n\nIn the context you provided, `worker_setup_hook` is an optional parameter in the `ray.init()` function, which is used to initialize Ray. You can pass your setup function as its value.\n\nHowever, if you want to use this in a cluster environment (e.g., using `ray up` command), you might need to create a custom runtime environment and include the setup hook in the environment configuration. The documentation on this is not very extensive at the moment, but you can refer to the following resources for more information:\n\n1. Ray Runtime Environments: <https://docs.ray.io/en/latest/cluster/runtime-envs.html>\n2. Ray Cluster Launch Configuration: <https://docs.ray.io/en/latest/cluster/launch-config.html>\n\nPlease note that the `worker_setup_hook` feature is experimental, and you might need to adjust your approach based on the specific requirements of your use case.",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. Ray Data supports a variety of file formats, including CSV and files stored in S3.\n3. Use `ray.data.read_images` to load the validation set from S3, or use `ray.data.read_csv` to load a CSV file.\n4. Ray Data can read from any filesystem supported by Arrow.\n\nHere's an example of loading the validation set from S3 using `ray.data.read_images`:\n```python\nimport ray\n\n# Load the validation set from S3\nvalidation_dataset = ray.data.read_images('s3://my-bucket/validation/*.jpg')\n```\nYou can replace `'s3://my-bucket/validation/*.jpg'` with the path to your own S3 bucket and validation set.",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.",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 Ray logging to display debug-level information. This will allow you to see any Ray system errors or exceptions that are raised during the execution of your Ray Serve application. Here's how you can do it:\n\n1. Start Ray with debug logging enabled:\n```bash\nexport RAY_BACKEND_LOG_LEVEL=debug\nray start\n```\n2. In your Ray Serve application, use the `@ray.remote` and `@serve.decorator` decorators to define your deployment and handle exceptions as needed.\n\nFor example:\n```python\nimport ray\nfrom ray import serve\n\n@ray.remote\nclass MyDeployment:\n def __init__(self):\n self.counter = 0\n\n def increment(self):\n self.counter += 1\n return self.counter\n\n def handle_exception(self, e):\n # Handle exceptions here\n print(f\"An exception occurred: {e}\")\n\n@serve.decorator\ndef my_deployment_handler(ray_deployment):\n async def handle(request):\n try:\n result = await ray_deployment.increment.remote()\n return {\"result\": result}\n except Exception as e:\n await ray_deployment.handle_exception.remote(e)\n return {\"error\": str(e)}\n return handle\n\nserve.start(detached=True)\nserve.run(my_deployment_handler.bind(MyDeployment.remote()))\n```\n\nIn this example, any exceptions that occur during the execution of the `increment` method will be caught and handled by the `handle_exception` method. The `my_deployment_handler` function wraps the Ray deployment and catches any exceptions that bubble up to the top level. These exceptions will be logged to the console, but you can modify this behavior to suit your needs.\n\nBy following these steps, you can track uncaught exceptions in your Ray Serve application and handle them as needed.",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 using the Python logger emitted by your Ray Serve endpoint in the Ray cluster, you should keep the driver running while debugging since, in development, logs are streamed to the driver Ray program. The logs will be visible in your terminal or Jupyter Notebook if you run Ray Jobs by executing the Ray Driver on the Head node directly or connecting via Ray Client. Note that if you execute the Driver directly on the Head Node of the Ray Cluster or run with Ray Client, the Driver logs are not accessible from the Dashboard; instead, see the terminal or Jupyter Notebook output to view the Driver logs.",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_dataset = train_dataset.take(100) # Takes the first 100 rows\n```\n\nOr, if you want to filter the dataset based on some condition, you can use `map_partitions`:\n\n```python\ndef filter_func(partition):\n return partition[partition['x'] < 10]\n\nsubset_dataset = train_dataset.map_partitions(filter_func)\n```\n\nThis will return a new Ray Dataset containing only the rows where the 'x' column is less than 10.",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' final checkpoints are saved. After the experiment is complete, you can then manually download the checkpoints from the cloud storage to analyze them.\n\nHere's an example of how to enable checkpointing at the end of the experiment:\n```python\ntuner = tune.Tuner(\n my_trainable,\n run_config=air.RunConfig(\n name=\"my-tune-exp\",\n checkpoint_at_end=True, # Save final checkpoint for each trial\n # Configure cloud storage for checkpointing\n )\n)\nresults = tuner.fit()\n```\nReplace `my_trainable` with your trainable function and configure the cloud storage accordingly. After the experiment is complete, you can then download the checkpoints from the cloud storage for analysis.",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```\nNote that if the Actor is detached, you can't get its handle via `ray.get()` or any other method after it has been killed.",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 in `ray.init()`, you can use Runtime Environments and pass the environment variables explicitly using `ray.init(runtime_env={\"env_vars\": {\"foo\": \"foo\"}})`. Here's an example:\n```python\nray.init(runtime_env={\"env_vars\": {\"FOO\": \"foo\"}})\nalgo = ppo.PPO(env=MyEnv, config={\"env_config\": {}} )\n```\nThis will set the `FOO` environment variable for the Ray workers.",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, and you can find the head node by checking the `is_head_node` field for each node. The head node will have `is_head_node` set to `true`.\n\nHere's an example of how you can filter the nodes to get only the head node:\n\n```bash\nhead_node=$(curl -s http://localhost:6379/api/nodes \\\n | jq -r '.[] | select(.is_head_node == true) | .node_id')\n```\n\nThis command uses `curl` to make a GET request to the `/api/nodes` endpoint, and then uses `jq` to parse the JSON response and filter the nodes to get only the head node. The `node_id` of the head node is then printed to the console.\n\nNote that you need to replace `localhost:6379` with the address of your Ray cluster's API server.",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 `ray.remote()` and `.remote()` on the function. Ray tasks cannot be resumed once canceled, so you need to create a new task. Here's an example:\n\n```python\n@ray.remote\ndef my_task():\n # Your task code here\n pass\n\n# Run the task\ntask_ref = my_task.remote()\n\n# Cancel the task\nray.cancel(task_ref)\n\n# Rerun the task\nnew_task_ref = my_task.remote()\n```\n\nKeep in mind that canceling a task and rerunning it may still result in the new task being canceled if the same condition that caused the initial cancellation still exists.",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```\nThis will allow `your_number` tasks to run simultaneously in the `map_batches` function.\n\nIn the context you provided, `max_concurrent` is also used in the `synchronous_parallel_sample` function and the `ConcurrencyLimiter` class to limit the number of concurrent tasks. So, you can use the `max_concurrent` parameter in a similar way in these functions to set the maximum number of concurrent tasks.\n\nFor example:\n```\ntrain_batch = synchronous_parallel_sample(worker_set=self.workers, max_env_steps=self.config[\"train_batch_size\"], max_concurrent=your_number)\n```\n\n```\nconcurrency_limiter = ConcurrencyLimiter(searcher, max_concurrent=your_number)\n```\n\nIn these examples, `your_number` is the maximum number of tasks that you want to allow to run concurrently.",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 use `ray.cluster_resources()[\"CPU\"]` to get the total number of CPUs in the Ray cluster.",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 with a `RuntimeEnvConfig` that specifies the packages to exclude in the `pip_packages_extra` field. Here's an example:\n```python\nfrom ray import runtime_env\n\nruntime_env = runtime_env.RuntimeEnv(\n runtime_env_config=runtime_env.RuntimeEnvConfig(\n pip=[\"emoji\"],\n pip_packages_extra=[\"pkg1\", \"pkg2\"] # packages to exclude\n ),\n env_vars={\"TF_WARNINGS\": \"none\"}\n)\n```\nIn this example, the `pip_packages_extra` list includes the packages \"pkg1\" and \"pkg2\", which will be excluded when creating the runtime environment.\n\nNote that the `exclude` option is not explicitly mentioned in the provided context, but the `pip_packages_extra` field in `RuntimeEnvConfig` can be used to exclude packages by specifying the package names in the list.",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)\n batch[\"label\"] = [output[0][\"label\"] for output in outputs]\n return batch\n\ndataset = ray.data.range(10)\ndataset = dataset.map_batches(process_batch, batch_format=\"pandas\")\n\n# View the first two batches of images\nprint(dataset.take(2))\n```\nIn this example, we define a function `process_batch` that takes a batch of data and applies a model to it, then adds the model's predictions to the batch as a new key \"label\". We then create a Ray Dataset `dataset` with the `ray.data.range()` function, and use `map_batches()` to apply `process_batch` to each batch in the dataset. We set `batch_format=\"pandas\"` to specify that we want the batches to be represented as Pandas DataFrames.\n\nAfter `map_batches()` is done, we can view the first two batches of images by calling `dataset.take(2)`. The \"label\" key added by `process_batch` will be included in the output.",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 --nodes <number_of_nodes> --head-node-ip <head_node_ip> --ray-start-args \"{\\\"object-store-memory\\\":5000000000,\\\"object-store-node-local-memory\\\":5000000000,\\\"logging-level\\\":\\\"INFO\\\",\\\"min-worker-log-level\\\":\\\"INFO\\\"}\"\n```\n\nReplace `<number_of_nodes>` with the number of nodes in the cluster and `<head_node_ip>` with the IP address of the head node.\n\nNote that multi-node Ray clusters are experimental on Windows and may encounter 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 context provided, your Ray tuner shows \"running\" but with almost 0% CPU usage because by default, Ray tasks and actors use 1 logical CPU for scheduling and 0 logical CPU for running. This means that even though the task is scheduled and running, it's not using a significant amount of CPU resources for execution. Additionally, when using Ray Data in conjunction with Ray Tune, it's important to ensure there are enough free CPUs for Ray Data to run on, as Tune tries to fully utilize cluster CPUs by default, which can prevent Ray Data from scheduling tasks and reduce performance.",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 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 size of the object store memory can be controlled using the `--object-store-memory` flag when starting a Ray instance. 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 the cluster, it would be more intelligent to set these variables to the IPs or hostnames of the machines where Grafana and Prometheus are running.\n\nFor example, if Grafana is hosted on a machine with IP `55.66.77.88` and Prometheus is hosted on a machine with IP `99.88.77.66`, you should set:\n\n* `RAY_GRAFANA_HOST=http://55.66.77.88:3000`\n* `RAY_PROMETHEUS_HOST=http://99.88.77.66:9090`\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\nIf all the environment variables are set properly, you should see time-series metrics in the Ray Dashboard.",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, to set the request timeout to 60 seconds, you can use:\n```yaml\nhttp_options:\n request_timeout_s: 60\n```\nThis config is global to your Ray cluster and 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. You can do this by using the `--num-gpus` option with the `ray start` command. If you don't specify this option, the GPU will still be available to the entrypoint script, but Ray will not provide isolation in terms of visible devices.\n\nHowever, Ray does not automatically handle GPU availability for multiple GPUs per trial. If you want to use multiple GPUs per trial, you should check out LightningTrainer.\n\nHere's an example of how to start Ray with one GPU:\n```css\nray start --num-gpus 1\n```\nAnd here's an example of how to make a direct request to the autoscaler to scale up GPU resources:\n```python\nimport ray\nray.init(num_gpus=1)\n```",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 configuring your Ray training job. These attributes allow you to set the maximum number of seconds that a task can run before it is automatically terminated.\n\nHere is an example of how to use `HorovodConfig.timeout_s` to set a timeout for a Ray job:\n\n```\nfrom ray.train.horovod import HorovodConfig\n\n# Create a HorovodConfig object\nconfig = HorovodConfig()\n\n# Set the timeout to 60 seconds\nconfig.timeout_s = 60\n\n# Use the config object to run your Ray job\nray.train.run_trainable(my_trainable, config=config)\n```\n\nIn this example, the `my_trainable` object would be the training function or class that you want to run using Ray. The `config` object is passed to the `run_trainable` function, which uses the `HorovodConfig` object to configure the Ray job.\n\nYou can use a similar approach to set a timeout using `TorchConfig.timeout_s`. Simply create a `TorchConfig` object, set the `timeout_s` attribute, and pass the config object to the `run_trainable` function.\n\nIf you want to set a timeout for a specific task within a Ray job, you can use the `RayDaskCallback` class to define a callback function that is executed at the start of the task. This callback function can use the `timer` function to record the start time of the task, and then use a conditional statement to check if the task has exceeded the specified timeout. If the timeout has been exceeded, the callback function can raise a `ray.exceptions.RayTaskTimeoutError` exception to terminate the task.\n\nHere is an example of how to use a callback function to set a timeout for a specific task:\n\n```\nimport timer\nfrom ray.dask import RayDaskCallback\n\nclass MyTimerCallback(RayDaskCallback):\n def _ray_pretask(self, key, object_refs):\n # Executed at the start of the Ray task.\n start_time = timer.start_time()\n return start_time\n\n def _ray_posttask(self, key, object_refs, started_at, status, traceback):\n # Executed after the Ray task has completed.\n end_time = timer.get_time()\n elapsed_time = end_time - started_at\n if elapsed_time > 60: # Timeout after 60 seconds\n raise ray.exceptions.RayTaskTimeoutError(\"Task exceeded timeout\")\n```\n\nIn this example, the `MyTimerCallback` class defines a `_ray_pretask` method that is executed at the start of the Ray task, and a `_ray_posttask` method that is executed after the task has completed. The `_ray_pretask` method records the start time of the task, and the `_ray_posttask` method calculates the elapsed time and raises a `RayTaskTimeoutError` exception if the task has exceeded the specified timeout.\n\nTo use this callback function with a Ray task, you can pass an instance of the `MyTimerCallback` class as the `callbacks` argument to the `run_task` function:\n\n```\nfrom ray import put, get, run_task\n\n# Create an instance of the MyTimerCallback class\ncallback = MyTimerCallback()\n\n# Define the Ray task\n@ray.remote\ndef my_task():\n # Task code here\n pass\n\n# Run the Ray task with the callback function\ntask_id = run_task(my_task, callbacks=[callback])\n```\n\nIn this example, the `my_task` function defines the code for the Ray task, and the `run_task` function is used to execute the task with the `callback` object as the `callbacks` argument. This will cause the `MyTimerCallback` class to be used as the callback function for the task, and the `_ray_pretask` and `_ray_posttask` methods will be executed at the start and end of the task, respectively.\n\nI hope this helps! Let me know if you have any questions or need further clarification.",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 approach based on the information provided about using the `RAY_TEMPDIR` variable for a local Ray cluster.",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 be directed to the custom directory. The temp directory can be specified when calling ray start or ray.init(). The temporary directory for the cluster files can be defined using the RAY\\_TEMPDIR environment variable, which should be accessible by the host, the outer container, and the inner container. In the inner container, you can control the directory name.",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 is used to specify a runtime environment for a Ray job or task using a JSON string. The JSON string should contain a dictionary with the following fields: `pip` (list of pip packages to install) and `env_vars` (environment variables).\n\nHere is an example of using `--runtime-env-json`:\n\n```\n--runtime-env-json '{\"pip\": [\"emoji\"], \"env_vars\": {\"TF_WARNINGS\": \"none\"}}'\n```\n\nThis example installs the `emoji` package and sets the `TF_WARNINGS` environment variable to `none`.\n\nNote: The above example is based on the context provided, specifically the mention of the `RuntimeEnv` class and its usage with `pip` and `env_vars`. However, the exact format of the JSON string may vary depending on the specific implementation of the `--runtime-env-json` flag.",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, the value of `maxConcurrentReplicas` should not be specified explicitly, as it is not a configurable parameter in the `autoscaling_config` dictionary. Instead, the autoscaling algorithm will consider other parameters such as `max_concurrent_queries`, `max_replicas_per_node`, and others to decide the target number of replicas for the deployment.",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's important to note that `target_num_ongoing_requests_per_replica` is just a target value for autoscaling and not a hard limit, hence `max_concurrent_queries` should be set appropriately to manage the number of pending queries in a single replica.",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 stateful entities in a distributed system. An actor's state may become \"UNHEALTHY\" due to various reasons, such as errors in the deployment's constructor, a crashed replica, or general system or machine faults. This is in contrast to tasks, which are typically better suited if the stateful aspect of an actor is not required. The provided context does not explicitly mention actors changing their state to unhealthy deliberately, suggesting that such a state change is more likely to be the result of unforeseen circumstances or 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 reported as 'DEAD' if the actor has died permanently. However, the text does not provide specific reasons for why an actor might die or become unresponsive.",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 overloading the head node with too many simultaneous connections, and application tasks or actors consuming a large amount of heap space, causing the node to run out of memory. Additionally, if the node has insufficient memory, the operating system may start killing worker or raylet processes, disrupting the application. OOM may also stall metrics, and if this happens on the head node, it may stall the dashboard or other control processes and cause the cluster to become unusable.",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 default temp directory for Ray can also be changed by specifying it when calling `ray.init()` or `ray start`.",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 Python SDK does not provide a direct method to get the dashboard link. You can use the Ray Jobs REST API or the Ray Client to get the dashboard URL. Alternatively, you can use the `ray.timeline` Python API to export the tracing file and then access the dashboard through the URL provided in the Ray cluster resources.",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 the messages mentioning workers being killed due to memory pressure.\n3. The worker being crashed unexpectedly due to SIGSEGV or other unexpected errors.\n\nIt is important to note that memory pressure (OOM) can be caused by application tasks or actors consuming a large amount of heap space, leading to the operating system killing worker or raylet processes and disrupting the application.",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 unable to establish a connection with 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 use the command `ray logs gcs_server.out --node-id <NODE_ID>` to check the GCS server logs for any errors or issues.\n2. Ensure that the worker node and the GCS server have the same version of Ray installed. If there is a version mismatch, the worker node may not be able to connect to the GCS server.\n3. If you have customized the worker init container, make sure that it is correctly checking the GCS server status and establishing a connection.\n4. If the GCS server is temporarily unavailable, worker nodes can still handle traffic for Serve applications, but they cannot be updated or recover from other failures. Once the GCS server is recovered, the cluster will return to normal behavior.\n\nIn your case, since the node is still up and running, it's possible that the GCS server is experiencing temporary issues or there is a configuration problem. Checking the GCS server logs should provide more information on the cause of the issue.",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 the endpoints for Prometheus to scrape. Additionally, Ray has the flexibility to export metrics into other observability platforms like Arize. The example provided in the context is just one way of exporting metrics using actor APIs.",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, either by adding more memory to the system or reducing the size of the objects being stored. Alternatively, you can configure a different directory for object spilling with more storage space. Object spilling can be disabled in Ray 1.2 by default, and it is recommended to check the version-specific object spilling documentation for steps to enable and configure object spilling.",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 primarily used by ML engineers and Python developers. It allows scaling of Python applications and acceleration of machine learning workloads. Ray Core provides core primitives such as tasks, actors, and objects for building and scaling distributed applications. You can turn your functions and classes into Ray tasks and actors, and work with Ray objects using the Ray Core CLI.",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 service that supports cron jobs and can interact with Ray, such as a cron daemon or a cloud scheduler.",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 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's the command you need:\n```python\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nThis is based on the context you provided, specifically the first code snippet.",3380"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3381}3382]3383}3384