llm-applications
4799 строк · 411.5 Кб
1{2"config": {3"experiment_name": "codellama-34b-instruct-hf",4"chunk_size": 700,5"chunk_overlap": 50,6"num_chunks": 9,7"embedding_model_name": "thenlper/gte-large",8"llm": "codellama/CodeLlama-34b-Instruct-hf",9"temperature": 0.0,10"max_context_length": 16384,11"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 error message you're seeing is because you're trying to use a NumPy array as a key in a dictionary, which is not allowed in Python.\n\nIn the `increase_brightness` function, you're trying to access the `image` key in the `batch` dictionary, which is a NumPy array. However, you can't use a NumPy array as a key in a dictionary.\n\nTo fix this error, you can convert the `image` key to a string or some other hashable type before using it as a key in the dictionary. For example:\n```\ndef increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n return batch\n```\nAlternatively, you can use a different data structure, such as a list or a tuple, to store the image data. For example:\n```\ndef increase_brightness(batch: List[np.ndarray]) -> List[np.ndarray]:\n batch[0] = np.clip(batch[0] + 4, 0, 255)\n return batch\n```\nI hope this helps! Let me know if you have any other questions.",44"llm": "codellama/CodeLlama-34b-Instruct-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": " In a Ray Serve application, autoscaling works by automatically increasing or decreasing the number of replicas based on the load. The Ray autoscaler is a process that runs on the head node and adjusts the number of nodes in the cluster based on the resources required by tasks, actors, or placement groups. When the resource demands of the Ray workload exceed the current capacity of the cluster, the autoscaler will try to increase the number of worker nodes. When worker nodes sit idle, the autoscaler will remove worker nodes from the cluster. Similarly, 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.",71"llm": "codellama/CodeLlama-34b-Instruct-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.nodes()` function. This function returns a list of the nodes in the cluster, along with their IP addresses and other information.\n\nFor example, to get the address of the head node in a Ray cluster, you can use the following code:\n```\nimport ray\n\nray.init()\n\nhead_node = ray.nodes()[0]\nprint(head_node[\"NodeManagerAddress\"])\n```\nThis will print the IP address of the head node, which you can then use to connect to the node using the `ray.connect()` function.\n\nAlternatively, you can use the `ray.util.state.get_node()` function to get the address of a specific node in the cluster. This function takes the ID of the node as an argument, and returns a `NodeState` object that contains information about the node, including its IP address.\n\nFor example, to get the address of a node with the ID \"node-1\", you can use the following code:\n```\nimport ray\nfrom ray.util.state import get_node\n\nray.init()\n\nnode = get_node(\"node-1\")\nprint(node.address)\n```\nThis will print the IP address of the node with the ID \"node-1\".",98"llm": "codellama/CodeLlama-34b-Instruct-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.",125"llm": "codellama/CodeLlama-34b-Instruct-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.",152"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nIt is not possible to use AsyncIO's await to wait for a Ray future like `await x.remote()`. Instead, you can use the `asyncio.wrap_future()` function to convert the Ray future into an asyncio future, which can then be awaited.\n\nHere is an example of how to use `asyncio.wrap_future()` to convert a Ray future into an asyncio future:\n```\nimport asyncio\n\n@ray.remote\ndef some_task():\n return 1\n\nasync def await_obj_ref():\n ref = some_task.remote()\n fut: asyncio.Future = asyncio.wrap_future(ref.future())\n print(await fut)\n\nasyncio.run(await_obj_ref())\n```\nThis code will print \"1\" after the `some_task` function has completed.\n\nIt is important to note that you should only use `asyncio.wrap_future()` to convert Ray futures into asyncio futures if you need to use the asyncio framework to manage your concurrent tasks. If you do not need to use asyncio, you can simply use the `ray.get()` function to wait for the Ray future to complete.",179"llm": "codellama/CodeLlama-34b-Instruct-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 data processing frameworks that can be used for big data analytics. Spark is a popular open-source data processing engine that can handle a wide range of data sources and data formats. Ray is a fast and scalable data processing framework that is designed for machine learning and data science workloads. Dask is a flexible data processing library that can handle large datasets and provide automatic data parallelism.\n\nIn terms of performance, Spark is generally considered to be faster than Dask for certain types of workloads, such as batch processing and ETL. However, Dask can be faster than Spark for certain types of workloads, such as interactive data analysis and machine learning. Ray is designed to be faster than Spark for certain types of workloads, such as deep learning and large-scale data processing.\n\nIn terms of ease of use, Spark is generally considered to be easier to use than Dask, as it has a more intuitive API and a larger community of users. Dask is also relatively easy to use, but it can be more complex to set up and configure than Spark. Ray is designed to be easy to use, with a simple and intuitive API that is similar to Spark.\n\nIn terms of scalability, all three frameworks are designed to scale horizontally and can handle large datasets and high-performance workloads. However, Spark is generally considered to be more scalable than Dask for certain types of workloads, such as batch processing and ETL. Ray is designed to be highly scalable for large-scale data processing and machine learning workloads.\n\nIn terms of fault tolerance, all three frameworks are designed to be fault-tolerant and can handle failures and errors gracefully. However, Spark is generally considered to be more fault-tolerant than Dask, as it has a more robust distributed architecture and can handle more types of failures. Ray is designed to be highly fault-tolerant for large-scale data processing and machine learning workloads.\n\nIn terms of integration with other tools and frameworks, all three frameworks can be integrated with a wide range of tools and frameworks, such as Hadoop, Kubernetes, and TensorFlow. However, Spark is generally considered to be more widely used and has a larger community of users, which can make it easier to find resources and support. Dask is also relatively easy to integrate with other tools and frameworks, and Ray is designed to be highly integrated with other tools and frameworks, such as TensorFlow and PyTorch.\n\nOverall, the choice of which framework to use will depend on the specific needs and requirements of the project. Spark is a popular and widely used framework that is well-suited for a wide range of data processing and machine learning workloads. Dask is a flexible and powerful framework that is well-suited for large-scale data processing and machine learning workloads. Ray is a fast and scalable framework that is well-suited for large-scale data processing and machine learning workloads, and is designed to be highly integrated with other tools and frameworks.",206"llm": "codellama/CodeLlama-34b-Instruct-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 overloads a node with more tasks than the resources allow to take advantage of the available resources on the node. By overloading the node, Ray can make more efficient use of the available resources and reduce the number of nodes required to complete the workload. However, it is important to ensure that the tasks are using the resources responsibly and not causing issues like OOM.",233"llm": "codellama/CodeLlama-34b-Instruct-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": " You should use Ray Client when you want to connect an interactive Python shell to a remote cluster.",260"llm": "codellama/CodeLlama-34b-Instruct-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 `scheduling_strategy` parameter in the `@ray.remote` decorator. For example:\n```\n@ray.remote(scheduling_strategy=\"SPREAD\")\ndef spread_func():\n return 2\n\n# Spread tasks across the cluster.\n[spread_func.remote() for _ in range(10)]\n\n# Spread actors across the cluster.\nactors = [SpreadActor.options(scheduling_strategy=\"SPREAD\").remote() for _ in range(10)]\n```\nIn this example, the `SPREAD` strategy will try to spread the tasks or actors among available nodes.\n\nAlternatively, you can use the `ScalingConfig` class to achieve the same results. For example:\n```\ntuner = tune.Tuner(\n tune.with_resources(\n train_fn,\n resources=ScalingConfig(\n trainer_resources={\"CPU\": 2, \"GPU\": 0.5, \"hdd\": 80},\n num_workers=2,\n resources_per_worker={\"CPU\": 1},\n ),\n )\n)\ntuner.fit()\n```\nIn this example, the `ScalingConfig` class is used to specify the resources required for the training function, and the number of workers to use. The `num_workers` parameter specifies the number of workers to use, and the `resources_per_worker` parameter specifies the resources to use for each worker.\n\nYou can also use the `ActorPoolStrategy` class to create a pool of actors that can be used to perform tasks in parallel. For example:\n```\nstrategy = ActorPoolStrategy(min_size=2, max_size=8)\n\nds.map_batches(\n BatchInferModel,\n batch_size=256,\n #num_gpus=1, # Uncomment this to run this on GPUs!\n compute=strategy,\n).take()\n```\nIn this example, the `ActorPoolStrategy` class is used to create a pool of actors that can be used to perform the `BatchInferModel` function in parallel. The `min_size` parameter specifies the minimum number of actors to use, and the `max_size` parameter specifies the maximum number of actors to use. The `compute` parameter specifies the strategy to use for computing the results.",287"llm": "codellama/CodeLlama-34b-Instruct-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": " The error message \"connection refused\" when running `ray debug` on a remote Ray cluster suggests that the Ray client is unable to connect to the Ray cluster. This could be due to a variety of reasons, such as a firewall blocking the connection or the Ray cluster not being properly configured.\n\nTo troubleshoot this issue, you can try the following steps:\n\n1. Check the Ray cluster's configuration to ensure that it is properly set up and that the Ray client can connect to it.\n2. Make sure that the firewall is not blocking the connection between the Ray client and the Ray cluster.\n3. Try connecting to the Ray cluster using a different method, such as using the `ray.init()` function with the `--address` argument set to the address of the Ray cluster.\n4. Check the Ray client's logs to see if there are any error messages that may indicate the cause of the connection refused error.\n\nIf none of these steps resolve the issue, you may need to provide more information about your Ray cluster and the environment in which you are running the Ray client in order to troubleshoot the problem further.",314"llm": "codellama/CodeLlama-34b-Instruct-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": " To set up the model to communicate gradient updates across machines using Ray AIR, you need to define a parameter server that will hold a copy of the model and apply the gradients received from the workers.\n\nHere's an example of how to define the parameter server:\n```python\n@ray.remote\nclass ParameterServer(object):\n def __init__(self, lr):\n self.model = ConvNet()\n self.optimizer = torch.optim.SGD(self.model.parameters(), lr=lr)\n```\nIn this example, the `ParameterServer` class is defined with a constructor that initializes the model and optimizer. The `lr` parameter is used to set the learning rate for the optimizer.\n\nTo use the parameter server in the training loop, you can create an instance of the `ParameterServer` class and use it to apply the gradients received from the workers. Here's an example of how to do this:\n```python\n# Create an instance of the parameter server\nps = ParameterServer.remote(1e-2)\n\n# Train the model in parallel\nfor epoch in range(num_epochs):\n # Compute gradients in parallel\n gradients = {}\n for worker in workers:\n gradients[worker.compute_gradients.remote(current_weights)] = worker\n\n # Apply gradients to the model\n ps.apply_gradients.remote(gradients)\n\n # Update the model\n current_weights = ps.get_weights.remote()\n```\nIn this example, the `ParameterServer` instance `ps` is created and used to apply the gradients received from the workers. The `apply_gradients` method is called with the gradients dictionary as an argument, and the `get_weights` method is called to retrieve the updated model weights.\n\nBy using a parameter server in this way, you can distribute the training of the model across multiple machines, while still using the same optimizer and model architecture. This can be useful for large-scale machine learning tasks where the model is too large to fit on a single machine.",341"llm": "codellama/CodeLlama-34b-Instruct-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 that provides a general-purpose serving layer for building online inference APIs. It is not a full-fledged ML platform and lacks the functionality for managing the lifecycle of your models, visualizing their performance, etc. Ray Serve primarily focuses on model serving and providing the primitives for you to build your own ML platform on top.\n\nRay Serve is not tied to any specific machine learning library or framework, but rather provides a general-purpose scalable serving layer. It 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.\n\nRay Serve supports complex model deployment patterns requiring the orchestration of multiple Ray actors, where different actors provide inference for different models. It handles both batch and online inference and can scale to thousands of models in production.\n\nIn summary, Ray Serve is a scalable model serving library that provides a general-purpose serving layer for building online inference APIs. It is not a full-fledged ML platform and lacks the functionality for managing the lifecycle of your models, visualizing their performance, etc. It 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.",368"llm": "codellama/CodeLlama-34b-Instruct-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. Define a callable class that will serve as the Serve deployment. This class should contain the business logic or ML model that will handle incoming requests.\n2. Use the `@serve.deployment` decorator on the class to define the deployment.\n3. Bind the deployment with optional arguments to the constructor.\n4. Deploy the deployment to a Ray cluster using the Serve CLI.\n5. Monitor your application using the Serve CLI and the Ray Serve dashboard.\n\nNote that Ray Serve supports any arbitrary Python code, so you can write all the steps as a single Python module, enabling rapid development and easy testing. You can then quickly deploy your Ray Serve LLM application to production, and each application step can independently autoscale to efficiently accommodate user traffic without wasting resources.",395"llm": "codellama/CodeLlama-34b-Instruct-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": " To determine the memory size needed for the deployment, you can use the following formula:\n\nmemory = (num_gpus * 16) + (object_store_memory * 16)\n\nIn this case, the `model_id` is set to \"EleutherAI/gpt-j-6B\", which requires 16GB of memory. The `revision` is set to \"float16\", which reduces the memory usage to 8GB. The `prompt` is a string of 100 characters, which requires a small amount of memory.\n\nTherefore, the total memory needed for this deployment is:\n\nmemory = (1 * 16) + (8 * 16) = 128GB\n\nSo, you will need a machine with at least 128GB of memory to run this deployment.",422"llm": "codellama/CodeLlama-34b-Instruct-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` from TensorFlow's `tf.distribute` module. This strategy allows you to run multiple workers on different machines, and it will automatically handle the communication and synchronization between the workers.\n\nHere's an example of how you can use this strategy to log the results from multiple workers into a single TensorBoard:\n```\nimport tensorflow as tf\n\n# Define the distributed strategy\nstrategy = tf.distribute.MultiWorkerMirroredStrategy()\n\n# Create a TensorBoard writer\nwriter = tf.summary.create_file_writer(\"logs/\")\n\n# Define the training function\n@tf.function\ndef train_step(inputs):\n # Perform the training step\n # ...\n # Log the results to TensorBoard\n with writer.as_default():\n tf.summary.scalar(\"loss\", loss, step=tf.summary.experimental.get_step())\n\n# Run the training function on multiple workers\nwith strategy.scope():\n for i in range(num_workers):\n train_step(inputs[i])\n```\nIn this example, the `train_step` function is defined as a TensorFlow function, and it logs the results to TensorBoard using the `tf.summary.scalar` function. The `MultiWorkerMirroredStrategy` is used to run the `train_step` function on multiple workers, and the results are automatically aggregated and logged to TensorBoard.\n\nTo visualize the results in TensorBoard, you can use the `tensorboard` command-line tool. For example:\n```\ntensorboard --logdir logs/\n```\nThis will start TensorBoard and open it in your web browser, where you can view the logged results.",449"llm": "codellama/CodeLlama-34b-Instruct-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 following code:\n```\nfrom ray.train import SyncConfig\n\nsync_config = SyncConfig(\n upload_dir=\"s3://my-bucket/my-experiment\",\n syncer=\"s3\",\n sync_period=300,\n sync_timeout=1800,\n sync_artifacts=True,\n sync_artifacts_on_checkpoint=True,\n sync_on_checkpoint=True\n)\n```\nThis will configure Ray AIR to upload the experiment directory to an S3 bucket every 300 seconds, with a timeout of 1800 seconds. It will also enable syncing of artifacts and checkpoints.\n\nNote that the `sync_on_checkpoint` parameter is deprecated and will be hard-deprecated in Ray 2.7. Instead, you should use the `sync_artifacts_on_checkpoint` parameter to control whether artifacts are synced when a checkpoint is saved.\n\nAlso note that the `syncer` parameter should be set to the name of the syncer you want to use. In this example, we are using the `s3` syncer to upload the experiment directory to an S3 bucket.",476"llm": "codellama/CodeLlama-34b-Instruct-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 the `kubectl logs` command.\n2. Check the logs of the Ray Pods using the `kubectl exec` command.\n3. Check the Driver logs if the Ray Job is submitted by the Job API.\n4. Check the Serve logs if the Ray Job is a Serve deployment.\n5. Check the Ray client logs if you are using Ray Client.\n6. Use the `ray job logs` command to follow the logs of the Ray Job.\n7. Use the `ray job logs` command with the `--address` and `--follow` options to follow the logs of the Ray Job.\n8. Check the Ray logs directly by accessing the log files on the Pods.\n9. Check the Ray Serve logs directly by accessing the log files on the pods.\n\nBy following these steps, you can quickly narrow down the root cause of the failed Ray job and resolve the issue.",503"llm": "codellama/CodeLlama-34b-Instruct-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": " To specify how many GPUs a serve deployment needs, you can use the `ray_actor_options` parameter and set the `num_gpus` field to the desired number of GPUs. For example:\n```\n@serve.deployment(ray_actor_options={\"num_gpus\": 2})\ndef func(*args):\n return do_something_with_my_gpu()\n```\nThis will allocate 2 GPUs for each replica of the deployment. You can also use fractions to allocate a portion of a GPU, for example:\n```\n@serve.deployment(ray_actor_options={\"num_gpus\": 0.5})\ndef func(*args):\n return do_something_with_my_gpu()\n```\nThis will allocate 0.5 GPUs for each replica of the deployment.\n\nIt's important to note that the `num_gpus` field is only used when the deployment is created, and it does not change the number of GPUs allocated to the deployment after it has been created. If you need to change the number of GPUs allocated to a deployment, you will need to recreate the deployment with the updated `num_gpus` field.",530"llm": "codellama/CodeLlama-34b-Instruct-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": " To debug the issue, you can use the following steps:\n\n1. Check the logs of the worker that died using `ray logs raylet.out -ip 172.31.183.172`.\n2. Check the logs of the worker that was killed using `ray logs worker-6f2ec5c8b0d5f5a66572859faf192d36743536c2e9702ea58084b037*out -ip 10.0.62.231`.\n3. Check the memory usage on the node using `ray logs raylet.out -ip 10.0.62.231`.\n4. Check the logs of the worker that was killed using `ray logs worker-6f2ec5c8b0d5f5a66572859faf192d36743536c2e9702ea58084b037*out -ip 10.0.62.231`.\n5. Check the logs of the worker that was killed using `ray logs worker-6f2ec5c8b0d5f5a66572859faf192d36743536c2e9702ea58084b037*out -ip 10.0.62.231`.\n6. Check the logs of the worker that was killed using `ray logs worker-6f2ec5c8b0d5f5a66572859faf192d36743536c2e9702ea58084b037*out -ip 10.0.62.231`.\n7. Check the logs of the worker that was killed using `ray logs worker-6f2ec5c8b0d5f5a66572859faf192d36743536c2e9702ea58084b037*out -ip 10.0.62.231`.\n8. Check the logs of the worker that was killed using `ray logs worker-6f2ec5c8b0d5f5a66572859faf192d36743536c2e9702ea58084b037*out -ip 10.0.62.231`.\n9. Check the logs of the worker that was killed using `ray logs worker-6f2ec5c8b0d5f5a66572859faf192d36743536c2e9702ea58084b037*out -ip 10.0.62.231`.\n10. Check the logs of the worker that was killed using `ray logs worker-6f2ec5c8b0d5f5a66572859faf192d36743536c2e9702ea58084b037*out -ip 10.0.62.231`.\n\nBy following these steps, you should be able to identify the cause of the issue and resolve it.",557"llm": "codellama/CodeLlama-34b-Instruct-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": " The possible reasons for nodes dying in a cluster include:\n\n1. Head node failure: When the head node fails, the entire Ray cluster fails.\n2. Raylet failure: When a raylet process fails, the corresponding node will be marked as dead and is treated the same as node failure.\n3. Node failure: A node can fail due to various reasons such as hardware failure, network issues, or software bugs.\n4. Memory pressure: When a node runs low on memory, tasks can be killed due to memory pressure.\n5. Overloading the head node: When the head node is overloaded with too many simultaneous connections, it can cause the node to fail.\n6. Placement group lifetime: A placement group can be detached from its creator, which means it will live as a global object independent of the creator.\n7. Max CPU fraction per node: This is an experimental feature that disallows placing bundles on nodes if it would cause the fraction of CPUs used by bundles from any placement group on the node to exceed a certain fraction.",584"llm": "codellama/CodeLlama-34b-Instruct-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": " To programmatically get a Ray remote cluster to a target size immediately without scaling up through autoscaler, you can use the `ray.autoscaler.sdk.request_resources()` function. This function takes in the number of CPUs and bundles as arguments and immediately attempts to scale the cluster to accommodate the requested resources, bypassing normal upscaling speed constraints.\n\nHere's an example of how to use this function:\n```\nimport ray\n\n# Request 2 CPUs and 1 GPU\nray.autoscaler.sdk.request_resources(num_cpus=2, bundles=[{\"GPU\": 1}])\n```\nThis will immediately attempt to scale the cluster to accommodate the requested resources, bypassing normal upscaling speed constraints.\n\nNote that this function only works if the autoscaler is already running and the cluster is configured to use it. If the autoscaler is not running, you will need to start it manually using the `ray.autoscaler.sdk.start()` function.",611"llm": "codellama/CodeLlama-34b-Instruct-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 set the `prefetch_batches` parameter to 0. This will disable the prefetching of batches and return the batches in the order they are generated.\n\nHere is an example:\n```\nimport ray\n\n# Create a dataset\nds = ray.data.range(100)\n\n# Disable async iter_batches\nds.iter_batches(prefetch_batches=0)\n```\nIn this example, the `iter_batches` method will return the batches in the order they are generated, without prefetching any batches. This can be useful if you want to process the batches in a specific order or if you want to avoid the overhead of prefetching batches.\n\nNote that disabling async iter_batches may increase the latency of the dataset, as the batches will be generated and returned in the order they are requested.",638"llm": "codellama/CodeLlama-34b-Instruct-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": " In the context of Ray Data, a batch and a block are different concepts. A batch is a user-facing object that represents a collection of rows, while a block is an internal abstraction that represents a subset of rows. Blocks are used to perform operations in parallel, and they are the building blocks of a Dataset.\n\nA Dataset operates over a sequence of Ray object references to blocks. Each block contains a disjoint subset of rows, and Ray Data loads and transforms these blocks in parallel. The batch size is the number of rows in a batch, and it is used to specify the number of rows to load and transform in parallel.\n\nIn contrast, a placement group bundle is a collection of resources that must be reserved on a single Ray node. It is used to ensure that a set of actors or tasks are placed on the same node, which can improve performance by reducing communication overhead.\n\nTo learn more about batch formats, read Configuring batch formats. To learn more about placement group bundles, read Placement Group Bundle.",665"llm": "codellama/CodeLlama-34b-Instruct-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 a Ray project, follow these steps:\n\n1. Clone the Ray repository from GitHub using the command `git clone https://github.com/ray-project/ray.git`.\n2. Install the necessary dependencies for development by running `pip install -Ur dreambooth/requirements.txt`.\n3. Prepare some directories and environment variables by running the following commands:\n```\n# TODO: If running on multiple nodes, change this path to a shared directory (ex: NFS)\nexport DATA_PREFIX=\"/tmp\"\nexport ORIG_MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport ORIG_MODEL_HASH=\"b95be7d6f134c3a9e62ee616f310733567f069ce\"\nexport ORIG_MODEL_DIR=\"$DATA_PREFIX/model-orig\"\nexport ORIG_MODEL_PATH=\"$ORIG_MODEL_DIR/models--${ORIG_MODEL_NAME/\\\\//--}/snapshots/$ORIG_MODEL_HASH\"\n```\n4. Build the Ray dashboard by running the following commands:\n```\ncd dashboard/client\nnpm ci\nnpm run build\n```\n5. Move back to the top level Ray directory and build Ray for Python:\n```\ncd ../..\npip install \"ray[default]\"\n```\n6. Install packages using `setup_commands` in the Ray Cluster configuration file or build necessary packages into a container image.\n7. Push local files to the cluster using `ray rsync_up`.\n\nNote: This is a general guide, and the specific steps may vary depending on your project requirements.",692"llm": "codellama/CodeLlama-34b-Instruct-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": " The issue you're experiencing with the `ray/rllib/evaluation/rollout_worker.py` module is likely due to a deadlock caused by the `ray.get()` function.\n\nThe `ray.get()` function is used to retrieve the results of a remote function call, but it can also cause a deadlock if the remote function is not properly configured. In your case, it seems that the remote function is not properly configured, causing the `ray.get()` function to block indefinitely.\n\nTo resolve this issue, you can try the following:\n\n1. Check the remote function's configuration to ensure that it is properly set up.\n2. Use the `ray.get()` function with the `timeout` parameter to specify a maximum amount of time to wait for the remote function to complete.\n3. Use the `ray.get()` function with the `poll_interval` parameter to specify how often to check the status of the remote function.\n4. Use the `ray.get()` function with the `num_cpus` parameter to specify the number of CPUs to use for the remote function.\n\nBy using these parameters, you can help ensure that the `ray.get()` function is properly configured and does not cause a deadlock.",719"llm": "codellama/CodeLlama-34b-Instruct-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": " \n[PYTHON]\ndef join_ray_datasets(dataset1, dataset2):\n return dataset1.zip(dataset2)\n[/PYTHON]\n",746"llm": "codellama/CodeLlama-34b-Instruct-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 using the `ray.get()` method.",773"llm": "codellama/CodeLlama-34b-Instruct-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 model checkpoint from the model in memory, you can use the `Checkpoint.from_model()` method. This method takes the model as an argument and stores the model state dictionary in the checkpoint.\n\nHere's an example:\n```\nfrom ray.train import Checkpoint\n\n# Create a checkpoint from the model\ncheckpoint = Checkpoint.from_model(model)\n```\nAlternatively, you can use the `Checkpoint.from_state_dict()` method to create a checkpoint from a model state dictionary. This method takes the state dictionary as an argument and stores it in the checkpoint.\n```\nfrom ray.train import Checkpoint\n\n# Create a checkpoint from the model state dictionary\ncheckpoint = Checkpoint.from_state_dict(model.state_dict())\n```\nOnce you have created the checkpoint, you can use the `to_directory()` method to write the checkpoint data to a directory.\n```\n# Write the checkpoint data to a directory\ncheckpoint.to_directory(\"my_checkpoint\")\n```\nYou can also use the `get_model()` method to retrieve the model stored in the checkpoint.\n```\n# Retrieve the model stored in the checkpoint\nmodel = checkpoint.get_model()\n```\nNote that the `Checkpoint` class also provides other methods for working with checkpoints, such as `set_metadata()`, `get_metadata()`, `set_preprocessor()`, `get_preprocessor()`, and `update_metadata()`. These methods can be used to store and retrieve additional information associated with the checkpoint.",800"llm": "codellama/CodeLlama-34b-Instruct-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 the central concept in Ray Serve. A deployment contains business logic or an ML model to handle incoming requests and can be scaled up to run across a Ray cluster. At runtime, a deployment consists of a number of replicas, which are individual copies of the class or function that are started in separate Ray Actors (processes). The number of replicas can be scaled up or down (or even autoscaled) to match the incoming request load. To define a deployment, use the @serve.deployment decorator on a Python class (or function for simple use cases). Then, bind the deployment with optional arguments to the constructor (see below).",827"llm": "codellama/CodeLlama-34b-Instruct-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": " In Ray Serve, user config refers to the configuration options that can be set for a deployment. These options can be set in the Serve config file or on the @serve.deployment decorator. The user config can be used to update the deployment's goal state, and it can be accessed through the serve config command in the Ray Serve CLI.\n\nTo use the user config in Ray Serve, you can follow these steps:\n\n1. Define the user config in the Serve config file or on the @serve.deployment decorator.\n2. Deploy the application using the serve deploy command in the Ray Serve CLI.\n3. Use the serve config command to view the current config of the application.\n4. Update the user config by modifying the Serve config file or using the reconfigure() method on the deployment.\n5. Reapply the updated config to the Ray cluster using the serve deploy command.\n\nNote that the user config is not the same as the Ray Serve config, which is the configuration of the Ray Serve system itself. The Ray Serve config is set when the Ray cluster is started, and it cannot be changed without restarting the cluster. The user config, on the other hand, can be changed at runtime without restarting the cluster.",854"llm": "codellama/CodeLlama-34b-Instruct-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 main difference between the \"PACK\" and \"SPREAD\" strategies is how they handle the placement of bundles on nodes.\n\nThe \"PACK\" strategy packs bundles into as few nodes as possible, while the \"SPREAD\" strategy places bundles across distinct nodes as even as possible. In other words, \"PACK\" tries to minimize the number of nodes used, while \"SPREAD\" tries to maximize the number of nodes used.\n\nFor example, if you have three bundles with the same resource requirements, the \"PACK\" strategy will try to place all three bundles on the same node, while the \"SPREAD\" strategy will try to place each bundle on a different node.\n\nThe \"STRICT_PACK\" and \"STRICT_SPREAD\" strategies are variations of \"PACK\" and \"SPREAD\", respectively, that enforce a stricter placement policy. \"STRICT_PACK\" requires that all bundles be placed on the same node, while \"STRICT_SPREAD\" requires that each bundle be placed on a different node.\n\nIn summary, the main difference between \"PACK\" and \"SPREAD\" is the trade-off between minimizing the number of nodes used and maximizing the number of nodes used. \"PACK\" tries to minimize the number of nodes used, while \"SPREAD\" tries to maximize the number of nodes used.",881"llm": "codellama/CodeLlama-34b-Instruct-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, you can deploy a Ray cluster for each user so that their workloads are isolated. A Ray cluster is a set of worker nodes connected to a common Ray head node, and it can be fixed-size or autoscale according to the resources requested by applications running on the cluster. Ray supports multi-tenancy, but it is not recommended in production due to the lack of strong resource isolation and priority support.",908"llm": "codellama/CodeLlama-34b-Instruct-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": " To specify the ScalingConfig for a Tuner run, you can pass it as an argument to the Tuner constructor along with the TuneConfig and the trainable.\n\nHere's an example:\n```\ntuner = tune.Tuner(\n trainable,\n param_space=space,\n tune_config=tune.TuneConfig(num_samples=10),\n scaling_config=ScalingConfig(num_workers=8, resources_per_worker={\"CPU\": 4, \"GPU\": 0.5}, use_gpu=True)\n)\n```\nIn this example, the ScalingConfig is set to use 8 workers, with each worker having 4 CPUs and 0.5 GPUs. The `use_gpu` parameter is set to `True` to enable GPU usage.\n\nNote that the ScalingConfig is only used when the `tune_config` parameter is set to `TuneConfig`. If you're using a different tuning algorithm, you may need to use a different configuration object.",935"llm": "codellama/CodeLlama-34b-Instruct-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": " To utilize the \"zero-copy\" feature of Ray for numpy arrays, you can use the `ray.data.from_numpy_refs()` function to create a Ray Dataset from a list of Ray object references to NumPy ndarrays. This will allow you to access the numpy arrays in the object store without copying them.\n\nHere is an example of how to use this feature:\n```\nimport numpy as np\nimport ray\n\n# Create a list of Ray object references to NumPy ndarrays\narr_refs = [ray.put(np.zeros(100)) for _ in range(10)]\n\n# Create a Ray Dataset from the list of object references\nds = ray.data.from_numpy_refs(arr_refs)\n\n# Access the numpy arrays in the object store without copying them\nfor arr in ds.to_numpy_refs():\n print(arr)\n```\nThis will print the contents of the numpy arrays in the object store without copying them.\n\nNote that this feature is only available for datasets that can be converted to NumPy ndarrays. If you are working with other types of data, you may need to use other methods to avoid copying the data.",962"llm": "codellama/CodeLlama-34b-Instruct-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 advised to create 'key pools' where an actor can hold 1=many keys. This is because the number of keys can be very large, and creating an actor for each key would be inefficient. By creating key pools, the number of actors can be reduced, which can improve performance.",989"llm": "codellama/CodeLlama-34b-Instruct-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. This method returns the checkpoint path of the best trial of the experiment, determined by comparing the last trial results using the metric and mode parameters passed to `tune.run()`.\n\nHere's an example of how to use this method:\n```python\nanalysis = ExperimentAnalysis(path)\nbest_checkpoint = analysis.get_best_checkpoint(metric=\"mean_accuracy\", mode=\"max\")\nprint(best_checkpoint)\n```\nThis will print the checkpoint path of the best trial, determined by the `mean_accuracy` metric and `max` mode.\n\nAlternatively, you can use the `get_last_checkpoint` method to get the last checkpoint of a specific trial. This method returns the checkpoint path of the trial with the highest `training_iteration`.\n```python\nanalysis = ExperimentAnalysis(path)\nlast_checkpoint = analysis.get_last_checkpoint()\nprint(last_checkpoint)\n```\nThis will print the checkpoint path of the last trial, determined by the `training_iteration` metric.\n\nYou can also use the `get_trial_checkpoints_paths` method to get the paths and metrics of all persistent checkpoints of a trial.\n```python\nanalysis = ExperimentAnalysis(path)\ntrial_checkpoints = analysis.get_trial_checkpoints_paths(trial=trial_id, metric=\"mean_accuracy\")\nprint(trial_checkpoints)\n```\nThis will print a list of tuples, where each tuple contains the checkpoint path and the corresponding metric value.",1016"llm": "codellama/CodeLlama-34b-Instruct-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 a singleton actor:\n\n* Ensures that only one instance of the actor is created and used throughout the application.\n* Allows for easy management of state between instances of the same class.\n* Can be used to implement a cache or a registry of objects.\n\nDisadvantages of using a singleton actor:\n\n* Can lead to a single point of failure if the actor fails.\n* Can make it difficult to scale the application horizontally.\n* Can make it difficult to test the application.",1043"llm": "codellama/CodeLlama-34b-Instruct-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": " The advantages of using a named actor in Ray are:\n\n1. Unique name: A named actor can be given a unique name within its namespace, which allows you to retrieve the actor from any job in the Ray cluster.\n2. Easy retrieval: You can retrieve the actor later somewhere in the code by using the `ray.get_actor()` method and passing in the name of the actor.\n3. Garbage collection: The actor will still be garbage-collected if no handles to it exist.\n4. Non-global named actors: In C++, you can create non-global named actors that are scoped to the current job.\n5. Sync call: The `ray.get_actor()` method is a sync call and it\u2019ll timeout after 60s.\n6. Better off using tasks: If you don\u2019t need the stateful part of an actor, you\u2019re mostly better off using tasks.",1070"llm": "codellama/CodeLlama-34b-Instruct-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": " To read a text file stored on S3 using Ray Data, you can use the `read_text` function and pass in the S3 path to the file as an argument. For example:\n```\nimport ray\n\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\n```\nThis will create a dataset with a single column named \"text\" that contains the contents of the text file. You can then use the `show` method to display the contents of the dataset.\n```\nds.show()\n```\nThis will display the contents of the text file, with each line of text displayed as a separate row in the dataset.\n\nAlternatively, you can use the `read_binary_files` function to read the text file as a binary file and then manually decode the data. For example:\n```\nimport ray\n\npath = \"s3://anonymous@ray-example-data/this.txt\"\nds = ray.data.read_binary_files(path)\n```\nThis will create a dataset with a single column named \"bytes\" that contains the binary data from the text file. You can then use the `decode` method to convert the binary data to a string. For example:\n```\nds = ds.map(lambda x: x.decode(\"utf-8\"))\n```\nThis will convert the binary data to a string using the UTF-8 encoding. You can then use the `show` method to display the contents of the dataset.\n```\nds.show()\n```\nThis will display the contents of the text file, with each line of text displayed as a separate row in the dataset.",1097"llm": "codellama/CodeLlama-34b-Instruct-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": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command. This command takes the cluster configuration file as an argument and returns the head node IP.\n\nHere's an example of how to use the `ray get_head_ip` command:\n```\nray get_head_ip my_cluster_config.yaml\n```\nThis will return the head node IP for the cluster defined in `my_cluster_config.yaml`.\n\nAlternatively, you can use the `ray list nodes` command to get a list of all nodes in the cluster, including the head node. The head node will have a `head` role, so you can filter the list to only show the head node by using the `--role` option:\n```\nray list nodes --role head\n```\nThis will return a list of all nodes in the cluster with the `head` role, which should include only the head node. The output will include the IP address of the head node.",1124"llm": "codellama/CodeLlama-34b-Instruct-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 code:\n```\ndef map_fn(batch):\n # Your code to process the batch and return a list of objects\n return [processed_object for processed_object in batch]\n\nds = ray.data.from_items([1, 2, 3, 4, 5])\nds = ds.map_batches(map_fn, batch_size=2)\n```\nIn this example, the `map_fn` function takes a batch of data and returns a list of processed objects. The `map_batches` method is then used to apply the `map_fn` function to the entire dataset, with a batch size of 2.\n\nNote that the `map_fn` function can also be a class, in which case it will run as a long-running Ray actor. In this case, you can use the `compute` argument to specify the `ActorPoolStrategy` with the number of parallel actors.",1151"llm": "codellama/CodeLlama-34b-Instruct-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` and `evaluation_duration_unit` parameters in the `evaluation_config` dictionary.\n\nFor example, to set a maximum episode length of 10 episodes, you can use the following configuration:\n```\nevaluation_config = {\n \"evaluation_duration\": 10,\n \"evaluation_duration_unit\": \"episodes\"\n}\n```\nAlternatively, you can set the maximum episode length in terms of time steps by using the `evaluation_duration_unit` parameter with the value \"timesteps\". For example, to set a maximum episode length of 200 time steps, you can use the following configuration:\n```\nevaluation_config = {\n \"evaluation_duration\": 200,\n \"evaluation_duration_unit\": \"timesteps\"\n}\n```\nNote that the `evaluation_duration` parameter specifies the maximum number of episodes or time steps to use for evaluation, and the `evaluation_duration_unit` parameter specifies whether the maximum is measured in episodes or time steps.",1178"llm": "codellama/CodeLlama-34b-Instruct-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` class and set the `max_failures` parameter to a value greater than 1. This will cause Ray Tune to retry the trial up to the specified number of failures before giving up and stopping the trial.\n\nHere is an example of how to use `FailureConfig` to retry a trial up to 3 times:\n```\nfrom ray import tune\n\n# Define the trial\ndef train_model(config):\n # Train the model\n pass\n\n# Define the parameter space\ntrial_space = {\n \"param1\": tune.choice([1, 2, 3]),\n \"param2\": tune.choice([4, 5, 6])\n}\n\n# Define the failure configuration\nfailure_config = tune.FailureConfig(max_failures=3)\n\n# Start the trial\ntuner = tune.Tuner(train_model, param_space=trial_space, failure_config=failure_config)\nresults = tuner.fit()\n```\nIn this example, the `train_model` function is called with the trial parameters, and the `tune.FailureConfig` class is used to specify the maximum number of failures to allow before giving up and stopping the trial. The `tuner.fit()` method is then called to start the trial and run it until it completes or fails. If the trial fails, Ray Tune will automatically retry it up to the specified number of failures before giving up and stopping the trial.",1205"llm": "codellama/CodeLlama-34b-Instruct-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, the Worker Actor can remain even on Supervisor/Driver failure if the Supervisor passes a reference to the Actor to another Actor. This is because the Actor is not owned by the Supervisor, but rather by the Worker that created it. Therefore, even if the Supervisor or Driver fails, the Actor will continue to run as long as the Worker is still alive.",1232"llm": "codellama/CodeLlama-34b-Instruct-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": " To read a large text file in S3 with Ray, you can use the `ray.data.read_text()` function and specify the path to the file in S3. For example:\n```\nimport ray\n\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\n```\nThis will read the text file from S3 and create a Ray dataset with a single column named \"text\" containing the text data. You can then manipulate the data using the various methods available in the Ray Data API.",1259"llm": "codellama/CodeLlama-34b-Instruct-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 get a ray dataset from pandas, you can use the `ray.data.from_pandas()` function. This function takes a pandas dataframe or a list of pandas dataframes as input and returns a ray dataset.\n\nHere is an example of how to use this function:\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\nray_dataset = ray.data.from_pandas(df)\n```\nYou can also create a ray dataset from a list of pandas dataframes by passing the list of dataframes as an argument to the `ray.data.from_pandas()` function.\n```\n# Create a list of pandas dataframes\ndfs = [df, df]\n\n# Create a ray dataset from the list of pandas dataframes\nray_dataset = ray.data.from_pandas(dfs)\n```\nNote that the `ray.data.from_pandas()` function returns a `MaterializedDataset` object, which is a lazy dataset that can be further processed using the `ray.data` API. To convert the dataset to a pandas dataframe, you can use the `to_pandas()` method of the `MaterializedDataset` object.",1286"llm": "codellama/CodeLlama-34b-Instruct-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`:\n```\nimport ray\n\n# Create a Ray Dataset from a list of dictionaries\ndata = [{\"x\": [x], \"y\": [2 * x]} for x in range(200)]\ndataset = ray.data.from_items(data)\n\n# Apply a function to each row of the dataset\ndef increment(batch):\n batch[\"y\"] = batch[\"y\"] + 1\n return batch\n\ndataset = dataset.map(increment)\n\nprint(dataset.take(4))\n```\nThis code will create a Ray Dataset from a list of dictionaries, and then apply the `increment` function to each row of the dataset using the `map` method. The `increment` function takes a batch of data and increments the value of the \"y\" key in each row by 1. The resulting dataset will contain the original data with the \"y\" values incremented by 1. The `take` method is used to print the first 4 rows of the dataset.",1313"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nHere is an example of using `ray.data.map` with a callable class as input:\n```\nimport ray\n\nray.init()\n\nclass MyCallable:\n def __init__(self, x):\n self.x = x\n\n def __call__(self, row):\n return row[\"id\"] * self.x\n\nds = ray.data.range(10)\nds = ds.map(MyCallable(2))\nprint(ds.take())\n```\nThis will output `[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]`.\n\nIn this example, `MyCallable` is a callable class that takes an `x` argument in its constructor and returns a function that multiplies the input row by `x`. The `map` method is then called on the `ds` dataset with an instance of `MyCallable` as the input. This will apply the `MyCallable` function to each row in the dataset, multiplying the `id` column by 2. The resulting dataset is then printed using the `take` method.",1340"llm": "codellama/CodeLlama-34b-Instruct-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 Tuner, you can use the `resources` parameter in the `tune.with_resources` function. For example:\n```\ntrain_model = tune.with_resources(train_model, {\"cpu\": 1, \"memory\": 1024})\n```\nThis will set the memory limit for each trial to 1024 MB.\n\nAlternatively, you can use the `TuneConfig` class to specify the resources for each trial. For example:\n```\ntune_config = TuneConfig(num_samples=100, max_concurrent_trials=10, resources={\"cpu\": 1, \"memory\": 1024})\n```\nThis will set the memory limit for each trial to 1024 MB and the maximum number of concurrent trials to 10.\n\nNote that the `resources` parameter in `tune.with_resources` and the `resources` parameter in `TuneConfig` are used to specify the resources for each trial, and they are not related to the `resources` parameter in the `ray.tune.search.Repeater` class, which is used to specify the resources for the search algorithm.",1367"llm": "codellama/CodeLlama-34b-Instruct-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": " \n[PYTHON]\ndef get_actor_id(actor_name):\n return ray.util.state.get_actor(actor_name)[\"actor_id\"]\n[/PYTHON]\n[TESTS]\n# Test case 1:\nassert get_actor_id(\"Actor\") == \"31405554844820381c2f0f8501000000\"\n# Test case 2:\nassert get_actor_id(\"Actor2\") == \"31405554844820381c2f0f8501000001\"\n# Test case 3:\nassert get_actor_id(\"Actor3\") == \"31405554844820381c2f0f8501000002\"\n[/TESTS]\n",1394"llm": "codellama/CodeLlama-34b-Instruct-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. The `ray.init()` function will return `True` if Ray has already been initialized and `False` otherwise.",1421"llm": "codellama/CodeLlama-34b-Instruct-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 should be executed as a Ray actor, which is a long-running process that can perform multiple tasks. This allows for more efficient use of resources and can improve performance.",1448"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nTo use WandbLogger with AccelerateTrainer, you need to do the following:\n\n1. Install Wandb: `pip install wandb`\n2. Set up your API keys as environment variables.\n3. Set `rank_zero_only.rank = None` to avoid Lightning creating a new experiment run on the driver node.\n4. Import the WandbLogger from PyTorch Lightning.\n5. Initialize the WandbLogger in the worker function.\n\nHere's an example:\n```python\nimport wandb\nfrom pytorch_lightning.loggers.wandb import WandbLogger\nfrom pytorch_lightning.utilities.rank_zero import rank_zero_only\n\ndef train_func_per_worker():\n ...\n\n wandb_logger = WandbLogger(\n name=\"demo-run\",\n project=\"demo-project\",\n id=\"unique_id\",\n offline=offline\n )\n```\nNote that you need to set `offline=True` if you're running in offline mode.",1475"llm": "codellama/CodeLlama-34b-Instruct-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 using `ray.put()`\n* Values returned from remote functions\n* Objects are reference counted and evicted when they fall out of scope\n* An object store server runs on each node\n* The size of the object store can be controlled by `--object-store-memory`\n* Objects are spilled to disk if the object store fills up\n* Data blocks stored in the object store are spilled to external storage once the capacity of the object store is used up.",1502"llm": "codellama/CodeLlama-34b-Instruct-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, you can use the `ray.cancel()` function and pass in the ObjectRef of the task you want to cancel. By default, this will send a `KeyboardInterrupt` to the task's worker if it is mid-execution. You can also pass `force=True` to force-exit the worker. Note that currently, Ray will not automatically retry tasks that have been cancelled.",1529"llm": "codellama/CodeLlama-34b-Instruct-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": " To send extra arguments in the `dataset.map_batches` function, you can use the `fn_args` and `fn_kwargs` parameters.\n\nFor example:\n```\nds.map_batches(\n my_function,\n batch_size=10,\n fn_args=[\"arg1\", \"arg2\"],\n fn_kwargs={\"kwarg1\": \"value1\", \"kwarg2\": \"value2\"}\n)\n```\nIn this example, the `my_function` function will be called with the arguments `arg1`, `arg2`, `kwarg1`, and `kwarg2`.\n\nAlternatively, you can use the `fn_constructor_args` parameter to pass arguments to the constructor of the class-based UDF.\n```\nds.map_batches(\n MyClass,\n batch_size=10,\n fn_constructor_args=[\"arg1\", \"arg2\"],\n fn_kwargs={\"kwarg1\": \"value1\", \"kwarg2\": \"value2\"}\n)\n```\nIn this example, the `MyClass` class will be instantiated with the arguments `arg1` and `arg2`, and the `my_function` method will be called with the arguments `kwarg1` and `kwarg2`.",1556"llm": "codellama/CodeLlama-34b-Instruct-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": " The history of jobs run on a Kuberay cluster is stored in the Global Control Store (GCS) of the Ray head node. The GCS is a key-value store that stores metadata about the Ray cluster, including information about the jobs that have been run.\n\nThe GCS is typically stored in an internal database on the head node, but it can also be stored in an external Redis database for fault tolerance. The GCS uses a binary format to store its data.",1583"llm": "codellama/CodeLlama-34b-Instruct-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 \"The actor ImplicitFunc is too large\" suggests that the actor is trying to store a large object in its memory, which is not allowed. To resolve this issue, you can try the following:\n\n1. Use `ray.put()` to put the large object in the Ray object store, and then pass the object reference to the actor.\n2. Use a smaller batch size in the `ds.map_batches()` function.\n3. Use a different compute strategy in the `ds.map_batches()` function, such as `ray.data.ActorPoolStrategy(min_size=2, max_size=8)`.\n4. Use a different model that is smaller in size.\n5. Use a different framework or library that is more optimized for large-scale machine learning tasks.",1610"llm": "codellama/CodeLlama-34b-Instruct-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": " To use Ray to distribute training for a custom neural net written using Keras in Databricks, you can follow these steps:\n\n1. Import Ray Train and Ray AIR Session:\n```python\nimport ray.train as train\n```\n2. Convert the training function to a distributed multi-worker training function using the `ray.train.torch.prepare_model` and `ray.train.torch.prepare_data_loader` utility functions. This will automatically wrap the model with `DistributedDataParallel` and place it on the right device, and add `DistributedSampler` to the data loaders.\n```python\ndef train_func_distributed():\n num_epochs = 3\n batch_size = 64\n\n dataset = get_dataset()\n dataloader = DataLoader(dataset, batch_size=batch_size)\n dataloader = train.torch.prepare_data_loader(dataloader)\n\n model = NeuralNetwork()\n model = train.torch.prepare_model(model)\n```\n3. Use the Ray Train API to interact with the distributed training process. You can find the latest version of the code here: `train.py`. The latest version might differ slightly from the code presented here.\n4. To distribute the training across multiple workers, you can use a Ray cluster. Each of Ray\u2019s five native libraries distributes a specific ML task:\n\t* Data: Scalable, framework-agnostic data loading and transformation across training, tuning, and prediction.\n\t* Train: Distributed multi-node and multi-core model training with fault tolerance that integrates with popular training libraries.\n\t* Tune: Scalable hyperparameter tuning to optimize model performance.\n\t* Serve: Scalable and programmable serving to deploy models for online inference, with optional microbatching to improve performance.\n\t* RLlib: Scalable distributed reinforcement learning workloads.\n5. To serve the trained model, you can use Ray Serve. In this example, we will show how to train a Tensorflow model and load the model from your file system in your Ray Serve deployment. You can also parse the JSON request and make a prediction.\n```python\nimport ray\n\n# Step 1: Create a Ray Dataset from in-memory Numpy arrays.\n# You can also create a Ray Dataset from many other sources and file\n# formats.\nds = ray.data.from_numpy(np.ones((1, 100)))\n\n# Step 2: Define a Predictor class for inference.\n# Use a class to initialize the model just once in `__init__`\n# and re-use it for inference across multiple batches.\nclass TorchPredictor:\n def __init__(self):\n # Load a dummy neural network.\n # Set `self.model` to your pre-trained PyTorch model.\n self.model = nn.Sequential(\n nn.Linear(in_features=100, out_features=1),\n nn.Sigmoid(),\n )\n self.model.eval()\n\n# Prepare everything for distributed training\n(\n model,\n optimizer,\n train_dataloader,\n eval_dataloader,\n lr_scheduler,\n) = accelerator.prepare(\n model, optimizer, train_dataloader, eval_dataloader, lr_scheduler\n)\n\n# Start training\n...\n\nfrom ray.train.torch import TorchTrainer\nfrom ray.train import ScalingConfig\n\n# Set up your dataset and model.\nimport numpy as np\nimport tensorflow as tf\n\n# Parse the command line arguments.\nparser = options.get_training_parser()\nadd_ray_args(parser)\nargs = options.parse_args_and_arch(parser)\noriginal_args = copy.deepcopy(args)\n\n# Main loop for fault-tolerant training.\nretry = True\nwhile retry:\n args = copy.deepcopy(original_args)\n\n # Initialize Ray.\n ray.init(address=args.ray_address)\n\n set_num_resources(args)\n set_batch_size(args)\n\n # Set up Ray distributed actors.\n Actor = ray.remote(num_cpus=1, num_gpus=int(not args.cpu))(RayDistributedActor)\n workers = [Actor.remote() for i in range(args.distributed_world_size)]\n```",1637"llm": "codellama/CodeLlama-34b-Instruct-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": " To use `ray.put` and `ray.get`, you need to first initialize Ray with `ray.init()`. Then, you can use `ray.put` to store an object in the object store and get its ID, which can be used to pass the object as an argument to a remote function or method call. To get the object from the object store, you can use `ray.get` and pass the object ID as an argument.\n\nHere's an example:\n```\nimport ray\n\nray.init()\n\n# Store an object in the object store\nobj = ray.put(1)\n\n# Get the object from the object store\nresult = ray.get(obj)\n\nprint(result) # prints 1\n```\nNote that `ray.put` is a synchronous operation, meaning it will block until the object is stored in the object store. `ray.get` is also a synchronous operation, meaning it will block until the object is retrieved from the object store.\n\nIt's important to avoid calling `ray.get` unnecessarily, as it can harm performance and fault tolerance. Instead, work with object references directly and only call `ray.get` at the end to get the final result.",1664"llm": "codellama/CodeLlama-34b-Instruct-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 use the `map_batches` function to apply a preprocessing function to each batch of data. You can then use the `materialize` function to materialize the preprocessed data in the Ray object store.\n\nHere is an example of how you can use Ray Data to preprocess many files:\n```\nimport ray\nfrom ray.data import read_csv\nfrom ray.data.preprocessors import Preprocessor\n\n# Define a preprocessing function\ndef preprocess_data(batch):\n # Apply preprocessing operations to the batch of data\n return batch\n\n# Read the data from a CSV file\ndata = read_csv(\"data.csv\")\n\n# Apply the preprocessing function to each batch of data\npreprocessed_data = data.map_batches(preprocess_data)\n\n# Materialize the preprocessed data in the Ray object store\npreprocessed_data.materialize()\n```\nThis code will read the data from a CSV file, apply the preprocessing function to each batch of data, and then materialize the preprocessed data in the Ray object store.\n\nYou can also use the `Preprocessor` class to define a preprocessing function and apply it to the data. Here is an example of how you can use the `Preprocessor` class to preprocess data:\n```\nimport ray\nfrom ray.data import read_csv\nfrom ray.data.preprocessors import Preprocessor\n\n# Define a preprocessing function\ndef preprocess_data(batch):\n # Apply preprocessing operations to the batch of data\n return batch\n\n# Create a Preprocessor object\npreprocessor = Preprocessor(preprocess_data)\n\n# Read the data from a CSV file\ndata = read_csv(\"data.csv\")\n\n# Apply the preprocessing function to each batch of data\npreprocessed_data = data.map_batches(preprocessor)\n\n# Materialize the preprocessed data in the Ray object store\npreprocessed_data.materialize()\n```\nThis code will read the data from a CSV file, create a `Preprocessor` object with the preprocessing function, and then apply the preprocessing function to each batch of data. Finally, it will materialize the preprocessed data in the Ray object store.",1691"llm": "codellama/CodeLlama-34b-Instruct-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": " The error message \"!!! FAIL serialization: cannot pickle '_thread.lock' object\" indicates that the Python object being serialized contains a non-serializable object of type '_thread.lock'. This is likely due to the use of the `threading` module in the code, which creates a lock object that cannot be serialized.\n\nTo resolve this issue, you can try using the `inspect_serializability` function from the `ray.util` module to identify the non-serializable object and fix it. Alternatively, you can use the `convert_to_msgpack_checkpoint` function from the `ray.util` module to convert the pickle checkpoint to a msgpack type checkpoint, which may be more compatible with the Ray framework.\n\nIt's also worth noting that the error message mentions that the issue may be related to the use of the `threading` module, so you may want to check your code to see if you're using this module in a way that's incompatible with the Ray framework.",1718"llm": "codellama/CodeLlama-34b-Instruct-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, you can follow the instructions in the Kuberay documentation.\n\nFirst, you need to add the Kuberay Helm chart repository to your local machine by running the following command:\n```\nhelm repo add kuberay https://ray-project.github.io/kuberay-helm/\n```\nNext, you can install the Kuberay operator and CRDs by running the following command:\n```\nhelm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n```\nThis will install the Kuberay operator and the CRDs required to deploy and manage Ray clusters on Kubernetes.\n\nOnce the Kuberay operator is installed, you can create a RayCluster CR by running the following command:\n```\nhelm install raycluster kuberay/ray-cluster --version 0.6.0\n```\nThis will create a RayCluster CR and deploy a Ray cluster on Kubernetes.\n\nNote that the Kuberay operator can function within a single namespace, but the use of CRDs has a cluster-wide scope.",1745"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nThe 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 instance of a class is used as a function. In the context of Ray data, the `__call__()` method is used to perform batch inference on a dataset.\n\nWhen the `__call__()` method is called on an instance of a class, it is passed a batch of data as an argument, and it is expected to return a batch of results. The `__call__()` method is used to perform the actual inference on the data, and it is typically implemented using a machine learning framework such as PyTorch or TensorFlow.\n\nIn the example code provided, the `__call__()` method is defined as a method of the `TorchPredictor` class, which is a class that is used to perform batch inference on a PyTorch model. The `__call__()` method takes a batch of input data as an argument, and it uses the PyTorch model to perform inference on the data. The results of the inference are then returned as a batch of output data.\n\nOverall, the `__call__()` method is a key component of the Ray data batch inference system, and it is used to perform the actual inference on the data.",1772"llm": "codellama/CodeLlama-34b-Instruct-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 use the `disconnect()` method. For example:\n```\nimport ray\n\nray.init(\"ray://<head_node_host>:<port>\")\n\n# Do some work\n\nray.disconnect()\n```\nAlternatively, you can also use the `ray.shutdown()` method to disconnect the client and shut down the Ray cluster.\n```\nimport ray\n\nray.init(\"ray://<head_node_host>:<port>\")\n\n# Do some work\n\nray.shutdown()\n```\nNote that when you disconnect a Ray client, any object or actor references held by the server on behalf of the client are dropped, as if directly disconnecting from the cluster. If the client disconnects unexpectedly, i.e. due to a network failure, the client will attempt to reconnect to the server for 30 seconds before all of the references are dropped. You can increase this time by setting the environment variable `RAY_CLIENT_RECONNECT_GRACE_PERIOD=N`, where `N` is the number of seconds that the client should spend trying to reconnect before giving up.",1799"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nTo submit a job with Python using local files, you can use the Ray Jobs API (CLI) or the Python SDK.\n\nUsing the Ray Jobs API (CLI), you can submit a job by running the following command:\n```\n$ ray job submit --address=\"http://<head-node-ip>:8265\" --runtime-env-json='{\"working_dir\": \"/data/my_files\", \"pip\": [\"emoji\"]}' -- python my_ray_script.py\n```\nThis command will submit a job with the specified runtime environment and execute the `my_ray_script.py` script.\n\nUsing the Python SDK, you can submit a job by creating a `JobSubmissionClient` object and calling the `submit_job` method. For example:\n```\nfrom ray.job_submission import JobSubmissionClient\n\n# If using a remote cluster, replace 127.0.0.1 with the head node's IP address.\nclient = JobSubmissionClient(\"http://127.0.0.1:8265\")\n\njob_id = client.submit_job(\n # Entrypoint shell command to execute\n entrypoint=\"python script.py\",\n # Path to the local directory that contains the script.py file\n runtime_env={\"working_dir\": \"./\"}\n)\n\nprint(job_id)\n```\nThis code will submit a job with the specified runtime environment and execute the `script.py` script.",1826"llm": "codellama/CodeLlama-34b-Instruct-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": " To do inference from a model trained by Ray Tune, you can use the `Checkpoint` class provided by Ray Train. The `Checkpoint` class allows you to save and load the model and its parameters, and you can use it to perform inference on new data.\n\nHere's an example of how to use the `Checkpoint` class to perform inference on a model trained by Ray Tune:\n```\nimport ray\nfrom ray.train import Checkpoint\n\n# Load the model and its parameters from the checkpoint\ncheckpoint = Checkpoint.load(\"path/to/checkpoint\")\nmodel = checkpoint.model\n\n# Perform inference on new data\nnew_data = ...\npredictions = model.predict(new_data)\n```\nNote that the `Checkpoint` class is only available in Ray Train, so you'll need to install Ray Train in order to use it. You can install Ray Train using the following command:\n```\npip install \"ray[train]\"\n```",1853"llm": "codellama/CodeLlama-34b-Instruct-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 perform batch inference without using PyTorch or TensorFlow directly. One way to do this is by using a library like Ray Data, which provides a simple and efficient way to perform batch inference with multiple GPU workers.\n\nTo use Ray Data for batch inference, you can follow these steps:\n\n1. Load your dataset into a Ray Dataset.\n2. Preprocess the data using Ray Data's map_batches() function.\n3. Load your pre-trained model and use it to perform inference on the preprocessed data.\n4. Evaluate the predictions and save the results to an external storage.\n\nHere is an example code snippet that demonstrates how to use Ray Data for batch inference with a pre-trained PyTorch model:\n```python\nimport ray\nfrom ray.data import from_numpy\nfrom ray.data.dataset import Dataset\nfrom ray.data.batch import Batch\nfrom ray.data.batch_inference import BatchInference\nfrom ray.data.batch_inference.pytorch import PyTorchBatchInference\n\n# Load the dataset into a Ray Dataset\nds = from_numpy(np.asarray([\"Complete this\", \"for me\"]))\n\n# Preprocess the data using Ray Data's map_batches() function\nds = ds.map_batches(lambda batch: batch.to_tensor())\n\n# Load the pre-trained model\nmodel = torch.load(\"model.pth\")\n\n# Use Ray Data to perform inference on the preprocessed data\ninference = PyTorchBatchInference(model, ds)\n\n# Evaluate the predictions and save the results to an external storage\npredictions = inference.predict()\n```\nIn this example, we first load the dataset into a Ray Dataset and preprocess the data using Ray Data's map_batches() function. We then load the pre-trained PyTorch model and use Ray Data to perform inference on the preprocessed data. Finally, we evaluate the predictions and save the results to an external storage.\n\nNote that this is just a simple example, and you may need to modify the code to fit your specific use case. Additionally, you will need to have the necessary dependencies installed, such as PyTorch and Ray Data.",1880"llm": "codellama/CodeLlama-34b-Instruct-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 high-performance distributed computing framework. It provides a simple, universal API for building distributed applications. Ray can be used for a wide range of applications, including machine learning, data processing, and simulations.",1907"llm": "codellama/CodeLlama-34b-Instruct-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 use more than that, the trial will be scheduled on a machine with at least that amount of GPU memory available. However, you will have to make sure that your model still fits on the fractional memory, as Ray does not automatically handle this for you. Additionally, if you want to use multiple GPUs per trial, you should check out LightningTrainer.",1934"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nThe query is asking how to pickle a variable defined in an actor's init method. The context provides examples of how to create actors and pass objects to actor method calls, but it does not provide information on how to pickle a variable defined in an actor's init method.\n\nTherefore, the answer is that it is not possible to pickle a variable defined in an actor's init method.",1961"llm": "codellama/CodeLlama-34b-Instruct-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 using Ray, you can use the `ray.util.collective.collective.allreduce()` function. This function takes a list of tensors as input and performs an all-reduce operation on each tensor in the list.\n\nHere is an example of how to use `allreduce()` to perform an all-reduce operation among a list of actors:\n```\nimport ray\n\n# Create a list of actors\nactors = [ray.remote(num_gpus=1) for i in range(5)]\n\n# Create a list of tensors to be all-reduced\ntensor_list = [torch.ones((10,), dtype=torch.float32) for i in range(5)]\n\n# Perform the all-reduce operation\nray.util.collective.collective.allreduce(tensor_list, \"default\")\n```\nIn this example, we create a list of five actors, each with one GPU, and a list of five tensors to be all-reduced. We then use the `allreduce()` function to perform the all-reduce operation on each tensor in the list. The `allreduce()` function takes the list of tensors as input and performs the all-reduce operation on each tensor in parallel.\n\nNote that the `allreduce()` function assumes that the tensors in the list are located on different GPUs. If the tensors are located on the same GPU, you can use the `ray.util.collective.collective.reduce()` function instead, which performs a reduce operation on a single tensor.",1988"llm": "codellama/CodeLlama-34b-Instruct-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 we specify a bundle with `{\"CPU\":0}` in the PlacementGroup, it will not be created because the bundle requires 0 CPUs, which is not a valid resource requirement.",2015"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nTo cancel a job from the UI, you can use the `ray job stop` command followed by the job ID. For example:\n```\n$ ray job stop raysubmit_tUAuCKubPAEXh6CW\n```\nThis will stop the job and all of its associated data.\n\nAlternatively, you can use the `ray.cancel` API to cancel a task. This will send a `KeyboardInterrupt` to the task's worker if it is mid-execution. You can also pass `force=True` to force-exit the worker.\n\nFor example:\n```\n@ray.remote\ndef blocking_operation():\n time.sleep(10e6)\n\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n\ntry:\n ray.get(obj_ref)\nexcept ray.exceptions.TaskCancelledError:\n print(\"Object reference was cancelled.\")\n```\nNote that currently, Ray will not automatically retry tasks that have been cancelled.\n\nIf you want to cancel a workflow, you can use the `ray.workflow.cancel` API. This will cancel the workflow and all of its associated data.\n\nFor example:\n```\nfrom ray import workflow\n\n@ray.remote\ndef wait_all(*deps) -> None:\n pass\n\n@ray.remote\ndef cancel(request_id: str) -> None:\n make_request(\"cancel\", request_id)\n\nif error:\n return workflow.continuation(\n wait_all.bind(\n cancel.bind(car_req_id),\n cancel.bind(hotel_req_id),\n cancel.bind(flight_req_id),\n )\n )\nelse:\n return result\n```\nNote that workflow checkpoints will still be saved in storage. To clean up saved checkpoints, see `workflow.delete()`.",2042"llm": "codellama/CodeLlama-34b-Instruct-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 the cluster when using Ray Serve, you can use the `serve deploy` command with the `--address` option, specifying the address of the remote Ray cluster's dashboard agent.\n\nFor example:\n```\nserve deploy config.yaml --address <remote dashboard agent address>\n```\nAlternatively, you can set the `RAY_ADDRESS` environment variable to the address of the remote Ray cluster's dashboard agent, and then run the `serve deploy` command without the `--address` option.\n\nFor more information, you can refer to the Ray Serve documentation on deploying to a remote cluster.",2069"llm": "codellama/CodeLlama-34b-Instruct-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": " To install the latest Ray nightly wheel, you can use the following command:\n\npip install -U \"ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp38-cp38-manylinux2014_x86_64.whl\"\n\nNote that this command will install the latest nightly wheel for Python 3.8. If you want to install a different version of Ray, you can replace the URL with the appropriate one from the Daily Releases (Nightlies) section of the documentation.",2096"llm": "codellama/CodeLlama-34b-Instruct-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\npip install -U \"ray[default]\"\n\nThis will install Ray with support for the dashboard and cluster launcher. If you want to install Ray with minimal dependencies, you can use the following command:\n\npip install -U \"ray\"\n\nAlternatively, you can install the latest Ray wheel from a specific commit on master by using the following template:\n\npip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/{COMMIT_HASH}/ray-{RAY_VERSION}-{PYTHON_VERSION}-{PYTHON_VERSION}-{OS_VERSION}.whl\n\nFor example, to install the Ray 3.0.0.dev0 wheels for Python 3.9, MacOS for commit 4f2ec46c3adb6ba9f412f09a9732f436c4a5d0c9, you can use the following command:\n\npip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/4f2ec46c3adb6ba9f412f09a9732f436c4a5d0c9/ray-3.0.0.dev0-cp39-cp39-macosx_10_15_x86_64.whl",2123"llm": "codellama/CodeLlama-34b-Instruct-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 the `unittest` module in Python. Here's an example of how to write a unit test for a Ray function:\n```\nimport unittest\nimport ray\n\n@ray.remote\ndef my_function(x):\n return x ** x\n\nclass MyTest(unittest.TestCase):\n def setUp(self):\n ray.init(num_cpus=4, num_gpus=0)\n\n def tearDown(self):\n ray.shutdown()\n\n def test_my_function(self):\n result = my_function.remote(2)\n self.assertEqual(result, 4)\n```\nIn this example, we define a Ray function `my_function` that takes an integer `x` and returns `x ** x`. We then define a unit test class `MyTest` that inherits from `unittest.TestCase`. In the `setUp` method, we initialize Ray with 4 CPUs and 0 GPUs. In the `tearDown` method, we shut down Ray.\n\nIn the `test_my_function` method, we call the `my_function.remote` method with the argument `2` and assert that the result is `4`.\n\nNote that in this example, we use the `ray.init` and `ray.shutdown` methods to initialize and shut down Ray. These methods are used to manage the Ray cluster and should be called in the `setUp` and `tearDown` methods of the unit test class, respectively.",2150"llm": "codellama/CodeLlama-34b-Instruct-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 the logs from being routed to the driver and will reduce the amount of output to stdout.\n\nAlternatively, you can use the `ray.experimental.tqdm_ray.safe_print()` function to print progress bars without corrupting the driver's stdout.\n\nYou can also use the `RAY_TQDM_PATCH_PRINT` environment variable to disable this feature.\n\nIt's worth noting that disabling log_to_driver may affect the performance of your application, as logs will no longer be copied to the driver's stdout.",2177"llm": "codellama/CodeLlama-34b-Instruct-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 2.1 with Ray Serve, you can follow these steps:\n\n1. Install the necessary packages: `pip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers`\n2. Download the `ray-service.stable-diffusion.yaml` file from the Ray project repository.\n3. Create a RayService using the downloaded file: `kubectl apply -f ray-service.stable-diffusion.yaml`\n4. Prepare a sample image file to test the Stable Diffusion model.\n5. Send a request to the text-to-image model using the `stable_diffusion_req.py` script.\n\nNote that you should test your Ray Serve script locally or in a RayCluster before deploying it to a RayService. Also, it's recommended to start a new Ray cluster, deploy the updated code to it using serve deploy, and then switch traffic from your old cluster to the new one for large-scale code updates in production.",2204"llm": "codellama/CodeLlama-34b-Instruct-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": " Actor handle is a reference to an actor in Ray. It is used to pass the actor around to other tasks and to call methods on the actor.",2231"llm": "codellama/CodeLlama-34b-Instruct-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` function. This will manually terminate the actor and release any resources it was using. It is important to note that this will not go through the normal Python `sys.exit` teardown logic, so any exit handlers installed in the actor using `atexit` will not be called.\n\nHere is an example of how to use `ray.kill` to kill a detached actor:\n```\nimport ray\n\n@ray.remote\nclass MyActor:\n pass\n\nactor_handle = MyActor.remote()\n\n# Kill the actor\nray.kill(actor_handle)\n```\nAlternatively, you can also use the `actor.__ray_terminate__.remote()` method to queue a termination task for the actor. This will allow the actor to finish any pending tasks and then exit gracefully.\n```\nimport ray\n\n@ray.remote\nclass MyActor:\n pass\n\nactor_handle = MyActor.remote()\n\n# Queue a termination task for the actor\nactor_handle.__ray_terminate__.remote()\n```\nIt is important to note that if you are using a detached actor, you should manually destroy it once you are sure that it is no longer needed. This can be done by calling `ray.kill` on the actor handle.",2258"llm": "codellama/CodeLlama-34b-Instruct-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 the pip package in the runtime environment if an old version exists, you can set the \"pip_check\" parameter to False in the runtime_env. This will disable the pip version check and allow the specified pip packages to be installed even if an older version is already installed.\n\nFor example:\n```\nRuntimeEnv(\n pip={\n \"packages\": [\"tensorflow\", \"requests\"],\n \"pip_check\": False,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"\n }\n)\n```\nThis will install the specified pip packages, even if an older version is already installed. The \"pip_version\" parameter specifies the version of pip to use for the installation.",2285"llm": "codellama/CodeLlama-34b-Instruct-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 of all rows in a Ray Data Dataset, you can use the `random_shuffle()` method. This method will randomly shuffle all rows in the dataset and return a new dataset with the shuffled rows.\n\nHere is an example of how to use `random_shuffle()` to do a global shuffle of all rows in a Ray Data Dataset:\n```\nimport ray\n\n# Create a Ray Data Dataset\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/sms_spam_collection_subset.txt\")\n\n# Do a global shuffle of all rows in the dataset\nds = ds.random_shuffle()\n```\nNote that `random_shuffle()` is a slow operation, and it can be expensive to perform a full global shuffle of all rows in a large dataset. If you only need to shuffle a subset of rows, you can use the `local_shuffle_buffer_size` parameter to specify the size of the buffer to use for shuffling. This will shuffle a subset of rows up to the specified buffer size during iteration, which can be more performant than a full global shuffle.\n\nFor more information on how to optimize shuffling in Ray Data, see the Optimize shuffling guide.",2312"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nThe namespace of an Actor can be specified while creating it using the `options` method. The `options` method takes a `name` parameter which specifies the name of the actor, and a `namespace` parameter which specifies the namespace of the actor. If no namespace is specified, the actor will be created in the anonymous namespace.\n\nHere is an example of how to create an actor with a specified namespace:\n```\nimport ray\n\nray.init(namespace=\"my_namespace\")\n\n@ray.remote\nclass MyActor:\n pass\n\nmy_actor = MyActor.options(name=\"my_actor\", namespace=\"my_namespace\").remote()\n```\nIn this example, the actor `my_actor` is created in the namespace `my_namespace`.\n\nIt is also possible to retrieve a named actor from a different namespace using the `ray.get_actor` method. The `ray.get_actor` method takes a `name` parameter which specifies the name of the actor, and a `namespace` parameter which specifies the namespace of the actor.\n```\nimport ray\n\nray.init(namespace=\"my_namespace\")\n\n@ray.remote\nclass MyActor:\n pass\n\nmy_actor = MyActor.options(name=\"my_actor\", namespace=\"my_namespace\").remote()\n\n# Retrieve the actor from a different namespace\nmy_actor = ray.get_actor(\"my_actor\", namespace=\"my_namespace\")\n```\nIn this example, the actor `my_actor` is retrieved from the namespace `my_namespace`.\n\nIt is important to note that named actors are only accessible within their namespaces. If an actor is created in a namespace, it cannot be accessed from a different namespace.",2339"llm": "codellama/CodeLlama-34b-Instruct-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 supports async.io through the use of the `@ray.remote` decorator. This decorator can be used to define both regular functions and actor methods as remote tasks, which can be executed asynchronously on separate Python workers. To invoke these remote functions or methods, the `remote` method is used, which immediately returns an object reference (a future) and then creates a task that will be executed on a worker process.\n\nIn addition, Ray also supports the use of the `asyncio` module for remote tasks. This allows for the use of async/await syntax for defining remote tasks, and enables the use of async/await syntax for invoking remote tasks.\n\nIt's worth noting that Ray does not support asyncio for remote tasks, and the `asyncio` module should only be used for defining and invoking remote tasks.",2366"llm": "codellama/CodeLlama-34b-Instruct-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, you can use the Ray debugger. First, run the program with the `--debug` flag, and then press `0` to select the breakpoint. This will allow you to jump into the call with the `remote` command and print the value of `n` with `p(n)`.",2393"llm": "codellama/CodeLlama-34b-Instruct-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": " The example of `ray.actor.exit_actor()` is provided in the context:\n```\n@ray.remote\nclass Actor:\n def exit(self):\n ray.actor.exit_actor()\n\nactor = Actor.remote()\nactor.exit.remote()\n```\nThis code defines an actor class with a method `exit` that calls `ray.actor.exit_actor()` to terminate the actor. The actor is then created with `Actor.remote()` and the `exit` method is called on the actor with `actor.exit.remote()`. This will terminate the actor.",2420"llm": "codellama/CodeLlama-34b-Instruct-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 logs inside an actor in Ray, you can use the `logging` module and configure the logger to log to a file. Here's an example:\n```\nimport ray\nimport logging\n\n@ray.remote\nclass MyActor:\n def __init__(self):\n self.logger = logging.getLogger(__name__)\n self.logger.setLevel(logging.INFO)\n self.logger.addHandler(logging.FileHandler('actor.log'))\n\n def foo(self):\n self.logger.info('Hello from actor!')\n```\nIn this example, the `MyActor` class is defined with a `logger` attribute that is initialized with the `__name__` of the actor. The `setLevel` method is used to set the logging level to `INFO`, and the `addHandler` method is used to add a `FileHandler` that logs to a file named `actor.log`.\n\nIn the `foo` method, the `logger.info` method is used to log a message to the file.\n\nYou can also use the `logging` module to customize the prefixes for Actor logs. For example, you can define the `__repr__` method for the Actor class to replace the Actor name with the Actor repr.\n```\nimport ray\nimport logging\n\n@ray.remote\nclass MyActor:\n def __init__(self, index):\n self.index = index\n self.logger = logging.getLogger(__name__)\n self.logger.setLevel(logging.INFO)\n self.logger.addHandler(logging.FileHandler('actor.log'))\n\n def foo(self):\n self.logger.info('Hello from actor!')\n\n def __repr__(self):\n return f\"MyActor(index={self.index})\"\n```\nIn this example, the `__repr__` method is defined to return a string that includes the Actor index. This will be used as the prefix for the Actor logs.",2447"llm": "codellama/CodeLlama-34b-Instruct-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": " To perform batch inference with GPT-2 on text data from an S3 bucket using Ray, you can follow these steps:\n\n1. Download the persistent Ray Train checkpoint locally and load the fine-tuned model weights and tokenizer from the checkpoint.\n2. Use the \ud83e\udd17 Transformers pipeline to generate predictions from the fine-tuned model.\n3. Set the task to \"text-generation\" and set device_map=\"auto\" for Ray Train to automatically place the model on the right device.\n4. Use Ray Data to read the text data from the S3 bucket and map the batches to the model using the TextClassifier class.\n5. Query the model in Python to perform batch inference.\n\nHere is an example code snippet to perform batch inference with GPT-2 on text data from an S3 bucket using Ray:\n```python\nimport os\nimport numpy as np\nimport ray\nfrom transformers import pipeline, AutoTokenizer, GPTJForCausalLM\nfrom ray.data import read_text\nfrom ray.data.dataset import Dataset\n\n# Download the persistent Ray Train checkpoint locally\nos.system(f\"awsv2 s3 sync s3://{checkpoint.path} /mnt/local_storage/\")\n\n# Load the fine-tuned model weights and tokenizer from the checkpoint\nmodel = GPTJForCausalLM.from_pretrained(checkpoint.path)\ntokenizer = AutoTokenizer.from_pretrained(checkpoint.path)\n\n# Set the task to \"text-generation\" and set device_map=\"auto\" for Ray Train\nray.train.set_task(\"text-generation\")\nray.train.set_device_map(\"auto\")\n\n# Use Ray Data to read the text data from the S3 bucket\nds = read_text(\"s3://anonymous@ray-example-data/this.txt\")\n\n# Map the batches to the model using the TextClassifier class\nds = ds.map_batches(TextClassifier, compute=ray.data.ActorPoolStrategy(size=2))\n\n# Query the model in Python to perform batch inference\nds.show(3)\n```\nNote that you will need to have the necessary dependencies installed, such as the \ud83e\udd17 Transformers library, and have the appropriate credentials set up to access the S3 bucket.",2474"llm": "codellama/CodeLlama-34b-Instruct-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 use the following command:\n\nray debug --ray-debugger-external\n\nThis will enable the Ray debugger and make it available externally to the node. Note that this is only safe to activate if the node is behind a firewall.\n\nAlternatively, you can set the environment variable RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1 on each node before starting Ray, which will print the full runtime_env setup log messages to the driver (the script that calls ray.init()).\n\nFor more information on debugging Ray applications and clusters, you can refer to the Ray documentation.",2501"llm": "codellama/CodeLlama-34b-Instruct-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": " To list the current Ray actors from Python, you can use the `ray.util.state.list_actors()` function. This function returns a list of `ray.util.state.common.ActorState` objects, each representing an actor in the cluster.\n\nHere's an example of how to use this function:\n```\nimport ray\n\n# List all actors in the cluster\nactors = ray.util.state.list_actors()\n\n# Print the actor IDs and class names\nfor actor in actors:\n print(f\"{actor.actor_id} {actor.class_name}\")\n```\nThis will print the actor IDs and class names of all actors in the cluster. You can also use the `ray.get_actor()` function to retrieve a specific actor by its ID.",2528"llm": "codellama/CodeLlama-34b-Instruct-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": " To kill a replica actor in Python using Ray, you can use the `ray.kill` function and pass the actor handle as an argument. For example:\n```\nimport ray\n\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nThis will kill the replica actor and prevent it from restarting. If you want to kill the actor but allow it to restart, you can pass `no_restart=False` to `ray.kill`.\n\nNote that killing an actor will interrupt any running tasks on the actor, causing them to fail immediately. Any atexit handlers installed in the actor will not be run. If you want to kill the actor but let pending tasks finish, you can call `actor.__ray_terminate__.remote()` instead to queue a termination task. Any atexit handlers installed in the actor will be run in this case.\n\nAlso note that garbage collection for actors has not been implemented yet, so this is currently the only way to terminate an actor gracefully. The ObjectRef resulting from the task can be waited on to wait for the actor to exit (calling `ObjectRef::Get` on it will throw a `RayActorException`).",2555"llm": "codellama/CodeLlama-34b-Instruct-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": " To specify that a task should run on a V100 GPU type in Ray, you can use the `accelerator_type` parameter in the `@ray.remote` decorator. For example:\n```\n@ray.remote(num_gpus=1, accelerator_type=ray.util.accelerators.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 task to run on a node that has a V100 GPU available. The `accelerator_type` parameter can be set to any of the available accelerator types, which can be found in the `ray.util.accelerators` module.",2582"llm": "codellama/CodeLlama-34b-Instruct-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": " To get started with Ray, you can follow these steps:\n\n1. Install Ray via pip install -U ray.\n2. Initialize Ray by running ray.init().\n3. Start a Ray cluster by running kind create cluster.\n4. Use the Ray Core quickstart guide to learn the basics of using Ray.\n5. Check out the Ray Serve quickstart guide to learn how to deploy and manage models with Ray Serve.\n6. Explore the Ray Serve advanced guides for more options and configurations.\n7. Use the Ray Serve dashboard to monitor and manage your deployments.\n8. Join the Ray community on Slack to ask questions and get help.\n\nNote: These steps are for getting started with Ray on a single machine or cluster of machines. If you want to use Ray on a cloud provider, you will need to follow the instructions for that specific cloud provider.",2609"llm": "codellama/CodeLlama-34b-Instruct-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 `conda` field in the `runtime_env` dictionary. For example:\n```\nruntime_env = {\"conda\": {\"python\": \"3.8\"}}\n```\nThis will use Python 3.8 in the runtime environment.\n\nAlternatively, you can use the `pip_version` field in the `runtime_env` dictionary to specify the version of pip to use. For example:\n```\nruntime_env = {\"pip_version\": \"==22.0.2;python_version=='3.8.11'\"}\n```\nThis will use pip version 22.0.2 and Python version 3.8.11 in the runtime environment.\n\nNote that the `conda` and `pip` keys of `runtime_env` cannot both be specified at the same time. If you need to use both, you can use the `conda` key and add your pip dependencies in the \"pip\" field in your conda environment.yaml.",2636"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nTo create an actor in a namespace in Ray, you can specify the namespace when initializing Ray or when creating the actor.\n\nIn Python, you can specify the namespace when initializing Ray using the `namespace` parameter:\n```\nray.init(namespace=\"my_namespace\")\n```\nAlternatively, you can specify the namespace when creating the actor using the `options` method:\n```\n@ray.remote\nclass MyActor:\n pass\n\nMyActor.options(name=\"my_actor\", namespace=\"my_namespace\").remote()\n```\nIn Java, you can specify the namespace when initializing Ray using the `ray.job.namespace` system property:\n```\nSystem.setProperty(\"ray.job.namespace\", \"my_namespace\");\nRay.init();\n```\nAlternatively, you can specify the namespace when creating the actor using the `setName` method:\n```\nRay.actor(MyActor::new).setName(\"my_actor\").setNamespace(\"my_namespace\").remote();\n```\nIn C++, you can specify the namespace when initializing Ray using the `ray_namespace` parameter:\n```\nray::RayConfig config;\nconfig.ray_namespace = \"my_namespace\";\nray::Init(config);\n```\nAlternatively, you can specify the namespace when creating the actor using the `SetName` method:\n```\nray::Actor(RAY_FUNC(MyActor::FactoryCreate)).SetName(\"my_actor\").SetNamespace(\"my_namespace\").Remote();\n```\nNote that named actors are scoped by namespace, so an actor with the same name in a different namespace is considered a different actor.",2663"llm": "codellama/CodeLlama-34b-Instruct-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 in Tune. To do this, you can set the `working_dir` parameter 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```\nAlternatively, you can use the `RAY_CHDIR_TO_TRIAL_DIR` environment variable to disable changing the working directory to the trial directory, and then use relative paths to access files in the original working directory.",2690"llm": "codellama/CodeLlama-34b-Instruct-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, your policies will be built solely on the CPU, even if GPUs are available on the machine.",2717"llm": "codellama/CodeLlama-34b-Instruct-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, but it requires more manual configuration. You can deploy a static Ray cluster to Kubernetes without using KubeRay, but it will not have the built-in autoscaling feature that KubeRay provides. To enable autoscaling, you will need to configure the Ray autoscaler in the RayCluster custom resource (CR) config.",2744"llm": "codellama/CodeLlama-34b-Instruct-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) and the KubeRay operator.\n\nFirst, create a RayCluster CR YAML manifest and apply it to your Kubernetes cluster using the `kubectl apply` command. This will create a Ray cluster with the specified configuration.\n\nTo manage the Ray cluster, you can use the `kubectl` command-line tool to interact with the RayCluster CR and the KubeRay operator. For example, you can use `kubectl get raycluster` to view the status of the Ray cluster, or `kubectl edit raycluster` to edit the configuration of the Ray cluster.\n\nYou can also use the `kubectl` command-line tool to manage the autoscaling of the Ray cluster. For example, you can use `kubectl autoscale raycluster` to enable or disable autoscaling, or `kubectl scale raycluster` to manually scale the Ray cluster up or down.\n\nIt's important to note that the RayCluster CR and the KubeRay operator are still in beta, so the configuration and management options may change in future releases.",2771"llm": "codellama/CodeLlama-34b-Instruct-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 not be killed. However, the node will be marked as dead and will be treated as a new node to the Ray cluster.",2798"llm": "codellama/CodeLlama-34b-Instruct-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": " To debug out of memory issues in Ray, you can use the `ray memory` command to track down ObjectRef references that may be causing an ObjectStoreFullError. You can also use the per Task and Actor memory usage graph to locate the source of the memory leak and the `htop` command to view the memory usage of the head node. Additionally, you can use the `memray` tool to debug memory issues in Serve actors.",2825"llm": "codellama/CodeLlama-34b-Instruct-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": " To use a callback in Trainer, you can add it to the `callbacks` parameter of the `Trainer` constructor. For example:\n```\nfrom transformers import Trainer, TrainingArguments\nfrom ray.train.huggingface.transformers import RayTrainReportCallback\n\n# Create a Trainer with the RayTrainReportCallback\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=small_train_ds,\n eval_dataset=small_eval_ds,\n compute_metrics=compute_metrics,\n callbacks=[RayTrainReportCallback()]\n)\n```\nThis will add the `RayTrainReportCallback` to the `Trainer`, which will report metrics and checkpoints to Ray Train.\n\nAlternatively, you can implement your own customized report callback by inheriting from the `TrainerCallback` class and overriding the `on_save()` method. For example:\n```\nfrom transformers.trainer_callback import TrainerCallback\n\nclass MyTrainReportCallback(TrainerCallback):\n def __init__(self):\n super().__init__()\n self.metrics = {}\n\n def on_save(self, iteration, trials, trial, result, **info):\n # Fetch the latest metric dictionary from TrainerState.log_history\n # and report it with the latest checkpoint to Ray Train\n pass\n```\nYou can then use this custom callback in the `Trainer` constructor instead of the `RayTrainReportCallback`.\n\nNote that you should ensure that the logging, evaluation, and saving frequencies are set correctly in the `TrainingArguments` to ensure that the callback is called at the appropriate times.",2852"llm": "codellama/CodeLlama-34b-Instruct-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 `--working-dir` option when running `ray.init()`. For example:\n```\nray.init(working_dir=\"/path/to/working/directory\")\n```\nAlternatively, you can specify the working directory in the `runtime_env` parameter of `ray.init()`:\n```\nray.init(runtime_env={\"working_dir\": \"/path/to/working/directory\"})\n```\nNote that the working directory is only used for the Ray client, and not for the Ray workers on the cluster. If you need to access files or directories on the cluster, you should use the `ray.get_context().get_trial_dir()` method to get the path to the trial directory on the cluster.",2879"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nTo create an actor instance with parameters in Ray, you can use the `ActorClass.options()` method to configure and override the actor instantiation parameters. This method takes the same arguments as `ray.remote()`.\n\nHere is an example of how to create an actor instance with parameters in Ray:\n```\nimport ray\n\n@ray.remote\nclass Counter:\n def __init__(self, value):\n self.value = value\n\n def increment(self):\n self.value += 1\n return self.value\n\n# Create an actor from this class.\ncounter = Counter.options(value=0).remote()\n```\nIn this example, the `Counter` class is defined with an `__init__()` method that takes a `value` parameter. The `options()` method is used to set the `value` parameter to 0 when creating the actor instance.\n\nYou can also use the `get_if_exists()` method to get a handle to an existing actor, or create a new actor if it doesn't exist. This method is available after you set a name for the actor via `ActorClass.options()`.\n```\nimport ray\n\n@ray.remote\nclass Counter:\n def __init__(self, value):\n self.value = value\n\n def increment(self):\n self.value += 1\n return self.value\n\n# Create an actor from this class.\ncounter = Counter.options(value=0, name=\"counter\").remote()\n\n# Get a handle to the actor if it exists, or create a new one if it doesn't.\ncounter = ray.get_if_exists(Counter, name=\"counter\")\n```\nIn this example, the `Counter` class is defined with an `__init__()` method that takes a `value` parameter. The `options()` method is used to set the `value` parameter to 0 and the `name` parameter to \"counter\" when creating the actor instance. The `get_if_exists()` method is used to get a handle to the actor if it exists, or create a new one if it doesn't.",2906"llm": "codellama/CodeLlama-34b-Instruct-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 used by an Actor, you can use the `ray.util.ActorPool.push` method. This method takes an actor as an argument and pushes it into the current list of idle actors.\n\nHere is an example of how to use `ray.util.ActorPool.push`:\n```\nimport ray\nfrom ray.util.actor_pool import ActorPool\n\n@ray.remote\nclass Actor:\n def double(self, v):\n return 2 * v\n\na1, a2 = Actor.remote(), Actor.remote()\npool = ActorPool([a1])\npool.push(a2)\n```\nIn this example, we define an actor class `Actor` with a method `double` that takes a value `v` and returns twice that value. We then create two instances of the actor, `a1` and `a2`, and push `a2` into the list of idle actors using `pool.push(a2)`.\n\nNote that you can also specify custom resources in `ray_actor_options` when creating an actor, for example to ensure that a deployment is scheduled on a specific node. For example:\n```\n@serve.deployment(ray_actor_options={\"resources\": {\"custom_resource\": 2}})\ndef func(*args):\n return do_something_with_my_custom_resource()\n```\nIn this example, we specify that the deployment should be scheduled on a node with 2 units of the \"custom_resource\" resource.",2933"llm": "codellama/CodeLlama-34b-Instruct-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 following command:\n```\nray cluster_resources()\n```\nThis will print the current working directory of the Ray cluster.",2960"llm": "codellama/CodeLlama-34b-Instruct-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 `log.info` message is not visible in the Ray logs because it is not being captured by the Ray logging system. The Ray logging system only captures logs from Ray actors and tasks, and not from the driver program.\n\nTo see the `log.info` message, you can use the `ray.util.state.get_log` API to retrieve the log file from the Ray cluster. You can also use the `ray logs` command to retrieve the log file from the Ray cluster.\n\nAlternatively, you can modify the logging configuration for Ray libraries to capture the `log.info` message. You can do this by importing the `logging` module and modifying the logging configuration for the Ray logger.",2987"llm": "codellama/CodeLlama-34b-Instruct-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, when using Ray Datasets to read a file, you can make sure the order of the data is preserved by setting the `preserve_order` flag in the `DataContext` to `True`.",3014"llm": "codellama/CodeLlama-34b-Instruct-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": " \"Ray will not retry tasks upon exceptions thrown by application code\" means that if a task fails due to an exception thrown by the application code, Ray will not automatically retry the task. The application code must handle the exception and decide whether to retry the task or not.",3041"llm": "codellama/CodeLlama-34b-Instruct-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 start` or `ray.init()` command and specify the `--log-dir` or `--logging-config` option.\n\nFor example:\n```\nray start --log-dir /path/to/log/directory\n```\nor\n```\nray.init(logging_config={\"log_dir\": \"/path/to/log/directory\"})\n```\nAlternatively, you can set the `RAY_LOG_DIR` environment variable to specify the log directory.\n\nNote that the log directory must be a valid directory path on the local file system.",3068"llm": "codellama/CodeLlama-34b-Instruct-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 without setting the min worker as 10, you can use the `ray up` command with the `--max-workers` option. For example:\n```\nray up --max-workers=10\n```\nThis will launch a Ray cluster with 10 worker nodes, without setting the min worker as 10.\n\nAlternatively, you can use the `ray start` command with the `--head` and `--block` options to start the head node and block until the cluster is ready. For example:\n```\nray start --head --block\n```\nThis will start the head node and block until the cluster is ready, without setting the min worker as 10.\n\nNote that the `ray up` command is the recommended way to launch a Ray cluster, as it provides more options and flexibility than the `ray start` command.",3095"llm": "codellama/CodeLlama-34b-Instruct-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 the Ray API to scale up a cluster, you can use the `ray.autoscaler.sdk.request_resources()` function to request additional resources. This function takes in the number of CPUs and bundles as arguments, and will immediately attempt to scale the cluster to accommodate the requested resources.\n\nHere is an example of how to use this function to scale up a cluster:\n```\nimport ray\n\n# Connect to the Ray cluster\nray.init()\n\n# Request additional resources\nray.autoscaler.sdk.request_resources(num_cpus=10, bundles=[{\"CPU\": 2}])\n```\nThis will request 10 CPUs and 2 CPU bundles, which will be added to the existing cluster. The autoscaler will then attempt to scale the cluster up to accommodate the requested resources.\n\nYou can also use the `ray up` command to scale up a cluster. This command takes in a cluster configuration file as an argument, and will create or update a Ray cluster based on the configuration.\n\nHere is 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 will create or update a Ray cluster with a minimum of 10 workers and a maximum of 20 workers. The autoscaler will then attempt to scale the cluster up to accommodate the requested resources.\n\nNote that the `ray up` command can also be used to create a new cluster from scratch, or to update an existing cluster with new configuration options.",3122"llm": "codellama/CodeLlama-34b-Instruct-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 Ray cloud launcher on AWS, you can use the `provider` section of the configuration file to specify the subnets.\n\nHere is an example of how to specify subnets in the `provider` section of the configuration file:\n```\nprovider:\n type: aws\n region: us-west-2\n subnets:\n - subnet-12345678\n - subnet-87654321\n```\nIn this example, the `subnets` field is used to specify the subnets that the head node and worker nodes should be launched in. You can replace `subnet-12345678` and `subnet-87654321` with the actual subnet IDs that you want to use.\n\nNote that you can also use the `availability_zones` field in the `provider` section to specify the availability zones that the subnets are located in. For example:\n```\nprovider:\n type: aws\n region: us-west-2\n subnets:\n - subnet-12345678\n - subnet-87654321\n availability_zones:\n - us-west-2a\n - us-west-2b\n```\nThis will launch the head node and worker nodes in the specified subnets, which are located in the `us-west-2a` and `us-west-2b` availability zones.",3149"llm": "codellama/CodeLlama-34b-Instruct-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": " The HTTP server error code log for Ray Serve can be found in the logs directory on the head node of the Ray cluster. The logs directory is located at `/tmp/ray/session_latest/logs`.",3176"llm": "codellama/CodeLlama-34b-Instruct-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 is a known problem that the Ray Dashboard may not be publicly exposed when running a remote Ray cluster with the VM Cluster Launcher, KubeRay operator, or manual configuration. This can cause the dashboard to disappear or not load properly.\n\nTo resolve this issue, you can try the following:\n\n1. Expose the dashboard port: You can expose the dashboard port by adding a port forwarding rule to your cluster configuration. For example, if you are using the VM Cluster Launcher, you can add the following line to your cluster configuration file:\n```\ndashboard_port: 8265\n```\nThis will expose the dashboard port on the head node.\n2. Use port forwarding: You can use port forwarding to access the dashboard from outside the head node. For example, if you are using the Ray Cluster Launcher, you can use the following command to forward the dashboard port to your local machine:\n```\nray dashboard cluster.yaml\n```\nThis will forward the dashboard port to your local machine, allowing you to access the dashboard in your browser.\n3. Use a reverse proxy: You can use a reverse proxy to access the dashboard from outside the head node. For example, you can use NGINX to set up a reverse proxy that forwards requests to the dashboard port on the head node.\n\nIt is important to note that exposing the dashboard port or using port forwarding may introduce security risks, as the dashboard may be accessible to unauthorized users. Therefore, it is recommended to use authentication and authorization mechanisms to protect the dashboard.",3203"llm": "codellama/CodeLlama-34b-Instruct-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": " In the Ray cluster launcher YAML, `max_workers` includes only worker nodes, not the head node.",3230"llm": "codellama/CodeLlama-34b-Instruct-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 rsync-up` command to upload the updated files to the cluster head node. You can then use the `ray rsync-down` command to download the updated files to your local machine.\n\nAlternatively, you can use the `ray.train.get_context().get_trial_dir()` method to get the path to the trial directory on the cluster, and then use the `os` module to write the updated files to the trial directory.\n\nIt's important to note that when using the `ray.train.get_context().get_trial_dir()` method, all workers on the same node will share the same working directory, so be sure to use the `ray.train.get_context().get_trial_dir()` method to get the path to the trial directory on the cluster, and then use the `os` module to write the updated files to the trial directory.",3257"llm": "codellama/CodeLlama-34b-Instruct-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 is already initiated, you can use the `ray.init()` function with the `_temp_dir` argument set to the new temporary directory path. This will update the root temporary directory for the Ray session.\n\nAlternatively, you can use the `ray.start()` function with the `--temp-dir` argument set to the new temporary directory path. This will also update the root temporary directory for the Ray session.\n\nNote that changing the root temporary directory will affect all Ray tasks and actors that are running in the session, so be sure to update the working directory file accordingly.",3284"llm": "codellama/CodeLlama-34b-Instruct-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": " \nTo force Ray head node to use a custom PEM file to SSH worker nodes, you can specify the `ssh_private_key` field in the `auth` section of the Ray cluster configuration file.\n\nFor 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 private key when connecting to the worker nodes via SSH.\n\nNote that you will also need to make sure that the public key corresponding to the private key is added to the project-wide metadata, as described in the Ray documentation.",3311"llm": "codellama/CodeLlama-34b-Instruct-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, running on the head node. It manages node membership and actor directory, and is also known as the Global Control Store. The GCS server takes up a significant amount of memory on the head node because it stores all the cluster-level metadata in-memory.\n\nTo 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, the following functions are not available: actor creation, deletion, and reconstruction; placement group creation, deletion, and reconstruction; resource management.\n\nWhen deploying a large cluster, the head node has extra stress due to additional system processes running on it, such as GCS. 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.\n\nIf the head node fails, the entire Ray cluster fails. To 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.",3338"llm": "codellama/CodeLlama-34b-Instruct-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": " To debug the issue of nodes being \"pending\" for a long time when starting a Ray cluster with `ray up`, you can follow these steps:\n\n1. Check the Ray cluster status using the `ray status` command. This will show you the current state of the cluster, including the number of nodes and their status.\n2. Check the Ray cluster logs. You can do this by running `cat /tmp/ray/session_latest/logs/monitor.*` on the head node. This will show you the logs of the Ray autoscaler, which is responsible for managing the cluster.\n3. Check the Ray cluster events. You can do this by running `ray events` on the head node. This will show you any events that have occurred in the cluster, including any errors or warnings.\n4. Check the Ray cluster configuration. Make sure that the configuration is correct and that the cluster is not running out of resources. You can do this by running `ray config` on the head node.\n5. Check the Ray cluster nodes. Make sure that the nodes are running and that they have enough resources to run the Ray jobs. You can do this by running `ray nodes` on the head node.\n6. Check the Ray cluster object store. Make sure that the object store is not full and that there is enough memory available for the Ray jobs. You can do this by running `ray object_store` on the head node.\n7. Check the Ray cluster actors. Make sure that the actors are not using all the resources in the cluster. You can do this by running `ray actors` on the head node.\n8. Check the Ray cluster Tune trials. Make sure that the Tune trials are not using all the resources in the cluster. You can do this by running `ray tune` on the head node.\n\nBy following these steps, you should be able to identify the cause of the issue and fix it.",3365"llm": "codellama/CodeLlama-34b-Instruct-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": " To install Ray 2.5.1 from GitHub or wheel, you can use the following commands:\n\n1. Install the latest official version of Ray from PyPI on Linux, Windows, and macOS:\n```\npip install -U \"ray[default]\"\n```\n2. Install Ray with support for the dashboard and cluster launcher:\n```\npip install -U \"ray[default]\"\n```\n3. Install Ray with minimal dependencies:\n```\npip install -U \"ray\"\n```\n4. Install Ray with data support:\n```\npip install -U \"ray[data]\"\n```\n5. Install Ray from a specific commit on master:\n```\npip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/{COMMIT_HASH}/ray-{RAY_VERSION}-{PYTHON_VERSION}-{PYTHON_VERSION}-{OS_VERSION}.whl\n```\nNote: Replace `{COMMIT_HASH}`, `{RAY_VERSION}`, `{PYTHON_VERSION}`, and `{OS_VERSION}` with the appropriate values for your system.",3392"llm": "codellama/CodeLlama-34b-Instruct-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": " To use the `worker_setup_hook` in a runtime environment to set up a worker node, you can specify the hook as a function in the `runtime_env` dictionary when initializing Ray. The function will be called on each worker node when it is created.\n\nHere is an example of how to use the `worker_setup_hook` in a runtime environment:\n```\nray.init(runtime_env={\n \"worker_setup_hook\": logging_setup_func\n})\n\ndef logging_setup_func():\n # Set up logging on the worker node\n pass\n```\nIn this example, the `logging_setup_func` function will be called on each worker node when it is created. You can use this function to set up logging or perform any other setup tasks that you want to run on each worker node.\n\nNote that the `worker_setup_hook` is only called on worker nodes that are created after the `ray.init` call. If you want to set up the worker nodes before the `ray.init` call, you can use the `worker_setup_commands` option in the `runtime_env` dictionary. This option allows you to specify a list of commands to run on each worker node before it is started.\n\nFor more information on using the `worker_setup_hook` and `worker_setup_commands` options in a runtime environment, see the Ray documentation on runtime environments.",3419"llm": "codellama/CodeLlama-34b-Instruct-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 using the `ray.data.read_csv()` or `ray.data.read_parquet()` methods.\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\nRay Data supports many different data sources and formats, including CSV, Parquet, and raw images. It also supports a wide variety of cloud providers, including AWS, GCP, and Azure.\n\nTo start a Ray cluster on AWS, you can use the Ray cluster launcher with the AWS Python SDK. This will allow you to start a Ray cluster on AWS and use Ray Data to load and process data.\n\nFor more information, you can refer to the Ray Data User Guide and the Ray Data Examples.",3446"llm": "codellama/CodeLlama-34b-Instruct-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 shuffling during `ray.data.map_batches`, you can use the `local_shuffle_buffer_size` parameter to shuffle a subset of rows up to a provided buffer size during iteration. This is more performant than a full global shuffle and can be configured by choosing the smallest value that achieves sufficient randomness.",3473"llm": "codellama/CodeLlama-34b-Instruct-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": " The order of the input data is not preserved after a map_batches operation.",3500"llm": "codellama/CodeLlama-34b-Instruct-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 mechanisms:\n\n1. Catch application-level failures: 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\u2019s return value. This wrapped exception will be thrown to any worker that tries to get the result, either by calling ray.get or if the worker is executing another task that depends on the object.\n2. Ray logging: Ray Serve uses Python\u2019s standard logging module with a logger named \"ray.serve\". By default, logs are emitted from actors both to stderr and on disk on each node at /tmp/ray/session_latest/logs/serve/. This includes both system-level logs from the Serve controller and proxy as well as access logs and custom user logs produced from within deployment replicas. In development, logs are streamed to the driver Ray program (the Python script that calls serve.run() or the serve run CLI command), so it\u2019s convenient to keep the driver running while debugging.\n3. Fault tolerance: Ray provides mechanisms to catch errors, retry failed code, and handle misbehaving code. See the pages for task and actor fault tolerance for more information on these mechanisms.\n4. Testing: We strongly recommend that you test your Ray Serve script locally or in a RayCluster before deploying it to a RayService. Please refer to rayserve-dev-doc.md for more details.\n5. Error handling: Ray APIs are future APIs (indeed, it is possible to convert Ray object references to standard Python future APIs), and the error handling model is the same. When any remote Tasks or Actors fail, the returned object ref contains an exception. When you call get API to the object ref, it raises an exception.",3527"llm": "codellama/CodeLlama-34b-Instruct-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. Specifically, you can use the `RaySystemError` exception to catch and handle the error.\n\nHere's an example:\n```python\nimport ray\n\n@ray.remote\ndef f():\n raise Exception(\"the real error\")\n\n@ray.remote\ndef g(x):\n return\n\ntry:\n result = f.remote()\n ray.get(result)\nexcept ray.exceptions.RaySystemError as e:\n print(\"Ray system error:\", e)\n```\nIn this example, the `f` function raises an exception, which is caught by the `g` function. The `g` function then returns the exception as a `RaySystemError` object. The `ray.get` function is used to retrieve the result of the `f` function, which is the `RaySystemError` object. The `except` block is used to catch the `RaySystemError` object and print an error message.\n\nYou can also use the `ray.exceptions.RayTaskError` exception to catch and handle task-level errors in Ray Serve. This exception is raised when a task fails due to a Python-level exception.\n\nIt's important to note that Ray Serve surfaces application-level failures as Python-level exceptions, so you should use the `ray.exceptions` module to catch and handle these exceptions.",3554"llm": "codellama/CodeLlama-34b-Instruct-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": " To view logs emitted by a Ray Serve endpoint in a Ray cluster, you can use the `ray logs` command. The logs are stored on each node in the cluster, and you can use the `--node-id` flag to specify the node from which you want to view the logs.\n\nFor example, to view the logs from a node with the ID `<NODE_ID>`, you can run the following command:\n```\nray logs --node-id <NODE_ID>\n```\nThis will print the logs from the specified node to the console. You can also use the `--glob` flag to filter the logs by a glob pattern. For example, to view only the logs from the `gcs_server.out` file, you can run the following command:\n```\nray logs --node-id <NODE_ID> --glob gcs_server.out\n```\nAlternatively, you can use the `ray.util.state.list_logs()` function to list the available log files on a node, and then use the `ray.util.state.get_log()` function to retrieve the contents of a specific log file. For example:\n```\nfrom ray.util.state import list_logs, get_log\n\n# List the available log files on a node\nlogs = list_logs(node_id=<NODE_ID>)\n\n# Retrieve the contents of a specific log file\nlog_contents = get_log(node_id=<NODE_ID>, log_file=<LOG_FILE>)\n```\nNote that the `ray.util.state` module is an internal developer API, and the functions it provides may change or be removed in future versions of Ray.",3581"llm": "codellama/CodeLlama-34b-Instruct-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": " To see logs for a failed Ray Serve deployment, you can check the following locations:\n\n1. /tmp/ray/session_latest/logs/serve/ on the head node and worker nodes.\n2. The Kuberay operator logs by running `kubectl logs -f <kuberay-operator-pod-name>`.\n3. The controller log and deployment log, which are located under /tmp/ray/session_latest/logs/serve/ in both the head node pod and worker node pod.\n\nYou can also use a logging stack such as ELK or Loki to search by deployment or replica.",3608"llm": "codellama/CodeLlama-34b-Instruct-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 `Dataset.take()` or `Dataset.take_all()` methods. These methods return a list of rows from the dataset.\n\nFor example, to take the first 10 rows of a dataset, you can use the following code:\n```\nimport ray\n\nds = ray.data.read_csv(\"s3://anonymous@air-example-data/iris.csv\")\n\nrows = ds.take(10)\nprint(rows)\n```\nThis will print a list of 10 rows from the dataset.\n\nAlternatively, you can use the `Dataset.sample()` method to take a random sample of rows from the dataset. For example:\n```\nimport ray\n\nds = ray.data.read_csv(\"s3://anonymous@air-example-data/iris.csv\")\n\nrows = ds.sample(10)\nprint(rows)\n```\nThis will print a list of 10 random rows from the dataset.",3635"llm": "codellama/CodeLlama-34b-Instruct-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 `Tuner.restore` method and set `resume_errored=True`. This will restore the experiment from the last available checkpoint when it is run.\n\nHere's an example:\n```python\ntuner = tune.Tuner.restore(\n os.path.expanduser(\"~/ray_results/tune_fault_tolerance_guide\"),\n trainable=trainable,\n resume_errored=True,\n)\ntuner.fit()\n```\nIn this example, the experiment is restored from the last available checkpoint when it is run. The `resume_errored=True` parameter tells Tune to resume the experiment from the last checkpoint in case of an error.\n\nNote that you can also restore the experiment from a cloud bucket path.",3662"llm": "codellama/CodeLlama-34b-Instruct-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` function. This will terminate the actor immediately, and any pending tasks will fail with a `RayActorError`. If you want to kill the actor but let pending tasks finish, you can call `actor.__ray_terminate__.remote()` instead.\n\nHere is an example of how to use `ray.kill` to kill a detached actor:\n```\nimport ray\n\n# Create a detached actor\nactor = ray.remote(lambda: None).options(lifetime=\"detached\").remote()\n\n# Kill the actor\nray.kill(actor)\n```\nNote that killing an actor will not automatically restart it, so you will need to create a new actor if you want to continue using it.",3689"llm": "codellama/CodeLlama-34b-Instruct-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": " To set environment variables in Ray init, you can use the `env_vars` field in the `runtime_env` dictionary. For example:\n```\nray.init(\n runtime_env={\n \"env_vars\": {\n \"MY_VAR\": \"my_value\"\n }\n }\n)\n```\nThis will set the environment variable `MY_VAR` to `my_value` for all Ray workers.\n\nAlternatively, you can use the `env` field in the `runtime_env` dictionary to set environment variables. For example:\n```\nray.init(\n runtime_env={\n \"env\": {\n \"MY_VAR\": \"my_value\"\n }\n }\n)\n```\nThis will also set the environment variable `MY_VAR` to `my_value` for all Ray workers.\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. You can also reference existing environment variables using `${ENV_VAR}` to achieve the appending behavior. If the environment variable doesn't exist, it becomes an empty string \"\".",3716"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nThe REST API for getting the head node ID is not explicitly mentioned in the provided context. However, based on the information provided, it can be inferred that the head node ID can be obtained using the `ray.runtime_context.RuntimeContext.get_node_id()` method.\n\nThis method returns the current node ID for the worker or driver, which is the ID of the node that the driver, task, or actor runs. The ID will be in hex format.\n\nAlternatively, you can use the `ray.util.state.get_node()` method to get a node by ID. This method takes the node ID as an argument and returns a `NodeState` object that contains information about the node, including its IP address and resources.\n\nIt's worth noting that the `ray.runtime_context.RuntimeContext.node_id` property is deprecated and may be removed in future Ray releases. The recommended way to get the node ID is to use the `ray.runtime_context.RuntimeContext.get_node_id()` method instead.",3743"llm": "codellama/CodeLlama-34b-Instruct-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": " To rerun a canceled Ray task, you can use the `ray.cancel` function to cancel the task and then resubmit the task. However, note that canceled tasks are not automatically retried, so you will need to resubmit the task manually. Additionally, recursive cancellation is available for async actor tasks, which cancels all child and Actor tasks.",3770"llm": "codellama/CodeLlama-34b-Instruct-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 max parallel concurrent scheduled tasks in map_batches, you can use the `set_max_concurrency` method of the `ConcurrencyLimiter` class. This method will set the maximum number of concurrent trials that the searcher can run.\n\nHere is an example of how to use the `set_max_concurrency` method:\n```\nfrom ray.tune.search import ConcurrencyLimiter\n\n# Create a searcher object\nsearcher = HyperOptSearch(metric=\"accuracy\")\n\n# Set the maximum number of concurrent trials\nsearcher.set_max_concurrency(max_concurrent=2)\n\n# Create a tuner object\ntuner = tune.Tuner(\n trainable,\n tune_config=tune.TuneConfig(\n search_alg=searcher\n ),\n)\n\n# Fit the tuner\ntuner.fit()\n```\nIn this example, the `set_max_concurrency` method is called on the `HyperOptSearch` object with the `max_concurrent` parameter set to 2. This means that the searcher will only run 2 concurrent trials at a time.\n\nYou can also use the `set_max_concurrency` method with other searchers, such as `ZOOptSearch`, `SkOptSearch`, `BayesOptSearch`, and `OptunaSearch`. The method is used in the same way as with `HyperOptSearch`.",3797"llm": "codellama/CodeLlama-34b-Instruct-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": " To get the number of CPUs from a Ray cluster, you can use the `ray.cluster_resources()` function. This function returns a dictionary of the resources available to the Ray cluster, including the number of CPUs.\n\nFor example:\n```\nimport ray\n\nray.init()\n\nprint(ray.cluster_resources())\n```\nThis will output a dictionary with the available resources, including the number of CPUs.\n\nAlternatively, you can use the `ray.num_cpus()` function to get the number of CPUs directly.\n```\nimport ray\n\nray.init()\n\nprint(ray.num_cpus())\n```\nThis will output the number of CPUs available to the Ray cluster.\n\nNote that the `ray.cluster_resources()` function returns a dictionary with all the available resources, while the `ray.num_cpus()` function only returns the number of CPUs.",3824"llm": "codellama/CodeLlama-34b-Instruct-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` context, you can specify a list of fields to exclude from the runtime environment. For example:\n```\nruntime_env = {\n \"exclude\": [\"py_modules\", \"working_dir\"]\n}\n```\nThis will exclude the `py_modules` and `working_dir` fields from the runtime environment.\n\nAlternatively, you can use the `exclude` option in the `ray.job_config.JobConfig.set_runtime_env` method to exclude specific fields from the runtime environment. For example:\n```\njob_config = ray.job_config.JobConfig()\njob_config.set_runtime_env(\n runtime_env={\n \"exclude\": [\"py_modules\", \"working_dir\"]\n }\n)\n```\nThis will exclude the `py_modules` and `working_dir` fields from the runtime environment for the job.\n\nNote that the `exclude` option only applies to the fields that are specified in the `runtime_env` dictionary. If you want to exclude all fields, you can use the `*` wildcard. For example:\n```\nruntime_env = {\n \"exclude\": \"*\"\n}\n```\nThis will exclude all fields from the runtime environment.",3851"llm": "codellama/CodeLlama-34b-Instruct-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": " \n\nThe query is asking for an example of how to use the `map_batches` method in Ray Data to convert external experiences to batch format.\n\nHere is an example of how to do this:\n```\nimport gymnasium as gym\nimport numpy as np\nimport os\n\nimport ray._private.utils\n\nfrom ray.rllib.models.preprocessors import get_preprocessor\nfrom ray.rllib.evaluation.sample_batch_builder import SampleBatchBuilder\nfrom ray.rllib.offline.json_writer import JsonWriter\n\n# Generate a batch of experiences\nexperiences = []\nfor i in range(10):\n observation = np.random.rand(4)\n action = np.random.rand(2)\n reward = np.random.rand()\n done = np.random.rand()\n experiences.append({\n \"observation\": observation,\n \"action\": action,\n \"reward\": reward,\n \"done\": done\n })\n\n# Convert the experiences to batch format\nbatch = SampleBatchBuilder()\nfor experience in experiences:\n batch.add_values(experience)\nbatch = batch.build_and_reset()\n\n# Write the batch to a JSON file\nwith JsonWriter(\"batch.json\") as writer:\n writer.write(batch)\n```\nThis code generates a batch of experiences and then converts them to batch format using the `SampleBatchBuilder` class from Ray RLlib. The resulting batch is then written to a JSON file using the `JsonWriter` class from Ray RLlib.\n\nNote that the `map_batches` method is not used in this example, as it is not necessary to convert the experiences to batch format in this case. However, if you have a large number of experiences that you need to convert to batch format, you can use the `map_batches` method to do so in parallel.",3878"llm": "codellama/CodeLlama-34b-Instruct-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. Run the command `ray.init()` in your Python script or terminal.\n2. Look for the line in the output that says \"View the Ray dashboard at http://127.0.0.1:8265\".\n3. The address of the Ray dashboard is the address you need to use to connect to the Ray cluster. In this example, the address is \"http://127.0.0.1:8265\".\n\nAlternatively, you can also set the `RAY_ADDRESS` environment variable to the address of the Ray cluster. This can be done by running the command `export RAY_ADDRESS=\"http://127.0.0.1:8265\"` in your terminal before running your Python script.",3905"llm": "codellama/CodeLlama-34b-Instruct-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 followed by the configuration file. For example:\n```\n$ ray up -y config.yaml\n```\nThis will start the Ray cluster on the nodes specified in the configuration file.\n\nAlternatively, you can use the `ray start` command to start the Ray cluster on a specific node. For example:\n```\n$ ray start --head --port=6379\n```\nThis will start the Ray cluster on the current node with the head node running on port 6379.\n\nOnce the Ray cluster is started, you can connect to it using the `ray.init` command. For example:\n```\n$ ray.init()\n```\nThis will connect to the latest Ray cluster.\n\nNote that the `ray up` and `ray start` commands are only available in Ray version 1.5 and later. If you are using an earlier version of Ray, you will need to use the `ray start` command to start the Ray cluster.",3932"llm": "codellama/CodeLlama-34b-Instruct-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 issue is likely due to the fact that the Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: <https://docs.ray.io/en/master/data/dataset-internals.html#data-and-tune>.\n\nTo ensure CPU resources are always available for Ray Data execution, limit the number of concurrent Tune trials with the max_concurrent_trials Tune option.\n\nYou can also try running `ray.data.DataContext.get_current().execution_options.verbose_progress = True` for detailed progress reporting.",3959"llm": "codellama/CodeLlama-34b-Instruct-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 such as GCS or the dashboard, and it is not recommended to run any tasks or actors on the head node by default. Instead, the head node should be reserved for cluster management and the worker nodes should be used for running tasks and actors. The object store memory size should be configured separately for the head node and the worker nodes, with the head node having a larger object store memory size to accommodate the memory demands of the cluster management system components.",3986"llm": "codellama/CodeLlama-34b-Instruct-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.start` command.\n\nThe `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 start Ray Serve with a proxy on every node, you can use the following command:\n```\nserve start --proxy-location EveryNode\n```\nTo start Ray Serve with a proxy on the head node only, you can use the following command:\n```\nserve start --proxy-location HeadOnly\n```\nTo disable the HTTP proxy, you can use the following command:\n```\nserve start --proxy-location Disabled\n```",4013"llm": "codellama/CodeLlama-34b-Instruct-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": " To set the `RAY_GRAFANA_HOST` and `RAY_PROMETHEUS_HOST` variables in the context of KubeRay, you can follow these steps:\n\n1. Identify the IP address of the head node in your KubeRay cluster. You can do this by running the command `kubectl get nodes` and looking for the node with the `ray-head` label.\n2. Set the `RAY_GRAFANA_HOST` variable to the IP address of the head node, followed by the port number that Grafana is running on (e.g. `http://<head_node_ip>:3000`).\n3. Set the `RAY_PROMETHEUS_HOST` variable to the IP address of the head node, followed by the port number that Prometheus is running on (e.g. `http://<head_node_ip>:9090`).\n\nFor example, if the IP address of the head node is `192.168.1.100`, you can set the variables as follows:\n```\nRAY_GRAFANA_HOST=http://192.168.1.100:3000\nRAY_PROMETHEUS_HOST=http://192.168.1.100:9090\n```\nNote that the `RAY_GRAFANA_IFRAME_HOST` variable is not required if you are using KubeRay, as the Grafana dashboard will be embedded in the Ray Dashboard using an iframe.",4040"llm": "codellama/CodeLlama-34b-Instruct-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 (Global Control Store) determines which Kubernetes pod to kill when using KubeRay autoscaling by using the Ray Autoscaler, which is a Ray cluster process that automatically scales a cluster up and down based on resource demand. The Ray Autoscaler decides to create a Ray pod, the Kubernetes Cluster Autoscaler can provision a Kubernetes node so that the pod can be placed. Similarly, after the Ray autoscaler decides to delete an idle pod, the Kubernetes Cluster Autoscaler can clean up the idle Kubernetes node that remains.",4067"llm": "codellama/CodeLlama-34b-Instruct-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` section:\n```\nrequest_timeout_s: <timeout_value>\n```\nReplace `<timeout_value>` with the desired timeout value in seconds. For example:\n```\nhttp_options:\n request_timeout_s: 30\n```\nThis sets the end-to-end timeout for HTTP requests to 30 seconds.\n\nNote that this config is global to your Ray cluster, and it cannot be updated during runtime.",4094"llm": "codellama/CodeLlama-34b-Instruct-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 use the `num_gpus` parameter in the `@ray.remote` decorator to specify the number of GPUs to use. You can also use the `ray.autoscaler.sdk.request_resources` function to request GPU resources.\n\nHere is an example code snippet that uses the GPU on an M1 laptop with Ray:\n```\nimport ray\nimport tensorflow as tf\n\n# Initialize Ray\nray.init()\n\n# Define a remote function that uses the GPU\n@ray.remote(num_gpus=1)\ndef use_gpu():\n # Import TensorFlow and use the GPU\n import tensorflow as tf\n tf.config.list_physical_devices('GPU')\n print(\"GPUs:\", tf.config.list_physical_devices('GPU'))\n\n# Call the remote function\nuse_gpu.remote()\n```\nThis code will use the GPU on your M1 laptop to perform the computation in the `use_gpu` function.\n\nNote that you need to have the GPU version of TensorFlow installed on your M1 laptop for this to work. You can install it using the following command:\n```\npip install tensorflow-gpu\n```\nAlso, make sure that you have the necessary drivers and software installed to use the GPU on your M1 laptop.",4121"llm": "codellama/CodeLlama-34b-Instruct-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": " To add a timeout for a Ray job, you can use the `--timeout` option when submitting the job. For example:\n```\nray job submit --timeout 300 --working-dir your_working_directory -- python script.py\n```\nThis will set a timeout of 300 seconds (5 minutes) for the job. If the job does not complete within this time, it will be terminated.\n\nAlternatively, you can use the `ray.get` function with a timeout parameter to set a timeout for a specific `get` operation. For example:\n```\nray.get(obj_ref, timeout=4)\n```\nThis will set a timeout of 4 seconds for the `get` operation. If the operation does not complete within this time, a `GetTimeoutError` will be raised.",4148"llm": "codellama/CodeLlama-34b-Instruct-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 in Ray, you can use the `ray.init()` function and specify the `temp_dir` parameter. For example:\n```\nray.init(temp_dir=\"/path/to/custom/tmp/dir\")\n```\nThis will set the temporary directory for the remote cluster to the specified path.\n\nAlternatively, you can set the `RAY_TEMPDIR` environment variable to the desired temporary directory path before running the `ray.init()` function. For example:\n```\nimport os\nos.environ[\"RAY_TEMPDIR\"] = \"/path/to/custom/tmp/dir\"\nray.init()\n```\nThis will set the temporary directory for the remote cluster to the specified path.\n\nNote that the temporary directory should be accessible by the host, the outer container, and the inner container. In the case where the shared directory has a different name on the host, you can set the `RAY_HOSTDIR` environment variable to the name of the shared directory on the host. For example:\n```\nimport os\nos.environ[\"RAY_HOSTDIR\"] = \"/path/to/shared/dir\"\nray.init()\n```\nThis will set the shared directory name on the host to the specified path.",4175"llm": "codellama/CodeLlama-34b-Instruct-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 the `--temp-dir` to a different directory than `/tmp`, Ray will spill objects to the custom directory.",4202"llm": "codellama/CodeLlama-34b-Instruct-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": " The `--runtime-env-json` option is used to specify a runtime environment for a Ray application. The runtime environment is a Python dictionary that can be used to specify configuration options for a job, task, or actor.\n\nHere is an example of how to use the `--runtime-env-json` option:\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```\nIn this example, the `--runtime-env-json` option is used to specify a runtime environment for the `local_dev:graph` deployment. The runtime environment is defined as a Python dictionary that includes environment variables, a working directory, and a list of Python packages to install using `pip`.\n\nThe `--runtime-env-json` option can also be used with the `ray.init()` function to specify a runtime environment for a Ray application. For example:\n```\nray.init(runtime_env=RuntimeEnv(env_vars={\"MY_ENV_VAR\": \"my-value\"}, working_dir=\"./project/src\", pip=[\"requests\", \"chess\"]))\n```\nIn this example, the `RuntimeEnv` class is used to define a runtime environment for the Ray application. The `env_vars` parameter is used to specify environment variables, the `working_dir` parameter is used to specify the working directory, and the `pip` parameter is used to specify a list of Python packages to install using `pip`.",4229"llm": "codellama/CodeLlama-34b-Instruct-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 a value greater than 1 if autoscaling configuration is specified. This is because `maxConcurrentReplicas` specifies the maximum number of replicas that can be running at any given time, and if it is set to 1, the autoscaler will not be able to scale up the number of replicas beyond 1.",4256"llm": "codellama/CodeLlama-34b-Instruct-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 greater than or equal to `target_num_ongoing_requests_per_replica`. This will ensure that the autoscaler can scale up the number of replicas as needed to handle the expected number of concurrent requests.",4283"llm": "codellama/CodeLlama-34b-Instruct-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": " The `smoothing_factor` is a multiplicative factor that controls the speed of upscaling or downscaling decisions in the autoscaling algorithm. It is used to amplify the response of the algorithm and can be adjusted based on the traffic volume and other factors.",4310"llm": "codellama/CodeLlama-34b-Instruct-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 due to various reasons such as:\n\n1. Deployment failure: The deployment may fail due to various reasons such as a crashed replica, a general system or machine error, or an error in the deployment's constructor.\n2. Unhealthy state: The deployment may become unhealthy after updating or has updated and become unhealthy afterwards.\n3. Drain state: The deployment may be in a drain state, which means that it is not accepting new requests but is still processing existing requests.\n4. Drained state: The deployment may be in a drained state, which means that it is not accepting new requests and is not processing existing requests.\n\nIn all these cases, the actor's state is changed to unhealthy, and the KubeRay operator initiates the creation of a new RayCluster to redirect network traffic to it and subsequently deletes the old RayCluster.",4337"llm": "codellama/CodeLlama-34b-Instruct-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": " To access logs for a dead node in Ray, you can use the `ray.util.state.list_logs` and `ray.util.state.get_log` APIs. These APIs allow you to retrieve logs from a specific node in the cluster, even if the node is no longer running.\n\nHere's an example of how to use these APIs:\n```\nimport ray\n\n# Get the node ID of the dead node\nnode_id = ray.list_nodes()[0].id\n\n# List all log files available on the dead node\nlog_files = ray.util.state.list_logs(node_id=node_id)\n\n# Get a specific log file from the dead node\nlog_file = ray.util.state.get_log(node_id=node_id, filename=\"gcs_server.out\")\n```\nNote that you need to have the `ray.util.state` module imported in your code in order to use these APIs.",4364"llm": "codellama/CodeLlama-34b-Instruct-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": " A node can change its status to dead for several reasons, including:\n\n1. Raylet failure: If a raylet process fails, the corresponding node will be marked as dead and treated the same as a node failure.\n2. Task was killed due to the node running low on memory.\n3. When a worker node fails, the actors running on it also fail, and Serve detects that the actors have failed, attempting to respawn the actors on the remaining, healthy nodes.\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. Placement group creation is pending scheduling: It could be because there\u2019s not enough resources, some of creation stage has failed (e.g., failed to commit placement groups because the node is dead).\n7. NodeAffinitySchedulingStrategy: A low-level strategy that allows a task or actor to be scheduled onto a particular node specified by its node id.\n8. Handling Failures and Node Preemption: If enabled, nodes will be stopped when the cluster scales down. If disabled, nodes will be terminated instead. Stopped nodes launch faster than terminated nodes.",4391"llm": "codellama/CodeLlama-34b-Instruct-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": " The reasons for spikes in node CPU utilization in a Ray cluster include:\n\n1. Insufficient resources: If the cluster is running low on resources, such as CPU or memory, it can lead to spikes in CPU utilization as nodes compete for available resources.\n2. Preprocessing compute time: If the preprocessing step of a task is computationally intensive, adding more CPU-only nodes can help parallelize the preprocessing and reduce CPU utilization.\n3. Memory usage: If a node is running low on memory, it can lead to spikes in CPU utilization as the node tries to free up memory by swapping or paging out data.\n4. Autoscaling: If the autoscaler is not configured correctly, it can lead to spikes in CPU utilization as nodes are added or removed from the cluster.\n5. Task scheduling: If tasks are not scheduled efficiently, it can lead to spikes in CPU utilization as nodes compete for available resources.",4418"llm": "codellama/CodeLlama-34b-Instruct-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 to deploy a RayService on EKS is at least r5dn.16xlarge.",4445"llm": "codellama/CodeLlama-34b-Instruct-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": " To configure the session name generated by Ray, you can use the `namespace` argument in the `ray.init()` function. For example:\n```\nray.init(namespace=\"my_namespace\")\n```\nThis will set the namespace for the session to \"my_namespace\". The session name will be generated based on the namespace and the timestamp.\n\nAlternatively, you can also use the `session_name` argument in the `ray.init()` function to specify a custom session name. For example:\n```\nray.init(session_name=\"my_session\")\n```\nThis will set the session name to \"my_session\".",4472"llm": "codellama/CodeLlama-34b-Instruct-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. The Python SDK provides a `JobSubmissionClient` class that allows you to submit jobs programmatically and get information about the job, including the dashboard link.\n\nHere's an example of how to use the `JobSubmissionClient` class to get the dashboard link for a job:\n```\nfrom ray.job_submission import JobSubmissionClient\n\n# Initialize the client\nclient = JobSubmissionClient(\"http://127.0.0.1:8265\")\n\n# Submit the job\nsubmission_id = client.submit_job(entrypoint=\"sleep 1\")\n\n# Get the job information, including the dashboard link\njob_info = client.get_job_info(submission_id)\nprint(job_info.dashboard_link)\n```\nIn this example, we first initialize the `JobSubmissionClient` class with the address of the Ray cluster head node and the port used by the Ray Dashboard (8265 by default). We then submit a job using the `submit_job` method and get the job information using the `get_job_info` method. The `job_info` object contains the dashboard link, which we can print to the console.",4499"llm": "codellama/CodeLlama-34b-Instruct-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 the instance where the node was running failing, or the raylet crashing unexpectedly (OOM, preempted node, etc.).",4526"llm": "codellama/CodeLlama-34b-Instruct-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": " The error message \"Health check failed for node\" indicates that the Ray GCS (Global Control Store) server process has failed in the head Pod. This can be caused by a variety of issues, such as a misconfigured Redis server or a networking issue. To troubleshoot the issue, you should inspect the log directory /tmp/ray/session_latest/logs/ in the head Pod for errors related to the GCS server.",4553"llm": "codellama/CodeLlama-34b-Instruct-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": " No, the DAGDriver is not required to deploy a serve application using RayServe. However, the DAGDriver can be used to route HTTP requests through a call graph, which can be useful for handling HTTP requests in a Ray Serve application.",4580"llm": "codellama/CodeLlama-34b-Instruct-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 is the path to the Python file that contains the Ray Serve application. This path should be importable by Serve at runtime.",4607"llm": "codellama/CodeLlama-34b-Instruct-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, the KubeRay documentation provides examples of using RayJob in KubeRay. The documentation includes a RayJob Quickstart guide and a RayJob example YAML file that can be used to create a RayJob custom resource in Kubernetes. The RayJob example YAML file includes a RayCluster spec that defines the RayCluster to be created for the job, as well as the entrypoint for the Ray job.",4634"llm": "codellama/CodeLlama-34b-Instruct-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 in `ray.util.metrics` for defining and exporting custom metrics for visibility into your applications. The API supports three types of metrics: Counter, Gauge, and Histogram, which correspond to the same Prometheus metric types. The metrics can be exported using the `ray.init` function with the `_metrics_export_port` parameter set to the desired port number.",4661"llm": "codellama/CodeLlama-34b-Instruct-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, you can use the following strategies:\n\n1. Use NVMe SSDs or other high-performance storage: This can help reduce the performance impact of object spilling.\n2. Increase the capacity of the object store: You can do this by increasing the memory available to the object store or by using a distributed object store.\n3. Configure object spilling to use multiple directories: This can help spread the IO load and disk space usage across multiple physical devices, reducing the risk of running out of disk space.\n4. Adjust the local disk utilization threshold: You can adjust the threshold to a value that is appropriate for your use case, or set it to 1 to disable the protection.\n5. Use a distributed object store: This can help reduce the performance impact of object spilling by allowing objects to be stored and retrieved from multiple nodes in the cluster.\n6. Use a faster storage device: If you are using an HDD, it is recommended that you specify a large buffer size (> 1MB) to reduce IO requests during spilling.\n\nIt is important to note that object spilling can have a performance impact, so it is important to carefully consider the trade-offs when using this feature.",4688"llm": "codellama/CodeLlama-34b-Instruct-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 small number of core primitives for building and scaling distributed applications.",4715"llm": "codellama/CodeLlama-34b-Instruct-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": " Ray does not have a built-in job scheduler, but you can use an external job scheduler to interact with your Ray cluster and trigger workflow runs. The recommended way to run a job on a Ray cluster is to use the Ray Jobs API, which consists of a CLI tool, Python SDK, and a REST API.",4742"llm": "codellama/CodeLlama-34b-Instruct-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": " The dependencies for the `read_images` API are:\n\n* `ray`\n* `pyarrow`\n* `PIL`\n* `numpy`\n* `requests`\n* `io`\n* `os`",4769"llm": "codellama/CodeLlama-34b-Instruct-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": " To kill a specific serve replica, you can use the `ray.kill()` function and pass in the replica handle. You can get the replica handle by using the `ray.get_actor()` function and passing in the replica's name.\n\nHere's an example:\n```\n>>> import ray\n>>> replica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\n>>> ray.kill(replica_handle, no_restart=True)\n```\nThis will kill the replica with the specified name and namespace, and prevent it from restarting.\n\nNote that killing a replica will not affect the other replicas in the deployment. The other replicas will continue to process requests as usual.",4796"llm": "codellama/CodeLlama-34b-Instruct-hf"4797}4798]4799}4800