llm-applications
4799 строк · 437.7 Кб
1{2"config": {3"experiment_name": "llama-2-70b-chat-hf",4"chunk_size": 700,5"chunk_overlap": 50,6"num_chunks": 9,7"embedding_model_name": "thenlper/gte-large",8"llm": "meta-llama/Llama-2-70b-chat-hf",9"temperature": 0.0,10"max_context_length": 4096,11"system_content": "Answer the query using the context provided. Be succinct.",12"assistant_content": "",13"docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",14"experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",15"references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",16"num_samples": 17717},18"results": [19{20"question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",21"sources": [22"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",23"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",24"https://docs.ray.io/en/master/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": " The document appears to provide information about Ray Data, a library for working with large datasets in Python. The document mentions various topics related to data processing and transformation, including batch formats, batch size, and data preprocessing.\n\nOne piece of advice for working with Ray Data and batches is to use the `map_batches()` method instead of `map()` or `flat_map()` when working with vectorized transformations. This is because `map_batches()` is designed to work efficiently with large datasets and can handle batches of data in parallel, leading to better performance.\n\nAnother piece of advice is to use the `batch_format` parameter when calling `map_batches()` to specify the format of the batches. Ray Data represents batches as dictionaries of NumPy ndarrays or pandas DataFrames by default, but you can configure the batch type by specifying `batch_format=\"numpy\"` or `batch_format=\"pandas\"`.\n\nAdditionally, the document mentions the `Preprocessor` class and its `transform_batch()` method, which can be used to transform data batches. The `Preprocessor` class is a base class for implementing custom data preprocessing logic.\n\nOverall, the document provides various tips and best practices for working with Ray Data and large datasets in Python, including using `map_batches()` for efficient data processing and configuring batch formats for optimal performance.",44"llm": "meta-llama/Llama-2-70b-chat-hf"45},46{47"question": "How does autoscaling work in a Ray Serve application?",48"sources": [49"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",50"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",51"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",52"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",53"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",54"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",55"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#load-metrics-are-based-on-application-semantics",56"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",57"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling"58],59"document_ids": [6013471,616363,625559,6313448,645969,655953,665971,6714726,681344769],70"answer": " Autoscaling in Ray Serve works by automatically adjusting the number of replicas based on the load of incoming requests. The Ray autoscaler, which runs on the head node or a sidecar container, monitors the resource demands of the workload and increases the number of worker nodes when necessary. When worker nodes are idle, the autoscaler removes them to maintain efficiency. The autoscaler only reacts to task and actor resource requests, not application metrics or physical resource utilization. Ray Serve scales down by terminating replica actors and removing nodes when possible. The autoscaler works with the Ray cluster launcher and can be configured using the Ray cluster API.",71"llm": "meta-llama/Llama-2-70b-chat-hf"72},73{74"question": "how do I get the address of a ray node",75"sources": [76"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",77"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",78"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",79"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",80"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",81"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",82"https://docs.ray.io/en/master/ray-core/api/doc/ray.nodes.html#ray-nodes",83"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",84"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints"85],86"document_ids": [87739,884817,89754,904446,916552,92493,934769,946502,95558796],97"answer": " To get the address of a Ray node, you can use the `ray.util.state.get_node` function, which takes the node ID as an argument and returns the node's address. If you don't know the node ID, you can use the `ray.nodes` function to get a list of all the nodes in the cluster, and then find the ID of the node you want to get the address for.\n\nHere's an example of how you can use these functions to get the address of a Ray node:\n```\nimport ray\n\n# Get a list of all the nodes in the cluster\nnodes = ray.nodes()\n\n# Find the ID of the node you want to get the address for\nnode_id = nodes[0].id\n\n# Get the address of the node\naddress = ray.util.state.get_node(node_id).address\n\nprint(address)\n```\nThis will print the address of the first node in the cluster. You can replace `nodes[0].id` with the ID of the node you want to get the address for.\n\nAlternatively, you can use the `ray logs` command to get the address of a Ray node. The `ray logs` command will show you the logs from the head node of the cluster, and the address of the head node will be included in the logs. To get the address of a different node, you can use the `--node-id` option followed by the ID of the node you want to get the address for.\n\nFor example:\n```\nray logs --node-id <NODE_ID>\n```\nThis will show you the logs from the node with the specified ID, and the address of the node will be included in the logs.\n\nYou can also use the `ray.util.state.list_logs` function to get a list of logs from a specific node. This function takes the node ID as an argument, and returns a list of logs from that node.\n\nFor example:\n```\nimport ray\n\n# Get a list of logs from a specific node\nlogs = ray.util.state.list_logs(node_id=<NODE_ID>)\n\n# Print the address of the node\nprint(logs[0].node_ip)\n```\nThis will print the address of the node that the logs are from.\n\nI hope this helps! Let me know if you have any other questions.",98"llm": "meta-llama/Llama-2-70b-chat-hf"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 supports NCCL (NVIDIA Collective Communication Library) as an optional high-performance communication backend for distributed CPUs and GPUs.",125"llm": "meta-llama/Llama-2-70b-chat-hf"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. Ray Train supports running DeepSpeed with other frameworks, including Lightning, Transformers, and Accelerate. You can configure DeepSpeed using the accelerate.Accelerator object in your training function, and Ray Train will automatically apply the configuration on the workers. The RayDeepSpeedStrategy in Ray Train allows you to run DeepSpeed with Accelerate, and PyTorch Lightning offers a DeepSpeed integration that provides a simple interface to configure the knobs for DeepSpeed.",152"llm": "meta-llama/Llama-2-70b-chat-hf"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": " It looks like you're asking about how to use AsyncIO with Ray to run remote tasks asynchronously.\n\nTo do this, you can use the `asyncio.wrap_future()` function to convert a Ray ObjectRef into an asyncio Future. Then, you can use the `asyncio.run()` function to run the asynchronous task.\n\nHere's an example code snippet that demonstrates this:\n```\nimport ray\nimport asyncio\n\n@ray.remote\ndef some_task():\n return 1\n\nasync def convert_to_asyncio_future():\n ref = some_task.remote()\n fut: asyncio.Future = asyncio.wrap_future(ref.future())\n print(await fut)\n\nasyncio.run(convert_to_asyncio_future())\n```\nThis code defines a remote task `some_task` using the `@ray.remote` decorator, and then uses the `asyncio.wrap_future()` function to convert the Ray ObjectRef into an asyncio Future. Finally, it uses the `asyncio.run()` function to run the asynchronous task.\n\nNote that you can also use the `asyncio.wait()` function to wait for multiple Futures simultaneously, as shown in the example code snippet you provided.",179"llm": "meta-llama/Llama-2-70b-chat-hf"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 tools for distributed data processing. Spark is a widely used open-source framework for big data processing that can handle batch processing, stream processing, and machine learning tasks. Ray is a high-performance distributed computing framework that is designed to handle complex data processing tasks and can be used for both batch processing and stream processing. Dask is a Python parallel computing library that allows for parallelizing existing serial code and is designed for large-scale data processing tasks.\n\nIn terms of performance, Spark and Ray are both designed to handle large-scale data processing tasks and can perform well on distributed datasets. Dask is designed to work with existing data processing libraries like NumPy and Pandas, and can be used to parallelize existing code, but may not be as optimized for distributed processing as Spark and Ray.\n\nIn terms of ease of use, Spark and Dask have more mature APIs and are easier to use, while Ray has a more complex API but offers more advanced features.\n\nIn terms of interoperability, Ray and Dask can be used together seamlessly, with Dask's collections and tasks executing on top of Ray's scheduler. Spark and Ray can also be used together, with Spark tasks executing on top of Ray's scheduler using the RayDP library.\n\nIn summary, the choice between Spark, Ray, and Dask will depend on the specific needs of the project. If you need a widely adopted and mature framework for big data processing, Spark may be the best choice. If you need a high-performance distributed computing framework that can handle complex data processing tasks, Ray may be the best choice. If you need a library that allows you to parallelize existing serial code and is designed for large-scale data processing tasks, Dask may be the best choice.",206"llm": "meta-llama/Llama-2-70b-chat-hf"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 does not overload a node with more tasks than the resources allow. Instead, it ensures that the sum of the resource requirements of all concurrently running tasks and actors on a given node does not exceed the node's total resources. This is achieved by specifying the resource requirements for each task or actor, such as CPU, GPU, and custom resources. The scheduler then decides which node to run the task on based on the available resources and the specified scheduling strategy.\n\nIf a task requires more resources than available on a node, Ray will not run the task on that node, and instead, it will be scheduled on a different node with sufficient resources. This way, Ray ensures that the node's resources are not overloaded, and the task can run successfully.\n\nIt's important to note that resource requirements do not impose limits on actual physical resource usage. It's the responsibility of the user to ensure that tasks or actors use no more resources than specified via resource requirements. Ray also does not provide CPU isolation for tasks or actors, and it's up to the operating system to schedule and run the task.\n\nIn summary, Ray's resource management mechanism ensures that the sum of the resource requirements of all concurrently running tasks and actors on a given node does not exceed the node's total resources, preventing overloading and ensuring efficient and successful task execution.",233"llm": "meta-llama/Llama-2-70b-chat-hf"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": " When should you use Ray Client?\n\nYou should use Ray Client when you want to connect an interactive Python shell to a remote cluster, allowing you to leverage a remote Ray cluster just like you would with Ray running on your local machine. It is useful for developing interactively in a local Python shell, but it requires a stable connection to the remote cluster and will terminate the workload if the connection is lost for more than 30 seconds. If you have a long-running workload that you want to run on your cluster, we recommend using Ray Jobs instead.",260"llm": "meta-llama/Llama-2-70b-chat-hf"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, you can use the `ActorPoolStrategy` and set the `min_size` and `max_size` parameters to the desired values. This will create a pool of actors with at least `min_size` and at most `max_size` workers, and each worker will run a portion of the data.\n\nHere's an example code snippet:\n```python\nimport ray\n\n# Create an actor pool with at least 2 and at most 8 workers\nstrategy = ActorPoolStrategy(min_size=2, max_size=8)\n\n# Define a function to run on each worker\n@ray.remote\ndef my_function(data):\n # Do something with the data\n return data\n\n# Create a list of data to process\ndata = [1, 2, 3, 4, 5]\n\n# Map the function over the data using the actor pool\nresults = ray.get([my_function.remote(d) for d in data])\n```\nIn this example, the `ActorPoolStrategy` will create a pool of actors with at least 2 and at most 8 workers, and each worker will run a portion of the data. The `my_function` function will be executed on each worker, and the results will be returned as a list of values.\n\nYou can also use the `ray.util.ActorPool` class to create a fixed pool of actors and run functions on them.\n```python\nimport ray\n\n# Create a fixed pool of 4 actors\npool = ray.util.ActorPool(4)\n\n# Define a function to run on each actor\n@ray.remote\ndef my_function(data):\n # Do something with the data\n return data\n\n# Create a list of data to process\ndata = [1, 2, 3, 4]\n\n# Run the function on each actor in the pool\nresults = [pool.map(my_function, d) for d in data]\n```\nIn this example, the `ray.util.ActorPool` class will create a fixed pool of 4 actors, and the `my_function` function will be run on each actor in the pool. The results will be returned as a list of values.\n\nYou can also use the `ray.nodes()` function to get a list of nodes in the cluster, and the `ray.cluster_resources()` function to get the current total cluster resources and the `ray.available_resources()` function to get the current available cluster resources.\n\nIt's also important to note that actors that don\u2019t require any resources (i.e., `num_cpus=0` with no other resources) are effectively spread across the cluster by randomly choosing a node in the cluster without considering resource utilization.",287"llm": "meta-llama/Llama-2-70b-chat-hf"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": " It's likely that the connection refused error is due to the Ray cluster not being ready to accept requests yet. When you run `ray debug`, it tries to connect to the Ray cluster to retrieve information about the running applications. However, if the cluster is not yet ready, it will refuse the connection.\n\nYou can try waiting for a few seconds before running `ray debug` again to give the cluster time to start up fully. If you continue to encounter this issue, it's possible that the dashboard or dashboard agent may have failed to start. You can check the logs for these components to see if there are any errors.\n\nAnother possibility is that the address you're using to connect to the Ray cluster is incorrect. Make sure that you're using the correct IP address and port for your Ray cluster. You can find this information in the Ray dashboard or by running `ray.init()` and looking at the output.",314"llm": "meta-llama/Llama-2-70b-chat-hf"315},316{317"question": "How does Ray AIR set up the model to communicate gradient updates across machines?",318"sources": [319"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",320"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#asynchronous-parameter-server-training",321"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",322"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",323"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",324"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",325"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",326"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",327"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#defining-the-parameter-server"328],329"document_ids": [3309845,3315338,3329820,333374,334156,335337,336183,33710019,3385329339],340"answer": " Ray AIR sets up the model to communicate gradient updates across machines by using a parameter server. 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. The workers communicate with the parameter server using Ray's remote actor functionality, which allows them to send gradients and receive updates to the model.",341"llm": "meta-llama/Llama-2-70b-chat-hf"342},343{344"question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",345"sources": [346"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",347"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",348"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",349"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",350"https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",351"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",352"https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",353"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",354"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice"355],356"document_ids": [35713621,35813974,35913614,36013597,36113420,36210282,363144,3645785,3655739366],367"answer": " Ray Serve is a scalable model serving library for building online inference APIs. It is framework-agnostic, allowing you to use a single toolkit to serve models built with different frameworks like PyTorch, Tensorflow, Keras, and Scikit-Learn. Ray Serve is particularly well-suited for model composition and many model serving, enabling you to build a complex inference service consisting of multiple ML models and business logic all in Python code. It supports complex model deployment patterns requiring the orchestration of multiple Ray actors, where different actors provide inference for different models.\n\nUnlike Modal and Seldon, Ray Serve is not a full-fledged ML platform and lacks the functionality for managing the lifecycle of your models, visualizing their performance, etc. Instead, it primarily focuses on model serving and providing the primitives for you to build your own ML platform on top.\n\nRay Serve is a good choice when you want to avoid locking yourself into a solution that is tied to a specific framework or cloud provider. It allows you to use containers and deploy your models on all major public clouds, K8s, as well as on bare-metal, on-premise machines.\n\nIn summary, Ray Serve is a scalable model serving library that allows you to build complex inference services using multiple ML models and business logic in Python code. It is framework-agnostic, supports model composition, and can be deployed on various infrastructure options, making it a good choice for those who want flexibility and avoid vendor lock-in.",368"llm": "meta-llama/Llama-2-70b-chat-hf"369},370{371"question": "How do I deploy an LLM workload on top of Ray Serve?",372"sources": [373"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",374"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",375"https://docs.ray.io/en/master/cluster/vms/user-guides/community/lsf.html#deploying-on-lsf",376"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",377"https://docs.ray.io/en/master/serve/api/index.html#serve",378"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",379"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",380"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",381"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air"382],383"document_ids": [38413618,38513677,3866382,3875636,38813983,38913598,39013416,39113647,39210282393],394"answer": " To deploy an LLM workload on top of Ray Serve, you can follow these steps:\n\n1. Write your LLM application as a Python module, using any arbitrary Python code.\n2. Use the @serve.deployment decorator to define a deployment, which contains the business logic or ML model to handle incoming requests.\n3. Bind the deployment with optional arguments to the constructor.\n4. Use the serve deploy command to deploy the config file to a Ray cluster over HTTP.\n5. Update the application using the CLI.\n6. Deploy to a remote cluster started with the Ray Cluster Launcher.\n7. Monitor your applications using the CLI and the Ray Serve dashboard.\n\nRay Serve supports batching and can integrate with other Ray components, such as Ray Data, to efficiently accommodate user traffic without wasting resources. Additionally, Ray Serve can scale up or down (or even autoscale) to match the incoming request load, making it a flexible and efficient choice for deploying LLM applications.",395"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Based on the context provided, the recommended memory size for the model \"EleutherAI/gpt-j-6B\" would be at least 16 GB, as it is specified in the note that the model requires 16 GB of memory to run.",422"llm": "meta-llama/Llama-2-70b-chat-hf"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 the `MultiWorkerMirroredStrategy` in TensorFlow and set the global batch size to be the same as the batch size in the single-worker code. Each worker processes the same size batch as in the single-worker code. You can then use TensorBoard to visualize the results by starting TensorBoard on the remote head machine and accessing it through a web interface.\n\nYou can also customize the directory of results by specifying `air.RunConfig(storage_path=..)` and then point TensorBoard to that directory to visualize the results. Additionally, you can use awless for easy cluster management on AWS.\n\nIt's important to note that when running many concurrent trials on a single node, logging can introduce significant overhead, and you should aim to report fewer results per second to avoid overwhelming the system. In general, around 50-100 results per second can still be managed without too much overhead when logging is enabled, but measures to decrease incoming results should be considered beyond that threshold.",449"llm": "meta-llama/Llama-2-70b-chat-hf"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 and set the `upload_dir`, `syncer`, `sync_period`, `sync_timeout`, `sync_artifacts`, and `sync_on_checkpoint` attributes.\n\nFor example:\n```\nsync_config = ray.train.SyncConfig(\n upload_dir='my-upload-dir',\n syncer='my-syncer',\n sync_period=300,\n sync_timeout=1800,\n sync_artifacts=True,\n sync_on_checkpoint=True\n)\n```\nThis configuration object can then be passed to the `ray.train.RunConfig` constructor as the `sync_config` argument.\n\nNote that the `ray.train.SyncConfig` class is deprecated and will be hard-deprecated in Ray 2.7. Instead, you can use the `ray.train.CheckpointConfig` class to specify your checkpointing strategy.",476"llm": "meta-llama/Llama-2-70b-chat-hf"477},478{479"question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",480"sources": [481"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",482"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",483"https://docs.ray.io/en/master/ray-observability/getting-started.html#job-profiling",484"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",485"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",486"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",487"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",488"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/observability.html#method-3-check-logs-of-ray-pods",489"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods"490],491"document_ids": [4925764,493444,494422,495415,49613523,4976631,4986157,4995855,5005665501],502"answer": " To quickly narrow down the root cause of a failed Ray job, you can follow these steps:\n\n1. Check the output of the Ray job using `kubectl logs -l=job-name=rayjob-sample`.\n2. Look for error messages or warning signs in the logs.\n3. If the job was submitted by the Job API, check the driver logs using `ray job logs 'raysubmit_xxxxxxxxxxxxxxxx' --address http://127.0.0.1:8265 --follow`.\n4. Check the worker logs by accessing the log files on the Pods using `kubectl exec -it $RAY_POD -n $YOUR_NAMESPACE -- bash`.\n5. If the job was submitted by Ray Client, check the client logs at `/tmp/ray/session_latest/logs` on the head node.\n6. Use a logging stack such as ELK or Loki to search the stored logs by deployment or replica.\n7. Check the Ray Serve logs directly by accessing the log files on the pods using `kubectl exec -it $RAY_POD -n $YOUR_NAMESPACE -- bash`.\n\nRemember to substitute the Ray job's submission ID in the command-line tools.",503"llm": "meta-llama/Llama-2-70b-chat-hf"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": " You can specify the number of GPUs a serve deployment needs by using the `ray_actor_options` parameter and setting the `num_gpus` value. For example, `@serve.deployment(ray_actor_options={\"num_gpus\": 1})` will reserve one GPU for each replica of the deployment. You can also set this value to a fraction to pack multiple replicas together on a machine with fewer GPUs than replicas. Additionally, you can use the `num_cpus` parameter to reserve a logical number of CPUs for each replica.",530"llm": "meta-llama/Llama-2-70b-chat-hf"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": " It seems that one of your worker nodes is crashing due to memory pressure, and Ray is killing the worker to prevent further damage. The error message suggests that the worker was using too much memory, and Ray's memory monitor killed it to prevent out-of-memory issues.\n\nTo debug this issue, you can use the following steps:\n\n1. Check the Ray logs for more information about the worker that crashed. You can use the command `ray logs raylet.out -ip <IP address of the node>` to view the logs.\n2. Look for any error messages or warnings related to memory usage in the logs.\n3. Check the memory usage of your tasks and workers using the command `ray logs worker-<worker ID>*out -ip <IP address of the node>`.\n4. Make sure that your tasks are not using too much memory. You can use the `ray up` command to see the memory usage of each task.\n5. Consider increasing the memory available to your workers or reducing the memory usage of your tasks.\n6. If the issue persists, you can try disabling log deduplication using the command `RAY_DEDUP_LOGS=0` to see more detailed logs.\n\nIt's also worth noting that the error message mentions that the worker was running a PyTorch training job, and that the `DistributedSampler` was used with the dataloaders. This may be a factor in the memory usage issue, and you may want to investigate further.",557"llm": "meta-llama/Llama-2-70b-chat-hf"558},559{560"question": "what are the possible reasons for nodes dying in a cluster?",561"sources": [562"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",563"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",564"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",565"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",566"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",567"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",568"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",569"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",570"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group"571],572"document_ids": [57313898,5744522,5754519,5764524,5779524,578554,5795639,5805129,5814790582],583"answer": " Possible reasons for nodes dying in a cluster include:\n\n* Head node failure, which can be simulated by killing the head pod or node\n* Raylet failure, which can occur due to memory pressure (OOM) or other reasons\n* Node failure, which can be caused by overloading the head node with too many simultaneous connections\n* Autoscaler terminating the cluster due to user action\n* Tasks being killed due to the node running low on memory\n* Placement group issues, such as exceeding the maximum CPU fraction per node.",584"llm": "meta-llama/Llama-2-70b-chat-hf"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": " You can programmatically get a Ray remote cluster to a target size immediately without scaling up through autoscaler by using the `ray.autoscaler.sdk.request_resources()` method. This method allows you to command the autoscaler to scale the cluster up to a desired size without waiting for the autoscaler to gradually scale up the cluster based on resource demand.\n\nHere's an example of how to use this method:\n```\nimport ray\n\n# Create a Ray cluster\ncluster = ray.init(num_cpus=2, num_gpus=1)\n\n# Request resources to scale the cluster to a target size\nray.autoscaler.sdk.request_resources(num_cpus=4, bundles=[{\"name\": \"my-bundle\", \"resources\": {\"CPU\": 2, \"GPU\": 1}}])\n```\nThis will immediately scale the cluster to have 4 CPUs and 2 GPUs, without waiting for the autoscaler to gradually scale up the cluster based on resource demand.\n\nNote that you can also use the `ray.autoscaler.sdk.request_resources()` method to scale the cluster down to a target size by specifying a lower number of resources than the current size of the cluster.",611"llm": "meta-llama/Llama-2-70b-chat-hf"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 with the `prefetch_batches` parameter set to 0. This will disable the prefetching of batches and make the iterator block until the next batch is available.\n\nHere's an example:\n```\nds = ray.data.range(5)\nds_iter = ds.iter_batches(prefetch_batches=0)\n```\nThis will return an iterator that yields batches of size 5, but it will block until the next batch is available instead of prefetching batches in the background.\n\nNote that disabling prefetching can lead to slower performance, especially if the dataset is large and the batch size is small. It's generally recommended to use prefetching to improve performance, but in some cases, such as when processing large datasets, it may be necessary to disable it to avoid running out of memory.",638"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Batch and block are both used in the context of Ray datasets, but they refer to different concepts:\n\n* Batch: A batch is a user-facing object that represents a set of data points used for training a model. It is a high-level abstraction that allows data to be processed in parallel.\n* Block: A block is an internal abstraction that represents a unit of data processing. It is a lower-level concept that is used to partition rows into smaller, distributed data blocks. A block is not directly accessible to the user, and it is used by Ray Data to perform operations in parallel.\n\nIn summary, a batch is a user-facing object that represents a set of data points, while a block is an internal abstraction that represents a unit of data processing.",665"llm": "meta-llama/Llama-2-70b-chat-hf"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 on Windows, follow these steps:\n\n1. Clone the Ray repository and go to the example directory: `git clone https://github.com/ray-project/ray.git`\n2. Install dependencies: `pip install -Ur dreambooth/requirements.txt`\n3. Prepare directories and environment variables:\n\t* `export DATA_PREFIX=\"/tmp\"`\n\t* `export ORIG_MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"`\n\t* `export ORIG_MODEL_HASH=\"b95be7d6f134c3a9e62ee616f310733567f069ce\"`\n\t* `export ORIG_MODEL_DIR=\"$DATA_PREFIX/model-orig\"`\n\t* `export ORIG_MODEL_PATH=\"$ORIG_MODEL_DIR/models--${ORIG_MODEL_NAME/\\\\//--}/snapshots/$ORIG_MODEL_HASH\"`\n4. Build Ray from source:\n\t* `cd ray`\n\t* `npm ci`\n\t* `npm run build`\n5. Build the dashboard:\n\t* `cd dashboard/client`\n\t* `npm ci`\n\t* `npm run build`\n6. Return to the top-level Ray directory: `cd ../..`\n7. Build Ray for Python:\n\t* Make sure you activate any Python virtual (or conda) environment you could be using.\n\t* `pip install \"ray[default]\"`\n\nNote: The instructions are based on the provided context and might not cover all possible scenarios.",692"llm": "meta-llama/Llama-2-70b-chat-hf"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": " It seems like you're experiencing issues with Ray Rollout Workers deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`. The error message mentions that the Ray cluster does not have any available CPUs, and the Dataset job will hang unless more CPUs are freed up.\n\nTo debug this issue, you can try the following steps:\n\n1. Check the Ray cluster's resource usage: You can use the Ray CLI to check the current resource usage of the cluster. You can do this by running the command `ray up` or `ray cluster` to see the current state of the cluster, including the number of available CPUs.\n2. Check the Dataset job's resource requirements: You can check the resource requirements of the Dataset job that is being executed by the Ray TrainWorker. You can do this by looking at the job's definition in the Ray CLI or by checking the job's configuration file.\n3. Check for any resource-intensive actors or Tune trials: As the error message suggests, resource-intensive actors or Tune trials can cause the Ray cluster to run out of available CPUs. You can check for any such actors or trials by looking at the Ray CLI output or by checking the Ray logs.\n4. Increase the number of CPUs available to the Ray cluster: If the issue is caused by a lack of available CPUs, you can try increasing the number of CPUs available to the Ray cluster. You can do this by adding more nodes to the cluster or by increasing the number of CPUs available on each node.\n5. Profile the RolloutWorker code: You can use profiling tools like cProfile or pstats to profile the RolloutWorker code and identify any performance bottlenecks or issues. This can help you understand where the code is spending most of its time and identify potential optimizations.\n\nBy following these steps, you should be able to identify and resolve the issue causing the Ray Rollout Workers to deadlock.",719"llm": "meta-llama/Llama-2-70b-chat-hf"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()` method to concatenate them horizontally, or the `union()` method to concatenate them across rows. You can also use the `concatenate_datasets()` function to concatenate multiple datasets.\n\nHere's an example of using `zip()` to join two datasets:\n```\nimport ray\n\nds1 = ray.data.range(5)\nds2 = ray.data.range(5)\n\njoined_ds = ds1.zip(ds2)\nprint(joined_ds.take_batch())\n```\nThis will output:\n```\n[{'id': 0, 'id_1': 0}, {'id': 1, 'id_1': 1}, {'id': 2, 'id_1': 2}, {'id': 3, 'id_1': 3}, {'id': 4, 'id_1': 4}]\n```\nHere's an example of using `union()` to join two datasets:\n```\nimport ray\n\nds1 = ray.data.range(5)\nds2 = ray.data.range(5)\n\njoined_ds = ds1.union(ds2)\nprint(joined_ds.take_all())\n```\nThis will output:\n```\n[0, 1, 2, 3, 4, 0, 1, 2, 3, 4]\n```\nYou can also use the `concatenate_datasets()` function to concatenate multiple datasets. Here's an example:\n```\nimport ray\n\nds1 = ray.data.range(5)\nds2 = ray.data.range(5)\nds3 = ray.data.range(5)\n\njoined_ds = ray.data.concatenate_datasets([ds1, ds2, ds3])\nprint(joined_ds.take_all())\n```\nThis will output:\n```\n[0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4]\n```\nNote that the `zip()` and `union()` methods will concatenate the datasets horizontally, while the `concatenate_datasets()` function will concatenate them across rows.",746"llm": "meta-llama/Llama-2-70b-chat-hf"747},748{749"question": "Is there a way to retrieve an object ref from its id?",750"sources": [751"https://docs.ray.io/en/master/ray-core/objects.html#objects",752"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",753"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",754"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait",755"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",756"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",757"https://docs.ray.io/en/master/ray-core/api/core.html#objects",758"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",759"https://docs.ray.io/en/master/ray-core/objects.html#objects"760],761"document_ids": [7624413,7635426,7644982,7654750,7664885,76714041,7684651,7695436,7704412771],772"answer": " Yes, you can retrieve an object ref from its ID by using the `ray.get()` method. The method takes a list of object refs or a single object ref as input and returns a list of object refs that are ready and a list of object IDs that are not ready. You can use the `ray.wait()` method to wait for the object to be available in the local object store before returning it. Additionally, you can use the `ray.put()` method to store an object in the object store and retrieve it later using its ID.",773"llm": "meta-llama/Llama-2-70b-chat-hf"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 `Checkpoint` from a model in memory, you can use the `Checkpoint.from_model()` method, which takes the model as an argument and stores its state in the checkpoint.\n\nHere's an example:\n```\nimport tensorflow as tf\n\n# Create a model\nmodel = tf.keras.applications.resnet.ResNet101()\n\n# Create a checkpoint from the model\ncheckpoint = tf.keras.checkpoint.Checkpoint.from_model(model)\n```\nThis will create a checkpoint that stores the model's state, including its weights and architecture. You can then use the `checkpoint` object to save the model's state to a file or directory, or to restore the model from a previous checkpoint.\n\nHere's an example of how to save the checkpoint to a file:\n```\n# Save the checkpoint to a file\ncheckpoint.save('my_model.ckpt')\n```\nAnd here's an example of how to restore the model from a checkpoint:\n```\n# Load the checkpoint from a file\ncheckpoint = tf.keras.checkpoint.Checkpoint.from_file('my_model.ckpt')\n\n# Restore the model from the checkpoint\nmodel = checkpoint.get_model()\n```\nNote that the `Checkpoint` class also has other methods and attributes that you can use to work with checkpoints, such as `get_metadata()`, `get_preprocessor()`, `set_metadata()`, `set_preprocessor()`, `to_directory()`, and `update_metadata()`. You can find more information about these methods and attributes in the TensorFlow documentation.",800"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Deployment in Ray Serve refers to a group of actors that can handle traffic and are defined as a single class with a number of options, including the number of replicas, each of which will map to a Ray actor at runtime. Requests to a deployment are load balanced across its replicas. A deployment can be scaled up or down to match the incoming request load. To define a deployment, use the @serve.deployment decorator on a Python class or function. Then, bind the deployment with optional arguments to the constructor.",827"llm": "meta-llama/Llama-2-70b-chat-hf"828},829{830"question": "What is user config in Ray Serve? how do I use it?",831"sources": [832"https://docs.ray.io/en/master/serve/api/index.html#serve-config",833"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",834"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",835"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#updating-user-config",836"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",837"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-4-prepare-your-ray-serve-python-scripts-and-run-the-ray-serve-application",838"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#dynamically-changing-parameters-without-restarting-your-replicas-user-config",839"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#adding-deleting-and-updating-applications",840"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"841],842"document_ids": [84313986,84413831,84513974,84613687,84713551,8485983,84913565,85013661,8516615852],853"answer": " User config in Ray Serve refers to the configuration settings that define a Serve application's desired state, such as the number of replicas, route prefix, and other deployment-specific settings. To use user config, you can specify it in the Serve config file or pass it in as an argument when deploying the application.\n\nFor example, you can define a user config in a YAML file like this:\n```\ndeployments:\n - name: MyApp\n user_config:\n threshold: 1.5\n```\nThen, you can deploy the application with the following command:\n```\nserve deploy my_app.yaml\n```\nThis will deploy the application with the specified user config. You can also update the user config by modifying the YAML file and re-deploying the application.\n\nAlternatively, you can pass the user config as an argument when deploying the application:\n```\nserve deploy my_app.yaml --user-config threshold=1.5\n```\nThis will deploy the application with the specified user config without the need to modify the YAML file.\n\nNote that user config can also be updated in-place by re-applying the Serve config file with the new user config settings. See the Ray Serve documentation for more information on in-place updates.",854"llm": "meta-llama/Llama-2-70b-chat-hf"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 difference between PACK and SPREAD strategy in Ray is:\n\n* PACK strategy packs bundles into as few nodes as possible, while SPREAD strategy places bundles across distinct nodes as even as possible.\n* PACK strategy is a soft policy, meaning that if bundles cannot be packed into a single node, they are spread to other nodes. STRICT_PACK strategy is a stricter version of PACK, where bundles must be placed into a single node on the cluster.\n* SPREAD strategy is a soft policy, meaning that if strict spreading is not feasible, bundles can be placed on overlapping nodes. STRICT_SPREAD strategy is a stricter version of SPREAD, where each bundle must be scheduled in a separate node.\n\nIn summary, PACK strategy prioritizes packing bundles into fewer nodes, while SPREAD strategy prioritizes spreading bundles across multiple nodes. STRICT_PACK and STRICT_SPREAD are stricter versions of PACK and SPREAD, respectively, that enforce a single node or separate nodes for each bundle.",881"llm": "meta-llama/Llama-2-70b-chat-hf"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": " To run Ray across multiple machines, it's recommended to start a Ray cluster for each user to ensure isolation of their workloads. A Ray cluster consists of a set of worker nodes connected to a common Ray head node, and can be fixed-size or autoscale up and down according to the resources requested by applications running on the cluster. Ray clusters can be deployed on various infrastructure, including laptops, single servers, multiple servers, Kubernetes, cloud providers, and on-premise environments.\n\nTo deploy a Ray cluster, you can use the `ray up` command to launch a cluster on your infrastructure of choice. Ray also provides a growing ecosystem of community integrations, including support for Kubernetes, cloud providers, and on-premise environments.\n\nIt's important to note that while Ray clusters support multi-tenancy, it's not recommended to run multiple jobs from different users simultaneously in a Ray cluster in production environments, as Ray currently lacks some features for multi-tenancy, such as strong resource isolation and priorities.",908"llm": "meta-llama/Llama-2-70b-chat-hf"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": " You can specify the ScalingConfig for a Tuner run by creating a ScalingConfig object and passing it as an argument to the Tuner constructor, like this:\n```\ntuner = tune.Tuner(\n objective,\n tune_config=tune.TuneConfig(\n metric=\"mean_loss\",\n mode=\"min\",\n search_alg=algo,\n num_samples=-1,\n time_budget_s=time_budget_s,\n ),\n param_space={\"steps\": 100},\n scaling_config=ScalingConfig(\n num_workers=8,\n resources_per_worker={\n \"CPU\": 4,\n \"GPU\": 0.5,\n },\n use_gpu=True,\n ),\n)\n```\nThis will configure the Tuner to use 8 workers, with 4 CPUs and 0.5 GPUs per worker, and to use GPUs for training. You can adjust the values of the ScalingConfig object to suit your needs.",935"llm": "meta-llama/Llama-2-70b-chat-hf"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": " How to utilize 'zero-copy' feature ray provide for numpy?\n\nYou can utilize the 'zero-copy' feature provided by Ray for numpy arrays by using the `ray.data.from_numpy_refs` function to create a Dataset from a list of Ray object references to numpy ndarrays. This function induces a copy of the data, but for zero-copy access to the underlying data, you can use the `ray.data.get_internal_block_refs` function. Additionally, you can set the `zero_copy_batch` parameter to `True` when calling `ray.put` to avoid copying the array every time it is passed to a task.",962"llm": "meta-llama/Llama-2-70b-chat-hf"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": " It is okay to spin up 1=1 actors for millions of keys that all have state, but it is advised to create 'key pools' where an actor can hold 1=many keys. This is because each actor can only handle one task at a time, and creating a separate actor for each key can lead to inefficient use of resources. By grouping keys into pools and assigning each pool to an actor, you can reduce the number of actors needed and improve overall performance.",989"llm": "meta-llama/Llama-2-70b-chat-hf"990},991{992"question": "How to find the best checkpoint from the trial directory?",993"sources": [994"https://docs.ray.io/en/master/train/api/doc/ray.train.Result.html#ray-train-result",995"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",996"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",997"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",998"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",999"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1000"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1001"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_trial_checkpoints_paths.html#ray-tune-experimentanalysis-get-trial-checkpoints-paths",1002"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result"1003],1004"document_ids": [10058414,10061431,10072188,10081645,10093759,10101430,101111460,10121692,101337601014],1015"answer": " To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` method of the `ExperimentAnalysis` class in Ray Tune. This method takes the path to a local trial directory and a metric to order the checkpoints by, and returns the path to the best checkpoint according to the specified metric.\n\nHere's an example of how to use this method:\n```\nfrom ray import tune\n\n# Load the trial directory\ntrial = tune.ExperimentAnalysis('path/to/trial/directory')\n\n# Get the best checkpoint\nbest_checkpoint = trial.get_best_checkpoint('mean_accuracy', 'max')\n\n# Print the path to the best checkpoint\nprint(best_checkpoint)\n```\nThis will print the path to the checkpoint with the highest mean accuracy in the trial directory.\n\nAlternatively, you can use the `get_last_checkpoint` method of the `ExperimentAnalysis` class to get the last checkpoint associated with the best-performing trial. This method takes a trial object and a metric to order the checkpoints by, and returns the path to the last checkpoint associated with the best-performing trial.\n\nHere's an example of how to use this method:\n```\nfrom ray import tune\n\n# Load the trial directory\ntrial = tune.ExperimentAnalysis('path/to/trial/directory')\n\n# Get the last checkpoint\nlast_checkpoint = trial.get_last_checkpoint('training_iteration', 'max')\n\n# Print the path to the last checkpoint\nprint(last_checkpoint)\n```\nThis will print the path to the last checkpoint associated with the best-performing trial in the trial directory, based on the specified metric.",1016"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Advantages of using singleton Actor:\n\n* Each actor instance is instantiated in its own process, ensuring isolation and independence of state.\n* Actors can be easily retrieved by name, allowing for efficient communication and coordination between actors.\n* Actors can be created with a globally unique name, enabling easy retrieval and avoiding name conflicts.\n\nDisadvantages of using singleton Actor:\n\n* Using class variables to manage state between instances of the same class is not supported, as each actor instance has its own copy of the class variables.\n* If the stateful part of an actor is not needed, using tasks instead is generally better off, as they are lighter weight and more efficient.\n* Closure capturing large objects can harm performance, and using global variables to share state between tasks and actors is an anti-pattern.",1043"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Advantages of using named actors in Ray:\n\n* Allows retrieval of actors from any job in the Ray cluster\n* Useful when cannot directly pass actor handle to task or when trying to access an actor launched by another driver\n* Actor will still be garbage-collected if no handles to it exist\n* Can be used to create an actor only if it doesn\u2019t exist, using get_if_exists option\n* Supports non-global named actors in C++, which means the actor name is only valid within the job and the actor cannot be accessed from another job\n* Named actors are scoped by namespace, and no namespace is assigned by default, they will be placed in an anonymous namespace\n* Allows getting a handle to a named actor using ray.get_actor, which is a sync call and has a timeout of 60s\n* Can be used to create an actor with a globally unique name, which can be used to retrieve the actor via ray.get_actor as long as the actor is still alive\n* Allows specifying the namespace to use for the actor, and the actor can be retrieved via ray.get_actor with the specified namespace\n* Lifetime of the actor can be specified, either None, which defaults to the actor will fate share with its creator and will be deleted once its refcount drops to zero, or \u201cdetached\u201d, which means the actor will live as a global object independent of the creator.",1070"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a succinct answer to the query using the context provided:\n\nTo read a text file stored on S3 using Ray Data, you can use the `read_text()` function and pass in the S3 path as the argument. For example:\n```\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\n```\nThis will create a Ray Data dataset containing the contents of the text file, with each line of text as a separate row. You can then use the `schema()` method to view the schema of the dataset.\n\nAlternatively, you can use the `read_binary_files()` function to read binary files from S3, and then manually decode the data. For example:\n```\nds = ray.data.read_binary_files(\"s3://anonymous@ray-example-data/pdf-sample_0.pdf\")\n```\nThis will create a Ray Data dataset containing the binary data from the PDF file. You can then use the `map()` method to decode the data and convert it into a text format, like this:\n```\nds = ds.map(lambda x: pd.read_csv(BytesIO(x)))\n```\nThis will create a new Ray Data dataset containing the decoded text data.",1097"llm": "meta-llama/Llama-2-70b-chat-hf"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": " You can use the `ray get_head_ip` command to get the IP address of the head node of your Ray cluster. You can also use `ray list nodes` to get the node ID or IP address of the head node, and then use `ray logs` or `ray get_head_ip` to get the IP address.\n\nAlternatively, you can use a Python script to programmatically discover the endpoints of the nodes in your Ray cluster by reading the Ray Cluster information using the `ray.nodes()` API.\n\nIf you are starting a Ray cluster locally, you can use `ray start --head` to start the head node, and then use `ray start --address=<head-node-address:port>` on each of the other nodes to connect to the head node.\n\nIf you are starting a Ray cluster remotely, you can follow the directions in the Ray documentation for Getting Started.\n\nIt's also worth noting that if you have a running Ray cluster (version >= 1.5), the Ray Client server is likely already running on port 10001 of the head node by default.",1124"llm": "meta-llama/Llama-2-70b-chat-hf"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 use the following approach:\n\n1. Define a function that takes a batch of data as input and returns a list of transformed objects.\n2. Use the `map_batches` method to apply the function to the entire dataset, specifying the function and any additional arguments or keywords arguments (such as `batch_size` or `compute`) as needed.\n\nHere's an example of how you might use `map_batches` to transform a dataset of images:\n```\nimport ray\nfrom ray.data.datasets import Dataset\n\n# Define a function to transform a batch of images\ndef transform_images(batch):\n # Do something with the images in the batch\n return [{\"id\": img[\"id\"] * 2} for img in batch]\n\n# Create a dataset from a list of images\nds = Dataset.from_items([{\"id\": 1}, {\"id\": 2}, {\"id\": 3}])\n\n# Apply the transformation function to the dataset\ntransformed_ds = ds.map_batches(transform_images, batch_size=32)\n\n# Print the first few elements of the transformed dataset\nprint(transformed_ds.take(5))\n```\nThis code will apply the `transform_images` function to each batch of 32 images in the dataset and return a list of transformed objects. The resulting dataset will contain the transformed objects, with the first few elements printed to the console.\n\nNote that the `map_batches` method can also be used with class-based UDFs, in which case you would need to specify the `compute` argument to specify the number of parallel actors to use. Additionally, you can use the `flat_map` method to create new records from existing ones, or the `map` method to transform one record at a time.",1151"llm": "meta-llama/Llama-2-70b-chat-hf"1152},1153{1154"question": "How do I set a maximum episode length when training with Rllib?",1155"sources": [1156"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",1157"https://docs.ray.io/en/master/rllib/rllib-replay-buffers.html#advanced-usage",1158"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",1159"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1160"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.reporting.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-reporting",1161"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",1162"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#evolution-strategies-es",1163"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.train_buffered.html#ray-rllib-algorithms-algorithm-algorithm-train-buffered",1164"https://docs.ray.io/en/master/rllib/rllib-offline.html#getting-started"1165],1166"document_ids": [116710821,116810724,116910510,117011458,117113177,117211048,117311925,117412332,1175105941176],1177"answer": " To set a maximum episode length when training with RLlib, you can use the `evaluation_duration` parameter and set it to the desired number of episodes or time steps. For example, to set a maximum episode length of 10 episodes, you can use the following code:\n```\nrllib.train(\n algorithm=algorithm,\n config=config,\n evaluation_duration=10,\n evaluation_duration_unit=\"episodes\"\n)\n```\nAlternatively, you can set the `evaluation_duration` parameter to a specific number of time steps, such as 200 time steps, and set the `evaluation_duration_unit` parameter to \"timesteps\".\n```\nrllib.train(\n algorithm=algorithm,\n config=config,\n evaluation_duration=200,\n evaluation_duration_unit=\"timesteps\"\n)\n```\nThis will set the maximum episode length to 200 time steps. Note that if you set `evaluation_duration_unit` to \"timesteps\", RLlib will round up the number of time steps specified to the nearest integer, so the actual maximum episode length may be slightly longer than the specified value.",1178"llm": "meta-llama/Llama-2-70b-chat-hf"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 trial-level fault tolerance. Specifically, you can set the `max_failures` parameter to specify the number of times a trial can fail before it is automatically retried. You can also set the `retry_delay` parameter to specify the time delay between retries.\n\nHere's an example of how to configure `FailureConfig` for a Ray Tune trial:\n```\nfrom ray import tune\n\n# Define the trial configuration\ntrial_config = tune.TrialConfig(\n max_failures=3, # Allow up to 3 failures before retrying\n retry_delay=60, # Wait 60 seconds before retrying\n)\n\n# Run the trial with Tune\ntuner = tune.Tuner(train_model, param_space=trial_space, trial_config=trial_config)\nresults = tuner.fit()\n```\nIn this example, the `max_failures` parameter is set to 3, which means that the trial will be retried up to 3 times before it is considered failed. The `retry_delay` parameter is set to 60 seconds, which means that Tune will wait 60 seconds before retrying the trial after a failure.\n\nYou can also use the `on_trial_error` and `on_trial_recover` callbacks to handle trial failures and retries. The `on_trial_error` callback is called after a trial instance fails, while the `on_trial_recover` callback is called after a trial instance fails but is scheduled for retry. You can use these callbacks to implement custom retry logic or to perform additional actions when a trial fails or recovers.\n\nFor example, you can use the `on_trial_error` callback to implement a custom retry mechanism that retries the trial immediately:\n```\nfrom ray import tune\n\n# Define the trial configuration\ntrial_config = tune.TrialConfig(\n max_failures=3, # Allow up to 3 failures before retrying\n retry_delay=0, # Retry immediately\n)\n\n# Run the trial with Tune\ntuner = tune.Tuner(train_model, param_space=trial_space, trial_config=trial_config)\nresults = tuner.fit()\n\n# Implement a custom retry mechanism using the on_trial_error callback\ndef on_trial_error(iteration, trials, trial, **info):\n # Retry the trial immediately\n tuner.restart_trial(trial)\n\ntuner.set_callback(on_trial_error)\n```\nIn this example, the `on_trial_error` callback is called after a trial instance fails. The `tuner.restart_trial(trial)` method is used to immediately retry the trial.\n\nYou can also use the `on_trial_recover` callback to perform additional actions when a trial recovers from a failure. For example, you can use this callback to print a message indicating that the trial has recovered:\n```\nfrom ray import tune\n\n# Define the trial configuration\ntrial_config = tune.TrialConfig(\n max_failures=3, # Allow up to 3 failures before retrying\n retry_delay=0, # Retry immediately\n)\n\n# Run the trial with Tune\ntuner = tune.Tuner(train_model, param_space=trial_space, trial_config=trial_config)\nresults = tuner.fit()\n\n# Implement a custom recovery mechanism using the on_trial_recover callback\ndef on_trial_recover(iteration, trials, trial, **info):\n print(f\"Trial {trial.id} has recovered from failure!\")\n\ntuner.set_callback(on_trial_recover)\n```\nIn this example, the `on_trial_recover` callback is called after a trial instance recovers from a failure. The `print` statement is used to print a message indicating that the trial has recovered.",1205"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Yes, it is possible to keep the worker actor up even if the supervisor passes a reference to the actor to another actor. This can be achieved by using the `lifetime` parameter in the `Actor` constructor and setting it to `detached`. This will decouple the actor's lifetime from the job and allow it to persist even after the driver process exits.\n\nHere's an example code snippet that demonstrates this behavior:\n```python\nimport ray\n\n# Create a detached actor\nactor = ray.Actor.options(name=\"MyActor\", lifetime=\"detached\").remote()\n\n# Pass the actor reference to another actor\nother_actor = ray.Actor.options(name=\"OtherActor\").remote()\nother_actor.set_actor(actor)\n\n# The worker actor will still be alive even after the supervisor dies\nprint(ray.get_actor(\"MyActor\").state)\n```\nIn this example, the `MyActor` actor is created with a `lifetime` parameter set to `detached`, which means that it will persist even after the driver process exits. The `OtherActor` actor is then created and passed a reference to the `MyActor` actor. Even if the supervisor actor dies, the `MyActor` actor will still be alive and accessible via the `OtherActor` actor.\n\nNote that setting `lifetime` to `detached` also means that the actor will not be automatically recovered if it fails. To enable automatic recovery, you can set `max_restarts` parameter in the actor's options.",1232"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a succinct answer to the query \"How do I read a large text file in S3 with Ray?\":\n\nYou can use the `ray.data.read_text()` function to read a large text file from S3. Here's an example:\n```\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\n```\nThis will create a Ray Data dataset containing the contents of the text file, with each line of text as a separate row. You can then use the `schema()` method to view the schema of the dataset, or use various transformation functions to process the data.",1259"llm": "meta-llama/Llama-2-70b-chat-hf"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 create a Ray Dataset from a pandas DataFrame, you can use the `ray.data.from_pandas()` function. This function takes a list of pandas DataFrames as input and returns a Ray Dataset.\n\nHere's an example:\n```\nimport pandas as pd\nimport ray\n\n# Create a pandas DataFrame\ndf = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\n\n# Create a Ray Dataset from the pandas DataFrame\nds = ray.data.from_pandas(df)\n\n# Print the Ray Dataset\nprint(ds)\n```\nThis will output:\n```\nMaterializedDataset(num_blocks=1, num_rows=3, schema={a: int64, b: int64})\n```\nNote that the `from_pandas()` function also accepts a list of pandas DataFrames as input, which allows you to create a Ray Dataset from multiple DataFrames at once.\n\nIf you want to convert a Ray Dataset to a pandas DataFrame, you can use the `to_pandas()` method of the Dataset object. Here's an example:\n```\n# Create a Ray Dataset\nds = ray.data.from_items([{\"a\": i} for i in range(3)])\n\n# Convert the Ray Dataset to a pandas DataFrame\ndf = ds.to_pandas()\n\n# Print the pandas DataFrame\nprint(df)\n```\nThis will output:\n```\n a\n0 0\n1 1\n2 2\n```\nNote that the `to_pandas()` method triggers the execution of any lazy transformations performed on the dataset, so it may take some time to complete. Also, if the dataset has more rows than the specified limit, an error will be raised.",1286"llm": "meta-llama/Llama-2-70b-chat-hf"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.map` to transform a dataset:\n```\nimport ray\n\n# Create a dataset from a range of integers\nds = ray.data.range(10000)\n\n# Map each row to a new dictionary with a transformed \"id\" key\nds = ds.map(lambda row: {\"id\": row[\"id\"] * 2})\n\n# Take the first 4 rows of the transformed dataset\nprint(ds.take(4))\n```\nThis will output:\n```\n[{'id': 0}, {'id': 2}, {'id': 4}, {'id': 6}]\n```\nIn this example, we created a dataset `ds` from a range of integers using `ray.data.range`. We then used `ds.map` to apply a transformation function to each row of the dataset. The transformation function takes a row as input, and returns a new dictionary with a transformed \"id\" key. Finally, we used `ds.take` to retrieve the first 4 rows of the transformed dataset.\n\nNote that `ray.data.map` can also be used with other dataset operations, such as `flat_map` and `filter`, to create more complex data pipelines. Additionally, as mentioned in the warning message, if you're using a vectorized transformation, it's often faster to use `map_batches` instead of `map`.",1313"llm": "meta-llama/Llama-2-70b-chat-hf"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```\nimport ray\n\nclass MyTransform(ray.data.CallableClass):\n def __init__(self):\n pass\n\n def __call__(self, row):\n return row[\"id\"] * 2\n\nray.init()\n\nds = ray.data.range(10000)\nds = ds.map(MyTransform())\nprint(ds)\n```\nThis will create a dataset with 10000 rows, where each row has a single column \"id\" containing a unique integer value. The `MyTransform` class is defined as a callable class, which means it can be used as a function to transform each row in the dataset. The `__call__` method of the class takes a row as input, and returns a new row with the \"id\" value multiplied by 2.\n\nThe `map` method is called on the dataset with the `MyTransform` class as input, which applies the transformation to each row in the dataset. The resulting dataset will have the same number of rows as the original dataset, but with the \"id\" values multiplied by 2.\n\nNote that the `ray.data.CallableClass` protocol is used to define a callable class that can be used with the `map` method. This protocol defines the `__call__` method that is called on each row in the dataset.",1340"llm": "meta-llama/Llama-2-70b-chat-hf"1341},1342{1343"question": "How to set memory limit for each trial in Ray Tuner?",1344"sources": [1345"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1346"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1347"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",1348"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1349"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",1350"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-trials-individually-with-a-timeout",1351"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria",1352"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",1353"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater"1354],1355"document_ids": [13562325,13571114,13582406,13592266,13602001,13612204,13622198,13631025,136420031365],1366"answer": " To set the memory limit for each trial in Ray Tune, you can use the `resources` parameter in the `TuneConfig` object. For example:\n```\nconfig = TuneConfig(\n num_samples=10,\n max_concurrent_trials=5,\n resources={\"cpu\": 1, \"memory\": 4}\n)\n```\nThis will set the maximum amount of CPU and memory that each trial can use. The `resources` parameter can also be set to a dictionary with other resource names, such as `\"gpu\"` or `\"ephemeral_storage\"`.\n\nAlternatively, you can use the `with_resources` method of the `Tuner` class to set the resources for a specific trial:\n```\ntuner = Tuner(\n trainable,\n run_config=RunConfig(stop={\"training_iteration\": 10, \"mean_accuracy\": 0.8}),\n resources={\"cpu\": 1, \"memory\": 4}\n)\n```\nThis will set the resources for the specific trial run by the `Tuner`.\n\nNote that the `resources` parameter is only applicable for trials that are run using the `Tuner` class. If you are using a custom trial loop, you will need to manually set the resources for each trial.",1367"llm": "meta-llama/Llama-2-70b-chat-hf"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": " You can use the `ray.runtime_context.RuntimeContext.get_actor_id()` method to get the actor ID of the current actor.\n\nAlternatively, you can use the `ray get actors <ACTOR_ID>` command to get the actor ID of a specific actor.\n\nYou can also use the `ray.util.state.get_actor()` method to get the actor ID of a specific actor by its name or ID.\n\nHere's an example of how to use these methods:\n```\n# Get the actor ID of the current actor\nactor_id = ray.runtime_context.RuntimeContext.get_actor_id()\n\n# Get the actor ID of a specific actor by its name\nactor_id = ray.util.state.get_actor('Actor')\n\n# Get the actor ID of a specific actor by its ID\nactor_id = ray.util.state.get_actor(31405554844820381c2f0f8501000000)\n```\nNote that the `ray.util.state.get_actor()` method returns a `ActorState` object, which contains information about the actor, including its ID.",1394"llm": "meta-llama/Llama-2-70b-chat-hf"1395},1396{1397"question": "can ray.init() can check if ray is all-ready initiated ?",1398"sources": [1399"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",1400"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1401"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",1402"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1403"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1404"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",1405"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#starting-ray",1406"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",1407"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users"1408],1409"document_ids": [14104785,14114811,14124194,14134190,14144188,14154189,14165386,14174186,141842041419],1420"answer": " Yes, `ray.init()` can check if Ray is already initialized. It returns `True` if Ray has already been initialized and `False` otherwise.",1421"llm": "meta-llama/Llama-2-70b-chat-hf"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 in `ray.data.map_batches` specifies that the function being applied to each batch should be executed as an actor, which is a long-running Ray process that can handle multiple tasks in parallel. This allows the function to be executed in parallel on multiple batches, speeding up the processing of large datasets.\n\nIn the context of the provided code, `compute=actor` is used to specify that the `TorchPredictor` class should be executed as an actor, which will fetch the model from the driver node only once and reuse it for all tasks assigned to the actor. This optimizes the batch prediction by reducing the overhead cost of fetching the model for each task.",1448"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a succinct answer to your question:\n\nTo use WandbLogger with AccelerateTrainer, you can follow these steps:\n\n1. Install Wandb using `pip install wandb`.\n2. Import the necessary modules: `from pytorch_lightning.loggers.wandb import WandbLogger; from pytorch_lightning.utilities.rank_zero import rank_zero_only`.\n3. Define your WandbLogger instance with the necessary parameters, such as `name`, `project`, `id`, and `offline`.\n4. In your AccelerateTrainer worker function, initialize the WandbLogger instance and log your training metrics using `wandb.log()`.\n5. Make sure to set `rank_zero_only.rank = None` to avoid creating a new experiment run on the driver node.\n\nHere's an example code snippet:\n```python\nfrom pytorch_lightning.loggers.wandb import WandbLogger\nfrom pytorch_lightning.utilities.rank_zero import rank_zero_only\n\ndef train_func_per_worker():\n # Initialize WandbLogger\n wandb_logger = WandbLogger(\n name=\"demo-run\",\n project=\"demo-project\",\n id=\"unique_id\",\n offline=offline\n )\n\n # Log training metrics using WandbLogger\n wandb_logger.log(metrics)\n\n # Set rank_zero_only.rank = None to avoid creating a new experiment run on the driver node\n rank_zero_only.rank = None\n```\nNote that you can also use the `WandbLoggerCallback` class to log your training metrics to Wandb. This class takes care of logging the metrics to Wandb automatically, and you don't need to manually call `wandb.log()` in your worker function.",1475"llm": "meta-llama/Llama-2-70b-chat-hf"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": " The following will be implicitly put into the object store:\n\n* Objects created via `ray.put()`\n* Objects returned from remote functions\n* Objects that are reference counted and evicted when they fall out of scope\n* Objects that are spilled to disk when the object store fills up (in Ray 1.3+).",1502"llm": "meta-llama/Llama-2-70b-chat-hf"1503},1504{1505"question": "How do I kill or cancel a ray task that I already started?",1506"sources": [1507"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1508"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",1509"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1510"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",1511"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1512"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",1513"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",1514"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",1515"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks"1516],1517"document_ids": [15184550,15194375,15204894,152114702,15224494,152311591,15244497,152514703,1526115921527],1528"answer": " To cancel a Ray task that has already started, you can use the `ray.cancel()` function and pass the ObjectRef of the task as an argument. By default, this will send a KeyboardInterrupt to the task's worker, which will cancel the task. You can also set the `force` argument to `True` to force-exit the worker. Note that Ray does not automatically retry tasks that have been cancelled.\n\nHere is an example code snippet that demonstrates how to cancel a Ray task:\n```\n# Define a remote function that takes a long time to complete\n@ray.remote\ndef blocking_operation():\n time.sleep(10e6)\n\n# Create an ObjectRef for the task\nobj_ref = blocking_operation.remote()\n\n# Cancel the task\nray.cancel(obj_ref)\n\n# Try to get the result of the task\ntry:\n ray.get(obj_ref)\nexcept ray.exceptions.TaskCancelledError:\n print(\"Object reference was cancelled.\")\n```\nIn this example, the `blocking_operation` function takes a long time to complete, so we cancel it using `ray.cancel()` and then try to get the result using `ray.get()`. Since the task was cancelled, we catch a `TaskCancelledError` exception and print a message indicating that the object reference was cancelled.\n\nNote that the behavior of `ray.cancel()` depends on the current state of the task. If the task has not been scheduled yet, Ray attempts to cancel the scheduling. If the task is running, Ray offers no mechanism for interruption. If the task is an async actor task, Ray seeks to cancel the associated asyncio.Task.\n\nAlso, note that cancellation is not always guaranteed, and the task might not be cancelled if the cancellation request does not get through to the executor. You can check if a task was successfully cancelled using `ray.get(actor_task_ref)`. If you want to cancel all child and actor tasks recursively, you can set the `recursive` argument to `True` when calling `ray.cancel()`.",1529"llm": "meta-llama/Llama-2-70b-chat-hf"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": " You can send extra arguments to the `map_batches` function by including them as additional positional arguments after the function or class that is being applied to the dataset. For example:\n```\nds.map_batches(ImageClassifier, batch_size=2, num_gpus=1)\n```\nIn this example, `ImageClassifier` is the user-defined function or class that is being applied to the dataset, `batch_size=2` specifies the number of images in each batch, and `num_gpus=1` specifies the number of GPUs needed for each model replica.\n\nYou can also include additional keyword arguments, such as `compute` and `batch_format`, to specify the compute strategy and the format of the batches, respectively. For example:\n```\nds.map_batches(ImageClassifier, batch_size=2, num_gpus=1, compute=ray.data.compute.ActorPoolStrategy(num_actors=4), batch_format=\"numpy\")\n```\nIn this example, `compute=ray.data.compute.ActorPoolStrategy(num_actors=4)` specifies that the function should be executed using 4 parallel actors, and `batch_format=\"numpy\"` specifies that the batches should be returned as NumPy arrays.\n\nIt's also worth noting that you can use the `fn_args` and `fn_kwargs` parameters to pass additional arguments to the user-defined function or class. For example:\n```\nds.map_batches(ImageClassifier, batch_size=2, num_gpus=1, fn_args=(4, \"float32\"), fn_kwargs={\"kernel\": \"rbf\"})\n```\nIn this example, `fn_args=(4, \"float32\")` specifies that the function should be called with the arguments `(4, \"float32\")`, and `fn_kwargs={\"kernel\": \"rbf\"}` specifies that the function should be called with the keyword argument `kernel=\"rbf\"`.",1556"llm": "meta-llama/Llama-2-70b-chat-hf"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 Kubernetes cluster in an external Redis database.",1583"llm": "meta-llama/Llama-2-70b-chat-hf"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 error message \"ValueError: The actor ImplicitFunc is too large\" suggests that the actor being created is too large and cannot be serialized. This can happen when the actor's definition includes a large object or array that is not properly stored in the Ray object store.\n\nTo resolve this issue, you can try the following:\n\n1. Check if the actor's definition includes any large objects or arrays that are not properly stored in the Ray object store. Make sure to use `ray.put()` to put large objects in the object store.\n2. Reduce the batch size of the data loader to avoid creating large actors. You can do this by setting the `batch_size` parameter in the `ds.map_batches()` function to a smaller value.\n3. Increase the number of workers in the actor pool by setting the `max_size` parameter in the `ActorPoolStrategy` constructor to a higher value. This will allow more actors to be created, reducing the size of each actor.\n4. Use the `reuse_actors` parameter in the `tune.TuneConfig` constructor to specify whether actors should be reused across tasks. If this parameter is set to `True`, actors will be reused, which can help reduce the size of each actor.\n5. Consider using a different type of actor, such as a `ray.actor.Actor` instead of an `ImplicitFunc`, which may be more efficient for large data sets.\n\nIt's also worth noting that the error message mentions that the actor pool will have at least 2 workers and at most 8 workers, so you may want to check if the actor pool is properly configured.",1610"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a summary of the provided context:\n\nThe context is discussing how to use Ray to distribute training for a custom neural network written using Keras in Databricks. It mentions that Ray Train is a library that allows for distributed training of machine learning models and provides a few changes to the code to enable distributed training. The changes include importing Ray Train and Ray AIR Session, using the `ray.train.torch.prepare_model` and `ray.train.torch.prepare_data_loader` utility functions to set up the model and data for distributed training, and using the `TorchTrainer` class to start training.\n\nAdditionally, the context mentions that Ray Clusters is a native library that distributes a specific ML task, and that Ray Serve is a library that allows for scalable and programmable serving to deploy models for online inference. The context also mentions that Ray Train integrates with popular training libraries and provides fault tolerance.\n\nFinally, the context provides an example of how to use Ray Train to set up multi-worker training with Keras, and mentions that the same approach can be used for other machine learning frameworks such as TensorFlow and Scikit-Learn.",1637"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a succinct answer to the query:\n\nTo use Ray's `put` and `get` functions, follow these steps:\n\n1. First, make sure you have initialized Ray with `ray.init()`.\n2. Use `ray.put()` to store an object in the object store. This function takes a value of any type and returns an `ObjectRef` that can be used to reference the object.\n3. Use `ray.get()` to retrieve an object from the object store. This function takes an `ObjectRef` or a list of `ObjectRefs` as input and returns the corresponding object or list of objects.\n\nHere's an example code snippet that demonstrates how to use `ray.put` and `ray.get`:\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Put an object in the object store\nobject_ref = ray.put(\"Hello, world!\")\n\n# Get the object from the object store\nobject = ray.get(object_ref)\n\nprint(object) # Output: \"Hello, world!\"\n```\nNote that `ray.put()` and `ray.get()` are asynchronous operations, so they return immediately and do not block. If you need to wait for the object to be available, you can use `ray.wait()` to wait for the object to be ready.\n\nAlso, it's important to avoid calling `ray.get()` unnecessarily, as it can harm performance. Instead, work with object references directly and only call `ray.get()` at the end to get the final result.",1664"llm": "meta-llama/Llama-2-70b-chat-hf"1665},1666{1667"question": "how do I use Ray Data to pre process many files?",1668"sources": [1669"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",1670"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",1671"https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",1672"https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors",1673"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data",1674"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",1675"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1676"https://docs.ray.io/en/master/train/user-guides.html#ray-train-user-guides",1677"https://docs.ray.io/en/master/data/user-guide.html#user-guides"1678],1679"document_ids": [16809178,16819222,16826849,16836914,16849212,16856838,16866835,16878110,168869111689],1690"answer": " To use Ray Data to preprocess many files, you can follow these steps:\n\n1. First, make sure to install Ray Data and its dependencies.\n2. Import the necessary libraries, such as `ray`, `ray.data`, and `numpy`.\n3. Use Ray Data's `read_dataset` function to load your data from files. You can specify the file format and other options as needed.\n4. Apply any necessary preprocessing operations to the data using Ray Data's `map_batches` function or other operations.\n5. If the preprocessed dataset is small enough to fit in Ray's object store memory, call `materialize()` on the dataset to store it in memory for faster access.\n6. If the preprocessed dataset is too large to fit in memory, consider using a different approach such as spilling the data to disk and reading it back as needed.\n7. Use the preprocessed dataset in your machine learning training pipeline.\n\nRemember to consider performance considerations such as using GPUs and optimizing data transfer between CPU and GPU. Also, consider using Ray Data preprocessors for tabular data preprocessing.",1691"llm": "meta-llama/Llama-2-70b-chat-hf"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": " It appears that you are trying to use Ray's `inspect_serializability` function to check the serializability of a function that contains a threading lock. However, the function cannot be pickled because the threading lock is not serializable.\n\nTo fix this issue, you can try using a different type of lock that is serializable, such as a `multiprocessing.Lock` or a `threading.RLock`. Alternatively, you can try using a different approach to achieve your desired functionality, such as using a synchronized method or a lock-free data structure.\n\nAdditionally, you may want to consider using Ray's `cloudpickle` serializer, which is designed to work around some of the limitations of the standard Python pickle serializer. You can enable `cloudpickle` by setting the `RAY_CLOUDPICKLE_ENABLED` environment variable to `1`. This may help to avoid the serialization issues you are encountering.",1718"llm": "meta-llama/Llama-2-70b-chat-hf"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 chart repository: `helm repo add kuberay https://ray-project.github.io/kuberay-helm/`\n2. Install the KubeRay operator: `helm install kuberay-operator kuberay/kuberay-operator --version 0.6.0`\n3. Install a RayCluster: `helm install raycluster kuberay/ray-cluster --version 0.6.0`\n\nThese steps will install the KubeRay operator and CRDs, allowing you to deploy and manage Ray applications on Kubernetes.",1745"llm": "meta-llama/Llama-2-70b-chat-hf"1746},1747{1748"question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",1749"sources": [1750"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1751"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1752"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",1753"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",1754"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",1755"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1756"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",1757"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging",1758"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text"1759],1760"document_ids": [17618107,17626788,176313697,1764140,17656955,17667955,17677380,1768590,176969981770],1771"answer": " The function for Ray data batch inference has to be named as `__call__()` because it is a special method in Python that is called when an object is used as a function. In the context of Ray data batch inference, the `__call__()` method is used to define a callable class that can be used to perform batch inference on a dataset.\n\nThe `__call__()` method takes a batch of data as input, performs inference on it using the model, and returns the results. The method is defined in the `TorchPredictor` class, which is a subclass of `ray.data.ActorPoolStrategy`. The `TorchPredictor` class is used to cache the loading of the model in its constructor, and the `__call__()` method is used to perform inference on each batch of data.\n\nThe use of `__call__()` method allows the batch inference code to be written in a concise and elegant way, and it is a common pattern in Python programming to use special methods like `__call__()` to define callable objects.",1772"llm": "meta-llama/Llama-2-70b-chat-hf"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. For example:\n```\ncli1.disconnect()\n```\nThis will disconnect the client from the Ray cluster. If you want to disconnect a client explicitly, you can set the `RAY_CLIENT_RECONNECT_GRACE_PERIOD` environment variable to a non-zero value before calling `disconnect()`. This will allow the client to reconnect to the cluster for a specified amount of time before dropping all object references.\n\nIt's important to note that if you're using Ray multi-client, you'll need to call `disconnect()` on each client object separately. Additionally, object references can only be used by the client from which they were obtained, so you'll need to make sure you're using the correct client object when accessing objects in the cluster.",1799"llm": "meta-llama/Llama-2-70b-chat-hf"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 an example:\n\n1. First, init Ray: `ray.init()`\n2. Create a JobSubmissionClient object: `client = JobSubmissionClient(\"http://<head-node-ip>:8265\")`\n3. Define the job: `job_id = client.submit_job(entrypoint=\"python my_script.py\", runtime_env={\"working_dir\": \"./\"})`\n4. Wait for the job to finish: `wait_until_status(job_id, [\"succeeded\"], timeout_seconds=5)`\n\nNote that you need to specify the path to the local directory that contains the script.py file in the runtime_env argument. Also, you can use the RAY_JOB_HEADERS environment variable to pass headers per HTTP request to the Cluster.",1826"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a succinct answer to the query:\n\nTo perform inference with a model trained by Ray Tune, you can use the `ray.data.read_csv()` function to read in your dataset, and then pass the data to the trained model to make predictions.\n\nHere's an example code snippet:\n```python\nimport ray\nfrom ray.train import ScalingConfig\nfrom ray.train.xgboost import XGBoostTrainer\n\n# Load the trained model\nmodel = ray.train.load(\"my_model\")\n\n# Read in the dataset\ndata = ray.data.read_csv(\"s3://anonymous@air-example-data/breast_cancer.csv\")\n\n# Make predictions on the data\npredictions = model.predict(data)\n```\nIn this example, we first load the trained model using the `ray.train.load()` function. We then read in the dataset using the `ray.data.read_csv()` function. Finally, we pass the data to the trained model to make predictions using the `model.predict()` method.\n\nNote that the `ray.train.load()` function returns a `Checkpoint` object, which contains the trained model and its hyperparameters. You can use this object to make predictions on new data.",1853"llm": "meta-llama/Llama-2-70b-chat-hf"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. Ray Datasets can be used to perform scalable offline batch inference with Torch models by mapping a pre-trained model over the data. Additionally, Ray Data can be used to perform object detection batch inference at scale with a pre-trained PyTorch model. To use GPUs for inference, the model and data can be moved to and from GPU, and the batch size can be specified in the ds.map_batches() call.",1880"llm": "meta-llama/Llama-2-70b-chat-hf"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 platform for building scalable, distributed applications. It provides a simple, Python-based API for building tasks and a distributed architecture for running them. Ray can be used to build a wide range of applications, including machine learning, data processing, and real-time analytics.\n\nSome of the key features and capabilities of Ray include:\n\n* Scalable task execution: Ray can run tasks on a cluster of machines, allowing you to scale your applications horizontally.\n* Distributed architecture: Ray provides a distributed architecture for running tasks, which allows you to take advantage of multiple machines to process large amounts of data.\n* Python-based API: Ray provides a simple, Python-based API for building tasks, which makes it easy to get started with the platform.\n* Support for various data sources: Ray supports a variety of data sources, including local files, HDFS, and cloud storage.\n* Integration with popular deep learning frameworks: Ray integrates with popular deep learning frameworks such as TensorFlow and PyTorch, making it easy to build and deploy machine learning models.\n* Support for real-time analytics: Ray provides support for real-time analytics, allowing you to process and analyze data in real-time.\n* Support for batch processing: Ray also supports batch processing, which allows you to process large amounts of data in a single pass.\n\nRay provides a number of tools and features to help you build and deploy your applications, including:\n\n* Ray.init: This command initializes a Ray cluster on a local machine or a remote cluster.\n* Ray Tutorials and Examples: Ray provides a number of tutorials and examples to help you get started with the platform.\n* Ray blogs: The Ray blog provides a wealth of information on using the platform, including tutorials, use cases, and best practices.\n* Ray on Cloud VMs: Ray can be run on cloud virtual machines, allowing you to easily scale your applications up or down as needed.\n* Ray.serve.deployment: This command deploys a Ray application to a cluster.\n* Ray on Kubernetes: Ray can be deployed on Kubernetes, which allows you to take advantage of the scalability and flexibility of Kubernetes.\n* Ray Jobs: Ray provides a number of pre-built jobs that you can use to quickly and easily build and deploy common data processing workflows.\n\nOverall, Ray is a powerful platform for building scalable, distributed applications, and provides a wide range of features and tools to help you build and deploy your applications.",1907"llm": "meta-llama/Llama-2-70b-chat-hf"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 the task uses more than the allocated amount, it will not be able to run. The task will only be scheduled on a machine that has the required amount of GPU memory available.\n\nIt is the user's responsibility to ensure that multiple trials can share GPUs and there is enough memory to do so. Ray does not automatically handle this for you. If you want to use multiple GPUs per trial, you should check out LightningTrainer.\n\nYou can use the `num_gpus` parameter in the `ray_actor_options` to specify the number of GPUs to allocate to the actor. You can also use the `num_gpus_per_worker` parameter to specify the number of GPUs to allocate per worker. These values can be fractional, but the precision of the fractional resource requirement is 0.0001, so you should avoid specifying a double that\u2019s beyond that precision.\n\nIt's also important to note that not all algorithms can take advantage of GPUs, and support for multi-GPU is currently only available for certain algorithms, such as PPO, IMPALA, DQN, and PG.",1934"llm": "meta-llama/Llama-2-70b-chat-hf"1935},1936{1937"question": "how to pickle a variable defined in actor\u2019s init method",1938"sources": [1939"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1940"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",1941"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",1942"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",1943"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",1944"https://docs.ray.io/en/master/ray-core/actors.html#actors",1945"https://docs.ray.io/en/master/rllib/actors.html#actors",1946"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1947"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs"1948],1949"document_ids": [19505192,19515194,19524435,19534241,195414682,19554469,195611576,19575191,19585201959],1960"answer": " To pickle a variable defined in an actor's init method, you can use the `ray.put()` method to store the variable in a remote object, and then pass the remote object to other tasks or actors.\n\nHere's an example:\n```\n@ray.remote\nclass Actor:\n def __init__(self, global_var):\n self.global_var = global_var\n\n def f(self):\n return ray.get(self.global_var.get_global_var.remote()) + 3\n\nglobal_var = GlobalVarActor.remote()\nactor = Actor.remote(global_var)\nray.get(global_var.set_global_var.remote(4))\n# This returns 7 correctly.\nassert ray.get(actor.f.remote()) == 7\n```\nIn this example, the `Actor` class defines a variable `global_var` in its init method, which is then used in the `f()` method. The `global_var` variable is stored in a remote object using `ray.put()`, and the remote object is passed to other tasks or actors using the `actor.f.remote()` method.\n\nNote that using global variables to share state between tasks and actors is not recommended, as it can lead to race conditions and other issues. Instead, it's recommended to encapsulate the global variables in an actor and pass the actor handle to other tasks and actors.",1961"llm": "meta-llama/Llama-2-70b-chat-hf"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, you can use the `ray.util.collective.collective.allreduce()` function. This function takes a list of tensors to be reduced, a group name, and an optional reduction operation. The tensors are reduced across the group and the result is returned.\n\nHere is an example of how to use the `allreduce()` function:\n```\nimport ray\n\n# Create a list of actors\nactors = [ray.actor.Actor(name=\"actor_{}\".format(i)) for i in range(5)]\n\n# Define a tensor to be reduced\ntensor = torch.ones((10,), dtype=torch.float32)\n\n# Perform an all_reduce operation\nray.util.collective.collective.allreduce(tensor, actors, group_name=\"my_group\")\n```\nThis will reduce the tensor across all actors in the `my_group` group and return the result.\n\nYou can also specify a reduction operation, such as `ReduceOp.SUM`, to perform a sum reduction instead of the default mean reduction.\n```\nray.util.collective.collective.allreduce(tensor, actors, group_name=\"my_group\", reduce_op=ReduceOp.SUM)\n```\nIt's also possible to perform an all_reduce operation with a custom reduction operation.\n```\ndef custom_reduction(tensor, actors):\n # Custom reduction logic\n return tensor + tensor\n\nray.util.collective.collective.allreduce(tensor, actors, group_name=\"my_group\", reduce_op=custom_reduction)\n```\nNote that the `allreduce()` function is a low-level API and requires careful handling of the tensors and the reduction operation. It's also important to ensure that the actors are properly initialized and connected to the same collective group.",1988"llm": "meta-llama/Llama-2-70b-chat-hf"1989},1990{1991"question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",1992"sources": [1993"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",1994"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",1995"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#bundles",1996"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",1997"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources.html#ray-rllib-algorithms-algorithm-config-algorithmconfig-resources",1998"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1999"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",2000"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",2001"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group"2002],2003"document_ids": [20044790,20055063,20065050,20075051,200812053,200911075,20105076,20115060,201247892013],2014"answer": " If a placement group is specified with `{\"CPU\": 0}`, it means that the placement group requires 0 CPU resources. This is not possible, as a placement group must reserve some amount of CPU resources in order to be created. Therefore, the creation of the placement group will fail.\n\nIn the context of the provided code snippets, this means that the `pending_pg` variable will not be created, and the `ray.get(pending_pg.ready(), timeout=5)` call will raise a timeout exception. This is because the placement group cannot be created because it requires 0 CPU resources, which is not possible.",2015"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a succinct answer to the query \"How to cancel a job from UI?\":\n\nYou can cancel a job from the UI by using the `ray job stop` command, followed by the job ID. For example:\n```\nray job stop raysubmit_tUAuCKubPAEXh6CW\n```\nThis will request the job to exit asynchronously. You can also use the `ray.cancel()` function to cancel a task by calling it on the returned ObjectRef. For example:\n```\nray.cancel(obj_ref)\n```\nWhere `obj_ref` is the ObjectRef returned by the task. Note that currently, Ray will not automatically retry tasks that have been cancelled.",2042"llm": "meta-llama/Llama-2-70b-chat-hf"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 a remote cluster when using Ray Serve, you can use the `serve deploy` command with the `--address` option followed by the remote cluster's dashboard agent address. For example:\n```\nserve deploy config.yaml --address <remote-cluster-dashboard-agent-address>\n```\nThis will deploy your project files to the remote cluster. You can also use the `serve run` command to run your application on the remote cluster, and the `serve config` and `serve status` commands to inspect your application's configuration and status on the remote cluster.\n\nNote that you may need to set up network access from a client to the remote cluster, or use port-forwarding to access the remote cluster's dashboard agent address. You can also use the `RAY_ADDRESS` environment variable to specify the address of the remote cluster's dashboard agent.",2069"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a summary of the instructions for installing Ray nightly wheels:\n\n1. Clean removal of previous install: `pip uninstall -y ray`\n2. Install Ray with support for the dashboard + cluster launcher: `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`\n3. Install Ray with minimal dependencies: `pip install -U LINK_TO_WHEEL.whl`\n4. Install Ray from a specific commit: `pip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/{COMMIT_HASH}/ray-{RAY_VERSION}-{PYTHON_VERSION}-{PYTHON_VERSION}-{OS_VERSION}.whl`\n5. Building Ray (Python Only): `pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp38-cp38-manylinux2014_x86_64.whl`\n\nNote that the `LINK_TO_WHEEL.whl` file should be replaced with the actual link to the wheel file you want to install. Also, the `COMMIT_HASH` should be replaced with the actual commit hash you want to install.",2096"llm": "meta-llama/Llama-2-70b-chat-hf"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, you can use the following command:\n\n`pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`\n\nReplace `LINK_TO_WHEEL.whl` with the link to the nightly wheel file that you want to install. You can find the links to the nightly wheels in the Ray documentation or on the Ray GitHub page.\n\nAlternatively, you can install the latest official version of Ray from PyPI on Linux, Windows, and macOS by using the following command:\n\n`pip install -U \"ray[air]\"`\n\nThis will install the latest version of Ray with support for machine learning applications. If you want to install Ray with minimal dependencies, you can use the following command:\n\n`pip install -U \"ray\"`\n\nIf you want to install Ray with support for reinforcement learning, you can use the following command:\n\n`pip install -U \"ray[rllib]\"`\n\nYou can also install Ray with specific dependencies by using the following command:\n\n`pip install -U \"ray[default]\"`\n\nThis will install Ray with the default dependencies, including the Dashboard and Cluster Launcher. If you don't want these components, you can use the following command:\n\n`pip install -U \"ray\"`\n\nYou can also install Ray with support for data processing by using the following command:\n\n`pip install -U \"ray[data]\"`\n\nI hope this helps! Let me know if you have any other questions.",2123"llm": "meta-llama/Llama-2-70b-chat-hf"2124},2125{2126"question": "how can I write unit tests for Ray code?",2127"sources": [2128"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",2129"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",2130"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2131"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",2132"https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",2133"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",2134"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2135"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",2136"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users"2137],2138"document_ids": [21395397,21405395,21414811,21424190,21434338,214414561,21455380,21466621,214742042148],2149"answer": " To write unit tests for Ray code, you can use a testing framework such as unittest or TestNG. You can create a test class that initializes a Ray cluster using ray.init() and then runs tests on the cluster. You can use ray.shutdown() to shut down the cluster after the tests are finished.\n\nHere's an example of a test class that uses unittest:\n```\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n def setUp(self):\n ray.init(num_cpus=4, num_gpus=0)\n\n def test_ray_code(self):\n # Run your Ray code here\n pass\n\n def tearDown(self):\n ray.shutdown()\n```\nYou can also use ray.cluster_utils.Cluster to create a mini-cluster for testing. This can be useful if you want to test code that uses Ray's distributed features.\n\nHere's an example of how to create a mini-cluster for testing:\n```\nimport ray\nfrom ray.cluster_utils import Cluster\n\nclass RayTest(unittest.TestCase):\n def setUp(self):\n self.cluster = Cluster(num_nodes=4, ray_namespace=\"my_ray_cluster\")\n self.cluster.init()\n\n def test_ray_code(self):\n # Run your Ray code here\n pass\n\n def tearDown(self):\n self.cluster.shutdown()\n```\nWhen writing tests for Ray code, it's important to be mindful of the resource quantity and to fix the resource quantity with ray.init(num_cpus=...). You should also be careful when running tests in parallel, as Ray programs can be a little tricky to test due to the nature of parallel programs.\n\nYou can also use ray.is_initialized() to check if the Ray context is initialized, and ray.is_shutdown() to check if the Ray context is shut down.\n\nIt's also a good practice to use a try-except block to ensure that the Ray context is properly shut down even if an exception is thrown during the test.\n```\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n def setUp(self):\n ray.init(num_cpus=4, num_gpus=0)\n\n def test_ray_code(self):\n try:\n # Run your Ray code here\n pass\n finally:\n ray.shutdown()\n```\nIt's also important to note that you can use ray.serve.Deployment to deploy your Ray code as a service, and test it using RayServeHandle.\n\nYou can also use ray.get_runtime_context() to get the current runtime context, which can be useful for testing purposes.\n\nYou can find more information and examples on the Ray documentation and tutorials.",2150"llm": "meta-llama/Llama-2-70b-chat-hf"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 set the `log_to_driver` parameter to `False` in the `ray.init()` function. This will prevent Ray from routing all worker logs to the driver, which can help reduce the amount of output on stdout.\n\nHere's an example of how you can do this:\n```\nimport ray\n\nray.init(log_to_driver=False)\n```\nThis will disable logging to the driver, and instead, the logs will be directed to the worker log files. You can also set the `RAY_TQDM_PATCH_PRINT` environment variable to `0` to disable the patching of the built-in `print` function, which can also help reduce the amount of output on stdout.\n\nAdditionally, you can use the `ray.get()` function with the `silent=True` argument to suppress the output of the function. For example:\n```\nray.get(task.remote(), silent=True)\n```\nThis will run the `task` function remotely and suppress the output.\n\nYou can also use the `redirect_worker_logs` parameter in the `ray.init()` function to redirect worker logs to a file or a directory. For example:\n```\nray.init(redirect_worker_logs='/path/to/logs')\n```\nThis will redirect all worker logs to the specified file or directory, instead of printing them to the driver.\n\nIt's worth noting that disabling logging to the driver can make it more difficult to troubleshoot issues with your Ray application, so you may want to consider alternative approaches to reducing the amount of output on stdout, such as logging to a file or using a logging framework that allows you to configure the log level and output destination.",2177"llm": "meta-llama/Llama-2-70b-chat-hf"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 a stable diffusion model using Ray Serve, follow these steps:\n\n1. Install Ray Serve and the necessary dependencies.\n2. Download the `ray-service.stable-diffusion.yaml` file and create a RayService configuration.\n3. Prepare a sample image file and forward the serve port of the head Pod.\n4. Clone the repository and prepare the sample image file.\n5. Send a request to the text-to-image model using the `stable_diffusion_req.py` script.\n\nNote: It is recommended to test the Ray Serve script locally or in a RayCluster before deploying it to a RayService.",2204"llm": "meta-llama/Llama-2-70b-chat-hf"2205},2206{2207"question": "what is actor_handle?",2208"sources": [2209"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",2210"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",2211"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",2212"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2213"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2214"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",2215"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",2216"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",2217"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors"2218],2219"document_ids": [222011587,222114698,22224487,22234860,22244615,222511588,22264489,222714699,222842002229],2230"answer": " An actor handle is a handle to an actor that can be passed around to various tasks, allowing them to interact with the actor and its state.",2231"llm": "meta-llama/Llama-2-70b-chat-hf"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` method, passing the actor handle as an argument. This will forcefully terminate the actor, causing any running tasks to fail immediately. If you want to allow pending tasks to finish before terminating the actor, you can call `actor.__ray_terminate__.remote()` instead. Note that for detached actors, calling `ray.kill` on an actor handle will destroy the actor and allow the name to be reused.",2258"llm": "meta-llama/Llama-2-70b-chat-hf"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 a runtime environment, you can specify the package version in the `pip` field of the `runtime_env` dictionary, like this:\n```\nruntime_env = {\n \"pip\": {\n \"packages\": [\"requests==2.27.1\"],\n \"pip_check\": False,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"\n }\n}\n```\nThis will install the specified version of the `requests` package and any dependencies that are not already satisfied by the base environment.\n\nAlternatively, you can specify a local requirements.txt file that includes the desired version of the package, like this:\n```\nruntime_env = {\n \"pip\": {\n \"packages\": [\"-r requirements.txt\"],\n \"pip_check\": False,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"\n }\n}\n```\nIn this case, the `requirements.txt` file should include the desired version of the package, like this:\n```\nrequests==2.27.1\n```\nNote that specifying a version in the `pip` field or in a requirements.txt file will override any version that is already installed in the base environment.",2285"llm": "meta-llama/Llama-2-70b-chat-hf"2286},2287{2288"question": "How do I do global shuffle with Ray?",2289"sources": [2290"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",2291"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",2292"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2293"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2294"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",2295"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",2296"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",2297"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",2298"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle"2299],2300"document_ids": [23017395,23026795,23036827,23049209,23056826,23069206,23076876,23086864,230968252310],2311"answer": " To do a global shuffle with Ray, you can use the `random_shuffle()` method on a Ray Data Dataset. This method shuffles all rows of the dataset and returns a new dataset with the shuffled rows.\n\nHere's an example:\n```\nimport ray\n\nds = ray.data.range(100)\nshuffled_ds = ds.random_shuffle()\n```\nThis will shuffle all rows of the dataset and return a new dataset with the shuffled rows.\n\nIf you want to shuffle the dataset in a specific order, you can pass a seed value to the `random_shuffle()` method. This will ensure that the same sequence of random numbers is used for each iteration, which can be useful for reproducibility.\n```\nds = ray.data.range(100)\nshuffled_ds = ds.random_shuffle(seed=42)\n```\nYou can also use the `local_shuffle_buffer_size` parameter to specify the size of the buffer used for shuffling. This can help improve performance by reducing the amount of data that needs to be moved around.\n```\nds = ray.data.range(100)\nshuffled_ds = ds.random_shuffle(local_shuffle_buffer_size=10)\n```\nKeep in mind that global shuffling can be slow and computationally expensive, especially for large datasets. If you don't need a full global shuffle, you can use the `local_shuffle_buffer_size` parameter to shuffle a subset of rows up to a provided buffer size during iteration. This can be more performant and can help reduce the amount of data that needs to be moved around.\n\nYou can also use the `push_based_shuffle` parameter to enable push-based shuffling, which can be faster and more efficient than the default pull-based shuffling.\n```\nds = ray.data.range(100)\nshuffled_ds = ds.random_shuffle(push_based_shuffle=True)\n```\nNote that push-based shuffling is still experimental and may not be suitable for all use cases. You can also specify the shuffle implementation during program execution by setting the `RAY_DATA_PUSH_BASED_SHUFFLE` environment variable.",2312"llm": "meta-llama/Llama-2-70b-chat-hf"2313},2314{2315"question": "How to find namespace of an Actor?",2316"sources": [2317"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",2318"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2319"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",2320"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",2321"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2322"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",2323"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace",2324"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2325"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces"2326],2327"document_ids": [23284521,23294623,23304504,23314871,23324626,23334528,23344751,23354625,233644992337],2338"answer": " To find the namespace of an actor, you can use the `ray.get_actor()` method and pass the name of the actor as an argument. The method will return the actor handle, which contains the namespace information.\n\nHere's an example code snippet:\n```python\nimport ray\n\n# Create an actor with a specified namespace\nactor = ray.remote(Actor, namespace=\"my_namespace\")\n\n# Get the actor handle\nactor_handle = ray.get_actor(actor.name)\n\n# Print the namespace of the actor\nprint(actor_handle.namespace)\n```\nIn this example, we create an actor with the name `my_actor` and specify the namespace as `my_namespace`. Then, we use the `ray.get_actor()` method to get the actor handle, which contains the namespace information. Finally, we print the namespace of the actor using the `actor_handle.namespace` attribute.",2339"llm": "meta-llama/Llama-2-70b-chat-hf"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 works with async.io by allowing you to use the async/await syntax when defining remote functions and actor methods. This allows you to write concurrent code that can run in parallel with other tasks in the Ray cluster. Ray also provides a way to run asynchronous tasks using the ray.get() method, which returns a future that can be used to get the result of the task when it's available. Additionally, Ray provides a way to define asynchronous actors using the @ray.remote decorator, which allows you to run actor methods concurrently with other tasks in the Ray cluster.",2366"llm": "meta-llama/Llama-2-70b-chat-hf"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, try the following steps:\n\n1. Separate the call to `ray.get()` from the call to the remote functions. This allows all remote functions to be spawned before waiting for the results, enabling parallelism.\n2. Pass a list of object references to `ray.get()` instead of calling it one by one. This reduces the number of times you need to wait for tasks to finish.\n3. Avoid calling `ray.get()` unnecessarily for intermediate steps. Work with object references directly, and only call `ray.get()` at the end to get the final result.\n4. Use a timeout with `ray.get()` to return early from a blocking call that takes too long.\n5. Use Ray's debugger to step into the call with the remote command and print the value of n with `p(n)`.\n6. Re-run the program after making changes to separate `ray.get()` from the remote functions, and pass a list of object references to `ray.get()`. This should result in the program running in parallel and completing in a shorter duration.\n\nBy following these steps, you should be able to debug and optimize your Ray program to run efficiently and avoid hanging `ray.get()` calls.",2393"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's an example of using `ray.actor.exit_actor()` to manually terminate an actor:\n```\nray.actor.exit_actor()\n```\nThis will kill the actor process and release resources associated/assigned to the actor. Note that this method of termination waits until any previously submitted tasks finish executing and then exits the process gracefully with `sys.exit`.\n\nIt's important to note that manual termination of an actor should be reserved for cases where an actor is unexpectedly hanging or leaking resources, and for detached actors, which must be manually destroyed. In most cases, Ray will automatically terminate actors that have gone out of scope.\n\nAlso, note that this method is only available when you download Ray via `pip install \"ray[default]\"`.",2420"llm": "meta-llama/Llama-2-70b-chat-hf"2421},2422{2423"question": "how to add log inside actor?",2424"sources": [2425"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",2426"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",2427"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",2428"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2429"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",2430"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2431"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2432"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",2433"https://docs.ray.io/en/master/ray-observability/getting-started.html#actors-view"2434],2435"document_ids": [2436414,2437540,2438520,243914324,244014202,244114286,244214295,244314106,24444342445],2446"answer": " To add a log inside an actor, you can use the `logger` module in Python. Here's an example:\n```\nimport logging\n\nclass MyActor:\n def __init__(self, index):\n self.index = index\n self.logger = logging.getLogger(__name__)\n\n def foo(self):\n self.logger.info(\"Hello from actor %d\" % self.index)\n```\nThis will create a logger for the actor class and log a message at the info level. You can also use the `ray.get` function to get the log of the actor.\n\nAlternatively, you can use the `ray.actor_logging` module to configure logging for actors. This module provides a convenient way to log messages from actors and tasks, and you can use it to customize the logging behavior for your actors.\n\nHere's an example of how to use `ray.actor_logging` to log messages from an actor:\n```\nimport ray\n\nclass MyActor:\n def __init__(self, index):\n self.index = index\n\n def foo(self):\n ray.actor_logging.info(\"Hello from actor %d\" % self.index)\n```\nThis will log a message at the info level for the actor. You can also use the `ray.actor_logging.debug`, `ray.actor_logging.warning`, and `ray.actor_logging.error` functions to log messages at different levels.\n\nNote that by default, all stdout and stderr of tasks and actors are redirected to the driver output. If you want to disable this feature, you can use the `ray.actor_logging.disable_redirect` function.",2447"llm": "meta-llama/Llama-2-70b-chat-hf"2448},2449{2450"question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",2451"sources": [2452"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2453"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt",2454"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",2455"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",2456"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2457"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",2458"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#next-steps",2459"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#next-steps",2460"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"2461],2462"document_ids": [24636999,246410470,246510259,246614582,24676998,24686843,246910219,247010137,247169382472],2473"answer": " Yes, you can use Ray to perform batch inference with GPT-2 on text data from an S3 bucket. You can follow the steps outlined in the provided tutorial to set up a Ray Serve deployment that accepts batches, configure the batch size, and query the model in Python. Additionally, you can use the `ray.data.read_text` function to read the text data from the S3 bucket and `ray.data.map_batches` to process the data in batches.\n\nHere's an example code snippet that demonstrates how to perform batch inference with GPT-2 on text data from an S3 bucket using Ray:\n```python\nimport ray\nfrom ray.data.datasets import Dataset\nfrom ray.data.transforms import MapBatches\nfrom transformers import pipeline\n\n# Set up a Ray Serve deployment that accepts batches\nray.init(num_cpus=2)\n\n# Read the text data from the S3 bucket\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\n\n# Process the data in batches using MapBatches\nds = ds.map_batches(TextClassifier, compute=ray.data.ActorPoolStrategy(size=2))\n\n# Show the first 3 batches\nds.show(3)\n```\nIn this example, `TextClassifier` is a callable class that sets up and invokes a pre-trained GPT-2 model. The `map_batches` function is used to process the text data in batches, and the `show` function is used to display the first 3 batches.\n\nNote that you'll need to install the necessary dependencies, such as Ray, Transformers, and PyTorch, and also make sure that you have the necessary credentials to access the S3 bucket.",2474"llm": "meta-llama/Llama-2-70b-chat-hf"2475},2476{2477"question": "How do I enable Ray debug logs?",2478"sources": [2479"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2480"https://docs.ray.io/en/master/ray-observability/user-guides/index.html#user-guides",2481"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",2482"https://docs.ray.io/en/master/cluster/cli.html#ray-start",2483"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#debugger-commands",2484"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-debug",2485"https://docs.ray.io/en/master/ray-contribute/debugging.html#debugging-for-ray-developers",2486"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",2487"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging"2488],2489"document_ids": [2490680,2491546,2492510,24935611,2494579,24954669,2496944,2497573,249843232499],2500"answer": " To enable Ray debug logs, you can set the environment variable RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1 on each node before starting Ray. This will print the full runtime_env setup log messages to the driver (the script that calls ray.init()).\n\nYou can also use the Ray debugger to debug your distributed applications. The debugger allows you to set breakpoints in your Ray tasks and actors and inspect variables in that context. To use the debugger, you can run the command `ray debug` to show all active breakpoints and exceptions. You can also use the `--address` option to override the address to connect to.\n\nNote that the Ray debugger is an experimental feature and is currently unstable. Interfaces are subject to change.",2501"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Here are the current Ray actors:\n\n* Actor (actor_id: 31405554844820381c2f0f8501000000, class_name: Actor, name: 96956, pid: ALIVE)\n* Actor (actor_id: f36758a9f8871a9ca993b1d201000000, class_name: Actor, name: 96955, pid: ALIVE)\n\nYou can use the `ray list actors` command to list all actors in the cluster. You can also use the `ray get actors <ACTOR_ID>` command to retrieve a specific actor by its ID. Additionally, you can use the `ray list actors --limit <NUM>` command to limit the number of actors returned in the list. You can also use the `ray list actors --filter <FILTER>` command to filter the actors by their state, class name, or other attributes.",2528"llm": "meta-llama/Llama-2-70b-chat-hf"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": " You can kill a replica actor using the `ray.kill()` method, passing the actor handle and a boolean parameter `no_restart` that indicates whether the actor should be restarted or not. In your case, you can use the following code:\n```\nray.kill(replica_handle, no_restart=True)\n```\nThis will forcefully terminate the actor, and it will not be restarted. Note that if the actor is a detached actor, subsequent calls to get its handle via `ray.get_actor()` will fail.\n\nAlso, you can use `ray.actor.exit_actor()` within the actor method to manually terminate the actor process and release resources associated/assigned to the actor.\n\nIt's important to note that force-killing an actor should be reserved for cases where an actor is unexpectedly hanging or leaking resources, and for detached actors, which must be manually destroyed.",2555"llm": "meta-llama/Llama-2-70b-chat-hf"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": " You can specify the GPU type in the `ray.remote` function by using the `accelerator_type` argument. For 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```\nThis will schedule the `train` function to run on a node with a Tesla V100 GPU.\n\nAlternatively, you can use the `ray.util.accelerators` module to specify the accelerator type. For example:\n```\nimport ray\nfrom ray.util.accelerators import NVIDIA_TESLA_V100\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```\nNote that the `accelerator_type` argument only applies to GPUs, and not to CPUs. If you want to specify the CPU type, you can use the `num_cpus` argument instead.",2582"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here are the answers to the queries using the context provided:\n\n1. How do I get started?\n\nTo get started, install Ray via `pip install -U ray`. Then, import and initialize Ray using `import ray; ray.init()`.\n\n2. How can I get started contributing to Tune?\n\nLook for issues labeled \"good first issue\" and \"help wanted\" on the Tune Github Project Board. Raise a new issue or PR related to Tune, and include \"[tune]\" in the title and add a tune label.\n\n3. How do I start Ray?\n\nStart Ray on your single machine or cluster of machines by following the instructions on the Ray documentation page.\n\n4. How do I create a Kind cluster?\n\nUse the command `kind create cluster` to create a Kind cluster.",2609"llm": "meta-llama/Llama-2-70b-chat-hf"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 the runtime environment, you can use the `python_version` key in the `pip` section of the `runtime_env` dictionary. For example:\n```\nruntime_env = {\n \"pip\": {\n \"packages\": [\"emoji\"],\n \"python_version\": \"3.8.11\"\n },\n \"env_vars\": {\"TF_WARNINGS\": \"none\"}\n}\n```\nThis will specify that the Python version 3.8.11 should be used for the job or task.\n\nAlternatively, you can use the `conda` key in the `runtime_env` dictionary to specify a Conda environment, which can also include a specified Python version. For example:\n```\nruntime_env = {\n \"conda\": {\n \"env_name\": \"myenv\",\n \"python_version\": \"3.8.11\"\n },\n \"env_vars\": {\"TF_WARNINGS\": \"none\"}\n}\n```\nThis will specify that the Conda environment `myenv` should be used, which has Python version 3.8.11 installed.\n\nNote that you can also specify a Python version in the `requirements.txt` file, which will be used by the `pip` package manager to install the required packages. For example:\n```\npython-version=3.8.11\n```\nThis will specify that the Python version 3.8.11 should be used for the job or task.\n\nIt's important to note that the `python_version` key in the `pip` section of the `runtime_env` dictionary takes precedence over the `python-version` requirement in the `requirements.txt` file.",2636"llm": "meta-llama/Llama-2-70b-chat-hf"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 namespace, you can use the `ray.init()` method to initialize the Ray cluster with the desired namespace. Then, you can create an actor using the `ray.actor()` method and specify the name of the actor, which must be unique within the namespace.\n\nHere's an example code snippet that demonstrates how to create an actor in a namespace:\n```python\nimport ray\n\n# Initialize the Ray cluster with the \"colors\" namespace\nray.init(namespace=\"colors\")\n\n# Create an actor with the name \"orange\"\norange = ray.actor(RAY_FUNC(Counter::FactoryCreate)).SetName(\"orange\").Remote()\n\n# Create another actor with the name \"purple\"\npurple = ray.actor(RAY_FUNC(Counter::FactoryCreate)).SetName(\"purple\").Remote()\n\n# Shutdown the Ray cluster\nray.shutdown()\n```\nIn this example, the `ray.init()` method is used to initialize the Ray cluster with the \"colors\" namespace. Then, the `ray.actor()` method is used to create two actors, \"orange\" and \"purple\", which are named and remote. Finally, the `ray.shutdown()` method is used to shut down the Ray cluster.\n\nNote that the `ray.init()` method must be called before creating any actors, and the namespace specified in the `ray.init()` method must match the namespace used when creating the actors.",2663"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Yes, you can specify multiple working directories by setting the `working_dir` argument in the `runtime_env` dictionary to a list of directories. For example:\n```\nruntime_env = {\n 'working_dir': ['/path/to/dir1', '/path/to/dir2']\n}\n```\nThis will set the working directory to `/path/to/dir1` and `/path/to/dir2` for the task.\n\nAlternatively, you can use the `RAY_CHDIR_TO_TRIAL_DIR` environment variable to disable the changing of the working directory to the trial directory, and use relative paths to access files in the original working directory.\n\nIt's important to note that if you're running on multiple machines, you must make sure that the directory is synced on all nodes, and use relative paths for read-only purposes.",2690"llm": "meta-llama/Llama-2-70b-chat-hf"2691},2692{2693"question": "what if I set num_cpus=0 for tasks",2694"sources": [2695"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2696"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",2697"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",2698"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2699"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",2700"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#code-example",2701"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2702"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",2703"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.entrypoint_num_cpus.html#ray-job-submission-jobinfo-entrypoint-num-cpus"2704],2705"document_ids": [270611063,2707623,27085936,27094850,27106716,27115234,27124853,27134877,271467032715],2716"answer": " If you set num_cpus=0 for tasks, it will run solely on the CPU, even if GPUs are available on the machine. This is useful for small tasks that cannot expect a greater speedup than the number of physical CPUs. Additionally, setting num_cpus=0 can prevent Ray workloads with non-zero CPU requirements from being scheduled on the head.",2717"llm": "meta-llama/Llama-2-70b-chat-hf"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 Kubernetes without using KubeRay, especially with the case that autoscaler is enabled. You can deploy a static Ray cluster to Kubernetes without using KubeRay, but it should be noted that this deployment method lacks the built-in autoscaling feature that KubeRay provides. To enable Ray autoscaling in the KubeRay RayCluster custom resource (CR) config, you can set enableInTreeAutoscaling:true and set the fields minReplicas and maxReplicas to constrain the number of replicas of an autoscaling workerGroup.",2744"llm": "meta-llama/Llama-2-70b-chat-hf"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 use the RayCluster custom resource (CR) to define the desired state of the cluster. The KubeRay operator will then manage the Ray pods to match the spec defined in the RayCluster CR.\n\nHere are the key steps to create a minimal RayCluster YAML manifest:\n\n1. Download the RayCluster YAML manifest for this tutorial using `curl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-cluster.gke-bucket.yaml`\n2. Include the following lines in every pod spec of your Ray cluster:\n\t* `spec:\n\t\tserviceAccountName: my-ksa`\n\t* `nodeSelector:\n\t\tiam.gke.io/gke-metadata-server-enabled: \"true\"`\n3. Create an autoscaling RayCluster custom resource by applying the following YAML manifest:\n```\nkubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/release-0.5/ray-operator/config/samples/ray-cluster.autoscaler.yaml\n```\nThis will create a RayCluster custom resource with autoscaling enabled. You can then use the `kubectl` command-line tool to manage the Ray cluster, such as scaling the number of worker nodes up or down using the `kubectl scale` command.\n\nFor more information on configuring and managing a Ray cluster on Kubernetes, you can refer to the Ray documentation and guides, such as the RayCluster Configuration guide and the KubeRay Autoscaling guide.",2771"llm": "meta-llama/Llama-2-70b-chat-hf"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": " If you shut down a raylet, the tasks and workers on that node will also be killed. The raylet is responsible for managing the tasks and workers on its node, and when it shuts down, it will gracefully terminate any active tasks and workers. However, if the raylet is shut down due to memory pressure or other reasons, the tasks and workers on that node will be killed and will not be restarted.\n\nIt's important to note that when a raylet is shut down, the corresponding node will be marked as dead and will be treated the same as a node failure. The raylet will be restarted on a different node, and any tasks or workers that were running on the original node will not be restarted.\n\nIf you want to shut down a ray cluster, you can use the `ray.shutdown()` function. This will shut down the ray cluster and delete all applications and actors. However, if you want to skip the setup commands and only run `ray stop` and `ray start` on all nodes, you can pass the `--restart-only` flag to the `update` call.\n\nAdditionally, you can use `ray.serve.shutdown()` to completely shut down Serve on the cluster, delete all applications, and shut down Serve system actors.\n\nIt's also important to note that when Ray Serve scales down and terminates some replica actors, it will try to do so in a way that results in the most nodes having no Ray actors or tasks running on them, at which point the Ray autoscaler will remove those nodes.",2798"llm": "meta-llama/Llama-2-70b-chat-hf"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": " If you'd like to debug out-of-memory issues in Ray, you can follow these steps:\n\n1. Familiarize yourself with the Ray Memory Management model.\n2. Check the memory usage of your cluster using the Ray dashboard or the `ray memory` command.\n3. Identify the tasks or actors that have high memory usage using the per-task and actor memory usage graph.\n4. Use the `htop` command to get a more detailed view of memory usage.\n5. If the issue is caused by a memory leak, use `memray` to debug and profile memory usage.\n6. Check the head node memory usage and reduce parallelism if necessary.\n7. Use the `ray memory` command to track down ObjectRef references that may be causing an ObjectStoreFullError.\n\nRelevant documentation:\n\n* Debugging Out of Memory: <https://ray.io/docs/debugging-out-of-memory>\n* Memory Management: <https://ray.io/docs/memory-management>\n* Out-of-Memory Prevention: <https://ray.io/docs/out-of-memory-prevention>\n* Head Node Out-of-Memory Error: <https://ray.io/docs/head-node-out-of-memory-error>\n* Debugging using \u2018ray memory\u2019: <https://ray.io/docs/debugging-using-ray-memory>",2825"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a summary of how to use callbacks in Ray Train:\n\n1. Define a callback class that inherits from `transformers.trainer_callback.TrainerCallback` or `ray.train.huggingface.transformers.RayTrainReportCallback`.\n2. Override the `on_save` method in your callback class to collect the latest metrics and report them to Ray Train.\n3. Add your callback class to the `Trainer` instance using the `add_callback` method.\n4. Prepare your trainer using the `prepare_trainer` method.\n5. Train your model using the `train` method.\n\nHere's an example code snippet that demonstrates how to use a custom callback in Ray Train:\n```python\nfrom transformers import Trainer, TrainingArguments\nfrom ray.train.huggingface.transformers import RayTrainReportCallback\n\n# Define a custom callback class\nclass MyTrainReportCallback(RayTrainReportCallback):\n def __init__(self):\n super().__init__()\n self.metrics = {}\n\n def on_save(self, trainer, checkpoint):\n # Collect the latest metrics\n self.metrics = trainer.get_metrics()\n # Report the metrics to Ray Train\n ray.train.report(self.metrics)\n\n# Initialize the trainer\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\n# Add the custom callback\ntrainer.add_callback(MyTrainReportCallback())\n\n# Prepare the trainer\ntrainer = prepare_trainer(trainer)\n\n# Train the model\ntrainer.train()\n```\nIn this example, we define a custom callback class `MyTrainReportCallback` that inherits from `RayTrainReportCallback`. In the `on_save` method, we collect the latest metrics using the `get_metrics` method and report them to Ray Train using the `ray.train.report` method. We then add the custom callback to the trainer using the `add_callback` method and prepare the trainer using the `prepare_trainer` method. Finally, we train the model using the `train` method.",2852"llm": "meta-llama/Llama-2-70b-chat-hf"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 use the `ray.init()` function and specify the `working_dir` parameter. For example:\n```\nray.init(working_dir='/path/to/current/working/directory')\n```\nThis will set the working directory for the Ray client to the specified directory. Note that this directory should contain the data and code that you want to use in your Ray job.\n\nAlternatively, you can also use the `os.getcwd()` function to get the current working directory and pass it to `ray.init()`:\n```\nimport os\n\nworking_dir = os.getcwd()\nray.init(working_dir=working_dir)\n```\nThis will set the working directory for the Ray client to the current working directory of your Python script.",2879"llm": "meta-llama/Llama-2-70b-chat-hf"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 parameters, you can use the `ray.actor.ActorClass.options()` method to configure and override the actor instantiation parameters. Then, you can call the `ray.actor.ActorClass.remote()` method to create an actor instance with the specified parameters.\n\nHere's an example:\n```\n# Define an actor class with a constructor that takes a parameter\nclass MyActor:\n def __init__(self, name):\n self.name = name\n\n# Create an actor instance with a parameter\nactor = MyActor.remote(name=\"John\")\n```\nIn this example, the `MyActor` class has a constructor that takes a `name` parameter. The `ray.actor.ActorClass.remote()` method is called with the `name` parameter, which is passed to the constructor to create an actor instance with the specified name.\n\nYou can also pass multiple parameters to the actor constructor by using the `**actor_options` parameter of the `ray.actor.ActorClass.options()` method. For example:\n```\n# Define an actor class with a constructor that takes multiple parameters\nclass MyActor:\n def __init__(self, name, age):\n self.name = name\n self.age = age\n\n# Create an actor instance with multiple parameters\nactor = MyActor.remote(name=\"John\", age=30)\n```\nIn this example, the `MyActor` class has a constructor that takes two parameters, `name` and `age`. The `ray.actor.ActorClass.remote()` method is called with a dictionary of actor options that includes the `name` and `age` parameters, which are passed to the constructor to create an actor instance with the specified name and age.",2906"llm": "meta-llama/Llama-2-70b-chat-hf"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 that is using an actor, you can use the `ray.util.ActorPool.push()` method to push a new actor into the current list of idle actors. Here's an example from the Ray documentation:\n```\nimport ray\nfrom ray.util.actor_pool import ActorPool\n\n@ray.remote\nclass Actor:\n def __init__(self):\n # Every memory allocation after `__enter__` method will be tracked.\n memray.Tracker(\"/tmp/ray/session_latest/logs/${ray.get_runtime_context().get_actor_id()}_mem_profile.bin\").__enter__()\n self.arr = [bytearray(b\"1\" * 1000000)]\n\n def append(self):\n self.arr.append(bytearray(b\"1\" * 1000000))\n\na = Actor.remote()\npool = ActorPool([a])\npool.push(a)\n```\nIn this example, a new actor is created and pushed into the actor pool using `pool.push(a)`. The actor can then be used to perform tasks by calling its methods, such as `a.append.remote()`.",2933"llm": "meta-llama/Llama-2-70b-chat-hf"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 Ray working directory, you can use the `ray.get(hello_world.remote())` command, which will print the working directory of the Ray cluster.\n\nAlternatively, you can use the `ray.cluster_resources()` command to get a list of resources in the Ray cluster, including the working directory.\n\nYou can also use the `ray job logs` command to follow the logs of a Ray job, which will show the working directory of the job.\n\nIt's important to note that the working directory of a Ray job is located in the `/tmp/ray/session_latest/runtime_resources/_ray_pkg_<hash of directory contents>` directory on the cluster. This means that relative paths in the remote tasks and actors in the code will work on the laptop and on the cluster without any code changes.",2960"llm": "meta-llama/Llama-2-70b-chat-hf"2961},2962{2963"question": "why I can not see log.info in ray log?",2964"sources": [2965"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2966"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2967"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",2968"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2969"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2970"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",2971"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",2972"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",2973"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"2974],2975"document_ids": [2976444,2977680,29786631,2979445,2980511,2981662,2982530,2983415,29844932985],2986"answer": " The query is: why I can not see log.info in ray log?\n\nThe answer is: The default logging level for Ray's logger is logging.INFO, but the log.info messages are not displayed in the Ray log by default. To view log.info messages, you need to modify the Ray logging configuration by setting the logging level to logging.DEBUG or higher. You can do this by importing the Ray logger and modifying its configuration, or by using the ray.util.state.list_logs() function to list all logs and then retrieving the desired log file using the ray.util.state.get_log() function. Additionally, you can use the --debug flag with the ray logs command to enable debug logging for a specific log file.",2987"llm": "meta-llama/Llama-2-70b-chat-hf"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 make sure the order of the data is preserved when reading a file using Ray Dataset by setting the `preserve_order` flag in the `DataContext`. This flag ensures that the data is read in a deterministic order, which is important for reproducibility. Here's an example code snippet:\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 data using Ray Dataset\ndataset = ray.data.read_numpy(\"path/to/file.npy\", preserve_order=True)\n```\nBy setting `preserve_order` to `True`, Ray Dataset will read the data in a deterministic order, ensuring that the order of the data is preserved.",3014"llm": "meta-llama/Llama-2-70b-chat-hf"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": " The statement \"Ray will *not* retry tasks upon exceptions thrown by application code\" means that by default, Ray will not retry a task if it fails due to an exception thrown by the application code. Instead, the task will be marked as failed and the exception will be surfaced to the user. However, the user can configure Ray to retry tasks upon application-level failures by setting the `retry_exceptions` argument to `True` or by passing a list of retryable exceptions.",3041"llm": "meta-llama/Llama-2-70b-chat-hf"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 `ray.init()` method and pass the `log_dir` parameter. For example:\n```\nray.init(log_dir='/path/to/logs')\n```\nThis will set the log directory for Ray to the specified path.\n\nAlternatively, you can also specify the log directory using the `RAY_LOG_DIR` environment variable. For example:\n```\nexport RAY_LOG_DIR='/path/to/logs'\nray.init()\n```\nThis will set the log directory for Ray to the value of the `RAY_LOG_DIR` environment variable.\n\nNote that the log directory should exist before starting Ray, and Ray will create a new folder in the log directory for each session.",3068"llm": "meta-llama/Llama-2-70b-chat-hf"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, you can use the `ray up` command with the `--min-workers` and `--max-workers` options set to 10.\n\nHere's an example command:\n```\nray up --min-workers 10 --max-workers 10 --yes\n```\nThis will create a Ray cluster with 10 worker nodes, in addition to the head node, and will use the default configuration for the worker nodes.\n\nIf you want to specify a different configuration for the worker nodes, you can use the `--node-config` option to provide a YAML file containing the configuration for the worker nodes.\n\nFor example, to specify a different instance type and number of CPUs for the worker nodes, you can use the following command:\n```\nray up --min-workers 10 --max-workers 10 --node-config worker_config.yaml --yes\n```\nWhere `worker_config.yaml` is a YAML file containing the configuration for the worker nodes, such as:\n```\nnode_config:\n instance_type: m5.4xlarge\n image_id: latest_dlami\n block_device_mappings:\n - device_name: /dev/sda1\n ebs:\n volume_size: 1000\n cpu: 2\n```\nThis will create a Ray cluster with 10 worker nodes, each with an m5.4xlarge instance type, 2 CPUs, and a 1000GB EBS volume.",3095"llm": "meta-llama/Llama-2-70b-chat-hf"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 use Ray API to scale up a cluster, you can use the `ray.autoscaler.sdk.request_resources()` method to command the autoscaler to scale the cluster up to a desired size. This method takes two arguments: `num_cpus` (optional) and `bundles` (optional). The `num_cpus` parameter specifies the number of CPUs to request, while the `bundles` parameter specifies a list of resource bundles to request.\n\nHere's an example of how to use this method:\n```\nray.autoscaler.sdk.request_resources(num_cpus=10, bundles=[{\"name\": \"my-bundle\", \"resources\": {\"cpu\": 10}}])\n```\nThis command will request 10 CPUs and 10 bundles with the specified name. The cluster will immediately attempt to scale to accommodate the requested resources, bypassing normal upscaling speed constraints.\n\nYou can also use the `ray up` command to scale up a cluster. This command takes several options, including `--min-workers`, `--max-workers`, `--no-restart`, and `--restart-only`. The `--min-workers` and `--max-workers` options specify the minimum and maximum number of worker nodes for the cluster, respectively. The `--no-restart` option skips restarting Ray services during the update, while the `--restart-only` option skips running setup commands and only restarts Ray.\n\nHere's an example of how to use the `ray up` command to scale up a cluster:\n```\nray up --min-workers 10 --max-workers 20\n```\nThis command will set the minimum and maximum number of worker nodes for the cluster to 10 and 20, respectively. If the cluster already has more than 10 worker nodes, it will not be affected by this command.",3122"llm": "meta-llama/Llama-2-70b-chat-hf"3123},3124{3125"question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",3126"sources": [3127"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3128"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3129"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",3130"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",3131"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",3132"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",3133"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents",3134"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",3135"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions"3136],3137"document_ids": [31386199,3139273,31406203,31415503,31426458,31436470,31446519,31456467,314661043147],3148"answer": " To specify a subnet in the deployment file for a Ray cluster on AWS, you can add the `subnets` field to the `provider` section of the YAML file, like this:\n```\nprovider:\n type: aws\n region: us-west-2\n subnets:\n - subnet-12345678\n - subnet-90123456\n```\nThis will specify that the Ray cluster should be launched in the subnets with the IDs `subnet-12345678` and `subnet-90123456`.\n\nYou can also specify the subnets using the `aws.subnets` field, like this:\n```\nprovider:\n type: aws\n region: us-west-2\n aws:\n subnets:\n - subnet-12345678\n - subnet-90123456\n```\nEither way, you will need to replace the `subnet-12345678` and `subnet-90123456` placeholders with the actual IDs of the subnets that you want to use.\n\nIt's also worth noting that you can specify multiple subnets in the `subnets` field, separated by commas. This can be useful if you want to spread your Ray cluster across multiple subnets for availability or performance reasons.",3149"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Here are the requested logs and their descriptions:\n\n1. HTTP server error code log for Ray serve: ray_serve_num_deployment_http_error_requests, ray_serve_num_deployment_grpc_error_requests, ray_serve_http_request_latency_ms, ray_serve_grpc_request_latency_ms\n2. Ray client logs: /tmp/ray/session_latest/logs on the head node.\n3. Ray Serve script logs: rayserve-dev-doc.md and rayservice-troubleshooting.md\n4. Ray dashboard logs: rayservice-troubleshooting.md\n5. Ray exception logs: ray.exceptions.RaySystemError\n6. Ray service logs: kubectl port-forward svc/rayservice-sample-head-svc --address 0.0.0.0 8265:8265\n7. Ray logging: ray.serve logger, /tmp/ray/session_latest/logs/serve/ on each node, and streamed to the driver Ray program in development.",3176"llm": "meta-llama/Llama-2-70b-chat-hf"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": " It seems like you are experiencing issues with accessing the Ray Dashboard on a remote cluster. The dashboard is not publicly exposed by default, and you need to set up port forwarding to access it from outside the head node.\n\nYou can use the `ray dashboard` command to set up port forwarding automatically. Run the following command on your local machine, replacing `cluster.yaml` with the configuration file you used to launch your cluster:\n```\nray dashboard cluster.yaml\n```\nThis will forward the dashboard port (default is 8265) on the head node to your local machine, and you should be able to access the dashboard in your local browser at `http://localhost:8265`.\n\nAlternatively, you can also set up port forwarding manually by using the `--dashboard-port` argument when running `ray start`. For example:\n```\nray start --dashboard-port 8265\n```\nThis will start the Ray cluster with the dashboard port set to 8265, and you can access the dashboard from outside the head node by using the IP address of the head node followed by `:8265`.\n\nNote that it is important to ensure that the port you choose is not already in use by another process on your local machine.",3203"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Does `max_workers` include the head node, or only worker nodes?\n\nThe `max_workers` parameter in the Ray cluster launcher YAML file only includes worker nodes and does not include the head node. The head node is considered a separate entity and is not included in the count of worker nodes. Therefore, when specifying `max_workers`, you should only consider the number of worker nodes you want to launch, and the head node will be added separately.",3230"llm": "meta-llama/Llama-2-70b-chat-hf"3231},3232{3233"question": "How to update files in working directory ?",3234"sources": [3235"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",3236"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",3237"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3238"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",3239"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",3240"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",3241"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",3242"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#outdated-function-definitions",3243"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#synchronizing-files-from-the-cluster-ray-rsync-up-down"3244],3245"document_ids": [32466632,32471775,32481790,32498894,32501068,32516214,32521066,3253619,325462223255],3256"answer": " To update files in the working directory, you can use the `ray.train.get_context().get_trial_dir()` method to get the path to the trial directory, and then use this path to save any outputs. This method is recommended instead of using the `TUNE_ORIG_WORKING_DIR` environment variable, which is deprecated.\n\nAlternatively, you can set the `RAY_CHDIR_TO_TRIAL_DIR` environment variable to `0` to disable the changing of the working directory, and then use relative paths to access files in the original working directory. However, this approach requires careful synchronization of files across nodes, and you should make sure that the directory is synced on all nodes if running on multiple machines.\n\nIt's also important to note that updating an existing cluster configuration (e.g., adding more files, changing dependencies) can be done by running `ray up` again on the existing cluster. This command checks for any changes in the local configuration and uploads new files and config to the cluster, following which Ray services/processes will be restarted.",3257"llm": "meta-llama/Llama-2-70b-chat-hf"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 has already been initiated, you can use the `ray.init` method and specify the `--working-dir` argument. This will upload the specified directory to the Ray cluster and make its contents available to the job.\n\nAlternatively, you can use the `ray.train.get_context().get_trial_dir()` method to get the path to the trial directory, which is a unique directory for each job that is created on the Ray cluster. This directory can be used to store outputs and other files that need to be preserved across job runs.\n\nIt's important to note that when Ray changes the working directory, it does so for all workers on the same node, so you should use the `ray.train.get_context().get_trial_dir()` method to ensure that outputs are saved in a unique directory for each job.\n\nYou can also disable the behavior of changing the working directory by setting the `RAY_CHDIR_TO_TRIAL_DIR` environment variable to `0`. This will allow you to access files in the original working directory, but you must use relative paths for read-only purposes, and you must ensure that the directory is synced on all nodes if running on multiple machines.\n\nFinally, you can use the `TUNE_ORIG_WORKING_DIR` environment variable to access paths relative to the original working directory. This environment variable is deprecated, and you should use the `RAY_CHDIR_TO_TRIAL_DIR` environment variable instead.",3284"llm": "meta-llama/Llama-2-70b-chat-hf"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 SSH key, you can specify the `ssh_private_key` parameter in the `auth` section of the `ray.yaml` configuration file. For example:\n```\nauth:\n ssh_user: ubuntu\n ssh_private_key: /path/to/your/key.pem\n```\nThis will tell Ray to use the specified SSH key for authentication with the head node.\n\nYou can also specify the `KeyName` parameter in the `head_node` and `worker_node` sections of the configuration file, like this:\n```\nhead_node:\n ...\n KeyName: my-key\n\nworker_node:\n ...\n KeyName: my-key\n```\nThis will tell Ray to use the specified key for authentication with the head and worker nodes.\n\nNote that you will need to make sure that the custom SSH key is installed on the head node and that the `~/.ssh/id_rsa.pub` file exists and contains the public key. You can also specify a different SSH key for the worker nodes by changing the `ssh_private_key` parameter in the `worker_node` section of the configuration file.",3311"llm": "meta-llama/Llama-2-70b-chat-hf"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 server is a centralized metadata server for a Ray cluster that runs on the head node. It manages node membership and actor directory, and is also known as the Global Control Store. The head node is a node that runs extra cluster-level processes like GCS and API server in addition to those processes running on a worker node.\n\nThe GCS server takes up memory on the head node, and its failure means that the entire Ray cluster fails. To make the GCS fault tolerant, HA Redis is required. When the GCS restarts, it loads all the data from the Redis instance and resumes regular functions. During the recovery period, some functions are not available, such as actor creation, deletion, and reconstruction, placement group creation, deletion, and reconstruction, and resource management.\n\nThe head node has extra stress due to additional system processes running on it like GCS, and a good starting hardware specification for the head node is 8 CPUs and 32 GB memory. The actual hardware specification depends on the workload and the size of the cluster. Metrics that are useful for deciding the hardware specification are CPU usage, memory usage, and network bandwidth usage.\n\nTo tolerate head node failures, we need to make GCS fault tolerant so that when we start a new head node, we still have all the cluster-level data. Ray system memory, GCS memory, and Raylet memory are the different ways that Ray applications use memory. The head node needs to open several ports, including the port of Ray (GCS server), listening port for Ray Client Server, and dashboard-grpc-port.\n\nIn summary, the GCS server is a critical component of a Ray cluster that runs on the head node and manages cluster-level metadata. It takes up memory on the head node, and its failure can cause the entire cluster to fail. To mitigate this risk, HA Redis can be used to make the GCS fault tolerant, and the head node should have sufficient hardware resources to handle the additional stress of running GCS and other system processes.",3338"llm": "meta-llama/Llama-2-70b-chat-hf"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": " It looks like you are experiencing issues with your Ray cluster, specifically with nodes being stuck in the \"Pending\" state. Here are some steps you can take to debug and resolve the issue:\n\n1. Check the Ray cluster status using `ray status`. This command will show you the current state of your cluster, including the number of nodes and their status.\n2. Check the logs for any errors or warnings. You can do this by running `cat /tmp/ray/session_latest/logs/monitor.*` to view the autoscaler monitor log.\n3. Make sure that your Docker resource limits are set high enough. You can check the current resource limits by running `kubectl describe pod raycluster-kuberay-xxxx-xxxxx`.\n4. Wait for the pods to reach the \"Running\" state. This may take a few minutes, and you can check the status by running `kubectl get pods`.\n5. If the issue persists, you can try restarting the Ray cluster using `ray up`. This will restart all Ray services, even if there are no configuration changes.\n6. If the issue still persists, you can try increasing the number of nodes in your Ray cluster. You can do this by updating the `node_count` parameter in your `ray.yaml` file and then running `ray up`.\n7. If none of the above steps work, you can try debugging the issue by looking at the Ray scheduler events. You can do this by setting `RAY_SCHEDULER_EVENTS=1` and then running `ray status`. This will show you detailed information about the scheduler events and may help you identify the root cause of the issue.\n\nI hope these suggestions help you resolve the issue with your Ray cluster. If you continue to experience problems, you may want to consider reaching out to the Ray community for further support.",3365"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a summary of the ways to install Ray from the provided context:\n\n1. Daily Releases (Nightlies): Use pip to install the nightly Ray wheels with the following command: `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`\n2. Installing from a specific commit: Use pip to install the Ray wheels of any particular commit on master with the following template: `pip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/{COMMIT_HASH}/ray-{RAY_VERSION}-{PYTHON_VERSION}-{PYTHON_VERSION}-{OS_VERSION}.whl`\n3. From Wheels: Install the latest official version of Ray from PyPI on Linux, Windows, and macOS by choosing the option that best matches your use case.\n\t* Recommended for machine learning applications: `pip install -U \"ray[air]\"`\n\t* For reinforcement learning support, install RLlib instead: `pip install -U \"ray[rllib]\"`\n\t* For general Python applications: `pip install -U \"ray[default]\"`\n\t* If you don\u2019t want Ray Dashboard or Cluster Launcher, install Ray with minimal dependencies instead: `pip install -U \"ray\"`\n\t* Advanced: Use the following command to install Ray with the specified components:\n\t\t+ `pip install -U \"ray\"`: Core\n\t\t+ `pip install -U \"ray[default]\"`: Core, Dashboard, Cluster Launcher\n\t\t+ `pip install -U \"ray[data]\"`: Core, Data",3392"llm": "meta-llama/Llama-2-70b-chat-hf"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": " You can use the `worker_setup_hook` parameter in the `ray.init()` function to set up worker nodes in a runtime environment. This parameter is a callable that is executed after the worker nodes are created and before tasks and actors are scheduled. You can use this hook to perform any necessary setup, such as installing required software or configuring the environment.\n\nHere is an example of how you can use the `worker_setup_hook` parameter:\n```\nray.init(runtime_env={\"worker_setup_hook\": my_setup_function})\n```\nIn this example, `my_setup_function` is a callable that will be executed on each worker node after it is created. You can define this function to perform any necessary setup, such as installing software or configuring the environment.\n\nIt's important to note that the `worker_setup_hook` parameter is only available in Ray 1.10.0 and later versions. In earlier versions of Ray, you can use the `worker_process_setup_hook` parameter instead.",3419"llm": "meta-llama/Llama-2-70b-chat-hf"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 follow these steps:\n\n1. Install Ray Data: `pip install -U \"ray[data]\"`\n2. Load your data into a Ray Dataset: Ray Data supports many different data sources and formats, including S3. You can use the `ray.data.read_csv()` function to read a CSV file from S3.\n3. Define a Python class to load the pre-trained model.\n4. Transform your dataset using the pre-trained model by calling `ds.map_batches()`.\n5. Get the final predictions by either iterating through the output or saving the results.\n\nYou can also use the Ray cluster launcher to start a Ray cluster on AWS, which allows you to scale your data processing to multiple machines.\n\nNote that Ray Data does not have any restrictions on cloud provider, ML framework, or data format, so you can use it with any cloud provider or ML framework of your choice.",3446"llm": "meta-llama/Llama-2-70b-chat-hf"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 dataset shuffling during a `ray.data.map_batches` operation, you can use the `local_shuffle_buffer_size` parameter to specify a buffer size for local shuffling. This will shuffle a subset of rows up to the specified buffer size during iteration, which can provide sufficient randomness without requiring a full global shuffle.\n\nHere's an example code snippet that demonstrates this approach:\n```python\nray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .local_shuffle_buffer_size(100)\n .map_batches(preprocess_function, batch_format=\"pandas\")\n```\nIn this example, the `local_shuffle_buffer_size` parameter is set to 100, which means that up to 100 rows will be shuffled locally during each iteration. This can help reduce the amount of data movement required and improve performance.\n\nNote that the `local_shuffle_buffer_size` parameter is only applicable when iterating over batches with shuffling enabled. If you're using a vectorized transformation, such as `map_batches`, you can also specify the `local_shuffle_buffer_size` parameter to enable local shuffling.",3473"llm": "meta-llama/Llama-2-70b-chat-hf"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.",3500"llm": "meta-llama/Llama-2-70b-chat-hf"3501},3502{3503"question": "ray serve returns generic internal service error when there is an internal failure, how do I get it to emit more detailed errors or logs?",3504"sources": [3505"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",3506"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",3507"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",3508"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",3509"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",3510"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3511"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3512"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",3513"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror"3514],3515"document_ids": [35164349,35174527,351813517,35194348,35205671,35214660,352213974,3523595,352446943525],3526"answer": " To get more detailed errors or logs from Ray Serve, you can use the following approaches:\n\n1. Check the Ray logs: Ray Serve uses Python's standard logging module, and logs are emitted from actors both to stderr and on disk on each node. You can find the logs at /tmp/ray/session_latest/logs/serve/.\n2. Use Ray's exception handling mechanism: 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 and stores this as the task's return value. You can catch this exception and inspect the wrapped exception to get more details about the error.\n3. Use Ray's logging and monitoring tools: Ray provides several tools for logging and monitoring, such as the Ray logger and the Ray dashboard. You can use these tools to get more insights into the system-level behavior and application-level details during runtime.\n4. Test your Ray Serve script locally or in a RayCluster before deploying it to a RayService: Ray Serve script errors can be caught and tested locally or in a RayCluster before deploying it to a RayService.\n5. Check the Ray documentation and error codes: Ray provides a list of error codes and their meanings in the documentation. You can check the documentation to understand the error codes and their corresponding solutions.\n6. Use Ray's fault tolerance mechanisms: Ray provides mechanisms to catch errors, retry failed code, and handle misbehaving code. You can use these mechanisms to handle application-level failures and system-level failures.\n7. Check the Ray Serve API documentation: Ray Serve API documentation provides information on how to use the Ray Serve API, including error handling and troubleshooting.\n8. Check the Ray SystemError exception: Ray SystemError exception can be thrown when Ray encountered a system error. You can catch this exception and inspect the error message to get more details about the error.",3527"llm": "meta-llama/Llama-2-70b-chat-hf"3528},3529{3530"question": "how do i track an uncaught exception in ray serve",3531"sources": [3532"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3533"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",3534"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",3535"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",3536"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",3537"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception",3538"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3539"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3540"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api"3541],3542"document_ids": [354313974,35444694,35454527,35464674,35475747,3548778,35494658,35504660,355146753552],3553"answer": " To track an uncaught exception in Ray Serve, you can use the `ray.exceptions` module. This module provides a number of exceptions that can be used to handle different types of errors that may occur in Ray Serve.\n\nFor example, you can use the `ray.exceptions.RaySystemError` exception to handle system errors that may occur in Ray Serve. This exception is raised when Ray encounters a system error, such as when the raylet is killed.\n\nYou can also use the `ray.exceptions.ObjectReconstructionFailedError` exception to handle errors that occur when an object cannot be reconstructed. This exception is raised when the object cannot be reconstructed because its lineage was evicted due to memory pressure.\n\nAdditionally, you can use the `ray.exceptions.TaskCancelledError` exception to handle errors that occur when a task is cancelled. This exception is raised when a task is cancelled, either because the task's placement group was removed or because the worker died unexpectedly.\n\nTo catch these exceptions, you can use a try-except block in your code. For example:\n```\ntry:\n # Your code here\nexcept ray.exceptions.RaySystemError as e:\n # Handle the error here\nexcept ray.exceptions.ObjectReconstructionFailedError as e:\n # Handle the error here\nexcept ray.exceptions.TaskCancelledError as e:\n # Handle the error here\n```\nBy using the `ray.exceptions` module, you can handle different types of errors that may occur in Ray Serve and provide appropriate error handling in your code.",3554"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a succinct answer to the query:\n\nTo view logs emitted by a Ray Serve endpoint in a Ray cluster using Python logger, you can use the `ray logs` command with the `--node-id` option to specify the node ID of the head node. Alternatively, you can use the `list_logs` function from the `ray.util.state` module to list all logs on a node and then download a particular log file using the `get_log` function.\n\nHere's an example command to view logs from a head node:\n```\nray logs --node-id <HEAD_NODE_ID>\n```\nYou can also use the `ray logs cluster` command to view logs from all nodes in the cluster.\n\nNote that the `ray logs` command only shows logs from the head node by default. To view logs from other nodes, you need to specify the `--node-id` option.",3581"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a concise answer to the query:\n\nTo see logs for a failed Ray Serve deployment, you can find them at /tmp/ray/session_latest/logs/serve/ on the head node. You can also use the Ray Dashboard to monitor the cluster and see the logs for each deployment. Additionally, you can use the Kuberay operator logs to troubleshoot application deployment failures. Built-in Ray Serve metrics are also available to monitor the performance of your application.",3608"llm": "meta-llama/Llama-2-70b-chat-hf"3609},3610{3611"question": "How to take a subset of a Ray Dataset?",3612"sources": [3613"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",3614"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",3615"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",3616"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",3617"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",3618"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#splitting-datasets",3619"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",3620"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",3621"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray"3622],3623"document_ids": [36249180,36258006,36266835,362710017,36288610,36299198,36309188,36316901,36321433633],3634"answer": " To take a subset of a Ray Dataset, you can use the `take()` method to retrieve a specified number of rows from the dataset. For example:\n```\nds = ray.data.from_items([{\"x\": [x], \"y\": [2 * x]} for x in range(200)])\nsubset = ds.take(10)\n```\nThis will return a new Ray Dataset containing the first 10 rows of the original dataset.\n\nAlternatively, you can use the `take_all()` method to retrieve all rows from the dataset, and then slice the resulting list to get a subset of the data. For example:\n```\nds = ray.data.from_items([{\"x\": [x], \"y\": [2 * x]} for x in range(200)])\nall_rows = ds.take_all()\nsubset = all_rows[:10]\n```\nThis will also return a new Ray Dataset containing the first 10 rows of the original dataset.\n\nNote that the `take()` and `take_all()` methods both return new Ray Datasets, so you can chain them together with other dataset operations to create complex data pipelines.",3635"llm": "meta-llama/Llama-2-70b-chat-hf"3636},3637{3638"question": "How do I load all checkpoints from trials of a Tune experiment launched with `tune.run`? I ran my initial experiment with cloud checkpointing, so I\u2019d need to download all the checkpoints to analyze them.",3639"sources": [3640"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",3641"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing",3642"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",3643"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints",3644"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-out-of-tune-using-checkpoints-other-artifacts",3645"https://docs.ray.io/en/master/tune/tutorials/overview.html#tune-feature-guides",3646"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#tunecontroller",3647"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",3648"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage"3649],3650"document_ids": [36512226,36522217,36532253,36542212,36552299,36562270,36572243,36582255,365923483660],3661"answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `train.get_checkpoint()` method. This method will return the latest checkpoint for a trial, or None if no checkpoint is available. You can also configure persistent storage options for a distributed Tune experiment to ensure that checkpoints are saved and loaded correctly. Additionally, you can use the `Checkpoint` class from the `ray.train` module to work with checkpoints.\n\nHere's an example of how to load all checkpoints from trials of a Tune experiment:\n```\nimport ray\nfrom ray.train import Checkpoint\n\n# Connect to the Ray cluster\nray.init()\n\n# Get the list of trials from the Tune experiment\ntrials = ray.get_trials()\n\n# Iterate over each trial and load its checkpoint\nfor trial in trials:\n checkpoint = train.get_checkpoint(trial.id)\n if checkpoint:\n # Do something with the checkpoint\n print(checkpoint.as_directory())\n```\nThis code will connect to the Ray cluster, get the list of trials from the Tune experiment, and then iterate over each trial to load its checkpoint using the `train.get_checkpoint()` method. The checkpoint will be stored in the `checkpoint` variable, and you can then do something with it, such as printing the directory where the checkpoint is saved.\n\nNote that the `train.get_checkpoint()` method will return None if no checkpoint is available for a trial, so you may want to add a check to see if a checkpoint is available before trying to work with it.",3662"llm": "meta-llama/Llama-2-70b-chat-hf"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 Ray, you can use the `ray.kill` method and pass the actor handle as an argument. This will forcefully terminate the actor and allow the name to be reused. Make sure to set `no_restart=False` if you want Ray to automatically restart the actor.\n\nHere's an example code snippet:\n```python\nimport ray\n\n# Create a detached actor\nactor_handle = ray.actor.ActorHandle(\"my_actor\", lifetime=\"detached\")\n\n# Kill the actor\nray.kill(actor_handle, no_restart=False)\n```\nNote that killing an actor will cause any pending tasks on the actor to fail immediately, and any atexit handlers installed in the actor will not be run. If you want to let pending tasks finish before terminating the actor, you can call `actor_handle.__ray_terminate__.remote()` instead.",3689"llm": "meta-llama/Llama-2-70b-chat-hf"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": " You can set environment variables in Ray by using the `env_vars` field in the `runtime_env` dictionary passed to `ray.init()`. For example:\n```\nray.init(runtime_env={\"pip\": [\"emoji\"], \"env_vars\": {\"TF_WARNINGS\": \"none\"}})\n```\nAlternatively, you can use the `ray.runtime_env.RuntimeEnv` class to create a runtime environment object and set the environment variables using the `env_vars` attribute. For example:\n```\nfrom ray.runtime_env import RuntimeEnv\n\nruntime_env = RuntimeEnv(pip=[\"emoji\"], env_vars={\"TF_WARNINGS\": \"none\"})\nray.init(runtime_env=runtime_env)\n```\nNote that environment variables already set on the cluster will still be visible to the Ray workers, so there is no need to include `os.environ` or similar in the `env_vars` field. Additionally, you can reference existing environment variables using `${ENV_VAR}` to achieve the appending behavior.",3716"llm": "meta-llama/Llama-2-70b-chat-hf"3717},3718{3719"question": "What is the rest api for getting the head node id?",3720"sources": [3721"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",3722"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.node_id.html#ray-runtime-context-runtimecontext-node-id",3723"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",3724"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_node_id.html#ray-runtime-context-runtimecontext-get-node-id",3725"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",3726"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3727"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",3728"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3729"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails"3730],3731"document_ids": [3732721,37334731,3734754,37354909,37364450,373714324,373814202,3739492,374066573741],3742"answer": " The REST API for getting the head node ID is `ray.runtime_context.RuntimeContext.get_node_id()`. This method returns the current node ID for the worker or driver, and it is deprecated. Instead, use `ray.util.state.get_node()` to get a node by ID.\n\nHere's an example of how to use `ray.util.state.get_node()` to get the head node ID:\n```\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Get the head node ID\nhead_node_id = ray.util.state.get_node(ray.runtime_context.RuntimeContext.node_id)\n\nprint(head_node_id)\n```\nThis will print the ID of the head node in the cluster. Note that this method can return `None` if the actor not found, or a `NodeState` object containing information about the node.",3743"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a summary of how to rerun a canceled Ray task:\n\n* If the task has been scheduled or interrupted, calling `ray.get` on the task reference will raise a `TaskCancelledError`.\n* To cancel a task, call `ray.cancel` on the task reference. If the task is an async actor task, Ray will seek to cancel the associated asyncio.Task.\n* If the task is a regular actor task or a threaded actor task, Ray offers no mechanism for interruption.\n* If the task is an unscheduled task, Ray attempts to cancel the scheduling. If the task is successfully canceled at this stage, invoking `ray.get` on the actor task reference will produce a `TaskCancelledError`.\n* To check if a task was successfully canceled, use `ray.get` on the actor task reference.\n* Ray attempts to cancel tasks on a best-effort basis, meaning cancellation isn\u2019t always guaranteed.\n* If the cancellation request doesn\u2019t get through to the executor, the task might not be canceled.\n* Ray tracks all child and actor tasks, and when the `recursive=True` argument is given, it cancels all child and actor tasks.",3770"llm": "meta-llama/Llama-2-70b-chat-hf"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 maximum number of concurrent scheduled tasks in `map_batches`, you can use the `ConcurrencyLimiter` class from the `ray.tune.search` module. This class allows you to specify a maximum number of concurrent trials for a searcher, and it will automatically manage the concurrency of the searcher's trials.\n\nHere's an example of how you can use `ConcurrencyLimiter` to set the maximum number of concurrent scheduled tasks in `map_batches`:\n```\nfrom ray.tune.search import ConcurrencyLimiter\n\n# Create a searcher that uses map_batches\nsearcher = HyperOptSearch(metric=\"accuracy\")\n\n# Create a ConcurrencyLimiter that manages the searcher's concurrency\nlimiter = ConcurrencyLimiter(searcher, max_concurrent=2)\n\n# Use the limiter to search for the optimal parameters\ntuner = tune.Tuner(trainable, tune_config=tune.TuneConfig(search_alg=limiter))\ntuner.fit()\n```\nIn this example, the `ConcurrencyLimiter` is created with a maximum number of concurrent trials set to 2. This means that the searcher will only run 2 trials concurrently, and it will wait for all concurrent trials to finish before updating the underlying searcher.\n\nYou can also use the `set_max_concurrency` method of the searcher to set the maximum number of concurrent trials. For example:\n```\nsearcher = HyperOptSearch(metric=\"accuracy\")\nsearcher.set_max_concurrency(2)\n```\nThis will set the maximum number of concurrent trials for the searcher to 2.\n\nNote that if you use a `ConcurrencyLimiter` to manage the concurrency of your searcher, it's not necessary to use the `set_max_concurrency` method of the searcher. The `ConcurrencyLimiter` will automatically manage the concurrency of the searcher's trials, and it will inherit the maximum number of concurrent trials from the `max_concurrent` parameter of the `ConcurrencyLimiter`.",3797"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a concise answer to your question:\n\nYou can get the number of CPUs available in a Ray cluster by running the command `ray.cluster_resources()` and looking for the `num_cpus` field in the output. This field represents the number of CPUs available in the cluster.\n\nAlternatively, you can check the Ray dashboard, which displays information such as the number of CPU cores available and the total utilization of the current Ray application. You can access the Ray dashboard by visiting the URL `http://127.0.0.1:8265` (or the address specified in your Ray configuration) and looking for the \"CPU\" field in the output.\n\nNote that Ray auto-detects the resources (e.g., CPU) available on each node, but you can also manually override this by passing custom resources to the `ray start` command. For example, to specify that a machine has 10 CPUs and 1 GPU available for use by Ray, you can run the command `ray start --num-cpus=10 --num-gpus=1`.",3824"llm": "meta-llama/Llama-2-70b-chat-hf"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 in the `runtime_env` configuration, you can specify a list of fields that should be excluded from the runtime environment. For example:\n```\nray.runtime_env.RuntimeEnvConfig(\n exclude=[\"py_modules\", \"working_dir\"],\n setup_timeout_seconds=600,\n eager_install=True\n)\n```\nThis will exclude the `py_modules` and `working_dir` fields from the runtime environment.\n\nAlternatively, you can also use the `exclude` option when creating a `RuntimeEnv` object:\n```\nray.runtime_env.RuntimeEnv(\n py_modules=[\"requests\", \"chess\"],\n working_dir=\"/app\",\n exclude=[\"py_modules\", \"working_dir\"]\n)\n```\nThis will create a `RuntimeEnv` object with the specified fields, while excluding the `py_modules` and `working_dir` fields.\n\nNote that when using the `exclude` option, the fields that are excluded will not be included in the runtime environment, but they will still be included in the job configuration. Therefore, if you need to access these fields in your job, you should not exclude them from the runtime environment.",3851"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's an example of a map batch with batch format set to \"pandas\":\n\nimport ray\n\n# Create a dataset from a range of numbers\ndataset = ray.data.range(10)\n\n# Set batch format to pandas\nbatch_format = \"pandas\"\n\n# Define a function to increase the brightness of an image\ndef increase_brightness(batch):\n batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n return batch\n\n# Apply the function to each batch\nds = dataset.map_batches(increase_brightness, batch_format=batch_format)\n\n# Print the first batch\nprint(ds.take_all())\n\nThis will output:\n\n image\n0 255\n1 255\n2 255\n3 255\n4 255\n\nIn this example, we set the batch format to \"pandas\" using the `batch_format` parameter in the `map_batches()` method. This tells Ray Data to represent each batch as a pandas DataFrame. We then define a function `increase_brightness()` that takes a batch as input, increases the brightness of the image, and returns the modified batch. We apply this function to each batch using the `map_batches()` method. Finally, we print the first batch using the `take_all()` method.\n\nNote that when we set `batch_format=\"pandas\"`, Ray Data will expect the function we pass to `map_batches()` to return a pandas DataFrame. If we return a NumPy ndarray instead, Ray Data will automatically convert it to a pandas DataFrame.",3878"llm": "meta-llama/Llama-2-70b-chat-hf"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 follow these steps:\n\n1. Check the environment variable RAY_ADDRESS.\n2. Check the address of the latest cluster started (found in /tmp/ray/ray_current_cluster).\n3. If no address is provided, start a new local Ray instance.\n\nAlternatively, you can pass the --address flag explicitly to each Ray Jobs CLI command, or prepend each command with RAY_ADDRESS=http://127.0.0.1:8265.\n\nYou can also view the Ray Dashboard at localhost:8265 to access the Ray cluster.",3905"llm": "meta-llama/Llama-2-70b-chat-hf"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 the CLI, you can use the `ray up` command with the `-y` flag to specify the configuration file. For example:\n```\nray up -y config.yaml\n```\nThis will start a Ray cluster on the cloud, creating a designated \u201chead node\u201d and worker nodes.\n\nAlternatively, you can use the `ray start` command to start a Ray cluster locally or remotely. To start a Ray cluster remotely, you can follow the directions in the \"Getting Started\" guide.\n\nTo start the head node, you can run the following command:\n```\nray start --head --port=6379\n```\nThis will print out the Ray cluster address, which can be passed to `ray start` on other machines to start the worker nodes.\n\nTo connect to the Ray cluster, you can call `ray.init` from one of the machines in the cluster. This will connect to the latest Ray cluster:\n```\nray.init()\n```\nYou can also manually set up a Ray cluster by using the `ray up` command with a configuration file that specifies the nodes in the cluster.\n\nTo connect to multiple Ray clusters, you can pass the `allow_multiple=True` flag to `ray.init`:\n```\nray.init(\"ray://<head_node_host_cluster>:10001\", allow_multiple=True)\n```\nThis will allow you to connect to multiple Ray clusters in one Python process.",3932"llm": "meta-llama/Llama-2-70b-chat-hf"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 query is asking why the Ray tuner shows \"running\" but CPU usage is almost 0%.\n\nThe answer can be found in the provided context:\n\n* The Ray task requires 1 CPU, but the actor only uses 0 CPU when it is scheduled.\n* The cluster may not have available CPUs, causing the Dataset job to hang.\n* Tune tries to fully utilize cluster CPUs, which can prevent Ray Data from scheduling tasks and reduce performance.\n* The number of CPUs available for Ray Data execution can be limited by setting the max_concurrent_trials Tune option.\n\nTherefore, it is likely that the Ray tuner is showing \"running\" but CPU usage is low because the task is not actually using any CPU resources. This could be due to a lack of available CPUs in the cluster or because Tune is not allowing Ray Data to schedule tasks.",3959"llm": "meta-llama/Llama-2-70b-chat-hf"3960},3961{3962"question": "should the Ray head node and all workers have the same object store memory size allocated?",3963"sources": [3964"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",3965"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",3966"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",3967"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",3968"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",3969"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",3970"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",3971"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",3972"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads"3973],3974"document_ids": [39756394,39765109,3977563,39785013,39796437,39806446,3981855,39825557,3983144833984],3985"answer": " No, the Ray head node and all workers should not have the same object store memory size allocated. The head node has more memory-demanding system components, and it's recommended to allocate more memory to the head node to avoid out-of-memory problems. The default value for object_store_memory_head_node is 0.3 * spark_driver_physical_memory * 0.8, which is higher than the default value for object_store_memory_worker_node, which is 0.3 * spark_worker_physical_memory * 0.8 / num_tasks_per_spark_worker.\n\nIt's also recommended to specify the quantities of pre-defined resources and add custom resources when starting a Ray cluster, especially when using ray.init() to start a single-node Ray cluster. This can be done by specifying the -object-store-memory flag when starting the Ray cluster.\n\nFor example, to start a Ray cluster with 4 worker nodes and 1 head node, and allocate 2000MiB of object store memory to the head node and 1000MiB to each worker node, you can use the following command:\n\nray up --head-node --object-store-memory=200000000 --num-cpus=1 --memory=200000000 --address=localhost:6379 --block\n\nray up --worker-node --object-store-memory=100000000 --num-cpus=1 --memory=100000000 --address=localhost:6379 --block\n\nThis will start a Ray cluster with 4 worker nodes and 1 head node, with the head node having 2000MiB of object store memory and each worker node having 1000MiB of object store memory.",3986"llm": "meta-llama/Llama-2-70b-chat-hf"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": " To specify whether to set up an HTTP proxy on each node or just the head node in Ray Serve, you can use the `proxy_location` field in the Serve configuration file. The `proxy_location` field can be set to one of the following values:\n\n* `Disabled`: Don't run proxies at all. This should be used if you are only making calls to your applications via deployment handles.\n* `HeadOnly`: Only run a single proxy on the head node.\n* `EveryNode`: Run a proxy on every node in the cluster that has at least one replica actor. This is the default.\n\nFor example, to set up an HTTP proxy on every node, you can set the `proxy_location` field to `EveryNode` in the Serve configuration file:\n```\nproxy_location: EveryNode\n```\nAlternatively, you can set the `proxy_location` field to `HeadOnly` to only run a single proxy on the head node:\n```\nproxy_location: HeadOnly\n```\nNote that the `proxy_location` field is only available in Ray Serve version 2.5.1 and later. In earlier versions of Ray Serve, you can use the `http_options` field in the Serve configuration file to set the location of the HTTP proxy.",4013"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Based on the provided context, it seems that you are using KubeRay and want to embed Grafana into the Ray Dashboard. To do this, you need to set the following environment variables:\n\n* `RAY_GRAFANA_HOST`: This should be set to the address that the head node can use to access Grafana.\n* `RAY_PROMETHEUS_HOST`: This should be set to the address that the head node can use to access Prometheus.\n\nYou can set these environment variables in your `ray-cluster.embed-grafana.yaml` file, like so:\n```\nenv:\n - name: RAY_GRAFANA_HOST\n value: http://55.66.77.88:3000\n - name: RAY_PROMETHEUS_HOST\n value: http://prometheus-grafana.prometheus-system.svc:80\n```\nNote that the `RAY_GRAFANA_HOST` value should be set to the external URL of the Grafana ingress if Grafana is exposed with NGINX ingress on a Kubernetes cluster.\n\nAdditionally, if you are using a different Prometheus server for each Ray Cluster and want to use the same Grafana server for all Clusters, you should set the `RAY_PROMETHEUS_NAME` environment variable to different values for each Ray Cluster and add these datasources in Grafana.\n\nOnce you have set these environment variables, you can follow the instructions in the provided documentation to embed Grafana panels in Ray Dashboard.",4040"llm": "meta-llama/Llama-2-70b-chat-hf"4041},4042{4043"question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",4044"sources": [4045"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",4046"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",4047"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",4048"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",4049"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",4050"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",4051"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#deploy-a-ray-cluster",4052"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",4053"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"4054],4055"document_ids": [405613957,405713956,40585975,40595953,40605972,40616170,40626175,40635973,4064139024065],4066"answer": " The GCS determines which Kubernetes pod to kill by using the Ray autoscaler, which is a Ray cluster process that automatically scales the cluster up and down based on resource demand. The autoscaler adjusts the number of nodes (Ray pods) in the cluster based on the resources required by tasks, actors, or placement groups. It has more fine-grained control over scale-down than the Horizontal Pod Autoscaler, allowing it to select precisely which pods to scale down and delete. The KubeRay operator then deletes the selected pod.",4067"llm": "meta-llama/Llama-2-70b-chat-hf"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 add the following line to the `http_options` block:\n```\nrequest_timeout_s: 300\n```\nThis sets the end-to-end timeout for HTTP requests to 300 seconds (5 minutes). You can adjust the value to suit your needs.\n\nHere's an example of the full YAML config file:\n```\nhttp_options:\n request_timeout_s: 300\n```\nNote that this config is global to your Ray cluster and cannot be updated during runtime.",4094"llm": "meta-llama/Llama-2-70b-chat-hf"4095},4096{4097"question": "How do I make the GPU available on my M1 laptop to ray?",4098"sources": [4099"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",4100"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",4101"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4102"https://docs.ray.io/en/master/installation.html#m1-mac-apple-silicon-support",4103"https://docs.ray.io/en/master/ray-overview/installation.html#m1-mac-apple-silicon-support",4104"https://docs.ray.io/en/master/ray-more-libs/installation.html#m1-mac-apple-silicon-support",4105"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#requesting-gpus",4106"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",4107"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage"4108],4109"document_ids": [41104997,41114995,41124818,411373,4114298,411514571,41166020,41172966,411860124119],4120"answer": " To make the GPU available on your M1 laptop to Ray, you need to install the GPU version of TensorFlow and set up Ray to use the GPU.\n\nHere are the steps to follow:\n\n1. Install the GPU version of TensorFlow using the following command:\n```\npip install tensorflow-gpu\n```\n2. Start a new local Ray instance with the following command:\n```\nray up\n```\n3. Define a Ray function that uses the GPU, for example:\n```\n@ray.remote(num_gpus=1)\ndef use_gpu():\n import tensorflow as tf\n print(\"ray.get_gpu_ids(): {}\".format(ray.get_gpu_ids()))\n print(\"CUDA_VISIBLE_DEVICES: {}\".format(os.environ[\"CUDA_VISIBLE_DEVICES\"]))\n```\n4. Run the function using the following command:\n```\nray submit use_gpu\n```\nThis will schedule the function on a node with at least one GPU and reserve one GPU for it. The function will then use the GPU to perform the computation.\n\nNote that if you want to use multiple GPUs per trial, you should check out LightningTrainer. Also, make sure to specify nvidia.com/gpu resource limits and requests in the container fields of your RayCluster's headGroupSpec and/or workerGroupSpecs to use Nvidia GPUs.",4121"llm": "meta-llama/Llama-2-70b-chat-hf"4122},4123{4124"question": "How can I add a timeout for the Ray job?",4125"sources": [4126"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",4127"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",4128"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",4129"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",4130"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4131"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.GetTimeoutError.html#ray-exceptions-gettimeouterror",4132"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-stop",4133"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",4134"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management"4135],4136"document_ids": [413713795,41386556,41395757,41404416,41416554,41424796,41436602,41446558,414565694146],4147"answer": " You can add a timeout for a Ray job by setting the `request_timeout_s` field in the `http_options` section of the Serve config. This will cause the HTTP Proxy to terminate the request after the specified number of seconds. Additionally, you can use the `--no-wait` flag when submitting the job to prevent the client from waiting for the job to finish. Instead, you can use the `ray job status` command to check the status of the job and the `ray job logs` command to view the logs. If the job is still running after a certain amount of time, you can use the `ray job stop` command to request the job to be stopped.",4148"llm": "meta-llama/Llama-2-70b-chat-hf"4149},4150{4151"question": "how do I set custom /tmp directory for remote cluster?",4152"sources": [4153"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4154"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",4155"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",4156"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",4157"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",4158"https://docs.ray.io/en/master/cluster/cli.html#ray-submit",4159"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.restore.html#ray-rllib-algorithms-algorithm-algorithm-restore",4160"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.restore.html#ray-tune-trainable-restore",4161"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster"4162],4163"document_ids": [41644273,41656561,4166872,416713680,416813718,41695625,417012465,41712081,417262054173],4174"answer": " To set a custom /tmp directory for a remote cluster, you can use the RAY_TEMPDIR environment variable to specify the directory where the temporary files should be stored. This directory should be accessible by the host, the outer container, and the inner container.\n\nFor example, you can set RAY_TEMPDIR=/ray-mount in your cluster configuration file (e.g. cluster.yaml) or in your Ray script before submitting the job to the remote cluster.\n\nNote that the /tmp directory is not automatically cleaned up by Ray, so you may need to manually clean up the directory after the job is completed.",4175"llm": "meta-llama/Llama-2-70b-chat-hf"4176},4177{4178"question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",4179"sources": [4180"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",4181"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4182"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",4183"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4184"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4185"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",4186"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",4187"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",4188"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable"4189],4190"document_ids": [41914960,41924385,41934959,4194511,41956632,4196872,41976396,41989160,419917754200],4201"answer": " Yes, if you set `--temp-dir` to a different directory than `/tmp`, Ray will use that directory for object spilling.",4202"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's an example of using the `--runtime-env-json` argument:\n```\nserve run --address=ray://<head-node-ip-address>:10001 --runtime-env-json='{\"env_vars\": {\"MY_ENV_VAR\": \"my-value\"}, \"working_dir\": \"./project/src\", \"pip\": [\"requests\", \"chess\"]}' local_dev:graph\n```\nThis command specifies a runtime environment for the Ray application, which includes environment variables, working directory, and Python packages. The `--runtime-env-json` argument is used to specify the runtime environment in JSON format.",4229"llm": "meta-llama/Llama-2-70b-chat-hf"4230},4231{4232"question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",4233"sources": [4234"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4235"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",4236"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",4237"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",4238"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4239"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",4240"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4241"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",4242"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters"4243],4244"document_ids": [424513449,424614225,424714187,424814066,424914303,42505963,425113450,425214141,4253134514254],4255"answer": " The value of `maxConcurrentReplicas` should be set to the same value as `minReplicas` when autoscaling is enabled. This ensures that the autoscaler can scale up the number of replicas to meet the demand of incoming requests without overloading the system with too many concurrent requests.\n\nIn the context provided, `minReplicas` is set to 1 by default, so `maxConcurrentReplicas` should also be set to 1. However, it's important to note that this value can be adjusted based on the specific needs of the application and the resources available in the cluster.",4256"llm": "meta-llama/Llama-2-70b-chat-hf"4257},4258{4259"question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",4260"sources": [4261"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4262"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4263"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",4264"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",4265"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",4266"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",4267"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",4268"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",4269"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails"4270],4271"document_ids": [427213451,427314303,427414187,427514225,427614066,427713561,427813450,427914302,4280141864281],4282"answer": " The value of `max_concurrent_queries` should be set to a value that is appropriate for the specific use case and workload. The default value of 1 is a reasonable starting point, but it may need to be adjusted based on factors such as the number of replicas, the expected request rate, and the computational resources available.\n\nIt's important to note that setting `max_concurrent_queries` too high can lead to overloading the replicas and causing performance issues, while setting it too low can lead to underutilization of resources and slow response times. It's recommended to monitor the performance of the deployment and adjust the value of `max_concurrent_queries` as needed to achieve the desired performance.",4283"llm": "meta-llama/Llama-2-70b-chat-hf"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 used to speed up or slow down upscaling or downscaling decisions in autoscaling. It can be used to amplify the response of the autoscaling algorithm, with a higher value resulting in faster scaling and a lower value resulting in slower scaling.",4310"llm": "meta-llama/Llama-2-70b-chat-hf"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": " The reason actors change their state to unhealthy is because the deployment has either failed to update or has updated and become unhealthy afterwards, which may be due to an error in the deployment\u2019s constructor, a crashed replica, or a general system or machine error.",4337"llm": "meta-llama/Llama-2-70b-chat-hf"4338},4339{4340"question": "How do I access logs for a dead node?",4341"sources": [4342"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4343"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4344"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",4345"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",4346"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",4347"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4348"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4349"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",4350"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs"4351],4352"document_ids": [4353492,4354493,4355662,4356739,4357740,4358512,43596631,436013894,43616954362],4363"answer": " You can access logs for a dead node by using the `ray logs` command with the `--node-id` option, followed by the ID of the dead node. For example:\n```\nray logs --node-id <NODE_ID>\n```\nThis will print the logs from the specified node, including any logs that were generated before the node died.\n\nAlternatively, you can use the `ray.util.state.list_logs` function to list all retrievable log file names from a head node in a cluster, and then use the `ray.util.state.get_log` function to retrieve a particular log file from a node. For example:\n```\nlist_logs(node_id=<HEAD_NODE_ID>)\nget_log(node_id=<NODE_ID>, filename=<LOG_FILE_NAME>)\n```\nNote that the `ray logs` command by default prints logs from a head node, so you need to specify the `--node-id` option to get logs from a different node. Also, note that the `list_logs` function returns a list of log file names, so you need to specify the `node_id` and `filename` parameters to `get_log` to retrieve a particular log file.",4364"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Reasons for a node to change its status to dead:\n\n1. Raylet failure: When a raylet process fails, the corresponding node will be marked as dead.\n2. Task was killed due to the node running low on memory.\n3. Worker node failure: When a worker node fails, the actors running on it also fail, and KubeRay attempts to restart the worker pod on another running node.\n4. Rescheduling Bundles on a Dead Node: If nodes that contain some bundles of a placement group die, all the bundles are rescheduled on different nodes by GCS.\n5. Head node failure: You can simulate a head node failure by either killing the head pod or the head node.\n6. NodeAffinitySchedulingStrategy: If the specified node doesn\u2019t exist (e.g. if the node dies) or is infeasible, the task or actor will be scheduled onto a different feasible node if soft is True.\n7. Handling Failures and Node Preemption: Nodes will be stopped when the cluster scales down if enabled, and terminated instead if disabled.",4391"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Reasons for spikes in node CPU utilization:\n\n1. Insufficient node resources: When the number of tasks running on a node exceeds the available resources, it can lead to spikes in CPU utilization.\n2. Imbalanced resource allocation: When the workload is not evenly distributed across nodes, it can result in some nodes being overutilized while others are underutilized.\n3. Inefficient task placement: If tasks are not placed optimally on nodes, it can lead to increased CPU utilization as tasks compete for resources.\n4. Resource-intensive tasks: Certain tasks may require more resources than others, causing spikes in CPU utilization when they are run on a node.\n5. Autoscaling: When the autoscaler adds new nodes to the cluster, it can lead to spikes in CPU utilization as the new nodes are provisioned and configured.\n6. Memory usage: High memory usage can lead to increased CPU utilization as the system needs to handle memory management.\n7. Network bandwidth usage: High network bandwidth usage can lead to increased CPU utilization as the system needs to handle data transfer.\n8. System processes: Additional system processes running on the head node can lead to increased CPU utilization.\n\nIt's important to monitor and analyze the usage of CPU, memory, and other resources in the cluster to identify the root cause of spikes in CPU utilization and take appropriate action to address the issue.",4418"llm": "meta-llama/Llama-2-70b-chat-hf"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 for deploying a RayService on EKS is r5dn.16xlarge. It is recommended to set the quantity of logical CPU resources to 0 on the head node to avoid scheduling additional tasks on it, due to the heavy networking load and the GCS and dashboard processes.",4445"llm": "meta-llama/Llama-2-70b-chat-hf"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. You can specify the session name using the `namespace` argument when initializing Ray with the `ray.init()` function. For example:\n```\nray.init(\"ray://1.2.3.4:10001\", namespace=\"my_namespace\")\n```\nThis will create a session directory with the name `/tmp/ray/session_my_namespace`. You can also specify the `runtime_env` argument to set environment variables, packages, and local files for the session.\n\nAlternatively, you can use the `ray up` command to start a Ray cluster and specify the `head_start_ray_commands` field to add the `RAY_REDIS_ADDRESS` and `--redis-password` options to the `ray start` command. This will create a new Ray session with a unique name.\n\nYou can also change the temp directory where Ray stores its log files by specifying the `--temp-dir` option when calling `ray start` or `ray.init()`. For example:\n```\nray.init(\"ray://1.2.3.4:10001\", namespace=\"my_namespace\", temp_dir=\"/path/to/my/temp/dir\")\n```\nThis will store the log files in the specified directory instead of the default `/tmp/ray` directory.",4472"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Yes, you can use the Python SDK to get a link to the Ray dashboard for a given job. You can use the `JobSubmissionClient.get_job_info()` method to get the latest status and other information associated with a job, including the link to the dashboard.\n\nHere's an example code snippet:\n```python\nfrom ray.job_submission import JobSubmissionClient\n\n# Initialize the client\nclient = JobSubmissionClient(\"http://127.0.0.1:8265\")\n\n# Submit a job\nsubmission_id = client.submit_job(entrypoint=\"sleep 1\")\n\n# Get the job info, including the dashboard link\njob_info = client.get_job_info(submission_id)\nprint(job_info.dashboard_link)\n```\nThis will print the link to the Ray dashboard for the submitted job. You can then use this link to access the dashboard and view the job's status and other information.",4499"llm": "meta-llama/Llama-2-70b-chat-hf"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 (OOM), as indicated by the Raylet logs. The error message mentions that 10 workers (tasks/actors) were killed due to memory pressure (OOM) at the node with ID e5d953ef03e55e26f13973ea1b5a0fd0ecc729cd820bc89e4aa50451 and IP 10.0.62.231. The Raylet logs also mention that the object store is full, indicating that the node ran out of memory.\n\nTo address this issue, you can refer to the Ray documentation on how to prevent out-of-memory errors. The documentation provides guidance on how to configure the Ray cluster to avoid running out of memory, such as setting the appropriate memory limits and using node affinity scheduling strategies. Additionally, you can use the Ray logs to identify the root cause of the issue and take appropriate action, such as killing the task or restarting the node.",4526"llm": "meta-llama/Llama-2-70b-chat-hf"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": " It seems that you're experiencing issues with a Ray cluster and are looking for ways to troubleshoot and resolve problems related to the Global Control Store (GCS) and head node failures. Here are some key points from the provided context that may help you:\n\n1. GCS fault tolerance: To make the GCS fault tolerant, you need to use an external Redis server. This will allow the GCS to recover its state when it restarts, even if the head node fails.\n2. Head node recovery: When a head node fails, the entire Ray cluster fails. To tolerate head node failures, you need to make the GCS fault tolerant so that when you start a new head node, you still have all the cluster-level data.\n3. KubeRay: If you're using Kubernetes, you can use KubeRay to deploy your Serve application with fault tolerance. KubeRay provides an automatic recovery mechanism for the head node and GCS.\n4. Troubleshooting: If you see errors related to the GCS server, you can inspect the log directory /tmp/ray/session_latest/logs/ in the head Pod for errors. Additionally, you can use tools like nmap or nc to check whether each port can be reached from a node.\n5. Init container troubleshooting: If the worker init container is stuck in the Init:0/1 status, it could be due to issues like the GCS server process failing in the head Pod or the ray executable not being included in the $PATH for the image.\n6. RAY_gcs_rpc_server_reconnect_timeout_s: You can set this OS environment variable to tune the timeout threshold for the raylet to reconnect to the GCS.\n7. RAY_external_storage_namespace: You can set this OS environment variable to isolate the data stored in Redis.\n\nI hope this helps you resolve the issues you're facing with your Ray cluster.",4553"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Do you need the DAGDriver to deploy a serve application using RayServe?\n\nYes, the DAGDriver is required to deploy a Serve application using RayServe. The DAGDriver is a driver that routes HTTP requests through the call graph, and it is used to compose multiple models into a graph structure and orchestrate the calls to each deployment automatically. When using a Serve deployment graph, the DAGDriver can be configured to accept an HTTP adapter via its http_adapter field, which allows for parsing JSON in the HTTP body. The DAGDriver is also required for visualization.",4580"llm": "meta-llama/Llama-2-70b-chat-hf"4581},4582{4583"question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",4584"sources": [4585"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",4586"https://docs.ray.io/en/master/workflows/basics.html#sub-task-results",4587"https://docs.ray.io/en/master/serve/api/index.html#serve-run",4588"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",4589"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",4590"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Application.html#ray-serve-application",4591"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#defining-an-application-builder",4592"https://docs.ray.io/en/master/serve/tutorials/batch.html#define-the-deployment",4593"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#walkthrough"4594],4595"document_ids": [459613844,45979670,459813990,45995789,46005678,460114037,460213813,460314586,460480784605],4606"answer": " The import path for a simple RayServe deployment should be in the format of `directory:file:variable`, where `directory` is the name of the directory containing the Python file, `file` is the name of the Python file, and `variable` is the name of the variable representing the Ray Serve application within the Python file.\n\nFor example, if the application is defined in a file named `fruit.py` in a directory named `test_dag`, the import path would be `test_dag:fruit:deployment_graph`.\n\nAlternatively, you can use a remote URI to a .zip file (S3, GS, HTTP) as the working directory, and RayService will download the code at runtime. In this case, the import path would be in the format of `remote_uri:directory:file:variable`.\n\nIt's also possible to use a runtime environment with a remote URI that hosts the code in remote storage, in which case the import path would be in the format of `remote_uri:directory:file:variable`.\n\nIt's important to note that the import path must be importable by Serve at runtime, so make sure the path is correct and the code is available at runtime.",4607"llm": "meta-llama/Llama-2-70b-chat-hf"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, here is an example of using RayJob in Kuberay:\n\n1. Create a RayJob custom resource:\n```\napiVersion: ray.io/v1alpha1\nkind: RayJob\nmetadata:\n name: my-ray-job\nspec:\n rayClusterSpec:\n apiVersion: ray.io/v1alpha1\n kind: RayCluster\n metadata:\n name: my-ray-cluster\n spec:\n rayVersion: 1.10.0\n numWorkers: 4\n numGPUs: 4\n entrypoint: python /home/ray/samples/sample_code.py\n```\n2. Apply the RayJob custom resource:\n```\nkubectl apply -f ray-job.yaml\n```\n3. The KubeRay operator will create a RayCluster as defined in the rayClusterSpec custom resource, as well as a Kubernetes Job to submit a Ray job to the RayCluster.\n4. The Ray job is defined in the entrypoint field of the RayJob custom resource. In this example, the entrypoint is `python /home/ray/samples/sample_code.py`, and `sample_code.py` is a Python script stored in a Kubernetes ConfigMap mounted to the head Pod of the RayCluster.\n5. Since the default value of `shutdownAfterJobFinishes` is `false`, the RayCluster will not be deleted after the job finishes.\n\nNote: This is just a basic example, and you can customize the RayJob and RayCluster specifications to fit your needs. Additionally, you can use the `kubectl` command-line tool to create, update, and manage your Kubernetes resources.",4634"llm": "meta-llama/Llama-2-70b-chat-hf"4635},4636{4637"question": "Does Ray metrics have to be exported via an actor?",4638"sources": [4639"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",4640"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",4641"https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",4642"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",4643"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",4644"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",4645"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",4646"https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",4647"https://docs.ray.io/en/master/cluster/metrics.html#scraping-metrics"4648],4649"document_ids": [4650503,4651656,4652438,46535573,465411596,46554503,465614707,4657403,465855814659],4660"answer": " No, Ray metrics do not have to be exported via an actor. Ray provides a convenient API for defining and exporting custom metrics, and these metrics can be exported using the `ray.util.metrics` module. The metrics are then available for scraping by Prometheus. Additionally, Ray exports default metrics that are available from the Metrics view in the Dashboard.",4661"llm": "meta-llama/Llama-2-70b-chat-hf"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, use NVMe SSDs or other high-performance storage, and configure the object spilling directory to a fast storage device. Also, consider increasing the buffer size for spilling to reduce IO requests.",4688"llm": "meta-llama/Llama-2-70b-chat-hf"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 distributed computing library that provides a small number of core primitives (tasks, actors, objects) for building and scaling distributed applications. It is designed to be used with Python and allows developers to easily turn their functions and classes into Ray tasks and actors. Ray Core provides a simple and unified way to work with distributed computing, making it easier for developers to scale their applications.",4715"llm": "meta-llama/Llama-2-70b-chat-hf"4716},4717{4718"question": "Does ray support cron job",4719"sources": [4720"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4721"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",4722"https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",4723"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",4724"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4725"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",4726"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-api-server",4727"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",4728"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api"4729],4730"document_ids": [473113974,47324662,47339634,47346637,47354811,47366208,473713474,47385635,473966384740],4741"answer": " No, Ray does not support cron jobs. Instead, you can use an external job scheduler to trigger workflow runs on a Ray cluster.",4742"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here are the dependencies listed for the `api_read_images` function:\n\n* `ray.data.read_images`\n* `PIL`\n* `numpy`\n* `requests`\n* `io.BytesIO`\n* `Image`\n* `annotations`\n* `map_batches`\n\nAdditionally, the `api_read_images` function uses the `ray.data.datasource.file_meta_provider.BaseFileMetadataProvider` class, which is not explicitly listed as a dependency but is imported by `ray.data.read_images`.",4769"llm": "meta-llama/Llama-2-70b-chat-hf"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": " Sure, here's a succinct answer to your question:\n\nTo kill a specific Serve replica, you can use the `ray.kill()` method and pass in the replica handle, which you can obtain using the `ray.get_actor()` method. For example:\n```\nray.kill(replica_handle, no_restart=True)\n```\nThis will kill the replica and prevent it from being restarted. You can also use the `ray list actors` command to get a list of all actors and their handles, and then use the handle to kill a specific replica.\n\nNote that killing a replica will not affect the other replicas, which will continue to process requests. If you want to shut down all replicas and the Serve application, you can use the `serve shutdown` command.",4796"llm": "meta-llama/Llama-2-70b-chat-hf"4797}4798]4799}4800