llm-applications
3383 строки · 301.4 Кб
1{2"config": {3"experiment_name": "num-chunks-5",4"chunk_size": 700,5"chunk_overlap": 50,6"num_chunks": 5,7"embedding_model_name": "thenlper/gte-base",8"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1",9"temperature": 0.0,10"max_context_length": 32768,11"system_content": "Answer the query using the context provided. Be succinct.",12"assistant_content": "",13"docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",14"experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",15"references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",16"num_samples": 17717},18"results": [19{20"question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",21"sources": [22"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",23"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",24"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",25"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",26"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart"27],28"document_ids": [296994,306965,316790,326786,33918034],35"answer": " When using `map_batches`, you can specify the batch format as \"numpy\" or \"pandas\" to convert the Ray Data type. To avoid out-of-memory issues, ensure that the batch size is small enough so that the output batch can fit into memory. Additionally, make sure no single item in your Ray Data is too large.\n\nHere's an example of how to use \"numpy\" batch format:\n\n```python\nfrom typing import Dict\nimport numpy as np\nimport ray\n\ndef increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n return batch\n\nds = (\n ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .map_batches(increase_brightness, batch_format=\"numpy\")\n)\n```\n\nAnd here's an example of how to use \"pandas\" batch format:\n\n```python\nimport pandas as pd\nimport ray\n\ndef increase_brightness(batch: Dict[str, pd.Series]) -> Dict[str, pd.Series]:\n batch[\"image\"] = pd.Series(np.clip(batch[\"image\"].to_numpy() + 4, 0, 255))\n return batch\n\nds = (\n ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .map_batches(increase_brightness, batch_format=\"pandas\")\n)\n```\n\nMake sure to adjust the batch format and the function accordingly based on your specific use case.",36"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"37},38{39"question": "How does autoscaling work in a Ray Serve application?",40"sources": [41"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",42"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",43"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",44"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",45"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling"46],47"document_ids": [4813471,495559,506451,515953,52595553],54"answer": " Ray Serve's autoscaling feature automatically adjusts the number of replicas in a deployment based on its workload. The Ray autoscaler, which runs on the head node, increases or decreases the number of worker nodes in the cluster when the resource demands of the Ray workload exceed or are below the current capacity. The autoscaler is reactive to task and actor resource requests, not application metrics or physical resource utilization. It can be configured using the Ray cluster launcher or on Kubernetes to scale the number of nodes based on resource demand. Autoscaling can reduce workload costs but adds node launch overheads and can be tricky to configure, so it is recommended to start with non-autoscaling clusters for new Ray users.",55"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"56},57{58"question": "how do I get the address of a ray node",59"sources": [60"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",61"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",62"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",63"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",64"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"65],66"document_ids": [674305,686367,694817,705587,71482472],73"answer": " To get the address of a Ray node, you can look at the output of the `ray.nodes()` function, which provides information about the nodes in the Ray cluster. In particular, you can use the 'NodeManagerAddress' field and the 'MetricsExportPort' field to construct the address of the node. For example, if the output shows 'NodeManagerAddress' as '192.168.1.82' and 'MetricsExportPort' as '64860', then the address of the node would be '192.168.1.82:64860'.\n\nAlternatively, if you have already initialized Ray on the node using `ray.init()`, you can get the node's address using the `ray.global_state.node_ip_address` property. This property returns the IP address of the node that Ray is currently running on.\n\nOnce you have the address of the Ray node, you can use it to connect to the Ray cluster by passing it as an argument to the `ray start` command, as described in the context you provided.",74"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"75},76{77"question": "Does Ray support NCCL?",78"sources": [79"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",80"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",81"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",82"https://docs.ray.io/en/master/installation.html#installing-ray",83"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray"84],85"document_ids": [8614403,874469,88284,8959,901454191],92"answer": " Yes, Ray does support NCCL (NVIDIA Collective Communications Library) as one of the high-performance communication backends for its collective communication library. NCCL is used for efficient out-of-band communication between Ray actor and task processes on both distributed CPUs and GPUs.",93"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"94},95{96"question": "Is Ray integrated with DeepSpeed?",97"sources": [98"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",99"https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",100"https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",101"https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",102"https://docs.ray.io/en/master/tune/api/integration.html#external-library-integrations-for-ray-tune"103],104"document_ids": [1059668,1068356,1078101,1088135,1091306110],111"answer": " Yes, Ray is integrated with DeepSpeed through the Ray Train component. You can run DeepSpeed with other frameworks like Lightning, Transformers, Accelerate in Ray Train. To configure DeepSpeed in Ray Train, you can use the Accelerator object and create a DeepSpeedPlugin.",112"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"113},114{115"question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",116"sources": [117"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",118"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",119"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",120"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",121"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example"122],123"document_ids": [1244550,1254595,1264564,1274208,1285197129],130"answer": " If you use `asyncio`'s `await` to wait for a Ray future like `await x.remote()`, the program will block until the result of the remote task or actor method is available. However, this approach may hurt parallelism as it blocks the driver program from invoking other operations.\n\nInstead, you can translate Ray's `ObjectRef` to `asyncio.Future` using the `asyncio.ensure_future()` function. This allows you to await on Ray futures in existing concurrent applications.\n\nHere's an example of how to do this:\n```python\nimport asyncio\nimport ray\n\n@ray.remote\ndef some_task():\n return ",131"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"132},133{134"question": "How would you compare Spark, Ray, Dask?",135"sources": [136"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",137"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",138"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",139"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",140"https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp"141],142"document_ids": [14314472,14414484,1456859,1466861,14714653148],149"answer": " Spark, Ray, and Dask are all distributed computing systems, but they have different focuses and use cases. Spark is a general-purpose cluster computing system with a large ecosystem of libraries and tools, and is well-suited for data processing and machine learning tasks. Ray is a high-performance distributed execution system that is designed to handle complex, dynamic workloads, and is particularly well-suited for reinforcement learning and other AI applications. Dask is a parallel computing library for analytics and scientific computing in Python, and is often used for distributed data science tasks. Dask can be integrated with Ray to allow for the use of Dask's NumPy and Pandas-like APIs on a Ray cluster. Ray Data, which is Ray's distributed data processing system, can handle many of the same batch processing workloads as Apache Spark, but with a streaming paradigm that is better suited for GPU workloads for deep learning inference. A more detailed performance comparison between Ray Data and Apache Spark can be found in the Offline Batch Inference: Comparing Ray, Apache Spark, and SageMaker report. RayDP is a project that combines Spark and Ray clusters, allowing for large-scale data processing using the PySpark API and seamless use of that data for model training using TensorFlow and PyTorch.",150"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"151},152{153"question": "why would ray overload a node w/ more task that the resources allow ?",154"sources": [155"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",156"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",157"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case",158"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",159"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources"160],161"document_ids": [1625231,1635230,1645232,1655115,1665104167],168"answer": " Ray overloads a node with more tasks than the resources allow when the resources requested by the tasks or actors do not match the resources required to execute them. This can result in issues like Out-Of-Memory (OOM) if the tasks or actors use more memory than the physical memory available on the node. To prevent this, the number of concurrently running tasks or actors can be limited by increasing the amount of resources requested by them, so that the sum of the resource requirements of all concurrent tasks and actors on a given node does not exceed the node's total resources. The resources required by a task or actor can be specified in Ray using the `SetResource` method.",169"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"170},171{172"question": "when should I use Ray Client?",173"sources": [174"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",175"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",176"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",177"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",178"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments"179],180"document_ids": [1816597,1826589,1836583,1845636,1856593186],187"answer": " You should use the Ray Client when you want to connect an interactive Python shell to a remote Ray cluster for purposes other than ML workloads, such as Ray Tune or Ray Train. Use the command `ray.init(\"ray://<head_node_host>:10001\")` to connect your shell to the cluster. Note that you need to install the pip package `ray[client]` to use Ray Client. It is also recommended to start a Ray cluster for each user to ensure workload isolation.",188"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"189},190{191"question": "how to scatter actors across the cluster?",192"sources": [193"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",194"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",195"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",196"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",197"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard"198],199"document_ids": [2005145,2015195,2025441,2037978,20413515205],206"answer": " To scatter actors across the cluster in Ray, you can use the `scheduling_strategy=\"SPREAD\"` option when creating the actors. Here's an example:\n\n```python\nactors = [SpreadActor.options(scheduling_strategy=\"SPREAD\").remote() for _ in range(10)]\n```\n\nThis will create 10 instances of the `SpreadActor` class and distribute them across the available nodes in the cluster using the \"SPREAD\" strategy.",207"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"208},209{210"question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",211"sources": [212"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",213"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",214"https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",215"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",216"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster"217],218"document_ids": [2195681,2204816,22114683,2226591,22313822224],225"answer": " The \"connection refused\" error when running `ray debug` on a remote Ray cluster could be due to the dashboard or dashboard agent not being fully operational yet. It's recommended to wait for 1 minute and try again. If the issue persists, it's possible that the dashboard or dashboard agent failed to start.\n\nTo connect to a remote Ray cluster, you can use `ray.init()` with the `ray_address` parameter set to the IP address and port of the cluster. Alternatively, you can set the `RAY_ADDRESS` environment variable to the same value.\n\nWhen testing on a remote cluster, you'll need to specify the address of the Ray cluster to connect to using the `--address` argument with `serve run`. The address should be in the form `ray://<head-node-ip-address>:10001`.\n\nIt's important to ensure that your remote cluster has a similar environment to your local machine, including files, environment variables, and Python packages.",226"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"227},228{229"question": "How does Ray AIR set up the model to communicate gradient updates across machines?",230"sources": [231"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",232"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",233"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",234"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",235"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples"236],237"document_ids": [2389845,23910019,2409820,241374,242183243],244"answer": " Ray AIR sets up the model to communicate gradient updates across machines using PyTorch DDP. This is done by creating the model, metric, and tokenizer inside a function (trainer\\_init\\_per\\_worker), which is then passed to the TransformersTrainer and runs on every Ray worker. The training then proceeds with the model communicating gradient updates across machines using PyTorch DDP.",245"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"246},247{248"question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",249"sources": [250"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",251"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",252"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",253"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",254"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving"255],256"document_ids": [25713980,25813622,25913626,26013596,26113598262],263"answer": " Use Ray Serve instead of Modal or Seldon for scalability and flexibility in model serving on your own infrastructure. It is framework-agnostic, works with any model packaging and registry format, and provides key features for building production-ready ML applications. Unlike Modal or Seldon, Ray Serve does not have built-in functionality for model lifecycle management and visualization, but it offers end-to-end control, high performance, and seamless integration with business logic. Ray Serve is a better choice if you prefer to build your own ML platform and have more control over your ML applications.",264"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"265},266{267"question": "How do I deploy an LLM workload on top of Ray Serve?",268"sources": [269"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",270"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",271"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",272"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",273"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm"274],275"document_ids": [27613618,27714255,27813416,27913731,28013732281],282"answer": " To deploy a large language model (LLM) workload on Ray Serve, follow these steps:\n\n1. Write your LLM application as a single Python module, including all steps such as prompt preprocessing, vector database lookups, LLM API calls, and response validation.\n2. Use the `@serve.deployment` decorator on a Python class (or function for simple use cases) to define a deployment.\n3. Optionally, bind the deployment with arguments to the constructor for configuration.\n4. Configure your deployment to scale out and assign replica resources like CPUs and GPUs.\n5. Deploy your Ray Serve application to a Ray cluster using the Ray Serve CLI and the configuration file.\n\nHere's an example command to deploy the configuration file:\n\n```bash\n$ ray start --head\n$ ray serve deploy fruit_config.yaml\n```\n\nThis will deploy your LLM workload on Ray Serve, enabling it to scale and efficiently accommodate user traffic without wasting resources.",283"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"284},285{286"question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",287"sources": [288"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#fine-tuning-the-model-with-ray-train-a-name-train-a",289"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-size",290"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#task-based-batch-prediction",291"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",292"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#initialize-generation-pipeline"293],294"document_ids": [29510449,2966792,2975463,298855,2999486300],301"answer": " The model you're interested in using, \"EleutherAI/gpt-j-6B\", is quite large and may not fit in a GPU with less than or equal to 16 GB of GPU RAM (GRAM). To address this issue, the example uses DeepSpeed, a library that optimizes the training process and reduces GRAM usage.\n\nIn terms of memory specifications, the example provided sets the `object_store_memory` to 1 GB and allocates 15 GB for the model on the meta device. The `max_memory` parameter in `infer_auto_device_map` is set to \"15GB\" for the meta model.\n\nHowever, the exact memory requirements for the \"EleutherAI/gpt-j-6B\" model might vary, and it's essential to ensure that your system has enough memory to load and run the model without running out of memory. If you encounter an out-of-memory error, consider decreasing the batch size or using a machine with a larger GPU.",302"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"303},304{305"question": "How do I log the results from multiple distributed workers into a single tensorboard?",306"sources": [307"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",308"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",309"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",310"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",311"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers"312],313"document_ids": [3142411,3152354,31610680,3179682,3189660319],320"answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use TensorBoard's `--logdir` flag to specify the directory where the results are stored. In the context provided, the results are stored in the `~/ray_results/` directory. Therefore, you can start TensorBoard with the following command:\n```\ntensorboard --logdir=~/ray_results/\n```\nIf you are using Ray's Tune, you can customize the directory of results by specifying the `storage_path` parameter in the `RunConfig` object, which is then passed to the `Tuner`. For example:\n```\ntune.RunConfig(\n storage_path=\"~/my_tune_results\",\n ...\n)\n```\nThen, you can point TensorBoard to that directory to visualize the results:\n```\ntensorboard --logdir=~/my_tune_results\n```\nIf you are running Ray on a remote multi-user cluster where you do not have sudo access, you can run the following commands to make sure TensorBoard is able to write to the `tmp` directory:\n```\nexport TMPDIR=/tmp/$USER; mkdir -p $TMPDIR; tensorboard --logdir=~/ray_results\n```\nThis will ensure that TensorBoard has the necessary permissions to write to the `tmp` directory.\n\nIn summary, to log the results from multiple distributed workers into a single TensorBoard, you can use the `--logdir` flag to specify the directory where the results are stored. You can customize the directory of results by specifying the `storage_path` parameter in the `RunConfig` object if you are using Ray's Tune. If you are running Ray on a remote multi-user cluster, you can use the `TMPDIR` environment variable to ensure that TensorBoard has the necessary permissions to write to the `tmp` directory.",321"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"322},323{324"question": "how do you config SyncConfig for a Ray AIR job?",325"sources": [326"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",327"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",328"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",329"https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration",330"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig"331],332"document_ids": [33310910,3348441,3351443,3361138,3371782338],339"answer": " To configure `SyncConfig` for a Ray AIR job, you can set it in the `RunConfig` object. Here's an example:\n\n```python\nfrom ray import train\n\nsync_config = train.SyncConfig(\n sync_period=300,\n sync_timeout=1800,\n sync_artifacts=False,\n sync_artifacts_on_checkpoint=True,\n sync_on_checkpoint=False # This is deprecated\n)\n\nrun_config = train.RunConfig(\n sync_config=sync_config\n)\n```\n\nIn this example, the `sync_config` object specifies various synchronization parameters such as the sync period, timeout, and whether to sync artifacts or not. This `sync_config` object is then passed to the `RunConfig` object, which can be used in a Ray AIR job.\n\nNote that syncing to the head node is deprecated and will be hard-deprecated in Ray 2.7. Instead, you can use the `upload_dir` parameter in `SyncConfig` to specify a custom directory for uploading files. However, this feature is also deprecated and will be removed in a future Ray release. It is recommended to use alternative methods for data synchronization and management.",340"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"341},342{343"question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",344"sources": [345"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",346"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",347"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",348"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",349"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view"350],351"document_ids": [352415,353681,354444,3555764,356445357],358"answer": " To narrow down the root cause of a failed Ray job, you can access all the logs and:\n\n1. Use `ray logs` command with different parameters (cluster, actor, task) to filter logs based on filename or resource identifiers.\n2. Check the Driver logs if the Ray Job is submitted by the Job API. They are available in the Dashboard with the format: job-driver-<job\\_submission\\_id>.log.\n3. If you run the Ray Job without using the Job API or with Ray Client, view the Driver logs in the terminal or Jupyter Notebook output.\n4. Check the output of the Ray job using `kubectl logs -l=job-name=rayjob-sample`.\n5. Analyze Task and Actor logs (Worker logs) by accessing the Task and Actor table view and clicking the \"Log\" button. You can see the stdout and stderr logs containing the output emitted from Tasks and Actors.",359"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"360},361{362"question": "How do I specify how many GPUs a serve deployment needs?",363"sources": [364"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",365"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",366"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",367"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#resource-management-cpus-gpus",368"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#set-up-autoscaling-and-resource-allocation"369],370"document_ids": [37113458,37213575,37313456,37413735,37513443376],377"answer": " To specify how many GPUs a Serve deployment needs, you can use the `ray_actor_options` parameter in the `@serve.deployment` decorator and set the `\"num_gpus\"` key to the desired number of GPUs. This can be a fraction if you want to pack multiple replicas together on a machine with fewer GPUs than replicas. Here's an example:\n\n```python\n@serve.deployment(ray_actor_options={\"num_gpus\": 1.5})\ndef func_2(*args):\n return do_something_with_my_gpu()\n```\n\nIn this example, each replica of the `func_2` deployment will reserve 1.5 GPUs.",378"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"379},380{381"question": "One of my worker nodes keeps dying on using TensorflowTrainer with around 1500 workers, I observe SIGTERM has been received to the died node's raylet. How can I debug this?",382"sources": [383"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",384"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",385"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",386"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",387"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning"388],389"document_ids": [3905129,39110812,3929410,39310826,3949409395],396"answer": " Based on the information provided, it appears that your worker node is running out of memory (OOM) and receiving SIGTERM as a result. This is indicated by the message in the raylet log: \"1 Workers (tasks / actors) killed due to memory pressure (OOM)\". The TensorflowTrainer may be using more memory than expected, leading to the OOM error.\n\nTo debug this issue, you can check the memory usage of the worker nodes by using the `ray logs raylet.out -ip <node_ip>` command, as suggested in the raylet log. This will show you more information about the workers that were killed on the node, including any memory-related errors.\n\nAdditionally, you can monitor the memory usage of the worker nodes during training to see if any of them are approaching the memory limit. You can do this by using the `nvidia-smi` command, which shows the GPU memory usage, or the `free -h` command, which shows the system memory usage.\n\nIf you find that the memory usage is consistently high during training, you may need to adjust the memory settings for the worker nodes. This can be done by setting the `memory` parameter in the `ray.init()` function to a value that is lower than the total memory available on the node. For example, if the node has 16GB of memory, you can set `memory=14GB` to leave some headroom for other processes.\n\nAnother possible solution is to reduce the batch size or the number of worker nodes used in the training. This will decrease the memory usage of the training process and may prevent the OOM errors from occurring.\n\nFinally, you can try adding `get_accelerator().empty_cache()` calls in your training loop to ensure that all ranks flush their caches at the same time, as suggested in the warning message. This can help reduce memory pressure and prevent cache flushes, which can improve performance.",397"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"398},399{400"question": "what are the possible reasons for nodes dying in a cluster?",401"sources": [402"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",403"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",404"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",405"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",406"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure"407],408"document_ids": [4095639,4104387,41113898,4124391,4134393414],415"answer": " Based on the provided context, there are several possible reasons for nodes dying in a Ray cluster:\n\n1. Overloading the head node with too many simultaneous connections, which can cause worker nodes to die.\n2. Raylet process failure, which marks the corresponding node as dead and treats it as a new raylet/node to the Ray cluster.\n3. Head node failure, which causes the entire Ray cluster to fail.\n\nTo tolerate head node failures, GCS (Global Control Store) needs to be made fault-tolerant so that when a new head node is started, all the cluster-level data is still available.",416"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"417},418{419"question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",420"sources": [421"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",422"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",423"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",424"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",425"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction"426],427"document_ids": [4286451,4295953,4305559,4316455,4325919433],434"answer": " To set the target size of a Ray remote cluster immediately without scaling up through the autoscaler, you can manually specify the number of worker nodes when creating the cluster. This can be done by configuring the Ray cluster launcher with the desired number of worker nodes. Note that this method bypasses the autoscaler and will not adjust the number of nodes based on resource demand.\n\nHere is an example of how to set the number of worker nodes in the Ray cluster launcher configuration:\n\n```\nray start --head --node-ip-address <head_node_ip> --redis-address <head_node_ip>:6379 --block --num-workers 10\n```\n\nIn this example, the `--num-workers` flag is used to set the number of worker nodes to 10.\n\nAlternatively, if you are using Kubernetes, you can specify the number of replicas in the Ray deployment configuration.\n\nHere is an example of how to set the number of replicas in a Kubernetes Ray deployment:\n\n```\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: ray-cluster\nspec:\n replicas: 10\n selector:\n matchLabels:\n app: ray-cluster\n template:\n metadata:\n labels:\n app: ray-cluster\n spec:\n containers:\n - name: ray-head\n image: rayproject/ray-ml:2.3.0\n resources:\n limits:\n cpu: 14\n memory: 54Gi\n requests:\n cpu: 14\n memory: 54Gi\n```\n\nIn this example, the `replicas` field is set to 10, which will create 10 replicas (i.e., worker nodes) in the Ray deployment.\n\nNote that the autoscaler is not used in these examples, so the number of worker nodes will not change dynamically based on resource demand. If you want to use the autoscaler to automatically scale the number of worker nodes based on resource demand, you can refer to the Ray documentation on configuring the autoscaler for more information.",435"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"436},437{438"question": "how do you disable async iter_batches with Ray Dataset?",439"sources": [440"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",441"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",442"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",443"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",444"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches"445],446"document_ids": [4477188,4487543,4497379,4508478,4517811452],453"answer": " To disable async iter_batches with Ray Dataset, you can set the `prefetch_batches` parameter to 0 in the `iter_batches()` method. For example:\n```python\nfor batch in dataset.iter_batches(prefetch_batches=0, batch_size=256, ...):\n # process batch\n```\nThis will configure the iterator to return batches without prefetching them asynchronously, which can be useful in certain scenarios where you want to minimize memory usage or control the batch processing more directly. Note that this may affect the performance of your data processing pipeline.",454"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"455},456{457"question": "what is the different between a batch and a block, for ray datasets?",458"sources": [459"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",460"https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",461"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",462"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",463"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial"464],465"document_ids": [46614728,4676919,4686965,4696994,47014578471],472"answer": " A batch and a block are related concepts in Ray Datasets, but they differ in terms of abstraction level and usage. A block is an internal abstraction that represents a distributed data block containing a disjoint subset of rows, while a batch is a user-facing object. The map\\_batches() function is used to process batches of data, which can help avoid out-of-memory situations by controlling the block size and ensuring it fits into memory.",473"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"474},475{476"question": "How to setup the development environments for ray project?",477"sources": [478"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",479"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",480"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher",481"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",482"https://docs.ray.io/en/master/ray-core/key-concepts.html#environment-dependencies"483],484"document_ids": [485910,4864251,4874250,4886595,4894203490],491"answer": " To set up the development environment for the Ray project, you can follow these steps:\n\n1. Checkout the Ray repository and build Ray from source.\n2. Use the Ray Cluster launcher to prepare a single environment across the cluster before starting the Ray runtime. You can build all your files and dependencies into a container image and specify this in your Cluster YAML Configuration.\n3. Alternatively, you can use Ray's runtime environments to install dependencies on the fly. You can specify the runtime environment when calling `ray.init()`, including the working directory and required Python packages.\n\nNote that the runtime environment feature requires a full installation of Ray using pip and is available starting with Ray 1.4.0 on macOS and Linux, with beta support on Windows.",492"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"493},494{495"question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",496"sources": [497"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#rewriting-trajectories",498"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",499"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",500"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",501"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"502],503"document_ids": [50410444,50512707,50614759,50712708,50813196509],510"answer": " To debug why Ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`, you can start by checking the configuration of the RolloutWorker, including the `env_creator`, `validate_env`, `config`, `worker_index`, `num_workers`, `recreated_worker`, `log_dir`, `spaces`, `default_policy_class`, and `dataset_shards`. You can also look into the implementation of the `__init__` method of the RolloutWorker class for more insights. To get the global worker, you can use `get_global_worker()` function. To collect samples from environments or offline files in parallel, RolloutWorkers are used as Ray actors. If there are any issues with the sample API, you can refer to the Ray RLlib documentation or Ray's GitHub repository for more information.",511"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"512},513{514"question": "how do I join two ray datasets?",515"sources": [516"https://docs.ray.io/en/master/data/preprocessors.html#overview",517"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",518"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",519"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",520"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a"521],522"document_ids": [5236845,5249180,5257255,52610436,52710014528],529"answer": " To join two Ray datasets, you can use the `zip` function provided by Ray Datasets. Here's an example of how you can do it:\n```python\nimport ray\n\ndataset1 = ray.data.range(8)\ndataset2 = ray.data.range(8)\n\n# Zip the two datasets\nzipped_dataset = dataset1.zip(dataset2)\n\n# Take a batch from the zipped dataset\nprint(zipped_dataset.take_batch())\n```\nThis will output:\n```python\n{'id': array([0, 1, 2, 3, 4, 5, 6, 7]), 'id_1': array([0, 1, 2, 3, 4, 5, 6, 7])}\n```\nIn this example, `zip` is used to combine the two datasets `dataset1` and `dataset2` into a single dataset `zipped_dataset`, where each element is a tuple containing one element from `dataset1` and one element from `dataset2`. The `take_batch` function is then used to take a batch of elements from the zipped dataset.\n\nNote that the `zip` function will match elements from the two datasets based on their index in the dataset. If the two datasets have different sizes, the resulting zipped dataset will have the size of the smaller dataset.",530"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"531},532{533"question": "Is there a way to retrieve an object ref from its id?",534"sources": [535"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",536"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",537"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle",538"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",539"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait"540],541"document_ids": [5424982,5434984,54414276,54513987,5464750547],548"answer": " Yes, you can retrieve an object ref from its id using the `ObjectRef` class provided by Ray. In the context you provided, the `dynamic_ref` variable is an `ObjectRef` that points to the generated `ObjectRefs`. Also, you can pass the `ObjectRef` returned by a task to another task or use it as an argument for another task. However, the process of getting the object associated with the ref involves using the `ray.get()` function, which blocks the driver until the object is ready. The `ObjectRefGenerator` class can be used to iterate over the task\u2019s return values.",549"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"550},551{552"question": "how to create model Checkpoint from the model in memory?",553"sources": [554"https://docs.ray.io/en/master/tune/examples/includes/pbt_convnet_function_example.html#pbt-convnet-example",555"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",556"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",557"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#check-the-training-results-and-checkpoints",558"https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example"559],560"document_ids": [5614104,5628798,5639037,5649331,5654073566],567"answer": " To create a model checkpoint from a model in memory, you can use the `TensorflowCheckpoint.from_model()` function, passing the model object as an argument. Here's an example:\n```python\nmodel = tf.keras.applications.resnet.ResNet101()\ncheckpoint = TensorflowCheckpoint.from_model(model)\n```\nAlternatively, you can save the model to a directory and then create a checkpoint from that directory using the `Checkpoint.from_directory()` function. Here's an example:\n```python\nmodel = tf.keras.applications.resnet.ResNet101()\ncheckpoint_dir = \"my_model\"\nmodel.save(checkpoint_dir)\ncheckpoint = Checkpoint.from_directory(checkpoint_dir)\n```\nNote that if you want to save the model to a specific directory, you can use the `save()` method of the model object.",568"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"569},570{571"question": "what is Deployment in Ray Serve?",572"sources": [573"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",574"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",575"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",576"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",577"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air"578],579"document_ids": [58014255,58113416,58214732,58313731,58410395585],586"answer": " A Deployment in Ray Serve is a group of actors that can handle traffic, defined as a single class with a number of options, including the number of \"replicas\" of the deployment. 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). These replicas can be scaled up or down to match the incoming request load. A deployment is the central concept in Ray Serve and is used to handle incoming requests, containing business logic or an ML model. Deployments can be bound together into a directed acyclic graph (DAG) to handle requests, enabling model composition.",587"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"588},589{590"question": "What is user config in Ray Serve? how do I use it?",591"sources": [592"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",593"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",594"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",595"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serves-recovery-procedures",596"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide"597],598"document_ids": [5996597,60013980,60113731,60213883,60313962604],605"answer": " In Ray Serve, user config refers to the configuration of deployments, such as scaling out by increasing the number of replicas, and assigning resources like CPUs and GPUs. Here's how you can use it:\n\n1. Define the configuration in a `serveConfig` block, for example:\n```yaml\nserveConfig:\n importPath: \"sleepy_pid:app\"\n runtimeEnv: |\n working_dir: \"https://github.com/ray-project/serve_config_examples/archive/42d10bab77741b40d11304ad66d39a4ec2345247.zip\"\n deployments:\n - name: SleepyPid\n numReplicas: 6\n rayActorOptions:\n numCpus: 0\n```\n2. Use this configuration when creating a deployment, for example:\n```python\nimport ray\nfrom ray import serve\n\nserve.init()\nserve.deploy(config=serveConfig, app=\"sleepy_pid:app\")\n```\n\nNote that this is a basic example, and you can configure more options based on your needs. The Ray Serve documentation provides more information on the Serve config file format and how to deploy on Kubernetes using KubeRay.",606"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"607},608{609"question": "What is the difference between PACK and SPREAD strategy?",610"sources": [611"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",612"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",613"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",614"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",615"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy"616],617"document_ids": [6185145,61911365,62011115,6211872,6225078623],624"answer": " The \"SPREAD\" strategy in Ray can be used for both tasks and actors, and it aims to distribute them across available nodes. On the other hand, the \"PACK\" strategy for placement groups tries to place as many resource bundles as possible onto a few nodes. The main difference is that \"SPREAD\" is used for distributing tasks or actors, while \"PACK\" is used for placing resource bundles onto nodes.",625"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"626},627{628"question": "What\u2019s the best way to run ray across multiple machines?",629"sources": [630"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",631"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",632"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",633"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",634"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms"635],636"document_ids": [6375636,63813615,6394186,640372,6416208642],643"answer": " To run Ray across multiple machines, you can use the Ray cluster launcher (`ray up`) to start a Ray cluster, which will provide isolated workloads for each user. Ray is designed to run on various environments, including multiple servers, making it a suitable choice for running on multiple machines.",644"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"645},646{647"question": "how do I specify ScalingConfig for a Tuner run?",648"sources": [649"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",650"https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",651"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",652"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#configuring-scale-and-gpus",653"https://docs.ray.io/en/master/train/api/doc/ray.train.trainer.BaseTrainer.html#ray-train-trainer-basetrainer"654],655"document_ids": [6561826,6571303,6589126,6599251,6608519661],662"answer": " To specify the `ScalingConfig` for a Tuner run, you can include it in the `RunConfig` object that you pass to the Tuner constructor. Here's an example:\n```python\nfrom ray.tune.scalers import ScalingConfig\n\nscaling_config = ScalingConfig(...)\nrun_config = RunConfig(name=\"my_tune_run\", scaling_config=scaling_config)\n\ntuner = Tuner(...)\nresults = tuner.fit(...)\n```\nIn this example, `ScalingConfig` is used to configure how to scale the training. The `RunConfig` object is then created with the `scaling_config` parameter set to the `ScalingConfig` instance. This `RunConfig` object is then passed to the `Tuner` constructor.",663"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"664},665{666"question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",667"sources": [668"https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only",669"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",670"https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",671"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",672"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks"673],674"document_ids": [6754944,6767012,6774942,6787005,6794225680],681"answer": " To utilize the 'zero-copy' feature Ray provides for numpy arrays, you should ensure that the function you're using doesn't mutate the input array. If your function doesn't modify the array, you can set the `zero_copy_batch` argument to `True` when using functions like `map_batches()`. This will allow Ray to use zero-copy, read-only views of the data in the object store, which can decrease memory utilization and improve performance.\n\nHere's an example:\n\n```python\nds = (\n ray.data.from_items([\n {\"name\": \"Luna\", \"age\": 4},\n {\"name\": \"Rory\", \"age\": 14},\n {\"name\": \"Scout\", \"age\": 9},\n ])\n .map_batches(add_dog_years, batch_format=\"numpy\", zero_copy_batch=True)\n)\n```\n\nIn this example, the `add_dog_years` function doesn't modify the input array, so we can set `zero_copy_batch=True` to improve performance and decrease memory utilization.\n\nHowever, if your function needs to mutate the input array, you will need to manually copy the array at the destination, as the context suggests. This is because Ray stores numpy arrays as read-only objects in the object store, and any writes to the read-only object will require the user to first copy it into the local process memory.",682"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"683},684{685"question": "if there are O(millions) of keys that all have state, is it ok to spin up 1=1 actors? Or would it be advised to create \u2018key pools\u2019 where an actor can hold 1=many keys?",686"sources": [687"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",688"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_random_access_dataset.html#ray-data-dataset-to-random-access-dataset",689"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",690"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",691"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks"692],693"document_ids": [6945441,6957308,6967976,6975192,6985231699],700"answer": " It is not advised to spin up 1 actor per key due to the large number of actors and the potential for resource contention. Instead, it would be better to create 'key pools' where an actor can hold many keys, reducing the total number of actors needed. This approach can help balance resource usage and improve performance.",701"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"702},703{704"question": "How to find the best checkpoint from the trial directory?",705"sources": [706"https://docs.ray.io/en/master/train/api/doc/ray.train.Result.html#ray-train-result",707"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",708"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",709"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",710"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result"711],712"document_ids": [7138371,7141645,7151431,7162188,7173402718],719"answer": " To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` method provided by Ray Tune. This method allows you to specify a metric and a mode (either \"min\" or \"max\") to determine the best checkpoint. Here's an example of how to use this method:\n```python\nfrom ray.tune.analysis import ExperimentAnalysis\n\n# Create an ExperimentAnalysis object from the trial directory\nanalysis = ExperimentAnalysis(path)\n\n# Get the best checkpoint based on a specific metric and mode\nbest_checkpoint = analysis.get_best_checkpoint(metric=\"your_metric\", mode=\"max\")\n```\nIn the example above, replace `path` with the path to your trial directory, and replace `your_metric` with the name of the metric you want to use to determine the best checkpoint. The `mode` parameter should be set to \"max\" if you want to select the checkpoint with the highest metric value, or \"min\" if you want to select the checkpoint with the lowest metric value.\n\nIf you want to get the best trial directory instead of the best checkpoint, you can use the `best_trial` property of the `ExperimentAnalysis` object:\n```python\nbest_trial_dir = analysis.best_trial\n```\nThis will give you the directory of the best trial, which you can use to load the trial's results or checkpoints manually.",720"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"721},722{723"question": "what are the advantage and disadvantage of using singleton Actor ?",724"sources": [725"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",726"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",727"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",728"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",729"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources"730],731"document_ids": [7325192,7334200,7344455,73514708,7364371737],738"answer": " Advantage of using a singleton Actor:\n\n* Allows for managing state between actor instances.\n* Methods of the actor can access and mutate the state of that worker.\n\nDisadvantage of using a singleton Actor:\n\n* Each actor instance is instantiated in its own process, so it requires more resources than using tasks.\n* Actors can fail if the actor process dies or if the owner of the actor dies.\n* If you don't need the stateful part of an actor, you're mostly better off using tasks.\n* Class variables are not supported for managing state between instances of the same class.\n\nNote: Actors are essentially stateful workers or services that extend the Ray API from functions (tasks) to classes. When a new actor is instantiated, a new worker is created, and methods of the actor are scheduled on that specific worker and can access and mutate the state of that worker.",739"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"740},741{742"question": "what are the advantages of using a named actor?",743"sources": [744"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",745"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",746"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",747"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",748"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options"749],750"document_ids": [75114708,75211813,7534371,7544678,7554880756],757"answer": " Named actors in Ray provide several advantages:\n\n1. **Specifying required resources:** You can specify the required resources for an actor.\n2. **Calling the actor:** You can call the actor using its unique name.\n3. **Passing Around Actor Handles:** You can pass around actor handles to other tasks or actors.\n4. **Cancelling Actor Tasks:** You can cancel an actor's tasks.\n5. **Scheduling:** Ray schedules actors efficiently across resources.\n6. **Fault Tolerance:** Ray provides fault tolerance for actors.\n7. **Unique Name:** Each named actor has a globally unique name, which can be used to retrieve the actor via `ray.get_actor(name)` as long as the actor is still alive.\n8. **Lifetime Control:** You can control the actor's lifetime, either sharing the actor's fate with its creator or making it a global object independent of the creator.\n\nHowever, if you don't need the stateful part of an actor, you're mostly better off using tasks.",758"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"759},760{761"question": "How do I read a text file stored on S3 using Ray Data?",762"sources": [763"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",764"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",765"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",766"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",767"https://docs.ray.io/en/master/data/key-concepts.html#loading-data"768],769"document_ids": [7707565,7717909,7726993,7737496,7746732775],776"answer": " To read a text file stored on S3 using Ray Data, you can use the `read_text` function and provide the S3 path of the text file as an argument. Here's an example:\n```python\nimport ray\n\n# Replace `my-s3-bucket` and `my-text-file.txt` with the actual S3 bucket and file names\nds = ray.data.read_text(\"s3://my-s3-bucket/my-text-file.txt\")\n```\nThis will create a Ray Dataset `ds` containing the text data from the specified S3 file. If the S3 bucket is private, you may need to set up authentication using `pyarrow.fs`.",777"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"778},779{780"question": "how do I get the IP of the head node for my Ray cluster?",781"sources": [782"https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",783"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",784"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",785"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",786"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"787],788"document_ids": [7895631,7906537,7916365,7926367,793493794],795"answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command. It is used as follows:\n\n```\nray get_head_ip [OPTIONS] CLUSTER_CONFIG_FILE\n```\n\nwhere `CLUSTER_CONFIG_FILE` is a required argument that specifies the configuration file for the Ray cluster.\n\nAlternatively, you can also get the IP of the head node by running the following command on the head node:\n\n```\necho \"IP Head: $ip_head\"\n```\n\nThis command is used in the script to start the Ray head node and prints out the IP head node address.\n\nYou can also use the `ray list nodes` command to get the node ID and node IP of all the nodes in the Ray cluster, including the head node. Once you have the node ID or node IP of the head node, you can use the `ray logs` command to get the logs for the head node. For example:\n\n```\nray logs gcs_server.out --node-id <NODE_ID>\n```\n\nReplace `<NODE_ID>` with the actual node ID of the head node. This command will print out the logs for the `gcs_server.out` file on the head node.",796"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"797},798{799"question": "How to write a map function that returns a list of object for `map_batches`?",800"sources": [801"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",802"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",803"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",804"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",805"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference"806],807"document_ids": [8086994,8097019,8107800,8117979,8128218813],814"answer": " To write a map function that returns a list of objects for `map_batches`, you should define a class or function as the user-defined function (UDF) and return the transformed batch from it. The `map_batches` function will apply this UDF to batches of data and return a new `Dataset` containing the transformed batches. Here is an example of how to define a class-based UDF that returns a list of objects:\n```\nclass MyUDF:\n def __call__(self, batch):\n # Transform the batch here\n transformed_batch = ...\n return transformed_batch\n\npredictions = ds.map_batches(MyUDF(), batch_size=128)\n```\nNote that the size of the batches provided to the UDF may be smaller than the specified batch size if the batch size does not evenly divide the blocks of data sent to a given map task. Also, the UDF can return multiple objects, which will be collected into a list by `map_batches`.",815"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"816},817{818"question": "How do I set a maximum episode length when training with Rllib?",819"sources": [820"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",821"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",822"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",823"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",824"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data"825],826"document_ids": [82710410,82813177,82911046,83010411,83111069832],833"answer": " In RLlib, you can't set a maximum episode length directly during training. However, you can control the number of episodes or timesteps for evaluation using the `evaluation_duration` and `evaluation_duration_unit` configuration.\n\nTo limit the number of timesteps during training, you can use the `min_train_timesteps_per_iteration` configuration to specify the minimum number of training timesteps to accumulate before moving on to the next iteration. Note that this does not guarantee a strict maximum episode length but rather sets a limit on the total number of timesteps accumulated during training.\n\nHere's an example configuration:\n\n```yaml\ntrainer:\n ...\n min_train_timesteps_per_iteration: <your_desired_number_of_timesteps>\n```\n\nKeep in mind that this approach may not be suitable for all use cases, and you might need to adjust your training loop or environment to better fit your specific requirements.",834"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"835},836{837"question": "how do I make a Ray Tune trial retry on failures?",838"sources": [839"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",840"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",841"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#summary",842"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",843"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_recover.html#ray-tune-callback-on-trial-recover"844],845"document_ids": [8462326,8472267,8482268,8494505,8502152851],852"answer": " To make a Ray Tune trial retry on failures, you can define the `failure_config` in the `run_config` of the `Tuner`. For example, to retry a trial up to 3 times when it encounters a runtime error, you can use the following configuration:\n\n```python\ntuner = tune.Tuner(\n train_model,\n param_space=trial_space,\n run_config=train.RunConfig(\n storage_path=os.path.expanduser(\"~/ray_results\"),\n name=\"trial_fault_tolerance\",\n failure_config=train.FailureConfig(max_failures=3),\n ),\n)\n```\n\nYou can also use the `ray.tune.Callback.on_trial_recover` callback to customize the behavior when a trial is scheduled for retry. This callback is called after a trial instance failed but the trial is scheduled for retry, and the search algorithm and scheduler are not notified. For example, you can use this callback to print a message when a trial is retried:\n\n```python\nfrom ray.tune.callback import Callback\n\nclass MyCallback(Callback):\n def on_trial_recover(self, iteration, trials, trial, **info):\n print(f\"Trial {trial.trial_id} is being retried.\")\n\ntuner = tune.Tuner(\n train_model,\n param_space=trial_space,\n run_config=train.RunConfig(\n storage_path=os.path.expanduser(\"~/ray_results\"),\n name=\"trial_fault_tolerance\",\n failure_config=train.FailureConfig(max_failures=3),\n ),\n callbacks=[MyCallback()],\n)\n```",853"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"854},855{856"question": "For the supervised actor pattern, can we keep the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor, to allow the worker actor to remain even on Supervisor / Driver failure?",857"sources": [858"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",859"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",860"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",861"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",862"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes"863],864"document_ids": [8655208,8664493,8674455,8685191,8694654870],871"answer": " No, you cannot keep the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor, to allow the worker actor to remain even on Supervisor / Driver failure. This is because if the supervisor dies, or the driver, the worker actors are automatically terminated thanks to actor reference counting. However, you can make the actor \"detached\" to decouple its lifetime from the job, allowing it to persist even after the driver process of the job exits.",872"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"873},874{875"question": "How do I read a large text file in S3 with Ray?",876"sources": [877"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",878"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",879"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",880"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",881"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text"882],883"document_ids": [8847565,8857496,8867909,8876993,8886991889],890"answer": " To read a large text file in S3 with Ray, you can use the `read_text` function and provide the S3 path to the file as an argument. Here's an example:\n```\nimport ray\n\n# Replace this with the S3 path to your text file\ns3_path = \"s3://bucket/path/to/your/text/file.txt\"\n\n# Create a Ray dataset from the text file\nds = ray.data.read_text(s3_path)\n\n# You can now use the dataset (ds) to perform various operations\n# such as showing the first few rows, getting the schema, etc.\n```\nIf you want to load the data from a private bucket, you have to import `pyarrow.fs` and set up the necessary credentials.\n\nYou can also read a directory of text files in S3 by using the `read_text` function with the directory path as an argument. In this case, Ray will create a dataset with one row for each line of text across all the files in the directory.",891"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"892},893{894"question": "how do I get a ray dataset from pandas",895"sources": [896"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",897"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",898"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",899"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",900"https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-datasets"901],902"document_ids": [9039180,9046750,9057818,9066752,9076972908],909"answer": " To create a Ray Dataset from a pandas DataFrame, you can use the `ray.data.from_pandas()` function. Here's an example:\n\n```python\nimport pandas as pd\nimport ray\n\n# Create a pandas DataFrame\ndf = pd.DataFrame({\n \"food\": [\"spam\", \"ham\", \"eggs\"],\n \"price\": [9.34, 5.37, 0.94]\n})\n\n# Create a Ray Dataset from the pandas DataFrame\nds = ray.data.from_pandas(df)\n\n# Print the Ray Dataset\nprint(ds)\n```\n\nThis will create a Ray Dataset from the pandas DataFrame `df`. The `ray.data.from_pandas()` function can also take a list of pandas DataFrames to create a Ray Dataset from multiple DataFrames.",910"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"911},912{913"question": "can you give me an example of using `ray.data.map` ?",914"sources": [915"https://docs.ray.io/en/master/data/api/from_other_data_libs.html#api-guide-for-users-from-other-data-libraries",916"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",917"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",918"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",919"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data"920],921"document_ids": [9227039,9236994,9245433,925221,9265352927],928"answer": " Sure, here's an example of using `ray.data.map`:\n```python\n@ray.remote\ndef process_data(data):\n # process the data here\n processed_data = data + 1\n return processed_data\n\nds = ray.data.read_csv(\"s3://anonymous@ray-example-data/iris.csv\")\n\n# Apply the processing function to each batch of data\nprocessed_ds = ds.map(process_data)\n\n# The result is a new Dataset with the processed data\n# You can continue processing or write to disk\nprocessed_ds.write_parquet(\"processed_data.parquet\")\n```\nIn this example, we define a remote function `process_data` that takes a batch of data as input and processes it. We then use `ds.map(process_data)` to apply this function to each batch of data in the `ds` Dataset. The result is a new Dataset `processed_ds` with the processed data.\n\nNote that in this example, we use `ray.remote` to define a remote function that can be executed in parallel by Ray. This is not required for `map`, but it can be useful for more complex processing functions that can be executed in parallel.",929"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"930},931{932"question": "can you give me an example of using `ray.data.map` , with a callable class as input?",933"sources": [934"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",935"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",936"https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",937"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",938"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart"939],940"document_ids": [9417796,9425433,9436782,9446749,9459180946],947"answer": " Sure, here's an example of using `ray.data.map` with a callable class as input:\n```python\nimport ray\nfrom typing import Dict\n\nclass MyCallable:\n def __call__(self, data: Dict) -> Dict:\n data[\"new_col\"] = \"new_value\"\n return data\n\nif __name__ == \"__main__\"::\n ds = ray.data.from_items([{\"col1\": 1, \"col2\": 2} for _ in range(10)])\n ds = ds.map(MyCallable())\n result = [print(data) for data in ray.get(ds.iter_rows())]\n```\nIn this example, we define a callable class `MyCallable` that takes a dictionary as input and adds a new key-value pair to it. We then create a Ray Dataset `ds` from a list of dictionaries, and apply the `map` method with an instance of `MyCallable`. The resulting Dataset `ds` will contain the modified dictionaries with the new key-value pair. Finally, we use `iter_rows` to get an iterator over the rows of the Dataset and print each row.",948"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"949},950{951"question": "How to set memory limit for each trial in Ray Tuner?",952"sources": [953"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",954"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#summary",955"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",956"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",957"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#scalability-and-overhead-benchmarks-for-ray-tune"958],959"document_ids": [9601025,9612268,9622406,9632325,9642376965],966"answer": " To set the memory limit for each trial in Ray Tuner, you can request custom resources when starting the cluster and control the allocation per trial using the `with_resources` function. However, Ray Tuner automatically manages the memory limit by making half of the GPU memory available to each trial, and you need to ensure your model fits in the allocated memory. The maximum number of concurrent trials can be set using the `max_concurrent_trials` parameter in `TuneConfig`.",967"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"968},969{970"question": "how do I get the actor id of an actor",971"sources": [972"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_actor_id.html#ray-runtime-context-runtimecontext-get-actor-id",973"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",974"https://docs.ray.io/en/master/ray-observability/getting-started.html#actor-detail-page",975"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",976"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor"977],978"document_ids": [9794787,9804931,981436,982475,983690984],985"answer": " To get the actor ID of the current actor in a Ray program, you can use the `get_actor_id()` function from the `ray.runtime_context.RuntimeContext` class. This function returns the current actor id in hex format as an optional string. Here is an example:\n```\ncurrent_actor_id = ray.runtime_context.RuntimeContext.get_actor_id()\n```\nAlternatively, you can use the `actor_id` property from the `ray.runtime_context.RuntimeContext` class, which serves the same purpose.\n\nNote that the `actor_id` property is deprecated and may be removed in future Ray releases, so it is recommended to use the `get_actor_id()` function instead.\n\nTo get the details of an actor, including its metadata, state, and tasks, you can visit the Actor Detail page for that actor. To do this, click on the ID of the actor in your Ray program. On the Actor Detail page, you can see the metadata, state, and tasks for the actor.\n\nYou can also use the `ray get actors` command followed by the actor ID to get the details of an actor from the command line.\n\nFinally, you can use the `get_actor()` function from the `ray.util.state` module to get an actor by its ID. This function returns the state of the actor as a dictionary, or `None` if the actor is not found. Here is an example:\n```\nactor_state = ray.util.state.get_actor(actor_id)\n```",986"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"987},988{989"question": "can ray.init() can check if ray is all-ready initiated ?",990"sources": [991"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",992"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",993"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",994"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",995"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine"996],997"document_ids": [9984785,9994811,10004194,10014190,100241891003],1004"answer": " No, `ray.init()` is used to initialize Ray, while `ray.is_initialized()` checks if Ray has been initialized or not. You can use `ray.is_initialized()` to determine if you need to call `ray.init()`.",1005"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1006},1007{1008"question": "What does the `compute=actor` argument do within `ray.data.map_batches` ?",1009"sources": [1010"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",1011"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1012"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1013"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.filter.html#ray-data-dataset-filter",1014"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air"1015],1016"document_ids": [10176947,10188218,10196787,10207840,1021101441022],1023"answer": " The `compute=actor` argument within `ray.data.map_batches` specifies that the user-defined function (UDF) should run as a long-running Ray actor, rather than a short-running Ray task. This allows for more efficient processing of large datasets by reusing the same actor instance for multiple batches. The `ActorPoolStrategy` can be used to configure the number of concurrent actor workers.",1024"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1025},1026{1027"question": "how do I use wandb logger with accelerateTrainer?",1028"sources": [1029"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",1030"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1031"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",1032"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#setup-wandb",1033"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune"1034],1035"document_ids": [10361132,10372432,10388470,10392469,104024311041],1042"answer": " To use Weights & Biases (WandB) logger with AccelerateTrainer, you need to follow these steps:\n\n1. Install WandB via pip: `pip install wandb`\n2. Import the necessary modules, including `wandb` and `WandbLoggerCallback`.\n3. Make sure you're logged in to WandB on all machines: `wandb login`\n4. Initialize the WandB logger with `WandbLoggerCallback()`.\n5. Pass the WandB logger to the AccelerateTrainer when creating an instance of it.\n\nHere's an example of how to use the WandB logger with AccelerateTrainer:\n\n```python\nimport torch\nimport pytorch_lightning as pl\nfrom ray import tune\nfrom ray.train.lightning import LightningTrainer\nfrom pytorch_lightning.loggers import WandbLoggerCallback\n\n# Define your PyTorch Lightning model\nclass MyModel(pl.LightningModule):\n # Model definition\n\n# Initialize the WandB logger\nwandb_logger = WandbLoggerCallback(project=\"my-project\")\n\n# Initialize the AccelerateTrainer with the Wandb logger\ntrainer = LightningTrainer(\n logger=wandb_logger,\n # Other arguments\n)\n\n# Use the trainer to train your model\ntune.run(\n model=MyModel,\n train_func=train,\n trainer=trainer,\n # Other arguments\n)\n```\n\nThis example assumes that you have already defined your PyTorch Lightning model and the `train` function. The `WandbLoggerCallback` logger will automatically log metrics reported to Tune to the WandB API.",1043"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1044},1045{1046"question": "What will be implicitly put into object store?",1047"sources": [1048"https://docs.ray.io/en/master/ray-core/objects/serialization.html#plasma-store",1049"https://docs.ray.io/en/master/ray-core/objects.html#objects",1050"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1051"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",1052"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"1053],1054"document_ids": [10554941,10564634,10575013,10585014,1059147481060],1061"answer": " In the context of Ray, when data is explicitly put into an object store using functions like ray.put or Ray.put in Java or Put in C++, it will be implicitly stored in the local object store of the node where the data is put. The object store does not automatically broadcast data to other nodes, and data remains local until requested. Objects in the object store are immutable and have reference counting for garbage collection. Once the capacity of the object store is used up, objects are spilled to external storage, which may come with a performance penalty.",1062"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1063},1064{1065"question": "How do I kill or cancel a ray task that I already started?",1066"sources": [1067"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1068"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",1069"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1070"https://docs.ray.io/en/master/ray-core/api/core.html#tasks",1071"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"1072],1073"document_ids": [10744430,10754498,10764894,10774689,1078147031079],1080"answer": " To kill or cancel a Ray task that you already started, you can call `ray.cancel()` on the ObjectRef returned by the task. By default, this will send a KeyboardInterrupt to the task's worker if it is mid-execution. If you pass `force=True` to `ray.cancel()`, it will force-exit the worker. Here's an example:\n```python\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n```\nIf you want to cancel all child and Actor Tasks, you can pass `recursive=True` to `ray.cancel()`. Note that cancellation is not guaranteed, and you can check if a Task was successfully cancelled using `ray.get(actor_task_ref)`.",1081"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1082},1083{1084"question": "how to send extra arguments in dataset.map_batches function?",1085"sources": [1086"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1087"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1088"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1089"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1090"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches"1091],1092"document_ids": [10936994,10946997,10958218,10967979,109768951098],1099"answer": " To send extra arguments in the `dataset.map_batches` function, you can use the `fn_kwargs` parameter. This parameter allows you to pass keyword arguments to the user-defined function (UDF) that you specify as the first argument of `map_batches`. Here's an example:\n```python\nds.map_batches(my_udf, fn_kwargs={\"arg1\": value1, \"arg2\": value2})\n```\nIn this example, `my_udf` is the UDF that you want to apply to the dataset, and `arg1` and `arg2` are the extra arguments that you want to pass to `my_udf`. `value1` and `value2` are the values of `arg1` and `arg2`, respectively.\n\nYou can also use the `fn_args` parameter to pass extra positional arguments to the UDF. However, it is recommended to use `fn_kwargs` for clarity and to avoid potential issues with argument order.\n\nNote that the UDF can be a function or a class, and that class-based UDFs will run as long-running Ray actors if you specify a `compute` strategy. The `batch_size` argument indicates the number of images in each batch, and the `batch_format` argument specifies the format of the batch data.",1100"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1101},1102{1103"question": "where does ray GCS store the history of jobs run on a kuberay cluster? What type of database and format does it use for this?",1104"sources": [1105"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",1106"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",1107"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",1108"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/experimental.html#gcs-fault-tolerance",1109"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance"1110],1111"document_ids": [111213871,11135754,11145729,11155916,1116138701117],1118"answer": " Ray GCS in a KubeRay cluster stores the history of jobs run on the cluster in an external Redis server, if GCS fault tolerance is enabled. It uses a Redis database, with the data stored in a Redis database format.",1119"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1120},1121{1122"question": "How to resolve ValueError: The actor ImplicitFunc is too large?",1123"sources": [1124"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#move-the-data-loader-to-the-training-function",1125"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler",1126"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",1127"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",1128"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference"1129],1130"document_ids": [11319858,11321895,11336150,11346908,113583671136],1137"answer": " The ValueError \"The actor ImplicitFunc is too large\" is likely caused by an actor function capturing a large object in scope. To resolve this issue, you can use `ray.put()` to put large objects in the Ray object store, and then pass them to the actor function. Additionally, you may want to consider reducing the size of the data loader or the batch size to reduce the memory usage of the actor.\n\nThe warning \"The actor TrainTrainable is very large (52 MiB)\" suggests that the actor function `TrainTrainable` is capturing a large array or object in scope. You can use `ray.put()` to put this object in the Ray object store and then pass it to the actor function to resolve the issue.\n\nThe warning \"To ensure full parallelization across an actor pool of size 4, the specified batch size should be at most 5\" suggests that the batch size is too large for the specified actor pool size. You can reduce the batch size to ensure full parallelization across the actor pool.\n\nThe message \"object_store_memory: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:05<00:00, 5.15s/it]\" suggests that the object store memory is fully utilized. You can increase the object store memory limit to accommodate larger objects or more actors.\n\nThe log messages related to PIL images and labels suggest that the data loader is processing image data and labels. You can use `ray.put()` to put large image data in the Ray object store and then pass it to the actor function to reduce the memory usage of the actor.\n\nThe code snippet related to `TorchPredictor` and `ActorPoolStrategy` suggests that you are using Ray's data APIs to perform distributed inference. You can configure the autoscaling actor pool that this inference stage uses, setting upper and lower bounds on the actor pool size, and even tweak the batch prefetching vs. inference task queueing tradeoff. This can help optimize the performance of the distributed inference.",1138"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1139},1140{1141"question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",1142"sources": [1143"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1144"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",1145"https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",1146"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",1147"https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others"1148],1149"document_ids": [1150228,11516887,115214653,11539675,1154143261155],1156"answer": " To distribute training for your custom Keras neural net in Ray on Databricks, you can follow these steps:\n\n1. Install the required packages: `!pip install -U \"ray[train]\" tensorflow`\n2. Set up your dataset and model using TensorFlow and Keras.\n3. Use the `TensorflowTrainer` class from `ray.train.tensorflow` to create a distributed training setup.\n\nHere's an example of how you can set this up:\n\n```python\nimport ray\nfrom ray.train.tensorflow import TensorflowTrainer\n\n# Set up your dataset and model\nimport numpy as np\nimport tensorflow as tf\n\n# Create a simple Keras model\nmodel = tf.keras.models.Sequential([\n tf.keras.layers.Dense(10, activation='relu', input_shape=(10,)),\n tf.keras.layers.Dense(1)\n])\n\n# Compile the model\nmodel.compile(optimizer='adam', loss='mse')\n\n# Create a Ray Dataset\nds = ray.data.from_numpy(np.random.rand(1000, 10))\n\n# Define the training function\ndef train_func(model, ds):\n model.fit(ds.train_shuffles(1), epochs=1)\n\n# Set up the trainer\ntrainer = TensorflowTrainer(\n train_func,\n scaling_config=ray.train.ScalingConfig(num_workers=4, use_gpu=False)\n)\n\n# Run the training\nresult = trainer.fit()\n```\n\nThis example sets up a simple Keras model, creates a Ray Dataset, and then uses the `TensorflowTrainer` class to distribute the training of the model across 4 workers. The `train_func` function defines the training loop, and the `ScalingConfig` object is used to configure the number of workers and whether to use GPUs.",1157"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1158},1159{1160"question": "how to use ray.put and ray,get?",1161"sources": [1162"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",1163"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1164"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1165"https://docs.ray.io/en/master/ray-core/api/core.html#objects",1166"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory"1167],1168"document_ids": [11695443,11704811,11716597,11724693,117350301174],1175"answer": " To use `ray.put` and `ray.get`, you can follow these steps:\n\n1. Use `ray.put(value)` to store a value in Ray's object store.\n2. Use `ray.get(object_ref)` to get the value from the object store, where `object_ref` is the return value of `ray.put()` or a task that produces an object.\n\nHere's an example:\n\n```python\n# Store an object in the object store\nmy_obj = ray.put(\"Hello, Ray!\")\n\n# Get the object from the object store\nstored_obj = ray.get(my_obj)\nprint(stored_obj) # Output: b'Hello, Ray!'\n```\n\nIn the context provided, `ray.put()` is used to store a value in the object store, and `f.remote(a)` creates a task that depends on the object stored with `ray.put(None)`. The `ray.wait()` function is used to wait for tasks to complete and return a list of finished and unfinished tasks.\n\nTo use the Ray Client, you need to initialize Ray using `ray.init(address=\"<address>\")` with the appropriate cluster address. Then, you can use the Ray API as usual.",1176"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1177},1178{1179"question": "how do I use Ray Data to pre process many files?",1180"sources": [1181"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",1182"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-data",1183"https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",1184"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#code-example",1185"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart"1186],1187"document_ids": [11889178,11899189,11906866,11915233,119291801193],1194"answer": " To preprocess many files using Ray Data, you can follow these general steps:\n\n1. Initialize Ray and create a Ray Dataset from your files. Ray Data supports creating datasets from various sources and file formats.\n2. Preprocess your Ray Dataset as needed. Ray Data provides a range of preprocessing operations that can be used to transform your data prior to training.\n3. Incorporate the preprocessed Ray Dataset into your Ray Train script for distributed ML training.\n\nHere is a simple code example to get you started:\n\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Create a Ray Dataset from your files\n# Replace `my_files` with a list of your file paths\ntrain_dataset = ray.data.read_parquet(my_files)\n\n# Preprocess your Ray Dataset\ndef preprocess(batch):\n # Add your preprocessing code here\n pass\n\ntrain_dataset = train_dataset.map_batches(preprocess)\n\n# Use the preprocessed Ray Dataset in your Ray Train script\n# ...\n```\n\nFor more information, you can refer to the Ray Data documentation on preprocessing and ingest for ML training: <https://docs.ray.io/en/latest/data/preprocessing.html>",1195"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1196},1197{1198"question": "can\u2019t pickle SSLContext objects",1199"sources": [1200"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",1201"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",1202"https://docs.ray.io/en/master/ray-core/objects/serialization.html#customized-serialization",1203"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1204"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting"1205],1206"document_ids": [12074939,12084945,12094947,12104957,121149531212],1213"answer": " Based on the context provided, Ray, a parallelization framework, uses a customized Pickle protocol version 5 for serialization. However, it seems like SSLContext objects cannot be pickled, as indicated by the error message in the inspection result: \"cannot pickle '_thread.lock' object\". To overcome this limitation, you might need to define a custom serialization process for SSLContext objects, if you have access to the code, by defining a `__reduce__` function inside the corresponding class.",1214"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1215},1216{1217"question": "How do I install CRDs in Kuberay?",1218"sources": [1219"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",1220"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",1221"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",1222"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",1223"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#getting-started-with-kuberay"1224],1225"document_ids": [12265720,12276103,12285647,12295817,123056461231],1232"answer": " To install CRDs in Kuberay, follow the instructions in Step 1 of the 'Instructions' section. Run the following command:\n\n```bash\nhelm repo add kuberay https://ray-project.github.io/kuberay-helm/\nhelm install kuberay-operator kuberay/kuberay-operator --version 0.6.0\n```\n\nThis command will install both CRDs and the Kuberay operator.",1233"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1234},1235{1236"question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",1237"sources": [1238"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",1239"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",1240"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",1241"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",1242"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors"1243],1244"document_ids": [1245140,124614578,12478038,12488161,124967881250],1251"answer": " The function for Ray data batch inference is named `__call__()` because it allows the instance of the class to be invoked like a function, taking a batch of data as an argument and returning the batch with predictions. This is a common convention in Python for creating custom callable objects.",1252"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1253},1254{1255"question": "How to disconnnect ray client?",1256"sources": [1257"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1258"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",1259"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",1260"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",1261"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-5-change-the-ray-image-from-rayproject-ray-ray-version-to-rayproject-ray-ml-ray-version"1262],1263"document_ids": [12646597,12656613,12666611,12676605,126859851269],1270"answer": " To disconnect a Ray client, you can use the `disconnect()` method on the Ray client object. Here's an example:\n```python\nimport ray\n\n# Connect to the Ray cluster\nray.init(\"ray://<head_node_host>:10001\")\n\n# ... Ray code here ...\n\n# Disconnect from the Ray cluster\nray.shutdown()\n```\nNote that calling `ray.init()` without any arguments will create a default global Ray client, which you can disconnect from using `ray.shutdown()`.\n\nAlso, when using Ray multi-client, you need to call `disconnect()` explicitly to close the connection. Object references can only be used by the client from which it was obtained.",1271"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1272},1273{1274"question": "how to submit job with python with local files?",1275"sources": [1276"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1277"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",1278"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1279"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",1280"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm-launch.html#slurm-launch-py"1281],1282"document_ids": [12836460,12846516,12856467,12866518,128765751288],1289"answer": " To submit a job with Python using local files, follow these steps:\n\n1. Write your Ray task or function using the Ray APIs, as shown in the 'script.py' example in the context.\n2. Initialize Ray by calling `ray.init()`.\n3. Use `ray job submit` command to submit the job. Make sure to specify the path to the working directory in the `--working-dir` argument.\n\nFor local clusters, the command would look like this:\n\n```bash\nray job submit --working-dir your_working_directory -- python script.py\n```\n\nReplace 'your_working_directory' with the actual path to the directory containing your script.\n\nFor remote clusters, replace '127.0.0.1' with the head node's IP address in the following command:\n\n```bash\nclient = JobSubmissionClient(\"http://127.0.0.1:8265\")\njob_id = client.submit_job(\n entrypoint=\"python script.py\",\n runtime_env={\"working_dir\": \"./\"}\n)\nprint(job_id)\n```\n\nRemember to replace 'script.py' with the actual path to your Python script in the remote cluster.",1290"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1291},1292{1293"question": "How do I do inference from a model trained by Ray tune.fit()?",1294"sources": [1295"https://docs.ray.io/en/master/data/batch_inference.html#using-models-from-ray-train",1296"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#putting-it-together",1297"https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",1298"https://docs.ray.io/en/master/tune/examples/bayesopt_example.html#running-tune-experiments-with-bayesopt",1299"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve"1300],1301"document_ids": [13026927,13033223,1304986,13053093,130627741307],1308"answer": " To do inference from a model trained by Ray Tune, you can use the Checkpoint returned by Ray Train and use it for batch inference with Ray Data. Here is an example of how you can use the Checkpoint for inference:\n```makefile\ncheckpoint = ray.train.checkpoint.Checkpoint.from_uri(checkpoint_uri)\nmodel = checkpoint.to_model()\n\ndef predict(dataset):\n predictions = model.predict(dataset)\n return predictions\n\ninference_dataset = ray.data.read_csv(\"s3://path/to/your/data.csv\")\ninference_result = inference_dataset.map(predict)\n```\nIn this example, `checkpoint_uri` is the URI of the trained model's checkpoint. The `to_model()` method is used to convert the Checkpoint to a model that can be used for inference. The `predict()` function takes a Ray Dataset as input and returns the predictions. Finally, the `map()` method is used to apply the `predict()` function to the inference dataset.",1309"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1310},1311{1312"question": "is there a way to load and run inference without using pytorch or tensorflow directly?",1313"sources": [1314"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1315"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1316"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",1317"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-2-inference-on-a-single-batch",1318"https://docs.ray.io/en/master/data/user-guide.html#user-guides"1319],1320"document_ids": [13216959,13226963,13236899,13248021,132568361326],1327"answer": " Yes, it is possible to load and run inference without using PyTorch or TensorFlow directly by using a pre-trained model from libraries like Hugging Face and performing inference on a Ray Dataset. The example you provided demonstrates how to do this using PyTorch models. You can create a Ray Dataset from in-memory Numpy arrays or other sources and file formats, and then map a pre-trained model over the data to perform batch inference. Additionally, you can use GPUs for inference by specifying the number of GPUs to use in the `ds.map_batches()` call and moving the model and data to and from the GPU in your code.",1328"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1329},1330{1331"question": "what does ray do",1332"sources": [1333"https://docs.ray.io/en/master/data/data.html#learn-more",1334"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1335"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",1336"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1337"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples"1338],1339"document_ids": [13406995,13414811,13425556,13436597,134453801345],1346"answer": " Ray is a universal array and task parallelism framework for Python. It allows you to parallelize and distribute computations across multiple CPUs and GPUs in your cluster. You can use Ray to parallelize machine learning algorithms, data processing pipelines, and other types of computations. To get started, you can read the Ray blogs, check out the Ray documentation including the 'ray.init()' function, Ray Jobs, and Ray Client usage, and explore Ray tutorials and examples.",1347"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1348},1349{1350"question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",1351"sources": [1352"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",1353"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",1354"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus",1355"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",1356"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources"1357],1358"document_ids": [135913457,13604999,13612738,13625116,136310251364],1365"answer": " If you specify a fractional GPU in the resource spec, such as 0.5 GPUs, and your program uses more than that, it may result in errors or unexpected behavior due to insufficient GPU resources. Ray supports fractional GPUs to allow multiple tasks and actors to share the same GPU, but each Ray worker will be scheduled on one machine, and you need to ensure that your model fits on the fractional memory.",1366"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1367},1368{1369"question": "how to pickle a variable defined in actor\u2019s init method",1370"sources": [1371"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",1372"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1373"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",1374"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",1375"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments"1376],1377"document_ids": [13785194,13795192,13804241,1381637,138246641383],1384"answer": " To pickle a variable defined in an actor's `__init__` method, you can pass the variable as an argument when calling the actor's method, as shown in the examples provided. However, you cannot pickle the actor object itself.\n\nIn your specific case, if you want to pickle the `global_var_actor` variable, you can modify the `f` method in the `Actor` class to accept `global_var_actor` as an argument:\n```python\n@ray.remote\nclass Actor:\n def __init__(self, global_var_actor):\n self.global_var_actor = global_var_actor\n\n def f(self, global_var_actor):\n return ray.get(global_var_actor.get_global_var.remote()) + 3\n```\nThen, when calling the `f` method, pass `global_var_actor` as an argument:\n```python\nglobal_var_actor = GlobalVarActor.remote()\nactor = Actor.remote(global_var_actor)\nray.get(global_var_actor.set_global_var.remote(4))\n# This returns 7 correctly.\nassert ray.get(actor.f.remote(global_var_actor)) == 7\n```\nNote that this is just for illustration purposes, and you may not need to pass `global_var_actor` as an argument in your specific use case if it is already available in the actor's context.",1385"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1386},1387{1388"question": "how do I do an all_reduce operation among a list of actors",1389"sources": [1390"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1391"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",1392"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",1393"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",1394"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference"1395],1396"document_ids": [13976787,1398673,1399788,14005441,140183691402],1403"answer": " To perform an all-reduce operation among a list of actors in Ray, you can use the `ray.util.state.list_actors()` function to get the actor handles, and then use the `ray.put()` and `ray.get()` functions to share data among the actors. However, Ray does not have a built-in all-reduce operation like some other distributed computing frameworks. You would need to implement the all-reduce logic yourself, such as by having each actor perform a reduction operation on its local data and then averaging the results across all the actors.\n\nHere's some example code to get you started:\n```python\nimport ray\nfrom ray.util.state import list_actors\n\n# Start Ray with 4 worker processes\nray.init(num_workers=4)\n\n# Define a function to perform the all-reduce operation\ndef all_reduce(value):\n # Get the actor handles\n actors = list_actctors()\n # Put the value on the object store\n obj_id = ray.put(value)\n # Initialize a list to hold the partial reductions\n partial_reductions = []\n # Compute the partial reduction for each actor\n for actor in actors:\n partial_reductions.append(ray.get(actor.remote.partial_reduce.remote(obj_id)))\n # Average the partial reductions\n reduced_value = sum(partial_reductions) / len(partial_reductions)\n # Return the reduced value\n return reduced_value\n\n# Define a function for the actors to perform a partial reduction\n@ray.remote\nclass MyActor:\n def partial_reduce(self, obj_id):\n # Get the value from the object store\n value = ray.get(obj_id)\n # Perform a partial reduction on the value\n partial_reduction = value * 2\n # Return the partial reduction\n return partial_reduction\n\n# Create some actors\nactors = [MyActor.remote() for _ in range(4)]\n\n# Perform the all-reduce operation\nresult = all_reduce(10)\n\n# Print the result\nprint(result) # Output: 80\n```\nIn this example, we define a `MyActor` class with a `partial_reduce` method that performs a partial reduction on a value. We then create four actors and use the `all_reduce` function to perform an all-reduce operation on the value 10. The `all_reduce` function gets the actor handles using `list_actors()`, puts the value on the object store using `ray.put()`, computes the partial reductions using `ray.get()` and the actor's `partial_reduce` method, and then averages the partial reductions to get the final reduced value. The result of the all-reduce operation is printed to the console.",1404"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1405},1406{1407"question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",1408"sources": [1409"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",1410"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",1411"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",1412"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",1413"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#bundles"1414],1415"document_ids": [14165063,14174790,14184789,14195051,142050501421],1422"answer": " Specifying a bundle with `{\"CPU\":0}` in the PlacementGroup will result in a bundle with no CPU resources. However, since the current cluster has only 2 CPUs left and a bundle with `{\"CPU\": 1}` is already created, creating a bundle with `{\"CPU\":0}` will not be possible as it would still require reserving CPU resources. Additionally, the `max_cpu_fraction_per_node` parameter (if set to less than 1.0) could also prevent creating a bundle with no CPU resources, as it sets aside CPUs that placement groups cannot occupy on nodes. Ultimately, the creation of the PlacementGroup will fail if there are not enough resources to create the specified bundles.",1423"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1424},1425{1426"question": "How to cancel job from UI?",1427"sources": [1428"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1429"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",1430"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",1431"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",1432"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel"1433],1434"document_ids": [14354430,14366478,14379791,14384498,143948941440],1441"answer": " To cancel a job from the UI, you can use the `ray job stop` command followed by the job ID. For example, `ray job stop raysubmit_tUAuCKubPAEXh6CW`. If you want to cancel a task, you can call `ray.cancel()` on the returned Object ref. For instance, `obj_ref = blocking_operation.remote(); ray.cancel(obj_ref)`.",1442"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1443},1444{1445"question": "how do I get my project files on the cluster when using Ray Serve? My workflow is to call `serve deploy config.yaml --address <remote dashboard agent address>`",1446"sources": [1447"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",1448"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1449"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",1450"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#development-workflow-with-serve-run",1451"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"1452],1453"document_ids": [145413738,145513732,14566484,145713696,145864651459],1460"answer": " To get your project files on the cluster when using Ray Serve, you can use the Ray Cluster Launcher to start a remote, multi-node cluster and then use the `serve deploy` command with the `--address` argument to specify your remote Ray cluster\u2019s dashboard agent address. The address should be of the form `[RAY_CLUSTER_URI]:[DASHBOARD_AGENT_PORT]`.\n\nAlternatively, if you have already started a remote Ray cluster, you can set the `RAY_ADDRESS` environment variable to the address of the Ray Dashboard for your cluster before using the `serve deploy` command.\n\nYou can also use the `serve run` command to run and test your application easily, either locally or on a remote cluster.\n\nFor more information, you can refer to the documentation on deploying Ray Serve applications to a remote cluster.",1461"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1462},1463{1464"question": "how do i install ray nightly wheel",1465"sources": [1466"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",1467"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",1468"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",1469"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",1470"https://docs.ray.io/en/master/installation.html#from-wheels"1471],1472"document_ids": [147314549,147463,1475288,1476286,1477611478],1479"answer": " To install the Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use pip to install the nightly wheel with support for the dashboard and cluster launcher by using the command `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`, where `LINK_TO_WHEEL.whl` is the link to the nightly wheel file. You can find the appropriate link for your system and Python version from the Ray documentation.",1480"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1481},1482{1483"question": "how do i install the latest ray nightly wheel?",1484"sources": [1485"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",1486"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",1487"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",1488"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",1489"https://docs.ray.io/en/master/installation.html#from-wheels"1490],1491"document_ids": [149214549,149363,1494288,1495286,1496611497],1498"answer": " To install the latest Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use the `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"` command, replacing `LINK_TO_WHEEL.whl` with the actual link to the nightly wheel you want to install. The nightly wheels can be found in the Daily Releases (Nightlies) section of the Ray documentation.",1499"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1500},1501{1502"question": "how can I write unit tests for Ray code?",1503"sources": [1504"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",1505"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",1506"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",1507"https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",1508"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples"1509],1510"document_ids": [15115397,15125395,15139668,15144403,151553801516],1517"answer": " To write unit tests for Ray code, you can follow these tips:\n\n1. Fix the resource quantity with `ray.init(num_cpus=...)` in the `setUp` method of your test case.\n2. If possible, share the Ray cluster across tests to avoid the overhead of creating a new cluster for each test.\n3. Consider creating a mini-cluster with `ray.cluster_utils.Cluster` for more control over the testing environment.\n4. Be careful when running tests in parallel to avoid resource contention and race conditions.\n5. When testing Ray code that involves Java and Python integration, use the appropriate Ray APIs to call Python code from Java and vice versa.\n6. Make sure to set the `ray.job.code-search-path` system property to the directory of your Python code.\n\nHere's an example test case using the `unittest` module:\n\n```python\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n def setUp(self):\n ray.init(num_cpus=4, num_gpus=0)\n\n def test_my_ray_code(self):\n # Test your Ray code here\n pass\n\n def tearDown(self):\n ray.shutdown()\n```\n\nMake sure to call `ray.shutdown()` in the `tearDown` method to release any resources used by Ray.",1518"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1519},1520{1521"question": "How I stop Ray from spamming lots of Info updates on stdout?",1522"sources": [1523"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",1524"https://docs.ray.io/en/master/cluster/cli.html#ray-up",1525"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",1526"https://docs.ray.io/en/master/cluster/cli.html#ray-start",1527"https://docs.ray.io/en/master/cluster/cli.html#ray-stop"1528],1529"document_ids": [1530410,15315616,153213929,15335611,153456131535],1536"answer": " To stop Ray from spamming lots of Info updates on stdout, you can try changing the log style to 'record' which outputs record-style without formatting. This can be done using the `--log-style record` command-line option.\n\nFor example, you can use the following command to start Ray with the 'record' log style:\n```css\nray start --log-style record\n```\nAlternatively, you can use the `ray stop` command to manually stop Ray processes on the local machine if you do not need them anymore. You can use the `--force` option to send SIGKILL instead of SIGTERM to forcefully terminate the processes.\n```css\nray stop --force\n```\nNote that changing the log style or stopping Ray processes may affect the functionality of your application, so use these options with caution.",1537"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1538},1539{1540"question": "how to deploy stable diffusion 2.1 with Ray Serve?",1541"sources": [1542"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",1543"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",1544"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#serve-a-stablediffusion-text-to-image-model-on-kubernetes",1545"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-7-submit-a-request-to-the-ray-serve-application",1546"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment"1547],1548"document_ids": [15496127,155014398,15516124,15525989,1553142551554],1555"answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install RayService:\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml\n\n2. Install the required libraries:\npip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers\n\n3. Save the Stable Diffusion code to a file named `stable_diffusion.py`.\n\n4. Deploy the Stable Diffusion model with Ray Serve.\n\n5. Forward the serve port of the head Pod:\nkubectl port-forward --address 0.0.0.0 $HEAD_POD 8000\n\n6. Prepare a sample image file (e.g., `stable_diffusion_example.png`).\n\nNote: The Python files for the Ray Serve application and its client are in the ray-project/serve_config_examples repo and the Ray documentation.",1556"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1557},1558{1559"question": "what is actor_handle?",1560"sources": [1561"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",1562"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",1563"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",1564"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.current_actor.html#ray-runtime-context-runtimecontext-current-actor",1565"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles"1566],1567"document_ids": [156814698,15694351,157011795,15714874,157243531573],1574"answer": " An actor handle is a reference to an actor object, which can be passed into other tasks and used to call the actor's remote functions or methods. It allows for communication and interaction with the actor from other parts of the program. Examples of actor handles are provided in the context for Python, Java, and C++. In Python, an actor handle can be obtained by calling `Counter.remote()`, and then passed to other tasks.",1575"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1576},1577{1578"question": "how to kill a r detached actors?",1579"sources": [1580"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",1581"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1582"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",1583"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",1584"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor"1585],1586"document_ids": [15874859,15884658,15894601,15904848,159145041592],1593"answer": " To kill a detached actor in Ray, you can use the `ray.kill()` function, passing the actor handle as an argument. For example:\n```python\nimport ray\n\nactor_handle = ray.remote(MyActorClass).remote()\n\n# ... do some work with the actor ...\n\nray.kill(actor_handle)\n```\nNote that this will immediately interrupt any running tasks on the actor and cause them to fail. If you want to let pending tasks finish before terminating the actor, you can call `actor.__ray_terminate__.remote()` instead.\n\nAfter calling `ray.kill()`, subsequent calls to get the actor handle via `ray.get_actor()` will fail. Also, note that detached actors must be manually destroyed once you are sure that they are no longer needed, as they are not automatically garbage-collected by Ray.",1594"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1595},1596{1597"question": "How to force upgrade the pip package in the runtime environment if an old version exists?",1598"sources": [1599"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",1600"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",1601"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",1602"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",1603"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages"1604],1605"document_ids": [16064624,16074292,16084380,16094260,161042671611],1612"answer": " To force upgrade the pip package in the runtime environment if an old version exists, you can specify the pip version in the runtime environment with \"pip\\_version\" parameter. For example:\n\nRuntimeEnv(\npip={\"packages\":[\"tensorflow\", \"requests\"], \"pip\\_check\": False,\n\"pip\\_version\": \"==22.0.2;python\\_version=='3.8.11'\"}\n)\n\nThis will ensure that pip version 22.0.2 is installed for Python version 3.8.11. If an old version of the package exists, it will be upgraded to the specified version. Note that you cannot specify both conda and pip keys in the runtime\\_env at the same time.",1613"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1614},1615{1616"question": "How do I do global shuffle with Ray?",1617"sources": [1618"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",1619"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",1620"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",1621"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",1622"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling"1623],1624"document_ids": [16256826,16266928,16276827,16286825,162992061630],1631"answer": " To perform a global shuffle with Ray, you can use the `random_shuffle()` function provided by Ray Data. Here is an example of how to do it:\n\n```\nimport ray\n\nctx = ray.data.DataContext.get_current()\nctx.use_push_based_shuffle = True\n\nds = (\n ray.data.range(1000)\n .random_shuffle()\n)\n```\n\nThis will randomly shuffle the data in the `ds` dataset. Note that shuffling data in Ray materializes the dataset in memory, so it is not streamed through memory.\n\nIf you want to use the experimental push-based shuffle implementation, you can set the `RAY_DATA_PUSH_BASED_SHUFFLE` environment variable to `1` when running your application. For example:\n\n```\n$ RAY_DATA_PUSH_BASED_SHUFFLE=1 python sort.py --num-partitions=10 --partition-size=1e7\n```\n\nThis will enable the push-based shuffle implementation for the `sort.py` script.\n\nFor more information on shuffle performance and tuning, you can refer to the Ray Data documentation on performance tips and tuning.",1632"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1633},1634{1635"question": "How to find namespace of an Actor?",1636"sources": [1637"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1638"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1639"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",1640"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1641"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace"1642],1643"document_ids": [16444719,16454871,16464709,16474880,164847511649],1650"answer": " To find the namespace of an Actor in Ray, you can use the `ray.get_actor()` function, which allows you to specify the namespace of the actor. If you don't know the namespace, you can call this function with `None` as the namespace argument, and it will return the actor handle if the actor with the given name exists in the current namespace. If the actor exists in a different namespace, you will need to specify that namespace when calling `ray.get_actor()`.\n\nHere's an example of how to use `ray.get_actor()` to find an actor in the current namespace:\n```python\nimport ray\n\n# Get a handle to the actor with name \"my_actor\" in the current namespace\nactor_handle = ray.get_actor(\"my_actor\")\n```\nIf you're not sure what namespace the actor is in, you can try calling `ray.get_actor()` with `None` as the namespace argument:\n```python\nimport ray\n\n# Try to get a handle to the actor with name \"my_actor\" in the current namespace\ntry:\n actor_handle = ray.get_actor(\"my_actor\", None)\nexcept ValueError:\n print(\"Actor not found in current namespace\")\n```\nIf the actor is not found in the current namespace, you can try specifying a different namespace:\n```python\nimport ray\n\n# Try to get a handle to the actor with name \"my_actor\" in the \"other_namespace\" namespace\ntry:\n actor_handle = ray.get_actor(\"my_actor\", \"other_namespace\")\nexcept ValueError:\n print(\"Actor not found in other namespace\")\n```\nNote that if the actor does not exist, `ray.get_actor()` will raise a `ValueError` exception.",1651"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1652},1653{1654"question": "How does Ray work with async.io ?",1655"sources": [1656"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",1657"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",1658"https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks",1659"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1660"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors"1661],1662"document_ids": [16634208,16644550,16654199,16666597,166745531668],1669"answer": " Ray's interaction with async.io is achieved by making every remote operation, such as tasks and actor methods, asynchronous. This means that the operation immediately returns a promise/future, which is essentially an identifier of the operation's result. To get the actual results, you need to call `ray.get()` on the IDs of the results, but this call blocks until the results are available, which can hurt parallelism.\n\nHowever, Ray integrates natively with asyncio, and you can use it alongside popular async frameworks like aiohttp, aioredis, etc. For example, you can define an async method in an actor class, and then invoke it using `ray.get()` in an async context, like `asyncio.run(async_get())`. This allows you to run multiple async actor methods concurrently in the event loop without blocking the driver program.",1670"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1671},1672{1673"question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",1674"sources": [1675"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",1676"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",1677"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-tasks",1678"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",1679"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance"1680],1681"document_ids": [168214432,16835235,16845423,16855236,168652191687],1688"answer": " To debug a hanging `ray.get()` call, there are a few things to consider. First, `ray.get()` is a blocking call, which means it waits until the requested result is available. If you call it in a loop, it can harm parallelism as the loop will not continue until the call to `ray.get()` is resolved.\n\nIn your case, the issue might be that you are not instantiating the recv side call for `ray.get([A.do_send.remote(target_rank=1)])`. To fix this, you can separate the call to `ray.get()` from the call to the remote functions, so all remote functions are spawned before waiting for the results. This way, they can run in parallel in the background.\n\nAdditionally, instead of calling `ray.get()` one by one, you can pass a list of object references to `ray.get()` to wait for all of the tasks to finish.\n\nFinally, avoid calling `ray.get()` unnecessarily for intermediate steps. Work with object references directly and only call `ray.get()` at the end to get the final result. This is because when `ray.get()` is called, objects must be transferred to the worker/node that calls `ray.get()`. If you don't need to manipulate the object, you probably don't need to call `ray.get()` on it!",1689"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1690},1691{1692"question": "can you show me an example of ray.actor.exit_actor()",1693"sources": [1694"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1695"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",1696"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1697"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1698"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle"1699],1700"document_ids": [17014615,17024601,17034618,17044622,170546061706],1707"answer": " Sure, here are examples of using `ray.actor.exit_actor()` in Python and C++:\n\nPython:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n def exit(self):\n ray.actor.exit_actor()\n\nactor = Actor.remote()\nactor.exit.remote()\n```\nC++:\n```cpp\n#include \"ray/ray.h\"\n\nRAY_EXPORT void exit_actor() {\n ray::Actor::Exit();\n}\n\nRAY_EXPORT ray::Status ExitActor(ray::ActorHandle<my_actor> actor_handle) {\n actor_handle.Kill();\n return ray::Status::OK();\n}\n```\nNote that calling `ray.actor.exit_actor()` or `actor_handle.Kill()` will immediately exit the actor process and release resources associated with the actor. It is generally not necessary to manually terminate actors, as they are automatically garbage collected. However, it can be useful in cases where an actor is unexpectedly hanging or leaking resources.\n\nIn C++, the `Kill()` method will cause the actor to immediately exit its process, causing any current, pending, and future tasks to fail with a `RayActorError`. If you would like Ray to automatically restart the actor, you can set a nonzero `max_restarts` in the `@ray.remote` options for the actor and pass the `no_restart=False` flag to `ray.kill()`.\n\nFor named and detached actors, calling `ray.kill()` on an actor handle will destroy the actor and allow the name to be reused.",1708"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1709},1710{1711"question": "how to add log inside actor?",1712"sources": [1713"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",1714"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",1715"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",1716"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#add-metadata-to-structured-logs",1717"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view"1718],1719"document_ids": [1720414,1721540,1722682,1723537,17244461725],1726"answer": " To add logging inside an actor, you can create a logger using the `logging` module and use it to log messages. Here's an example:\n```python\nimport logging\n\nclass MyActor(object):\n def log(self, msg):\n logger = logging.getLogger(__name__)\n logger.info(msg)\n\nactor = MyActor.remote()\nray.get(actor.log.remote(\"A log message for an actor.\"))\n```\nBy default, all stdout and stderr of Tasks and Actors are redirected to the Driver output. If you want to disable this feature, you can view the Configuring Logging documentation.\n\nNote that if a task is from a concurrent actor (i.e. an async actor or a threaded actor), the log of the tasks are expected to be interleaved. In this case, you can use `ray logs actor --id` to get the entire actor log.\n\nAdditionally, if you need to add metadata to structured logs, you can fetch the metadata of Jobs, Tasks, or Actors with Ray\u2019s `ray.runtime_context.get_runtime_context` API. For example, to get the job ID, you can use `ray.get_runtime_context().get_job_id`.",1727"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1728},1729{1730"question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",1731"sources": [1732"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt",1733"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",1734"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",1735"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",1736"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text"1737],1738"document_ids": [173910923,17407002,17415292,17426853,174370011744],1745"answer": " Sure, here's a script that performs batch inference with GPT-2 on text data from an S3 bucket using Ray Train and Hugging Face's \ud83e\udd17 Transformers pipeline:\n```python\nimport os\nimport ray\nfrom transformers import pipeline, AutoTokenizer, GPT2ForSequenceClassification\n",1746"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1747},1748{1749"question": "How do I enable Ray debug logs?",1750"sources": [1751"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",1752"https://docs.ray.io/en/master/ray-contribute/debugging.html#debugging-for-ray-developers",1753"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",1754"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",1755"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging"1756],1757"document_ids": [1758510,1759944,1760573,1761680,176243721763],1764"answer": " To enable Ray debug logs, you can set the environment variable `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1` on each node before starting Ray. This will print the full runtime\\_env setup log messages to the driver. For example, you can add this variable in the setup\\_commands section in the Ray Cluster configuration file. Note that this feature requires a full installation of Ray using `pip install \"ray[default]\"`.",1765"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1766},1767{1768"question": "How do I list the current Ray actors from python?",1769"sources": [1770"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1771"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",1772"https://docs.ray.io/en/master/ray-core/actors/actor-utils.html#actor-pool",1773"https://docs.ray.io/en/master/ray-observability/key-concepts.html#ray-states",1774"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started"1775],1776"document_ids": [1777475,1778488,17794543,1780402,17814771782],1783"answer": " To list the current Ray actors from Python, you can use the following command:\n```python\nfrom ray.util.state import list_actors\nlist_actors(filters=[(\"state\", \"=\", \"ALIVE\")])\n```\nThis will return a list of all the currently alive actors in the Ray cluster.",1784"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1785},1786{1787"question": "I want to kill the replica actor from Python. how do I do it?",1788"sources": [1789"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",1790"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",1791"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1792"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1793"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill"1794],1795"document_ids": [17964848,179713912,17984502,17994615,180048591801],1802"answer": " To kill a replica actor from Python, you can use the `ray.kill()` function and pass the actor handle to it. Here is an example:\n\n```python\nimport ray\n\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\n\nAlternatively, if you want to kill the actor immediately, you can delete all actor handles referring to it, which will cause them to complete any outstanding work and then shut down. If you only have one reference to an actor handle, calling `del actor` could trigger actor deletion. Note that your program may have multiple references to the same ActorHandle, and actor termination will not occur until the reference count goes to 0.\n\nYou can also manually terminate an actor from within one of the actor methods by calling `ray.actor.exit_actor()`. This will kill the actor process and release resources associated/assigned to the actor. However, this approach should generally not be necessary as actors are automatically garbage collected. The ObjectRef resulting from the task can be waited on to wait for the actor to exit.",1803"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1804},1805{1806"question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",1807"sources": [1808"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",1809"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",1810"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",1811"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",1812"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus"1813],1814"document_ids": [18154994,18166548,18174582,18184997,181950001820],1821"answer": " To specify that a task should run on a V100 GPU type, you can use the `num_gpus` argument in the `@ray.remote` decorator when defining the remote function. However, this only indicates the number of GPUs required for the task, it does not specify the GPU type.\n\nRay uses the CUDA_VISIBLE_DEVICES environment variable to assign GPUs to tasks or actors. The actual assignment of GPUs to tasks is done by Ray and it's not possible to specify a particular GPU type (e.g. V100) in the task declaration.\n\nIt's up to the user to ensure that the task is compatible with the assigned GPU and makes use of its capabilities. For example, if the task uses TensorFlow, the user should install the GPU version of TensorFlow and configure TensorFlow to use the assigned GPU.\n\nIn summary, to run a task on a V100 GPU, you should:\n\n1. Make sure that the node where the task will run has a V100 GPU available.\n2. Use the `num_gpus` argument in the `@ray.remote` decorator to indicate the number of GPUs required for the task.\n3. Ensure that the task is compatible with the assigned GPU and makes use of its capabilities.",1822"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1823},1824{1825"question": "How do I get started?",1826"sources": [1827"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",1828"https://docs.ray.io/en/master/cluster/vms/index.html#learn-more",1829"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",1830"https://docs.ray.io/en/master/serve/index.html#learn-more",1831"https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started"1832],1833"document_ids": [18346335,18356210,18364702,183713629,183861891839],1840"answer": " To get started with Ray, follow these steps:\n1. Install Ray using `pip install -U ray`.\n2. Import and initialize Ray: `import ray; ray.init()`.\n3. For Ray Serve applications, refer to the quick start tutorials and examples in the 'Getting Started' and 'Examples' sections.\n4. If you want to launch a Ray cluster manually, refer to the on-premise cluster setup guide.\n\nFor more information, consult the Ray docs, which provide user guides, API references, and additional examples.",1841"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1842},1843{1844"question": "How to specify python version in runtime_env?",1845"sources": [1846"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",1847"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",1848"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",1849"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",1850"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"1851],1852"document_ids": [18534624,18544253,18554466,18564610,1857138561858],1859"answer": " To specify the Python version in a RuntimeEnv, you can include it in the `pip_version` field under the `pip` key, using the syntax `\"pip_version\": \"==22.0.2;python_version=='3.8.11'\"`. This example sets the pip version to 22.0.2 and the Python version to 3.8.11. Here is an example of how to use it:\n```\nRuntimeEnv(\n pip={\"packages\":[\"tensorflow\", \"requests\"], \"pip_check\": False,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"}\n)\n```\nAlternatively, you can use the `conda` key to specify a conda environment that has the desired Python version.\n```\nRuntimeEnv(\n conda={\"name\": \"my_conda_env\", \"python_version\": \"3.8.11\"}\n)\n```",1860"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1861},1862{1863"question": "how to create a Actor in a namespace?",1864"sources": [1865"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1866"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",1867"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1868"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1869"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"1870],1871"document_ids": [18724719,18734709,18744640,18754871,187646371877],1878"answer": " To create an actor in a specific namespace, you can use the `options` method to specify the namespace when creating the actor. Here's an example in Python:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\nctx = ray.init(\"ray://localhost:10001\")\n\n# Create an actor with the specified namespace.\nActor.options(name=\"my_actor\", namespace=\"actor_namespace\", lifetime=\"detached\").remote()\n```\nNote that the namespace must be specified when you first connect to the cluster, using the `ray.init` function with the `namespace` argument. Named actors are only accessible within their namespaces.",1879"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1880},1881{1882"question": "Can I specify multiple working directories?",1883"sources": [1884"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",1885"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",1886"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",1887"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",1888"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable"1889],1890"document_ids": [18911775,18921790,18934328,18944282,189523711896],1897"answer": " No, you cannot specify multiple working directories in a single Ray program. However, you can use either local paths or remote URIs to specify a working directory in the runtime environment. If you need to access files relative to the original working directory, you can disable the behavior of changing the working directory by setting the `RAY_CHDIR_TO_TRIAL_DIR` environment variable to 0. In this case, you should use relative paths for read-only purposes and ensure that the directory is synced on all nodes if running on multiple machines.",1898"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1899},1900{1901"question": "what if I set num_cpus=0 for tasks",1902"sources": [1903"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",1904"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",1905"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",1906"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#configuring-parallelism-with-omp-num-threads",1907"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote"1908],1909"document_ids": [19105113,19115936,19126511,191313461,191448501915],1916"answer": " If you set `num_cpus=0` for tasks, it means that you are not reserving any CPU resources for those tasks. However, it's important to note that if you don't explicitly set the `num_cpus` parameter, the default value is 1 CPU resource for tasks. If you set `num_cpus=0`, it could affect the scheduling and running of your tasks, and you might encounter unexpected behavior. Therefore, it's recommended to explicitly set the `num_cpus` parameter for your tasks to avoid any surprises.",1917"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1918},1919{1920"question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",1921"sources": [1922"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",1923"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",1924"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",1925"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",1926"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites"1927],1928"document_ids": [19295818,19305953,19315975,19325963,193357521934],1935"answer": " Yes, it is possible to have Ray on K8s without using KubeRay and still enable autoscaling, but it requires additional configuration. You can use the Kubernetes Cluster Autoscaler to provision nodes for Ray pods that the Ray autoscaler decides to create. However, this method doesn't provide the built-in autoscaling feature that KubeRay provides. To enable Ray autoscaling with KubeRay, you need to set `enableInTreeAutoscaling:true` in the KubeRay RayCluster custom resource (CR) config, and set the `minReplicas` and `maxReplicas` fields to constrain the number of replicas of an autoscaling worker group. This requires KubeRay v0.3.0+ and Ray 1.10 or higher.",1936"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1937},1938{1939"question": "how to manually configure and manage Ray cluster on Kubernetes",1940"sources": [1941"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",1942"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",1943"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",1944"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",1945"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide"1946],1947"document_ids": [19485717,19495917,19505918,19515526,195257831953],1954"answer": " To manually configure and manage a Ray cluster on Kubernetes, you can use the RayCluster custom resource (CR) and the KubeRay operator. First, create a RayCluster CR configuration in yaml format, specifying the desired Ray version and other parameters. Then, use `kubectl apply -f raycluster.yaml` to deploy the Ray cluster. You can manage and monitor the cluster using the Ray Dashboard and, optionally, integrations with Prometheus and Grafana. The RayService provides Kubernetes-native support for Ray clusters and Ray Serve applications, allowing you to use `kubectl` to create and update the cluster and its applications.",1955"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1956},1957{1958"question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",1959"sources": [1960"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",1961"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",1962"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",1963"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",1964"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors"1965],1966"document_ids": [196714512,19685130,19695129,19704393,19715581972],1973"answer": " Yes, if you shutdown a raylet, the tasks and workers on that node will also get killed. This is because the raylet is responsible for managing the resources and workers on a node. If the raylet is shutdown, the node is treated as failing and all tasks and workers on that node will be killed.\n\nIn your case, it seems that a task has failed with an OutOfMemoryError. The raylet has killed some workers on the node due to memory pressure. To address this issue, you can consider provisioning more memory on the node or reducing task parallelism by requesting more CPUs per task. You can also adjust the kill threshold or disable worker killing by setting the appropriate environment variables when starting Ray.\n\nYou can refer to the documentation on how to prevent out of memory issues in Ray: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>. The Ray Dashboard's metrics page and event page also provide out-of-memory killer-specific events and metrics.",1974"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1975},1976{1977"question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",1978"sources": [1979"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",1980"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",1981"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",1982"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#what-s-the-out-of-memory-error",1983"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management"1984],1985"document_ids": [1986548,1987547,1988559,1989549,199050101991],1992"answer": " To debug out-of-memory issues in Ray, first familiarize yourself with Ray's Memory Management model. If you're experiencing issues, locate the source of the memory leak by viewing the documentation on \"Find per Task and Actor Memory Usage.\" This section explains how to debug out-of-memory errors and provides information on the per Task and Actor memory usage graph. You can also use the CLI command `htop` as an alternative. The documentation \"What\u2019s the Out-of-Memory Error?\" provides context on the Linux Out of Memory killer and how Ray's application-level memory monitor addresses this problem. For further information, you can refer to the \"Debugging Out of Memory\" documentation.",1993"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1994},1995{1996"question": "How to use callback in Trainer?",1997"sources": [1998"https://docs.ray.io/en/master/train/examples/transformers/transformers_torch_trainer_basic.html#ray-train-basic-example-for-huggingface-transformers",1999"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_end.html#ray-train-lightning-raytrainreportcallback-on-init-end",2000"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_start.html#ray-train-lightning-raytrainreportcallback-on-init-start",2001"https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune",2002"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_batch_end.html#ray-train-lightning-raytrainreportcallback-on-batch-end"2003],2004"document_ids": [20059704,20068531,20078760,20082390,200982062010],2011"answer": " To use a callback in the Trainer provided in the context, you can add it to the trainer object like this: `trainer.add_callback(YourCallback())`.\n\nIn the given context, `RayTrainReportCallback` is added to the trainer object using this method.\n\nIf you want to create a custom callback, you can create a class that inherits from `pl.Callback` and implement the methods you want to use. For example, `on_batch_end` is a method you can implement to execute code after each batch is trained. Note that some callback hooks are deprecated and will be removed in future versions, so it's recommended to use the alternatives suggested in the documentation.\n\nIn the provided query, there is no direct mention of how to use a callback specifically in the `Trainer` class, but the concept of using callbacks in Ray Tune is explained. However, Ray Tune callbacks are different from PyTorch Lightning callbacks used in the provided context.",2012"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2013},2014{2015"question": "How to provide current working directory to ray?",2016"sources": [2017"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2018"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2019"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2020"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",2021"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable"2022],2023"document_ids": [20246627,20256625,20266597,20275757,202823712029],2030"answer": " To provide the current working directory to Ray, you can specify the `working_dir` in the runtime environment when calling `ray.init()`. This will upload the working directory on the laptop to the Ray cluster. Once the Ray actor is launched as a remote actor, its current working directory will be set to the corresponding trial directory. Therefore, you can use relative paths in your remote task definitions without needing to modify your code for different environments.",2031"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2032},2033{2034"question": "how to create an actor instance with parameter?",2035"sources": [2036"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",2037"https://docs.ray.io/en/master/ray-core/cross-language.html#python-calling-java",2038"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2039"https://docs.ray.io/en/master/ray-core/actors.html#actors",2040"https://docs.ray.io/en/master/rllib/actors.html#actors"2041],2042"document_ids": [20435391,20444398,20455192,20464329,2047117742048],2049"answer": " To create an actor instance with a parameter, you can use the `.remote()` method with the constructor and arguments. For example, if you have a class `ActorClass` with a constructor that takes an argument `num_samples`, you can create an actor instance like this: `actor_instance = ActorClass.remote(num_samples)`. This creates and runs the actor on a remote worker process, and the return value is an actor handle that can be used to call its methods.",2050"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2051},2052{2053"question": "how to push a custom module to ray which is using by Actor ?",2054"sources": [2055"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2056"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",2057"https://docs.ray.io/en/master/ray-core/actors.html#actors",2058"https://docs.ray.io/en/master/rllib/actors.html#actors",2059"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors"2060],2061"document_ids": [20624844,206314685,20644327,206511772,206642002067],2068"answer": " To push a custom module to Ray, which is used by an Actor, you need to ensure that the module is available on the Ray worker processes where the Actor is running. You can achieve this by using the `put()` function to transfer the module to the Ray workers before creating the Actor.\n\nHere's an example of how to do this:\n\n1. Create your custom module, e.g., `my_module.py`:\n```python\n# my_module.py\ndef my_function():\n return \"Hello from my_module!\"\n```\n1. Modify your Actor class to use the custom module:\n```python\n# actor.py\nimport ray\nimport my_module # Import the custom module\n\n@ray.remote\nclass MyActor:\n def __init__(self):\n pass\n\n def execute_my_function(self):\n result = my_module.my_function() # Use the custom module\n return result\n```\n1. Transfer the custom module to the Ray workers:\n```python\n# main.py\nimport ray\nimport actor\n\n# Initialize Ray\nray.init()\n\n# Transfer the custom module to Ray workers\nray.put(actor)\n\n# Create the Actor\nmy_actor = ray.remote(actor.MyActor).remote()\n\n# Call the Actor method that uses the custom module\nprint(ray.get(my_actor.execute_my_function.remote()))\n```\nNow, when you run `main.py`, the custom module `my_module.py` will be available to the Actor running on the Ray worker processes.",2069"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2070},2071{2072"question": "how to print ray working directory?",2073"sources": [2074"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",2075"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2076"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#print",2077"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",2078"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"2079],2080"document_ids": [20815757,2082511,2083600,20845732,208565972086],2087"answer": " To print the working directory of a Ray cluster, you can use the following command:\n```css\nray job submit --address <cluster_address> -- python -c \"import ray; ray.init(); print(ray.global_state.get_current_node_resource().directory)\"\n```\nReplace `<cluster_address>` with the address of your Ray cluster.\n\nHowever, if you are looking for the logging directory of Ray, by default, it is stored in a `/tmp/ray/session_*/logs` directory. Ray uses `/tmp/ray` (for Linux and macOS) as the default temp directory. To change the temp and the logging directory, specify it when you call `ray start` or `ray.init()`. A new Ray session creates a new folder to the temp directory, and the latest session folder is symlinked to `/tmp/ray/session_latest`.",2088"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2089},2090{2091"question": "why I can not see log.info in ray log?",2092"sources": [2093"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2094"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",2095"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",2096"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2097"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger"2098],2099"document_ids": [2100680,2101535,21026445,2103444,21045302105],2106"answer": " The Ray logging system defaults to logging.INFO level, so if you're not seeing log.info messages, they might be getting filtered out. You can try changing the logging level to logging.DEBUG to see if that makes your log messages appear. Also, note that the log files are located in the directory /tmp/ray/session_*/logs on each Ray node\u2019s file system.\n\nTo modify the Ray logger, you can use the following code:\n```python\nimport logging\n\nlogger = logging.getLogger(\"ray\")\nlogger.setLevel(logging.DEBUG) # Set the logging level to DEBUG\n```\nSimilarly, you can modify the logging configuration for Ray libraries by specifying the appropriate logger name.",2107"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2108},2109{2110"question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",2111"sources": [2112"https://docs.ray.io/en/master/data/examples/batch_training.html#filtering-a-dataset-on-read-a-class-anchor-id-filter-ds-a",2113"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",2114"https://docs.ray.io/en/master/data/user-guide.html#user-guides",2115"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2116"https://docs.ray.io/en/master/data/saving-data.html#saving-data"2117],2118"document_ids": [21197870,21207360,21216834,21226839,212367982124],2125"answer": " No, when using Ray Dataset to read a file, the order of the data is not guaranteed to be preserved. Ray Dataset uses parallelism and column selection to optimize reading large datasets, which can result in changes to the original order of the data. If maintaining the original order is important, consider using a different method or applying a sorting operation after reading the data.",2126"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2127},2128{2129"question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",2130"sources": [2131"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",2132"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",2133"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",2134"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",2135"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions"2136],2137"document_ids": [21384505,21394414,21404423,21414460,214244532143],2144"answer": " The statement \"Ray will not retry tasks upon exceptions thrown by application code\" means that if a task fails due to an exception caused by the user's code, Ray will not automatically retry the task by default. This is different from system failures or specified application-level failures, where Ray will retry failed tasks. However, the user can change this behavior and control whether application-level errors are retried by setting the `retry_exceptions` argument to True in the `ray.remote()` or `.options()` functions.",2145"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2146},2147{2148"question": "how do I specify the log directory when starting Ray?",2149"sources": [2150"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",2151"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2152"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",2153"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",2154"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr"2155],2156"document_ids": [21576445,2158511,21595861,2160510,216158802162],2163"answer": " To specify the log directory when starting Ray, you can use the `--logs-dir` option with the `ray start` command or set the `log_dir` parameter when calling `ray.init()`. Here is an example:\n\n```\nray start --logs-dir /path/to/logs\n```\n\nor\n\n```\nimport ray\nray.init(log_dir=\"/path/to/logs\")\n```\n\nThis will change the default log directory from `/tmp/ray/session_*/logs` to the specified directory.\n\nYou can also redirect Ray logs to stderr by setting the `RAY_LOG_TO_STDERR` environment variable to `1` on all Ray nodes. However, this is not recommended and may break certain features like Worker log redirection to Driver.\n\nFor Clusters on VMs, it is not recommended to redirect logs to stderr. Instead, follow this guide to persist logs.",2164"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2165},2166{2167"question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",2168"sources": [2169"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2170"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-3-create-a-mini-cluster-with-ray-cluster-utils-cluster",2171"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",2172"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#walkthrough-using-ray-with-slurm",2173"https://docs.ray.io/en/master/cluster/vms/index.html#overview"2174],2175"document_ids": [21766400,21775401,21785555,21796531,218062092181],2182"answer": " To launch a Ray cluster with 1",2183"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2184},2185{2186"question": "how to use ray api to scale up a cluster",2187"sources": [2188"https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",2189"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-ray-settings",2190"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",2191"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",2192"https://docs.ray.io/en/master/ray-overview/index.html#overview"2193],2194"document_ids": [21955604,21964321,21976451,21985953,21993642200],2201"answer": " To use the Ray API to scale up a cluster, you can use the `ray.autoscaler.sdk.request_resources()` function in the Python SDK. Additionally, you can configure the Ray autoscaler using the Ray cluster launcher or on Kubernetes to automatically scale the cluster up and down based on resource demand. For data scientists and machine learning practitioners, Ray lets you scale jobs without needing infrastructure expertise.",2202"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2203},2204{2205"question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",2206"sources": [2207"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",2208"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2209"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",2210"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",2211"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws"2212],2213"document_ids": [22146199,2215273,22166629,22175503,221863042219],2220"answer": " To specify a subnet in the Ray cloud launcher deployment file for AWS, you can add the `subnet_id` field under the `provider` section of the YAML file. Here is an example:\n```makefile\ncluster_name: my-aws-cluster\n\nprovider:\n type: aws\n region: us-west-2\n subnet_id: subnet-01234567890123456 # specify the subnet ID here\n```\nMake sure to replace `subnet-01234567890123456` with the actual ID of your desired subnet.",2221"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2222},2223{2224"question": "where I can find HTTP server error code log for Ray serve",2225"sources": [2226"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2227"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2228"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",2229"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",2230"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications"2231],2232"document_ids": [223313980,223413517,22355802,223614255,223757472238],2239"answer": " HTTP server error code logs for Ray Serve can be found in the logs at /tmp/ray/session\\_latest/logs/serve/ on each node's disk. These logs are also streamed to the driver Ray program during development, such as when using the serve.run() function or the serve run CLI command. Additionally, the Ray dashboard, accessible at http://localhost:8265/#/serve, provides visibility into Serve applications and their logs.",2240"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2241},2242{2243"question": "I am running ray cluster on amazon and I have troubles displaying the dashboard. When a I tunnel the dashboard port from the headnode to my machine, the dashboard opens, and then it disappears (internal refresh fails). Is it a known problem? What am I doing wrong?",2244"sources": [2245"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",2246"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",2247"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",2248"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",2249"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster"2250],2251"document_ids": [22525530,22535528,22546223,22555526,2256137402257],2258"answer": " Yes, it's a known issue when accessing the Ray Dashboard from outside the head node. This could be due to the dashboard port not being publicly exposed or internal refresh failures.\n\nHere are some suggestions:\n\n1. Ensure that the dashboard port is properly forwarded during cluster setup. You can customize the dashboard port using the `--dashboard-port` argument when starting the Ray cluster.\n2. If you're using the VM Cluster Launcher, use port forwarding to expose the dashboard port.\n3. Make sure the Ray Dashboard agent's default port (52365) or the specified port is available when Ray starts. If the port is unavailable, the dashboard agent's HTTP server will fail, but Ray will continue to run.\n4. For security reasons, avoid exposing the Ray Dashboard publicly without proper authentication.\n\nIf the issue persists, you might want to check the Ray Dashboard configurations based on how you launch the Ray Clusters, as integrations with Prometheus and Grafana are optional for enhanced Dashboard experience.",2259"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2260},2261{2262"question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",2263"sources": [2264"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",2265"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",2266"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",2267"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2268"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis"2269],2270"document_ids": [22715557,22725555,22736299,2274256,227565092276],2277"answer": " In the Ray cluster launcher YAML, `max_workers` in the `available_node_types.<node_type_name>.node_type.max_workers` field refers to the maximum number of worker nodes, and does not include the head node. The head node is managed separately and its workers are controlled by the autoscaler. The `max_workers` field in `cluster.yaml` specifies the maximum number of worker nodes to launch in addition to the head node.",2278"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2279},2280{2281"question": "How to update files in working directory ?",2282"sources": [2283"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",2284"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",2285"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",2286"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",2287"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads"2288],2289"document_ids": [22901775,22911790,22922369,22938894,229466252295],2296"answer": " To update files in the working directory, you can use the `to_directory()` function to write checkpoint data to a specific directory. Additionally, you can use the `update_metadata(metadata)` function to update the metadata stored with the checkpoint. If you want to write to a file directly, you can use the `open()` function with the appropriate file path. When writing to files, be mindful of multiple workers saving to the same location and use `ray.train.get_context().get_trial_dir()` as the path to save any outputs. Also, note that the working directory is set to the trial directory, so you don't need to worry about specifying the full path when opening files.",2297"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2298},2299{2300"question": "How I can update working directory file when ray allready initiated ?",2301"sources": [2302"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2303"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",2304"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",2305"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",2306"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments"2307],2308"document_ids": [23096625,23104531,23112371,23121790,231365952314],2315"answer": " To update the working directory file when Ray is already initiated, you can't directly change the working directory. However, you can set a new working directory for new actors by specifying the `working_dir` in the `runtime_env` when calling `ray.init()`. Any outputs should be saved to the trial directory, accessed using `ray.train.get_context().get_trial_dir()`.\n\nIf you need to change the temp directory, you can pass `--temp-dir={your temp path}` to `ray start` or provide the `_temp_dir` argument to `ray.init()`. Note that these methods are not officially stable.",2316"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2317},2318{2319"question": "how can I force ray head node to use custom pem file to ssh worker node?",2320"sources": [2321"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2322"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",2323"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2324"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2325"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart"2326],2327"document_ids": [23286385,23296205,23306406,23316356,23322722333],2334"answer": " To force the Ray head node to use a custom PEM file for SSHing into the worker node, you can specify the path to your custom SSH private key in the `ssh_private_key` field of the `auth` section in the configuration file (`config.yaml`). For example:\n\n```yaml\nauth:\n ssh_user: ubuntu\n ssh_private_key: /path/to/your/custom/key.pem\n```\n\nMake sure that the specified private key has been added to the project-wide metadata if you are using a managed cluster service. Also, ensure that the corresponding public key has been added to the `authorized_keys` file on the worker node.",2335"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2336},2337{2338"question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",2339"sources": [2340"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2341"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",2342"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#running-out-of-file-descriptors-too-may-open-files",2343"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",2344"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-operating-system-settings"2345],2346"document_ids": [234714740,23484375,2349603,2350563,235143152352],2353"answer": " The Global Control Service (GCS) is a centralized metadata server for a Ray cluster, running on the Ray head node. It manages node membership and actor directory, and handles actor, placement groups, and node management. However, it stores all data in-memory, making it not fault-tolerant by default.\n\nThe head node, which runs GCS and other cluster-level processes, can consume significant memory due to memory-demanding system components like GCS or the dashboard. If the head node has the same memory capacity as worker nodes and runs the same number of tasks and actors, it can easily face out-of-memory issues.\n\nIn your case, the GCS server might be taking up so much memory because of numerous connections from other components in Ray. If there are more than 1024 connections, it can raise error messages like \"Too many open files.\" To prevent this, you can adjust the max file descriptors limit per process via the ulimit command, such as \"ulimit -n 65536\" for the host configuration.\n\nTo alleviate memory pressure on the head node, you can consider not running any tasks and actors on the head node by specifying \"--num-cpus=0\" when starting a head node with \"ray start --head.\" If you use Kuberay, there are additional configuration options available.",2354"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2355},2356{2357"question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",2358"sources": [2359"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",2360"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",2361"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#troubleshooting",2362"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-for-using-tune-with-spot-instances-aws",2363"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource"2364],2365"document_ids": [2366467,23675692,23682428,23692421,237057242371],2372"answer": " If some nodes are \"pending\" for a long time when starting a cluster with `ray up`, it might be due to insufficient resources in the Kubernetes cluster. Here are some steps to debug:\n\n1. Check if your Kubernetes cluster has enough resources. You can check the current resource usage and allocate more resources if necessary.\n2. Check the recent failures with `kubectl describe pod <pod-name>`. This can help you identify any issues that might be causing the pods to fail.\n3. Try increasing the Docker resource limits if the pods are stuck in the \"Pending\" state.\n4. You can also try restarting the Ray cluster with `ray up CLUSTER.YAML --restart-only` and then killing a random node with `ray kill-random-node tune-default.yaml --hard`. This can help you check if the Ray cluster is working correctly.\n\nIf the issue persists, you can refer to the Ray documentation for more troubleshooting steps.",2373"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2374},2375{2376"question": "how to install Ray 2.5.1 from github or wheel?",2377"sources": [2378"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",2379"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",2380"https://docs.ray.io/en/master/installation.html#from-wheels",2381"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2382"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies"2383],2384"document_ids": [238514545,2386286,238761,238863,23892882390],2391"answer": " To install Ray 2.5.1, you can use the wheels provided in the \"Daily Releases (Nightlies)\" section of the context. However, Ray does not provide specific wheel files for version 2.5.1, so you can choose the closest version that matches your requirements.\n\nHere are the steps to install Ray with the dashboard and cluster launcher support:\n\n1. Clean removal of previous Ray install:\n```\npip uninstall -y ray\n```\n2. Install Ray with support for the dashboard and cluster launcher:\n```bash\npip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"\n```\nReplace `LINK_TO_WHEEL.whl` with the link to the Ray wheel file you want to install.\n\nIf you prefer to install Ray without the dashboard and cluster launcher, you can use the following command instead:\n```bash\npip install -U LINK_TO_WHEEL.whl\n```\nAgain, replace `LINK_TO_WHEEL.whl` with the link to the Ray wheel file you want to install.\n\nNote that Ray does not provide a wheel file for version 2.5.1 directly, so you may need to choose the closest version that matches your requirements.",2392"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2393},2394{2395"question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",2396"sources": [2397"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",2398"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-component-logs",2399"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2400"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2401"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"2402],2403"document_ids": [2404542,2405518,24064632,24074610,2408131992409],2410"answer": " To use `worker_setup_hook` in a runtime environment for setting up on worker node creation, you can define a callable function and pass it to the `worker_process_setup_hook` parameter when initializing the Ray cluster with `ray.init()`. Here's an example:\n\n```python\ndef logging_setup_func():\n # Your setup code here\n pass\n\nray.init(runtime_env={\"worker_process_setup_hook\": logging_setup_func})\n```\n\nAlternatively, you can create a runtime environment using the `RuntimeEnv` class and specify the `worker_process_setup_hook` parameter:\n\n```python\nfrom ray.runtime_env import RuntimeEnv\n\ndef logging_setup_func():\n # Your setup code here\n pass\n\nray_env = RuntimeEnv(worker_process_setup_hook=logging_setup_func)\nray.init(runtime_env=ray_env)\n```\n\nThe `logging_setup_func` will be called after the worker starts and before tasks and actors are scheduled. Make sure the function is defined and passed as a callable object when creating the runtime environment.",2411"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2412},2413{2414"question": "how to use Ray dataset on aws",2415"sources": [2416"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",2417"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",2418"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2419"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",2420"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset"2421],2422"document_ids": [24236732,24247959,24256846,24267865,242781592428],2429"answer": " To use Ray dataset on AWS, you can read data from an S3 bucket using the `ray.data.read_csv` function, as shown in the example `ds = ray.data.read_csv(\"s3://anonymous@air-example-data/iris.csv\")`. Before using Ray 2.5, you could also create datasets from Python objects, but this is no longer supported in Ray 2.5. Instead, Ray Data now requires schemas for all datasets and has a fixed default batch format of NumPy.\n\nAdditionally, you can use the Ray cluster launcher to start a Ray cluster on AWS, GCP, or Azure clouds, and use any ML framework of your choice. Ray Data supports a wide variety of data formats, including CSV, Parquet, and raw images.\n\nHere's an example of how to create a dataset using Ray:\n\n```python\nimport ray\n\n# Define the S3 link to the dataset\ns3_link = \"s3://anonymous@air-example-data/iris.csv\"\n\n# Create a dataset from the S3 link\nds = ray.data.read_csv(s3_link)\n\n# Show the first batch of the dataset\nds.show(limit=1)\n```\n\nNote: Before running the code, make sure to set up your AWS credentials and permissions to access the S3 bucket.",2430"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2431},2432{2433"question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",2434"sources": [2435"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2436"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",2437"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",2438"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2439"https://docs.ray.io/en/master/data/data-internals.html#execution-memory"2440],2441"document_ids": [24426994,24436928,24447395,24456841,244669652447],2448"answer": " To avoid dataset shuffling during a `ray.data.map_batches()` operation, ensure that you are not calling any functions that cause shuffling, such as `random_shuffle()`, `sort()`, or `groupby()`. Shuffling operations can be slow and can materialize the dataset in memory, which may not be desirable for large datasets. Instead, you can iterate over batches with shuffling by using the `map_batches()` function. When using `map_batches()`, make sure that the batch size is small enough to fit into memory and avoid potential out-of-memory situations.",2449"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2450},2451{2452"question": "Is the order of the input data preserved after a map_batches operation?",2453"sources": [2454"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2455"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",2456"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",2457"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",2458"https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms"2459],2460"document_ids": [24616994,24627738,24636965,24648218,246568132466],2467"answer": " No, the order of the input data is not preserved after a `map_batches` operation. In the provided context, the dataset is sorted by \"id\" after being randomly shuffled, demonstrating that the order can change. However, `map_batches` is designed to be faster than `map()` when the transformation is vectorized.",2468"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2469},2470{2471"question": "ray serve returns generic internal service error when there is an internal failure, how do I get it to emit more detailed errors or logs?",2472"sources": [2473"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",2474"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",2475"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#common-issues",2476"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2477"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray"2478],2479"document_ids": [24805671,24814423,24825670,248313980,24845952485],2486"answer": " To get more detailed errors or logs when Ray Serve returns a generic internal service error due to an internal failure, you can:\n\n1. Test your Ray Serve script locally or in a RayCluster before deploying it to a RayService, as recommended in Issue 1. This can help you catch any issues early on.\n2. Implement fault tolerance mechanisms provided by Ray to handle application-level failures, such as catching errors, retrying failed code, and handling misbehaving code, as described in the Fault Tolerance section.\n3. Check for common issues such as incorrect Ray Serve script, incorrect serveConfigV2, missing dependencies, incorrect import\\_path, and issues with creating or updating Serve applications, as listed in the Common issues section.\n4. Refer to the Ray Serve API documentation for more information on how to use the API and handle errors.\n5. Understand the kinds of failures that can exist in Ray, such as Task or Actor failures, and how to identify and fix them, as explained in the \"What Kind of Failures Exist in Ray?\" section.\n\nEnsure that your Ray image includes all the required dependencies and handle any dependency issues as necessary. When a remote Task or Actor fails, the returned object ref contains an exception, which you can catch and handle appropriately.",2487"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2488},2489{2490"question": "how do i track an uncaught exception in ray serve",2491"sources": [2492"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",2493"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2494"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception",2495"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayTaskError.html#ray-exceptions-raytaskerror",2496"https://docs.ray.io/en/master/ray-observability/reference/api.html#state-apis-exceptions"2497],2498"document_ids": [24994560,250013980,2501778,25024779,25036652504],2505"answer": " To track an uncaught exception in Ray Serve, you can use the RayStateApiException and RayTaskError classes provided by Ray. These exceptions are stored in the object store for each of the task's outputs when a task throws an exception during execution. When an object is retrieved from the object store, the Python method checks to see if the object is a RayTaskError and if it is, an exception is thrown propagating the error message. However, RaySystemError is a system error and it is not related to Ray Serve task execution. It is usually thrown when the raylet is killed.",2506"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2507},2508{2509"question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",2510"sources": [2511"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2512"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",2513"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",2514"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",2515"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs"2516],2517"document_ids": [251813517,2519680,2520445,2521415,25225352523],2524"answer": " To view logs for your Ray Serve endpoint in the Ray cluster, you can check the logs on disk at the location `/tmp/ray/session_latest/logs/serve/`. These logs are also emitted to stderr. In development, logs are streamed to the driver Ray program, which is the Python script that calls `serve.run()` or the `serve run` CLI command.\n\nIf you're using Ray Jobs, worker logs are always captured in the Ray logging directory, and driver logs are captured only for Ray Jobs submitted via Jobs API. You can access these logs using the Dashboard UI, CLI, or Python SDK.\n\nKeep in mind that if you execute the Driver directly on the Head Node of the Ray Cluster or run with Ray Client, the Driver logs are not accessible from the Dashboard. In this case, you can view the Driver logs in your terminal or Jupyter Notebooks.",2525"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2526},2527{2528"question": "where can I see logs for a failed ray serve deployment",2529"sources": [2530"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",2531"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",2532"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2533"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",2534"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging"2535],2536"document_ids": [253713960,253814255,253913523,254013514,2541135172542],2543"answer": " To view logs for a failed Ray serve deployment, you can follow these steps:\n\n1. Check the Kuberay operator logs by running `kubectl logs -f <kuberay-operator-pod-name>`. These logs contain information about the Serve application deployment event and health checks.\n2. Examine 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. These logs contain information about specific deployment failure reasons and autoscaling events.\n3. You can also find a copy of these logs at `/tmp/ray/session_latest/logs/serve/`. You can parse these stored logs with a logging stack such as ELK or Loki to be able to search by deployment or replica.\n4. Use the Ray dashboard to get a high-level overview of your Ray cluster and Ray Serve application\u2019s states. The dashboard includes details such as the number of deployment replicas currently running, logs for your Serve controller, deployment replicas, and proxies, and the Ray nodes running in your Ray cluster. You can access the Ray dashboard at port 8265 at your cluster\u2019s URI.\n\nNote that Ray Serve uses Python\u2019s standard logging module with a logger named \"ray.serve\", and logs are emitted from actors both to stderr and on disk on each node at `/tmp/ray/session_latest/logs/serve/`.",2544"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2545},2546{2547"question": "How to take a subset of a Ray Dataset?",2548"sources": [2549"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",2550"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",2551"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",2552"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",2553"https://docs.ray.io/en/master/data/overview.html#ray-data-overview"2554],2555"document_ids": [25569180,255710014,255810436,25597274,256068392561],2562"answer": " To take a subset of a Ray Dataset, you can use the `ray.data.range()` function, which allows for easy creation of synthetic datasets for testing or benchmarking. You can also use the `take()` method to get a specified number of rows from the dataset. For example, to get the first 10 rows of a dataset, you can use `dataset.take(10)`.\n\nIn addition, you can use the `map_partitions()` method to apply a function to partitions of the dataset, which can be useful for filtering or transforming the data. For example, to create a subset of the dataset that only includes rows with a certain value in a particular column, you can use `map_partitions()` to apply a function that filters the data based on that column.\n\nHere is an example of how you might use these methods to take a subset of a Ray Dataset:\n```\n# Create a Ray Dataset from a range of integers\ndataset = ray.data.range(1000)\n\n# Use map_partitions() to filter the dataset and only include rows with even values in the 'id' column\nfiltered_dataset = dataset.map_partitions(lambda partition: [row for row in partition if row['id'] % 2 == 0])\n\n# Use take() to get the first 10 rows of the filtered dataset\nsubset = filtered_dataset.take(10)\n```\nThis will create a Ray Dataset with the first 10 rows of the filtered dataset, which only includes rows with even values in the 'id' column.",2563"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2564},2565{2566"question": "How do I load all checkpoints from trials of a Tune experiment launched with `tune.run`? I ran my initial experiment with cloud checkpointing, so I\u2019d need to download all the checkpoints to analyze them.",2567"sources": [2568"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",2569"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",2570"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",2571"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",2572"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis"2573],2574"document_ids": [25752226,25762439,25772347,25782253,257914732580],2581"answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `get_all_configs()` and `get_best_checkpoint()` functions from the `ExperimentAnalysis` class.\n\nFirst, you need to create an `ExperimentAnalysis` object by providing the path to the experiment's state JSON file. You can get this file from the `local_dir` parameter you specified when launching the experiment.\n\nThen, you can use `get_all_configs()` to get a list of all configurations, and iterate through them to get the best checkpoint for each trial using `get_best_checkpoint()`.\n\nHere's an example:\n\n```python\nfrom ray import tune\nfrom ray.tune.analysis import ExperimentAnalysis\n\n# Create ExperimentAnalysis object\nanalysis = ExperimentAnalysis(experiment_checkpoint_path=\"<path_to_state_json>\")\n\n# Get all configurations\nconfigs = analysis.get_all_configs()\n\n# Iterate through configurations and load best checkpoint for each trial\nfor config in configs:\n trial_name = config[\"trial_id\"]\n best_checkpoint_path = analysis.get_best_checkpoint(trial_name)\n print(f\"Best checkpoint for trial {trial_name}: {best_checkpoint_path}\")\n```\n\nReplace `<path_to_state_json>` with the path to your experiment's state JSON file.\n\nRegarding your cloud checkpointing, you can download the checkpoints to your local machine by copying the files from the cloud storage to your local directory. The path to the checkpoints is stored in the `best_checkpoint_path` variable in the example above. You can use the appropriate method provided by your cloud storage service to download the files.",2582"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2583},2584{2585"question": "How can I kill a \"detached\" Actor ?",2586"sources": [2587"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",2588"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",2589"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",2590"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",2591"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance"2592],2593"document_ids": [25944658,25954859,25964502,25974493,259844552599],2600"answer": " To kill a \"detached\" Actor in Ray using C++, you can't do it directly in C++ as of now because customizing the lifetime of an actor hasn't been implemented in C++ yet. However, you can kill a detached actor using Ray's Python API.\n\nFirst, you need to get the `ActorHandle` of the detached actor. Then, you can call `ray.kill(actor)` to terminate the actor forcefully. This will interrupt any running tasks on the actor and cause them to fail immediately.\n\nHere's an example:\n\n```python\nimport ray\n\n# Assuming `detached_actor` is your detached actor's handle\nray.kill(detached_actor)\n```\n\nKeep in mind that this will not run any `atexit` handlers installed in the actor. If you want to kill the actor but let pending tasks finish, you can call `detached_actor.__ray_terminate__.remote()` instead. This will queue a termination task and run any atexit handlers installed in the actor before it shuts down.",2601"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2602},2603{2604"question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",2605"sources": [2606"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",2607"https://docs.ray.io/en/master/rllib-env.html#configuring-environments",2608"https://docs.ray.io/en/master/rllib/rllib-env.html#configuring-environments",2609"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#environment-variables-are-not-passed-from-the-driver-process-to-worker-processes",2610"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment"2611],2612"document_ids": [26136595,26143,261510502,2616609,261793432618],2619"answer": " To set environment variables in `ray.init()`, you can use the `runtime_env` argument and include the `env_vars` key. For example, to set the `foo` environment variable, you can use:\n\n```python\nray.init(runtime_env={\"env_vars\": {\"foo\": \"foo\"}})\n```\n\nIn the context provided, you can modify the `ray.init()` call as follows:\n\n```python\nray.init(\n \"ray://1.2.3.4:10001\",\n namespace=\"my_namespace\",\n runtime_env={\n \"working_dir\": \"files/my_project\",\n \"pip\": [\"toolz\", \"requests\"],\n \"env_vars\": {\"foo\": \"foo\"},\n },\n)\n```\n\nThis will set the `foo` environment variable for all the Ray workers.",2620"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2621},2622{2623"question": "What is the rest api for getting the head node id?",2624"sources": [2625"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",2626"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",2627"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",2628"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",2629"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"2630],2631"document_ids": [26326535,2633721,2634754,26354909,26364932637],2638"answer": " The rest API for getting the head node ID is not explicitly mentioned in the provided context. However, you can obtain the head node ID using `ray.runtime_context.RuntimeContext.get_node_id()` in the Ray Python SDK. Alternatively, you can use the `ray list nodes` command in the CLI to get the node ID/IP and then use it with the `ray logs` command to get logs from a specific node.",2639"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2640},2641{2642"question": "how to rerun a canceled ray task",2643"sources": [2644"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2645"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",2646"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2647"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2648"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"2649],2650"document_ids": [26514897,26524498,26534359,265411802,2655147022656],2657"answer": " Based on the provided context, you cannot rerun a canceled Ray task as cancelled tasks aren't retried in Ray. However, you can recreate and rerun the task using the same function that created it initially. To cancel a task, you can call `ray.cancel()` on the returned object ref. Only async actor tasks can be interrupted; Ray does not offer a mechanism for interrupting single-threaded or multi-threaded actor tasks.",2658"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2659},2660{2661"question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",2662"sources": [2663"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",2664"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",2665"https://docs.ray.io/en/master/ray-core/actors/async_api.html#setting-concurrency-in-async-actors",2666"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#pattern-using-ray-wait-to-limit-the-number-of-pending-tasks",2667"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#dividing-data-into-batches-and-spawning-a-ray-task-for-each-batch-to-be-ran-in-parallel"2668],2669"document_ids": [26707738,26711787,26724581,26735246,267452892675],2676"answer": " In the provided context, there is no direct way to set the maximum parallel concurrent scheduled tasks in `map_batches()`. However, you can control the concurrency of Ray tasks by adjusting the `max_concurrency` flag when creating Ray actors.\n\nIn your specific use case, since you're using `map_batches()`, you can control the concurrency by modifying the resource requirements of each task instead of limiting the number of concurrent tasks. This is because Ray automatically decides task parallelism based on resource availability.\n\nHere's an example of how to modify the resource requirements of each task:\n\n```python\n@ray.remote(num_cpus=0.5) # Modify the number of CPUs or GPUs as needed\nclass MyTask:\n def process_batch(self, batch):\n # Your processing logic here\n ...\n\n# Use the task in map_batches\nds.map_batches(MyTask.process_batch)\n```\n\nKeep in mind the note in the provided context:\n\n> This method is meant primarily to limit how many tasks should be in flight at the same time. It can also be used to limit how many tasks can run concurrently, but it is not recommended, as it can hurt scheduling performance. Ray automatically decides task parallelism based on resource availability, so the recommended method for adjusting how many tasks can run concurrently is to modify each task\u2019s resource requirements instead.",2677"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2678},2679{2680"question": "How do I get the number of cpus from ray cluster?",2681"sources": [2682"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",2683"https://docs.ray.io/en/master/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",2684"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",2685"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",2686"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core"2687],2688"document_ids": [26895936,26905693,269110003,2692614,269354132694],2695"answer": " To get the number of CPUs in a Ray cluster, you can check the Ray dashboard which displays information such as the number of CPU cores available. Alternatively, you can use the `ray.cluster_resources()` function in Python to get the cluster resources, which includes the number of CPUs.\n\nIn the provided context, the Ray cluster has 1 head Pod with 4 physical CPUs and 1 worker Pod with 1 CPU. Additionally, the `num-cpus` parameter is set to 0 in the head Pod to prevent any Ray workloads with non-zero CPU requirements from being scheduled on the head.",2696"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2697},2698{2699"question": "How to use the exclude option to the runtime_env",2700"sources": [2701"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",2702"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2703"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",2704"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2705"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig"2706],2707"document_ids": [27084466,270913856,27104826,27114610,271247642713],2714"answer": " To use the \"exclude\" option with the runtime environment in Ray, you can create a RuntimeEnvConfig object with the desired configuration options and then pass it to the RuntimeEnv class. However, the \"exclude\" option is not explicitly mentioned in the provided context. It's possible that it's not a direct option of the RuntimeEnv or RuntimeEnvConfig classes.\n\nTo exclude certain files or directories when creating a runtime environment, you can specify the working\\_dir and py\\_modules options in the RuntimeEnv class to point to the desired directory or Python modules, excluding any unwanted files or directories.\n\nIf you want to modify the runtime environment of a JobConfig, you can use the JobConfig.set\\_runtime\\_env() method and pass in a dictionary containing the desired runtime environment options. However, there is no explicit \"exclude\" option mentioned in this method's context.\n\nIf you could provide more context or clarify what you want to exclude, I could give a more specific answer.",2715"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2716},2717{2718"question": "show a map batch example with batch_format",2719"sources": [2720"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2721"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2722"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2723"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",2724"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air"2725],2726"document_ids": [27276994,272814727,27297019,27306786,2731103652732],2733"answer": " Here's an example of using `map_batches()` with `batch_format`:\n\n```python\nfrom typing import Dict\nimport numpy as np\nimport ray\n\ndef increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n batch[\"image\"] = np.clip(batch[\"image\"] + 4, 0, 255)\n return batch\n\nds = (\n ray.data.read_images(\"s3://anonymous@ray-example-data/image-datasets/simple\")\n .map_batches(increase_brightness, batch_format=\"dict\")\n)\n```\n\nIn this example, we read image data and increase the brightness of each image in the dataset. The `batch_format` is set to \"dict\", which specifies that the batches should be represented as dictionaries. The `map_batches()` function applies the `increase_brightness` function to each batch of data.",2734"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2735},2736{2737"question": "how to find local ray address",2738"sources": [2739"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2740"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",2741"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",2742"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2743"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"2744],2745"document_ids": [27464817,27476422,27486183,27496484,275064652751],2752"answer": " To find the local Ray cluster address, you can look for the address of the latest cluster started in the /tmp/ray/ray_current_cluster directory. If that's empty, you can also check the environment variable RAY\\_ADDRESS. If you're using a Ray cluster started on VMs or Kubernetes, you'll need to follow instructions for setting up network access from a client. Once you have the Ray Dashboard address, you can set the RAY\\_ADDRESS environment variable to it for the Ray Jobs CLI to find your Ray cluster.",2753"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2754},2755{2756"question": "How to start ray cluster on multiple node via CLI?",2757"sources": [2758"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-3-create-a-mini-cluster-with-ray-cluster-utils-cluster",2759"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",2760"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",2761"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",2762"https://docs.ray.io/en/master/cluster/vms/index.html#overview"2763],2764"document_ids": [27655401,27666203,27676599,27685636,276962092770],2771"answer": " To start a Ray cluster on multiple nodes via CLI, you can use the Ray cluster launcher's `ray up` command with a configuration file that specifies the head node and worker nodes. Here's an example of how to use this command:\n\n1. Set up your Ray cluster:\n If you have a running Ray cluster (version >= 1.5), Ray Client server is likely already running on port 10001 of the head node by default. Otherwise, you\u2019ll want to create a Ray cluster. To start a Ray cluster locally, you can run `ray start --head`. To start a Ray cluster remotely, you can follow the directions in Getting Started.\n\n2. Create a configuration file (e.g. `config.yaml`) that specifies the head node and worker nodes:\n\n ```\n cluster:\n name: my-ray-cluster\n head_node:\n instance_type: m5.xlarge\n image_id: ami-0abcdef1234567890\n availability_zone: us-west-2a\n security_group_ids:\n - sg-0abcdef1234567890\n subnet_id: subnet-0abcdef1234567890\n worker_nodes:\n - instance_type: m5.xlarge\n image_id: ami-0abcdef1234567890\n availability_zone: us-west-2a\n security_group_ids:\n - sg-0abcdef1234567890\n subnet_id: subnet-0abcdef1234567890\n count: 2\n ```\n\n3. Use the Ray CLI to start the cluster:\n\n ```\n $ ray up -y config.yaml\n ```\n\nThis will start a Ray cluster with a head node and two worker nodes on the specified cloud provider. You can modify the configuration file to suit your needs, such as changing the instance type, image ID, and number of worker nodes.",2772"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2773},2774{2775"question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",2776"sources": [2777"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",2778"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",2779"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",2780"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",2781"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup"2782],2783"document_ids": [27845068,27856934,278610283,27879898,27886222789],2790"answer": " The Ray tuner shows \"running\" but CPU usage is almost 0% because the Ray actor and Ray Data both require CPUs to run tasks, and by default, Ray Tune tries to fully utilize cluster CPUs. If there are not enough free CPUs for Ray Data to run on, it can prevent Ray Data from scheduling tasks, reducing performance or causing workloads to hang. To ensure CPU resources are always available for Ray Data execution, you can limit the number of concurrent Tune trials with the max\\_concurrent\\_trials Tune option.",2791"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2792},2793{2794"question": "should the Ray head node and all workers have the same object store memory size allocated?",2795"sources": [2796"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",2797"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",2798"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",2799"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",2800"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources"2801],2802"document_ids": [2803563,28045557,28055013,28065014,280751092808],2809"answer": " No, the Ray head node and worker nodes do not need to have the same object store memory size allocated. In fact, it is recommended to allocate less object store memory to the head node to avoid out-of-memory issues, as the head node has more memory-demanding system components and may run additional processes such as the GCS or the dashboard. The object store memory size can be controlled using the `--object-store-memory` flag when starting a Ray cluster. Additionally, the object store memory is not a logical resource for scheduling, but users can manually specify the quantities of pre-defined resources and add custom resources when starting the Ray cluster.",2810"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2811},2812{2813"question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",2814"sources": [2815"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",2816"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",2817"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",2818"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",2819"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.ProxyLocation.html#ray-serve-config-proxylocation"2820],2821"document_ids": [282214075,282314081,282413958,282514010,2826142792827],2828"answer": " In Ray Serve, you can specify whether to set up an HTTP proxy on each node or just the head node using the `ProxyLocation` configuration. The options are:\n\n- `HeadOnly`: start one HTTP server on the head node (this is the default)\n- `EveryNode`: start one HTTP server per node\n- `NoServer`: disable HTTP server\n\nYou can set this configuration using the `ray.serve.config.ProxyLocation` enum. For example:\n\n```\nfrom ray.serve.config import ProxyLocation\n\nray.serve.config.proxy_location = ProxyLocation.EVERY_NODE\n```\n\nThis configuration determines where the HTTP proxies will be run to receive ingress traffic to the cluster. Note that this is different from the `num_cpus` configuration, which is used to reserve CPU cores for each internal Serve HTTP proxy actor (this configuration is now deprecated).",2829"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2830},2831{2832"question": "Want to embed Grafana into the Ray Dashboard, given that I am using KubeRay\n\nGiven the context that Prometheus and Grafana are not running on my Head node, and that I am using KubeRay, how should I be setting the following variables?\n\u2022 `RAY_GRAFANA_HOST`\n\u2022 `RAY_PROMETHEUS_HOST`\nAnd is there a way to set them more intelligently, given that head node IP is changing every time we reconfigure our cluster?",2833"sources": [2834"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",2835"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-11-embed-grafana-panels-in-ray-dashboard",2836"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",2837"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",2838"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster"2839],2840"document_ids": [28415544,28426086,28435543,28445545,284560612846],2847"answer": " Given that Prometheus and Grafana are not running on your Head node and you are using KubeRay, you should set `RAY_PROMETHEUS_HOST` to the address that the head node can use to access Prometheus in your KubeRay cluster, and `RAY_GRAFANA_HOST` to the address that the head node can use to access Grafana in your KubeRay cluster.\n\nSince the head node IP is changing every time you reconfigure your cluster, you can set `RAY_GRAFANA_IFRAME_HOST` to a stable address that the user's browsers can use to access Grafana and embed visualizations, such as a load balancer URL or a NodePort service.\n\nFor example, if Prometheus is hosted on `http://prom-server.prometheus.svc.cluster.local:9090` and Grafana is hosted on `http://grafana-server.grafana.svc.cluster.local:3000`, you can set the variables as follows:\n\n```\nRAY_PROMETHEUS_HOST=http://prom-server.prometheus.svc.cluster.local:9090\nRAY_GRAFANA_HOST=http://grafana-server.grafana.svc.cluster.local:3000\nRAY_GRAFANA_IFRAME_HOST=http://grafana-server.grafana.svc.cluster.local:3000\n```\n\nNote that `RAY_PROMETHEUS_NAME` should be set to a different value for each Ray Cluster if you use a different Prometheus server for each cluster, and add these datasources in Grafana.",2848"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2849},2850{2851"question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",2852"sources": [2853"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#experiment-results-kuberay-v0-6-0",2854"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-1-create-a-new-kubernetes-cluster",2855"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",2856"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",2857"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp"2858],2859"document_ids": [28605779,28615772,28625975,286313902,286461702865],2866"answer": " The GCS (KubeRay) determines which Kubernetes pod to kill when using KubeRay autoscaling based on the memory usage of the KubeRay operator Pod, which is highly correlated to the number of Pods in the Kubernetes cluster. When the Kubernetes cluster does not have enough computing resources, the GKE Autopilot adds a new Kubernetes node into the cluster. The Ray Autoscaler and the Kubernetes Cluster Autoscaler complement each other in creating and deleting Ray pods and Kubernetes nodes. It is recommended to configure the RayCluster so that only one Ray pod fits per Kubernetes node.",2867"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2868},2869{2870"question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",2871"sources": [2872"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2873"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",2874"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2875"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",2876"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema"2877],2878"document_ids": [287913854,288013762,288113852,28824300,2883139912884],2885"answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you can include the following in your YAML config:\n\n```yaml\nhttp_options:\n request_timeout_s: <your_timeout_value>\n```\n\nReplace `<your_timeout_value>` with the desired number of seconds for the end-to-end timeout for HTTP requests. This config is global to your Ray cluster and cannot be updated during runtime.",2886"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2887},2888{2889"question": "How do I make the GPU available on my M1 laptop to ray?",2890"sources": [2891"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#gpu-support",2892"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",2893"https://docs.ray.io/en/master/installation.html#installing-ray",2894"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",2895"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray"2896],2897"document_ids": [28984991,28995105,290059,2901284,2902145412903],2904"answer": " To make the GPU available on your M1 laptop for Ray, you don't need to do anything specific since Ray natively supports GPU as a pre-defined resource type for Apple silicon (M1) hardware. Ray also provides GPU isolation by automatically setting the CUDA\\_VISIBLE\\_DEVICES environment variable, which most machine learning frameworks will respect for GPU assignment.",2905"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2906},2907{2908"question": "How can I add a timeout for the Ray job?",2909"sources": [2910"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",2911"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s",2912"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",2913"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",2914"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s"2915],2916"document_ids": [29175757,29188655,291913762,29204639,292184812922],2923"answer": " To add a timeout for a Ray job, you can use the `timeout_s` parameter in the configuration classes such as `HorovodConfig` or `TorchConfig`. For example, you can set `ray.train.horovod.HorovodConfig.timeout_s = 300` to set an end-to-end timeout of 300 seconds for a Horovod job.\n\nAdditionally, when calling `ray.get()` to retrieve the result of a remote function, you can also specify a timeout using the `timeout` parameter. For example, `ray.get(obj_ref, timeout=4)` will time out if the result is not ready within 4 seconds.",2924"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2925},2926{2927"question": "how do I set custom /tmp directory for remote cluster?",2928"sources": [2929"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",2930"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2931"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",2932"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#cluster-setting",2933"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"2934],2935"document_ids": [2936872,29376482,29386517,29399337,294064652941],2942"answer": " To set a custom `/tmp` directory for a remote cluster, you can use the `ray_temp_root_dir` configuration when starting the Ray cluster. This will create a subdirectory under the specified path for the cluster's temporary data.\n\nFor example, if you want to set the temporary directory to `/mnt/mytmp` for a remote cluster with configuration file `cluster.yaml`, you can use the following command:\n```css\nray dashboard cluster.yaml --ray-temp-root-dir=/mnt/mytmp\n```\nThis will create a subdirectory like `ray-{head_port}-{random_suffix}` under `/mnt/mytmp` for the cluster's temporary data.\n\nNote that this directory needs to be accessible by the host, the outer container, and the inner container, as mentioned in the context.",2943"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2944},2945{2946"question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",2947"sources": [2948"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",2949"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",2950"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2951"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",2952"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment"2953],2954"document_ids": [29554960,29564531,2957511,29584959,29598722960],2961"answer": " Yes, if you set the `--temp-dir` flag to a different directory, Ray object spilling will occur in the custom directory. However, to configure the directory for object spilling specifically (regardless of the value of `--temp-dir`), you should use the `object_spilling_config` parameter in `ray.init()`, as shown in the context.",2962"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2963},2964{2965"question": "can you give me an example for *`--runtime-env-json`*",2966"sources": [2967"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",2968"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",2969"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",2970"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",2971"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster"2972],2973"document_ids": [29744342,29754253,29764466,29774310,2978138262979],2980"answer": " The `--runtime-env-json` flag is used to specify a runtime environment when running a task or actor using Ray. The value for this flag is a JSON string that describes the runtime environment. Here's an example of using this flag:\n```bash\nserve run --address=ray://<head-node-ip-address>:10001 \\\n --runtime-env-json='{\"env_vars\": {\"MY_ENV_VAR\": \"my-value\"}, \"working_dir\": \"./project/src\", \"pip\": [\"requests\", \"chess\"]}' \\\n local_dev:graph\n```\nIn this example, the runtime environment includes three parts:\n\n1. Environment variables: `{\"MY_ENV_VAR\": \"my-value\"}`\n2. Working directory: `./project/src`\n3. Python packages: `[\"requests\", \"chess\"]`\n\nThese settings will be applied to the task or actor being run.",2981"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2982},2983{2984"question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",2985"sources": [2986"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-autoscaler",2987"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",2988"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",2989"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",2990"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"2991],2992"document_ids": [29936479,299413450,299514225,299614187,2997143032998],2999"answer": " The context does not provide a specific value for `maxConcurrentReplicas` when autoscaling configuration is specified. It should be set based on the needs of the specific use case, considering the resources available and the desired performance. It's important to ensure that the quota for node types is set correctly and to consider tuning the `upscaling_speed` for faster autoscaling.",3000"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3001},3002{3003"question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",3004"sources": [3005"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3006"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3007"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",3008"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",3009"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema"3010],3011"document_ids": [301213451,301314303,301414187,301514225,3016140323017],3018"answer": " The value of `max_concurrent_queries` is not directly dependent on `target_num_ongoing_requests_per_replica`. The former is the maximum number of pending queries in a single replica, while the latter is the expected number of ongoing requests per replica. The appropriate value for `max_concurrent_queries` would depend on the specific system's capacity and performance requirements.",3019"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3020},3021{3022"question": "what is a `smoothing_factor`",3023"sources": [3024"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3025"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3026"https://docs.ray.io/en/master/tune/examples/includes/pb2_example.html#pb2-example",3027"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",3028"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining"3029],3030"document_ids": [303113455,303213454,30333900,30341925,303519203036],3037"answer": " A `smoothing_factor` is a multiplicative factor that speeds up or slows down the pace of upscaling or downscaling decisions made by an autoscaler. A higher value for the `smoothing_factor` increases the sensitivity of the autoscaler to changes in the metric, resulting in more rapid upscaling or downscaling decisions. Conversely, a lower value for the `smoothing_factor` makes the autoscaler less sensitive to changes in the metric, resulting in more gradual upscaling or downscaling decisions. The `smoothing_factor` can be adjusted to control the aggressiveness of the autoscaler and to prevent rapid, potentially unnecessary changes to the number of replicas.",3038"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3039},3040{3041"question": "What is the reason actors change their state to unhealthy?",3042"sources": [3043"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",3044"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",3045"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",3046"https://docs.ray.io/en/master/serve/api/index.html#get-api-serve-deployments-status",3047"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status"3048],3049"document_ids": [30504371,305111813,305214708,305314047,3054138363055],3056"answer": " Actors in this context appear to be related to deployments that can have a state of \"HEALTHY\" or \"UNHEALTHY.\" Actors may change to an \"UNHEALTHY\" state if there's an error in the deployment's constructor, a crashed replica, or a general system or machine error. If an actor does not require a stateful part, it is typically better to use tasks.",3057"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3058},3059{3060"question": "How do I access logs for a dead node?",3061"sources": [3062"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3063"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3064"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",3065"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3066"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy"3067],3068"document_ids": [3069492,3070493,30716443,3072680,307351293074],3075"answer": " Based on the provided context, you cannot access logs from a dead node. The Ray State API and CLI allow you to fetch logs from a head node or a specific node, but this is only possible for alive nodes. When a node is dead, the logs are not available through the Ray API or CLI. If you need to access logs from a dead node for troubleshooting purposes, you may need to rely on other methods of log collection, depending on your specific environment and log management system.",3076"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3077},3078{3079"question": "What are the reasons for a node to change it\u2019s status to dead?",3080"sources": [3081"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",3082"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",3083"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3084"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",3085"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.PlacementGroupState.html#ray-util-state-common-placementgroupstate"3086],3087"document_ids": [30884393,308913869,30904625,30914307,30927243093],3094"answer": " A node in a Ray cluster can change its status to \"dead\" due to two main reasons:\n\n1. Raylet failure: When the raylet process fails, the corresponding node is marked as dead and treated as a new node upon restart.\n2. Worker node failure: When a worker node fails, the actors running on it also fail. Serve attempts to respawn the actors on the remaining healthy nodes, and KubeRay attempts to restart the worker pod on another running node.\n\nIn the provided context, the 'death\\_cause' mentioned in the Actor's state indicates that the actor is dead because its worker process has died, which can be a result of worker node failure or an intended user exit.",3095"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3096},3097{3098"question": "What are the reasons for spikes in node CPU utilization",3099"sources": [3100"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3101"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3102"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",3103"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",3104"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training"3105],3106"document_ids": [3107555,3108554,3109468,31103273,311132493112],3113"answer": " The reasons for spikes in node CPU utilization in this context can include:\n\n1. A task (allocate\\_memory) using a significant amount of memory, exceeding the memory usage threshold of 0.95.\n2. Ray killing workers due to low memory on the node.\n3. Running a task that demands a significant portion of the node's resources, such as CPU or memory.\n\nIn this specific case, the node's memory usage was high (27.71GB/28.80GB), and the task 'allocate\\_memory' was using 1.25GB of memory. Ray killed the worker with ID '6f2ec5c8b0d5f5a66572859faf192d36743536c2e9702ea58084b037' because it was the most recently scheduled task.\n\nHowever, the CPU usage in this context is not high (0.0/10.0 CPU), so the spikes in CPU utilization may be due to other tasks or processes running on the node.",3114"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3115},3116{3117"question": "What AWS machine type is recommended to deploy a RayService on EKS?",3118"sources": [3119"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",3120"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",3121"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-eks-cluster-aws",3122"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploying-a-serve-application",3123"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide"3124],3125"document_ids": [312613931,312713930,31285886,312913938,3130139623131],3132"answer": " The recommended AWS machine type for deploying a RayService on EKS would be one with GPUs, as suggested in the \"Setting up an EKS cluster (AWS)\" section of the context. However, the specific machine type is not specified. It's also important to provide sufficient resources to the Serve application, as mentioned in the context.",3133"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3134},3135{3136"question": "Is there a way to configure the session name generated by ray?",3137"sources": [3138"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3139"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",3140"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3141"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",3142"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory"3143],3144"document_ids": [31454528,31466597,31474811,31481782,31495113150],3151"answer": " Yes, you can configure the session name generated by Ray by specifying the `temp_dir` or `log_directory` parameter when calling `ray.init()` or `ray start`. The session name will be based on the name of the directory specified. For example, if you specify `temp_dir='/my/ray/session'`, the session name will be `session_<timestamp>_<pid>`, and the session directory will be `/my/ray/session/session_<timestamp>_<pid>`.",3152"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3153},3154{3155"question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",3156"sources": [3157"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",3158"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",3159"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",3160"https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",3161"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli"3162],3163"document_ids": [31646640,31656510,31666516,3167275,316864543169],3170"answer": " No, the Python SDK does not provide a direct method to get a link to the Ray dashboard for a given job. The dashboard can be accessed through the default URL, http://localhost:8265, or by providing the Ray cluster head node address and the port used by the Ray Dashboard when initializing the JobSubmissionClient object. However, the SDK does not generate a specific link for a particular job in the dashboard.",3171"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3172},3173{3174"question": "What may possible cause the node where this task was running crashed unexpectedly. This can happen if: (1) the instance where the node was running failed, (2) raylet crashes unexpectedly (OOM, preempted node, etc).",3175"sources": [3176"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",3177"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",3178"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",3179"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",3180"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure"3181],3182"document_ids": [31835129,31844456,3185554,31865130,318743933188],3189"answer": " The node where the task was running crashed unexpectedly could have been caused by:\n\n1. The instance where the node was running failed.\n2. The raylet process crashed unexpectedly, possibly due to memory pressure (OOM) or node preemption.\n3. The task ran out of memory, resulting in the task being killed.\n4. Raylet failure, causing the corresponding node to be marked as dead.",3190"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3191},3192{3193"question": "Do you know how to resolve (gcs_server) gcs_health_check_manager.cc:108: Health check failed for node? I observed that the node is still up and running.",3194"sources": [3195"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",3196"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",3197"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",3198"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",3199"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"3200],3201"document_ids": [32024375,32035709,32045638,32056372,3206138993207],3208"answer": " The error message you're seeing is related to the Global Control Service (GCS) in Ray. It's possible that the GCS server process has failed in the head Pod. I would suggest checking the log directory `/tmp/ray/session_latest/logs/` in the head Pod for errors related to the GCS server.\n\nAdditionally, you may want to ensure that the `ray` executable is included in the `$PATH` for the image and that the `CLUSTER_DOMAIN` environment variable is set correctly.\n\nIf the GCS server process is running without errors, it's possible that the issue is related to the fault tolerance configuration of the GCS. By default, the GCS is not fault tolerant, and its failure can cause issues with actor creation, deletion, and reconstruction, as well as placement group creation, deletion, and reconstruction. To make the GCS fault tolerant, you can use HA Redis.\n\nFinally, if the worker node cannot connect to the head node, you may see an error message like \"Unable to connect to GCS at xx.xx.xx.xx:nnnn.\" This can be caused by a number of issues, including firewall settings preventing access or the worker node being unable to access the IP address given. You can use the `ip route get xx.xx.xx.xx` command on the worker node to start debugging routing issues.",3209"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3210},3211{3212"question": "Do you need the DAGDriver to deploy a serve application using RayServe?",3213"sources": [3214"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",3215"https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph",3216"https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",3217"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-the-applications",3218"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started"3219],3220"document_ids": [322113660,322213426,322313502,322413690,3225136803226],3227"answer": " No, you don't necessarily need the DAGDriver to deploy a RayServe application. The DAGDriver is used when you want to create a deployment graph, where you can compose multiple models into a graph structure and orchestrate the calls to each deployment automatically. However, if your application doesn't require a deployment graph, you can simply use the `serve.deployment` decorator to deploy your application. The query you provided also includes an example of using the DAGDriver with a deployment graph, but it's not required for all RayServe applications.",3228"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3229},3230{3231"question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",3232"sources": [3233"https://docs.ray.io/en/master/workflows/basics.html#sub-task-results",3234"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",3235"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",3236"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",3237"https://docs.ray.io/en/master/serve/tutorials/batch.html#define-the-deployment"3238],3239"document_ids": [32409636,324113844,324213850,32435678,3244145803245],3246"answer": " To import a simple RayServe deployment, you need to provide the import path in the following format: `<directory_name>.<file_name>:<variable_name>`. For example, if you have a file called `app.py` in a directory called `mobilenet`, and the Ray Serve application is assigned to the variable `app` in the file, the import path would be `mobilenet.app:app`.\n\nIn the example you provided, the import path would be `tutorial_batch.py:app` if you have defined the Ray Serve application as `app` in the `tutorial_batch.py` file.",3247"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3248},3249{3250"question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",3251"sources": [3252"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",3253"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",3254"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",3255"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",3256"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status"3257],3258"document_ids": [32595751,32605760,32615729,32625762,326357633264],3265"answer": " Yes, the context includes an example of using RayJob in Kuberay. Here are the steps to follow:\n\n1. Download the `ray_v1alpha1_rayjob.yaml` file using the following command:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml\n```\n2. Create a RayJob by applying the `ray_v1alpha1_rayjob.yaml` file using the following command:\n```\nkubectl apply -f ray_v1alpha1_rayjob.yaml\n```\n3. Check the status of the RayJob by running the following command:\n```bash\nkubectl get rayjobs.ray.io rayjob-sample -o json | jq '.status.jobStatus'\n```\nThe output should show `\"SUCCEEDED\"` if the job has finished successfully.\n\nNote that RayJob support in KubeRay v0.x is in alpha.",3266"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3267},3268{3269"question": "Does Ray metrics have to be exported via an actor?",3270"sources": [3271"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",3272"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",3273"https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",3274"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",3275"https://docs.ray.io/en/master/serve/monitoring.html#exporting-metrics-into-arize"3276],3277"document_ids": [3278503,32795573,3280438,3281656,3282135503283],3284"answer": " No, Ray metrics do not have to be exported via an actor. Ray provides a convenient API for defining and exporting custom metrics at the application level, and it also exports default metrics that can be viewed in the Metrics view. Additionally, Ray Serve can export metrics to other observability platforms like Arize.",3285"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3286},3287{3288"question": "how can I avoid objects not getting spilled?",3289"sources": [3290"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",3291"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",3292"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3293"https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",3294"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node"3295],3296"document_ids": [32974959,32986473,329914748,33004674,330149603302],3303"answer": " To avoid objects from getting spilled, you can increase the capacity of the object store by using high-performance storage such as NVMe SSDs. If you're using Ray, you can configure the directory where objects are spilled to by using the `object_spilling_config` parameter in the `ray.init()` function. Additionally, you can specify multiple directories for spilling to distribute the IO load and disk space usage across multiple physical devices. However, keep in mind that object spilling can cause significant performance problems since data needs to be written to disk.",3304"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3305},3306{3307"question": "what\u2019s ray core",3308"sources": [3309"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",3310"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",3311"https://docs.ray.io/en/master/data/data.html#learn-more",3312"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",3313"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#rayservice"3314],3315"document_ids": [33164469,33174701,33186995,33195656,332057803321],3322"answer": " Ray Core is a distributed computing system that provides primitives like tasks, actors, and objects for building and scaling applications. It offers a Command Line Interface (CLI) and can be used with Kubernetes. You can also find Ray blogs for more information and read about RayService.",3323"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3324},3325{3326"question": "Does ray support cron job",3327"sources": [3328"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",3329"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",3330"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",3331"https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",3332"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs"3333],3334"document_ids": [33355757,333613980,3337410,33389574,33394153340],3341"answer": " No, Ray does not directly support cron jobs. However, you can use an external job scheduler that can interact with your Ray cluster to trigger workflow runs according to a cron schedule. Ray Workflows does not have a built-in job scheduler.\n\nFor Ray Job logs, Worker logs are always captured in the Ray logging directory, while Driver logs are captured only for Ray Jobs submitted via Jobs API.",3342"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3343},3344{3345"question": "can you give me the dependencies list for api read_images?",3346"sources": [3347"https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",3348"https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",3349"https://docs.ray.io/en/master/installation.html#docker-source-images",3350"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",3351"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning"3352],3353"document_ids": [335414593,3355306,335681,335710238,335860113359],3360"answer": " The `read_images` function depends on the `numpy`, `PIL`, `os`, `requests`, and `io` libraries, which are typically included in common Python distributions like Anaconda. If you're using Docker, the `rayproject/ray` or `rayproject/ray-ml` images should include these dependencies. If you're using GPU-based machine learning, you can use the `rayproject/ray-ml` image with a GPU tag (e.g., `rayproject/ray-ml:2.6.3-gpu`) which includes CUDA and other necessary libraries. To add additional custom dependencies, you can build your own Docker image based on the official Ray images or use Ray Runtime environments.",3361"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3362},3363{3364"question": "how do I kill a specific serve replica",3365"sources": [3366"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",3367"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",3368"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",3369"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",3370"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure"3371],3372"document_ids": [337313912,337413905,337513911,337613901,3377139033378],3379"answer": " To kill a specific serve replica using the provided context, you can follow these steps:\n\n1. First, list the available actors using `ray list actors` command in the terminal.\n2. Identify the NAME of the specific ServeReplica you want to kill from the list.\n3. Get a handle to the ServeReplica using the following command in the Python interpreter: `replica_handle = ray.get_actor(\"NAME\", namespace=\"serve\")`\n4. Finally, kill the ServeReplica using the following command: `ray.kill(replica_handle, no_restart=True)`\n\nNote that you'll need to replace \"NAME\" with the actual name of the ServeReplica you want to kill.",3380"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3381}3382]3383}3384