llm-applications
4799 строк · 411.0 Кб
1{2"config": {3"experiment_name": "mixtral-8x7b-instruct-v0.1",4"chunk_size": 700,5"chunk_overlap": 50,6"num_chunks": 9,7"embedding_model_name": "thenlper/gte-large",8"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1",9"temperature": 0.0,10"max_context_length": 32768,11"system_content": "Answer the query using the context provided. Be succinct.",12"assistant_content": "",13"docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",14"experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",15"references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",16"num_samples": 17717},18"results": [19{20"question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",21"sources": [22"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",23"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",24"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",25"https://docs.ray.io/en/master/data/key-concepts.html#transforming-data",26"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",27"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",28"https://docs.ray.io/en/master/data/working-with-tensors.html#transforming-tensor-data",29"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.MultiHotEncoder.transform_batch.html#ray-data-preprocessors-multihotencoder-transform-batch",30"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.transform_batch.html#ray-data-preprocessor-preprocessor-transform-batch"31],32"document_ids": [337081,346790,356786,366733,3714727,388046,396777,407421,41724142],43"answer": " When using `map_batches`, you can specify the batch format as \"numpy\" or \"pandas\" by including `batch_format` in the function call. This will configure the format of the batches in your dataset. If you're having trouble with type conversions, make sure that the format you're returning from your function matches the expected format.\n\nFor example, if you want to increase the brightness of an image dataset using numpy arrays, you can do:\n```python\nfrom typing import Dict\nimport numpy as np\nimport ray\n\ndef increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n return batch\n\nds = (\n ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .map_batches(increase_brightness, batch_format=\"numpy\")\n)\n```\nSimilarly, if you want to use pandas DataFrames, you can do:\n```python\nimport pandas as pd\n\ndef increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n return batch\n\nds = (\n ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .map_batches(increase_brightness, batch_format=\"pandas\")\n)\n```\nNote that in both cases, the function returns a `Dict[str, np.ndarray]`, which matches the expected format.",44"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"45},46{47"question": "How does autoscaling work in a Ray Serve application?",48"sources": [49"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",50"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",51"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",52"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",53"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",54"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",55"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#load-metrics-are-based-on-application-semantics",56"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",57"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling"58],59"document_ids": [6013471,616363,625559,6313448,645969,655953,665971,6714726,681344769],70"answer": " Ray Serve's autoscaling feature adjusts the number of replicas in a deployment based on its load. The Ray autoscaler, a process that runs on the head node, increases or decreases the number of worker nodes in the cluster when the resource demands of the Ray workload exceed or are below the current capacity. It reacts to task and actor resource requests, not application metrics or physical resource utilization. When Ray Serve scales down, it tries to terminate replica actors in a way that allows the Ray autoscaler to remove nodes with no Ray actors or tasks running on them. The Ray Serve Autoscaler is an application-level autoscaler that sits on top of the Ray Autoscaler, requesting the required number of replica actors and leaving the Ray Autoscaler to handle the addition or removal of Ray nodes as needed.",71"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"72},73{74"question": "how do I get the address of a ray node",75"sources": [76"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",77"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",78"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",79"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",80"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",81"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",82"https://docs.ray.io/en/master/ray-core/api/doc/ray.nodes.html#ray-nodes",83"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",84"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints"85],86"document_ids": [87739,884817,89754,904446,916552,92493,934769,946502,95558796],97"answer": " To get the address of a Ray node, you can use the `ray.nodes()` function which returns information about the nodes in the Ray cluster. This function is for debugging purposes only and the returned information includes the node's address.\n\nAlternatively, if you have the node ID, you can use the `ray.util.state.get_node(id)` function to get the node's information including its address.\n\nAlso, if you are using a Ray Cluster started on VMs or Kubernetes, you can follow the instructions for setting up network access from a client and set the `RAY_ADDRESS` environment variable to the Ray Dashboard address.\n\nIn summary, you can use `ray.nodes()` or `ray.util.state.get_node(id)` to get the address of a Ray node, and make sure to set up network access if you are using a remote cluster.",98"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"99},100{101"question": "Does Ray support NCCL?",102"sources": [103"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",104"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",105"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",106"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray",107"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",108"https://docs.ray.io/en/master/installation.html#installing-ray",109"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",110"https://docs.ray.io/en/master/ray-overview/index.html#overview",111"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api"112],113"document_ids": [1144662,11514391,1164811,11714537,118284,11959,1206835,121364,12213974123],124"answer": " Yes, Ray does support NCCL (NVIDIA Collective Communications Library) as one of the optional high-performance communication backends in its Collective Communication Lib (ray.util.collective). NCCL is used for efficient out-of-band communication between Ray actor and task processes, and it operates on both distributed CPUs and GPUs. This makes Ray suitable for distributed ML programs.",125"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"126},127{128"question": "Is Ray integrated with DeepSpeed?",129"sources": [130"https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",131"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.deepspeed_engine.html#ray-train-lightning-raydeepspeedstrategy-deepspeed-engine",132"https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",133"https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",134"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",135"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.strategy_name.html#ray-train-lightning-raydeepspeedstrategy-strategy-name",136"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#fine-tune-vicuna-13b-with-lightning-and-deepspeed",137"https://docs.ray.io/en/master/train/deepspeed.html#training-with-deepspeed",138"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning"139],140"document_ids": [1418232,1428996,1438331,1448249,1458881,1468578,1479334,1488207,1499393150],151"answer": " Yes, Ray is integrated with DeepSpeed through the Ray Train module. You can use DeepSpeed with Lightning, Transformers, and Accelerate in Ray Train. The integration allows you to run DeepSpeed with Ray Train's distributed computing capabilities across a cluster. Ray Train also supports DeepSpeed configurations and ignores certain Accelerate configuration options, such as the number of machines and GPUs, to automatically set them according to Ray AIR configs. The provided context includes examples of using DeepSpeed with Ray Train and its integrations.",152"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"153},154{155"question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",156"sources": [157"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",158"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example",159"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",160"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",161"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",162"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",163"https://docs.ray.io/en/master/ray-core/walkthrough.html#running-a-task",164"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",165"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures"166],167"document_ids": [1684600,1695197,1704611,1714599,1725248,1734598,1744485,1754603,1764601177],178"answer": " If you use AsyncIO's `await` keyword to wait for a Ray future, like `await x.remote()`, you can do so by converting the Ray ObjectRef to an asyncio Future using `asyncio.wrap_future()`. This allows you to await on Ray futures in existing concurrent applications that use asyncio. However, it's important to note that you cannot use async/await syntax directly in remote tasks, as Ray does not support asyncio for remote tasks. Instead, you can wrap the async function with a synchronous wrapper to run the task remotely.",179"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"180},181{182"question": "How would you compare Spark, Ray, Dask?",183"sources": [184"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",185"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",186"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",187"https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",188"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",189"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",190"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",191"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",192"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray"193],194"document_ids": [19514465,19614479,1976847,19814647,19914502,2006846,20114467,2026809,20314469204],205"answer": " Spark, Ray, and Dask are all distributed computing systems, but they have different focuses and use cases.\n\nApache Spark is a general-purpose cluster-computing system that can handle batch and stream processing, as well as SQL queries, machine learning, and graph processing. It has a wide range of supported languages including Python, Scala, Java, and R.\n\nRay is a high-performance distributed execution engine that is designed to handle large-scale machine learning and graph applications. It has a unified abstraction for in-memory data processing and can scale to thousands of nodes. Ray can be used as a scheduler for Dask, allowing you to build data analyses using Dask's collections and execute them on a Ray cluster.\n\nDask is a flexible parallel computing library for analytic computing, built with the Python ecosystem in mind. It provides big data collections that mimic the APIs of NumPy and Pandas, allowing those abstractions to represent larger-than-memory data and/or allowing operations on that data to be run on a multi-machine cluster. Dask-on-Ray is a project that allows you to use Dask and Ray libraries in the same application without having two different clusters, and to execute Dask computations on a Ray cluster.\n\nIn summary, Spark is a general-purpose distributed computing system, Ray is a high-performance distributed execution engine for machine learning and graph applications, and Dask is a flexible parallel computing library for analytic computing that can be used with Ray as a scheduler.",206"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"207},208{209"question": "why would ray overload a node w/ more task that the resources allow ?",210"sources": [211"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",212"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",213"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",214"https://docs.ray.io/en/master/ray-core/tasks.html#scheduling",215"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",216"https://docs.ray.io/en/master/ray-core/scheduling/index.html#locality-aware-scheduling",217"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",218"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",219"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case"220],221"document_ids": [2225231,2235112,2245104,2254376,2265230,2275152,2286504,2295115,2305232231],232"answer": " Ray's scheduling system ensures that the total resources required by concurrently running tasks and actors on a node do not exceed the node's total resources. Overloading a node with more tasks than it can handle might occur if the tasks' resource requirements are not correctly specified or if the tasks use more resources than specified. To prevent this, you can reduce the number of concurrent tasks or increase the resources available to them. Additionally, you can specify a task or actor's logical resource requirements to ensure they only run on a node with sufficient resources.",233"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"234},235{236"question": "when should I use Ray Client?",237"sources": [238"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",239"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",240"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",241"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",242"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",243"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",244"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",245"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",246"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve"247],248"document_ids": [2496615,2506611,2516608,2526612,2536613,2544811,25513974,2564204,25713442258],259"answer": " You should use the Ray Client when you want to connect an interactive Python shell to a remote cluster for development purposes. However, it's important to note that Ray Client has limitations and may not work as expected for ML workloads like Ray Tune or Ray Train. For those cases, it's recommended to use the Ray Jobs API instead. Additionally, ensure that you have the required pip package ray[client] installed.",260"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"261},262{263"question": "how to scatter actors across the cluster?",264"sources": [265"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",266"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",267"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",268"https://docs.ray.io/en/master/serve/architecture.html#high-level-view",269"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",270"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",271"https://docs.ray.io/en/master/ray-core/scheduling/index.html#default",272"https://docs.ray.io/en/master/ray-core/api/utility.html#utility",273"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference"274],275"document_ids": [2765145,2771027,2785195,27913466,2805391,2816787,2825143,2834677,2848119285],286"answer": " To scatter actors across the cluster in Ray, you can use the `scheduling_strategy` parameter and set it to \"SPREAD\" when creating the actor. Here's an example:\n\n```python\n@ray.remote(scheduling_strategy=\"SPREAD\")\nclass SpreadActor:\n pass\n\n# Spread actors across the cluster.\nactors = [SpreadActor.remote() for _ in range(10)]\n```\n\nThis will distribute the actors across the available nodes in the cluster.",287"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"288},289{290"question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",291"sources": [292"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",293"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",294"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",295"https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",296"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",297"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",298"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#connect-to-the-cluster",299"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",300"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"301],302"document_ids": [3035681,3044815,3056610,30614678,3074266,3086619,3096526,31013718,3116551312],313"answer": " The connection refused error you're encountering when running `ray debug` on your remote Ray cluster is likely due to the Dashboard, Dashboard Agent, or GCS not being fully operational yet. These components may take a few seconds to start up after the head Pod is ready. If you continue to encounter this issue after waiting for 1 minute, it's possible that the dashboard or dashboard agent may have failed to start.\n\nTo connect to a remote Ray cluster, you should use `ray.init(address=\"ray://<head_node_ip_address>:10001\")` with the appropriate IP address. Make sure your remote cluster has a similar environment to your local machine, including files, environment variables, and Python packages.\n\nAdditionally, ensure that the Ray Dashboard is running by executing `ray dashboard cluster.yaml` in a separate shell. This command will forward the remote port 8265 to port 8265 on localhost.\n\nIf you still face issues, check if the Ray Cluster is running and accessible using its default HTTP address (http://127.0.0.1:8265 or ray://<head_node_ip_address>:10001).",314"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"315},316{317"question": "How does Ray AIR set up the model to communicate gradient updates across machines?",318"sources": [319"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",320"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#asynchronous-parameter-server-training",321"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",322"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",323"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",324"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",325"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",326"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",327"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#defining-the-parameter-server"328],329"document_ids": [3309845,3315338,3329820,333374,334156,335337,336183,33710019,3385329339],340"answer": " Ray AIR sets up the model to communicate gradient updates across machines by using data-parallel training and setting up a parameter server. The parameter server holds a copy of the model and receives gradients from the workers, applies them to the model, and sends the updated model back to the workers. This is facilitated by the `ParameterServer` class defined as a remote process with the `@ray.remote` decorator. The workers asynchronously compute gradients given their current weights and send these gradients to the parameter server. When the parameter server finishes applying the new gradient, it sends a copy of the current weights back to the worker, which then updates its weights and repeats the process.",341"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"342},343{344"question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",345"sources": [346"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",347"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",348"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",349"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",350"https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",351"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",352"https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",353"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",354"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice"355],356"document_ids": [35713621,35813974,35913614,36013597,36113420,36210282,363144,3645785,3655739366],367"answer": " Use Ray Serve instead of Modal or Seldon for model serving on your own infrastructure, with scalability and flexibility across public clouds, K8s, and on-premise machines. Ray Serve is framework-agnostic, not tied to specific ML libraries or frameworks, and supports model composition. Unlike Modal or Seldon, Ray Serve lacks full-fledged ML platform features such as model lifecycle management and performance visualization. You can't \"just do it via containers\" because Ray Serve provides additional scalability, flexibility, and model composition capabilities beyond what containers offer.",368"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"369},370{371"question": "How do I deploy an LLM workload on top of Ray Serve?",372"sources": [373"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",374"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",375"https://docs.ray.io/en/master/cluster/vms/user-guides/community/lsf.html#deploying-on-lsf",376"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",377"https://docs.ray.io/en/master/serve/api/index.html#serve",378"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",379"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",380"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",381"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air"382],383"document_ids": [38413618,38513677,3866382,3875636,38813983,38913598,39013416,39113647,39210282393],394"answer": " To deploy an LLM (large language model) workload on top of Ray Serve, follow these steps:\n\n1. Write your LLM application as a single Python module, including all necessary steps like prompt preprocessing, vector database lookups, LLM API calls, and response validation.\n2. Save this Python module and the Ray Serve configuration file (YAML format) in the same directory.\n3. If you're using a local, single-node Ray cluster, start Ray with the command `ray start --head`.\n4. Deploy your Ray Serve config file using the CLI with the command `serve deploy <config_file_path>`.\n5. For deploying on a remote, multi-node cluster, use the Ray Cluster Launcher.\n\nFor multiple Ray users, it's recommended to start a Ray cluster for each user to ensure workload isolation.\n\nRay Serve is built on top of Ray, providing scalability across many machines and flexible scheduling support, such as fractional GPUs. Deployments are the central concept in Ray Serve, containing business logic or an ML model to handle incoming requests and scalable across a Ray cluster. Use the `@serve.deployment` decorator on a Python class or function to define a deployment.\n\nRefer to the provided context for more information on deploying multiple applications, using Ray Serve with LSF, and deploying to VM environments.",395"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"396},397{398"question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",399"sources": [400"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",401"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",402"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",403"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",404"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",405"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",406"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",407"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",408"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air"409],410"document_ids": [4114878,41214121,41314137,41414299,41514183,41614062,41714221,41810280,41910260420],421"answer": " The memory you need depends on the model, but the context suggests that a GPU with 16 GB or more memory is required for the \"EleutherAI/gpt-j-6B\" model. The `object_store_memory` in your configuration would be the setting to specify the memory size.",422"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"423},424{425"question": "How do I log the results from multiple distributed workers into a single tensorboard?",426"sources": [427"https://docs.ray.io/en/master/train/examples/tf/tensorflow_mnist_example.html#running-distributed-training-of-a-tensorflow-model-on-mnist-with-ray-train",428"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",429"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",430"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#example-logging-to-mlflow-and-tensorboard",431"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#quickstart",432"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",433"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",434"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-tune-results-on-a-single-node",435"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-concurrent-tune-trials"436],437"document_ids": [4389544,43910927,4402354,4419144,4428286,443231,4442411,4452383,4462380447],448"answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use TensorBoard's `--logdir` flag and point it to a shared directory that is accessible by all workers. In the context provided, it looks like the results are being saved to `~/ray_results`. Therefore, you can start TensorBoard as follows:\n```bash\ntensorboard --logdir=~/ray_results\n```\nYou can then access TensorBoard in your web browser at `http://localhost:6006`. Note that this command should be run on the machine where the results are being saved. If the workers are saving results to a shared network directory, you can run this command on any machine that has access to that directory.",449"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"450},451{452"question": "how do you config SyncConfig for a Ray AIR job?",453"sources": [454"https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration",455"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",456"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",457"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningConfigBuilder.checkpointing.html#ray-train-lightning-lightningconfigbuilder-checkpointing",458"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",459"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",460"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_timeout.html#ray-train-syncconfig-sync-timeout",461"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",462"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.checkpoint_config.html#ray-train-runconfig-checkpoint-config"463],464"document_ids": [4651138,46610466,4678441,4688371,4691443,47010464,4711852,4721782,4739098474],475"answer": " To configure `SyncConfig` for a Ray AIR job, you can use the `ray.train.SyncConfig` class, which allows you to specify various synchronization options such as `upload_dir`, `syncer`, `sync_period`, `sync_timeout`, and others.\n\nHere's an example of how to use `SyncConfig` in a `RunConfig` object:\n```python\nfrom ray import train\n\nsync_config = train.SyncConfig(\n upload_dir=\"my_upload_dir\",\n sync_period=300,\n sync_timeout=1800,\n sync_artifacts=True,\n sync_artifacts_on_checkpoint=True,\n)\n\nrun_config = train.RunConfig(\n sync_config=sync_config,\n storage_path=\"my_storage_path\",\n)\n```\nNote that synchronization of checkpoints and other artifacts from worker nodes to the head node is no longer supported in Ray AIR. This means that the checkpoints and artifacts saved by trials scheduled on worker nodes will not be accessible during or after the run.\n\nAdditionally, be aware that the use of `syncer` and `upload_dir` is deprecated and may be hard-deprecated in Ray 2.7. Instead, consider using other synchronization methods or tools.",476"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"477},478{479"question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",480"sources": [481"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",482"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",483"https://docs.ray.io/en/master/ray-observability/getting-started.html#job-profiling",484"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",485"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",486"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",487"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",488"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/observability.html#method-3-check-logs-of-ray-pods",489"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods"490],491"document_ids": [4925764,493444,494422,495415,49613523,4976631,4986157,4995855,5005665501],502"answer": " To quickly narrow down the root cause of a failed Ray job, follow these steps:\n\n1. Access all the logs related to the Ray job.\n2. Use the `kubectl logs -l=job-name=rayjob-sample` command to view the logs in your cluster, organized by node and log file name.\n3. Utilize the search functionality provided in the Logs view to find specific log messages.\n4. Check the driver logs, if available, for more information on the job submission.\n5. If the Ray job is submitted via the Job API, you can profile the job for more insights.\n6. If needed, access the log files directly on the Pods for detailed system and user-level logs.\n\nRemember to parse and store logs using a logging stack like ELK or Loki for easier searching and analysis.",503"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"504},505{506"question": "How do I specify how many GPUs a serve deployment needs?",507"sources": [508"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",509"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",510"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",511"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",512"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#resource-management-cpus-gpus",513"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",514"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",515"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#usage-example",516"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#resources"517],518"document_ids": [51913458,52011062,52113575,52213456,52313778,52410283,52511063,5266420,5275927528],529"answer": " To specify how many GPUs a Serve deployment needs, you can use the `ray_actor_options` parameter in the `@serve.deployment` decorator and set the `\"num_gpus\"` key to the desired number of GPUs. This can be a fractional value to pack multiple replicas together on a machine with fewer GPUs than replicas. For example:\n```\n@serve.deployment(ray_actor_options={\"num_gpus\": 1})\ndef func(*args):\n return do_something_with_my_gpu()\n```\nThis will create a deployment where each replica uses a single GPU. If you want to use inter-node model parallelism, you can increase `num_gpus`. However, this requires nodes with multiple GPUs. If the machine does not have the required number of GPUs available, a `RuntimeError` will be thrown by the respective worker. If you set `num_gpus=0`, your policies will be built solely on the CPU, even if GPUs are available on the machine.",530"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"531},532{533"question": "One of my worker nodes keeps dying on using TensorflowTrainer with around 1500 workers, I observe SIGTERM has been received to the died node's raylet. How can I debug this?",534"sources": [535"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",536"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",537"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",538"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",539"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",540"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",541"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",542"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",543"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop"544],545"document_ids": [5465129,5479493,54810333,549557,550555,55110339,5529483,55310456,5549326555],556"answer": " The worker nodes are dying due to memory pressure (OOM), which means the nodes are running out of memory. This can be caused by the TensorFlow binary not being optimized for the specific CPU instructions in performance-critical operations. To address the out-of-memory issue, you can refer to the documentation on how to prevent OOM in Ray: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>. Additionally, Ray's memory monitor periodically prints the aggregated out-of-memory killer summary to Ray drivers.\n\nTo debug the issue, you can:\n\n1. Check the memory usage on the nodes using `ray logs raylet.out -ip <node_ip>` as suggested in the logs.\n2. Enable the appropriate CPU instructions in TensorFlow by rebuilding it with the appropriate compiler flags.\n3. Monitor the logs for OOM warnings and errors.\n4. Consider adjusting the batch size, learning rate, or other hyperparameters to reduce memory usage.\n5. Ensure that the data being used is properly preprocessed and does not consume excessive memory.\n6. If using distributed training, ensure that the `DistributedSampler` is used correctly to avoid replicating samples on each device.",557"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"558},559{560"question": "what are the possible reasons for nodes dying in a cluster?",561"sources": [562"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",563"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",564"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",565"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",566"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",567"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",568"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",569"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",570"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group"571],572"document_ids": [57313898,5744522,5754519,5764524,5779524,578554,5795639,5805129,5814790582],583"answer": " Possible reasons for nodes dying in a Ray cluster include:\n\n1. Head node failure, which causes the entire cluster to fail.\n2. Raylet process failure, resulting in the corresponding node being marked as dead.\n3. Node failure due to memory pressure, causing worker processes to be killed.\n4. User action, such as terminating the cluster.\n5. Overloading the head node with too many simultaneous connections.\n\nTo make the cluster fault-tolerant to head node failures, you need to make the GCS fault-tolerant so that when a new head node starts, you still have all the cluster-level data.",584"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"585},586{587"question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",588"sources": [589"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",590"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",591"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",592"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",593"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",594"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",595"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",596"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",597"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster"598],599"document_ids": [6006363,6016715,6026365,6035953,6049510,6056337,606851,6076457,608854609],610"answer": " To programmatically set the target size of a Ray remote cluster immediately, you can use the `ray.autoscaler.sdk.request_resources()` function within your Ray program. This function bypasses normal upscaling speed constraints and commands the autoscaler to scale the cluster up to the desired size.\n\nHere's an example usage:\n\n```python\nimport ray\nfrom ray.autoscaler.sdk.request_resources import request_resources\n\n# Connect to the Ray cluster\nray.init(address=\"<your_cluster_address>\")\n\n# Request resources to set the target size of the cluster\nrequest_resources(num_cpus=<desired_cluster_size>)\n```\n\nReplace `<your_cluster_address>` with the address of your Ray cluster and `<desired_cluster_size>` with the number of CPUs you want the cluster to scale up to.\n\nKeep in mind that this method requires an active Ray program running on the cluster. If you need to set the target size before starting your Ray program, you may need to use other configuration options depending on your cluster setup (e.g., Kubernetes, Azure, etc.).",611"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"612},613{614"question": "how do you disable async iter_batches with Ray Dataset?",615"sources": [616"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",617"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",618"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",619"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.streaming_split.html#ray-data-dataset-streaming-split",620"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",621"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches",622"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#define-your-training-function",623"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",624"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat"625],626"document_ids": [6277081,6287379,6297188,6307414,6317380,6327811,6339361,6349906,6357328636],637"answer": " To disable async iter\\_batches with Ray Dataset, you can use the `iter_batches()` method which returns an iterator over batches of data. It doesn't have an async counterpart. Here is an example:\n```python\nds = ray.data.range(100)\nfor batch in ds.iter_batches():\n pass\n```\nNote that this operation will trigger execution of the lazy transformations performed on this dataset.",638"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"639},640{641"question": "what is the different between a batch and a block, for ray datasets?",642"sources": [643"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",644"https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",645"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",646"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",647"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#batch-training-with-ray-core",648"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.batch_to_block.html#ray-data-block-blockaccessor-batch-to-block",649"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",650"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",651"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-data"652],653"document_ids": [65414728,6556872,65614727,6577081,6585274,6597150,6606895,66114584,6626859663],664"answer": " In the context of Ray Datasets, a \"block\" refers to an internal abstraction for a partition of data, while a \"batch\" is a user-facing object representing a group of rows. A batch can be converted to a block using the `batch_to_block` method. The batch size is the number of data points used for one gradient update in model training.",665"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"666},667{668"question": "How to setup the development environments for ray project?",669"sources": [670"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-from-source",671"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",672"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",673"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#development-workflow",674"https://docs.ray.io/en/master/serve/api/index.html#serve-run",675"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-on-linux-macos-full",676"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",677"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher",678"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments"679],680"document_ids": [681877,682910,68310168,68413709,68513990,686889,6874251,6884250,6896614690],691"answer": " To set up the development environment for the Ray project, follow these steps:\n\n1. Clone the Ray repository:\n ```\n git clone https://github.com/ray-project/ray.git\n ```\n\n2. Change to the example directory and install dependencies:\n ```\n cd doc/source/templates/05_dreambooth_finetuning\n pip install -Ur dreambooth/requirements.txt\n ```\n\n3. Prepare some directories and environment variables:\n - Set up `DATA_PREFIX`, `ORIG_MODEL_NAME`, `ORIG_MODEL_HASH`, `ORIG_MODEL_DIR`, and `ORIG_MODEL_PATH` environment variables as shown in the context.\n\n4. If you're using Linux or MacOS, build the Ray dashboard by following the instructions provided in the context.\n\n5. Build Ray for Python by activating any Python virtual (or conda) environment and running the build command from the Ray project directory.\n\n6. When initializing Ray, you can specify a `runtime_env` to define the dependencies your Ray application needs to run, including files, packages, environment variables, and more. This is an optional step but can be helpful for managing dependencies.\n\nFor more information on runtime environments and building Ray on Windows, refer to the context provided.",692"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"693},694{695"question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",696"sources": [697"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#rewriting-trajectories",698"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.__init__.html#ray-rllib-evaluation-rollout-worker-rolloutworker-init",699"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",700"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",701"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.set_weights.html#ray-rllib-evaluation-rollout-worker-rolloutworker-set-weights",702"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",703"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",704"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.sample.html#ray-rllib-evaluation-rollout-worker-rolloutworker-sample",705"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model"706],707"document_ids": [70810845,70913196,71012708,71112707,71212965,71310211,714640,71512739,71610218717],718"answer": " To debug why Ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`, you can check the following:\n\n1. Ensure that there are available CPUs in the Ray cluster. The deadlocking issue might be due to the lack of available CPUs for the Ray cluster to execute the tasks. You can refer to the warning message in the logs for more details.\n2. Check the configuration of the rollout worker, such as the `env_creator`, `default_policy_class`, and `config` parameters. Make sure that they are correctly defined and initialized.\n3. Verify that the `sample()` function is called correctly and that it returns a valid `SampleBatch` object. You can print the `SampleBatch` object to check its contents.\n4. Check the Ray logs for any error messages or warnings related to the deadlocking issue. You can also use the `ray profiler` to diagnose any performance issues or bottlenecks in the Ray cluster.\n5. If the issue persists, you can try reducing the complexity of the environment or the policy to isolate the root cause of the deadlocking issue. You can also try using a different algorithm or policy to see if the issue is specific to a particular implementation.",719"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"720},721{722"question": "how do I join two ray datasets?",723"sources": [724"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",725"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",726"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",727"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",728"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",729"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",730"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#load-and-preprocess-datasets",731"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-ml-libraries",732"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes"733],734"document_ids": [7358006,7366835,7379180,7389188,7397255,7407289,7419348,7426756,7436809744],745"answer": " To join two Ray datasets, you can use the `zip` function if the datasets have the same number of rows and you want to combine corresponding rows. Here's an example:\n```python\nds1 = ray.data.range(5)\nds2 = ray.data.range(5)\njoined_dataset = ds1.zip(ds2)\n```\nIf you want to concatenate the datasets vertically (i.e., add the rows of the second dataset to the end of the first dataset), you can use the `union` function:\n```python\nds1 = ray.data.range(5)\nds2 = ray.data.range(5, 10)\ncombined_dataset = ds1.union(ds2)\n```\nNote that the `union` function will trigger execution of any lazy transformations performed on the dataset.",746"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"747},748{749"question": "Is there a way to retrieve an object ref from its id?",750"sources": [751"https://docs.ray.io/en/master/ray-core/objects.html#objects",752"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",753"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",754"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait",755"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",756"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",757"https://docs.ray.io/en/master/ray-core/api/core.html#objects",758"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",759"https://docs.ray.io/en/master/ray-core/objects.html#objects"760],761"document_ids": [7624413,7635426,7644982,7654750,7664885,76714041,7684651,7695436,7704412771],772"answer": " Yes, you can retrieve an object from its object reference using the `ray.get()` function. This function blocks until the object corresponding to the object reference is available in the local object store. If the object is not in the local object store, it will be shipped from an object store that has it.\n\nHere is an example of how to use `ray.get()` to retrieve an object:\n\n```\nobject_ref = ray.put(object_to_store)\nretrieved_object = ray.get(object_ref)\n```\n\nYou can also pass a list of object references to `ray.get()` to retrieve multiple objects in parallel:\n\n```\nobject_refs = [ray.put(object_to_store) for object_to_store in objects_to_store]\nretrieved_objects = ray.get(object_refs)\n```\n\nIn this case, the order of the objects in the `retrieved_objects` list will be the same as the order of the object references in the `object_refs` list.",773"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"774},775{776"question": "how to create model Checkpoint from the model in memory?",777"sources": [778"https://docs.ray.io/en/master/tune/examples/includes/pbt_convnet_function_example.html#pbt-convnet-example",779"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",780"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#check-the-training-results-and-checkpoints",781"https://docs.ray.io/en/master/tune/examples/tune-pytorch-cifar.html#the-train-function",782"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",783"https://docs.ray.io/en/master/tune/examples/includes/xgboost_dynamic_resources_example.html#xgboost-dynamic-resources-example",784"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#enabling-checkpointing-to-retrieve-the-model",785"https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example",786"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint"787],788"document_ids": [7894104,7909037,7919331,7923858,7938798,7943964,7959857,7964073,7979066798],799"answer": " To create a model checkpoint from a model in memory, you can use the `tf.keras.checkpoints.Checkpoint` class in TensorFlow. Here's an example of how to do it:\n```python\nimport tensorflow as tf\n\n# Create the model\nmodel = tf.keras.applications.ResNet101()\n\n# Create a checkpoint object\ncheckpoint = tf.keras.checkpoints.Checkpoint(model=model)\n",800"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"801},802{803"question": "what is Deployment in Ray Serve?",804"sources": [805"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",806"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",807"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",808"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment",809"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",810"https://docs.ray.io/en/master/serve/tutorials/java.html#converting-to-a-ray-serve-deployment",811"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",812"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",813"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy"814],815"document_ids": [81614255,81713416,81814732,81914150,82013679,82114560,82213677,82313776,82413987825],826"answer": " A Deployment in Ray Serve is a group of actors that handle incoming traffic. It consists of a number of replicas, which are individual copies of a class or function running in separate Ray processes. The number of replicas can be scaled up or down to match the request load. Deployments are defined using the `@serve.deployment` decorator on a Python class or function. They can be combined into a Deployment graph for model composition, where requests are passed through multiple stages of processing.",827"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"828},829{830"question": "What is user config in Ray Serve? how do I use it?",831"sources": [832"https://docs.ray.io/en/master/serve/api/index.html#serve-config",833"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",834"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",835"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#updating-user-config",836"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",837"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-4-prepare-your-ray-serve-python-scripts-and-run-the-ray-serve-application",838"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#dynamically-changing-parameters-without-restarting-your-replicas-user-config",839"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#adding-deleting-and-updating-applications",840"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"841],842"document_ids": [84313986,84413831,84513974,84613687,84713551,8485983,84913565,85013661,8516615852],853"answer": " In Ray Serve, user config refers to the configuration that can be set for a specific deployment in a Serve application. It can be used to configure parameters such as the name, number of replicas, and route prefix for a deployment. The user config can be set in the Serve config file or on the `@serve.deployment` decorator.\n\nTo use the user config, you can define a `user_config` field in the deployment's entry in the Serve config file. For example:\n```\ndeployments:\n - name: Model\n user_config:\n threshold: 1.5\n```\nAlternatively, you can set the user config when creating the deployment using the `user_config` argument of the `serve.deployment` function.\n\nTo update the user config for a deployment, you can use the `reconfigure()` method of the deployment object. This method takes a new user config dictionary as an argument and updates the deployment's configuration accordingly.\n\nTo get the current config of a Serve application, you can use the `serve config` command in the Ray CLI. This command will return the latest config file that the Ray Cluster received, which represents the Serve application's goal state.\n\nI hope this helps! Let me know if you have any other questions.",854"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"855},856{857"question": "What is the difference between PACK and SPREAD strategy?",858"sources": [859"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",860"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",861"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",862"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",863"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",864"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",865"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",866"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",867"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.placement_strategy.html#ray-train-scalingconfig-placement-strategy"868],869"document_ids": [8701872,87112053,87211075,8735075,8745078,8755077,8764789,8775145,8788773879],880"answer": " The PACK and SPREAD strategies are two of the placement group strategies in Ray that determine how bundles, which represent resource requirements, are placed on nodes.\n\nThe PACK strategy places bundles onto as few nodes as possible, trying to utilize the resources of each node to the fullest extent. This strategy is useful when you want to maximize the locality of your tasks and minimize the communication overhead between nodes.\n\nOn the other hand, the SPREAD strategy places bundles across distinct nodes as evenly as possible. This strategy is useful when you want to distribute the load uniformly across nodes and avoid overloading a single node.\n\nIn summary, PACK prioritizes resource utilization and locality, while SPREAD prioritizes load balancing and avoiding overloading.",881"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"882},883{884"question": "What\u2019s the best way to run ray across multiple machines?",885"sources": [886"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",887"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",888"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",889"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",890"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",891"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",892"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",893"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",894"https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure"895],896"document_ids": [8975636,8984644,89914710,9005600,901372,90213615,9034186,9045634,9059809906],907"answer": " The best way to run Ray across multiple machines is to deploy a Ray cluster. A Ray cluster consists of a head node and worker nodes connected together. You can start a fixed-size Ray cluster or one that autoscales according to the resources requested by the applications running on the cluster. It's also recommended to start a separate Ray cluster for each user to ensure workload isolation. Ray clusters can be deployed on various environments, including laptops, Kubernetes, and major cloud providers.",908"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"909},910{911"question": "how do I specify ScalingConfig for a Tuner run?",912"sources": [913"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",914"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",915"https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",916"https://docs.ray.io/en/master/tune/key-concepts.html#tune-trials",917"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",918"https://docs.ray.io/en/master/tune/examples/pbt_ppo_example.html#example",919"https://docs.ray.io/en/master/tune/examples/nevergrad_example.html#optional-passing-the-hyper-parameter-space-into-the-search-algorithm",920"https://docs.ray.io/en/master/tune/examples/flaml_example.html#incorporating-a-time-budget-to-the-experiment",921"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#more-resources"922],923"document_ids": [9241826,9259126,9261303,927967,9289127,9292741,9302569,9312860,9329256933],934"answer": " To specify the `ScalingConfig` for a Tuner run, you can pass it as an argument to the `RunConfig` object, which you can then pass to the `Tuner` constructor. Here's an example:\n```python\nfrom ray.tune. ScalingConfig import ScalingConfig\n\nscaling_config = ScalingConfig(\n num_workers=8,\n resources_per_worker={\"CPU\": 4, \"GPU\": 0.5},\n use_gpu=True,\n)\n\nrun_config = RunConfig(\n name=\"my_tune_run\",\n scaling_config=scaling_config,\n)\n\ntuner = Tuner(\n trainable,\n param_space=space,\n tune_config=tune.TuneConfig(num_samples=10),\n run_config=run_config,\n)\n\nresults = tuner.fit()\n```\nIn this example, we create a `ScalingConfig` object that specifies the number of workers, the resources to allocate to each worker, and whether to use a GPU. We then create a `RunConfig` object and pass the `ScalingConfig` to it. Finally, we create a `Tuner` object and pass the `RunConfig` to it using the `run_config` argument. When we call `tuner.fit()`, the `ScalingConfig` will be used to configure the resources for the Tuner run.",935"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"936},937{938"question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",939"sources": [940"https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",941"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_numpy_refs.html#ray-data-dataset-to-numpy-refs",942"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",943"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",944"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",945"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",946"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",947"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy.html#ray-data-from-numpy",948"https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only"949],950"document_ids": [9514942,9527644,9534537,9544945,9554225,9567085,9577102,9587467,9594944960],961"answer": " To utilize the zero-copy feature Ray provides for numpy, you should store numpy arrays as read-only objects in the object store. When using numpy arrays in Ray workers on the same node, they can be read without copying. To convert a Ray Dataset into a distributed set of NumPy ndarrays with zero-copy access to the underlying data, use `Dataset.to_arrow()` or `Dataset.get_internal_block_refs()`. To create a Dataset from a list of Ray object references to NumPy ndarrays, use `ray.data.from_numpy_refs()`.\n\nTo avoid copying arrays when using functions like `map_batches()`, set `zero_copy_batch=True` if the function does not mutate its input.\n\nHere's an example of using `ray.data.from_numpy_refs()`:\n\n```python\nimport numpy as np\nimport ray\n\n# Create numpy arrays and store them as object references in Ray object store\nnumpy_arrays = [np.zeros((100, 100)) for _ in range(10)]\narray_refs = [ray.put(arr) for arr in numpy_arrays]\n\n# Create a Ray Dataset from the list of object references\nds = ray.data.from_numpy_refs(array_refs)\n\n# Use the Dataset with zero-copy access to the underlying data\n# ...\n```\n\nRemember that if you need to mutate the numpy arrays, you should manually copy them at the destination, as the zero-copy deserialization feature provided by Ray disables mutation.",962"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"963},964{965"question": "if there are O(millions) of keys that all have state, is it ok to spin up 1=1 actors? Or would it be advised to create \u2018key pools\u2019 where an actor can hold 1=many keys?",966"sources": [967"https://docs.ray.io/en/master/data/examples/random-access.html#architecture",968"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",969"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",970"https://docs.ray.io/en/master/data/examples/random-access.html#performance",971"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",972"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",973"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",974"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_random_access_dataset.html#ray-data-dataset-to-random-access-dataset",975"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile"976],977"document_ids": [9788000,9798119,9804878,9818002,9828146,9835231,9848101,9857308,986641987],988"answer": " Based on the context provided, it is not advised to spin up one actor per key due to the potential overhead and inefficiency. Instead, creating key pools where an actor can hold many keys would be a more advisable approach. This is because each actor can provide a certain number of records per second, and creating a separate actor for each key may not provide a linear increase in performance. Additionally, having more workers than clients is generally recommended for sufficient throughput. The number of remote actors to use depends on the workload and cluster setup, and there are cases where starting more than one actor per node may be beneficial, such as for multi-GPU training or in a heterogeneous cluster.",989"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"990},991{992"question": "How to find the best checkpoint from the trial directory?",993"sources": [994"https://docs.ray.io/en/master/train/api/doc/ray.train.Result.html#ray-train-result",995"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",996"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",997"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",998"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",999"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1000"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1001"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_trial_checkpoints_paths.html#ray-tune-experimentanalysis-get-trial-checkpoints-paths",1002"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result"1003],1004"document_ids": [10058414,10061431,10072188,10081645,10093759,10101430,101111460,10121692,101337601014],1015"answer": " To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` function, which is part of the Ray Tune library. This function allows you to specify a metric and a mode (either \"min\" or \"max\") to determine the best checkpoint. Here's an example of how to use this function:\n```\nfrom ray.tune.experiment import ExperimentAnalysis\n\nanalysis = ExperimentAnalysis.from_directory(path)\nbest_checkpoint = analysis.get_best_checkpoint(metric=\"your_metric\", mode=\"max\")\n```\nIn this example, `path` is the path to the trial directory, and `your_metric` is the name of the metric you want to use to determine the best checkpoint. The `mode` parameter specifies whether to choose the checkpoint with the minimum or maximum value of the specified metric.\n\nIf you want to get the best trial based on a specific metric and mode, you can use the `best_checkpoint` property of the `ExperimentAnalysis` class. Here's an example:\n```\nanalysis = ExperimentAnalysis.from_directory(path)\nbest_checkpoint = analysis.best_checkpoint\n```\nIn this case, the best checkpoint will be determined based on the metric and mode that were passed to the `tune.run()` function when the experiment was created.\n\nNote that the `get_best_checkpoint` function and the `best_checkpoint` property will return a `Checkpoint` object, which you can use to restore the model and continue training from the best checkpoint.",1016"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1017},1018{1019"question": "what are the advantage and disadvantage of using singleton Actor ?",1020"sources": [1021"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1022"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1023"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",1024"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",1025"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#default-concurrency-group",1026"https://docs.ray.io/en/master/ray-core/patterns/index.html#design-patterns-anti-patterns",1027"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",1028"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",1029"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"1030],1031"document_ids": [10325192,10334505,103414708,103511597,10364641,10375244,10384638,10394630,104046241041],1042"answer": " A singleton actor is an actor with a globally unique name that can be retrieved later. This can be useful for creating actors that provide a global service.\n\nAdvantages of using a singleton actor:\n\n* It allows for the creation of global services that can be accessed from anywhere in the application.\n* It ensures that only one instance of the actor is created, which can be useful for managing resources or maintaining a consistent state.\n\nDisadvantages of using a singleton actor:\n\n* It can introduce global state into the application, which can make it more difficult to reason about the behavior of the system as a whole.\n* It can also make it more difficult to test the actor in isolation, as it may depend on global state or other parts of the application.\n\nIt's also important to note that if you don't need the stateful part of an actor, you're mostly better off using tasks.\n\nAdditionally, it's an anti-pattern to use closure capturing large objects as it harms performance and using global variables to share state between tasks and actors.",1043"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1044},1045{1046"question": "what are the advantages of using a named actor?",1047"sources": [1048"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1049"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",1050"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1051"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1052"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1053"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1054"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1055"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",1056"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources"1057],1058"document_ids": [10594623,10604629,10614625,10624871,10634624,10644880,10654505,106614708,1067115971068],1069"answer": " Named actors offer the following advantages:\n\n1. Unique identification: Actors can be given a unique name within their namespace, allowing you to retrieve the actor from any job in the Ray cluster.\n2. Get-or-create functionality: Ray provides a `get_if_exists` option for actor creation that creates an actor only if it doesn\u2019t exist.\n3. Accessibility: Named actors can be accessed and retrieved later, even if you cannot directly pass the actor handle to the task that needs it or if you are trying to access an actor launched by another driver.\n4. Scoping: Named actors are scoped by namespace, and if no namespace is assigned, they will be placed in an anonymous namespace by default.\n\nNamed actors are useful when you need to maintain state and access the actor from different parts of your code or from other jobs in the Ray cluster. However, if you don\u2019t need the stateful part of an actor, you\u2019re mostly better off using tasks.",1070"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1071},1072{1073"question": "How do I read a text file stored on S3 using Ray Data?",1074"sources": [1075"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1076"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1077"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1078"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",1079"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1080"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",1081"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1082"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1083"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data"1084],1085"document_ids": [10867565,10877783,10887496,10897261,10906994,10916779,10926996,10937217,109479091095],1096"answer": " To read a text file stored on S3 using Ray Data, you can use the `read_text` function and provide the S3 path to the file as an argument. Here's an example:\n```python\nimport ray\n\nds = ray.data.read_text(\"s3://your-s3-bucket/your-text-file.txt\")\n```\nThis will create a Ray Data object `ds` that you can use for further processing. Note that you may need to configure your AWS credentials to access the S3 bucket.",1097"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1098},1099{1100"question": "how do I get the IP of the head node for my Ray cluster?",1101"sources": [1102"https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",1103"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",1104"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",1105"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",1106"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",1107"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",1108"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",1109"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats",1110"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster"1111],1112"document_ids": [11135631,11146501,1115493,11165587,11174446,11186502,11196409,11206416,112166161122],1123"answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command and provide the path to your cluster configuration file as an argument. The command will return the IP address of the head node. Here is the syntax:\n```css\nray get_head_ip [OPTIONS] CLUSTER_CONFIG_FILE\n```\nFor example, if your cluster configuration file is located at `/path/to/cluster_config.yaml`, you can run:\n```\nray get_head_ip /path/to/cluster_config.yaml\n```\nThis will return the IP address of the head node.\n\nAlternatively, if you have already started the Ray cluster, you can find the IP address of the head node by looking at the output of the `ray start` command. When you start the head node, the command will print out the Ray cluster address, which includes the IP address of the head node and the port number. Here is an example:\n```arduino\nRay cluster address: 10.0.0.1:6379\n",1124"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1125},1126{1127"question": "How to write a map function that returns a list of object for `map_batches`?",1128"sources": [1129"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1130"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1131"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1132"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1133"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1134"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1135"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1136"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",1137"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference"1138],1139"document_ids": [11407082,11417091,11427640,11437081,11447800,11457959,11467086,11477738,114880701149],1150"answer": " To write a map function that returns a list of objects for `map_batches`, you can define a function or class that takes a batch of data as input and yields or returns one or multiple processed batches as a list of objects. Here's an example of a function that takes a batch of dictionaries and returns a list of dictionaries with each dictionary containing the original data and the square of the 'age' value:\n\n```python\ndef map_fn(batch: Dict[str, np.ndarray]) -> List[Dict[str, np.ndarray]]:\n result = []\n for item in batch:\n item['age_squared'] = item['age'] ** 2\n result.append(item.copy())\n return result\n\nds = ... # Initialize your Dataset\nprocessed_ds = ds.map_batches(map_fn, batch_size=1024)\n```\n\nIn this example, the `map_fn` function takes a batch of dictionaries, processes each dictionary, and appends a copy of the processed dictionary to the `result` list. The function then returns the `result` list as a batch of processed data.\n\nKeep in mind that if your function returns large objects, you should yield outputs in chunks to avoid memory issues.",1151"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1152},1153{1154"question": "How do I set a maximum episode length when training with Rllib?",1155"sources": [1156"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",1157"https://docs.ray.io/en/master/rllib/rllib-replay-buffers.html#advanced-usage",1158"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",1159"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1160"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.reporting.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-reporting",1161"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",1162"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#evolution-strategies-es",1163"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.train_buffered.html#ray-rllib-algorithms-algorithm-algorithm-train-buffered",1164"https://docs.ray.io/en/master/rllib/rllib-offline.html#getting-started"1165],1166"document_ids": [116710821,116810724,116910510,117011458,117113177,117211048,117311925,117412332,1175105941176],1177"answer": " In RLlib, you can set a maximum episode length by using the `evaluation_duration` parameter in the configuration of your training run. This parameter determines the maximum number of episodes that will be run during each evaluation step.\n\nFor example, if you want to set the maximum episode length to 1000 time steps, you can do so by setting `evaluation_duration` to 1 and `evaluation_duration_unit` to \"episodes\" in the configuration of your training run, like this:\n```\nconfig = {\n \"evaluation_duration\": 1,\n \"evaluation_duration_unit\": \"episodes\",\n \"max_episode_length\": 1000,\n # other configuration parameters...\n}\n```\nThis will cause RLlib to terminate each episode after it has reached 1000 time steps, and to move on to the next evaluation step.\n\nNote that the `max_episode_length` parameter is not a built-in configuration option in RLlib, so you will need to add it to the configuration dictionary yourself. This parameter can be used to set the maximum length of episodes during both online training and offline evaluation.",1178"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1179},1180{1181"question": "how do I make a Ray Tune trial retry on failures?",1182"sources": [1183"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1184"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1185"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1186"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_error.html#ray-tune-callback-on-trial-error",1187"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_recover.html#ray-tune-callback-on-trial-recover",1188"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",1189"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.MedianStoppingRule.html#ray-tune-schedulers-medianstoppingrule",1190"https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.LoggerCallback.on_trial_recover.html#ray-tune-logger-loggercallback-on-trial-recover",1191"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune"1192],1193"document_ids": [11942326,11951114,11962266,11971980,11982152,11991926,12001728,12011966,1202115541203],1204"answer": " To make a Ray Tune trial retry on failures, you can use the `FailureConfig` to configure failure handling of individual trials. Here's an example:\n\n```python\nfrom ray import tune\n\n# Define your trainable function here\n\n# Create a FailureConfig instance\nconfig = tune.FailureConfig(max_retries=3) # retry up to 3 times\n\n# Create a Tuner instance with the FailureConfig\ntuner = tune.Tuner(train_model, param_space=trial_space, failure_config=config)\n\n# Run the trial\nresults = tuner.fit()\n```\n\nIn this example, the `max_retries` parameter is set to 3, meaning that if a trial fails, Ray Tune will automatically retry it up to 3 times before giving up. You can adjust the number of retries as needed.\n\nAdditionally, you can use the `ray.tune.Callback.on_trial_error` and `ray.tune.Callback.on_trial_recover` callbacks to customize behavior when a trial fails and is retried. These callbacks are called after a trial instance fails (errored) and before it is scheduled for retry, respectively.",1205"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1206},1207{1208"question": "For the supervised actor pattern, can we keep the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor, to allow the worker actor to remain even on Supervisor / Driver failure?",1209"sources": [1210"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",1211"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",1212"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1213"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1214"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",1215"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#how-to-write-fault-tolerant-ray-applications",1216"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1217"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#advanced-detached-placement-group",1218"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#worker-recovery"1219],1220"document_ids": [12215208,12224561,12234575,12244631,12255195,12264351,12275191,12285095,1229115511230],1231"answer": " No, 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 if the supervisor dies, or the driver, the worker actors are automatically terminated thanks to actor reference counting. This is true even if actors are nested to multiple levels to form a tree. However, you can make the actor's lifetime \"detached\" so that it can persist even after the driver process of the job exits. But this does not prevent the actor from being terminated when the supervisor dies.",1232"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1233},1234{1235"question": "How do I read a large text file in S3 with Ray?",1236"sources": [1237"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1238"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1239"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1240"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",1241"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1242"https://docs.ray.io/en/master/data/loading-data.html#handling-compressed-files",1243"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1244"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1245"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text"1246],1247"document_ids": [12487565,12497783,12507496,12517261,12526994,12536747,12546996,12557217,125669931257],1258"answer": " To read a large text file in S3 with Ray, you can use the `read_text` function and provide the S3 path to the file as an argument. Here's an example:\n```\nimport ray\n\nds = ray.data.read_text(\"s3://your-bucket-name/path/to/your/text-file.txt\")\n```\nThis will create a Ray DataFrame `ds` that contains the text of the file, with one row for each line of text.\n\nYou can also use the `read_binary_files` function to read the raw binary data of the file, and then decode the data manually if needed.\n\nFor example:\n```\nds = ray.data.read_binary_files(\"s3://your-bucket-name/path/to/your/text-file.txt\")\ndecoded_data = ds.map(decode_text)\n```\nWhere `decode_text` is a function that you define to decode the binary data into text.\n\nNote that you can use the `read_csv` function to read compressed text files, such as those with a `.gz` or `.bz2` extension, by specifying the compression type in the `arrow_open_stream_args` argument. For example:\n```\nds = ray.data.read_csv(\n \"s3://your-bucket-name/path/to/your/compressed-text-file.csv.gz\",\n arrow_open_stream_args={\"compression\": \"gzip\"},\n)\n```",1259"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1260},1261{1262"question": "how do I get a ray dataset from pandas",1263"sources": [1264"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-pandas",1265"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1266"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",1267"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",1268"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1269"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",1270"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1271"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",1272"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_pandas.html#ray-data-dataset-to-pandas"1273],1274"document_ids": [12756806,12766749,12776901,12786752,12799180,12807818,128110017,12827363,128373021284],1285"answer": " To convert a pandas DataFrame to a Ray dataset, you can use the `ray.data.from_pandas()` function. Here's an example:\n```\nimport pandas as pd\nimport ray\n\ndf = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\nds = ray.data.from_pandas(df)\n```\nAlternatively, if you have a list of pandas DataFrames, you can pass the list to the `ray.data.from_pandas()` function to create a Ray dataset from the list. For example:\n```\ndf1 = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\ndf2 = pd.DataFrame({\"a\": [7, 8, 9], \"b\": [10, 11, 12]})\nds = ray.data.from_pandas([df1, df2])\n```\nNote that the resulting Ray dataset will be divided into multiple blocks, each of which can be processed independently. You can use Ray's data APIs to perform operations on the dataset, such as mapping a function to each batch of data in the dataset.\n\nFor more information, see the Ray documentation on converting datasets to pandas and loading data from single-node data libraries.",1286"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1287},1288{1289"question": "can you give me an example of using `ray.data.map` ?",1290"sources": [1291"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1292"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1293"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1294"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1295"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1296"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",1297"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",1298"https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",1299"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch"1300],1301"document_ids": [13027796,13037274,13049180,13057081,13066835,13078046,13088006,13096773,131079711311],1312"answer": " Sure, here's an example of using `ray.data.Dataset.map`:\n```python\nimport ray\n\n# Create a Ray Dataset from a range of integers\nds = ray.data.range(10)\n\n# Define a function to apply to each row\ndef square(row):\n return row * row\n\n# Use the map method to apply the function to each row\nds_squared = ds.map(square)\n\n# Print the resulting Dataset\nprint(ds_squared.take()) # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n```\nIn this example, we create a Ray Dataset `ds` containing the integers from 0 to 9. We then define a function `square` that takes a row (an integer) and returns its square. We use the `map` method to apply this function to each row of the Dataset, resulting in a new Dataset `ds_squared` containing the squares of the original integers. Finally, we print the contents of the new Dataset using the `take` method.",1313"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1314},1315{1316"question": "can you give me an example of using `ray.data.map` , with a callable class as input?",1317"sources": [1318"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1319"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",1320"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1321"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1322"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1323"https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",1324"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",1325"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1326"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a"1327],1328"document_ids": [13297796,1330250,13317274,13327636,13336749,13347628,1335247,133610014,1337103031338],1339"answer": " Sure, here's an example of using `ray.data.map` with a callable class as input:\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Define a class with a callable method\n@ray.remote\nclass Adder(object):\n def __init__(self, value):\n self.value = value\n \n def add(self, row):\n",1340"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1341},1342{1343"question": "How to set memory limit for each trial in Ray Tuner?",1344"sources": [1345"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1346"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1347"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",1348"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1349"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",1350"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-trials-individually-with-a-timeout",1351"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria",1352"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",1353"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater"1354],1355"document_ids": [13562325,13571114,13582406,13592266,13602001,13612204,13622198,13631025,136420031365],1366"answer": " To set a memory limit for each trial in Ray Tuner, you can request custom resources when configuring the trial. However, Ray Tune does not directly support setting a memory limit for each trial. Instead, you can request a specific amount of GPU or CPU memory by using the `tune.with_resources` function and specifying the `\"gpu\"` or `\"cpu\"` resource with the desired amount.\n\nHere's an example of how to set a trial to use 2 GB of GPU memory:\n```python\ntrain_model = tune.with_resources(train_model, {\"gpu\": 0.5}) # 0.5 GPU with 2 GB memory\n```\nNote that this method relies on the cluster providing the resources you request. Make sure your cluster has enough memory available for each trial. Also, keep in mind that each Ray worker (and thus each Ray Tune Trial) will only be scheduled on one machine, so you need to ensure that the machine can provide all the resources you requested.\n\nFor more information on configuring resources in Ray Tune, you can refer to the documentation: <https://docs.ray.io/en/latest/tune/resources.html>",1367"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1368},1369{1370"question": "how do I get the actor id of an actor",1371"sources": [1372"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_actor_id.html#ray-runtime-context-runtimecontext-get-actor-id",1373"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",1374"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1375"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor",1376"https://docs.ray.io/en/master/ray-observability/getting-started.html#actor-detail-page",1377"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1378"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",1379"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1380"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"1381],1382"document_ids": [13834787,13844931,1385475,1386690,1387436,1388474,1389750,1390476,13914771392],1393"answer": " To get the actor ID of the current actor in a Ray program, you can use the `get_actor_id()` function from the `ray.runtime_context.RuntimeContext` class. This function returns the current actor id in hex format. Here is an example:\n```\nfrom ray.runtime_context import RuntimeContext\n\ncurrent_actor_id = RuntimeContext.get_actor_id()\nprint(current_actor_id)\n```\nAlternatively, you can use the `get_actor()` function from the `ray.util.state` module to get the actor state by specifying the actor ID. If the actor is found, the function returns the actor state, which includes the actor ID as well as other information about the actor. If the actor is not found, the function returns `None`. Here is an example:\n```\nfrom ray.util.state import get_actor\n\nactor_state = get_actor(id=\"<ACTOR_ID>\")\nif actor_state is not None:\n actor_id = actor_state.actor_id\n print(actor_id)\n```\nNote that the `get_actor()` function is intended for internal developer use and may change in future Ray releases. It is recommended to use the `get_actor_id()` function to get the current actor ID in a Ray program.\n\nThe `get_actor()` function can also be used to get the state of a single task using the get API, as shown in the context. The `ray logs actor` command can be used to access logs for a specific actor.\n\nFor more information and examples, please see the Ray documentation.",1394"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1395},1396{1397"question": "can ray.init() can check if ray is all-ready initiated ?",1398"sources": [1399"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",1400"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1401"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",1402"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1403"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1404"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1405"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#starting-ray",1406"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",1407"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users"1408],1409"document_ids": [14104785,14114811,14124194,14134190,14144188,14154189,14165386,14174186,141842041419],1420"answer": " No, `ray.init()` is used to initialize Ray, not to check if it is already initialized. To check if Ray is already initialized, you can use `ray.is_initialized()`. This function will return `True` if Ray has been initialized and `False` otherwise.",1421"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1422},1423{1424"question": "What does the `compute=actor` argument do within `ray.data.map_batches` ?",1425"sources": [1426"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1427"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",1428"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1429"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1430"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1431"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",1432"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1433"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map.html#ray-util-actorpool-map",1434"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text"1435],1436"document_ids": [14376787,14386885,14398108,14408070,14417988,14425465,14436981,14444708,144569991446],1447"answer": " The `compute=actor` argument within `ray.data.map_batches` is used to specify that the function provided to `map_batches` should be run as a long-running Ray actor, rather than as a short-running Ray task. This can be useful for performance reasons, as actors can cache data between task invocations, reducing the overhead of fetching the data for each task. In the context of the provided text, the `TorchPredictor` class is being used as a Ray actor to predict on batches of data.",1448"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1449},1450{1451"question": "how do I use wandb logger with accelerateTrainer?",1452"sources": [1453"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",1454"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1455"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1456"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1457"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",1458"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",1459"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",1460"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",1461"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries"1462],1463"document_ids": [14648470,14652432,14662463,14679530,14682431,14691132,14702466,14719529,147291361473],1474"answer": " To use Weights & Biases (WandB) logger with `accelerateTrainer`, you can follow these steps:\n\n1. Install WandB: `pip install wandb`\n2. Import the necessary libraries, including WandB and the `WandbLogger` from PyTorch Lightning.\n3. Create a `WandbLogger` instance with your desired configuration, such as the project name and group.\n4. Initialize the `accelerateTrainer` and pass the `WandbLogger` as the logger argument.\n\nHere's an example:\n\n```python\nimport torch\nimport pytorch_lightning as pl\nfrom pytorch_lightning.loggers import WandbLogger\nfrom accelerate import Accelerator, DeepSpeedConfig, DeepSpeedPowerAwareTraining\n\n# Initialize WandB logger\nwandb_logger = WandbLogger(project=\"my_project\", group=\"my_group\")\n\n# Initialize Accelerator\naccelerator = Accelerator()\n\n# Create a PyTorch Lightning model\nmodel = MyPLModel()\n\n# Prepare the model for training\nmodel, train_loader, eval_loader = accelerator.prepare(model, train_loader, eval_loader)\n\n# Create DeepSpeed configuration (optional)\ndeepspeed_config = DeepSpeedConfig(\n gradient_accumulation_steps=1,\n wall_clock_breakdown=True,\n activation_checkpointing=True,\n)\n\n# Initialize DeepSpeed (optional)\naccelerator = Accelerator(\n gradient_accumulation_steps=deepspeed_config.gradient_accumulation_steps,\n mixed_precision=deepspeed_config.mixed_precision,\n log_with=wandb_logger,\n deepspeed=DeepSpeedPowerAwareTraining(config_params=deepspeed_config),\n)\n\n# Train the model\ntrainer = accelerator.create_deepspeed_trainer(limit_train_batches=10)\ntrainer.fit(model, train_loader, eval_loader)\n```\n\nReplace `MyPLModel`, `train_loader`, and `eval_loader` with your actual model, dataloaders, and other configurations. The example above assumes you're using DeepSpeed with `accelerateTrainer`, but you can remove the DeepSpeed-related parts if you're not using it.",1475"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1476},1477{1478"question": "What will be implicitly put into object store?",1479"sources": [1480"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1481"https://docs.ray.io/en/master/ray-core/objects/serialization.html#plasma-store",1482"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1483"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1484"https://docs.ray.io/en/master/data/data-internals.html#object-store-memory",1485"https://docs.ray.io/en/master/ray-core/objects.html#objects",1486"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",1487"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",1488"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"1489],1490"document_ids": [14915013,14924941,14935014,149414748,14956896,14964414,149714094,149814122,1499141381500],1501"answer": " When an object is created in the object store via `ray.put` or returned as a value from a remote function, it is implicitly put into the object store memory. Objects are reference counted and evicted when they fall out of scope. By default, the object store reserves 30% of available memory, which can be controlled using the `--object-store-memory` flag. Objects will be spilled to disk if the object store fills up. Each node has its own object store, and data is not automatically broadcasted to other nodes. Objects read via `ray.get` are put into the object store shared memory.",1502"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1503},1504{1505"question": "How do I kill or cancel a ray task that I already started?",1506"sources": [1507"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1508"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",1509"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1510"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",1511"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1512"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",1513"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1514"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",1515"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks"1516],1517"document_ids": [15184550,15194375,15204894,152114702,15224494,152311591,15244497,152514703,1526115921527],1528"answer": " To kill or cancel a Ray task that you have already started, you can call `ray.cancel()` on the ObjectRef returned by the task. This will send a KeyboardInterrupt to the task's worker if it is mid-execution. If you want to force-exit the worker, you can pass `force=True` to `ray.cancel()`. Note that Ray will not automatically retry tasks that have been cancelled.\n\nHere is an example of how to cancel a Ray task:\n```\n@ray.remote\ndef blocking_operation():\n time.sleep(10e6)\n\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n\ntry:\n ray.get(obj_ref)\nexcept ray.exceptions.TaskCancelledError:\n print(\"Object reference was cancelled.\")\n```\nThis cancellation approach aligns with the standards presented in asyncio task cancellation. Note that asyncio.Task won't be interrupted in the middle of execution if you don't await within the async function. The cancellation is not guaranteed and you can check if a task was successfully cancelled using `ray.get(actor_task_ref)`. When the `recursive=True` argument is given, Ray cancels all child and Actor Tasks.",1529"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1530},1531{1532"question": "how to send extra arguments in dataset.map_batches function?",1533"sources": [1534"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1535"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1536"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",1537"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1538"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1539"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1540"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1541"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",1542"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches"1543],1544"document_ids": [15457082,15467081,15478607,15487959,15498070,15507988,15517640,15526956,155368611554],1555"answer": " To send extra arguments in the `dataset.map_batches` function, you can use the `fn_kwargs` parameter. This parameter allows you to pass keyword arguments to the user-defined function (UDF) that you are using in the `map_batches` function.\n\nHere is an example of how you can use `fn_kwargs` to send extra arguments:\n```\nextra_arg1 = \"some value\"\nextra_arg2 = 10\n\nds.map_batches(\n ImageClassifier,\n batch_size=128,\n compute=ActorPoolStrategy(num_actors=4),\n fn_kwargs={\n \"arg1\": extra_arg1,\n \"arg2\": extra_arg2,\n },\n)\n```\nIn this example, the `ImageClassifier` UDF will receive two extra arguments (`arg1` and `arg2`) when it is called by the `map_batches` function. You can access these arguments inside the UDF using the `self.arg1` and `self.arg2` syntax (assuming that you are using a class-based UDF).\n\nFor more information and examples, you can refer to the Ray documentation on the `map_batches` function: <https://docs.ray.io/en/latest/data/map-batches.html>",1556"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1557},1558{1559"question": "where does ray GCS store the history of jobs run on a kuberay cluster? What type of database and format does it use for this?",1560"sources": [1561"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance",1562"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",1563"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",1564"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",1565"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",1566"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",1567"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/experimental.html#gcs-fault-tolerance",1568"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",1569"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob"1570],1571"document_ids": [15725830,157313871,157413470,157513870,15765642,15775647,15785916,15795763,158061491581],1582"answer": " Ray GCS stores the history of jobs run on a Kuberay cluster in an external Redis database for fault tolerance. The data is stored in a key-value store format. This is achieved by passing the RAY\\_REDIS\\_ADDRESS env var and --redis-password in the Ray head node section of the Kubernetes deployment config file.",1583"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1584},1585{1586"question": "How to resolve ValueError: The actor ImplicitFunc is too large?",1587"sources": [1588"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",1589"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#move-the-data-loader-to-the-training-function",1590"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#verify-and-save-results",1591"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1592"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1593"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler",1594"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",1595"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",1596"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction"1597],1598"document_ids": [15996150,16009858,16017992,16028119,16038108,16041895,160510267,16069908,160754651608],1609"answer": " The ValueError \"The actor ImplicitFunc is too large\" is likely caused by the actor's constructor implicitly capturing a large object, such as a large array or model. To resolve this issue, you can put large objects in the Ray object store using `ray.put()` and retrieve them using `ray.get()` in the actor's constructor.\n\nAdditionally, you may want to consider using actor-based batch prediction to optimize the process. This involves defining a callable class with a constructor to load/cache the model and a method to take in a file and perform prediction. This can help reduce the overhead cost of fetching the model for each task.\n\nIt's also worth noting that if you're using `tune.TuneConfig()`, you cannot use actor-based strategies if `reuse_actors` is set to True. A `ValueError` will be raised in that case.\n\nFinally, ensure that the batch size is not too large for the actor pool size. The warning suggests that the batch size should be at most 5 for an actor pool of size 4.",1610"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1611},1612{1613"question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",1614"sources": [1615"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",1616"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1617"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#distributed-training",1618"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",1619"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1620"https://docs.ray.io/en/master/train/huggingface-accelerate.html#training-with-huggingface-accelerate",1621"https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others",1622"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1623"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#training"1624],1625"document_ids": [16269845,1627226,162810155,1629370,16306940,16318247,163214313,1633228,163455151635],1636"answer": " To distribute training for your custom Keras neural net in Ray on Databricks, you can follow these steps:\n\n1. Import Ray Train and Ray AIR Session: `import ray.train as train`\n2. Define a training function that accepts a configuration dict with hyperparameters.\n3. Use `ray.train.torch.prepare_model` to wrap the model with DistributedDataParallel and place it on the right device.\n4. Use `ray.train.torch.prepare_data_loader` to set up the DataLoader with DistributedSampler.\n5. Load the data shard for each worker with `session.get_dataset_shard(\"train\")`.\n6. Iterate over the dataset with `train_dataset.iter_torch_batches()`.\n\nHere's an example of how to define the `train_func_distributed` function:\n\n```python\nfrom ray import train\nimport tensorflow as tf # Your custom neural net model should be defined here\n\ndef train_func_distributed(config):\n num_epochs = config.get(\"num_epochs\")\n batch_size = config.get(\"batch_size\")\n\n # Load your custom neural net model\n model = YourModel()\n model = train.torch.prepare_model(model)\n\n # Set up the data loader\n dataset = get_dataset()\n dataloader = DataLoader(dataset, batch_size=batch_size)\n dataloader = train.torch.prepare_data_loader(dataloader)\n\n # Initialize the optimizer and other training components\n optimizer = ...\n lr_scheduler = ...\n\n # Prepare everything for distributed training\n (model, optimizer, train_dataloader, eval_dataloader, lr_scheduler) = accelerator.prepare(\n model, optimizer, train_dataloader, eval_dataloader, lr_scheduler\n )\n\n # Start training\n for epoch in range(num_epochs):\n for batch in train_dataloader:\n # Perform a training step\n ...\n\n # Report the results back to Ray Train\n ...\n```\n\nFinally, create a `TorchTrainer` instance and call the `fit` method to start the distributed training:\n\n```python\ntrainer = TorchTrainer(\n train_func_distributed,\n scaling_config=ScalingConfig(num_workers=4, use_gpu=use_gpu)\n)\n\nresults = trainer.fit()\n```\n\nMake sure to replace `YourModel` with your custom Keras neural net model and adjust the training code accordingly.",1637"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1638},1639{1640"question": "how to use ray.put and ray,get?",1641"sources": [1642"https://docs.ray.io/en/master/ray-core/api/core.html#objects",1643"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",1644"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",1645"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1646"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",1647"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",1648"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",1649"https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",1650"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"1651],1652"document_ids": [16534651,16545443,16554849,16564205,16574884,16585219,16594209,16604775,166148111662],1663"answer": " To use `ray.put` and `ray.get`, you need to understand their functionalities. `ray.put` stores an object in the Ray object store and returns its ID, which can be used to pass the object as an argument to any remote function or method call. `ray.get`, on the other hand, returns an object or list of objects from the object ID or list of object IDs. It's a synchronous (i.e., blocking) operation.\n\nHere's a simple example:\n\n```python\nimport ray\nray.init()\n\n# Store an object in the object store\nobject_id = ray.put(\"some object\")\n\n# Get the object from the object store\nobject = ray.get(object_id)\n```\n\nNote that calling `ray.get()` unnecessarily can harm performance. It's best practice to wait as long as possible before calling `ray.get()`, or even design your program to avoid having to call `ray.get()` at all.",1664"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1665},1666{1667"question": "how do I use Ray Data to pre process many files?",1668"sources": [1669"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",1670"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",1671"https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",1672"https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors",1673"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data",1674"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",1675"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1676"https://docs.ray.io/en/master/train/user-guides.html#ray-train-user-guides",1677"https://docs.ray.io/en/master/data/user-guide.html#user-guides"1678],1679"document_ids": [16809178,16819222,16826849,16836914,16849212,16856838,16866835,16878110,168869111689],1690"answer": " To preprocess many files using Ray Data, you can follow these steps:\n\n1. Use Ray Data to load and preprocess data for distributed machine learning training pipelines in a streaming fashion.\n2. If you are working with tabular data, use Ray Data preprocessors, which implement common data preprocessing operations.\n3. Apply the preprocessors on the dataset before passing the dataset into a Trainer.\n\nAdditionally, if your preprocessed Dataset is small enough to fit in Ray object store memory, you can materialize the preprocessed dataset in Ray\u2019s built-in object store by calling `materialize()` on the preprocessed dataset. This method computes the entire preprocessed dataset and pins it in the Ray object store memory, so when iterating over the dataset repeatedly, the preprocessing operations do not need to be re-run.\n\nRefer to the Ray Data documentation for more information on using Ray Data for preprocessing and ingest for ML training, as well as performance considerations.",1691"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1692},1693{1694"question": "can\u2019t pickle SSLContext objects",1695"sources": [1696"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1697"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1698"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",1699"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1700"https://docs.ray.io/en/master/ray-core/objects/serialization.html#known-issues",1701"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#tls-verification",1702"https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#checkpoints-are-py-version-specific-but-can-be-converted-to-be-version-independent",1703"https://docs.ray.io/en/master/serve/tutorials/java.html#calling-ray-serve-deployment-with-http",1704"https://docs.ray.io/en/master/ray-core/configure.html#step-3-set-the-environment-variables-for-both-ray-head-and-worker-to-enable-tls"1705],1706"document_ids": [17074953,17084952,17094939,17104957,17114958,17126592,171310551,171414578,171544001716],1717"answer": " The error message indicates that `SSLContext` objects cannot be pickled, which is causing the serialization to fail. This is not a specific issue with Ray, but rather a limitation of the pickle module in Python.\n\nTo troubleshoot this issue, you can use the `inspect_serializability` function from `ray.util` to identify the non-serializable object within the function or object instance.\n\nIn this case, it seems like the non-serializable object is an `SSLContext` object used for TLS verification in HTTPS client connections.\n\nIf you want to use TLS with Ray, you can set the appropriate environment variables for both the Ray head and worker to enable TLS, as described in the provided context. However, this may not solve the pickling issue with `SSLContext` objects.\n\nIf you need to serialize and deserialize `SSLContext` objects for some reason, you may need to explore alternative serialization formats that can handle these types of objects, such as cloudpickle or msgpack. However, this may require additional effort and may not be fully supported in Ray.",1718"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1719},1720{1721"question": "How do I install CRDs in Kuberay?",1722"sources": [1723"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",1724"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",1725"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",1726"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",1727"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-2-create-a-raycluster-cr",1728"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-3-install-a-kuberay-operator",1729"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-3-install-a-kuberay-operator",1730"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-2-install-the-kuberay-operator",1731"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-1-install-a-kuberay-cluster"1732],1733"document_ids": [17345720,17356103,17365817,17375647,17385981,17395774,17406057,17416044,174259801743],1744"answer": " To install CRDs in Kuberay, follow these steps:\n\n1. Add the Kuberay Helm repository:\nhelm repo add kuberay https://ray-project.github.io/kuberay-helm/\n\n2. Install the Kuberay operator and CRDs:\nhelm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n\nThe CRDs are installed as part of the Kuberay operator installation.",1745"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1746},1747{1748"question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",1749"sources": [1750"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1751"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1752"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",1753"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",1754"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",1755"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1756"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",1757"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging",1758"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text"1759],1760"document_ids": [17618107,17626788,176313697,1764140,17656955,17667955,17677380,1768590,176969981770],1771"answer": " The function for Ray data batch inference is named `__call__()` because it is a special method in Python classes that allows an instance of the class to be called like a function. In the context of Ray data batch inference, this method is defined in a callable class that performs inference on each batch of data. When the `map_batches()` function is called on a Ray data object, it applies this class's `__call__()` method to each batch of data, allowing for parallel and efficient batch inference.",1772"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1773},1774{1775"question": "How to disconnnect ray client?",1776"sources": [1777"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1778"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",1779"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",1780"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",1781"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",1782"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",1783"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1784"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",1785"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks"1786],1787"document_ids": [17886615,17896626,17906624,179114507,17926608,17934613,17944894,17956612,179643751797],1798"answer": " To disconnect a Ray client, you can call the `disconnect()` method on the client object. Here's an example:\n```python\ncli1.disconnect()\ncli2.disconnect()\n```\nNote that when the client disconnects, any object or actor references held by the server on behalf of the client are dropped. If the client disconnects unexpectedly, the client will attempt to reconnect to the server for a certain period of time before giving up. You can increase this time by setting the environment variable `RAY_CLIENT_RECONNECT_GRACE_PERIOD`.\n\nAlso, when using Ray multi-client, the client won't be disconnected automatically, so you need to call `disconnect()` explicitly to close the connection. Object references can only be used by the client from which it was obtained.",1799"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1800},1801{1802"question": "how to submit job with python with local files?",1803"sources": [1804"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1805"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job",1806"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1807"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",1808"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",1809"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",1810"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",1811"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#jobsubmissionclient",1812"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job"1813],1814"document_ids": [18156550,18164255,18176553,18186578,18196579,18206575,18216595,18226540,182365811824],1825"answer": " To submit a job with Python using local files, you can use the Ray Jobs API. Here's a step-by-step guide:\n\n1. Install Ray: `pip install ray[default]`\n2. Create a Python script (e.g., `my_script.py`) that you want to run in the job.\n3. Set up the runtime environment, including the working directory and required packages.\n4. Submit the job with the `ray job submit` command, specifying the script and runtime environment.\n\nHere's an example:\n\n```bash\n# Set up the runtime environment (replace '/path/to' with your actual paths)\nexport RAY_JOB_HEADERS='{\"working_dir\": \"/path/to/working_dir\", \"pip\": [\"my_package\"]}'\n\n# Submit the job\nray job submit -- python my_script.py\n```\n\nIn this example, the `my_script.py` file should be located in the specified working directory, and the required package `my_package` will be installed in the job's environment.\n\nMake sure to replace `/path/to/working_dir` and `my_package` with your actual paths and package names.",1826"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1827},1828{1829"question": "How do I do inference from a model trained by Ray tune.fit()?",1830"sources": [1831"https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",1832"https://docs.ray.io/en/master/tune/examples/includes/tune_basic_example.html#tune-basic-example",1833"https://docs.ray.io/en/master/data/batch_inference.html#using-models-from-ray-train",1834"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",1835"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",1836"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#configuring-the-search-space-and-starting-ray-tune",1837"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1838"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",1839"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning"1840],1841"document_ids": [1842986,18434111,18446962,18451080,1846117,18472767,1848234,1849127,185010901851],1852"answer": " To do inference from a model trained by Ray tune.fit(), you can use the Checkpoint returned by Ray Train to make predictions with the trained model. Here is an example of how to do this:\n\n1. Train a model with Ray Train by calling `tune.run()` and passing in an objective function that trains the model and reports the training progress to Ray Tune.\n2. Get the best checkpoint from the results of the Tune run by calling `tune.get_best_checkpoint()`.\n3. Load the trained model from the checkpoint by calling `train.get_checkpoint_to_restore()` and passing in the best checkpoint.\n4. Use the loaded model to make predictions by calling the model's `forward()` method and passing in the input data.\n\nHere is some example code that demonstrates this process:\n\n```python\nfrom ray import tune\nfrom ray.train import TrainConfig, Checkpoint\n\n# Train a model with Ray Train\nresults = tune.run(objective)\n\n# Get the best checkpoint\nbest_checkpoint = tune.get_best_checkpoint(results, metric=\"mean_loss\", mode=\"min\")\n\n# Load the trained model from the checkpoint\ncheckpoint_dir = best_checkpoint.get_best_result().get(\"artifacts\", {}).get(\"checkpoint\", \"\")\ntrain_config = TrainConfig(checkpoint_dir=checkpoint_dir)\nmodel = MyTrainedModel.from_checkpoint(train_config.checkpoint_dir)\n\n# Use the loaded model to make predictions\ninput_data = ...\npredictions = model.forward(input_data)\n```\n\nNote that this is just a general outline of the process, and the specific details will depend on the type of model you are using and how you have implemented your training and inference code.",1853"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1854},1855{1856"question": "is there a way to load and run inference without using pytorch or tensorflow directly?",1857"sources": [1858"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1859"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-2-inference-on-a-single-batch",1860"https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",1861"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",1862"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1863"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1864"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",1865"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",1866"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch"1867],1868"document_ids": [18696978,18707976,18716725,18728010,18736980,18746979,18758043,18766947,187779701878],1879"answer": " Yes, it is possible to load and run inference without using PyTorch or TensorFlow directly by using Ray Datasets, which allows you to perform scalable offline batch inference with Torch models by mapping a pre-trained model over your data. The example provided demonstrates how to do this using a ResNet18 model and a Ray Dataset. The Ray Dataset can be created from various sources and file formats, including in-memory Numpy arrays and S3 buckets. The preprocessing of data and model inference can be parallelized across multiple GPUs for faster performance.",1880"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1881},1882{1883"question": "what does ray do",1884"sources": [1885"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1886"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",1887"https://docs.ray.io/en/master/data/data.html#learn-more",1888"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",1889"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",1890"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",1891"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",1892"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",1893"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli"1894],1895"document_ids": [18964811,18975380,18986910,18996208,190014255,19016015,19025656,19035556,190446621905],1906"answer": " Ray is a scalable and flexible framework for building and running distributed applications. Here are some things Ray can do, based on the context provided:\n\n* It can be initialized using the `ray.init()` function.\n* There are tutorials and examples available to help you get started with Ray.\n* You can read the Ray blogs for more information and best practices.\n* Ray can be run on Cloud VMs for easy scaling and deployment.\n* It has a built-in serving system called `ray.serve`, which can be deployed using `ray.serve.deployment()`.\n* There is an open-source Ray project available at ray-project/ray#29753.\n* Ray can be run on Kubernetes for even more advanced scaling and deployment options.\n* It also has a Ray Core CLI for managing Ray applications.\n* You can use Ray Jobs for more information on running and managing distributed tasks and actors.",1907"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1908},1909{1910"question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",1911"sources": [1912"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",1913"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",1914"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",1915"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1916"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",1917"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",1918"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1919"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",1920"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus"1921],1922"document_ids": [192313457,19241025,19254999,192611062,19272966,192812049,192911068,19305116,193127301932],1933"answer": " If you specify a fractional GPU in the resource spec, and your program uses more than that allocated fraction, it may result in memory conflicts and errors. It's the user's responsibility to ensure that multiple trials can share GPUs and there is enough memory to do so. Ray does not automatically handle this for you.",1934"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1935},1936{1937"question": "how to pickle a variable defined in actor\u2019s init method",1938"sources": [1939"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1940"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",1941"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",1942"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",1943"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",1944"https://docs.ray.io/en/master/ray-core/actors.html#actors",1945"https://docs.ray.io/en/master/rllib/actors.html#actors",1946"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1947"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs"1948],1949"document_ids": [19505192,19515194,19524435,19534241,195414682,19554469,195611576,19575191,19585201959],1960"answer": " In the given context, it is not possible to pickle a variable defined in an actor's `__init__` method directly. However, you can pass objects to actor method calls either by value or by reference. If you want to use a mutable object across different actor instances, you should pass it by reference.\n\nHere's an example of passing an object by reference to an actor method:\n\n```python\n@ray.remote\nclass GlobalVarActor:\n def __init__(self):\n self.global_var = 0\n\n def set_global_var(self, value):\n self.global_var = value\n\n def get_global_var(self):\n return self.global_var\n\nglobal_var_actor = GlobalVarActor.remote()\nray.get(global_var_actor.set_global_var.remote(4))\n\n@ray.remote\nclass Actor:\n def __init__(self, global_var_actor):\n self.global_var_actor = global_var_actor\n\n def f(self):\n return ray.get(self.global_var_actor.get_global_var.remote()) + 3\n\nactor = Actor.remote(global_var_actor)\n\n# This returns 7 correctly.\nassert ray.get(actor.f.remote()) == 7\n```\n\nIn this example, we pass the `global_var_actor` object by reference to the `Actor` constructor. This allows the `Actor` instance to access and modify the `global_var` attribute of the `GlobalVarActor` instance.",1961"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1962},1963{1964"question": "how do I do an all_reduce operation among a list of actors",1965"sources": [1966"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",1967"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",1968"https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",1969"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1970"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",1971"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#collective-communication",1972"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayFSDPStrategy.reduce.html#ray-train-lightning-rayfsdpstrategy-reduce",1973"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",1974"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile"1975],1976"document_ids": [1977788,197814443,1979659,19806787,1981673,198214415,19838854,19848101,19856411986],1987"answer": " To perform an all\\_reduce operation among a list of actors in Ray, you can use the `ray.util.collective.allreduce()` function. However, this function is not explicitly mentioned in the provided context. Therefore, I'll describe how to use it based on related functions and concepts.\n\nFirst, you need to define a collective group for the actors. In the provided context, a `Worker` class is defined with a `compute()` method that includes an allreduce operation. You can create actors from this class and add them to a collective group.\n\nHere's an example of how to create a collective group and perform an allreduce operation:\n\n```python\nfrom ray import put, get, worker\n\n# Create two actors A and B and create a collective group\nA = Worker.remote()\nB = Worker.remote()\n\n# Add actors to a collective group\ngroup = worker.get_current_group_membership()\ngroup.add(A)\ngroup.add(B)\n\n# Perform allreduce on a tensor\ntensor_a = put(get(A.buffer.remote()))\ntensor_b = put(get(B.buffer.remote()))\n\nreduced_tensor = ray.util.collective.allreduce(tensor_a, group=group)\n\n# Print the reduced tensor\nprint(reduced_tensor)\n```\n\nThis example assumes that the `Worker` class and the `buffer` attribute are defined as in the provided context. The `put()` function is used to wrap the tensors in a Ray object, and `get()` is used to retrieve the tensor data. The `allreduce()` function is then called with the tensor and the collective group as arguments.\n\nKeep in mind that the `ray.util.collective.allreduce()` function is not explicitly mentioned in the provided context, so the above example might not work as-is in your use case. You might need to modify it based on your specific requirements and the available functions in your Ray version.",1988"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1989},1990{1991"question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",1992"sources": [1993"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",1994"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",1995"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#bundles",1996"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",1997"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",1998"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1999"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",2000"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",2001"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group"2002],2003"document_ids": [20044790,20055063,20065050,20075051,200812053,200911075,20105076,20115060,201247892013],2014"answer": " Specifying a bundle with `{\"CPU\":0}` in the PlacementGroup will not reserve any CPU resources for that group, as the value 0 indicates no CPU resources are required. However, if `_max_cpu_fraction_per_node` is set to a value less than 1.0 in the PlacementGroup, it would still consider the CPU resources reserved for the bundle with `{\"CPU\": 0}`, and it might prevent other bundles from being placed on the same node, depending on the value of `_max_cpu_fraction_per_node`.",2015"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2016},2017{2018"question": "How to cancel job from UI?",2019"sources": [2020"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",2021"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",2022"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",2023"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2024"https://docs.ray.io/en/master/workflows/comparison.html#trip-booking",2025"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",2026"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",2027"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2028"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks"2029],2030"document_ids": [20314550,20324375,20336559,20344894,20359757,20366710,20379791,203811590,203944922040],2041"answer": " To cancel a job from the UI, you can use the `ray job stop` command followed by the job ID. For example, `ray job stop raysubmit_tUAuCKubPAEXh6CW`. If you want to cancel a task or actor task, you can call `ray.cancel()` on the returned ObjectRef.",2042"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2043},2044{2045"question": "how do I get my project files on the cluster when using Ray Serve? My workflow is to call `serve deploy config.yaml --address <remote dashboard agent address>`",2046"sources": [2047"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",2048"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2049"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",2050"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2051"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#development-workflow-with-serve-run",2052"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",2053"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",2054"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",2055"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy"2056],2057"document_ids": [205813680,20596562,206013677,20616552,206213659,206313719,20646577,206513831,2066139872067],2068"answer": " To get your project files on the cluster when using Ray Serve, you can use the `serve run` command with the `--address` and `--working-dir` flags. Set the `--address` flag to the Ray cluster's head node IP address and the `--working-dir` flag to the local directory containing your project files. This command will connect to the remote cluster, upload the working directory, and run your Ray Serve application.\n\nExample command:\n```bash\nserve run --address=ray://<head-node-ip-address>:10001 --working-dir=\"./project/src\" local_dev:graph\n```\nReplace `<head-node-ip-address>` with your remote cluster head node IP address. The local directory specified by `--working-dir` must contain the necessary files for your Ray Serve application.",2069"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2070},2071{2072"question": "how do i install ray nightly wheel",2073"sources": [2074"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",2075"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2076"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2077"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2078"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",2079"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",2080"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",2081"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",2082"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray"2083],2084"document_ids": [208514545,2086288,208763,20886335,208966,2090291,209114551,2092883,209354122094],2095"answer": " To install the Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use the `pip install` command with the appropriate link to the nightly wheel. The links for the nightly wheels can be found in the context provided, in the sections for your specific operating system and Python version. For example, if you are using Linux and Python 3.9, you can use the following command to install the nightly wheel with support for the dashboard and cluster launcher:\n\n```\npip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"\n```\n\nBe sure to replace `LINK_TO_WHEEL.whl` with the actual link to the nightly wheel for your specific configuration.",2096"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2097},2098{2099"question": "how do i install the latest ray nightly wheel?",2100"sources": [2101"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2102"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",2103"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2104"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2105"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",2106"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",2107"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",2108"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",2109"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels"2110],2111"document_ids": [2112288,211314545,211463,21156335,2116291,211714551,211866,211914541,21202862121],2122"answer": " To install the latest Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use pip to install the nightly wheel with support for the dashboard and cluster launcher by using the command `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`, where `LINK_TO_WHEEL.whl` is the link to the nightly wheel file from the Ray website. Note that the specific link will depend on your operating system and Python version.",2123"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2124},2125{2126"question": "how can I write unit tests for Ray code?",2127"sources": [2128"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",2129"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",2130"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2131"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",2132"https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",2133"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",2134"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2135"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",2136"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users"2137],2138"document_ids": [21395397,21405395,21414811,21424190,21434338,214414561,21455380,21466621,214742042148],2149"answer": " To write unit tests for Ray code, you can follow these tips:\n\n1. Initialize Ray with a fixed number of resources using `ray.init(num_cpus=...)`.\n2. Share the Ray cluster across tests if possible.\n3. Create a mini-cluster with `ray.cluster_utils.Cluster`.\n4. Be cautious when running tests in parallel.\n5. Use `ray.init()` and `ray.shutdown()` to start and stop the Ray context.\n6. When testing with Ray Serve, use `Serve.getDeployment(\"deployment_name\")` to get the deployment handle.\n7. For normal Ray code, use the `.remote()` function to execute tasks in a different process.\n\nHere's an example of how to write a unit test for Ray code:\n\n```python\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n def setUp(self):\n ray.init(num_cpus=4, num_gpus=0)\n\n def tearDown(self):\n ray.shutdown()\n\n def test_my_ray_code(self):\n # Initialize your Ray objects here\n my_object = MyRayObject.remote(arg1, arg2)\n\n # Run your tests here\n result = ray.get(my_object.some_method.remote(arg3))\n self.assertEqual(result, expected_result)\n```\n\nReplace `MyRayObject`, `some_method`, `arg1`, `arg2`, `arg3`, and `expected_result` with your actual Ray objects and test parameters.",2150"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2151},2152{2153"question": "How I stop Ray from spamming lots of Info updates on stdout?",2154"sources": [2155"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",2156"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#disable-logging-to-the-driver",2157"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#distributed-progress-bars-tqdm",2158"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",2159"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2160"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",2161"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",2162"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",2163"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay"2164],2165"document_ids": [21666558,2167523,2168529,2169524,21704209,2171519,2172410,21735128,2174139292175],2176"answer": " To stop Ray from spamming lots of Info updates on stdout, you can try the following steps:\n\n1. Disable logging to the driver by setting `log_to_driver=False` in `ray.init`. This will prevent task and actor logs from being copied to the driver's stdout.\n2. You can also try setting `RAY_TQDM_PATCH_PRINT=0` to disable the patching of the built-in `print` function, which may improve performance if there are more than a couple thousand updates per second.\n3. Ray deduplicates logs that appear redundantly across multiple processes by default. You can adjust the buffering time for subsequent log messages by changing the relevant configuration settings.\n4. If you are using `ray.get()` to retrieve object references, it may result in spamming of stdout. In such cases, you can try using asyncio or other concurrency primitives to avoid blocking the event loop.\n5. If none of the above steps work, you can try stopping the job using the command `ray job stop raysubmit_tUAuCKubPAEXh6CW` and then re-submit the job with the necessary configuration changes.\n\nRefer to the provided context for more details on each step.",2177"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2178},2179{2180"question": "how to deploy stable diffusion 2.1 with Ray Serve?",2181"sources": [2182"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",2183"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",2184"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#serve-a-stablediffusion-text-to-image-model-on-kubernetes",2185"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-7-submit-a-request-to-the-ray-serve-application",2186"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",2187"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#code-updates",2188"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",2189"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-5-send-a-request-to-the-text-to-image-model",2190"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"2191],2192"document_ids": [21936127,219414409,21956124,21965989,21976128,219813692,21995671,22006130,2201136222202],2203"answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install Ray and Ray Serve: `pip install \"ray[serve]\"`\n2. Prepare your Ray Serve application code (e.g., `stable_diffusion.py`)\n3. Download the RayService configuration: `curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml`\n4. Create a RayService: `kubectl apply -f ray-service.stable-diffusion.yaml`. This configuration should include the necessary settings, such as tolerations and runtime environment.\n5. Send a request to the Stable Diffusion model by following steps 5.1-5.3 in the context, which include downloading `stable_diffusion_req.py`, setting your prompt, and sending a request to the model.\n\nNote: Before deploying to a RayService, test your Ray Serve script locally or in a RayCluster. Also, consider the best practice for large-scale code updates mentioned in the warning.",2204"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2205},2206{2207"question": "what is actor_handle?",2208"sources": [2209"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",2210"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",2211"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",2212"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2213"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2214"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",2215"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",2216"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",2217"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors"2218],2219"document_ids": [222011587,222114698,22224487,22234860,22244615,222511588,22264489,222714699,222842002229],2230"answer": " Actor handle is a reference to an actor object that can be passed into other tasks or remote functions. It allows you to call methods on the actor and access its state. In the provided context, `counter` is an actor handle that is passed to tasks `f.remote()` and `foo()` in Python and Java respectively.",2231"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2232},2233{2234"question": "how to kill a r detached actors?",2235"sources": [2236"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",2237"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2238"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",2239"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2240"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2241"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",2242"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2243"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2244"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance"2245],2246"document_ids": [22474633,22484859,22492157,22504615,22514613,22524579,22534848,22544620,225545612256],2257"answer": " To kill a detached actor in Ray, you can use the `ray.kill()` function, passing in the actor's handle as an argument. This will immediately terminate the actor, causing any current, pending, and future tasks to fail with a `RayActorError`. Here's an example:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nactor_handle = Actor.remote()\n\nray.kill(actor_handle)\n```\nNote that this will not go through the normal teardown logic, so any exit handlers installed in the actor using `std::atexit` or `atexit` will not be called. If you would like Ray to automatically restart the actor, make sure to set a nonzero `max_restarts` in the `@ray.remote` options for the actor, then pass the `flag no_restart=False` to `ray.kill()`.\n\nFor named and detached actors, calling `ray.kill()` on an actor handle destroys the actor and allows the name to be reused. A remote actor will be terminated when all actor handles to it in Python are deleted, which will cause them to complete any outstanding work and then shut down. If you want to kill actors immediately, you can also call `ray.kill(actor)`.\n\nGarbage collection for actors has not been implemented yet, so this is currently the only way to terminate an actor gracefully. The object ref resulting from the task can be waited on to wait for the actor to exit. Note that this method of termination waits until any previously submitted tasks finish executing and then exits the process gracefully with `sys.exit()`.",2258"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2259},2260{2261"question": "How to force upgrade the pip package in the runtime environment if an old version exists?",2262"sources": [2263"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2264"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2265"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2266"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",2267"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2268"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",2269"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2270"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",2271"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"2272],2273"document_ids": [22744329,22754717,22764327,22774260,22784330,22794267,22804328,22814268,228242832283],2284"answer": " To force upgrade a pip package in the runtime environment if an old version exists, you can specify the pip version in the runtime environment with a \"==\" followed by the desired version number. However, this will not automatically upgrade the package if it already exists in the environment.\n\nIf you want to ensure that the package is upgraded to the specified version, you can add the \"--force-reinstall\" option to the pip install command in the runtime environment. Here's an example:\n```python\nRuntimeEnv(\n pip={\n \"packages\": [\n \"tensorflow\",\n \"requests\",\n \"certifi==2021.10.8;python_version=='3.8.11'\",\n ],\n \"pip_check\": False,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\",\n \"command_hooks\": [\n {\n \"hook\": \"pip_install\",\n \"args\": [\"--force-reinstall\"],\n }\n ],\n }\n)\n```\nIn this example, the \"command\\_hooks\" field is used to specify a hook that will be run before installing the pip packages. The \"pip\\_install\" hook is built-in and will run the pip install command with the specified arguments. In this case, the \"--force-reinstall\" argument will force pip to reinstall the package even if it already exists in the environment, ensuring that it is upgraded to the specified version.",2285"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2286},2287{2288"question": "How do I do global shuffle with Ray?",2289"sources": [2290"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",2291"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",2292"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2293"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2294"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2295"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2296"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",2297"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",2298"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle"2299],2300"document_ids": [23017395,23026795,23036827,23049209,23056826,23069206,23076876,23086864,230968252310],2311"answer": " To do a global shuffle with Ray, you can use the `random_shuffle()` function on a Ray dataset. Here is an example:\n```\nimport ray\n\n# Create a Ray dataset\nds = ray.data.range(1000)\n\n# Do a global shuffle of all rows in this dataset\nds = ds.random_shuffle()\n```\nThis will shuffle all the rows in the dataset, so that the order of the rows is random. Note that this can be a slow operation for large datasets. If you want to improve the performance of the shuffle, you can try iterating over batches of rows with shuffling, or setting the `DataContext.use_push_based_shuffle` flag to use a push-based shuffle algorithm.\n\nFor more information on how to optimize shuffling, and which approach to choose, see the [Optimize shuffling guide](https://docs.ray.io/en/latest/data/optimize-shuffling.html).",2312"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2313},2314{2315"question": "How to find namespace of an Actor?",2316"sources": [2317"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",2318"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2319"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2320"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",2321"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2322"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",2323"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace",2324"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2325"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces"2326],2327"document_ids": [23284521,23294623,23304504,23314871,23324626,23334528,23344751,23354625,233644992337],2338"answer": " To find the namespace of an Actor in Ray, you can use the `namespace` argument in the `ray.get_actor()` function. If the namespace is not specified, it will default to the current namespace. Here is an example:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\n# Create an actor with a name in a specified namespace\ncounter = Actor.options(name=\"some_name\", namespace=\"my_namespace\").remote()\n\n# Retrieve the actor later somewhere, specifying the namespace\ncounter = ray.get_actor(\"some_name\", namespace=\"my_namespace\")\n```\n\nIf you don't know the namespace of the actor, you can list all the actors in all namespaces using the Ray dashboard or the Ray CLI. The namespace of each actor will be listed next to its name.\n\nNote that if an actor is created without specifying a namespace, it will be placed in an anonymous namespace, which means it will not be accessible from other namespaces.",2339"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2340},2341{2342"question": "How does Ray work with async.io ?",2343"sources": [2344"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",2345"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",2346"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",2347"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2348"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",2349"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",2350"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",2351"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",2352"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users"2353],2354"document_ids": [23554598,23564603,23574599,23584208,23595443,23604360,23614636,23624611,236342052364],2365"answer": " Ray can work with async.io by using the async/await syntax and integrating with popular async frameworks like aiohttp and aioredis. You can define async methods in Ray actors, and Ray will automatically detect whether an actor supports async calls or not. However, Ray does not support asyncio for remote tasks, but you can wrap the async function with a synchronous wrapper to run the task. The `.remote()` postfix is used for every remote function, remote class declaration, or invocation of a remote class method, and it makes the operation asynchronous. `ray.put()` stores an object in the object store and returns its ID, while `ray.get()` returns an object or list of objects from the object ID or list of object IDs. `ray.wait()` returns two lists of object references, one with finished tasks we\u2019re waiting for and one with unfinished tasks.",2366"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2367},2368{2369"question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",2370"sources": [2371"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",2372"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2373"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",2374"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",2375"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",2376"https://docs.ray.io/en/master/ray-core/patterns/ray-get-too-many-objects.html#anti-pattern-fetching-too-many-objects-at-once-with-ray-get-causes-failure",2377"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",2378"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#stepping-between-ray-tasks",2379"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get"2380],2381"document_ids": [23825235,23834209,23844416,23855236,23865219,23875169,23884361,2389581,239042142391],2392"answer": " To debug a hanging `ray.get()` call, you should avoid calling `ray.get()` in a loop, as it is a blocking call that waits until the requested result is available. This can harm parallelism, as it makes the loop wait for the previous function call to finish before spawning the next call. Instead, you should separate the call to `ray.get()` from the call to the remote functions, so that all remote functions are spawned before waiting for the results. This way, they can run in parallel in the background.\n\nIn your specific case, you can try passing a list of object references to `ray.get()` instead of calling it one by one, to wait for all of the tasks to finish. This can help you avoid unnecessary calls to `ray.get()` for intermediate steps and improve the performance of your Ray program.",2393"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2394},2395{2396"question": "can you show me an example of ray.actor.exit_actor()",2397"sources": [2398"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2399"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2400"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2401"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2402"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",2403"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2404"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2405"https://docs.ray.io/en/master/ray-core/api/core.html#actors",2406"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.AsyncioActorExit.html#ray-exceptions-asyncioactorexit"2407],2408"document_ids": [24094618,24104613,24114620,24124619,24134579,24144859,24154615,24164650,241747812418],2419"answer": " The `ray.actor.exit_actor()` function is used to manually terminate a Ray actor from within one of the actor methods. This will kill the actor process and release resources associated with the actor. Here is an example of how to use it:\n\n```python\n@ray.remote\nclass Actor:\n def exit(self):\n ray.actor.exit_actor()\n\nactor = Actor.remote()\nactor.exit.remote()\n```\n\nNote that manual termination of actors is not usually necessary, as actors are automatically garbage collected. The object ref resulting from the task can be waited on to wait for the actor to exit.\n\nAlternatively, you can use `ray.kill(actor_handle)` to terminate an actor forcefully. This should be reserved for cases where an actor is unexpectedly hanging or leaking resources, and for detached actors, which must be manually destroyed.\n\nHere is an example of how to use `ray.kill()`:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nactor_handle = Actor.remote()\n\nray.kill(actor_handle)\n```\n\nNote that this method of termination waits until any previously submitted tasks finish executing and then exits the process gracefully with `sys.exit()`. You can see the actor is dead as a result of the user's `exit_actor()` call by running `ray list actors --detail`.",2420"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2421},2422{2423"question": "how to add log inside actor?",2424"sources": [2425"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",2426"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",2427"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",2428"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2429"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",2430"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2431"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2432"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",2433"https://docs.ray.io/en/master/ray-observability/getting-started.html#actors-view"2434],2435"document_ids": [2436414,2437540,2438520,243914324,244014202,244114286,244214295,244314106,24444342445],2446"answer": " To add logging inside an actor, you can define a log method within the actor class that uses the Python logging library to log messages. Here's an example:\n```python\nimport logging\nimport ray\n\n@ray.remote\nclass MyActor:\n def __init__(self):\n self.logger = logging.getLogger(__name__)\n\n def log(self, msg):\n self.logger.info(msg)\n\n def do_something(self):\n # some code here\n self.log(\"Log message from actor\")\n```\nBy default, all stdout and stderr of Tasks and Actors are redirected to the Driver output. If you want to disable this feature and redirect the logs to a file or another output, you can view the Configuring Logging documentation.\n\nAdditionally, you can customize the prefixes for Actor logs by defining the `__repr__` method for the Actor class to replace the Actor name with a custom string, such as the Actor index. This can be useful for distinguishing between log messages from different Actors.",2447"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2448},2449{2450"question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",2451"sources": [2452"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2453"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt",2454"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",2455"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",2456"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2457"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",2458"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#next-steps",2459"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#next-steps",2460"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"2461],2462"document_ids": [24636999,246410470,246510259,246614582,24676998,24686843,246910219,247010137,247169382472],2473"answer": " Sure, here's a script that performs batch inference with GPT-2 on text data from an S3 bucket using Ray and the Hugging Face Transformers library:\n```python\nimport os\nimport ray\nfrom transformers import GPT2Model, GPT2Tokenizer\n\n# Download the GPT-2 model and tokenizer from Hugging Face\nmodel_name = \"gpt2\"\ntokenizer = GPT2Tokenizer.from_pretrained(model_name)\nmodel = GPT2Model.from_pretrained(model_name)\n\n# Set up Ray\nray.init()\n\n# Read text data from S3 bucket\ns3_bucket = \"your-s3-bucket\"\ns3_key = \"path/to/your/text/data\"\nds = ray.data.read_text(f\"s3://{s3_bucket}/{s3_key}\").map_batches(batch_size=1024)\n\n# Define a function to perform inference on a batch of text data\ndef infer_batch(batch):\n inputs = tokenizer(batch[\"text\"], padding=True, truncation=True, return_tensors=\"pt\")\n input_ids = inputs[\"input_ids\"]\n attention_mask = inputs[\"attention_mask\"]\n outputs = model(input_ids, attention_mask=attention_mask)\n generated_text = model.generate(input_ids, attention_mask=attention_mask, max_length=20, num_return_sequences=1)\n batch[\"output\"] = [sequences[0].tolist() for sequences in generated_text]\n return batch\n\n# Apply the inference function to each batch of text data\nds = ds.map_batches(infer_batch)\n\n# Write the inference results to S3 bucket\noutput_s3_bucket = \"your-output-s3-bucket\"\noutput_s3_key = \"path/to/your/output/data\"\nds.write_text(f\"s3://{output_s3_bucket}/{output_s3_key}\")\n```\nThis script reads text data from an S3 bucket, applies GPT-2 inference to each batch of text data, and writes the inference results to another S3 bucket. You can adjust the batch size and other parameters to fit your specific use case. Note that this script assumes that you have already set up Ray and have the necessary AWS credentials to access the S3 bucket.",2474"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2475},2476{2477"question": "How do I enable Ray debug logs?",2478"sources": [2479"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2480"https://docs.ray.io/en/master/ray-observability/user-guides/index.html#user-guides",2481"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",2482"https://docs.ray.io/en/master/cluster/cli.html#ray-start",2483"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#debugger-commands",2484"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-debug",2485"https://docs.ray.io/en/master/ray-contribute/debugging.html#debugging-for-ray-developers",2486"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",2487"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging"2488],2489"document_ids": [2490680,2491546,2492510,24935611,2494579,24954669,2496944,2497573,249843232499],2500"answer": " To enable Ray debug logs, you can use the `--log-style` flag with the `ray` command-line interface (CLI) and set it to 'pretty' or 'record' to specify the log style. For example:\n\n```\nray start --log-style pretty\n```\n\nAlternatively, you can set the `RAY_LOG_STYLE` environment variable to 'pretty' or 'record' to configure the log style. For example:\n\n```\nexport RAY_LOG_STYLE=pretty\nray start\n```\n\nIf you want to enable runtime environment (runtime\\_env) debugging log streaming, you can set the `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED` environment variable to 1 on each node before starting Ray. For example, you can use the `setup_commands` field in the Ray Cluster configuration file to set this environment variable. This will print the full runtime\\_env setup log messages to the driver (the script that calls `ray.init()`).\n\nFor more information about configuring Ray's logging system, you can refer to the \"Configuring Logging\" guide in the User Guides section of the Ray documentation.",2501"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2502},2503{2504"question": "How do I list the current Ray actors from python?",2505"sources": [2506"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2507"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",2508"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",2509"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2510"https://docs.ray.io/en/master/ray-observability/key-concepts.html#ray-states",2511"https://docs.ray.io/en/master/rllib/actors.html#actors",2512"https://docs.ray.io/en/master/ray-core/actors.html#actors",2513"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",2514"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"2515],2516"document_ids": [2517472,2518788,2519673,2520475,2521402,252211575,25234467,252414680,252546232526],2527"answer": " To list the current Ray actors from Python, you can use the `ray.util.state.list_actors()` function, which is part of the Ray internal developer API. Here is an example of how to use this function:\n\n```python\nimport ray\nfrom ray.util.state import list_actors\n\n# Start Ray\nray.init()\n\n# List actors\nactors = list_actors()\n\n# Print actor information\nfor actor in actors:\n print(actor)\n```\n\nAlternatively, you can use the `ray list actors` command in the Ray CLI to list the actors from the command line.\n\nFor more information and options for listing actors, you can refer to the Ray documentation on listing actors: <https://docs.ray.io/en/latest/cluster/command-guide.html#list-actors>.",2528"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2529},2530{2531"question": "I want to kill the replica actor from Python. how do I do it?",2532"sources": [2533"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",2534"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2535"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2536"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2537"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2538"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2539"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2540"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",2541"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure"2542],2543"document_ids": [254413912,25454859,25464848,25474620,25484613,25494860,25504618,25514579,2552139162553],2554"answer": " To kill a replica actor in Ray from Python, you can use the `ray.kill()` function, providing the actor handle as an argument. Here is an example:\n```\nimport ray\n\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nThis will forcefully terminate the actor, interrupting any running tasks and preventing it from being restarted. If you want to allow the actor to be restarted, you can set the `no_restart` parameter to `False`.\n\nKeep in mind that the actor will not be terminated immediately if it has any pending tasks. To allow the actor to complete its current tasks before terminating, you can call its `__ray_terminate__` method instead of using `ray.kill()`. This will queue a termination task that will be executed after the current tasks have finished.\n\nFor more information, you can refer to the Ray documentation on killing actors.",2555"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2556},2557{2558"question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",2559"sources": [2560"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",2561"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#accelerator-types",2562"https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",2563"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",2564"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",2565"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",2566"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-workload-scheduling",2567"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",2568"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options"2569],2570"document_ids": [25714994,25725006,25734366,25746590,25754996,25764997,25776017,25784697,257947022580],2581"answer": " To specify that a task should run on a V100 GPU, you can use the `@ray.remote(num_gpus=1, accelerator_type=\"NVIDIA_TESLA_V100\")` decorator when defining the task. This will tell Ray to schedule the task to a node that has a free Tesla V100 GPU and assign it to the task.\n\nHere is an example:\n```\n@ray.remote(num_gpus=1, accelerator_type=\"NVIDIA_TESLA_V100\")\ndef train(data):\n return \"This function was run on a node with a Tesla V100 GPU\"\n\nray.get(train.remote(1))\n```\nYou can also use the `RemoteFunction.options` method to override the default resource requirements for a task. For example:\n```\ntrain_options = {\"num_gpus\": 1, \"accelerator_type\": \"NVIDIA_TESLA_V100\"}\ntrain_remote = train.options(**train_options)\nray.get(train_remote.remote(1))\n```\nNote that the `num_gpus` and `accelerator_type` arguments only refer to logical resources used for scheduling purposes. The actual GPU utilization is not controlled or limited by Ray. It is up to the task to make use of the assigned GPU.\n\nAlso note that the available accelerator types include Nvidia GPUs, and the `NVIDIA_TESLA_V100` accelerator type is automatically detected by Ray.",2582"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2583},2584{2585"question": "How do I get started?",2586"sources": [2587"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",2588"https://docs.ray.io/en/master/ray-overview/getting-started.html#getting-started",2589"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-autoscaler-private-fake-multi-node-test-utils-dockercluster",2590"https://docs.ray.io/en/master/serve/index.html#learn-more",2591"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",2592"https://docs.ray.io/en/master/tune/faq.html#how-can-i-get-started-contributing-to-tune",2593"https://docs.ray.io/en/master/serve/advanced-guides/index.html#advanced-guides",2594"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",2595"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-1-create-a-kind-cluster"2596],2597"document_ids": [25986482,2599220,2600864,260113625,26024483,26031049,260413827,26054185,260660432607],2608"answer": " To get started with Ray, first install it using pip: `pip install -U ray`. Then, import and initialize Ray in your application with `import ray` and `ray.init()`. If you're new to Ray Serve, start with the Ray Serve Quickstart. If you're looking to contribute to Tune, check out the issues labeled \"good first issue\" and \"help wanted\" on the Tune Github project board. For starting Ray on a cluster, follow the instructions on the Starting Ray page after installing Ray. To create a Kind cluster, use the command `kind create cluster`.",2609"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2610},2611{2612"question": "How to specify python version in runtime_env?",2613"sources": [2614"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2615"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2616"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2617"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",2618"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2619"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",2620"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",2621"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2622"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv"2623],2624"document_ids": [26254330,26264717,26274329,26284661,262913856,26304253,26314327,26324283,263347122634],2635"answer": " To specify the Python version in runtime\\_env, you can use the `conda` key in the runtime\\_env dictionary to create a new conda environment with a specific Python version. Here is an example:\n```python\nruntime_env = {\n \"conda\": {\n \"name\": \"my_env\",\n \"dependencies\": {\n \"python\": \"3.9\",\n \"pip\": [\"my_package\"]\n }\n }\n}\n```\nIn this example, a new conda environment named \"my\\_env\" will be created with Python 3.9 and the package \"my\\_package\" installed using pip. Note that the `conda` key cannot be used with the `pip` key in the same runtime\\_env dictionary, so you cannot specify both pip packages and a Python version using the conda environment. If you need to use both pip and conda packages, you can create a conda environment with the desired Python version and specify the pip packages in the `pip` key of the runtime\\_env dictionary.",2636"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2637},2638{2639"question": "how to create a Actor in a namespace?",2640"sources": [2641"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",2642"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2643"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2644"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2645"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2646"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2647"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2648"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2649"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces"2650],2651"document_ids": [26524521,26534626,26544625,26554623,26564513,26574504,26584509,26594499,266045162661],2662"answer": " To create an actor in a specific namespace, you can specify the namespace while creating the actor using the `options` function and the `name` and `namespace` parameters. Here's an example in Python:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nctx = ray.init(\"ray://localhost:10001\")\n\n# Create an actor with a specified namespace.\nActor.options(name=\"my_actor\", namespace=\"actor_namespace\", lifetime=\"detached\").remote()\n```\nIn this example, an actor named \"my\\_actor\" is created in the \"actor\\_namespace\" namespace. Note that the current job's namespace does not affect the created actor's namespace.",2663"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2664},2665{2666"question": "Can I specify multiple working directories?",2667"sources": [2668"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",2669"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2670"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",2671"https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.PathPartitionParser.html#ray-data-datasource-pathpartitionparser",2672"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2673"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",2674"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",2675"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",2676"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function"2677],2678"document_ids": [26791775,26804277,26814301,26827297,26834273,268410170,26851790,268610169,268710662688],2689"answer": " No, you cannot specify multiple working directories in Ray. However, you can disable the behavior of changing the working directory to the trial directory by setting the RAY\\_CHDIR\\_TO\\_TRIAL\\_DIR=0 environment variable. This will allow access to files in the original working directory, but you should use relative paths for read-only purposes and ensure the directory is synced on all nodes if running on multiple machines. Alternatively, you can use the TUNE\\_ORIG\\_WORKING\\_DIR environment variable, but it is deprecated and may be removed in the future.",2690"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2691},2692{2693"question": "what if I set num_cpus=0 for tasks",2694"sources": [2695"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2696"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",2697"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",2698"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2699"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",2700"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#code-example",2701"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2702"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",2703"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.entrypoint_num_cpus.html#ray-job-submission-jobinfo-entrypoint-num-cpus"2704],2705"document_ids": [270611063,2707623,27085936,27094850,27106716,27115234,27124853,27134877,271467032715],2716"answer": " If you set `num_cpus=0` for tasks, your policies will be built solely on the CPU, even if GPUs are available on the machine. However, if your tasks are very small or have variable duration, you may not see speedups. Additionally, if your tasks require a significant amount of memory, you may need to allocate more memory resources to prevent out-of-memory errors. The `num_cpus` parameter specifies the quantity of CPU cores to reserve for a task or actor, and can be specified as an integer or float. It is optional for the entrypoint command of a Ray job.",2717"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2718},2719{2720"question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",2721"sources": [2722"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",2723"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",2724"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",2725"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",2726"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",2727"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",2728"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",2729"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",2730"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice"2731],2732"document_ids": [27335953,27345818,27355752,27365969,273713957,27385963,27395656,27405642,274157422742],2743"answer": " Yes, it is possible to have Ray on K8s without using KubeRay and still have autoscaling enabled. You can use Kubernetes' built-in autoscaling features, such as the Kubernetes Cluster Autoscaler for EKS or AutoPilot for GKE. However, this would require manual configuration of the Ray autoscaler settings within the RayCluster custom resource (CR) config.",2744"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2745},2746{2747"question": "how to manually configure and manage Ray cluster on Kubernetes",2748"sources": [2749"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",2750"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",2751"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",2752"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",2753"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",2754"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",2755"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gke-gcs-bucket.html#create-a-minimal-raycluster-yaml-manifest",2756"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",2757"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart"2758],2759"document_ids": [27605717,27615918,27625917,27635656,27645642,27655605,27666034,27675953,276859582769],2770"answer": " To manually configure and manage a Ray cluster on Kubernetes, you can follow these steps:\n\n1. Install `kubectl` and Helm, and prepare a Kubernetes cluster.\n2. Deploy a KubeRay operator on the cluster.\n3. Create a `RayCluster` custom resource (CR) to define the desired state of a Ray cluster. The CR should include the Ray version, enable in-tree autoscaling, and any other desired configurations.\n4. Use `kubectl apply -f raycluster.yaml` to deploy the Ray cluster based on the CR.\n5. Use the KubeRay autoscaling feature to automatically scale the cluster up and down based on resource demand.\n\nYou can find detailed instructions and examples in the RayCluster Quickstart guide and the RayCluster Configuration guide.\n\nAdditionally, you can use the Cluster Management CLI to manage Ray clusters, and the KubeRay Autoscaling guide to configure the Ray autoscaler on Kubernetes.",2771"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2772},2773{2774"question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",2775"sources": [2776"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",2777"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",2778"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",2779"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",2780"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options",2781"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",2782"https://docs.ray.io/en/master/serve/api/doc/ray.serve.shutdown.html#ray-serve-shutdown",2783"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",2784"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling"2785],2786"document_ids": [27875129,27884524,27896216,27904519,279114500,27926390,279314043,279414507,2795134482796],2797"answer": " Shutting down a raylet will not directly kill tasks and workers on that node. However, it may mark the node as dead and trigger a restart of services, which could kill currently working jobs. If you want to avoid this, you can pass --no-restart to the update call. Note that this action is different from ray.shutdown(), which is used to shut down the entire Ray cluster, and ray.serve.shutdown(), which is used to shut down Ray Serve on the cluster.",2798"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2799},2800{2801"question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",2802"sources": [2803"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",2804"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",2805"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",2806"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management",2807"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#what-s-the-out-of-memory-error",2808"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",2809"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",2810"https://docs.ray.io/en/master/serve/monitoring.html#profiling-memory",2811"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory"2812],2813"document_ids": [2814547,2815548,2816559,28175010,2818549,28195118,2820562,282113549,282250162823],2824"answer": " To debug out-of-memory issues in Ray, you should first locate the tasks and actors that have high memory usage by viewing the per task and actor memory usage graph or using the CLI command `htop`. The memory usage is measured in RSS - SHR.\n\nIf you are using Ray 2.2 or later, there is an application-level memory monitor that can help prevent out-of-memory errors. You can enable and configure the memory monitor to detect and resolve memory issues.\n\nIf you notice a memory leak on a Serve actor, you can use `memray` to debug the issue. Set the environment variable `RAY_SERVE_ENABLE_MEMORY_PROFILING=1` and run your Serve application. The Serve actors will run a `memray` tracker that logs their memory usage to bin files. You can then use the `memray flamegraph [bin file]` command to generate a flamegraph of the memory usage.\n\nThe `ray memory` command can also be used to help track down object references that may be causing an `ObjectStoreFullError`. Running `ray memory` from the command line while a Ray application is running will give you a dump of all object references that are currently held by the driver, actors, and tasks in the cluster.\n\nFor more information, you can refer to the Ray documentation on \"Debugging Out of Memory\" and \"Out-Of-Memory Prevention\".",2825"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2826},2827{2828"question": "How to use callback in Trainer?",2829"sources": [2830"https://docs.ray.io/en/master/train/examples/transformers/transformers_torch_trainer_basic.html#ray-train-basic-example-for-huggingface-transformers",2831"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",2832"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.html#ray-train-huggingface-transformers-raytrainreportcallback",2833"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",2834"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.html#ray-tune-callback",2835"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_end.html#ray-train-lightning-raytrainreportcallback-on-init-end",2836"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_start.html#ray-train-lightning-raytrainreportcallback-on-init-start",2837"https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",2838"https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune"2839],2840"document_ids": [28419553,28429165,28438635,28448206,28451405,28468531,28478760,28489934,284923902850],2851"answer": " To use a callback in the Trainer, you can add it to the trainer object using the `add_callback` method. For example, to use the `RayTrainReportCallback` provided by Hugging Face, you can add it to the trainer as shown below:\n```\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=small_train_ds,\n eval_dataset=small_eval_ds,\n compute_metrics=compute_metrics,\n)\n\ntrainer.add_callback(RayTrainReportCallback())\n```\nAlternatively, you can implement your own custom callback by creating a class that inherits from `transformers.trainer_callback.TrainerCallback` and overriding the relevant methods. For example:\n```\nfrom transformers.trainer_callback import TrainerCallback\n\nclass MyTrainReportCallback(TrainerCallback):\n def __init__(self):\n super().__init__()\n self.metrics = {}\n\n def on_train_begin(self, args, state, control, **kwargs):\n # Do something at the beginning of training\n pass\n\n def on_train_end(self, args, state, control, **kwargs):\n # Do something at the end of training\n pass\n\n def on_step_end(self, args, state, step_output, **kwargs):\n # Do something at the end of each step\n pass\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=small_train_ds,\n eval_dataset=small_eval_ds,\n compute_metrics=compute_metrics,\n callbacks=[MyTrainReportCallback()],\n)\n```\nYou can then use the `add_callback` method to add the custom callback to the trainer. The `on_train_begin`, `on_train_end`, and `on_step_end` methods are just examples, and you can override other methods as needed to implement your desired behavior.",2852"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2853},2854{2855"question": "How to provide current working directory to ray?",2856"sources": [2857"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2858"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2859"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",2860"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",2861"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2862"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2863"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2864"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2865"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit"2866],2867"document_ids": [28686632,28696633,28704262,28711790,2872511,28736562,28746551,28756552,287665962877],2878"answer": " To provide the current working directory to Ray, you can specify it in the runtime environment when calling `ray.init()`. Here's an example:\n\n```python\nimport os\nimport ray\n\n# Create a working directory and add a file to it\nos.makedirs(\"/tmp/my_working_dir\", exist_ok=True)\nwith open(\"/tmp/my_working_dir/data.txt\", \"w\") as f:\n f.write(\"Hello, Ray!\")\n\n# Specify the runtime environment with the working directory\nray.init(runtime_env={\"working_dir\": \"/tmp/my_working_dir\"})\n```\n\nIn this example, the current working directory is `/tmp/my_working_dir`, which contains a file named `data.txt`. By specifying the working directory in the runtime environment, Ray will upload the contents of this directory to the cluster and make it available to the Ray workers. Relative paths in the remote tasks and actors will work on the laptop and on the cluster without any code changes.",2879"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2880},2881{2882"question": "how to create an actor instance with parameter?",2883"sources": [2884"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",2885"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2886"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",2887"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",2888"https://docs.ray.io/en/master/rllib/actors.html#actors",2889"https://docs.ray.io/en/master/ray-core/actors.html#actors",2890"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",2891"https://docs.ray.io/en/master/ray-core/api/core.html#actors",2892"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments"2893],2894"document_ids": [28954876,28965192,28974629,289814682,289911576,29004469,29015391,29024650,290344352904],2905"answer": " To create an actor instance with a parameter, you can use the `remote()` function followed by the class name and pass the parameter as an argument. For example, if you have a class named `Greeter` with a constructor that takes a value, you can create an actor instance with a specific value like this:\n```python\nimport ray\n\n@ray.remote\nclass Greeter:\n def __init__(self, value):\n self.value = value\n\n def say_hello(self):\n return self.value\n\n# Create an actor instance with a value of 42\ngreeter = Greeter.remote(42)\n\n# Call the say_hello method on the actor\nprint(ray.get(greeter.say_hello.remote())) # prints 42\n```\nIn this example, we define a `Greeter` class with a constructor that takes a `value` parameter. We then create an actor instance with `Greeter.remote(42)`, passing the value `42` as an argument. We can then call methods on the actor instance using the `.` operator and the `.remote()` method to execute them remotely.",2906"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2907},2908{2909"question": "how to push a custom module to ray which is using by Actor ?",2910"sources": [2911"https://docs.ray.io/en/master/ray-core/api/core.html#actors",2912"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",2913"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.push.html#ray-util-actorpool-push",2914"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#memory-profiling-ray-tasks-and-actors",2915"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#custom-resources-accelerator-types-and-more",2916"https://docs.ray.io/en/master/rllib/actors.html#actors",2917"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",2918"https://docs.ray.io/en/master/ray-core/actors.html#actors",2919"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema"2920],2921"document_ids": [29224650,29235443,29244758,2925568,292613459,292711575,292814680,29294467,2930142192931],2932"answer": " To push a custom module to Ray which is used by an Actor, you can use the `ray.put()` function to put the module into Ray's object store, and then get the module using `ray.get()` when creating the Actor.\n\nHere is an example of how you can do this:\n\n```python\nimport ray\n\n# Put the custom module into the object store\nray.put(my_custom_module)\n\n# Define the Actor class\n@ray.remote\nclass MyActor:\n def __init__(self):\n # Get the custom module from the object store\n self.custom_module = ray.get(my_custom_module)\n\n def do_something(self):\n # Use the custom module\n result = self.custom_module.some_function()\n return result\n\n# Create the Actor\nmy_actor = MyActor.remote()\n\n# Call the do_something method on the Actor\nresult = ray.get(my_actor.do_something.remote())\n```\n\nIn this example, `my_custom_module` is the custom module that you want to use in your Actor. You can put the module into the object store using `ray.put(my_custom_module)`, and then get the module using `ray.get(my_custom_module)` when creating the Actor.\n\nNote that this method will load the custom module into the object store every time the Actor is created. If you want to avoid this overhead, you can consider other methods such as using a shared file system or a package manager to install the custom module on all the nodes in the Ray cluster.",2933"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2934},2935{2936"question": "how to print ray working directory?",2937"sources": [2938"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2939"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2940"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2941"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#submit-the-workload",2942"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#submit-the-workload",2943"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2944"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#method-2-submit-a-ray-job-to-the-raycluster-via-ray-job-submission-sdk",2945"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",2946"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs"2947],2948"document_ids": [29496632,29506551,2951511,29526179,29536528,29546633,29555732,29566157,295765302958],2959"answer": " To print the working directory in Ray, you can use the following code:\n```python\nimport ray\nray.init()\nprint(ray.working_dir)\n```\nThe `ray.working_dir` returns the working directory of the Ray cluster. Note that this will return the directory on the cluster, not your local machine. If you want to print the local working directory, you can simply use `os.getcwd()`.\n\nIn the context provided, the working directory is uploaded to the Ray cluster when running `ray.init()` using the following command:\n```python\nray.init(working_dir='/path/to/local/working/directory')\n```\nThis will upload the local working directory to the Ray cluster and make it available for the remote tasks and actors.",2960"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2961},2962{2963"question": "why I can not see log.info in ray log?",2964"sources": [2965"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2966"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2967"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",2968"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2969"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2970"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",2971"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",2972"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",2973"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"2974],2975"document_ids": [2976444,2977680,29786631,2979445,2980511,2981662,2982530,2983415,29844932985],2986"answer": " Based on the provided context, the reason you can't see `log.info` in Ray logs is because the default logging level for Ray's logger is `logging.INFO`. This means that `log.info` messages will be displayed, but if you can't see them, it might be due to filtering or not looking in the correct location.\n\nTo view the Ray logs, you can use the Ray Dashboard, access logs through the Ray client, or use Ray's CLI or Python SDK APIs. If you are running the Ray Job by executing the Ray Driver on the Head node directly or connecting via Ray Client, you can view the Driver logs in your terminal or Jupyter Notebooks.\n\nFor Ray Jobs, Worker logs are always captured in the Ray logging directory, while Driver logs are only captured for Ray Jobs submitted via Jobs API. Make sure you are checking the correct logs based on your use case.",2987"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2988},2989{2990"question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",2991"sources": [2992"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",2993"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",2994"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2995"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",2996"https://docs.ray.io/en/master/data/user-guide.html#user-guides",2997"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",2998"https://docs.ray.io/en/master/data/data-internals.html#reading-files",2999"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",3000"https://docs.ray.io/en/master/data/examples/batch_training.html#transforming-a-dataset-in-parallel-using-custom-functions-a-class-anchor-id-transform-ds-a"3001],3002"document_ids": [30039210,30047362,30056835,30067865,30076911,30087495,30096874,30109188,301178753012],3013"answer": " Yes, you can preserve the order of data when using Ray Dataset to read a file. To do this, you need to enable deterministic execution in Ray Datasets by setting the `preserve_order` flag in the DataContext. Here is an example:\n```python\nimport ray\n\n# Preserve ordering in Ray Datasets for reproducibility.\nctx = ray.data.DataContext.get_current()\nctx.execution_options.preserve_order = True\n\n# Read the file using Ray Dataset.\ndataset = ray.data.read_parquet('path/to/file')\n```\nThis will ensure that the order of the data is preserved when reading the file using Ray Dataset.",3014"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3015},3016{3017"question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",3018"sources": [3019"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",3020"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3021"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3022"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",3023"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",3024"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",3025"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3026"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",3027"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions"3028],3029"document_ids": [30304377,30314542,30324535,30334527,30344348,30354546,30364657,30374564,303846583039],3040"answer": " This means that if any exception is thrown by the user's code during the execution of a task, Ray will not re-run that task by default. The user can change this behavior and specify which exceptions should be retried by using the `retry_exceptions` argument in the `ray.remote()` function.",3041"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3042},3043{3044"question": "how do I specify the log directory when starting Ray?",3045"sources": [3046"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",3047"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",3048"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",3049"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3050"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#mounting-the-ray-log-directory",3051"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",3052"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",3053"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3054"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger"3055],3056"document_ids": [3057511,30586360,30595861,3060680,30615871,3062510,30635880,30644811,30655303066],3067"answer": " To specify the log directory when starting Ray, you can use the `--temp-dir` or `--logs-dir` option with the `ray start` or `ray.init()` command. For example:\n\n```\nray start --temp-dir /path/to/temp --logs-dir /path/to/logs\n```\n\nor\n\n```\nimport ray\nray.init(temp_dir=\"/path/to/temp\", logs_dir=\"/path/to/logs\")\n```\n\nThis will set the temp directory and log directory to the specified paths. Note that the temp directory is used as the default log directory if the logs directory is not specified.\n\nYou can also redirect Ray logs to stderr by setting the `RAY_LOG_TO_STDERR` environment variable to `1` on all Ray nodes. However, this is not recommended and may break certain features like worker log redirection to driver.\n\nFor more information on configuring logging in Ray, you can refer to the \"Configuring Logging\" guide.",3068"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3069},3070{3071"question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",3072"sources": [3073"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3074"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3075"https://docs.ray.io/en/master/cluster/cli.html#ray-up",3076"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3077"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3078"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",3079"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",3080"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",3081"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node"3082],3083"document_ids": [30846337,30856328,30865615,30876203,30886392,30896522,3090851,30914193,309265013093],3094"answer": " To launch a Ray cluster with 10 nodes without setting the minimum worker count as 10, you can create or update a Ray cluster configuration file (e.g., `config.yaml`) with the following content:\n\n```yaml\ncluster_name: my-ray-cluster\nmax_workers: 9\n\nray_worker:\n min_workers: 0\n max_workers: 9\n resources: {\"CPU\": 2}\n\nprovider:\n type: <your_cloud_provider> # e.g., aws, azure, gcp\n region: <your_region>\n availability_zone: <your_az>\n\n...\n```\n\nReplace `<your_cloud_provider>`, `<your_region>`, and `<your_az>` with the appropriate values for your cloud provider, region, and availability zone.\n\nThen, run the following command to start the Ray cluster:\n\n```bash\n$ ray up -y config.yaml\n```\n\nThis command will start a Ray cluster with a head node and up to 9 worker nodes, without setting the minimum worker count as 10.",3095"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3096},3097{3098"question": "how to use ray api to scale up a cluster",3099"sources": [3100"https://docs.ray.io/en/master/cluster/vms/index.html#overview",3101"https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",3102"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",3103"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",3104"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",3105"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",3106"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",3107"https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started",3108"https://docs.ray.io/en/master/cluster/cli.html#ray-up"3109],3110"document_ids": [31116209,31125604,311314710,31145600,31154644,31166363,31176715,31186189,311956153120],3121"answer": " To scale up a Ray cluster, you can use the `ray up` command with the `--min-workers` and `--max-workers` options to specify the minimum and maximum number of worker nodes for the cluster. For example:\n\n```\nray up --min-workers 10 --max-workers 20 my_cluster_config.yaml\n```\n\nThis will create or update the Ray cluster using the configuration in `my_cluster_config.yaml`, and will set the minimum number of worker nodes to 10 and the maximum number of worker nodes to 20.\n\nAlternatively, you can use the `ray.autoscaler.sdk.request_resources` function within a Ray program to command the autoscaler to scale the cluster up to a desired size. This will immediately attempt to scale the cluster to accommodate the requested resources, bypassing normal upscaling speed constraints. For example:\n\n```\nray.autoscaler.sdk.request_resources(num_cpus=50)\n```\n\nThis will request 50 CPUs from the autoscaler, which will attempt to scale the cluster up to accommodate this request.\n\nFor more information, you can refer to the Ray Cluster Management API documentation and the Configuring Autoscaling guide.",3122"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3123},3124{3125"question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",3126"sources": [3127"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3128"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3129"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3130"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",3131"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",3132"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",3133"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents",3134"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",3135"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions"3136],3137"document_ids": [31386199,3139273,31406203,31415503,31426458,31436470,31446519,31456467,314661043147],3148"answer": " To specify a subnet in the Ray cloud launcher deployment file for AWS, you can include the subnet information in the 'provider' section of the configuration file. However, the provided context does not include information on how to specify a subnet in the Ray cloud launcher configuration file.\n\nBut, according to the context, it seems that the Ray cloud launcher uses the AWS Python SDK (Boto3) to launch clusters on AWS. Therefore, you can specify a subnet when creating a cluster using Boto3 by including the 'subnet_id' parameter in the 'SubnetId' field of the 'Placement' parameter in the 'create_cluster' function.\n\nHere's an example of how to specify a subnet in a Boto3 script:\n```python\nec2 = boto3.resource('ec2')\n\n# Create a placement\nplacement = ec2.Placement(availability_zone='us-west-2a', subnet_id='subnet-01234567890abcdef0')\n\n# Create a cluster\nresponse = ec2.create_cluster(\n cluster_name='my-cluster',\n placement=placement,\n # Other parameters...\n)\n```\nIn this example, replace 'subnet-01234567890abcdef0' with the ID of the subnet you want to use.\n\nTherefore, you can modify the Ray cloud launcher configuration file to include the 'subnet\\_id' parameter in the 'Placement' section of the 'provider' section. However, this may require modifying the Ray cloud launcher code to accept the 'subnet\\_id' parameter and pass it to Boto3.",3149"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3150},3151{3152"question": "where I can find HTTP server error code log for Ray serve",3153"sources": [3154"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3155"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",3156"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",3157"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",3158"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",3159"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",3160"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",3161"https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally",3162"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"3163],3164"document_ids": [316513974,316613541,31676631,31685671,31695747,31704694,31715802,317213437,3173135173174],3175"answer": " To find the HTTP server error code log for Ray Serve, you can check the Ray client logs which can be found at /tmp/ray/session\\_latest/logs on the head node. Additionally, you can check the Serve page in the Ray dashboard at <http://localhost:8265/#/serve> and look for any error codes in the deployment status. Ray Serve uses Python\u2019s standard logging module with a logger named \"ray.serve\", and logs are emitted both to stderr and on disk on each node at /tmp/ray/session\\_latest/logs/serve/. In development, logs are streamed to the driver Ray program, so it\u2019s convenient to keep the driver running while debugging.",3176"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3177},3178{3179"question": "I am running ray cluster on amazon and I have troubles displaying the dashboard. When a I tunnel the dashboard port from the headnode to my machine, the dashboard opens, and then it disappears (internal refresh fails). Is it a known problem? What am I doing wrong?",3180"sources": [3181"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",3182"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",3183"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",3184"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",3185"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",3186"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",3187"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",3188"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",3189"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster"3190],3191"document_ids": [31925530,31935528,3194418,31955526,31966562,31976561,31986223,31995543,3200136813201],3202"answer": " Yes, it seems like you are experiencing a known issue with accessing the Ray Dashboard on a remote cluster. This happens because the dashboard port is not publicly exposed by default when launching a remote Ray cluster.\n\nTo access the Ray Dashboard from outside the head node, you need to set up port forwarding. If you started your remote cluster with the Ray Cluster Launcher, you can use the `ray dashboard` command to set up port forwarding automatically.\n\nHere's an example command to set up port forwarding:\n```css\nray dashboard cluster.yaml\n```\nReplace `cluster.yaml` with the configuration file you used to launch your cluster.\n\nOnce you have set up port forwarding, you should be able to access the Ray Dashboard in your local browser at `localhost:8265`. However, if you are still experiencing issues with the dashboard disappearing or internal refreshes failing, it could be due to network connectivity issues or firewall settings.\n\nMake sure that the port 8265 is open in your security group settings for the head node and that there are no firewalls blocking the connection. You can also try increasing the dashboard's refresh interval or reducing the amount of data displayed in the dashboard to see if that resolves the issue.\n\nAdditionally, it is recommended to set up Prometheus and Grafana for enhanced dashboard experience and critical features such as Metrics View.",3203"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3204},3205{3206"question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",3207"sources": [3208"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3209"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",3210"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3211"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3212"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",3213"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",3214"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",3215"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",3216"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"3217],3218"document_ids": [3219256,32206292,32216345,32226392,32236522,32246227,32256366,32266367,322763373228],3229"answer": " In the Ray cluster launcher YAML, `max_workers` includes only worker nodes, not the head node. This is consistent across different versions and documentation provided.",3230"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3231},3232{3233"question": "How to update files in working directory ?",3234"sources": [3235"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3236"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",3237"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3238"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",3239"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",3240"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",3241"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",3242"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#outdated-function-definitions",3243"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#synchronizing-files-from-the-cluster-ray-rsync-up-down"3244],3245"document_ids": [32466632,32471775,32481790,32498894,32501068,32516214,32521066,3253619,325462223255],3256"answer": " To update files in the working directory, you can use `ray.train.get_context().get_trial_dir()` as the path to save any outputs. This ensures that the files are saved to the trial directory, not the shared working directory. Additionally, you can use `ray rsync_up` to upload files from your local machine to the cluster head node. If you want to update the cluster configuration, you can use `ray up` again on the existing cluster to check for any changes to synced files and upload the new files.",3257"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3258},3259{3260"question": "How I can update working directory file when ray allready initiated ?",3261"sources": [3262"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3263"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3264"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3265"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3266"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3267"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",3268"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",3269"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3270"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory"3271],3272"document_ids": [32736632,32744385,32754811,32766555,32771790,32781775,32794263,32806633,32815113282],3283"answer": " To update the working directory file when Ray is already initiated, you can use the `ray.put()` and `ray.get()` functions to store and retrieve data in the object store, which is shared across all workers in the cluster. This allows you to modify data in a central location that can be accessed by all workers.\n\nAlternatively, you can use the `ray.train.get_context().get_trial_dir()` function to get the path to the trial directory, which is a unique directory for each training run, and save any outputs to this directory. This will ensure that each worker writes to its own separate directory and avoids overwriting each other's data.\n\nIt is important to note that the working directory is shared across all workers on the same node, so it is recommended to use the trial directory or the object store to avoid conflicts and overwriting each other's data.",3284"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3285},3286{3287"question": "how can I force ray head node to use custom pem file to ssh worker node?",3288"sources": [3289"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3290"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3291"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3292"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3293"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3294"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3295"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",3296"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3297"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"3298],3299"document_ids": [33006330,33016307,3302261,33036340,33046318,3305272,33066205,3307268,330863143309],3310"answer": " To force the Ray head node to use a custom PEM file for SSHing into the worker node, you can specify the `ssh_private_key` field in the `auth` section of the configuration file. For example:\n\n```\nauth:\n ssh_user: ubuntu\n ssh_private_key: /path/to/your/custom/key.pem\n```\n\nThis will tell Ray to use your custom PEM file for SSH authentication when launching worker nodes. Note that you should also ensure that the corresponding public key has been added to the authorized keys on the worker node.",3311"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3312},3313{3314"question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",3315"sources": [3316"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3317"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",3318"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",3319"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",3320"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-operating-system-settings",3321"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",3322"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",3323"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",3324"https://docs.ray.io/en/master/ray-core/configure.html#head-node"3325],3326"document_ids": [332714740,33284508,33296375,3330563,33314457,333213870,33334522,33345011,333543903336],3337"answer": " The GCS (Global Control Service) server is a centralized metadata server for a Ray cluster, running on the Ray head node and managing node membership and actor directory. It's also known as the Global Control Store. The head node has extra system processes like GCS, which can cause high memory usage. The GCS is not fault-tolerant by default, but using HA Redis can make it fault-tolerant. The GCS fault tolerance is crucial for tolerating head node failures, allowing the Ray cluster to continue functioning even when the head node crashes.",3338"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3339},3340{3341"question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",3342"sources": [3343"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",3344"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",3345"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",3346"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",3347"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",3348"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource",3349"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3350"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",3351"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm"3352],3353"document_ids": [3354467,3355852,33566147,33579510,33586215,33595724,3360254,336110208,336298983363],3364"answer": " If you're encountering nodes that are \"pending\" for a long time when starting a cluster with `ray up`, you can debug this issue by following these steps:\n\n1. Check the Ray cluster status by running `ray status`. This command will provide you with detailed information about the cluster, including the status of each node.\n2. If you observe any failures or issues in the output of `ray status`, you can check the autoscaler monitor log (`cat /tmp/ray/session_latest/logs/monitor.*`) for more details.\n3. If the issue persists, you can try restarting the cluster by running `ray up`. This command will restart all Ray services, which may help resolve any issues.\n4. If you're using Kubernetes to manage your Ray cluster, you can check the status of the pods by running `kubectl describe pod <pod-name>`. If the pods are stuck in the \"Pending\" state, you can check for errors and ensure that your Docker resource limits are set high enough.\n5. You can also try increasing the number of nodes in your cluster or reducing the number of actors to free up resources.\n\nIn the provided context, there are no specific errors or issues mentioned that would indicate the cause of the \"pending\" nodes. However, the warning message about a resource request that cannot be scheduled suggests that all cluster resources are claimed by actors. Therefore, reducing the number of actors or adding more nodes to the cluster may help resolve the issue.",3365"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3366},3367{3368"question": "how to install Ray 2.5.1 from github or wheel?",3369"sources": [3370"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",3371"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",3372"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",3373"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3374"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",3375"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",3376"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",3377"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",3378"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels"3379],3380"document_ids": [3381288,338214545,338363,33846335,3385291,338614551,338766,338814541,33892863390],3391"answer": " To install Ray 2.5.1 from a GitHub commit, you need to find the commit hash and use it to download the corresponding wheel. However, Ray wheels for version 2.5.1 are not available on the given S3 bucket. The latest Ray wheels available are for version 3.0.0.dev0.\n\nIf you still want to install Ray 2.5.1, you can do so by building it from source. Here's how to do it:\n\n1. Clone the Ray repository:\n```bash\ngit clone https://github.com/ray-project/ray.git\n```\n2. Checkout the 2.5.1 tag:\n```bash\ncd ray\ngit checkout v2.5.1\n```\n3. Create a virtual environment and activate it:\n```bash\npython3 -m venv ray-env\nsource ray-env/bin/activate\n```\n4. Install the required dependencies:\n```bash\npip install -r requirements.txt\n```\n5. Build and install Ray:\n```bash\npip install -e .\n```\n\nNote that this will install Ray 2.5.1 with minimal dependencies. If you want to install Ray with support for the dashboard and cluster launcher, you can use the following command instead:\n```bash\npip install -e \".[default]\"\n```",3392"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3393},3394{3395"question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",3396"sources": [3397"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",3398"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.__init__.html#ray-rllib-evaluation-worker-set-workerset-init",3399"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3400"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",3401"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3402"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#worker-setup-commands",3403"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3404"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3405"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options"3406],3407"document_ids": [3408542,340912971,34104287,341110762,34124720,34136259,34146202,34154288,341648813417],3418"answer": " To use `worker_setup_hook` in a runtime environment to set up on worker node creation, you can specify it when calling `ray.init()` by providing a dictionary for `runtime_env` with `worker_setup_hook` set to the callable function you want to use for setup. Here's an example:\n\n```python\nimport ray\n\ndef logging_setup_func():\n # Your logging setup code here\n pass\n\nray.init(runtime_env={\"worker_setup_hook\": logging_setup_func})\n```\n\nThis will call the `logging_setup_func` function after the worker starts and before tasks and actors are scheduled. Note that `worker_setup_hook` is an experimental feature, and you can find more information on other runtime environment configurations in the provided context.",3419"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3420},3421{3422"question": "how to use Ray dataset on aws",3423"sources": [3424"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",3425"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",3426"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",3427"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",3428"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",3429"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",3430"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",3431"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",3432"https://docs.ray.io/en/master/data/key-concepts.html#datasets"3433],3434"document_ids": [34356732,34368006,34376839,34389188,34397865,34406935,34416835,34426467,344367313444],3445"answer": " To use Ray dataset on AWS, you can read data from an S3 bucket using the `ray.data.read_csv` function, which supports reading data from any filesystem supported by Arrow. Here's an example:\n```python\nds = ray.data.read_csv(\"s3://my-bucket/path/to/data.csv\")\n```\nBefore using Ray on AWS, you need to launch a Ray cluster on AWS using the Ray cluster launcher with the AWS Python SDK. Ray Data is built on Ray, so it easily scales to many machines, and you can use any ML framework of your choice, including PyTorch, HuggingFace, or Tensorflow. Ray Data supports a wide variety of data formats, including CSV, Parquet, and raw images.\n\nTo start, install Ray Data using:\n```\npip install -U \"ray[data]\"\n```\nThen, follow the guide on \"Launching Ray Clusters on AWS\" to start an AWS Ray cluster. After that, you can use Ray dataset on AWS by reading data from an S3 bucket using the `ray.data.read_csv` function.",3446"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3447},3448{3449"question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",3450"sources": [3451"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3452"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",3453"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",3454"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",3455"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",3456"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",3457"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",3458"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",3459"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition"3460],3461"document_ids": [34627081,34636864,34647395,34659207,34666795,34678046,346810017,34696876,347073523471],3472"answer": " To avoid shuffling of your dataset during `ray.data.map_batches()`, you can set the `local_shuffle_buffer_size` parameter in `iter_batches()` or `iter_torch_batches()` after randomizing each block of your dataset using `randomize_block_order()`. This will enable local shuffling during iteration, providing a more performant alternative to global shuffling methods like `random_shuffle()`.\n\nExample:\n\n```python\nds = ray.data.read_text(\n \"s3://anonymous@ray-example-data/sms_spam_collection_subset.txt\"\n).randomize_block_order()\n\nfor split, dataset in ray_datasets.items():\n ray_datasets[split] = dataset.map_batches(preprocess_function, batch_format=\"pandas\")\n```\n\nThen, when iterating over the dataset during training:\n\n```python\nlocal_shuffle_buffer_size = 100 # Choose an appropriate size\nfor batch in ray_datasets[split].iter_batches(local_shuffle_buffer_size=local_shuffle_buffer_size):\n # Process the batch\n pass\n```\n\nThis way, you avoid shuffling the dataset during `map_batches()` while still maintaining randomness during iteration.",3473"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3474},3475{3476"question": "Is the order of the input data preserved after a map_batches operation?",3477"sources": [3478"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",3479"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3480"https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms",3481"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3482"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",3483"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",3484"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3485"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",3486"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"3487],3488"document_ids": [34897640,34907081,34916813,34927091,34937800,34947738,34957085,34967737,349770883498],3499"answer": " Yes, the order of the input data is preserved after a `map_batches` operation. The function applied to each batch during `map_batches` operates on the batch as a whole, so the relative order of the data within the batch is maintained. Additionally, if you sort or shuffle the data and then use `map_batches`, the output will respect the ordering of the input data.",3500"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3501},3502{3503"question": "ray serve returns generic internal service error when there is an internal failure, how do I get it to emit more detailed errors or logs?",3504"sources": [3505"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",3506"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",3507"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3508"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",3509"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",3510"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3511"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3512"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",3513"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror"3514],3515"document_ids": [35164349,35174527,351813517,35194348,35205671,35214660,352213974,3523595,352446943525],3526"answer": " To get more detailed errors or logs when Ray Serve returns a generic internal service error due to an internal failure, you can:\n\n1. Check for Python-level exceptions: Ray surfaces application-level failures as Python-level exceptions. When a task on a remote worker or actor fails due to a Python-level exception, Ray wraps the original exception in a RayTaskError. This wrapped exception will be thrown to any worker that tries to get the result.\n\n2. Use Ray logging: Ray logging can help you understand system-level behavior and surface application-level details during runtime. Ray Serve uses Python\u2019s standard logging module with a logger named \"ray.serve\". By default, logs are emitted from actors both to stderr and on disk on each node at /tmp/ray/session_latest/logs/serve/. In development, logs are streamed to the driver Ray program, so it\u2019s convenient to keep the driver running while debugging.\n\nFor more specific Ray exceptions, you can refer to the ray.exceptions module, which includes errors like RaySystemError, ObjectReconstructionFailedLineageEvictedError, RuntimeEnvSetupError, CrossLanguageError, and others. These exceptions can provide more context when debugging Ray applications.",3527"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3528},3529{3530"question": "how do i track an uncaught exception in ray serve",3531"sources": [3532"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3533"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",3534"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",3535"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",3536"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",3537"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception",3538"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3539"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3540"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api"3541],3542"document_ids": [354313974,35444694,35454527,35464674,35475747,3548778,35494658,35504660,355146753552],3553"answer": " To track an uncaught exception in Ray Serve, you can catch and handle the Ray-specific exceptions, such as `RayError`, `RayTaskError`, and `RayActorError`, in your Ray Serve deployment's function. When an uncaught exception occurs, Ray will wrap the original exception in a `RayTaskError` and store it as the task's return value.\n\nHere's an example of how to catch and handle exceptions in a Ray Serve deployment:\n\n```python\nimport ray\nfrom ray import serve\n\n@serve.deployment\nclass MyServeDeployment:\n def __init__(self):\n self.counter = 0\n\n @serve.endpoint\n def increase_counter(self, request):\n try:\n self.counter += 1\n if self.counter % 10 == 0:\n raise Exception(\"A deliberate exception for testing.\")\n return {\"counter\": self.counter}\n except Exception as e:\n # Log or handle the exception here\n print(f\"Caught an exception: {e}\")\n # You can also re-raise the exception if needed\n # raise\n return {\"counter\": self.counter, \"error\": str(e)}\n\nif __name__ == \"__main__\":\n serve.start(detached=True)\n MyServeDeployment.deploy()\n```\n\nIn this example, when an uncaught exception occurs in the `increase_counter` function, the code catches the exception, logs it, and returns an error message in the response.\n\nTo monitor and track these exceptions, you can check the Ray dashboard or use centralized logging solutions to collect and analyze the logs.",3554"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3555},3556{3557"question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",3558"sources": [3559"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3560"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3561"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",3562"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3563"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3564"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",3565"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",3566"https://docs.ray.io/en/master/serve/monitoring.html#json-logging-format",3567"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"3568],3569"document_ids": [357013517,3571680,3572445,35734323,357413524,3575519,3576444,357713525,35784933579],3580"answer": " To view logs for your Ray Serve endpoint in the Ray cluster, you can check the logs on disk at `/tmp/ray/session_latest/logs/serve/` on each node. By default, logs are also streamed to the driver Ray program (the Python script that calls `serve.run()` or the `serve run` CLI command). If you're running the driver directly on the head node or with Ray Client, you can view the logs in the terminal or Jupyter Notebook output.\n\nTo access logs using the Python SDK, you can use the `list_logs` or `ray logs` functions, providing the node ID or node IP from `ray list nodes`. If you have enabled JSON-formatted logging in the Serve log file, the logs will have a specific format with fields like `levelname`, `asctime`, `deployment`, `replica`, and `message`.\n\nFor more information, refer to the Ray Logging documentation.",3581"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3582},3583{3584"question": "where can I see logs for a failed ray serve deployment",3585"sources": [3586"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3587"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",3588"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",3589"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#inspect-deeper",3590"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",3591"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3592"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",3593"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",3594"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"3595],3596"document_ids": [359713523,35986631,3599444,360013658,360113442,360213517,360313960,360413537,3605135213606],3607"answer": " You can see the logs for a failed Ray serve deployment in the following locations:\n\n1. /tmp/ray/session\\_latest/logs/serve/ on the head node and worker node pods. These logs contain information about specific deployment failure reasons and autoscaling events.\n2. Kuberay operator logs by running `kubectl logs -f <kuberay-operator-pod-name>`. The Kuberay operator logs contain information about the Serve application deployment event and Serve application health checks.\n\nThese logs can help you troubleshoot application deployment failures in Ray Serve.",3608"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3609},3610{3611"question": "How to take a subset of a Ray Dataset?",3612"sources": [3613"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",3614"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",3615"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",3616"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",3617"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",3618"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#splitting-datasets",3619"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",3620"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",3621"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray"3622],3623"document_ids": [36249180,36258006,36266835,362710017,36288610,36299198,36309188,36316901,36321433633],3634"answer": " To take a subset of a Ray Dataset, you can use the `take` or `take_all` methods. For example, `train_dataset.take(10)` will return the first 10 elements of the `train_dataset` Ray Dataset. If you want to create a new Ray Dataset with a subset of the original, you can use the `ray.data.from_items` function to create a new Ray Dataset from a list or iterable of items. For example:\n\n```python\nsubset_dataset = ray.data.from_items(train_dataset.take(10))\n```\n\nThis will create a new Ray Dataset `subset_dataset` with the first 10 elements of `train_dataset`. Note that the `take` method returns an iterable of rows, so you need to convert it to a list or other iterable before passing it to `ray.data.from_items`.\n\nAlternatively, if you want to create a subset of the Ray Dataset based on some condition, you can use the `filter` method. For example, to create a new Ray Dataset with all elements where the `x` value is greater than 100, you can do:\n\n```python\nsubset_dataset = train_dataset.filter(lambda batch: batch[\"x\"] > 100)\n```\n\nThis will return a new Ray Dataset with all batches where the `x` value is greater than 100.",3635"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3636},3637{3638"question": "How do I load all checkpoints from trials of a Tune experiment launched with `tune.run`? I ran my initial experiment with cloud checkpointing, so I\u2019d need to download all the checkpoints to analyze them.",3639"sources": [3640"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",3641"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing",3642"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",3643"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints",3644"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-out-of-tune-using-checkpoints-other-artifacts",3645"https://docs.ray.io/en/master/tune/tutorials/overview.html#tune-feature-guides",3646"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#tunecontroller",3647"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",3648"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage"3649],3650"document_ids": [36512226,36522217,36532253,36542212,36552299,36562270,36572243,36582255,365923483660],3661"answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can utilize the `tune.Tuner.restore` method. Since you've used cloud checkpointing, first, you need to download all the checkpoints to your local machine. After downloading, you can restore the experiment as follows:\n\n```python\nfrom ray import tune\n\ntuner = tune.Tuner.restore(\n os.path.expanduser(\"path/to/your/downloaded/checkpoint\"), # replace with your path\n trainable=your_trainable_function,\n resume_errored=True,\n)\ntuner.fit()\n```\n\nThis will restore the experiment from the provided checkpoint path. Make sure to replace `your_trainable_function` with the actual trainable function used in your experiment.\n\nAfter restoring the experiment, you can analyze the trials and their checkpoints using various Tune features and functionalities.",3662"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3663},3664{3665"question": "How can I kill a \"detached\" Actor ?",3666"sources": [3667"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3668"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3669"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3670"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3671"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3672"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3673"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3674"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3675"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance"3676],3677"document_ids": [36784633,36794859,36804631,36814615,36824613,36834579,36844620,36854848,368645613687],3688"answer": " To kill a \"detached\" Actor in C++, you can use the `Kill()` function of the actor handle. Here is an example:\n\n```\nactor_handle.Kill();\n```\n\nThis will cause the actor to immediately exit its process, causing any current, pending, and future tasks to fail with a `RayActorError`.\n\nIn Python, you can use the `ray.kill()` function to manually terminate the actor. Here is an example:\n\n```\nray.kill(actor_handle)\n```\n\nThis will cause the actor to immediately exit its process, causing any current, pending, and future tasks to fail with a `RayActorError`. If you would like Ray to automatically restart the actor, make sure to set a nonzero `max_restarts` in the `@ray.remote` options for the actor, then pass the flag `no_restart=False` to `ray.kill`.\n\nFor named and detached actors, calling `ray.kill` on an actor handle destroys the actor and allows the name to be reused.",3689"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3690},3691{3692"question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",3693"sources": [3694"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",3695"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",3696"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3697"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3698"https://docs.ray.io/en/master/serve/api/index.html#serve-run",3699"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",3700"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",3701"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#set-up-ray-a-name-setup-a",3702"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.init_method.html#ray-train-torch-torchconfig-init-method"3703],3704"document_ids": [37056614,37069343,37074811,37084285,370913990,3710542,37114253,371210295,371391183714],3715"answer": " To set environment variables in `ray.init()`, you can include the `env_vars` option in the `runtime_env` dictionary. For example, to set the `foo` environment variable to the value `\"foo\"`, you can do:\n\n```python\nray.init(\n runtime_env={\n \"env_vars\": {\"foo\": \"foo\"}\n }\n)\n```\n\nIn this example, the `env_vars` dictionary contains the key-value pair `{\"foo\": \"foo\"}`, which sets the `foo` environment variable to the value `\"foo\"`.\n\nYou can also reference existing environment variables using `${ENV_VAR}` to achieve the appending behavior. If the environment variable doesn\u2019t exist, it becomes an empty string `\"\"`.\n\nFor example, if you have an existing environment variable `BAR` and you want to set the `foo` environment variable to the value of `BAR` with a prefix `foo_`, you can do:\n\n```python\nray.init(\n runtime_env={\n \"env_vars\": {\"foo\": \"${BAR}_prepended\"}\n }\n)\n```\n\nIn this example, if the `BAR` environment variable is set to `\"bar_value\"`, the `foo` environment variable will be set to `\"bar_value_prepended\"`.",3716"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3717},3718{3719"question": "What is the rest api for getting the head node id?",3720"sources": [3721"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",3722"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.node_id.html#ray-runtime-context-runtimecontext-node-id",3723"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",3724"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_node_id.html#ray-runtime-context-runtimecontext-get-node-id",3725"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",3726"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3727"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",3728"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3729"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails"3730],3731"document_ids": [3732721,37334731,3734754,37354909,37364450,373714324,373814202,3739492,374066573741],3742"answer": " The REST API for getting the head node ID in Ray is not explicitly stated in the provided context. However, you can use the `ray.util.state.get_node` function to get node information by node ID, and then check if the node is a head node using the `is_head_node` field.\n\nHere's an example of how to use `ray.util.state.get_node` to get node information:\n```python\nimport ray\nfrom ray.util.state import get_node\n\nnode_id = ray.runtime_context.RuntimeContext.node_id\nnode = get_node(node_id)\n\nif node.is_head_node:\n print(f\"The node ID {node_id} is a head node.\")\nelse:\n print(f\"The node ID {node_id} is not a head node.\")\n```\nTo use this code as a REST API, you would need to wrap it in a web framework such as Flask or FastAPI. However, this is beyond the scope of the provided context.",3743"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3744},3745{3746"question": "how to rerun a canceled ray task",3747"sources": [3748"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",3749"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",3750"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",3751"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",3752"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",3753"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",3754"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",3755"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",3756"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks"3757],3758"document_ids": [37594897,37604375,37614550,37624494,376314702,376411591,37654497,376614703,3767115923768],3769"answer": " It's not possible to rerun a canceled Ray task as canceled tasks aren't retried in Ray. If you want to re-run the task, you need to create a new task reference and execute it again. Also, note that only async actor tasks can be interrupted, and Ray offers no mechanism for interruption for single-threaded or multi-threaded actor tasks.",3770"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3771},3772{3773"question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",3774"sources": [3775"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",3776"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.sigopt.SigOptSearch.html#ray-tune-search-sigopt-sigoptsearch",3777"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.hyperopt.HyperOptSearch.set_max_concurrency.html#ray-tune-search-hyperopt-hyperoptsearch-set-max-concurrency",3778"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.zoopt.ZOOptSearch.set_max_concurrency.html#ray-tune-search-zoopt-zooptsearch-set-max-concurrency",3779"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ConcurrencyLimiter.html#ray-tune-search-concurrencylimiter",3780"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.html#ray-tune-search-optuna-optunasearch",3781"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.skopt.SkOptSearch.set_max_concurrency.html#ray-tune-search-skopt-skoptsearch-set-max-concurrency",3782"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.bayesopt.BayesOptSearch.set_max_concurrency.html#ray-tune-search-bayesopt-bayesoptsearch-set-max-concurrency",3783"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.set_max_concurrency.html#ray-tune-search-optuna-optunasearch-set-max-concurrency"3784],3785"document_ids": [37867738,37872072,37881654,37891340,37901464,37911867,37921547,37931597,379416103795],3796"answer": " To set the max parallel concurrent scheduled tasks in `map_batches`, you can use the `ConcurrencyLimiter` class from `ray.tune.search`. This class allows you to limit the maximum number of concurrent trials for a given searcher.\n\nHere's an example of how you can use it:\n```python\nfrom ray.tune.search import ConcurrencyLimiter\n\n# Create your searcher object\nsearch_alg = ...\n\n# Wrap the searcher with the ConcurrencyLimiter\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=<your_value>)\n\n# Use the wrapped searcher in your training loop\ntuner = tune.Tuner(\n trainable,\n tune_config=tune.TuneConfig(\n search_alg=search_alg\n ),\n)\ntuner.fit()\n```\nIn this example, replace `<your_value>` with the maximum number of concurrent tasks you want to allow.\n\nNote that the `ConcurrencyLimiter` class works by managing the `max_concurrent` parameter of the underlying searcher. Therefore, the searcher you use must have a `set_max_concurrency` method that allows you to set this parameter. The searchers provided by Ray Tuner, such as `HyperOptSearch`, `ZOOptSearch`, `SkOptSearch`, `BayesOptSearch`, and `OptunaSearch`, all have this method.",3797"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3798},3799{3800"question": "How do I get the number of cpus from ray cluster?",3801"sources": [3802"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",3803"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",3804"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-6-profile-using-ray-dashboard",3805"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",3806"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",3807"https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",3808"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",3809"https://docs.ray.io/en/master/data/performance-tips.html#tuning-read-parallelism",3810"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-8-a-loop-of-restarting-the-raycluster-occurs-when-the-kubernetes-cluster-runs-out-of-resources-kuberay-v0-6-1-or-earlier"3811],3812"document_ids": [381310003,38145936,38156049,38166504,38175413,38184380,38196013,38206815,382156933822],3823"answer": " To get the number of CPUs in a Ray cluster, you can use the `ray.cluster_resources()` function in Python. This function returns a dictionary that includes the number of CPUs and GPUs available in the cluster. Here's an example:\n```\nimport ray\nray.init()\nprint(ray.cluster_resources())\n```\nThis will output a dictionary that includes the number of CPUs and GPUs available in the cluster, like this:\n```\n{'cpu': 4.0, 'gpu': 0.0}\n```\nIn this example, the cluster has 4 CPUs and 0 GPUs available.\n\nAlternatively, if you are using a RayCluster, you can specify the number of CPUs available on the head and worker nodes when you create the cluster. For example:\n```\nrayCluster.create(num_workers=1, num_cpus_per_worker=1, num_cpus_for_head=4)\n```\nThis will create a cluster with 1 worker node that has 1 CPU, and a head node with 4 CPUs. You can then check the number of CPUs available in the cluster using `ray.cluster_resources()` as described above.\n\nIt's worth noting that Ray auto-detects the resources available on each node, but you can also manually override this by passing custom resources to the `ray start` command. For example, if you wish to specify that a machine has 10 CPUs and 1 GPU available for use by Ray, you can do this with the flags `--num-cpus=10` and `--num-gpus=1`.\n\nFor more information, see the [Ray documentation](https://docs.ray.io/en/latest/).",3824"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3825},3826{3827"question": "How to use the exclude option to the runtime_env",3828"sources": [3829"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",3830"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.set_runtime_env.html#ray-job-config-jobconfig-set-runtime-env",3831"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",3832"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3833"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_runtime_env_string.html#ray-runtime-context-runtimecontext-get-runtime-env-string",3834"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-6-runtime-env",3835"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.runtime_env.html#ray-job-submission-jobinfo-runtime-env",3836"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",3837"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig"3838],3839"document_ids": [38404661,38414826,384213856,38434712,38444912,38455684,38466665,38474291,384847643849],3850"answer": " To use the `exclude` option with the runtime environment (`runtime_env`) in Ray, you can use the `set_runtime_env` method of the `JobConfig` class and pass a runtime environment dictionary with the `exclude` key set to a list of strings specifying the dependencies you want to exclude.\n\nHere is an example of how to use the `exclude` option:\n```\nfrom ray import job_config\n\njob_config = job_config.JobConfig()\nruntime_env = {\n \"pip\": [\"some-package\"],\n \"exclude\": [\"some-package-to-exclude\"]\n}\njob_config.set_runtime_env(runtime_env)\n```\nIn this example, the `some-package` will be installed, but `some-package-to-exclude` will be excluded and not installed.\n\nNote that the `exclude` option is not a top-level key in the `RuntimeEnvConfig` class, but can be included in the runtime environment dictionary passed to the `set_runtime_env` method.",3851"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3852},3853{3854"question": "show a map batch example with batch_format",3855"sources": [3856"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3857"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.sample_batch.SampleBatch.split_by_episode.html#ray-rllib-policy-sample-batch-samplebatch-split-by-episode",3858"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",3859"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3860"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",3861"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",3862"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",3863"https://docs.ray.io/en/master/rllib/rllib-offline.html#example-converting-external-experiences-to-batch-format",3864"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format"3865],3866"document_ids": [38677081,386813299,386910266,387014727,38716956,38727640,38737091,387410622,387567903876],3877"answer": " Here's an example of using `map_batches()` with `batch_format=\"pandas\"`:\n\n```python\nimport pandas as pd\n\n# Assume ds is a Ray Dataset\n\ndef process_batch(batch):\n # Process the batch as a pandas DataFrame\n batch_df = pd.DataFrame(batch)\n # Perform some operations, such as adding a new column\n batch_df['new_column'] = some_operation(batch_df)\n return batch_df.to_dict('records')\n\npreds = (\n ds\n .repartition(100)\n .map_batches(\n process_batch,\n batch_size=4,\n batch_format=\"pandas\",\n compute=ray.data.ActorPoolStrategy(),\n num_gpus=1,\n )\n)\n\n# After map_batches is done, you can view your generated text\npreds.take_all()\n```\n\nThis example assumes that the input batches in the Ray Dataset `ds` are dictionaries and that you want to process them as pandas DataFrames. The `process_batch` function converts the batch to a DataFrame, performs some operations, and then converts it back to a dictionary before returning it. The `map_batches()` function then applies this function to each batch in the dataset.",3878"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3879},3880{3881"question": "how to find local ray address",3882"sources": [3883"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3884"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3885"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",3886"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",3887"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",3888"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3889"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",3890"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3891"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core"3892],3893"document_ids": [38946552,38954817,38966531,38976183,38984187,38994818,390013988,390113974,390254133903],3904"answer": " To find the local Ray address, you can look for the address of the Ray dashboard in the output when starting a local Ray cluster. For example, when you run \"ray start\" or \"ray up\", the output will typically include a message like \"View the Ray dashboard at <http://localhost:8265>\" or \"View the Ray dashboard at <http://127.0.0.1:8265>\". This is the address of the Ray dashboard, which you can use as the Ray address.\n\nAlternatively, you can find the Ray address by checking the environment variable RAY\\_ADDRESS, or by checking the address of the latest cluster started in /tmp/ray/ray\\_current\\_cluster.\n\nYou can also explicitly pass the Ray address to the Ray Jobs CLI using the --address flag, or by setting the RAY\\_ADDRESS environment variable. The Ray address can be a concrete address (e.g., localhost:<port>) or a remote cluster address prefixed with \"ray://\" (e.g., ray://123.45.67.89:50005).",3905"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3906},3907{3908"question": "How to start ray cluster on multiple node via CLI?",3909"sources": [3910"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3911"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",3912"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",3913"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",3914"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",3915"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",3916"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",3917"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",3918"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster"3919],3920"document_ids": [39216203,39226616,39235605,39246501,39254193,39266500,39276510,39286622,392962063930],3931"answer": " To start a Ray cluster on multiple nodes via CLI, follow these steps:\n\n1. Install Ray on each machine using `pip install -U \"ray[default]\"`.\n2. Create a configuration file (e.g., `config.yaml`) for the cluster specification.\n3. Use the Ray CLI to start the cluster with `ray up -y config.yaml`.\n\nThis command will create a designated head node and worker nodes on the cloud. If you need to modify the Ray Client server port, use `--ray-client-server-port=...` with the `ray start` command in the configuration file.\n\nFor more advanced use cases or manual setup, refer to the Ray documentation on managing Ray clusters.",3932"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3933},3934{3935"question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",3936"sources": [3937"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",3938"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",3939"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",3940"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",3941"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",3942"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",3943"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",3944"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",3945"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status"3946],3947"document_ids": [39485068,3949625,395010218,39519397,39526879,3953622,39549901,39551113,39564683957],3958"answer": " The Ray tuner shows \"running\" but CPU usage is almost 0% because there are no available CPUs for Ray to use. The Ray cluster is currently occupied by Actors or Tune trials, preventing Ray Data from scheduling tasks. To resolve this issue, limit the number of concurrent Tune trials with the max\\_concurrent\\_trials Tune option to ensure there are enough free CPUs for Ray Data to run on.",3959"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3960},3961{3962"question": "should the Ray head node and all workers have the same object store memory size allocated?",3963"sources": [3964"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3965"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",3966"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",3967"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",3968"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",3969"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",3970"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",3971"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",3972"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads"3973],3974"document_ids": [39756394,39765109,3977563,39785013,39796437,39806446,3981855,39825557,3983144833984],3985"answer": " Yes, it is recommended to have the same object store memory size allocated for the Ray head node and all worker nodes. This is because the Ray head node has more memory-demanding system components and can easily have out-of-memory problems if the head node has the same memory capacity as worker nodes and if you execute the same number of tasks and actors from a head node. By specifying a consistent amount of object store memory for both the head node and worker nodes, you can ensure that there is a consistent amount of memory available for storing objects and prevent potential out-of-memory issues.",3986"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3987},3988{3989"question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",3990"sources": [3991"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",3992"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",3993"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",3994"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3995"https://docs.ray.io/en/master/serve/api/index.html#serve-start",3996"https://docs.ray.io/en/master/serve/architecture.html#how-does-serve-ensure-horizontal-scalability-and-availability",3997"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",3998"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3999"https://docs.ray.io/en/master/serve/develop-and-deploy.html#build-serve-config-files-for-production-deployment"4000],4001"document_ids": [400214022,400314055,400414025,400514288,400613994,400713476,400813958,400914287,4010134394011],4012"answer": " In Ray Serve, you can specify whether to set up an HTTP proxy on each node or just the head node using the `ProxyLocation` configuration field. The options are \"Disabled\", \"HeadOnly\", and \"EveryNode\", with \"EveryNode\" being the default. This configuration can be set in the Serve config file or via the command line using the `--proxy-location` option.",4013"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4014},4015{4016"question": "Want to embed Grafana into the Ray Dashboard, given that I am using KubeRay\n\nGiven the context that Prometheus and Grafana are not running on my Head node, and that I am using KubeRay, how should I be setting the following variables?\n\u2022 `RAY_GRAFANA_HOST`\n\u2022 `RAY_PROMETHEUS_HOST`\nAnd is there a way to set them more intelligently, given that head node IP is changing every time we reconfigure our cluster?",4017"sources": [4018"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",4019"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",4020"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",4021"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-grafana-host-location",4022"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",4023"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-11-embed-grafana-panels-in-ray-dashboard",4024"https://docs.ray.io/en/master/cluster/metrics.html#simplist-setting-up-grafana-with-ray-provided-configurations",4025"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",4026"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana"4027],4028"document_ids": [40295544,40306061,40315543,40325547,40335545,40346086,40355597,40366084,403760854038],4039"answer": " To embed Grafana into the Ray Dashboard when using KubeRay, you should set the `RAY_GRAFANA_HOST` and `RAY_PROMETHEUS_HOST` environment variables as follows:\n\n* `RAY_GRAFANA_HOST`: Set this to the external URL of the Grafana ingress if both Grafana and the Ray Cluster are on the same Kubernetes cluster. If Grafana is hosted on a different machine or on a non-default port, set this to the address that the head node and the browsers of dashboard users can access.\n* `RAY_PROMETHEUS_HOST`: Set this to an address that the head node can use to access Prometheus.\n\nSince the head node IP is changing every time you reconfigure your cluster, you can set `RAY_GRAFANA_HOST` to the external URL of the Grafana ingress instead of the head node IP. This way, the address remains consistent even if the head node IP changes.\n\nThe `RAY_PROMETHEUS_NAME` variable is used to select a different data source to use for the Grafana dashboard panels. You can set this to a different value for each Ray Cluster if you are using a different Prometheus server for each Ray Cluster and the same Grafana server for all Clusters.\n\nNote that the `RAY_GRAFANA_IFRAME_HOST` variable is optional and is used to specify an address that the user's browsers can use to access Grafana and embed visualizations. If this variable is not set, Ray Dashboard uses the value of `RAY_GRAFANA_HOST`.\n\nHere's an example of how to set these variables in a Kubernetes environment:\n\n```yaml\nenv:\n - name: RAY_GRAFANA_HOST\n value: http://grafana.example.com\n - name: RAY_PROMETHEUS_HOST\n value: http://prometheus.example.com\n - name: RAY_PROMETHEUS_NAME\n value: Prometheus\n```\n\nReplace `http://grafana.example.com` and `http://prometheus.example.com` with the actual addresses that the head node can use to access Grafana and Prometheus, respectively. Replace `Prometheus` with the actual name of the Prometheus data source in Grafana.",4040"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4041},4042{4043"question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",4044"sources": [4045"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",4046"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",4047"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",4048"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",4049"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",4050"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",4051"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#deploy-a-ray-cluster",4052"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",4053"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"4054],4055"document_ids": [405613957,405713956,40585975,40595953,40605972,40616170,40626175,40635973,4064139024065],4066"answer": " When using KubeRay autoscaling in a GCS (GKE, EKS, etc.), the Ray Autoscaler and the Kubernetes Cluster Autoscaler work together to manage Ray pods and Kubernetes nodes. After the Ray autoscaler decides to create or delete a Ray pod, the Kubernetes Cluster Autoscaler provisions or cleans up the corresponding Kubernetes node. The Ray Autoscaler has more fine-grained control over scale-down than the Horizontal Pod Autoscaler, allowing it to select precisely which pods to scale down. It is recommended to configure your RayCluster so that only one Ray pod fits per Kubernetes node.",4067"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4068},4069{4070"question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",4071"sources": [4072"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",4073"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",4074"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",4075"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4076"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",4077"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4078"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s",4079"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",4080"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s"4081],4082"document_ids": [408313854,408413795,408514020,40864287,408714044,408814288,40898481,40904765,409186554092],4093"answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you can include the following:\n\n```yaml\nhttp_options:\n request_timeout_s: <value_in_seconds>\n```\n\nReplace `<value_in_seconds>` with the desired duration for the end-to-end timeout of HTTP requests.\n\nHere's an example of a complete Ray Serve YAML config file with the `request_timeout_s` set:\n\n```yaml\nversion: 2\n\ndeployments:\n - name: my_deployment\n ...\n",4094"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4095},4096{4097"question": "How do I make the GPU available on my M1 laptop to ray?",4098"sources": [4099"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",4100"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",4101"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4102"https://docs.ray.io/en/master/installation.html#m1-mac-apple-silicon-support",4103"https://docs.ray.io/en/master/ray-overview/installation.html#m1-mac-apple-silicon-support",4104"https://docs.ray.io/en/master/ray-more-libs/installation.html#m1-mac-apple-silicon-support",4105"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#requesting-gpus",4106"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",4107"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage"4108],4109"document_ids": [41104997,41114995,41124818,411373,4114298,411514571,41166020,41172966,411860124119],4120"answer": " To make the GPU available for Ray on your M1 laptop, you need to follow these steps:\n\n1. Install miniforge for Apple Silicon.\n2. Activate the miniforge environment.\n3. Install Ray as you normally would.\n4. Install the GPU version of TensorFlow to make use of the GPU in your function.\n5. Define your Ray remote function with `num_gpus=1` to assign a GPU to the function.\n\nHere is an example:\n```python\n@ray.remote(num_gpus=1)\ndef use_gpu():\n import tensorflow as tf\n # Use TensorFlow to make use of the GPU\n```\nNote that it is up to the function to actually make use of the GPU. Ray will reserve one GPU for it while it is being executed.",4121"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4122},4123{4124"question": "How can I add a timeout for the Ray job?",4125"sources": [4126"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",4127"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",4128"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",4129"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",4130"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4131"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.GetTimeoutError.html#ray-exceptions-gettimeouterror",4132"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-stop",4133"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",4134"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management"4135],4136"document_ids": [413713795,41386556,41395757,41404416,41416554,41424796,41436602,41446558,414565694146],4147"answer": " To add a timeout for a Ray job, you can use the `timeout` argument in the `ray.get()` function when retrieving the object reference. This will raise a `GetTimeoutError` if the result is not ready within the specified timeout. Here's an example:\n\n```python\nfrom ray.exceptions import GetTimeoutError\n\n@ray.remote\ndef long_running_function():\n time.sleep(8)\n\nobj_ref = long_running_function.remote()\ntry:\n ray.get(obj_ref, timeout=4)\nexcept GetTimeoutError:\n print(\"`get` timed out.\")\n```\n\nIn this example, the `long_running_function` will sleep for 8 seconds, and the `ray.get(obj_ref, timeout=4)` will time out if the result is not ready within 4 seconds.\n\nNote that this only sets a timeout for the `ray.get()` function, not for the entire Ray job. If you want to set a timeout for the entire job, you can use the `ray job stop` command with the `--no-wait` option to stop the job after a certain period of time.",4148"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4149},4150{4151"question": "how do I set custom /tmp directory for remote cluster?",4152"sources": [4153"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4154"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",4155"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",4156"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",4157"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",4158"https://docs.ray.io/en/master/cluster/cli.html#ray-submit",4159"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.restore.html#ray-rllib-algorithms-algorithm-algorithm-restore",4160"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.restore.html#ray-tune-trainable-restore",4161"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster"4162],4163"document_ids": [41644273,41656561,4166872,416713680,416813718,41695625,417012465,41712081,417262054173],4174"answer": " To set a custom `/tmp` directory for a remote cluster in Ray, you can use the `RAY_TEMPDIR` environment variable when initializing the Ray cluster. This variable defines where the temporary directory for the cluster files should be created, and it should be accessible by the host, the outer container, and the inner container.\n\nHere is an example of how to set the `RAY_TEMPDIR` variable in a cluster configuration file:\n```\nray:\n _address: <head-node-address>:6379\n _object-store-name: <object-store-name>\n _redis-password: <redis-password>\n temp-dir: /path/to/custom/tmp\n```\nThen, when starting the Ray cluster, you can specify the configuration file using the `ray start --config` command.\n\nIt's important to note that the temporary directory cannot be set per-task or per-actor; it can only be set per-job using `ray.init()`.",4175"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4176},4177{4178"question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",4179"sources": [4180"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",4181"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4182"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",4183"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4184"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4185"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",4186"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",4187"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",4188"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable"4189],4190"document_ids": [41914960,41924385,41934959,4194511,41956632,4196872,41976396,41989160,419917754200],4201"answer": " Yes, if you set the `object_spilling_config` parameter in `ray.init()`, Ray will spill objects to the custom directory specified in the configuration. The default spill directory is `/tmp`, but you can change it to any other directory by providing the `directory_path` parameter.\n\nHere's an example of how to configure object spilling to a custom directory:\n```python\nimport json\nimport ray\n\nray.init(\n _system_config={\n \"object_spilling_config\": json.dumps(\n {\"type\": \"filesystem\", \"params\": {\"directory_path\": \"/path/to/custom/directory\"}}\n )\n }\n)\n```\nNote that you can also specify multiple directories for spilling to spread the IO load and disk space usage across multiple physical devices if needed.",4202"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4203},4204{4205"question": "can you give me an example for *`--runtime-env-json`*",4206"sources": [4207"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",4208"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",4209"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",4210"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",4211"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",4212"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4213"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",4214"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",4215"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv"4216],4217"document_ids": [42184308,42194319,42204253,42214661,422213720,42234712,42244716,422514217,422647134227],4228"answer": " The `--runtime-env-json` option is used to specify a runtime environment for a job or task in Ray. Here's an example of how to use it with JSON:\n\n```python\nserve run --address=ray://<head-node-ip-address>:10001 --runtime-env-json='{\n \"env_vars\": {\"MY_ENV_VAR\": \"my-value\"},\n \"working_dir\": \"./project/src\",\n \"pip\": [\"requests\", \"chess\"]\n}' local_dev:graph\n```\n\nThis example sets environment variables, working directory, and Python packages for the runtime environment. You can also specify the runtime environment via a YAML file or a Python dictionary using the `runtime_env` parameter in the Ray API.",4229"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4230},4231{4232"question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",4233"sources": [4234"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4235"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",4236"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",4237"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",4238"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4239"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",4240"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4241"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",4242"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters"4243],4244"document_ids": [424513449,424614225,424714187,424814066,424914303,42505963,425113450,425214141,4253134514254],4255"answer": " The context does not provide a specific value for `maxConcurrentReplicas` when autoscaling configuration is specified. It only mentions that the `autoscaling_config` field is optional and if provided, it will enable autoscaling for the deployment's number of replicas. The actual value of `maxConcurrentReplicas` would depend on the specific requirements of the application and should be determined based on factors such as available resources and desired performance.",4256"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4257},4258{4259"question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",4260"sources": [4261"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4262"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4263"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",4264"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",4265"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",4266"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",4267"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4268"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4269"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails"4270],4271"document_ids": [427213451,427314303,427414187,427514225,427614066,427713561,427813450,427914302,4280141864281],4282"answer": " The value of `max_concurrent_queries` is not directly derived from `target_num_ongoing_requests_per_replica`. `max_concurrent_queries` is the maximum number of pending queries in a single replica, while `target_num_ongoing_requests_per_replica` is how many ongoing requests are expected to run concurrently per replica. The former is used to set a limit on the number of queries waiting to be processed, while the latter is used by the autoscaler to determine if more or fewer replicas are needed.",4283"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4284},4285{4286"question": "what is a `smoothing_factor`",4287"sources": [4288"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4289"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4290"https://docs.ray.io/en/master/tune/examples/includes/pb2_example.html#pb2-example",4291"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",4292"https://docs.ray.io/en/master/rllib/package_ref/utils.html#tensorflow-utilities",4293"https://docs.ray.io/en/master/rllib/package_ref/utils.html#numpy-utilities",4294"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.numpy.fc.html#ray-rllib-utils-numpy-fc",4295"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#twin-delayed-ddpg-td3",4296"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg"4297],4298"document_ids": [429913454,430013455,43013991,43021920,430311114,430411120,430513324,430611867,4307118494308],4309"answer": " A `smoothing_factor` is a multiplicative factor that speeds up or slows down each upscaling or downscaling decision made by an autoscaling algorithm. It can be used to adjust the sensitivity of the application to changes in traffic volume. For example, increasing the `upscale_smoothing_factor` will cause the application to scale up its resources more quickly in response to high traffic, while decreasing the `downscale_smoothing_factor` will make the application scale down more conservatively in response to low traffic.",4310"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4311},4312{4313"question": "What is the reason actors change their state to unhealthy?",4314"sources": [4315"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",4316"https://docs.ray.io/en/master/serve/api/index.html#get-api-serve-deployments-status",4317"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4318"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",4319"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",4320"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",4321"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",4322"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",4323"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#other-possible-scenarios-that-trigger-a-new-raycluster-preparation"4324],4325"document_ids": [432613836,432714006,432814294,432914708,43304505,433111597,433213935,4333747,433458144335],4336"answer": " Actors can become unhealthy if there's an error in the deployment's constructor, a crashed replica, or a general system or machine error. This information is reported in the \"message\" field of the status update, and the status can be checked using the \"serve status\" command.",4337"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4338},4339{4340"question": "How do I access logs for a dead node?",4341"sources": [4342"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4343"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4344"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",4345"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",4346"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",4347"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4348"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4349"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",4350"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs"4351],4352"document_ids": [4353492,4354493,4355662,4356739,4357740,4358512,43596631,436013894,43616954362],4363"answer": " Based on the provided context, you cannot access the logs from a dead node. However, you can access the logs for a specific node in a Ray cluster using the Ray logs API or CLI. Here's how you can do it using Python SDK:\n\n1. Get the node ID or IP from `ray list nodes` command.\n2. Use the `list_logs` function to list all retrievable log file names from the head node:\n```python\nfrom ray.util.state import list_logs\nlist_logs(node_id=<NODE_ID>)\n```\n3. Use the `get_log` function to retrieve a particular log file from a node:\n```python\nfrom ray.util.state import get_log\nget_log(filename=\"gcs_server.out\", node_id=<NODE_ID>)\n```\nReplace `<NODE_ID>` with the actual node ID or IP. Note that you can also use the `ray logs` CLI command with the `--node-id` option to retrieve logs.\n\nIf you need to access logs after the cluster or some of the nodes are stopped or terminated, you need to store and persist the logs. The context provides instructions for processing and exporting logs for clusters on VMs and KubeRay Clusters.",4364"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4365},4366{4367"question": "What are the reasons for a node to change it\u2019s status to dead?",4368"sources": [4369"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",4370"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4371"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",4372"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#rescheduling-bundles-on-a-dead-node",4373"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",4374"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.PlacementGroupState.html#ray-util-state-common-placementgroupstate",4375"https://docs.ray.io/en/master/ray-core/scheduling/index.html#nodeaffinityschedulingstrategy",4376"https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#handling-failures-and-node-preemption",4377"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#provider-cache-stopped-nodes"4378],4379"document_ids": [43804524,4381554,438213869,43835098,438413898,4385724,43865147,43879228,438862834389],4390"answer": " In the given context, a node can change its status to dead due to the following reasons:\n\n1. Raylet process failure: When a raylet process fails, the corresponding node is marked as dead.\n2. Node running low on memory: If a task is killed due to the node's memory running low, the node can be considered dead.\n3. Worker node failure: When a worker node fails, the actors running on it also fail, and KubeRay detects the node failure, marking it as dead.\n4. Head node failure: Killing the head pod or the head node can result in head node failure, and the corresponding node is marked as dead.\n\nAdditionally, nodes can be stopped when the cluster scales down if `provider.cache_stopped_nodes` is enabled, but this is not a failure scenario and is of low importance.",4391"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4392},4393{4394"question": "What are the reasons for spikes in node CPU utilization",4395"sources": [4396"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",4397"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#adding-cpu-only-nodes-to-your-cluster",4398"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4399"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",4400"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",4401"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",4402"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",4403"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4404"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training"4405],4406"document_ids": [4407468,44089227,4409554,44106375,44116364,44123015,44133008,4414555,441530024416],4417"answer": " Spikes in node CPU utilization can be caused by:\n\n1. Increased computational demands from tasks or actors.\n2. Bottlenecking in preprocessing compute time, leading to the addition of more CPU-only nodes.\n3. System processes running on the head node, such as GCS, in large clusters.\n4. Autoscaling configuration issues, where resource demands are not satisfied, causing queuing and node launch overheads.\n\nIn the provided context, there are no specific reasons mentioned for spikes in CPU utilization. However, the logs show that the node has 12.1/16.0 GiB memory in use, and one of the workers was killed due to exceeding the memory usage threshold. This might indirectly contribute to increased CPU usage, as the system tries to manage memory and handle tasks on the remaining resources.",4418"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4419},4420{4421"question": "What AWS machine type is recommended to deploy a RayService on EKS?",4422"sources": [4423"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-aks-microsoft-azure",4424"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",4425"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",4426"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",4427"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",4428"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",4429"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",4430"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",4431"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides"4432],4433"document_ids": [44345887,4435372,443613622,443713962,443813931,44395656,444013930,44416376,444256424443],4444"answer": " The recommended AWS machine type to deploy a RayService on EKS (Elastic Kubernetes Service) should have networking characteristics at least as good as an r5dn.16xlarge. For Ray clusters deployed using KubeRay, set `rayStartParams: {\"num-cpus\": \"0\"}` on the head node. This is because of the heavy networking load, and to avoid scheduling additional tasks on the head node.",4445"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4446},4447{4448"question": "Is there a way to configure the session name generated by ray?",4449"sources": [4450"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4451"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4452"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4453"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",4454"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",4455"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4456"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4457"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",4458"https://docs.ray.io/en/master/cluster/cli.html#ray-attach"4459],4460"document_ids": [44614384,44624811,446313974,44646613,44654514,4466511,44674823,44684662,446956284470],4471"answer": " Yes, you can configure the session name generated by Ray by using the `namespace` argument in the `ray.init()` function or the Ray Client. The session name will be set to the provided namespace. For example:\n```python\nray.init(namespace=\"my_session_name\")\n```\nor\n```python\nray.init(\"ray://1.2.3.4:10001\", namespace=\"my_session_name\")\n```\nAdditionally, you can specify a custom temporary directory for the Ray process using the `_temp_dir` argument in `ray.init()`. This will also affect the session directory path. For example:\n```python\nray.init(_temp_dir=\"/path/to/my/custom/temp/dir\")\n```\nThis will set the session directory to `/path/to/my/custom/temp/dir/ray/session_<session_name>`.",4472"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4473},4474{4475"question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",4476"sources": [4477"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",4478"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#python-sdk-api-reference",4479"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli",4480"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",4481"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",4482"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.get_job_info.html#ray-job-submission-jobsubmissionclient-get-job-info",4483"https://docs.ray.io/en/master/cluster/running-applications/job-submission/rest.html#ray-jobs-rest-api",4484"https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",4485"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job"4486],4487"document_ids": [44886575,44896539,44906547,44916640,44926637,44936671,44946571,4495275,449665784497],4498"answer": " Based on the provided context, the Ray Jobs Python SDK does not seem to have a direct method to get a link to the Ray dashboard for a given job. The SDK is more focused on submitting and managing jobs programmatically.\n\nHowever, you can access the Ray dashboard through the default URL, which is `http://localhost:8265` for a local Ray cluster. If you are using a remote Ray cluster, you would replace `localhost` with the address of the Ray head node.\n\nIf you need to get the Ray cluster head node address and the port used by the Ray Dashboard programmatically, you might need to implement that separately in your application, as this information is not directly provided by the JobSubmissionClient object in the SDK.",4499"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4500},4501{4502"question": "What may possible cause the node where this task was running crashed unexpectedly. This can happen if: (1) the instance where the node was running failed, (2) raylet crashes unexpectedly (OOM, preempted node, etc).",4503"sources": [4504"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",4505"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4506"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4507"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.LocalRayletDiedError.html#ray-exceptions-localrayletdiederror",4508"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.TaskUnschedulableError.html#ray-exceptions-taskunschedulableerror",4509"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4510"https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#understanding-objectlosterrors",4511"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.WorkerCrashedError.html#ray-exceptions-workercrashederror",4512"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure"4513],4514"document_ids": [45155129,45164658,4517554,45184858,45194830,4520557,45214559,45224906,452345244524],4525"answer": " The node where the task was running crashed unexpectedly due to memory pressure, which caused the raylet to kill 1 worker over the last time period. This is indicative of an out-of-memory (OOM) error. The error message also suggests that the node's memory may have been insufficient to handle the task's memory requirements, leading to the crash. To address the out-of-memory issue, refer to the documentation provided in the error message.",4526"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4527},4528{4529"question": "Do you know how to resolve (gcs_server) gcs_health_check_manager.cc:108: Health check failed for node? I observed that the node is still up and running.",4530"sources": [4531"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",4532"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",4533"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",4534"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",4535"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",4536"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",4537"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",4538"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",4539"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"4540],4541"document_ids": [45425638,45436505,454413870,45454508,45464522,454713871,45485709,45494515,4550147404551],4552"answer": " The error message you're seeing, \"Health check failed for node,\" suggests that the worker node is having trouble connecting to the GCS (Global Control Service) running on the head node. This could be due to several reasons, such as:\n\n* The head node is not running or is inaccessible at the specified address.\n* A different version of Ray is running at the specified address.\n* The specified address is wrong.\n* There are firewall settings preventing access.\n\nTo troubleshoot this issue, you can check if the head node is running and accessible at the specified address. You can also check if there are any firewall settings preventing access. Additionally, you can use a tool such as nmap or nc to check whether each port can be reached from a node.\n\nIf the GCS is not fault tolerant, then a head node failure will cause the entire Ray cluster to fail. To make the GCS fault tolerant, you can use HA Redis. This will allow the GCS to recover its state by reading from Redis when it restarts.\n\nIf you are using Kubernetes but not KubeRay, you can also make the GCS fault tolerant by backing it with Redis. When the GCS restarts, it will recover the state by reading from Redis. If the raylet fails to reconnect to the GCS for more than 60 seconds, the raylet will exit and the corresponding node will fail. This timeout threshold can be tuned by the OS environment variable RAY\\_gcs\\_rpc\\_server\\_reconnect\\_timeout\\_s. You can also set the OS environment variable RAY\\_external\\_storage\\_namespace to isolate the data stored in Redis.",4553"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4554},4555{4556"question": "Do you need the DAGDriver to deploy a serve application using RayServe?",4557"sources": [4558"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",4559"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-the-applications",4560"https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph",4561"https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",4562"https://docs.ray.io/en/master/serve/tutorials/gradio-dag-visualization.html#requirement-driver",4563"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",4564"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",4565"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",4566"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"4567],4568"document_ids": [456913741,457013655,457113426,457213502,457314524,457413859,457513652,457613650,4577147324578],4579"answer": " No, you don't need the DAGDriver to deploy a RayServe application. The DAGDriver is used to route HTTP requests through your call graph and can be configured with optional HTTP adapters for HTTP request handling. However, it is not a requirement for deploying RayServe applications. You can deploy RayServe applications using the `serve deploy` command with a configuration file or programmatically using the RayServe API.",4580"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4581},4582{4583"question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",4584"sources": [4585"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",4586"https://docs.ray.io/en/master/workflows/basics.html#sub-task-results",4587"https://docs.ray.io/en/master/serve/api/index.html#serve-run",4588"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",4589"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",4590"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Application.html#ray-serve-application",4591"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#defining-an-application-builder",4592"https://docs.ray.io/en/master/serve/tutorials/batch.html#define-the-deployment",4593"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#walkthrough"4594],4595"document_ids": [459613844,45979670,459813990,45995789,46005678,460114037,460213813,460314586,460480784605],4606"answer": " To provide the import path for a simple RayServe deployment, you need to specify the path to the application, which includes the directory name, Python file name, and the variable representing the Ray Serve application. For example, \"mobilenet.mobilenet:app\". You can also provide a runtime environment (runtime\\_env) that contains the necessary dependencies for your application. The runtime environment can be a local YAML file or a remote URI. The import path should be importable by Serve at runtime, and you can use the \"import_path\" field in the config file to specify it.",4607"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4608},4609{4610"question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",4611"sources": [4612"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",4613"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",4614"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",4615"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",4616"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",4617"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",4618"https://docs.ray.io/en/master/serve/develop-and-deploy.html#deploy-ray-serve-in-production",4619"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",4620"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status"4621],4622"document_ids": [46235751,46245760,46255752,46266144,46275647,46285658,462913441,463013926,463157634632],4633"answer": " Yes, the context provides an example of using RayJob in Kuberay. You can download the RayJob configuration file using the command `curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml` and create a RayJob using `kubectl apply -f ray_v1alpha1_rayjob.yaml`. The RayJob spec is defined in the ray-job.batch-inference.yaml file, which contains a spec for the RayCluster that will be created for the job.",4634"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4635},4636{4637"question": "Does Ray metrics have to be exported via an actor?",4638"sources": [4639"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",4640"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",4641"https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",4642"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",4643"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",4644"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",4645"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",4646"https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",4647"https://docs.ray.io/en/master/cluster/metrics.html#scraping-metrics"4648],4649"document_ids": [4650503,4651656,4652438,46535573,465411596,46554503,465614707,4657403,465855814659],4660"answer": " No, Ray metrics do not have to be exported via an actor. Metrics can be exported using the `ray.util.metrics` module, as shown in the example provided. Ray also collects and exposes physical stats, internal stats, and custom application metrics that can be exported as time series data. Ray runs a metrics agent per node to export system and application metrics, which can be scraped by Prometheus. The Dashboard agent process is responsible for aggregating and reporting metrics to the endpoints for Prometheus to scrape.",4661"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4662},4663{4664"question": "how can I avoid objects not getting spilled?",4665"sources": [4666"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",4667"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",4668"https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",4669"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",4670"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",4671"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",4672"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#out-of-core-data-processing",4673"https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",4674"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node"4675],4676"document_ids": [46776374,46784959,46794447,468014748,46814960,46824963,468314485,46841247,468549624686],4687"answer": " To avoid objects not getting spilled and potentially causing performance problems, you can:\n\n1. Use high-performance storage such as NVMe SSDs.\n2. Configure the directory where objects are spilled to by using `ray.init()` and setting the `object_spilling_config` parameter.\n3. Specify multiple directories for spilling to spread the IO load and disk space usage across multiple physical devices.\n4. If using an HDD, specify a large buffer size (> 1MB) to reduce IO requests during spilling.\n\nEnabling object spilling will allow for out-of-core data processing for memory-intensive distributed applications, but it comes with a performance penalty as data needs to be written to disk.",4688"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4689},4690{4691"question": "what\u2019s ray core",4692"sources": [4693"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",4694"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",4695"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",4696"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4697"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#want-to-learn-more",4698"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",4699"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",4700"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",4701"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api"4702],4703"document_ids": [47044662,47054481,47064673,47074811,47085444,47095656,47106835,4711367,4712139744713],4714"answer": " Ray Core is a part of the Ray framework that provides a small set of primitives for building and scaling distributed applications. It includes tasks, actors, and objects, and is supported by a Core API, Scheduling API, Runtime Env API, and various utility tools. Ray Core can be initialized with the `ray.init()` function. You can learn more about Ray Core in the \"Learning Ray\" book or the Ray Core Examples Gallery. Ray Core, along with Ray AI Libraries, makes up the Ray unified compute framework for ML workloads.",4715"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4716},4717{4718"question": "Does ray support cron job",4719"sources": [4720"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4721"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",4722"https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",4723"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",4724"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4725"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",4726"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-api-server",4727"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",4728"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api"4729],4730"document_ids": [473113974,47324662,47339634,47346637,47354811,47366208,473713474,47385635,473966384740],4741"answer": " No, Ray does not directly support cron jobs. However, you can use an external job scheduler that can interact with your Ray cluster to trigger workflow runs, including cron jobs. Ray Workflows does not have a built-in job scheduler, but you can use the Ray Jobs API for job submission and management on a Ray cluster. Jobs are bound to the lifetime of a Ray cluster, and retries or different runs with different parameters should be handled by the submitter.",4742"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4743},4744{4745"question": "can you give me the dependencies list for api read_images?",4746"sources": [4747"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4748"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4749"https://docs.ray.io/en/master/data/working-with-images.html#reading-images",4750"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",4751"https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",4752"https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",4753"https://docs.ray.io/en/master/installation.html#docker-source-images",4754"https://docs.ray.io/en/master/serve/tutorials/object-detection.html#serving-an-object-detection-model",4755"https://docs.ray.io/en/master/serve/http-guide.html#list-of-built-in-adapters"4756],4757"document_ids": [47587361,47597360,47606720,476110191,4762306,476314587,476481,476514472,4766135114767],4768"answer": " The dependencies for the `read_images` API include:\n\n* The `ray` library\n* The `pyarrow` library for handling file system operations\n* The `numpy` library for handling image data\n* The `PIL` library (Pillow) for opening and manipulating image files\n* The `requests` library for downloading image files (in the provided custom function)\n* The `os` library for handling file paths\n* The `io` library for handling byte streams\n\nThese dependencies are required for the `read_images` function to work correctly.",4769"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4770},4771{4772"question": "how do I kill a specific serve replica",4773"sources": [4774"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",4775"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",4776"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",4777"https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-deployments",4778"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",4779"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",4780"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",4781"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",4782"https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-applications"4783],4784"document_ids": [478513912,478613911,478713717,478814007,478913908,479013448,479113892,479213915,4793140154794],4795"answer": " To kill a specific serve replica, you can follow these steps:\n\n1. Get the list of actors by running `ray list actors` in the Python environment.\n2. Identify the NAME of the specific ServeReplica you want to kill from the list.\n3. Get a handle to the replica by running `replica_handle = ray.get_actor(\"NAME\", namespace=\"serve\")` in the Python environment.\n4. Kill the replica by running `ray.kill(replica_handle, no_restart=True)` in the Python environment.\n\nReplace \"NAME\" with the actual name of the replica you want to kill.",4796"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4797}4798]4799}4800