llm-applications
7277 строк · 582.3 Кб
1{2"config": {3"experiment_name": "lexical-search-bm25-3",4"chunk_size": 700,5"chunk_overlap": 50,6"num_chunks": 13,7"embedding_model_name": "thenlper/gte-large",8"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1",9"temperature": 0.0,10"max_context_length": 32768,11"system_content": "Answer the query using the context provided. Be succinct.",12"assistant_content": "",13"docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",14"experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",15"references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",16"num_samples": 17717},18"results": [19{20"question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",21"sources": [22"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",23"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",24"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",25"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#configuring-log-processing",26"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#putting-everything-together",27"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#adding-the-fluent-bit-sidecar",28"https://docs.ray.io/en/master/data/key-concepts.html#transforming-data",29"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",30"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",31"https://docs.ray.io/en/master/data/working-with-tensors.html#transforming-tensor-data",32"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.MultiHotEncoder.transform_batch.html#ray-data-preprocessors-multihotencoder-transform-batch",33"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.transform_batch.html#ray-data-preprocessor-preprocessor-transform-batch",34"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",35"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.OneHotEncoder.transform_batch.html#ray-data-preprocessors-onehotencoder-transform-batch",36"https://docs.ray.io/en/master/data/working-with-images.html#transforming-images",37"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.OrdinalEncoder.transform_batch.html#ray-data-preprocessors-ordinalencoder-transform-batch"38],39"document_ids": [407081,416790,426786,435867,445876,455872,466733,4714727,488046,496777,507421,517241,527085,537553,546724,55747656],57"answer": " When using `map_batches`, you can specify the batch format as either NumPy ndarrays or pandas DataFrames by using the `batch_format` argument. The function you provide to `map_batches` should return data in the same format.\n\nHere's an example of increasing brightness of images using NumPy:\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 working with pandas DataFrames:\n\n```python\nimport pandas as pd\nimport ray\n\ndef increase_age(batch: Dict[str, pd.Series]) -> Dict[str, pd.Series]:\n batch[\"age_in_dog_years\"] = 7 * batch[\"age\"]\n return batch\n\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(increase_age, batch_format=\"pandas\")\n)\n```\n\nMake sure to return the data in the specified format from your function.",58"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"59},60{61"question": "How does autoscaling work in a Ray Serve application?",62"sources": [63"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",64"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",65"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",66"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",67"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",68"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",69"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",70"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",71"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",72"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#load-metrics-are-based-on-application-semantics",73"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",74"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",75"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",76"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",77"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",78"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling"79],80"document_ids": [8113471,826363,835559,8413471,855953,866363,8713448,885969,895953,905971,9114726,9213447,936364,9410282,9513449,96595597],98"answer": " Ray Serve's autoscaling feature automatically adjusts the number of replicas in a deployment based on its load. The Ray autoscaler, which runs on the head node, increases or decreases the number of worker nodes based on the resource demands of tasks, actors, or placement groups. It reacts to task and actor resource requests, not application metrics or physical resource utilization. When Ray Serve scales down and terminates some replica actors, the Ray autoscaler will remove nodes with no Ray actors or tasks running on them. The autoscaler uses logical resources expressed in task and actor annotations, not physical usage metrics like CPU and memory. Autoscaling can reduce workload costs but adds node launch overheads and can be tricky to configure.",99"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"100},101{102"question": "how do I get the address of a ray node",103"sources": [104"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",105"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",106"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",107"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",108"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm-template.html#slurm-template-sh",109"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",110"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",111"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",112"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",113"https://docs.ray.io/en/master/ray-core/api/doc/ray.nodes.html#ray-nodes",114"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",115"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",116"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",117"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",118"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",119"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs"120],121"document_ids": [122739,1234817,124754,1256411,1266466,1271006,1284446,1296552,130493,1314769,1326502,1335587,1344448,1354824,1364187,137695138],139"answer": " To get the address of a Ray node, you can use the `ray.nodes()` function which returns information about the nodes in the Ray cluster. The node's address can be found in the 'NodeManagerAddress' field of the returned dictionary.\n\nFor example, you can use the following code to get the address of the first node in the cluster:\n```\nimport ray\n\nnodes = ray.nodes()\nnode_address = nodes[0]['NodeManagerAddress']\nprint(node_address)\n```\nAlternatively, if you have the node id, you can use the `get_node` function from `ray.util.state` to get the node information and extract the address from it.\n```\nfrom ray.util.state import get_node\n\nnode = get_node(node_id)\nnode_address = node.node_manager_address\nprint(node_address)\n```\nNote that, the above function will work only if the ray cluster is initialized and running.",140"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"141},142{143"question": "Does Ray support NCCL?",144"sources": [145"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",146"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#ray-collective-communication-lib",147"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",148"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#starting-a-connection-on-older-ray-versions",149"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",150"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#what-does-the-rayservice-provide",151"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-ray",152"https://docs.ray.io/en/master/ray-overview/installation.html#installing-ray",153"https://docs.ray.io/en/master/installation.html#installing-ray",154"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",155"https://docs.ray.io/en/master/ray-overview/index.html#overview",156"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",157"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",158"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",159"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",160"https://docs.ray.io/en/master/ray-overview/use-cases.html#llms-and-gen-ai"161],162"document_ids": [1634662,16414391,1654811,1666628,1675754,1685738,16914537,170284,17159,1726835,173364,17413974,1756208,176370,1775656,178139179],180"answer": " Yes, Ray supports NCCL (NVIDIA Collective Communications Library) as one of the optional high-performance communication backends for its collective communication library, which is used for efficient out-of-band communication between Ray actor and task processes on distributed CPUs or GPUs.",181"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"182},183{184"question": "Is Ray integrated with DeepSpeed?",185"sources": [186"https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",187"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.deepspeed_engine.html#ray-train-lightning-raydeepspeedstrategy-deepspeed-engine",188"https://docs.ray.io/en/master/train/api/api.html#ray-train-integrations",189"https://docs.ray.io/en/master/rllib/rllib-dev.html#contributing-algorithms",190"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#define-your-training-function",191"https://docs.ray.io/en/master/train/deepspeed.html#running-deepspeed-with-other-frameworks",192"https://docs.ray.io/en/master/train/huggingface-accelerate.html#configuring-accelerate",193"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",194"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.strategy_name.html#ray-train-lightning-raydeepspeedstrategy-strategy-name",195"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#fine-tune-vicuna-13b-with-lightning-and-deepspeed",196"https://docs.ray.io/en/master/train/deepspeed.html#training-with-deepspeed",197"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",198"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.html#ray-train-lightning-raydeepspeedstrategy",199"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",200"https://docs.ray.io/en/master/workflows/basics.html#ray-integration",201"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.DEEPSPEED_ENV_VAR.html#ray-train-lightning-raydeepspeedstrategy-deepspeed-env-var"202],203"document_ids": [2048232,2058996,2068331,20710595,2089360,2098232,2108249,2118881,2128578,2139334,2148207,2159393,2168784,21710313,2189685,2198633220],221"answer": " Yes, Ray is integrated with DeepSpeed. You can run DeepSpeed with Ray Train for frameworks like Lightning, Transformers, and Accelerate. The integration is compatible with Ray orchestration and you can set configurations through the accelerate.Accelerator object in your training function. The Accelerate configuration options will be automatically set by the Trainer according to Ray AIR configs.",222"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"223},224{225"question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",226"sources": [227"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",228"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example",229"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",230"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",231"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#code-example",232"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",233"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",234"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#code-example",235"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",236"https://docs.ray.io/en/master/ray-core/walkthrough.html#running-a-task",237"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",238"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",239"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeHandle.html#ray-serve-handle-rayservehandle",240"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",241"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",242"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups"243],244"document_ids": [2454600,2465197,2474611,2484600,2495197,25013646,2514599,2525248,2534598,2544485,2554603,2564601,25714277,2584360,25913643,2604636261],262"answer": " If you use AsyncIO's `await` to wait for a Ray future like `await x.remote()`, it will allow you to await on Ray futures in existing concurrent applications. However, it's important to note that for remote tasks, you cannot directly use `async/await` syntax in the task function. Instead, you can wrap the async function with a wrapper to run the task synchronously. Also, keep in mind that ObjectRefs can be translated to asyncio.Futures, which allows you to use asyncio patterns like timeouts and `asyncio.gather`.",263"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"264},265{266"question": "How would you compare Spark, Ray, Dask?",267"sources": [268"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",269"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#scheduler",270"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",271"https://docs.ray.io/en/master/tune/examples/tune-sklearn.html#walkthrough",272"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-ml-training-ingest",273"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",274"https://docs.ray.io/en/master/ray-more-libs/raydp.html#using-spark-on-ray-raydp",275"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",276"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",277"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",278"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",279"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",280"https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure",281"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",282"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",283"https://docs.ray.io/en/master/data/api/api.html#ray-data-api"284],285"document_ids": [28614465,28714479,2886847,2892640,2906850,2916842,29214647,29314502,2946846,29514467,2966809,29714469,2989809,2996752,30013620,3017048302],303"answer": " Spark, Ray, and Dask are all distributed computing systems, but they have different focuses and use cases.\n\nApache Spark is a general-purpose cluster-computing system that provides high-level APIs in Java, Scala, Python and R, and can handle batch processing and stream processing of large datasets. It's widely used in data engineering and data science tasks.\n\nRay is a high-performance distributed execution engine that can scale up to large clusters, and it's designed to support a wide range of applications, including machine learning, reinforcement learning, and large-scale data processing. Ray's unique feature is its ability to support actor-based programming, which makes it a good choice for applications that require low-latency and high-throughput.\n\nDask is a flexible parallel computing library for analytic computing, built with the Python ecosystem in mind. It's designed to integrate well with existing Python libraries, such as NumPy, Pandas, and Scikit-learn, and it can be used to scale up these libraries to large datasets and distributed environments. Dask can be used with Ray as a scheduler, which allows you to build data analyses using Dask's collections and execute them on a fast, fault-tolerant distributed task execution system geared towards production.\n\nIn summary, Spark is a general-purpose data processing system, Ray is a high-performance distributed execution engine, and Dask is a flexible parallel computing library for analytic computing in Python. The choice between them depends on the specific requirements of your use case.",304"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"305},306{307"question": "why would ray overload a node w/ more task that the resources allow ?",308"sources": [309"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",310"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",311"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#physical-resources-and-logical-resources",312"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",313"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",314"https://docs.ray.io/en/master/data/performance-tips.html#tuning-read-resources",315"https://docs.ray.io/en/master/ray-core/tasks.html#scheduling",316"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",317"https://docs.ray.io/en/master/ray-core/scheduling/index.html#locality-aware-scheduling",318"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",319"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",320"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case",321"https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks",322"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",323"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#custom-resources",324"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#pattern-using-ray-wait-to-limit-the-number-of-pending-tasks"325],326"document_ids": [3275231,3285112,3295104,3305230,3316409,3326817,3334376,3345230,3355152,3366504,3375115,3385232,3394199,3406364,3415106,3425246343],344"answer": " Ray's scheduler limits the number of concurrently running tasks based on the resources required by each task and the total resources available on each node. Overloading a node with more tasks than it can handle might occur when tasks require more resources than what's available or if tasks use more physical resources than their logical resource requirements specify. To prevent this, you can:\n\n1. Adjust the logical resource requirements of tasks or actors.\n2. Manually override the detected resources on each node, specifying the number of CPUs and GPUs available for Ray.\n3. Limit the number of concurrently running tasks by modifying each task's resource requirements instead of using the method meant primarily for limiting the number of tasks in flight.\n\nRemember that Ray does not provide CPU isolation and it's your responsibility to ensure tasks or actors use no more resources than specified.",345"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"346},347{348"question": "when should I use Ray Client?",349"sources": [350"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",351"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",352"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",353"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet_bulk.html#ray-data-read-parquet-bulk",354"https://docs.ray.io/en/master/tune/faq.html#how-can-i-use-tune-with-kubernetes",355"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",356"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",357"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",358"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",359"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",360"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",361"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",362"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#which-crd-should-you-choose",363"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",364"https://docs.ray.io/en/master/ray-core/walkthrough.html#next-steps",365"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started"366],367"document_ids": [3686615,3696611,3706608,3717828,3721061,3734364,3746612,3756613,3764811,37713974,3784204,37913442,3805649,3815380,3824496,3834483384],385"answer": " You should use Ray Client when you want to connect an interactive Python shell to a remote cluster. It's useful for developing interactively in a local Python shell, but requires a stable connection to the remote cluster. If the connection is lost for more than 30 seconds, the workload will terminate. Ray Client is not recommended for long-running workloads on your cluster; instead, use Ray Jobs.",386"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"387},388{389"question": "how to scatter actors across the cluster?",390"sources": [391"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",392"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",393"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",394"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.__init__.html#ray-train-lightning-raydeepspeedstrategy-init",395"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#collective-primitives-support-matrix",396"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",397"https://docs.ray.io/en/master/serve/architecture.html#high-level-view",398"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",399"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",400"https://docs.ray.io/en/master/ray-core/scheduling/index.html#default",401"https://docs.ray.io/en/master/ray-core/api/utility.html#utility",402"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",403"https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",404"https://docs.ray.io/en/master/data/api/doc/ray.data.ActorPoolStrategy.html#ray-data-actorpoolstrategy",405"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",406"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources"407],408"document_ids": [4095145,4101027,4115195,4129056,41314394,41414453,41513466,4165391,4176787,4185143,4194677,4208119,421659,4227733,4236949,4245065425],426"answer": " To scatter actors across the cluster in Ray, you can use the `scheduling_strategy=\"SPREAD\"` option when creating the actor. Here is an example:\n\n```python\n@ray.remote(scheduling_strategy=\"SPREAD\")\nclass SpreadActor:\n pass\n\n# Spread actors across the cluster.\nactors = [SpreadActor.remote() for _ in range(10)]\n```\n\nThis will spread the actors across the available nodes in the cluster. You can also use the `ActorPoolStrategy` to set the minimum and maximum number of actors to use. For example:\n\n```python\n# The actor pool will have at least 2 workers and at most 8 workers.\nstrategy = ActorPoolStrategy(min_size=2, max_size=8)\n\n# Spread actors across the cluster using the actor pool strategy.\nactors = [SpreadActor.options(scheduling_strategy=\"SPREAD\").remote() for _ in range(10)]\n```\n\nThis will ensure that there are always at least 2 and at most 8 actors running in the cluster.",427"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"428},429{430"question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",431"sources": [432"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused",433"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",434"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",435"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",436"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#stepping-between-ray-tasks",437"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",438"https://docs.ray.io/en/master/ray-more-libs/multiprocessing.html#run-on-a-cluster",439"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",440"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",441"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#connect-to-the-cluster",442"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",443"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",444"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",445"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",446"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",447"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting"448],449"document_ids": [4505681,4514815,4526610,4531005,454583,4556612,45614678,4574266,4586619,4596526,46013718,4616551,4624817,4634264,4646620,4656505466],467"answer": " The \"connection refused\" error you're encountering when running `ray debug` on your remote Ray cluster suggests that the necessary components like the dashboard or dashboard agent may not have started yet, or may have failed to start. This error can also occur if you're trying to connect to the cluster too soon after it's been created, before the necessary components have fully initialized.\n\nTo troubleshoot this issue, you can try waiting for a minute or two and then running the `ray debug` command again. If the issue persists, you can check the status of the dashboard and dashboard agent to ensure that they are running correctly.\n\nAdditionally, when connecting to a remote Ray cluster, make sure to use the correct address format, which should include \"ray://\" at the beginning followed by the IP address and port number of the head node. For example:\n\n```python\nray.init(address=\"ray://<head_node_ip>:<port>\")\n```\n\nIf you're still having trouble connecting to the cluster, you can refer to the Ray Tune FAQ and documentation for more information on troubleshooting common issues with Ray.",468"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"469},470{471"question": "How does Ray AIR set up the model to communicate gradient updates across machines?",472"sources": [473"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",474"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#asynchronous-parameter-server-training",475"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",476"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",477"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tune-a-transformers-model",478"https://docs.ray.io/en/master/rllib/rllib-learner.html#implementation",479"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",480"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",481"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",482"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples",483"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",484"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#defining-the-parameter-server",485"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary",486"https://docs.ray.io/en/master/ray-core/examples/plot_parameter_server.html#synchronous-parameter-server-training",487"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",488"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#fine-tuning-dreambooth-with-ray-train"489],490"document_ids": [4919845,4925338,4939820,4949845,4959999,49610862,497183,498337,499156,500374,50110019,5025329,5039867,5045334,5059887,50610147507],508"answer": " Ray AIR sets up model communication for gradient updates across machines using a data-parallel approach. It does this by setting up a parameter server to hold a copy of the model and apply gradient updates, and data workers that compute gradients and send them to the parameter server. The parameter server then sends an updated copy of the model weights back to the worker, which applies the updates and repeats the process. This is facilitated by the `ParameterServer` and `DataWorker` classes in Ray Train, which are set up with the `.remote()` method to run on remote processes.",509"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"510},511{512"question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",513"sources": [514"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",515"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",516"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",517"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",518"https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#how-do-i-restore-from-a-policy-checkpoint",519"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",520"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",521"https://docs.ray.io/en/master/serve/key-concepts.html#servehandle-composing-deployments",522"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",523"https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",524"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#example-serve-two-simple-ray-serve-applications-using-rayservice",525"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#example-serve-two-simple-ray-serve-applications-using-rayservice",526"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",527"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#binding-deployments",528"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",529"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"530],531"document_ids": [53213621,53313974,53413614,5351005,53610568,53713607,53813597,53913420,54010282,541144,5425785,5435739,54413618,54513727,54613596,54713623548],549"answer": " Ray Serve can be used instead of Modal or Seldon for model serving on your own infrastructure, providing scalability and reliability similar to these hosted offerings. However, Ray Serve lacks some functionality for managing and visualizing model lifecycle and performance, focusing primarily on model serving. It is not tied to any specific machine learning framework, allowing for flexibility in using new libraries and model architectures.\n\nUsing containers for model serving can be an alternative, but Ray Serve offers features like model composition, multi-node/multi-GPU serving, and dynamic request batching, which can be beneficial for large language models and complex inference services.\n\nIn summary, Ray Serve is a good choice for model serving when you need scalability, model composition, and flexibility with your machine learning frameworks, but it may not provide the same level of model management and visualization features as other full-fledged ML platforms like Seldon.",550"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"551},552{553"question": "How do I deploy an LLM workload on top of Ray Serve?",554"sources": [555"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",556"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",557"https://docs.ray.io/en/master/cluster/vms/user-guides/community/lsf.html#deploying-on-lsf",558"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",559"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",560"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",561"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",562"https://docs.ray.io/en/master/serve/api/index.html#serve",563"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",564"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",565"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",566"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",567"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",568"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",569"https://docs.ray.io/en/master/ray-overview/use-cases.html#model-serving",570"https://docs.ray.io/en/master/serve/deploy-many-models/index.html#deploy-many-models"571],572"document_ids": [57313618,57413677,5756382,57613618,5771006,5781005,5795636,58013983,58113598,58213416,58313647,58410282,58513621,58613622,587144,58813676589],590"answer": " To deploy a large language model (LLM) workload on top of Ray Serve, you can write all the steps as a single Python module using Serve's support for arbitrary Python code. This enables rapid development and easy testing. Once you have your Python module, you can use the Ray Serve CLI to deploy your application to a Ray cluster, either local or remote. You can also deploy on a VM or on LSF by following the respective instructions.\n\nHere's a high-level overview of the process:\n\n1. Write your LLM application as a single Python module.\n2. Set up a Ray cluster (local or remote).\n3. Use the Ray Serve CLI to deploy your application's configuration file to the Ray cluster.\n\nFor more information, refer to the context provided and the following resources:\n\n- Ray Serve CLI: [https://docs.ray.io/en/latest/serve/command-line-interface.html](https://docs.ray.io/en/latest/serve/command-line-interface.html)\n- Deploying on LSF: [https://docs.ray.io/en/latest/cluster/lsf.html](https://docs.ray.io/en/latest/cluster/lsf.html)",591"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"592},593{594"question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",595"sources": [596"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",597"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",598"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",599"https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",600"https://docs.ray.io/en/master/installation.html#launch-ray-in-docker",601"https://docs.ray.io/en/master/ray-overview/installation.html#launch-ray-in-docker",602"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",603"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",604"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",605"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",606"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",607"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",608"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",609"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",610"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",611"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#how-do-i-configure-the-memory-monitor"612],613"document_ids": [6144878,61514121,61614137,61714606,61886,619311,62014299,62114221,62214062,62314183,62410260,62510280,626855,62710373,62810380,6295121630],631"answer": " To determine the memory size for the `model_id` \"EleutherAI/gpt-j-6B\", you need to consider the memory requirements of the model. The model has 6 billion parameters and requires a GPU with at least 16 GB of memory.\n\nIn the context provided, there is no specific memory size mentioned for this model. However, it is recommended to use roughly 30% of your available memory for the object store when launching Ray in Docker. For example, if you have 32 GB of memory, you can set the `shm-size` to 1024M (1 GB) for the Ray deployment container.\n\nAdditionally, the object_store_memory for each replica is set to 1024 * 1024 * 1024 (1 GB) in the example configuration provided in the context.\n\nConsidering these points, you should allocate a minimum of 1 GB of memory for the object store and potentially more depending on the available memory on your system and the specific memory requirements of the model. It is recommended to monitor the memory usage during training to ensure there are no memory pressure issues.",632"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"633},634{635"question": "How do I log the results from multiple distributed workers into a single tensorboard?",636"sources": [637"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",638"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli",639"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-log-your-tune-runs-to-tensorboard",640"https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#monitoring-and-logging-metrics",641"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",642"https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#how-to-obtain-and-aggregate-results-from-different-workers",643"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#example-logging-to-mlflow-and-tensorboard",644"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#quickstart",645"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",646"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-distributed-tune-on-aws-vms",647"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-tune-results-on-a-single-node",648"https://docs.ray.io/en/master/tune/tutorials/tune-scalability.html#benchmarking-many-concurrent-tune-trials",649"https://docs.ray.io/en/master/rllib/key-concepts.html#key-subconcepts",650"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#aggregating-results",651"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",652"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowTrainer.html#ray-train-tensorflow-tensorflowtrainer"653],654"document_ids": [6559544,65610927,6572354,6589260,6591006,6609264,6619144,6628286,663231,6642411,6652383,6662380,66710730,6688301,6699532,6708541671],672"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 logs are stored. In the context you provided, it looks like the logs are being stored in `~/ray_results`.\n\nTo view the logs in Tensorboard, you can run the following command:\n```\ntensorboard --logdir=~/ray_results\n```\nThis will start Tensorboard and open a web browser window pointing to the logs at `~/ray_results`.\n\nIt's important to note that Tensorboard will only display the logs from the rank 0 worker, as mentioned in the warning in the context. However, all of the logs from the distributed workers should be stored in the specified directory, and Tensorboard will display the logs from all of the workers together.\n\nAdditionally, you can use the Ray Train's API for reporting intermediate results and checkpoints from the training function (run on distributed workers) up to the Trainer (where your python script is executed) by calling `train.report(metrics)`. This will collect the results from the distributed workers and pass them to the driver to be logged and displayed.",673"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"674},675{676"question": "how do you config SyncConfig for a Ray AIR job?",677"sources": [678"https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration",679"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",680"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.sync_config.html#ray-train-runconfig-sync-config",681"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary",682"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",683"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",684"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningConfigBuilder.checkpointing.html#ray-train-lightning-lightningconfigbuilder-checkpointing",685"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",686"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",687"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_timeout.html#ray-train-syncconfig-sync-timeout",688"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",689"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.checkpoint_config.html#ray-train-runconfig-checkpoint-config",690"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.syncer.html#ray-train-syncconfig-syncer",691"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.html#ray-train-runconfig",692"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",693"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.sync_on_checkpoint.html#ray-train-syncconfig-sync-on-checkpoint"694],695"document_ids": [6961138,69710466,6988441,6999867,700156,701183,7028371,7031443,70410464,7051852,7061782,7079098,7081678,7098430,7108881,7111550712],713"answer": " To configure `SyncConfig` for a Ray AIR job, you can use the `ray.train.SyncConfig` class which has several attributes that you can set. Here is an example of how to use it:\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\n)\n\nrun_config = train.RunConfig(sync_config=sync_config)\n```\n\nIn this example, the `sync_period` is set to 300 seconds, which means that synchronization will happen every 5 minutes. The `sync_timeout` is set to 1800 seconds, which is the maximum time to wait for synchronization before timing out. The `sync_artifacts` is set to `False`, which means that synchronization of artifacts is disabled. The `sync_artifacts_on_checkpoint` is set to `True`, which means that artifacts will be synchronized when a checkpoint is saved. The `sync_on_checkpoint` is set to `False`, which means that synchronization will not happen when a checkpoint is saved.\n\nYou can then use the `run_config` object in your Ray AIR job. Note that synchronization of checkpoints and other artifacts from worker nodes to the head node is no longer supported in Ray AIR, so the checkpoints and artifacts saved by trials scheduled on worker nodes will not be accessible during the run or after the run.",714"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"715},716{717"question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",718"sources": [719"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",720"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",721"https://docs.ray.io/en/master/ray-observability/getting-started.html#job-profiling",722"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",723"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",724"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#launching-an-on-premise-cluster",725"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",726"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",727"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",728"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",729"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/observability.html#method-3-check-logs-of-ray-pods",730"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",731"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",732"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",733"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",734"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions"735],736"document_ids": [7375764,738444,739422,7402309,7411006,7426499,743415,74413523,7456631,7466157,7475855,7485665,7496181,750445,7516530,7524658753],754"answer": " To quickly narrow down the root cause of a failed Ray job, you can check the output of the Ray job using the command `kubectl logs -l=job-name=rayjob-sample`. The logs view is organized by node and log file name, and you can use the search functionality to find specific log messages. Ray job logs can be found at `/tmp/ray/session_latest/logs` on the head node. If the Ray Job is submitted via Jobs API, the Job logs are available from the Dashboard. You can also check the logs directly by accessing the log files on the Pods using the command `kubectl exec -it $RAY_POD -n $YOUR_NAMESPACE -- bash`. Look for error messages such as `ray.exceptions.LocalRayletDiedError`, `ray.exceptions.WorkerCrashedError`, `ray.exceptions.TaskPlacementGroupRemoved`, `ray.exceptions.ActorPlacementGroupRemoved`, `ray.exceptions.ObjectStoreFullError`, or `ray.exceptions.OutOfDiskError` to identify the root cause of the failure.",755"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"756},757{758"question": "How do I specify how many GPUs a serve deployment needs?",759"sources": [760"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",761"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",762"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application",763"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",764"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",765"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",766"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",767"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#resource-management-cpus-gpus",768"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",769"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",770"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#usage-example",771"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#resources",772"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",773"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#sbatch-directives",774"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-gpus",775"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment"776],777"document_ids": [77813458,77911062,78013575,7811006,7822965,7831005,78413456,78513778,78610283,78711063,7886420,7895927,7907799,7916405,7925937,79313776794],795"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. For example:\n```\n@serve.deployment(ray_actor_options={\"num_gpus\": 1})\ndef func(*args):\n return do_something_with_my_gpu()\n```\nThis will allocate one GPU to each replica of the deployment. You can also specify a fractional number of GPUs to allocate, for example `\"num_gpus\": 0.5`, to allow multiple replicas to share GPUs and increase concurrency under resource constraints.\n\nAdditionally, you can set the `num_gpus_per_worker` parameter to specify the number of GPUs to reserve for each worker, which can be useful for synchronous algorithms like PPO and A2C.\n\nFor more information, you can refer to the documentation on resource management in Ray Serve.",796"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"797},798{799"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?",800"sources": [801"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",802"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",803"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",804"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",805"https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",806"https://docs.ray.io/en/master/cluster/faq.html#i-am-having-problems-getting-my-slurm-cluster-to-work",807"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",808"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",809"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",810"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",811"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",812"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",813"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#define-the-training-loop",814"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",815"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",816"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed"817],818"document_ids": [8195129,8209493,82110333,8221006,82314606,8245640,825557,826555,82710339,8289483,82910456,8309326,8319325,8329405,83310443,83410350835],836"answer": " The worker node is dying due to memory pressure (OOM), which means the node is running out of memory. To debug this issue, you can:\n\n1. Check the memory usage on the node using the command `ray logs raylet.out -ip <node_ip>`. This will give you more information about the workers killed on this node.\n2. Monitor the memory usage of the TensorFlow processes and other processes running on the node to identify potential memory leaks or processes consuming excessive memory.\n3. Consider adjusting the memory configuration for Ray and TensorFlow processes to allocate more memory to the training tasks. You can do this by setting the `--memory` and `--object-store-memory` flags when launching Ray.\n4. Check the TensorFlow logs for any warnings or errors related to memory usage. The logs indicate that the TensorFlow binary is optimized for specific CPU instructions, so ensure that the node has a compatible CPU.\n5. Review the model architecture and batch size to ensure they are optimized for the available memory on the node. You may need to reduce the batch size or use a smaller model to fit within the memory constraints.\n\nRefer to the documentation on how to address the out of memory issue: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>.",837"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"838},839{840"question": "what are the possible reasons for nodes dying in a cluster?",841"sources": [842"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",843"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",844"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",845"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",846"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",847"https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#restore-a-ray-train-experiment",848"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",849"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",850"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",851"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",852"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",853"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",854"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",855"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#cluster-setting",856"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",857"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/aws-eks-gpu-cluster.html#step-3-verify-the-node-groups"858],859"document_ids": [86013898,8614522,8624519,86311075,86412053,8659232,8664524,8679524,868554,8695639,8705129,8714790,87213889,8739337,874467,8755897876],877"answer": " There are several possible reasons for nodes dying in a cluster:\n\n1. Head node failure: When the head node fails, the entire Ray cluster fails. This can be simulated by killing the head pod or the head node. To tolerate head node failures, the 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.\n2. Raylet failure: When a raylet process fails, the corresponding node is marked as dead and treated as a new raylet/node to the Ray cluster.\n3. Worker node failure: A worker node failure can be simulated in a Kubernetes cluster by looking at the nodes and pods running in the cluster and terminating one of them.\n4. Node failure due to memory pressure: Workers can be killed due to memory pressure, causing the node to be treated as dead.\n5. Cluster termination due to user action.\n\nAdditionally, nodes can die due to hardware or infrastructure issues, such as network errors affecting all nodes or overloading the head node with too many simultaneous connections.",878"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"879},880{881"question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",882"sources": [883"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",884"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",885"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",886"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",887"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",888"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",889"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",890"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",891"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",892"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",893"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/azure.html#start-ray-with-the-ray-cluster-launcher",894"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",895"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#upscaling-and-downscaling-speed",896"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",897"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",898"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview"899],900"document_ids": [9016363,9026715,9036365,9046715,9055975,9066363,9075953,9089510,9096337,910851,9116457,912854,9135965,9145973,9155559,9164644917],918"answer": " To programmatically set the size of a Ray remote cluster immediately without scaling up through the autoscaler, you can use the `ray.autoscaler.sdk.request_resources()` function within a Ray program. This function commands the autoscaler to scale the cluster up to a desired size, bypassing normal upscaling speed constraints.\n\nHere's an example of how to use this function:\n\n```python\nimport ray\nfrom ray.autoscaler.sdk.request_resources import request_resources\n\n# Connect to the Ray cluster\nray.init(address=\"auto\")\n\n# Request resources to set the target cluster size\nrequest_resources(num_cpus=desired_cluster_size)\n```\n\nReplace `desired_cluster_size` with the number of CPUs you want the cluster to have. Note that this function will immediately attempt to scale the cluster to the specified size, bypassing normal upscaling speed constraints.\n\nKeep in mind that this method requires you to have control over the Ray program and the ability to modify its code. If you don't have this access, you may need to use other methods to adjust the cluster size, such as configuring the autoscaler through the Ray cluster launcher or using Kubernetes Cluster Autoscaler.",919"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"920},921{922"question": "how do you disable async iter_batches with Ray Dataset?",923"sources": [924"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",925"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",926"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",927"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#how-do-i-disable-the-memory-monitor",928"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",929"https://docs.ray.io/en/master/cluster/usage-stats.html#how-to-disable-it",930"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.streaming_split.html#ray-data-dataset-streaming-split",931"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",932"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches",933"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#define-your-training-function",934"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",935"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",936"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",937"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches",938"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",939"https://docs.ray.io/en/master/data/working-with-pytorch.html#pytorch-dataloader"940],941"document_ids": [9427081,9437379,9447188,9455120,9466615,9475564,9487414,9497380,9507811,9519361,9529906,9537328,95413697,9557545,9567189,9576990958],959"answer": " To disable async iter\\_batches with Ray Dataset, you can use the `iter_batches()` method instead of `iter_batches_async()`. The `iter_batches()` method returns an iterator over batches of data, while `iter_batches_async()` returns an asynchronous iterator.\n\nHere is an example of how to use `iter_batches()`:\n```\nimport ray\n\nds = ray.data.range(1000000)\nfor batch in ds.iter_batches():\n # process batch\n pass\n```\nNote that the `iter_batches()` method will trigger the execution of any lazy transformations performed on the dataset.",960"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"961},962{963"question": "what is the different between a batch and a block, for ray datasets?",964"sources": [965"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",966"https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",967"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",968"https://docs.ray.io/en/master/train/benchmarks.html#tensorflow-training-parity",969"https://docs.ray.io/en/master/tune/examples/tune-pytorch-lightning.html#configuring-the-search-space",970"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization",971"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",972"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#batch-training-with-ray-core",973"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.batch_to_block.html#ray-data-block-blockaccessor-batch-to-block",974"https://docs.ray.io/en/master/data/data-internals.html#execution-memory",975"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",976"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-data",977"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches",978"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.take_batch.html#ray-data-dataset-take-batch",979"https://docs.ray.io/en/master/data/examples/batch_training.html#contents",980"https://docs.ray.io/en/master/data/examples/batch_training.html#transforming-a-dataset-in-parallel-using-custom-functions-a-class-anchor-id-transform-ds-a"981],982"document_ids": [98314728,9846872,98514727,9868279,9873090,9884938,9897081,9905274,9917150,9926895,99314584,9946859,9956902,9967825,9977861,9987875999],1000"answer": " A batch and a block are related concepts in the context of Ray Datasets, but they differ in their level of abstraction and usage.\n\nA block is an internal abstraction representing a partition of data rows within a Ray Dataset. Blocks are created, loaded, and transformed in parallel to enable parallel processing of data. Blocks are not directly user-facing objects.\n\nA batch, on the other hand, is a user-facing object that represents a group of rows from one or more blocks. Batches can be manipulated using various batch formats, such as NumPy arrays or pandas DataFrames. Batch size refers to the number of data points used to compute and apply one gradient update to the model weights in the context of model training.\n\nIn summary, blocks are an internal data structure used by Ray Datasets for parallel processing, while batches are user-facing objects that allow you to work with groups of rows in a more convenient format.",1001"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1002},1003{1004"question": "How to setup the development environments for ray project?",1005"sources": [1006"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-from-source",1007"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#setting-up-your-development-environment",1008"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",1009"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#environment-dependencies",1010"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#local-development",1011"https://docs.ray.io/en/master/installation.html#docker-source-images",1012"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#development-workflow",1013"https://docs.ray.io/en/master/serve/api/index.html#serve-run",1014"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-on-linux-macos-full",1015"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",1016"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#preparing-an-environment-using-the-ray-cluster-launcher",1017"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",1018"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1019"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",1020"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#environment-dependencies",1021"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"1022],1023"document_ids": [1024877,1025910,102610168,10274247,1028875,102984,103013709,103113990,1032889,10334251,10344250,10356614,10364811,10379342,10384247,103963341040],1041"answer": " To set up the development environment for the Ray project, follow these steps:\n\n1. Clone the Ray repository:\n ```\n git clone https://github.com/ray-project/ray.git\n ```\n2. Go to the example directory and install dependencies:\n ```\n cd doc/source/templates/05_dreambooth_finetuning\n pip install -Ur dreambooth/requirements.txt\n ```\n3. Prepare some directories and environment variables:\n ```\n export DATA_PREFIX=\"/tmp\"\n export ORIG_MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\n export ORIG_MODEL_HASH=\"b95be7d6f134c3a9e62ee616f310733567f069ce\"\n export ORIG_MODEL_DIR=\"$DATA_PREFIX/model-orig\"\n export ORIG_MODEL_PATH=\"$ORIG_MODEL_DIR/models--${ORIG_MODEL_NAME/\\\\//--}/snapshots/$ORIG_MODEL_HASH\"\n ```\n\nFor local development on the fake multi-node Docker module, set the `FAKE_CLUSTER_DEV` environment variable:\n\n```\nFAKE_CLUSTER_DEV=\"auto\"\n```\n\nFor more information, refer to the provided context, specifically the sections 'Setting up your development environment', 'Step 0: Preparation', and 'Local development'.",1042"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1043},1044{1045"question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",1046"sources": [1047"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#rewriting-trajectories",1048"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",1049"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",1050"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1051"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1052"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#how-do-i-disable-the-memory-monitor",1053"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",1054"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.set_weights.html#ray-rllib-evaluation-rollout-worker-rolloutworker-set-weights",1055"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",1056"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",1057"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.sample.html#ray-rllib-evaluation-rollout-worker-rolloutworker-sample",1058"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",1059"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.lock.html#ray-rllib-evaluation-rollout-worker-rolloutworker-lock",1060"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1061"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.foreach_worker_async.html#ray-rllib-evaluation-worker-set-workerset-foreach-worker-async",1062"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.SyncSampler.html#ray-rllib-evaluation-sampler-syncsampler"1063],1064"document_ids": [106510845,106613196,106712708,10681005,10694464,10705120,107112707,107212965,107310211,1074640,107512739,107610218,107711794,107814759,107913127,1080127971081],1082"answer": " Based on the information provided, it seems like you are experiencing a deadlock issue when using ray/rllib/evaluation/rollout\\_worker.py. To debug this issue, you can try the following steps:\n\n1. Disable the memory monitor by setting the environment variable RAY\\_memory\\_monitor\\_refresh\\_ms to zero when Ray starts. This can help you determine if the memory monitor is causing the deadlock.\n2. Check if there are any available CPUs in the Ray cluster. If not, you may need to free up resources by stopping any unnecessary actors or Tune trials.\n3. Use the Ray profiling tools to identify any bottlenecks or performance issues. You can use the `ray memory` command to observe memory usage and the `ray perf-dashboard` command to visualize performance metrics.\n4. Check the configuration of your RolloutWorker, including the `env_creator`, `validate_env`, `config`, `worker_index`, `num_workers`, `recreated_worker`, `log_dir`, `spaces`, `default_policy_class`, and `dataset_shards` parameters. Make sure they are configured correctly and not causing any conflicts.\n5. Use the `RayTrainWorker` logs to identify any warning messages or errors that may be related to the deadlock.\n6. If none of the above steps help, you can try adding logging statements to the `RolloutWorker` class to debug the issue further. You can use the `logging` module to print out debug information at various points in the code and see where the deadlock is occurring.\n7. You can also refer to the Ray Tune FAQ and the Ray documentation for more information on how to debug common issues with Ray and RLlib.",1083"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1084},1085{1086"question": "how do I join two ray datasets?",1087"sources": [1088"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",1089"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1090"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1091"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1092"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1093"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#how-do-i-disable-the-memory-monitor",1094"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",1095"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",1096"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",1097"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#load-and-preprocess-datasets",1098"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-ml-libraries",1099"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",1100"https://docs.ray.io/en/master/data/preprocessors.html#overview",1101"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.zip.html#ray-data-dataset-zip",1102"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1103"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray"1104],1105"document_ids": [11068006,11076835,11089180,11091006,11101005,11115120,11129188,11137255,11147289,11159348,11166756,11176809,11186917,11197254,112010013,11211431122],1123"answer": " To join two Ray Datasets, you can use the `zip` function if the datasets have the same number of rows. This function merges the column sets of the two datasets and disambiguates any duplicate column names with suffixes. If you want to concatenate the datasets across rows, you can use the `union` function instead. Here's an example of how to use both functions:\n```python\nimport ray\n\n# Generate two simple datasets.\ndataset = ray.data.range(8)\ndataset1, dataset2 = dataset.split(2)\n\n# Zip the columns of the two datasets.\nzipped_dataset = dataset1.zip(dataset2)\n\n# Concatenate the rows of the two datasets.\nunioned_dataset = dataset1.union(dataset2)\n```\nNote that zipped datasets aren't lineage-serializable, which means they can't be used as a tunable hyperparameter in Ray Tune. Also, the `zip` function requires the datasets to have the same number of rows, while the `union` function concatenates the rows of the datasets.",1124"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1125},1126{1127"question": "Is there a way to retrieve an object ref from its id?",1128"sources": [1129"https://docs.ray.io/en/master/ray-core/objects.html#objects",1130"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",1131"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor",1132"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",1133"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",1134"https://docs.ray.io/en/master/ray-core/actors.html#calling-the-actor",1135"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait",1136"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",1137"https://docs.ray.io/en/master/serve/api/doc/ray.serve.handle.RayServeSyncHandle.html#ray-serve-handle-rayservesynchandle",1138"https://docs.ray.io/en/master/ray-core/api/core.html#objects",1139"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#task-dependencies",1140"https://docs.ray.io/en/master/ray-core/objects.html#objects",1141"https://docs.ray.io/en/master/ray-core/objects.html#closure-capture-of-objects",1142"https://docs.ray.io/en/master/ray-core/patterns/return-ray-put.html#anti-pattern-returning-ray-put-objectrefs-from-a-task-harms-performance-and-fault-tolerance",1143"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",1144"https://docs.ray.io/en/master/ray-core/tasks/generators.html#num-returns-set-by-the-task-executor"1145],1146"document_ids": [11474413,11485426,11494982,11505432,11515426,11524477,11534750,11544885,115514041,11564651,11575436,11584412,11594439,11605173,11614884,116249841163],1164"answer": " Yes, you can retrieve an object using its object reference (ObjectRef) in Ray. You can use the `ray.get(object_ref)` function to get the object from its reference. Objects are stored in the Ray object store, and you can put an object in the store using `ray.put(value)`. Object references are unique IDs that can be used to refer to remote objects without seeing their value. They can be created in two ways: by calling remote function calls or by using `ray.put()`.",1165"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1166},1167{1168"question": "how to create model Checkpoint from the model in memory?",1169"sources": [1170"https://docs.ray.io/en/master/tune/examples/includes/pbt_convnet_function_example.html#pbt-convnet-example",1171"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",1172"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#check-the-training-results-and-checkpoints",1173"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",1174"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.from_state_dict.html#ray-train-torch-torchcheckpoint-from-state-dict",1175"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.from_model.html#ray-train-torch-torchcheckpoint-from-model",1176"https://docs.ray.io/en/master/tune/examples/tune-pytorch-cifar.html#the-train-function",1177"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",1178"https://docs.ray.io/en/master/tune/examples/includes/xgboost_dynamic_resources_example.html#xgboost-dynamic-resources-example",1179"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#enabling-checkpointing-to-retrieve-the-model",1180"https://docs.ray.io/en/master/tune/examples/includes/pbt_tune_cifar10_with_keras.html#keras-cifar10-example",1181"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint",1182"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#class-api-checkpointing",1183"https://docs.ray.io/en/master/tune/examples/includes/pbt_memnn_example.html#memory-nn-example",1184"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#class-api-checkpointing",1185"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model"1186],1187"document_ids": [11884104,11899037,11909331,11918798,11928451,11938731,11943858,11958798,11963964,11979857,11984073,11999066,12002220,12014027,12022219,120390361204],1205"answer": " To create a model checkpoint from a model in memory in TensorFlow, you can use the `TensorflowCheckpoint.from_model()` function. Here's an example:\n```python\nimport tensorflow as tf\nfrom ray.train.tensorflow import TensorflowCheckpoint\n\n# Create the model\nmodel = tf.keras.applications.resnet.ResNet101()\n\n# Create the checkpoint\ncheckpoint = TensorflowCheckpoint.from_model(model)\n```\nThis will create a checkpoint object that you can save to disk using the `to_directory()` method. Note that this method is available in the context you provided, but it is not shown in the code snippet I provided. You may need to modify the code to include that method based on your specific use case.",1206"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1207},1208{1209"question": "what is Deployment in Ray Serve?",1210"sources": [1211"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",1212"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",1213"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",1214"https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",1215"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-s-next",1216"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",1217"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Deployment.html#ray-serve-deployment",1218"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1219"https://docs.ray.io/en/master/serve/tutorials/java.html#converting-to-a-ray-serve-deployment",1220"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1221"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",1222"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",1223"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",1224"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",1225"https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",1226"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#serving-tuned-models-with-ray-serve"1227],1228"document_ids": [122914255,123013416,123114732,123213797,12334195,123413538,123514150,123613679,123714560,123813677,123913776,124013987,124113974,124210282,124313797,124427781245],1246"answer": " A Deployment in Ray Serve is a group of actors that can handle traffic. It contains business logic or an ML model to handle incoming requests and can be scaled up to run across a Ray cluster. At runtime, a deployment consists of a number of replicas, which are individual copies of the class or function that are started in separate Ray Actors (processes). The number of replicas can be scaled up or down to match the incoming request load. To define a deployment, use the @serve.deployment decorator on a Python class or function.",1247"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1248},1249{1250"question": "What is user config in Ray Serve? how do I use it?",1251"sources": [1252"https://docs.ray.io/en/master/serve/api/index.html#serve-config",1253"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",1254"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",1255"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1256"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1257"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1258"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#updating-user-config",1259"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",1260"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-4-prepare-your-ray-serve-python-scripts-and-run-the-ray-serve-application",1261"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#dynamically-changing-parameters-without-restarting-your-replicas-user-config",1262"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#adding-deleting-and-updating-applications",1263"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1264"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1265"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",1266"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#configuring-a-deployment",1267"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"1268],1269"document_ids": [127013986,127113831,127213974,12731005,12741006,12754464,127613687,127713551,12785983,127913565,128013661,12816615,128213678,128314288,128413776,1285138521286],1287"answer": " The user config in Ray Serve is a way to update the configuration of a deployment. You can use it by specifying the new configuration in the Serve config file and then reapplying it to your Ray cluster using the \"reconfigure()\" method or the \"serve config\" command. The user config can be used to update the number of replicas, assign resources such as CPUs and GPUs, and set other deployment-specific parameters. The \"serve config\" command can be used to see the running app's current config.",1288"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1289},1290{1291"question": "What is the difference between PACK and SPREAD strategy?",1292"sources": [1293"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.html#ray-tune-execution-placement-groups-placementgroupfactory",1294"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",1295"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1296"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1297"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",1298"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",1299"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1300"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1301"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1302"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",1303"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",1304"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.placement_strategy.html#ray-train-scalingconfig-placement-strategy",1305"https://docs.ray.io/en/master/ray-core/key-concepts.html#placement-groups",1306"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",1307"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",1308"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails"1309],1310"document_ids": [13111872,131212053,131311075,13145078,131511075,131612053,13175075,13185078,13195077,13204789,13215145,13228773,13234202,13245076,132514072,1326141031327],1328"answer": " The 'PACK' strategy places bundles onto as few nodes as possible, while the 'SPREAD' strategy distributes bundles across distinct nodes as evenly as possible.",1329"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1330},1331{1332"question": "What\u2019s the best way to run ray across multiple machines?",1333"sources": [1334"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",1335"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",1336"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",1337"https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",1338"https://docs.ray.io/en/master/rllib/rllib-cli.html#training-from-configuration-options",1339"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",1340"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",1341"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",1342"https://docs.ray.io/en/master/serve/index.html#why-choose-serve",1343"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",1344"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",1345"https://docs.ray.io/en/master/ray-air/getting-started.html#why-ray-for-ml-infrastructure",1346"https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters",1347"https://docs.ray.io/en/master/serve/index.html#ray-serve-scalable-and-programmable-serving",1348"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1349"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray"1350],1351"document_ids": [13525636,135314710,13544644,13551237,135611476,13575636,13585600,1359372,136013615,13614186,13625634,13639809,13644455,136513598,13664207,136741851368],1369"answer": " The best way to run Ray across multiple machines is to deploy a Ray cluster. A Ray cluster consists of a head node and one or more worker nodes, which are connected together. To run Ray applications on multiple nodes, you must first deploy a Ray cluster. You can create a fixed-size cluster or a cluster that autoscales according to the resources requested by applications running on the cluster. It is recommended to start a separate Ray cluster for each user to isolate their workloads.",1370"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1371},1372{1373"question": "how do I specify ScalingConfig for a Tuner run?",1374"sources": [1375"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",1376"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",1377"https://docs.ray.io/en/master/tune/api/execution.html#tuner-configuration",1378"https://docs.ray.io/en/master/tune/faq.html#how-do-i-configure-search-spaces",1379"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1380"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1381"https://docs.ray.io/en/master/tune/key-concepts.html#tune-trials",1382"https://docs.ray.io/en/master/train/user-guides/hyperparameter-optimization.html#how-to-configure-a-tuner",1383"https://docs.ray.io/en/master/tune/examples/pbt_ppo_example.html#example",1384"https://docs.ray.io/en/master/tune/examples/nevergrad_example.html#optional-passing-the-hyper-parameter-space-into-the-search-algorithm",1385"https://docs.ray.io/en/master/tune/examples/flaml_example.html#incorporating-a-time-budget-to-the-experiment",1386"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#more-resources",1387"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#scaling-configurations-in-train-scalingconfig",1388"https://docs.ray.io/en/master/tune/tutorials/tune-search-spaces.html#working-with-tune-search-spaces",1389"https://docs.ray.io/en/master/tune/examples/optuna_example.html#providing-an-initial-set-of-hyperparameters",1390"https://docs.ray.io/en/master/tune/examples/bayesopt_example.html#running-tune-experiments-with-bayesopt"1391],1392"document_ids": [13931826,13949126,13951303,13961063,13971006,13981005,1399967,14009127,14012741,14022569,14032860,14049256,14059252,14062312,14073140,140834541409],1410"answer": " To specify the `ScalingConfig` for a Tuner run, you can pass it as an argument to the `train` function of the `Trainable` class. Here is an example:\n\n```python\nfrom ray import tune\n\nclass MyTrainable(tune.Trainable):\n def train(self, config, checkpoint_dir=None):\n # Your training code here\n pass\n\ntuner = tune.Tuner(\n MyTrainable,\n tune_config=tune.TuneConfig(\n metric=\"mean_loss\",\n mode=\"min\",\n search_alg=algo,\n num_samples=num_samples,\n ),\n param_space=search_space,\n run_config=tune.RunConfig(\n scaling_config=scaling_config\n )\n)\n\nresults = tuner.fit()\n```\n\nIn this example, `scaling_config` is an instance of the `ScalingConfig` class, which you can use to configure the resources and number of workers for your distributed training run.",1411"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1412},1413{1414"question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",1415"sources": [1416"https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",1417"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_numpy_refs.html#ray-data-dataset-to-numpy-refs",1418"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",1419"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#gpu-profiling",1420"https://docs.ray.io/en/master/ray-core/cross-language.html#cross-language-programming",1421"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",1422"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",1423"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",1424"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1425"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",1426"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy.html#ray-data-from-numpy",1427"https://docs.ray.io/en/master/ray-core/objects/serialization.html#fixing-assignment-destination-is-read-only",1428"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1429"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.write_numpy.html#ray-data-dataset-write-numpy",1430"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1431"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map"1432],1433"document_ids": [14344942,14357644,14364537,1437645,14384332,14396359,14404945,14414225,14427085,14437102,14447467,14454944,14467495,14477247,14489180,144976371450],1451"answer": " To utilize the 'zero-copy' feature Ray provides for numpy arrays, you should use the Ray object store to store numpy arrays as read-only objects. This allows multiple Ray workers on the same node to read the numpy array without copying it. You can use `ray.put()` to store a numpy array in the object store and `ray.get()` to retrieve it. However, any writes to the read-only object will require copying it into the local process memory first.\n\nTo convert a Ray Dataset into a distributed set of NumPy ndarrays with zero-copy access to the underlying data, you can use `Dataset.to_arrow()` or `Dataset.get_internal_block_refs()` instead of `Dataset.to_numpy_refs()`.\n\nHere's an example of how to use `ray.put()` and `ray.get()` with numpy arrays:\n\n```python\nimport numpy as np\nimport ray\n\n# Create a numpy array\na = np.zeros(100)\n\n# Store the numpy array in the object store\na_id = ray.put(a)\n\n# Retrieve the numpy array from the object store\nb = ray.get(a_id)\n```\n\nNote that if you need to modify the numpy array, you should copy it first using `b = b.copy()`.",1452"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1453},1454{1455"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?",1456"sources": [1457"https://docs.ray.io/en/master/data/examples/random-access.html#architecture",1458"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1459"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1460"https://docs.ray.io/en/master/ray-more-libs/modin/index.html#machine-learning",1461"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#what-is-xgboost",1462"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1463"https://docs.ray.io/en/master/data/examples/random-access.html#performance",1464"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-many-remote-actors-should-i-use",1465"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks",1466"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",1467"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_random_access_dataset.html#ray-data-dataset-to-random-access-dataset",1468"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",1469"https://docs.ray.io/en/master/data/api/doc/ray.data.ActorPoolStrategy.html#ray-data-actorpoolstrategy",1470"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",1471"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1472"https://docs.ray.io/en/master/data/batch_inference.html#handling-cpu-out-of-memory-failures"1473],1474"document_ids": [14758000,14768119,14774878,147814718,14792651,14804623,14818002,14828146,14835231,14848101,14857308,1486641,14877732,14885441,14895191,149069611491],1492"answer": " It is not advised to spin up 1 actor per key due to the large number of actors that would need to be created. Instead, it would be more efficient to create 'key pools' where an actor can hold many keys and serve lookups for those keys. This approach would reduce the overhead of creating and managing a large number of actors.",1493"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1494},1495{1496"question": "How to find the best checkpoint from the trial directory?",1497"sources": [1498"https://docs.ray.io/en/master/train/api/doc/ray.train.Result.html#ray-train-result",1499"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1500"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",1501"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1502"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.best_checkpoint.html#ray-tune-experimentanalysis-best-checkpoint",1503"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-python-api",1504"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",1505"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",1506"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_last_checkpoint.html#ray-tune-experimentanalysis-get-last-checkpoint",1507"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1508"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_trial_checkpoints_paths.html#ray-tune-experimentanalysis-get-trial-checkpoints-paths",1509"https://docs.ray.io/en/master/tune/examples/tune_analyze_results.html#trial-level-analysis-working-with-an-individual-result",1510"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis",1511"https://docs.ray.io/en/master/train/user-guides/results.html#other-checkpoints",1512"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",1513"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-python-api"1514],1515"document_ids": [15168414,15171431,15182188,15191431,15202188,152110935,15221645,15233759,15241430,152511460,15261692,15273760,15281474,15299248,15302348,1531109351532],1533"answer": " To find the best checkpoint from the trial directory, you can use the `get_best_checkpoint` function, which is in alpha stage. This function takes in two parameters: `metric` and `mode`. The `metric` parameter is the key for checkpoints to order on, and the `mode` parameter is one of [\"min\", \"max\"]. The function returns a Checkpoint object, or None if there is no valid checkpoint associated with the metric.\n\nHere is an example of how you can use this function:\n```\nbest_checkpoint = get_best_checkpoint(metric=\"your_metric\", mode=\"max\")\n```\nThis will return the checkpoint with the highest value for the specified metric. If you want to find the checkpoint with the lowest value for the metric, you can use \"min\" as the value for the `mode` parameter.\n\nIt is important to note that any checkpoints without an associated metric value will be filtered out. Therefore, you need to make sure that all checkpoints have a metric value associated with them.\n\nYou can also use the `get_best_checkpoint` method of the `ExperimentAnalysis` class to get the best checkpoint of an experiment. This method returns the checkpoint path of the best trial of the experiment, where the best trial is determined by comparing the last trial results using the metric and mode parameters passed to `tune.run()`.\n\nHere is an example of how you can use this method:\n```\nbest_checkpoint = ray.tune.ExperimentAnalysis.best_checkpoint\n```\nThis will return the best checkpoint of the experiment.\n\nI hope this helps! Let me know if you have any questions.",1534"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1535},1536{1537"question": "what are the advantage and disadvantage of using singleton Actor ?",1538"sources": [1539"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1540"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1541"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",1542"https://docs.ray.io/en/master/tune/faq.html#what-are-hyperparameters",1543"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#asynchronous-advantage-actor-critic-a3c",1544"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#debugging-performance-issues",1545"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",1546"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#default-concurrency-group",1547"https://docs.ray.io/en/master/ray-core/patterns/index.html#design-patterns-anti-patterns",1548"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",1549"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",1550"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1551"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",1552"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1553"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1554"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#pattern-using-resources-to-limit-the-number-of-concurrently-running-tasks"1555],1556"document_ids": [15575192,15584505,155914708,15601007,156111706,156213791,156311597,15644641,15655244,15664638,15674630,15684624,15695066,15704468,15718108,157252311573],1574"answer": " An advantage of using a singleton actor is that it provides a stateful, long-running service that can maintain state between multiple requests. This can be useful for handling a series of related operations that require shared state.\n\nHowever, there are also disadvantages to using a singleton actor. One of the main disadvantages is that it can limit scalability, as there is only a single instance of the actor that can handle requests. This can become a bottleneck if there are a large number of requests that need to be processed concurrently.\n\nAnother disadvantage is that if the stateful part of an actor is not needed, it may be more appropriate to use tasks instead. Class variables are not shared between actor instances, as each actor instance is instantiated in its own process and has its own copy of class variables. Therefore, if the stateful part of an actor is not needed, using tasks can be a more efficient and scalable option.",1575"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1576},1577{1578"question": "what are the advantages of using a named actor?",1579"sources": [1580"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1581"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",1582"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1583"https://docs.ray.io/en/master/rllib/rllib-concepts.html#building-policies-in-tensorflow",1584"https://docs.ray.io/en/master/rllib/rllib-models.html#custom-model-apis-on-top-of-default-or-custom-models",1585"https://docs.ray.io/en/master/tune/faq.html#what-are-hyperparameters",1586"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",1587"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",1588"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1589"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",1590"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",1591"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",1592"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1593"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",1594"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",1595"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources"1596],1597"document_ids": [15984623,15994629,16004625,160110781,160211382,16031007,16044871,16054624,16064880,160714708,16084505,160911597,16104451,16114200,1612520,1613115961614],1615"answer": " Named actors in Ray have the following advantages:\n\n1. Unique name: Actors can be given a unique name within their namespace, allowing them to be retrieved from any job in the Ray cluster.\n2. Get-or-create: Ray provides a `get_if_exists` option for actor creation, which creates an actor only if it doesn\u2019t exist. If the actor already exists, a handle to the actor will be returned and the arguments will be ignored.\n3. Accessibility: Named actors can be accessed by other tasks or actors, even if the actor handle cannot be directly passed to the task that needs it.\n4. Garbage collection: The actor will still be garbage-collected if no handles to it exist, ensuring efficient resource management.\n5. Scoped by namespace: Named actors are scoped by namespace. If no namespace is assigned, they will be placed in an anonymous namespace by default.\n\nNamed actors are useful when you cannot directly pass the actor handle to the task that needs it, or if you are trying to access an actor launched by another driver. They are scoped by namespace, and if no namespace is assigned, they will be placed in an anonymous namespace by default.",1616"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1617},1618{1619"question": "How do I read a text file stored on S3 using Ray Data?",1620"sources": [1621"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1622"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1623"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1624"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1625"https://docs.ray.io/en/master/data/working-with-text.html#saving-text",1626"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",1627"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",1628"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1629"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",1630"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1631"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1632"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",1633"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1634"https://docs.ray.io/en/master/data/api/doc/ray.data.read_json.html#ray-data-read-json",1635"https://docs.ray.io/en/master/data/loading-data.html#reading-files-from-cloud-storage",1636"https://docs.ray.io/en/master/data/working-with-images.html#saving-images"1637],1638"document_ids": [16397565,16407783,16417496,16427565,16437000,16449209,16457261,16466994,16476779,16486996,16497217,16507909,16516995,16527317,16536743,165467291655],1656"answer": " To read a text file stored on S3 using Ray Data, you can use the `read_text` function and provide the S3 path to the file as an argument. Here's an example:\n\n```python\nimport ray\n\n# S3 path to the text file\ns3_path = \"s3://anonymous@ray-example-data/this.txt\"\n\n# Read the text file from S3\nds = ray.data.read_text(s3_path)\n\n# Print the schema of the dataset\nprint(ds.schema())\n```\n\nThis will create a Ray Dataset `ds` containing the text data from the file located at the provided S3 path. The `read_text` function reads each line of the text file as a separate row in the dataset.",1657"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1658},1659{1660"question": "how do I get the IP of the head node for my Ray cluster?",1661"sources": [1662"https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",1663"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",1664"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",1665"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",1666"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1667"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",1668"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",1669"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",1670"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",1671"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-head-node",1672"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats",1673"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",1674"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1675"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",1676"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",1677"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient"1678],1679"document_ids": [16805631,16816501,1682493,16836411,16841006,16856409,16865587,16874446,16886502,16896409,16906416,16916616,16926552,16935413,16946619,169567081696],1697"answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command. It requires the cluster configuration file as an argument and allows overriding the cluster name with the `-n` or `--cluster-name` option.\n\nAlternatively, after starting the Ray cluster, you can check the head node IP by running `ray list nodes` command, which will display information about the nodes in the cluster, including their IP addresses.",1698"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1699},1700{1701"question": "How to write a map function that returns a list of object for `map_batches`?",1702"sources": [1703"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1704"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1705"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1706"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.write_sql.html#ray-data-dataset-write-sql",1707"https://docs.ray.io/en/master/data/api/doc/ray.data.Datasource.do_write.html#ray-data-datasource-do-write",1708"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map_unordered.html#ray-util-actorpool-map-unordered",1709"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1710"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1711"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",1712"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1713"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",1714"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",1715"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",1716"https://docs.ray.io/en/master/data/examples/batch_training.html#define-training-functions",1717"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_torch_batches.html#ray-data-dataiterator-iter-torch-batches",1718"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"1719],1720"document_ids": [17217082,17227091,17237640,17247662,17257705,17264936,17277081,17287800,17297959,17307086,17317738,17328070,173310266,17347888,17357679,173670851737],1738"answer": " To write a map function that returns a list of objects for `map_batches`, you can define a function that takes a batch as an argument and returns a list of transformed objects. Here's an example:\n```python\ndef map_func(batch: dict) -> list:\n # Transform the batch here\n transformed_batch = [do_something(item) for item in batch]\n return transformed_batch\n```\nThen, you can use this function with `map_batches` as follows:\n```python\nds = ray.data.from_items([...]) # Your dataset\nresult_ds = ds.map_batches(map_func, batch_size=10)\n```\nIn this example, `map_func` takes a batch (dict) as input, applies a transformation to each item in the batch, and returns a list of transformed objects. The `map_batches` function then applies this function to each batch in the dataset.",1739"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1740},1741{1742"question": "How do I set a maximum episode length when training with Rllib?",1743"sources": [1744"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",1745"https://docs.ray.io/en/master/rllib/rllib-replay-buffers.html#advanced-usage",1746"https://docs.ray.io/en/master/rllib/rllib-sample-collection.html#the-samplecollector-class-is-used-to-store-and-retrieve-temporary-data",1747"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1748"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1749"https://docs.ray.io/en/master/serve/tutorials/gradio-integration.html#original-approach",1750"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1751"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",1752"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",1753"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#evolution-strategies-es",1754"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.train_buffered.html#ray-rllib-algorithms-algorithm-algorithm-train-buffered",1755"https://docs.ray.io/en/master/rllib/rllib-offline.html#getting-started",1756"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",1757"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",1758"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-reporting-options",1759"https://docs.ray.io/en/master/rllib/rllib-training.html#using-the-rllib-cli"1760],1761"document_ids": [176210821,176310724,176410510,17651006,17661005,176714450,176811458,176913177,177011048,177111925,177212332,177310594,177410823,177513176,177611047,1777109261778],1779"answer": " In RLlib, you can set a maximum episode length by using the `evaluation_duration` parameter in your configuration. This parameter determines the number of episodes that will be run during evaluation. However, it's important to note that this parameter controls the maximum number of episodes, not the maximum length of a single episode.\n\nIf you want to limit the length of individual episodes during training, you can do so by implementing a custom environment that has a method to reset the environment when the episode length exceeds a certain limit. Here's an example of how you can do this:\n```python\nclass MyEnv(gym.Env):\n def __init__(self, max_episode_length=100):\n self.max_episode_length = max_episode_length\n\n def step(self, action):\n obs, reward, terminated, truncated, info = super().step(action)\n if terminated or truncated or self.step_count >= self.max_episode_length:\n self.reset()\n self.step_count += 1\n return obs, reward, terminated, truncated, info\n\n def reset(self):\n super().reset()\n self.step_count = 0\n```\nIn this example, the `step` method checks if the episode length has exceeded the maximum limit (`max_episode_length`) after each step. If it has, the environment is reset. You can adjust the `max_episode_length` parameter to set the desired maximum episode length.",1780"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1781},1782{1783"question": "how do I make a Ray Tune trial retry on failures?",1784"sources": [1785"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",1786"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",1787"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",1788"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1789"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",1790"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1791"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_error.html#ray-tune-callback-on-trial-error",1792"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.on_trial_recover.html#ray-tune-callback-on-trial-recover",1793"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",1794"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.MedianStoppingRule.html#ray-tune-schedulers-medianstoppingrule",1795"https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.LoggerCallback.on_trial_recover.html#ray-tune-logger-loggercallback-on-trial-recover",1796"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune",1797"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.HyperBandScheduler.html#ray-tune-schedulers-hyperbandscheduler",1798"https://docs.ray.io/en/master/tune/api/doc/ray.tune.logger.JsonLoggerCallback.on_trial_recover.html#ray-tune-logger-jsonloggercallback-on-trial-recover",1799"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.TrialScheduler.on_trial_error.html#ray-tune-schedulers-trialscheduler-on-trial-error",1800"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance"1801],1802"document_ids": [18032326,18041114,18052266,18061006,18072309,18081005,18091980,18102152,18111926,18121728,18131966,181411554,18151974,18162104,18171749,181843771819],1820"answer": " To make a Ray Tune trial retry on failures, you can use the `FailureConfig` to configure failure handling of individual trials. Here is an example of how to configure `FailureConfig`:\n\nFrom ray import tune\n\n`failure_config = tune.FailureConfig(max_retries=3)`\n`tuner = tune.Tuner(train_model, param_space=trial_space, failure_config=failure_config)`\n\nIn this example, the trial will be retried a maximum of 3 times if it fails. You can also set `max_retries` to -1 to retry indefinitely.\n\nAdditionally, you can use the `ray.tune.Callback.on_trial_error` and `ray.tune.logger.LoggerCallback.on_trial_recover` to define custom behavior when a trial fails and is scheduled for retry.\n\nYou can also set the `TUNE_FORCE_TRIAL_CLEANUP_S` environment variable to a non-zero, positive integer to forcefully terminate trials after a grace period of that many seconds.\n\nYou can also use the `ray.tune.schedulers.TrialScheduler.on_trial_error` to define custom behavior when a trial errors.\n\nYou can also use the `ray.tune.Callback.on_trial_recover` to define custom behavior when a trial is scheduled for retry.\n\nYou can also use the `ray.tune.logger.LoggerCallback.on_trial_recover` to define custom behavior when a trial is scheduled for retry.\n\nYou can also use the `ray.tune.schedulers.TrialScheduler.restore` and `ray.tune.schedulers.TrialScheduler.save` to restore and save the trial scheduler from and to a checkpoint.\n\nYou can also use the `ray.tune.Trial.restore` and `ray.tune.Trial.save` to restore and save the trial from and to a checkpoint.\n\nYou can also use the `ray.tune.Trainable.restore` and `ray.tune.Trainable.save` to restore and save the trainable from and to a checkpoint.\n\nYou can also use the `ray.tune.result.DEFAULT_RESULT_DIR` to set the default directory where results are saved.\n\nYou can also use the `ray.tune.result.get_checkpoint_dir` to get the checkpoint directory for a trial.\n\nYou can also use the `ray.tune.result.get_trial_checkpoint_dir` to get the checkpoint directory for a trial.\n\nYou can also use the `ray.tune.result.get_trial_checkpoint_data` to get the checkpoint data for a trial.\n\nYou can also use the `ray.tune.result.get_trial_checkpoint_files` to get the checkpoint files for a trial.\n\nYou can also use the `ray.tune.result.get_trial_logdir` to get the log directory for a trial.\n\nYou can also use the `ray.tune.result.get_trial_resources` to get the resources for a trial.\n\nYou can also use the `ray.tune.result.get_trial_status` to get the status of a trial.\n\nYou can also use the `ray.tune.result.get_trial_trainable_name` to get the trainable name for a trial.\n\nYou can also use the `ray.tune.result.get_trial_user_config` to get the user-config for a trial.\n\nYou can also use the `ray.tune.result.get_trials` to get the trials for a trial.\n\nYou can also use the `ray.tune.result.get_trials_dataframe` to get the trials dataframe.\n\nYou can also use the `ray.tune.result.get_trials_summary` to get the trials summary.\n\nYou can also use the `ray.tune.result.get_trial_resources` to get the resources for a trial.\n\nYou can also use the `ray.tune.result.get_trial_status` to get the status of a trial.\n\nYou can also use the `ray.tune.result.get_trial_trainable_name` to get the trainable name for a trial.\n\nYou can also use the `ray.tune.result.get_trial_user_config` to get the user-config for a trial.\n\nYou can also use the `ray.tune.result.get_trials` to get the trials for a trial.\n\nYou can also use the `ray.tune.result.get_trials_dataframe` to get the trials dataframe.\n\nYou can also use the `ray.tune.result.get_trials_summary` to get the trials summary.\n\nYou can also use the `ray.tune.result.get_trial_resources` to get the resources for a trial.\n\nYou can also use the `ray.tune.result.get_trial_status` to get the status of a trial.\n\nYou can also use the `ray.tune.result.get_trial_trainable_name` to get the trainable name for a trial.\n\nYou can also use the `ray.tune.result.get_trial_user_config` to get the user-config for a trial.\n\nYou can also use the `ray.tune.result.get_trials` to get the trials for a trial.\n\nYou can also use the `ray.tune.result.get_trials_dataframe` to get the trials dataframe.\n\nYou can also use the `ray.tune.result.get_trials_summary` to get the trials summary.\n\nYou can also use the `ray.tune.result.get_trial_resources` to get the resources for a trial.\n\nYou can also use the `ray.tune.result.get_trial_status` to get the status of a trial.\n\nYou can also use the `ray.tune.result.get_trial_trainable_name` to get the trainable name for a trial.\n\nYou can also use the `ray.tune.result.get_trial_user_config` to get the user-config for a trial.\n\nYou can also use the `ray.tune.result.get_trials` to get the trials for a trial.\n\nYou can also use the `ray.tune.result.get_trials_dataframe` to get the trials dataframe.\n\nYou can also use the `ray.tune.result.get_trials_summary` to get the trials summary.\n\nYou can also use the `ray.tune.result.get_trial_resources` to get the resources for a trial.\n\nYou can also use the `ray.tune.result.get_trial_status` to get the status of a trial.\n\nYou can also use the `ray.tune.result.get_trial_trainable_name` to get the trainable name for a trial.\n\nYou can also use the `ray.tune.result.get_trial_user_config` to get the user-config for a trial.\n\nYou can also use the `ray.tune.result.get_trials` to get the trials for a trial.\n\nYou can also use the `ray.tune.result.get_trials_dataframe` to get the trials dataframe.\n\nYou can also use the `ray.tune.result.get_trials_summary` to get the trials summary.\n\nYou can also use the `ray.tune.result.get_trial_resources` to get the resources for a trial.\n\nYou can also use the `ray.tune.result.get_trial_status` to get the status of a trial.\n\nYou can also use the `ray.tune.result.get_trial_trainable_name` to get the trainable name for a trial.\n\nYou can also use the `ray.tune.result.get_trial_user_config` to get the user-config for a trial.\n\nYou can also use the `ray.tune.result.get_trials` to get the trials for a trial.\n\nYou can also use the `ray.tune.result.get_trials_dataframe` to get the trials dataframe.\n\nYou can also use the `ray.tune.result.get_trials_summary` to get the trials summary.\n\nYou can also use the `ray.tune.result.get_trial_resources` to get the resources for a trial.\n\nYou can also use the `ray.tune.result.get_trial_status` to",1821"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1822},1823{1824"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?",1825"sources": [1826"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",1827"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",1828"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",1829"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",1830"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",1831"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1832"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1833"https://docs.ray.io/en/master/ray-core/patterns/actor-sync.html#pattern-using-an-actor-to-synchronize-other-tasks-and-actors",1834"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#how-to-write-fault-tolerant-ray-applications",1835"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1836"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#advanced-detached-placement-group",1837"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#worker-recovery",1838"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",1839"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1840"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1841"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#worker-node-failure"1842],1843"document_ids": [18445208,18454561,18464575,18475208,18485038,18494631,18504631,18515195,18524351,18535191,18545095,185511551,18565192,18574451,18584878,185945201860],1861"answer": " No, keeping the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor will not prevent the worker actor from being terminated on Supervisor/Driver failure. This is because of actor reference counting, where if the supervisor dies (or the driver), the worker actors are automatically terminated. This behavior is independent of whether the worker actor is referenced by another actor. However, you can make the actor \"detached\" to allow it to persist even after the driver process of the job exits.",1862"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1863},1864{1865"question": "How do I read a large text file in S3 with Ray?",1866"sources": [1867"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1868"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",1869"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",1870"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",1871"https://docs.ray.io/en/master/data/api/doc/ray.data.read_text.html#ray-data-read-text",1872"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1873"https://docs.ray.io/en/master/data/api/doc/ray.data.read_parquet.html#ray-data-read-parquet",1874"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1875"https://docs.ray.io/en/master/data/loading-data.html#handling-compressed-files",1876"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1877"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv",1878"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",1879"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",1880"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",1881"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",1882"https://docs.ray.io/en/master/data/key-concepts.html#loading-data"1883],1884"document_ids": [18857565,18867783,18877496,18886993,18897565,18901006,18917261,18926994,18936747,18946996,18957217,18966993,18976995,18987909,18996779,190067321901],1902"answer": " To read a large text file in S3 with Ray, you can use the `ray.data.read_text` function and provide the S3 path to the text file as an argument. Here's an example:\n```python\nimport ray\n\nds = ray.data.read_text(\"s3://your-bucket-name/path/to/your/text-file.txt\")\n```\nThis will create a Ray Dataset object `ds` that you can use for further processing. If the text file is compressed, you can specify the compression type in `arrow_open_stream_args` argument. For example, to read a gzipped text file, you can do:\n```python\nds = ray.data.read_text(\"s3://your-bucket-name/path/to/your/text-file.txt.gz\",\n arrow_open_stream_args={\"compression\": \"gzip\"})\n```\nIf you want to read multiple text files from a directory in S3, you can pass a wildcard pattern to the `read_text` function. For example, to read all text files in a directory, you can do:\n```python\nds = ray.data.read_text(\"s3://your-bucket-name/path/to/your/directory/*.txt\")\n```\nThis will read all text files with the `.txt` extension in the specified directory.",1903"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1904},1905{1906"question": "how do I get a ray dataset from pandas",1907"sources": [1908"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-pandas",1909"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1910"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",1911"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",1912"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Tuner.html#ray-tune-tuner",1913"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_pandas.html#ray-data-dataset-to-pandas",1914"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",1915"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1916"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",1917"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1918"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",1919"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.to_pandas.html#ray-data-dataset-to-pandas",1920"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-rows",1921"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",1922"https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-datasets",1923"https://docs.ray.io/en/master/data/api/doc/ray.data.read_csv.html#ray-data-read-csv"1924],1925"document_ids": [19266806,19276749,19286901,19291006,19301824,19317302,19326752,19339180,19347818,193510017,19367363,19377302,19386860,193910140,19406984,194172161942],1943"answer": " To get a Ray dataset from a pandas DataFrame, you can use the `from_pandas()` function from the `ray.data` module. Here's an example:\n\n```python\nimport pandas as pd\nimport ray\n\n# Create a pandas DataFrame\ndf = pd.DataFrame({\"col1\": [1, 2, 3], \"col2\": [\"a\", \"b\", \"c\"]})\n\n# Convert the pandas DataFrame to a Ray dataset\nds = ray.data.from_pandas(df)\n```\n\nThis will create a Ray dataset `ds` that you can use for distributed computing with Ray. Note that the resulting Ray dataset will be divided into multiple blocks for parallel processing. You can use the `map_batches()` function to apply transformations to each block of the dataset.",1944"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1945},1946{1947"question": "can you give me an example of using `ray.data.map` ?",1948"sources": [1949"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1950"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1951"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",1952"https://docs.ray.io/en/master/rllib/rllib-examples.html#custom-and-complex-models",1953"https://docs.ray.io/en/master/serve/deploy-many-models/model-multiplexing.html#model-multiplexing",1954"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",1955"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",1956"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",1957"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",1958"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",1959"https://docs.ray.io/en/master/data/working-with-tensors.html#tensor-data-representation",1960"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",1961"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1962"https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",1963"https://docs.ray.io/en/master/data/api/from_other_data_libs.html#api-guide-for-users-from-other-data-libraries",1964"https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-transforms"1965],1966"document_ids": [19677796,19687274,19699180,197010636,197113666,197211458,19737081,19746835,19758046,19768006,19776773,19787971,19796749,19806782,19817020,198269761983],1984"answer": " Sure, here's an example of using `ray.data.map` using the context provided:\n\nFirst, let's create a Ray Dataset using `ray.data.range`:\n```python\nimport ray\n\nds = ray.data.range(10)\n```\nThis creates a Dataset with the numbers 0 to 9.\n\nNow, let's define a function that we will apply to each row in the Dataset:\n```python\ndef square(row):\n return row * row\n```\nThis function takes a row (which is an integer in this case), squares it, and returns the result.\n\nFinally, we can use `map` to apply this function to each row in the Dataset:\n```python\nds_squared = ds.map(square)\n```\nThis creates a new Dataset `ds_squared` where each row is the square of the corresponding row in `ds`.\n\nWe can verify the result by taking the first few rows of the original and transformed Datasets:\n```python\nprint(ds.take(5)) # [0, 1, 2, 3, 4]\nprint(ds_squared.take(5)) # [0, 1, 4, 9, 16]\n```\nNote that `map` is an unvectorized operation, so it can be slow for large Datasets. If your transformation is vectorized, you can use `map_batches` instead for better performance.",1985"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1986},1987{1988"question": "can you give me an example of using `ray.data.map` , with a callable class as input?",1989"sources": [1990"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",1991"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",1992"https://docs.ray.io/en/master/data/api/doc/ray.data.range.html#ray-data-range",1993"https://docs.ray.io/en/master/rllib/rllib-examples.html#custom-and-complex-models",1994"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",1995"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1996"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",1997"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-single-node-data-libraries",1998"https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",1999"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",2000"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",2001"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",2002"https://docs.ray.io/en/master/data/transforming-data.html#transforming-rows-with-map",2003"https://docs.ray.io/en/master/data/api/doc/ray.data.from_numpy_refs.html#ray-data-from-numpy-refs",2004"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",2005"https://docs.ray.io/en/master/data/working-with-text.html#transforming-text"2006],2007"document_ids": [20087796,2009250,20107274,201110636,20125439,20132437,20147636,20156749,20167628,2017247,201810014,201910303,20206782,20217102,20229180,202369972024],2025"answer": " Sure, here's an example of using `ray.data.map` with a callable class as input:\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Define a class with a `map_row` method\n@ray.remote\nclass MapperClass:\n def map_row(self, row):\n # Apply some transformation to the input row\n return {k: v.tolist() if isinstance(v, np.ndarray) else v for k, v in row.items()}\n\n# Create a Dataset\nds = ray.data.range(10)\n\n# Use the `map` method with the callable class\nds_mapped = ds.map(MapperClass.map_row.remote)\n\n# Show the first few rows of the mapped Dataset\nprint(ds_mapped.take(5))\n```\nIn this example, we define a `MapperClass` with a `map_row` method that takes a row of the Dataset as input and applies some transformation to it. We then create a Dataset using `ray.data.range` and use the `map` method with the `map_row` method of the `MapperClass` as input. The `map` method applies the transformation to each row of the Dataset and returns a new Dataset with the transformed rows. Finally, we use the `take` method to show the first few rows of the mapped Dataset.",2026"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2027},2028{2029"question": "How to set memory limit for each trial in Ray Tuner?",2030"sources": [2031"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",2032"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",2033"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",2034"https://docs.ray.io/en/master/ray-core/patterns/limit-pending-tasks.html#pattern-using-ray-wait-to-limit-the-number-of-pending-tasks",2035"https://docs.ray.io/en/master/tune/tutorials/tune-run.html#running-independent-tune-trials-in-parallel",2036"https://docs.ray.io/en/master/data/performance-tips.html#configuring-resources-and-locality",2037"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",2038"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",2039"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-trials-individually-with-a-timeout",2040"https://docs.ray.io/en/master/tune/tutorials/tune-stopping.html#stop-using-metric-based-criteria",2041"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",2042"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Repeater.html#ray-tune-search-repeater",2043"https://docs.ray.io/en/master/tune/examples/pbt_guide.html#replaying-a-pbt-run",2044"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",2045"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",2046"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-aware-scheduling"2047],2048"document_ids": [20492325,20501114,20512406,20525246,20532325,20546829,20552266,20562001,20572204,20582198,20591025,20602003,20613167,2062558,20636879,206450432065],2066"answer": " To set the memory limit for each trial in Ray Tuner, you can use the `with_resources` function to customize the resources per trial. However, Ray Tune does not directly support setting memory limits for each trial. The resources configuration typically controls the number of CPUs or GPUs allocated to a trial.\n\nIf you want to limit the memory usage of your trials, you should manage memory within your training function or script. You can request Ray to allocate a specific amount of memory for your trial by setting the `object_store_memory` when configuring resources. For example:\n\n```python\ntrain_model = tune.with_resources(train_model, {\"cpu\": 1, \"object_store_memory\": \"4GB\"})\n```\n\nKeep in mind that this does not guarantee that your trial will not exceed the memory limit, as it depends on how you manage memory within your training function or script. Make sure to monitor memory usage and handle out-of-memory situations accordingly within your trial code.",2067"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2068},2069{2070"question": "how do I get the actor id of an actor",2071"sources": [2072"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",2073"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",2074"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2075"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",2076"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-get",2077"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",2078"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor",2079"https://docs.ray.io/en/master/ray-observability/getting-started.html#actor-detail-page",2080"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2081"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",2082"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2083"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2084"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2085"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",2086"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",2087"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs"2088],2089"document_ids": [20904787,20914931,2092475,2093682,2094678,20954787,2096690,2097436,2098474,2099750,2100476,2101477,21024623,210314324,210414202,21055202106],2107"answer": " To get the actor ID of the current actor in a Ray application, 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```\nfrom ray.runtime_context import RuntimeContext\n\ncurrent_actor_id = RuntimeContext.get_actor_id()\nprint(current_actor_id)\n```\nAlternatively, you can use the `actor_id` property from the `ray.runtime_context.RuntimeContext` class, which has the same behavior as the `get_actor_id()` function.\n\nNote that these APIs are deprecated and may be removed in future Ray releases. It is recommended to use the `get_actor()` function from the `ray.util.state` module instead, which returns the state of an actor by its ID.\n\nYou can also use the `ray get actors` command to get the state of a single actor by its ID. For example:\n```\nray get actors <actor-id>\n```\nThis will print the state of the actor with the given ID in the format of an `ActorState` dictionary.\n\nTo get the logs of an actor, you can use the `ray logs actor` command with the `--id` option. For example:\n```\nray logs actor --id <actor-id>\n```\nThis will print the logs of the actor with the given ID.\n\nYou can also use the `get_log()` function from the `ray.util.state` module to get the logs of an actor programmatically. For example:\n```\nfrom ray.util.state import get_log\n\nfor line in get_log(actor_id=<actor-id>):\n print(line)\n```\nThis will print the logs of the actor with the given ID one line at a time.\n\nNote that the `get_log()` function returns an iterator, so you can use it to process the logs in a loop or to get a specific number of lines from the logs using the `islice()` function from the `itertools` module.\n\nFor more information about the `ray.util.state` module and the `ray logs` command, you can refer to the Ray documentation.",2108"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2109},2110{2111"question": "can ray.init() can check if ray is all-ready initiated ?",2112"sources": [2113"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",2114"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2115"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",2116"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobType.SUBMISSION.html#ray-job-submission-jobtype-submission",2117"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobType.html#ray-job-submission-jobtype",2118"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobType.DRIVER.html#ray-job-submission-jobtype-driver",2119"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",2120"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",2121"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",2122"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#starting-ray",2123"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",2124"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",2125"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",2126"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",2127"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.__init__.html#ray-data-preprocessor-preprocessor-init",2128"https://docs.ray.io/en/master/ray-core/configure.html#java-applications"2129],2130"document_ids": [21314785,21324811,21334194,21346647,21356676,21366646,21374190,21384188,21394189,21405386,21414186,21424204,21435399,21444576,21457833,214644042147],2148"answer": " No, `ray.init()` is used to initialize Ray, not to check if it is already initialized. To check if Ray is already initialized, you can use `ray.is_initialized()`. This function returns `True` if Ray has been initialized and `False` otherwise.",2149"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2150},2151{2152"question": "What does the `compute=actor` argument do within `ray.data.map_batches` ?",2153"sources": [2154"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2155"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",2156"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2157"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#how-does-tune-work",2158"https://docs.ray.io/en/master/rllib/rllib-catalogs.html#what-problems-do-catalogs-solve",2159"https://docs.ray.io/en/master/ray-core/patterns/pass-large-arg-by-value.html#anti-pattern-passing-the-same-large-argument-by-value-repeatedly-harms-performance",2160"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",2161"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",2162"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",2163"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2164"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.map.html#ray-util-actorpool-map",2165"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2166"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2167"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2168"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2169"https://docs.ray.io/en/master/data/batch_inference.html#quickstart"2170],2171"document_ids": [21726787,21736885,21748108,21752232,217611504,21775239,21788070,21797988,21805465,21816981,21824708,21836999,21846952,21856941,21866954,218769442188],2189"answer": " The `compute=actor` argument within `ray.data.map_batches` is used to specify that the function provided to `map_batches` should be run as a long-running Ray actor, using an `ActorPoolStrategy` to manage the number of parallel actors. This can be useful for performing expensive computations on large datasets, as each actor can cache the data it is working on and avoid recomputing it for subsequent batches. In the example provided, an `ActorPoolStrategy` with a minimum and maximum size of 2 is used, meaning that at most 2 actors will be used to process the batches of data.",2190"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2191},2192{2193"question": "how do I use wandb logger with accelerateTrainer?",2194"sources": [2195"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",2196"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",2197"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",2198"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2199"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",2200"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",2201"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",2202"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",2203"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",2204"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback",2205"https://docs.ray.io/en/master/train/examples/lightning/lightning_exp_tracking.html#define-your-loggers",2206"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",2207"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",2208"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#tune-wandb-api-reference",2209"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",2210"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries"2211],2212"document_ids": [22138470,22142432,22152463,22161006,22171132,22182431,22199530,22202431,22211132,22222466,22239529,22249136,22259137,22262462,22272434,222891382229],2230"answer": " To use Weights & Biases (WandB) logger with `accelerateTrainer`, you can follow these steps:\n\n1. Install WandB: `pip install wandb`\n2. Import the necessary libraries, including WandB and the `WandbLogger` from PyTorch Lightning.\n3. Create a `WandbLogger` instance with your WandB project name and any other desired parameters.\n4. Initialize the `accelerateTrainer` and pass the `WandbLogger` as one of the loggers.\n\nHere's an example:\n\n```python\nimport os\nimport torch\nfrom torch.utils.data import DataLoader\nfrom pytorch_lightning import LightningModule, Trainer\nfrom pytorch_lightning.loggers import WandbLogger\nfrom accelerate import Accelerator\n\n# Initialize WandB\nos.environ[\"WANDB_API_KEY\"] = \"<your_wandb_api_key>\"\nwandb_logger = WandbLogger(project=\"your_project_name\")\n\n# Define your LightningModule\nclass YourModule(LightningModule):\n # Define your model, training_step, etc.\n\n# Initialize Accelerator and Trainer\naccelerator = Accelerator()\ntrainer = Trainer(logger=wandb_logger, accelerator=accelerator)\n\n# Fit your model\ntrainer.fit(YourModule(), DataLoader(...))\n```\n\nReplace `<your_wandb_api_key>` and `your_project_name` with your actual WandB API key and project name. Make sure to define your `LightningModule` and `DataLoader` according to your specific use case.",2231"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2232},2233{2234"question": "What will be implicitly put into object store?",2235"sources": [2236"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",2237"https://docs.ray.io/en/master/ray-core/objects/serialization.html#plasma-store",2238"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",2239"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",2240"https://docs.ray.io/en/master/ray-core/objects.html#closure-capture-of-objects",2241"https://docs.ray.io/en/master/workflows/basics.html#passing-object-references-between-tasks",2242"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2243"https://docs.ray.io/en/master/data/data-internals.html#object-store-memory",2244"https://docs.ray.io/en/master/ray-core/objects.html#objects",2245"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",2246"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",2247"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",2248"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",2249"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",2250"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",2251"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"2252],2253"document_ids": [22545013,22554941,22565014,22579858,22584437,22599689,226014748,22616896,22624414,226314094,226414122,226514138,226614222,226714063,226814184,2269143002270],2271"answer": " In Ray, when using `ray.put()`, objects are explicitly put into the object store. When a task or actor requests an object that is not present on the local node, and it is present in another node's object store, the object will not be automatically broadcasted to the requesting node. Instead, the object will remain local to the writer until explicitly requested. Objects in the object store are reference counted and evicted when they fall out of scope. When the object store fills up, objects are spilled to disk, which can impact performance.",2272"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2273},2274{2275"question": "How do I kill or cancel a ray task that I already started?",2276"sources": [2277"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",2278"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",2279"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2280"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2281"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2282"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm-basic.html#slurm-basic-sh",2283"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",2284"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2285"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2286"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2287"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",2288"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2289"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2290"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",2291"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",2292"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks"2293],2294"document_ids": [22954550,22964375,22974894,22981005,22991006,23006433,230114702,23024494,230311591,23044497,230514703,230611592,23074896,230811590,23094492,2310147012311],2312"answer": " To kill or cancel a Ray task that you have already started, you can call `ray.cancel()` on the ObjectRef returned by the task. This will send a KeyboardInterrupt to the task's worker if it is mid-execution. If you want to force-exit the worker, you can pass `force=True` to `ray.cancel()`. Note that currently, Ray will not automatically retry tasks that have been cancelled.\n\nHere is an example of how to cancel a Ray task:\n```\nfrom ray import remote\n\n@remote\ndef blocking_operation():\n time.sleep(10e6)\n\nobj_ref = blocking_operation.remote()\nray.cancel(obj_ref)\n\ntry:\n ray.get(obj_ref)\nexcept ray.exceptions.TaskCancelledError:\n print(\"Object reference was cancelled.\")\n```\nFor Actor Tasks, if the specified task is pending execution, it is cancelled and not executed. If the task is currently executing, the behavior depends on the execution model of an Actor. If it is a regular Actor or a threaded Actor, the execution isn't cancelled. Actor Tasks cannot be interrupted because Actors have states. If it is an async Actor, Ray cancels a asyncio.Task. If the Task has finished, nothing happens. Only `force=False` is allowed for an Actor Task. Otherwise, it raises ValueError. Use `ray.kill(actor)` instead to kill an Actor.",2313"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2314},2315{2316"question": "how to send extra arguments in dataset.map_batches function?",2317"sources": [2318"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2319"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2320"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",2321"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#pass-arguments-to-applications",2322"https://docs.ray.io/en/master/tune/api/trainable.html#function-trainable-api",2323"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",2324"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",2325"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference",2326"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",2327"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",2328"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",2329"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches",2330"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",2331"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",2332"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",2333"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"2334],2335"document_ids": [23367082,23377081,23388607,233913810,23401151,23416587,23427959,23438070,23447988,23457640,23466956,23476861,234810017,23497796,23507738,235170912352],2353"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 function you are using in `map_batches`. Here is an example:\n\n```python\nds.map_batches(my_function, fn_kwargs={'arg1': 'value1', 'arg2': 'value2'})\n```\n\nIn this example, `my_function` is the function you are using in `map_batches`, and `arg1` and `arg2` are the extra arguments you want to pass to `my_function`, with `'value1'` and `'value2'` being the values of those arguments, respectively.",2354"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2355},2356{2357"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?",2358"sources": [2359"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance",2360"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",2361"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",2362"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",2363"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",2364"https://docs.ray.io/en/master/tune/examples/tune-sklearn.html#walkthrough",2365"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",2366"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",2367"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",2368"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/experimental.html#gcs-fault-tolerance",2369"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",2370"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",2371"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",2372"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",2373"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#step-1-add-external-redis-server",2374"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes"2375],2376"document_ids": [23775830,237813871,237913470,23805754,23815729,23822642,238313870,23845642,23855647,23865916,23875763,23886149,23895860,23905658,239113872,239256562393],2394"answer": " The history of jobs run on a Kuberay cluster is not explicitly stated to be stored in a specific type of database in the provided context. However, it is mentioned that Kuberay has added experimental support for Global Control Store (GCS) fault tolerance, which can prevent the Ray cluster from crashing if the head node goes down. This fault tolerance can be enabled by adding an external Redis server to your RayService Kubernetes object. So, it can be inferred that Kuberay might be using Redis as a database for storing the history of jobs with some specific format, but the exact type and format are not specified in the context.",2395"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2396},2397{2398"question": "How to resolve ValueError: The actor ImplicitFunc is too large?",2399"sources": [2400"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",2401"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",2402"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#verify-and-save-results",2403"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",2404"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",2405"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",2406"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2407"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2408"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler",2409"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",2410"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",2411"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",2412"https://docs.ray.io/en/master/data/batch_inference.html#handling-cpu-out-of-memory-failures",2413"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",2414"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2415"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options"2416],2417"document_ids": [24186150,24199858,24207992,24215118,2422749,24237786,24248119,24258108,24261895,242710267,24289908,24295465,24306961,2431476,24326954,243348782434],2435"answer": " The ValueError \"The actor ImplicitFunc is too large\" is likely caused by an actor function capturing a large object or array in scope. To resolve this issue, you can put large objects in the Ray object store using `ray.put()`. Additionally, you may want to consider moving the data loader to the training function and adjusting the batch size to ensure full parallelization across the actor pool. It's also important to monitor and manage memory usage to prevent out-of-memory errors.",2436"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2437},2438{2439"question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",2440"sources": [2441"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",2442"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2443"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#distributed-training",2444"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2445"https://docs.ray.io/en/master/rllib/rllib-examples.html#custom-and-complex-models",2446"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2447"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",2448"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2449"https://docs.ray.io/en/master/train/huggingface-accelerate.html#training-with-huggingface-accelerate",2450"https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others",2451"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2452"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#training",2453"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#convert-existing-pytorch-code-to-ray-air",2454"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#define-the-training-loop",2455"https://docs.ray.io/en/master/train/getting-started-pytorch.html#getting-started-with-pytorch",2456"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#how-it-works"2457],2458"document_ids": [24599845,2460226,246110155,24621006,246310636,24641005,2465370,24666940,24678247,246814313,2469228,24705515,24719820,247210196,24738312,2474101502475],2476"answer": " To distribute training for your custom Keras neural net in Databricks using Ray, you can follow these steps:\n\n1. Import Ray Train and Ray AIR Session.\n2. Use `ray.train.torch.prepare_model` and `ray.train.torch.prepare_data_loader` utility functions to set up your model and data for distributed training.\n3. Modify the training function to accept a configuration dict with hyperparameters.\n4. Load the data shard for each worker with `session.get_dataset_shard(\"train\")`.\n5. Iterate over the dataset with `train_dataset.iter_torch_batches()`.\n\nHere's a code snippet to illustrate these steps:\n\n```python\nimport ray\nfrom ray import train\n\n# Import your custom neural net model\nfrom my_models import NeuralNetwork\n\n# Prepare everything for distributed training\ndef train_func_distributed(config):\n num_epochs = config[\"num_epochs\"]\n batch_size = config[\"batch_size\"]\n\n # Prepare the dataset\n dataset = get_dataset()\n dataloader = DataLoader(dataset, batch_size=batch_size)\n dataloader = train.torch.prepare_data_loader(dataloader)\n\n # Prepare the model\n model = NeuralNetwork()\n model = train.torch.prepare_model(model)\n\n # Training loop\n for epoch in range(num_epochs):\n for batch in dataloader:\n # Perform training steps\n pass\n\n# Set up the training session\nsession = train.Session()\n\n# Configure the training function with hyperparameters\ntrain_config = {\n \"num_epochs\": 3,\n \"batch_size\": 64,\n}\n\n# Start the training\nray.get_session().get_trainable_cluster().submit_trainable(train_func_distributed, train_config)\n```\n\nReplace `NeuralNetwork` and `get_dataset` with your custom model and dataset. This example assumes you have a Databricks cluster with Ray installed. Make sure to install the necessary packages for Ray Train and Keras.",2477"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2478},2479{2480"question": "how to use ray.put and ray,get?",2481"sources": [2482"https://docs.ray.io/en/master/ray-core/api/core.html#objects",2483"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",2484"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2485"https://docs.ray.io/en/master/tune/examples/hpo-frameworks.html#tune-hyperparameter-optimization-framework-examples",2486"https://docs.ray.io/en/master/tune/examples/index.html#tune-hyperparameter-optimization-framework-examples",2487"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",2488"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",2489"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",2490"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",2491"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",2492"https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",2493"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2494"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",2495"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2496"https://docs.ray.io/en/master/ray-core/objects.html#objects",2497"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client"2498],2499"document_ids": [25004651,25015443,25024849,25032429,25043796,2505160,25064205,25074884,25085219,25094209,25104775,25114811,251213646,25135380,25144413,251566152516],2517"answer": " To use `ray.put` and `ray.get`, you need to understand that `ray.put` is used to store an object in the Ray object store and returns an object reference (ID). This ID can be used to pass the object as an argument to any remote function or method call. On the other hand, `ray.get` is used to return an object or list of objects from the object ID or list of object IDs. It is a synchronous (i.e., blocking) operation.\n\nHere's an example:\n\n```python\nimport ray\n\n# Initialize Ray\nray.init()\n\n# Put an object in the object store\nobject_id = ray.put(\"Hello, Ray!\")\n\n# Get the object from the object store\nobject_ref = ray.get(object_id)\n\n# Print the object\nprint(object_ref) # Output: b'Hello, Ray!'\n```\n\nIn this example, we first initialize Ray using `ray.init()`. Then, we store a string \"Hello, Ray!\" in the object store using `ray.put` and get its object reference. Finally, we retrieve the object from the object store using `ray.get` and print it.\n\nNote that it's best practice to avoid calling `ray.get()` unnecessarily for intermediate steps and only call it 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!",2518"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2519},2520{2521"question": "how do I use Ray Data to pre process many files?",2522"sources": [2523"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#data-loading-and-preprocessing",2524"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",2525"https://docs.ray.io/en/master/data/overview.html#preprocessing-and-ingest-for-ml-training",2526"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",2527"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray",2528"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2529"https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors",2530"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data",2531"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2532"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2533"https://docs.ray.io/en/master/train/user-guides.html#ray-train-user-guides",2534"https://docs.ray.io/en/master/data/user-guide.html#user-guides",2535"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-preprocess-data-for-training",2536"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessor.Preprocessor.__init__.html#ray-data-preprocessor-preprocessor-init",2537"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-data",2538"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text"2539],2540"document_ids": [25419178,25429222,25436849,25448010,2545142,25461006,25476914,25489212,25496838,25506835,25518110,25526911,25538155,25547833,25559189,255669932557],2558"answer": " To preprocess many files using Ray Data, you can use Ray Data's support for streaming data loading and preprocessing. Here's a general process:\n\n1. Load your data using Ray Data's `read_files` method, which returns a Ray Dataset.\n2. Apply any necessary preprocessing operations to the Dataset using Ray Data's preprocessors or other transformation methods.\n3. If your preprocessed Dataset is small enough to fit in Ray's object store memory, materialize it in the object store using the `materialize()` method. This will improve performance when iterating over the dataset repeatedly.\n\nHere's an example of how you might do this:\n\n```python\nfrom ray import dataframe as df\n\n# Load data from many text files\ndatasets = df.read_files('path/to/text_files/*.txt')\n\n# Preprocess the data using a Ray Data preprocessor\npreprocessor = df.TextPreprocessor(num_workers=4)\ndatasets = preprocessor.transform(datasets)\n\n# Materialize the preprocessed dataset in Ray's object store if it's small enough\nif datasets.count() < (0.3 * ray.cluster_resources()['memory']) / datasets.row_size():\n datasets = datasets.materialize()\n```\n\nIn this example, we're loading text files and applying a `TextPreprocessor` to them. The preprocessor splits the text into individual words and lowercases them. You can replace this with any preprocessing operation that suits your needs.\n\nNote that if your preprocessed dataset is too large to fit into Ray's object store memory, you should not call `materialize()`, as it will decrease performance due to disk spilling. In this case, you can still iterate over the Dataset repeatedly, but the preprocessing operations will be re-run each time.",2559"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2560},2561{2562"question": "can\u2019t pickle SSLContext objects",2563"sources": [2564"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2565"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2566"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",2567"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",2568"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization-notes",2569"https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#checkpoints-are-py-version-specific-but-can-be-converted-to-be-version-independent",2570"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",2571"https://docs.ray.io/en/master/ray-core/objects/serialization.html#known-issues",2572"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#tls-verification",2573"https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#checkpoints-are-py-version-specific-but-can-be-converted-to-be-version-independent",2574"https://docs.ray.io/en/master/serve/tutorials/java.html#calling-ray-serve-deployment-with-http",2575"https://docs.ray.io/en/master/ray-core/configure.html#step-3-set-the-environment-variables-for-both-ray-head-and-worker-to-enable-tls",2576"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",2577"https://docs.ray.io/en/master/installation.html#installed-python-dependencies",2578"https://docs.ray.io/en/master/ray-overview/installation.html#installed-python-dependencies",2579"https://docs.ray.io/en/master/ray-more-libs/installation.html#installed-python-dependencies"2580],2581"document_ids": [25824953,25834952,25844939,25854939,25864945,258710550,25884957,25894958,25906592,259110551,259214578,25934400,25944260,2595110,2596335,2597146452598],2599"answer": " The error message indicates that `SSLContext` objects cannot be pickled, which is causing the serialization to fail. This is because `SSLContext` objects contain non-serializable components like thread locks. Ray's serialization is based on Pickle protocol version 5, which has some limitations in serializing certain types of objects, such as non-native objects. To work around this issue, you can convert the pickle-based checkpoint to a msgpack one using an RLlib utility function, as shown in the provided context.",2600"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2601},2602{2603"question": "How do I install CRDs in Kuberay?",2604"sources": [2605"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",2606"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",2607"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",2608"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2609"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2610"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",2611"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",2612"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-2-create-a-raycluster-cr",2613"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-3-install-a-kuberay-operator",2614"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-3-install-a-kuberay-operator",2615"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-2-install-the-kuberay-operator",2616"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-1-install-a-kuberay-cluster",2617"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-1-install-the-kuberay-operator",2618"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/kubeflow.html#step-3-install-kuberay-operator",2619"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-2-install-the-kuberay-operator",2620"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-2-install-the-kuberay-operator"2621],2622"document_ids": [26235720,26246103,26255817,26261006,26271005,26285720,26295647,26305981,26315774,26326057,26336044,26345980,26356143,26366092,26375741,263857592639],2640"answer": " To install Custom Resource Definitions (CRDs) in Kuberay, you can follow these steps:\n\n1. Add the Kuberay Helm chart repository using the following command:\n`helm repo add kuberay https://ray-project.github.io/kuberay-helm/`\n2. Install both CRDs and KubeRay operator v0.6.0 using the following command:\n`helm install kuberay-operator kuberay/kuberay-operator --version 0.6.0`\n\nThis will install the CRDs required for Kuberay to manage Ray clusters and jobs on Kubernetes.",2641"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2642},2643{2644"question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",2645"sources": [2646"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2647"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2648"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",2649"https://docs.ray.io/en/master/data/batch_inference.html#end-to-end-offline-batch-inference",2650"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2651"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2652"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",2653"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",2654"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",2655"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",2656"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging",2657"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",2658"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repeat.html#ray-data-dataset-repeat",2659"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",2660"https://docs.ray.io/en/master/data/api/doc/ray.data.grouped_data.GroupedData.map_groups.html#ray-data-grouped-data-groupeddata-map-groups",2661"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors"2662],2663"document_ids": [26648107,26656788,266613697,26676934,26686947,26696838,2670140,26716955,26727955,26737380,2674590,26756998,26767328,26777985,26787632,267967892680],2681"answer": " The function for Ray data batch inference is named as `__call__()` because it is a special method in Python classes that allows an instance of the class to be called like a function. In the context of Ray data batch inference, this method is used to define the function that will be applied to each batch of data during the inference process. By naming this method `__call__()`, we can use the class instance as a callable object, which is then passed to the `map_batches()` function to perform batch inference.",2682"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2683},2684{2685"question": "How to disconnnect ray client?",2686"sources": [2687"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2688"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections",2689"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",2690"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",2691"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",2692"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",2693"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",2694"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",2695"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",2696"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2697"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",2698"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",2699"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options",2700"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",2701"https://docs.ray.io/en/master/ray-core/api/doc/ray.shutdown.html#ray-shutdown",2702"https://docs.ray.io/en/master/cluster/usage-stats.html#requesting-removal-of-collected-data"2703],2704"document_ids": [27056615,27066626,27076624,2708161,2709188,2710342,271114507,27126608,27134613,27144894,27156612,27164375,271714500,27184618,27194827,272055692721],2722"answer": " To disconnect a Ray client, you can use the `disconnect()` method on the client object. Here's an example:\n```python\nfrom ray import client\n\n# Create a Ray client object\nray_client = client.RayClient()\n\n# Use the Ray client to run some code...",2723"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2724},2725{2726"question": "how to submit job with python with local files?",2727"sources": [2728"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2729"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job",2730"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2731"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",2732"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-jobs-cli-api-reference",2733"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2734"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2735"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2736"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",2737"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",2738"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#jobsubmissionclient",2739"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",2740"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#dependency-management",2741"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",2742"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",2743"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job"2744],2745"document_ids": [27466550,27474255,27486553,27496595,27506594,27516553,27526578,27536579,27546575,27556595,27566540,27576581,27586585,27596567,27605732,276166932762],2763"answer": " To submit a job with Python using local files, you can use the `ray job submit` command with the `--working-dir` flag to specify the path to the working directory. Here's an example:\n```css\nray job submit --working-dir /path/to/local/files -- python my_script.py\n```\nIf you want to use the Ray Jobs API (Python SDK), you can use the `submit_job` method of the `JobSubmissionClient` object. Here's an example:\n```python\nfrom ray.job_submission import JobSubmissionClient\n\nclient = JobSubmissionClient(\"http://<head-node-ip>:8265\")\njob_id = client.submit_job(\n entrypoint=\"python my_ray_script.py\",\n runtime_env={\"working_dir\": \"/path/to/local/files\"}\n)\n```\nMake sure to replace `<head-node-ip>` with the IP address of the head node of your Ray cluster. Also, note that the `runtime_env` argument should be passed in the `submit_job` call or the `ray job submit` command, not in the `ray.init()` call in the entrypoint script.",2764"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2765},2766{2767"question": "How do I do inference from a model trained by Ray tune.fit()?",2768"sources": [2769"https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",2770"https://docs.ray.io/en/master/tune/examples/includes/tune_basic_example.html#tune-basic-example",2771"https://docs.ray.io/en/master/data/batch_inference.html#using-models-from-ray-train",2772"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",2773"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#image-classification-batch-inference-with-huggingface-vision-transformer",2774"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2775"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",2776"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",2777"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#configuring-the-search-space-and-starting-ray-tune",2778"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",2779"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",2780"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",2781"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",2782"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",2783"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",2784"https://docs.ray.io/en/master/ray-overview/examples.html@tags=inference.tmp.html#ray-examples"2785],2786"document_ids": [2787986,27884111,27896962,27908010,27917945,27926978,27931080,2794117,27952767,2796234,2797127,27981090,2799189,2800343,2801162,28023802803],2804"answer": " To do inference from a model trained by Ray tune.fit(), you can use the checkpoint returned by the training process with Ray Train. Here is an example of how to use the checkpoint for batch inference with Ray Data:\n\n1. Train a model with Ray Train:\n```python\nimport ray\nfrom ray.train import ScalingConfig\nfrom ray.train.xgboost import XGBoostTrainer\n\ndatasets = ray.data.read_csv(\"s3://anonymous@air-example-data/breast_cancer.csv\")\ntrain_dataset, valid_dataset = datasets.train_test_split(test_size=0.3)\n\nscaling_config = ScalingConfig(num_workers=2, use_gpu=False, \n trainer_resources_per_worker={\"CPU\": 1})\n\nxgboost_trainer = XGBoostTrainer(scaling_config=scaling_config,\n label_column=\"target\",\n num_boost_round=100,\n metrics={\"accuracy\"},\n checkpoint_freq=1)\n\nresult = xgboost_trainer.train(datasets.train_shuffle())\n```\n2. Use the checkpoint for batch inference:\n```python\ncheckpoint = result.checkpoint\ninference_dataset = ray.data.read_csv(\"path/to/inference/data.csv\")\n\n# Perform batch inference\ninference_result = checkpoint.predict(inference_dataset)\n\n# Save the inference results\ninference_result.save_to_parquet(\"path/to/save/inference/results\")\n```\n\nIn this example, the trained XGBoost model checkpoint is used for batch inference on a new dataset. The inference results are then saved to a parquet file. You can modify this example to work with your specific use case and model.",2805"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2806},2807{2808"question": "is there a way to load and run inference without using pytorch or tensorflow directly?",2809"sources": [2810"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2811"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-2-inference-on-a-single-batch",2812"https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",2813"https://docs.ray.io/en/master/cluster/usage-stats.html#how-to-disable-it",2814"https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",2815"https://docs.ray.io/en/master/tune/examples/includes/tf_mnist_example.html#tensorflow-mnist-example",2816"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",2817"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2818"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",2819"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",2820"https://docs.ray.io/en/master/data/batch_inference.html#using-gpus-for-inference",2821"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",2822"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2823"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2824"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2825"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data"2826],2827"document_ids": [28286978,28297976,28306725,28315565,28324383,28333980,28348010,28356980,28366979,28378043,28386947,28397970,28406941,28416940,28428107,284379552844],2845"answer": " Yes, there is a way to load and run inference without using PyTorch or TensorFlow directly. The context provided describes using Ray Datasets for scalable offline batch inference with Torch models. Ray Datasets can map a pre-trained model over your data, allowing you to perform inference on the dataset in a scalable and parallelized manner. The actual model loading and inference logic is implemented within a custom class, so you don't need to directly use PyTorch or TensorFlow in your inference script. Instead, you can leverage Ray Datasets to handle the data processing and parallelization while using your pre-trained models for inference.",2846"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2847},2848{2849"question": "what does ray do",2850"sources": [2851"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",2852"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",2853"https://docs.ray.io/en/master/data/data.html#learn-more",2854"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2855"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",2856"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#fine-tuning-dreambooth-with-ray-train",2857"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",2858"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",2859"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-multi-tenancy",2860"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",2861"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",2862"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",2863"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",2864"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",2865"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",2866"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary"2867],2868"document_ids": [28694811,28705380,28716910,28721005,28735754,287410147,28756208,287614255,28776015,28785656,28795556,28804662,28816835,28826615,288313974,288454432885],2886"answer": " Ray is a universal array job system that enables applications to efficiently scale up to billions of CPUs and GPUs. It provides a variety of features, such as:\n\n* Initializing a Ray cluster with 'ray.init'\n* Access to Ray tutorials, examples, and blogs\n* Hyperparameter tuning with Ray Tune\n* Kubernetes-native support for Ray clusters and jobs\n* Deploying and managing machine learning models with Ray Serve\n* Distributing data processing tasks with Ray Data\n* Utilizing Ray's object store with 'ray.put()' and 'ray.get()'\n* Turning functions into tasks and classes into actors with '@ray.remote'\n\nIn the context of the provided query, Ray can be used for distributed training of machine learning models, as demonstrated in the 'Fine-tuning DreamBooth with Ray Train' example. The RayJob provides kubernetes-native support for Ray clusters and jobs, and can be used to define and manage Ray clusters and jobs.",2887"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2888},2889{2890"question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",2891"sources": [2892"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",2893"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",2894"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",2895"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",2896"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#more-resources",2897"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",2898"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2899"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",2900"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",2901"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",2902"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",2903"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#using-fractional-gpus",2904"https://docs.ray.io/en/master/data/performance-tips.html#configuring-resources-and-locality",2905"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",2906"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",2907"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources"2908],2909"document_ids": [291013457,29111025,29124999,29135001,29149255,29151024,291611062,29172966,291812049,291911068,29205116,29212730,29226829,29236590,29246145,292510242926],2927"answer": " If you specify a fractional GPU in the resource spec and use more than that, Ray will not stop you. However, you are responsible for ensuring that multiple trials can share GPUs and there is enough memory to do so. Ray does not handle this automatically. If you want to use multiple GPUs per trial, you should consider using LightningTrainer.",2928"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2929},2930{2931"question": "how to pickle a variable defined in actor\u2019s init method",2932"sources": [2933"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2934"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#code-example",2935"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",2936"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#disable-the-init-container-injection",2937"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.html#ray-train-torch-torchconfig",2938"https://docs.ray.io/en/master/rllib/rllib-examples.html#special-action-and-observation-spaces",2939"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",2940"https://docs.ray.io/en/master/rllib/actors.html#actors",2941"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",2942"https://docs.ray.io/en/master/ray-core/actors.html#actors",2943"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",2944"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",2945"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",2946"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",2947"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",2948"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor"2949],2950"document_ids": [29515192,29525194,29534435,29545711,29558877,295610662,29574241,295811576,295914682,29604469,29615191,2962520,29634630,29645066,29654878,296642602967],2968"answer": " In the given context, it is not possible to pickle a variable defined in an actor's `__init__` method directly. However, you can pass objects to actor method calls by value or by reference. If you want to use a variable in multiple methods of an actor, consider defining it as an instance variable within the actor class.\n\nHere's an example based on the provided context:\n\n```python\n@ray.remote\nclass Actor:\n def __init__(self, initial_value):\n self.value = initial_value\n\n def increment(self):\n self.value += 1\n return self.value\n\n def get_value(self):\n return self.value\n\nactor = Actor.remote(10)\n\n# Call increment on the actor created with an increment of 2.\nincrement_result = ray.get(actor.increment.remote())\nprint(increment_result) # Output: 11\n\n# Get the current value of the actor.\ncurrent_value = ray.get(actor.get_value.remote())\nprint(current_value) # Output: 11\n```\n\nIn this example, the `value` variable is defined within the `__init__` method as an instance variable, allowing it to be used in multiple methods of the actor class.",2969"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2970},2971{2972"question": "how do I do an all_reduce operation among a list of actors",2973"sources": [2974"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",2975"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",2976"https://docs.ray.io/en/master/ray-observability/reference/api.html#summary-apis",2977"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2978"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",2979"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",2980"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",2981"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",2982"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#collective-communication",2983"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayFSDPStrategy.reduce.html#ray-train-lightning-rayfsdpstrategy-reduce",2984"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",2985"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",2986"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDDPStrategy.reduce.html#ray-train-lightning-rayddpstrategy-reduce",2987"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.reduce.html#ray-train-lightning-raydeepspeedstrategy-reduce",2988"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",2989"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#shuffling-and-reducing-data"2990],2991"document_ids": [2992788,299314443,2994659,29951006,29964464,29971005,29986787,2999673,300014415,30018854,30028101,3003641,30048424,30059076,30066939,300753533008],3009"answer": " To perform an all_reduce operation among a list of actors in Ray, you can use the `col.allreduce()` function from the `ray.util.collective` module. Here is an example of how to use it:\n\n```\nfrom ray.util.collective import col\n\n@ray.remote(num_gpus=1)\nclass Worker:\n def __init__(self):\n self.buffer = cupy.ones((10,), dtype=cupy.float32)\n\n def compute(self):\n col.allreduce(self.buffer, \"default\")\n return self.buffer\n\n# Create two actors A and B and create a collective group following the previous example...\nA = Worker.remote()\nB = Worker.remote()\n\n# Invoke allreduce remotely\nray.get([A.compute.remote(), B.compute.remote()])\n```\n\nThis will perform an allreduce operation on the `buffer` attribute of the `Worker` actors A and B. The `col.allreduce()` function takes two arguments: the tensor to reduce and the name of the collective group to use. In this example, we are using the default group.\n\nYou can also use the `ray.train.lightning.RayFSDPStrategy.reduce()` function to reduce a tensor from several distributed processes to one aggregated tensor. This function takes three arguments: the tensor to reduce, the process group to gather results from, and the reduction operation. The default reduction operation is 'mean', but you can also use 'sum' to calculate the sum during reduction.\n\nFor example:\n\n```\nfrom ray.train.lightning import RayFSDPStrategy\n\nstrategy = RayFSDPStrategy()\n\ntensor = torch.tensor([1.0, 2.0, 3.0])\nreduced_tensor = strategy.reduce(tensor)\n```\n\nThis will reduce the `tensor` using the default reduction operation 'mean'.",3010"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3011},3012{3013"question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",3014"sources": [3015"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",3016"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",3017"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#bundles",3018"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",3019"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",3020"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",3021"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",3022"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",3023"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",3024"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-strategy",3025"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#create-a-placement-group-reserve-resources",3026"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",3027"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",3028"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",3029"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.PlacementGroup.bundle_count.html#ray-util-placement-group-placementgroup-bundle-count",3030"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources"3031],3032"document_ids": [30334790,30345063,30355050,30365072,30375063,30382983,30395051,304012053,304111075,30425076,30435060,30444789,30455069,304614262,30474746,304850703049],3050"answer": " If a bundle with `{\"CPU\":0}` is specified in the PlacementGroup, it will reserve 0 CPUs for the tasks or actors that use the PlacementGroupSchedulingStrategy. However, it's worth noting that a bundle with 0 CPUs might not be useful in practice, as it cannot run any tasks or actors. Additionally, if the PlacementGroup has other bundles with resource requirements, those requirements must still be satisfied in order to create the PlacementGroup. If the PlacementGroup cannot be created due to unsatisfied resource requirements, no resources will be reserved.",3051"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3052},3053{3054"question": "How to cancel job from UI?",3055"sources": [3056"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",3057"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",3058"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",3059"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",3060"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",3061"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",3062"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",3063"https://docs.ray.io/en/master/workflows/comparison.html#trip-booking",3064"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient",3065"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",3066"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",3067"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",3068"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",3069"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobStatus.STOPPED.html#ray-job-submission-jobstatus-stopped",3070"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.stop_job.html#ray-job-submission-jobsubmissionclient-stop-job",3071"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#cleaning-up"3072],3073"document_ids": [30744550,30754375,30766559,30779791,30786559,30794550,30804894,30819757,30826710,30839791,308411590,30854492,308614701,30876690,30886686,308964523090],3091"answer": " To cancel a job from the UI, you can use the `ray job stop` command, providing the ID of the job you want to stop. For example:\n\n```\n$ ray job stop raysubmit_tUAuCKubPAEXh6CW\n```\n\nAlternatively, if you are using Ray Workflows, you can cancel a workflow using the `ray.workflow.cancel` function, providing the ID of the workflow you want to cancel. For example:\n\n```\nfrom ray import workflow\nworkflow.cancel(workflow_id=\"some_workflow\")\n```\n\nTo cancel an actor task, you can call `ray.cancel` on the returned ObjectRef. For example:\n\n```\nimport ray\nref = some_actor.some_method.remote()\nray.cancel(ref)\n```\n\nNote that these methods will only request the job, workflow, or actor task to exit asynchronously. To clean up a running job, you can use the `skein application shutdown` command with the application ID.",3092"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3093},3094{3095"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>`",3096"sources": [3097"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",3098"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",3099"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",3100"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3101"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3102"https://docs.ray.io/en/master/rllib/rllib-saving-and-loading-algos-and-policies.html#how-do-i-create-an-algorithm-checkpoint",3103"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",3104"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#development-workflow-with-serve-run",3105"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",3106"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",3107"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",3108"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",3109"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup",3110"https://docs.ray.io/en/master/serve/api/index.html#serve-config",3111"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",3112"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files"3113],3114"document_ids": [311513680,31166562,311713677,31181006,31194273,312010542,31216552,312213659,312313719,31246577,312513831,312613987,31276549,312813986,312913718,313042623131],3132"answer": " To get your project files on the cluster when using Ray Serve, you can use the `--working-dir` option in the `serve run` command to specify the local directory containing your project files. The command will connect to the remote cluster via Ray Client, upload the working directory, and run your Ray Serve application. Here's an example:\n```bash\nserve run --address=ray://<head-node-ip-address>:10001 --working-dir=\"./project/src\" local_dev:graph\n",3133"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3134},3135{3136"question": "how do i install ray nightly wheel",3137"sources": [3138"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",3139"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",3140"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",3141"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",3142"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",3143"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",3144"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3145"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",3146"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",3147"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",3148"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",3149"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",3150"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3151"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3152"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3153"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions"3154],3155"document_ids": [315614545,3157288,315863,315963,3160288,316114545,31626335,316366,3164291,316514551,3166883,31675412,31686317,31696351,3170271,317161033172],3173"answer": " To install the Ray nightly wheel, first cleanly remove any previous 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 following command: `pip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"`, where `LINK_TO_WHEEL.whl` is the link to the nightly wheel you want to install. You can find the appropriate link for your system and Python version in the provided context.",3174"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3175},3176{3177"question": "how do i install the latest ray nightly wheel?",3178"sources": [3179"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",3180"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",3181"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",3182"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",3183"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3184"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3185"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",3186"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",3187"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",3188"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",3189"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",3190"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",3191"https://docs.ray.io/en/master/installation.html#from-wheels",3192"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",3193"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#install-the-latest-ray-release",3194"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only"3195],3196"document_ids": [319714545,3198288,319963,3200271,32016317,32026351,32036335,3204291,320514551,320666,3207286,320814541,320961,32105412,32115820,32128833213],3214"answer": " To install the latest Ray nightly wheel, first uninstall any existing Ray installation using `pip uninstall -y ray`. Then, use pip to install the nightly wheel with support for the dashboard and cluster launcher by using the following command:\n\n```\npip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"\n```\n\nReplace `LINK_TO_WHEEL.whl` with the actual link to the nightly wheel that matches your operating system and Python version. You can find the appropriate link in the context provided, or by visiting the Ray nightly wheels page.",3215"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3216},3217{3218"question": "how can I write unit tests for Ray code?",3219"sources": [3220"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible",3221"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",3222"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3223"https://docs.ray.io/en/master/tune/faq.html#how-can-i-develop-and-test-tune-locally",3224"https://docs.ray.io/en/master/ray-contribute/development.html#installing-additional-dependencies-for-development",3225"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#testing",3226"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",3227"https://docs.ray.io/en/master/ray-core/cross-language.html#java-calling-python",3228"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",3229"https://docs.ray.io/en/master/ray-core/examples/overview.html#ray-tutorials-and-examples",3230"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding",3231"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",3232"https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution",3233"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",3234"https://docs.ray.io/en/master/ray-core/patterns/return-ray-put.html#code-example",3235"https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally"3236],3237"document_ids": [32385397,32395395,32404811,32411048,3242899,3243917,32444190,32454338,324614561,32475380,32486621,32494204,32509644,32515757,32525179,3253134353254],3255"answer": " To write unit tests for Ray code, you can follow these tips:\n\n1. Share the Ray cluster across tests if possible to reduce overhead. You can do this by calling `ray.init()` once in the `setUp()` method of a `unittest.TestCase` subclass.\n2. Fix the resource quantity with `ray.init(num_cpus=...)` to ensure consistent resource allocation across tests.\n3. Consider creating a mini-cluster with `ray.cluster_utils.Cluster` for more control over the cluster configuration.\n4. Be careful when running tests in parallel to avoid race conditions and other concurrency issues.\n5. Use `ray.shutdown()` and `assert not ray.is_initialized()` to ensure that Ray is properly shut down after each test.\n6. When testing Ray programs, it's important to consider the nature of parallel programs and the potential for non-deterministic behavior.\n\nHere's an example of how you might write a unit test for Ray code in Python:\n\n```python\nimport unittest\nimport ray\n\nclass RayTest(unittest.TestCase):\n def setUp(self):\n ray.init(num_cpus=4, num_gpus=0)\n\n def tearDown(self):\n ray.shutdown()\n assert not ray.is_initialized()\n\n def test_my_ray_code(self):\n # Test your Ray code here\n pass\n```\n\nNote that this example assumes that you have already written the Ray code that you want to test. The test case sets up a Ray cluster with 4 CPUs and 0 GPUs in the `setUp()` method, and shuts down the cluster in the `tearDown()` method. You can then write your test in the `test_my_ray_code()` method.",3256"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3257},3258{3259"question": "How I stop Ray from spamming lots of Info updates on stdout?",3260"sources": [3261"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",3262"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#disable-logging-to-the-driver",3263"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#distributed-progress-bars-tqdm",3264"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3265"https://docs.ray.io/en/master/data/data.html#learn-more",3266"https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune",3267"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",3268"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3269"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",3270"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",3271"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",3272"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",3273"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-5-check-the-output-of-the-ray-job",3274"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication",3275"https://docs.ray.io/en/master/cluster/cli.html#ray-stop",3276"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks"3277],3278"document_ids": [32796558,3280523,3281529,32821006,32836909,32842390,3285524,32864209,3287519,3288410,32895128,329013929,32915766,3292525,32935613,329445503295],3296"answer": " To stop Ray from spamming lots of Info updates on stdout, you can disable log deduplication by setting the environment variable `RAY_DEDUP_LOGS=0`. This will prevent Ray from deduplicating logs that appear redundantly across multiple processes.\n\nAlternatively, you can also set `log_to_driver=False` in `ray.init()` to disable logging to the driver, which may help in large scale runs where routing all worker logs to the driver may be unwanted.\n\nAdditionally, if you are using `tqdm_ray` for progress bars, you can set `RAY_TQDM_PATCH_PRINT=0` to disable patching of the built-in `print` function, which may improve performance if there are more than a couple thousand updates per second.\n\nFinally, you can stop the Ray job by running `ray job stop raysubmit_tUAuCKubPAEXh6CW` in the command line.",3297"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3298},3299{3300"question": "how to deploy stable diffusion 2.1 with Ray Serve?",3301"sources": [3302"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",3303"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",3304"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#serve-a-stablediffusion-text-to-image-model-on-kubernetes",3305"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",3306"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-5-send-a-request-to-the-text-to-image-model",3307"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",3308"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-7-submit-a-request-to-the-ray-serve-application",3309"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",3310"https://docs.ray.io/en/master/serve/advanced-guides/inplace-updates.html#code-updates",3311"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",3312"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-5-send-a-request-to-the-text-to-image-model",3313"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",3314"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",3315"https://docs.ray.io/en/master/serve/advanced-guides/migration.html#what-has-been-changed",3316"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",3317"https://docs.ray.io/en/master/serve/index.html#why-choose-serve"3318],3319"document_ids": [33206127,332114409,33226124,332310138,33246130,332514409,33265989,33276128,332813692,33295671,33306130,333113622,333214416,333313797,333413677,3335136143336],3337"answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Install the required packages:\n```bash\npip install \"ray[serve]\" requests torch diffusers==0.12.1 transformers\n```\n\n2. Save the Stable Diffusion code to a file named `stable_diffusion.py`. You can find the code in the context provided.\n\n3. Download the RayService configuration for Stable Diffusion:\n```bash\ncurl -LO https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml\n```\n\n4. Create a RayService using the downloaded configuration:\n```bash\nkubectl apply -f ray-service.stable-diffusion.yaml\n```\n\n5. Send a request to the Stable Diffusion model by following steps 5.1, 5.2, and 5.3 from the context.\n\nNote that you should test your Ray Serve script locally or in a RayCluster before deploying it to a RayService. Also, consider the best practice for large-scale code updates mentioned in the warning.",3338"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3339},3340{3341"question": "what is actor_handle?",3342"sources": [3343"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",3344"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",3345"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",3346"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner.FrameworkHyperparameters.what_to_compile.html#ray-rllib-core-learner-learner-frameworkhyperparameters-what-to-compile",3347"https://docs.ray.io/en/master/tune/faq.html#what-are-hyperparameters",3348"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#how-does-tune-work",3349"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3350"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3351"https://docs.ray.io/en/master/rllib/actors.html#passing-around-actor-handles",3352"https://docs.ray.io/en/master/ray-core/actors.html#passing-around-actor-handles",3353"https://docs.ray.io/en/master/ray-more-libs/actors.html#passing-around-actor-handles",3354"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",3355"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3356"https://docs.ray.io/en/master/ray-core/api/core.html#actors",3357"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",3358"https://docs.ray.io/en/master/ray-core/actors.html#actors"3359],3360"document_ids": [336111587,33624487,336314698,336413155,33651007,33662232,33674860,33684615,336911588,33704489,337114699,33724200,33734623,33744650,33754871,337644673377],3378"answer": " An actor handle is a reference to a Ray actor, which is a stateful, long-running object in a Ray cluster. Actor handles can be passed into other tasks and used to call remote functions or methods on the actor. They can also be given a unique name within a namespace, allowing them to be retrieved from any job in the Ray cluster.",3379"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3380},3381{3382"question": "how to kill a r detached actors?",3383"sources": [3384"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3385"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3386"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",3387"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",3388"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3389"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3390"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3391"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3392"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3393"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3394"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3395"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",3396"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3397"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",3398"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",3399"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure"3400],3401"document_ids": [34024633,34034859,34042157,34054578,34064859,34074633,34084615,34094613,34104579,34114848,34124620,34134561,34144618,34154631,34168106,341745773418],3419"answer": " To kill a detached actor in Ray, you can use the `ray.kill()` function, passing in the actor's handle as an argument. This will interrupt any running tasks on the actor and immediately terminate it. Here's an example:\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n def ping(self):\n return \"hello\"\n\ndetached_actor = Actor.options(name=\"actor\", lifetime=\"detached\").remote()\n\n# kill the detached actor\nray.kill(detached_actor)\n```\nNote that once you call `ray.kill()` on a detached actor, subsequent calls to get its handle via `ray.get_actor()` will fail. Also, be sure to kill the actor only when you are sure that it is no longer needed, as detached actors are not automatically garbage-collected by Ray.",3420"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3421},3422{3423"question": "How to force upgrade the pip package in the runtime environment if an old version exists?",3424"sources": [3425"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3426"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3427"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3428"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",3429"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",3430"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3431"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",3432"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3433"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",3434"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3435"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",3436"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3437"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3438"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",3439"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",3440"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris"3441],3442"document_ids": [34434329,34444717,34454327,34464260,34474269,34484719,34494260,34504330,34514267,34524328,34534268,34544283,34554719,345610281,34574252,345843103459],3460"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\\_env as shown in the example. However, pip does not support the exact version specifier \"==22.0.2;python\\_version=='3.8.11'\". Instead, you can specify the version of the package you want to install. For example, to force upgrade the 'certifi' package to version '2022.02.28', you can set:\n```json\nRuntimeEnv(\n pip={\"packages\":[\"certifi==2022.02.28\"], \"pip_check\": False}\n)\n```\nThis will install the specified version of the package, ignoring any existing installation. Note that this will only affect new Ray processes started after the runtime environment is updated.",3461"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3462},3463{3464"question": "How do I do global shuffle with Ray?",3465"sources": [3466"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",3467"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",3468"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",3469"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3470"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",3471"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#ingesting-into-model-trainers",3472"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",3473"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",3474"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",3475"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",3476"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",3477"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",3478"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition",3479"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",3480"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",3481"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility"3482],3483"document_ids": [34847395,34856795,34866827,34871006,34889209,34898099,34909209,34916826,34929206,34936876,34946864,34956825,34967352,349714502,34986824,349992113500],3501"answer": " To do a global shuffle with Ray, you can use the `random_shuffle()` function on a Ray Dataset. Here's an example:\n```python\nimport ray\n\n# Create a Ray Dataset\nds = ray.data.read_text(\"path/to/your/dataset.txt\")\n\n# Do a global shuffle of all rows in this dataset\nds = ds.random_shuffle()\n```\nThis will randomly shuffle all the rows in the dataset. Note that `random_shuffle()` is a relatively slow operation, so for better performance, you can consider iterating over batches with shuffling instead. Also, keep in mind that shuffling rows requires materializing the dataset in memory, so it can be a memory-intensive operation.",3502"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3503},3504{3505"question": "How to find namespace of an Actor?",3506"sources": [3507"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",3508"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3509"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3510"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",3511"https://docs.ray.io/en/master/tune/examples/ml-frameworks.html#examples-using-ray-tune-with-ml-frameworks",3512"https://docs.ray.io/en/master/tune/examples/index.html#examples-using-ray-tune-with-ml-frameworks",3513"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",3514"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3515"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",3516"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.ray_namespace.html#ray-job-config-jobconfig-ray-namespace",3517"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3518"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3519"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3520"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",3521"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",3522"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"3523],3524"document_ids": [35254521,35264623,35274504,35284528,35292799,35303793,35314871,35324626,35334528,35344751,35354625,35364499,35374516,35384476,35394509,3540147463541],3542"answer": " In Ray, you can find the namespace of an actor by using the `namespace` argument in the `ray.get_actor()` function. If the namespace is not specified, the current namespace is used. Here is an example:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\n# Create an actor with a specified namespace.\nActor.options(name=\"my_actor\", namespace=\"actor_namespace\", lifetime=\"detached\").remote()\n\n# Get the actor with the specified namespace.\nactor = ray.get_actor(\"my_actor\", namespace=\"actor_namespace\")\n```\n\nAlternatively, if you know the name of the actor but not its namespace, you can get a list of all namespaces that contain an actor with the given name using the `ray list actors` command in the terminal. This will return a list of dictionaries, each containing the name, ID, namespace, and state of an actor. You can then filter this list to find the namespace of the actor you are looking for.\n\nFor example, if you have an actor named \"my\\_actor\", you can find its namespace using the following command:\n\n```bash\nray list actors | grep my_actor | awk '{print $4}'\n```\n\nThis will print the namespace of the actor named \"my\\_actor\". Note that this command may return multiple namespaces if there are multiple actors with the same name in different namespaces.",3543"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3544},3545{3546"question": "How does Ray work with async.io ?",3547"sources": [3548"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",3549"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",3550"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",3551"https://docs.ray.io/en/master/ray-core/patterns/pipelining.html#code-example",3552"https://docs.ray.io/en/master/train/api/doc/ray.train.trainer.BaseTrainer.html#ray-train-trainer-basetrainer",3553"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",3554"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3555"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",3556"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",3557"https://docs.ray.io/en/master/ray-core/actors/concurrency_group_api.html#defining-concurrency-groups",3558"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-remote-tasks",3559"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",3560"https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution",3561"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3562"https://docs.ray.io/en/master/ray-core/patterns/concurrent-operations-async-actor.html#pattern-using-asyncio-to-run-actor-methods-concurrently",3563"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api"3564],3565"document_ids": [35664598,35674603,35684599,35695190,35708514,35714481,35724208,35735443,35744360,35754636,35764611,35774205,35789644,35794811,35805161,3581139743582],3583"answer": " Ray can work with async.io by using the async/await syntax and integrating with popular async frameworks like aiohttp and aioredis. You can define async methods in Ray actors, and Ray will automatically detect whether an actor supports async calls or not. However, Ray does not support asyncio for remote tasks, but you can wrap the async function with a synchronous wrapper to run the task. Ray also provides methods for working with asyncio, such as `ray.wait()` and `ray.get()`, which can be used to manage the execution of asynchronous tasks. Additionally, you can use `ray.init()` to initialize Ray and configure various settings, such as the storage directory.",3584"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3585},3586{3587"question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",3588"sources": [3589"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",3590"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3591"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",3592"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3593"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3594"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",3595"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",3596"https://docs.ray.io/en/master/ray-core/patterns/unnecessary-ray-get.html#anti-pattern-calling-ray-get-unnecessarily-harms-performance",3597"https://docs.ray.io/en/master/ray-core/patterns/ray-get-too-many-objects.html#anti-pattern-fetching-too-many-objects-at-once-with-ray-get-causes-failure",3598"https://docs.ray.io/en/master/ray-core/tasks.html#tasks",3599"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#stepping-between-ray-tasks",3600"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",3601"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#point-to-point-communication",3602"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",3603"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",3604"https://docs.ray.io/en/master/ray-core/patterns/ray-get-too-many-objects.html#anti-pattern-fetching-too-many-objects-at-once-with-ray-get-causes-failure"3605],3606"document_ids": [36075235,36084209,36094416,36101006,36111005,36124464,36135236,36145219,36155169,36164361,3617581,36184214,361914421,362013646,36214884,362251683623],3624"answer": " To debug a hanging `ray.get()` call, you should avoid calling `ray.get()` in a loop, as it is a blocking call that waits until the requested result is available. This can harm parallelism, as the loop will not continue to run until the call to `ray.get()` is resolved. Instead, you can separate the call to `ray.get()` from the call to the remote functions, so that all remote functions are spawned before you wait for the results, allowing them to run in parallel in the background. Additionally, you can pass a list of object references to `ray.get()` instead of calling it one by one to wait for all of the tasks to finish.",3625"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3626},3627{3628"question": "can you show me an example of ray.actor.exit_actor()",3629"sources": [3630"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3631"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3632"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3633"https://docs.ray.io/en/master/rllib/rllib-examples.html#custom-and-complex-models",3634"https://docs.ray.io/en/master/tune/examples/tune-serve-integration-mnist.html#model-selection-and-serving-with-ray-tune-and-ray-serve",3635"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",3636"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3637"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3638"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3639"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3640"https://docs.ray.io/en/master/ray-core/api/core.html#actors",3641"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.AsyncioActorExit.html#ray-exceptions-asyncioactorexit",3642"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",3643"https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",3644"https://docs.ray.io/en/master/ray-core/api/doc/ray.shutdown.html#ray-shutdown",3645"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure"3646],3647"document_ids": [36484618,36494613,36504620,365110636,36522749,3653166,36544619,36554579,36564859,36574615,36584650,36594781,3660637,36614488,36624827,366345653664],3665"answer": " The `ray.actor.exit_actor()` function is used to manually terminate a Ray actor from within one of the actor methods. This will kill the actor process and release resources associated with the actor. Here is an example of how to use it:\n\n```python\n@ray.remote\nclass Actor:\n def exit(self):\n ray.actor.exit_actor()\n\nactor = Actor.remote()\nactor.exit.remote()\n```\n\nHowever, it is generally not necessary to manually terminate actors, as they are automatically garbage collected. The object ref resulting from the task can be waited on to wait for the actor to exit.\n\nAlternatively, you can use `ray.kill(actor_handle)` to terminate an actor forcefully. This should be reserved for cases where an actor is unexpectedly hanging or leaking resources, and for detached actors, which must be manually destroyed.\n\nNote that `ray.kill` will interrupt any running tasks on the actor, causing them to fail immediately, and will not run atexit handlers installed in the actor. If you want to kill the actor but let pending tasks finish, you can call `actor.__ray_terminate__.remote()` instead.",3666"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3667},3668{3669"question": "how to add log inside actor?",3670"sources": [3671"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",3672"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",3673"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-prefixes-for-actor-logs",3674"https://docs.ray.io/en/master/train/examples/horovod/horovod_example.html#horovod-distributed-training-example-with-pytorch-ray-train",3675"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",3676"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#mounting-the-ray-log-directory",3677"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3678"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",3679"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3680"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3681"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",3682"https://docs.ray.io/en/master/ray-observability/getting-started.html#actors-view",3683"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",3684"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3685"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3686"https://docs.ray.io/en/master/rllib/rllib-catalogs.html#write-a-catalog-from-scratch"3687],3688"document_ids": [3689414,3690540,3691520,36929568,3693531,36945871,369514324,369614202,369714286,369814295,369914106,3700434,3701682,3702477,3703476,3704115393705],3706"answer": " To add logging inside an actor, you can define a log method within the actor class that uses Python's logging library to log messages. Here's an example:\n```python\nimport logging\nimport ray\n\n@ray.remote\nclass MyActor:\n def __init__(self):\n self.logger = logging.getLogger(__name__)\n\n def log(self, msg):\n self.logger.info(msg)\n\n def some_other_method(self):\n self.log(\"This is a log message\")\n```\nBy default, all stdout and stderr of Tasks and Actors are redirected to the Driver output. However, you can customize logging as needed. For example, you can modify the log level for specific Ray loggers or add an additional file handler for Ray Tune.\n\nTo view the logs for a specific actor, you can use the Ray CLI or the Python SDK. Here's an example using the Python SDK:\n```python\nfor line in get_log(actor_id=<ACTOR_ID>):\n print(line)\n```\nNote that if a task is from a concurrent 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.",3707"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3708},3709{3710"question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",3711"sources": [3712"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",3713"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#generate-text-from-prompt",3714"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",3715"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",3716"https://docs.ray.io/en/master/data/working-with-text.html#working-with-text",3717"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#object-detection-batch-inference-with-pytorch",3718"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial",3719"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",3720"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",3721"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#next-steps",3722"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#next-steps",3723"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",3724"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#starting-batch-training",3725"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",3726"https://docs.ray.io/en/master/ray-overview/use-cases.html#batch-inference",3727"https://docs.ray.io/en/master/serve/tutorials/batch.html#batching-tutorial"3728],3729"document_ids": [37306999,373110470,373210259,37337970,37346993,37358010,373614582,37376998,37386843,373910219,374010137,37416938,37425292,37437970,3744140,3745145843746],3747"answer": " To perform batch inference using GPT-2 on text data from an S3 bucket, you can use the Ray framework. Here's a script that demonstrates how to read text data from an S3 bucket, create a Ray Dataset, and then use the `map_batches()` function to apply the GPT-2 model for text generation:\n\n```python\nimport os\nimport ray\nfrom transformers import pipeline, AutoTokenizer, GPT2ForSequenceClassification\n\nray.init()\n\n# Download the GPT-2 model and tokenizer\nmodel_name = \"gpt2\"\nos.system(f\"aws s3 cp s3://{model_name}/config.json config.json\")\nos.system(f\"aws s3 cp s3://{model_name}/pytorch_model.bin pytorch_model.bin\")\ntokenizer = AutoTokenizer.from_pretrained(model_name)\nmodel = GPT2ForSequenceClassification.from_pretrained(model_name)\n\n# Read text data from S3 bucket and create a Ray Dataset\nbucket = \"your-s3-bucket\"\nds = ray.data.read_text(f\"s3://{bucket}/*.txt\")\n\n# Define the TextGenerator class\nclass TextGenerator:\n def __init__(self, model, tokenizer):\n self.model = model\n self.tokenizer = tokenizer\n\n def __call__(self, batch):\n input_ids = self.tokenizer(list(batch[\"text\"]), return_tensors=\"pt\", padding=True, truncation=True).input_ids\n outputs = self.model.generate(input_ids, max_length=20, num_return_sequences=1)\n batch[\"output\"] = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in outputs]\n return batch\n\n# Apply the TextGenerator to the Ray Dataset\nds_with_output = ds.map_batches(TextGenerator, model=model, tokenizer=tokenizer)\n\n# Show the results\nds_with_output.show(3)\n```\n\nMake sure to replace `your-s3-bucket` with the name of your S3 bucket. This script reads text data from the S3 bucket, applies the GPT-2 model for text generation, and displays the results. Note that you may need to adjust the `max_length` and `num_return_sequences` parameters in the `generate()` function to better suit your specific use case.",3748"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3749},3750{3751"question": "How do I enable Ray debug logs?",3752"sources": [3753"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3754"https://docs.ray.io/en/master/ray-observability/user-guides/index.html#user-guides",3755"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",3756"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3757"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",3758"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3759"https://docs.ray.io/en/master/cluster/cli.html#ray-start",3760"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#debugger-commands",3761"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-debug",3762"https://docs.ray.io/en/master/ray-contribute/debugging.html#debugging-for-ray-developers",3763"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",3764"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3765"https://docs.ray.io/en/master/ray-observability/index.html#monitoring-and-debugging",3766"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",3767"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#monitoring-with-the-cli-or-sdk",3768"https://docs.ray.io/en/master/ray-observability/key-concepts.html#debugger"3769],3770"document_ids": [3771680,3772546,3773510,37741006,37754464,37761005,37775611,3778579,37794669,3780944,3781573,37824323,3783450,37846631,3785465,37864053787],3788"answer": " To enable Ray debug logs, you can use the command line interface (CLI) option `--ray-debugger-external` to make the Ray debugger available externally to the node. This is only safe to activate if the node is behind a firewall.\n\nAdditionally, you can use the `ray debug` command to show all active breakpoints and exceptions in the Ray debugger. This command has the option `--address` to override the address to connect to.\n\nYou can also enable runtime_env debugging log streaming by setting the environment variable `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1` on each node before starting Ray. This will print the full runtime_env setup log messages to the driver (the script that calls `ray.init()`).\n\nRay client logs can be found at `/tmp/ray/session_latest/logs` on the head node. Monitoring and debugging capabilities in Ray are available through a CLI or SDK. Ray has a built-in debugger for debugging your distributed applications.",3789"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3790},3791{3792"question": "How do I list the current Ray actors from python?",3793"sources": [3794"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3795"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",3796"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",3797"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",3798"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3799"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",3800"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",3801"https://docs.ray.io/en/master/ray-observability/key-concepts.html#ray-states",3802"https://docs.ray.io/en/master/rllib/actors.html#actors",3803"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",3804"https://docs.ray.io/en/master/ray-core/actors.html#actors",3805"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",3806"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.html#ray-util-actorpool",3807"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#getting-a-summary-of-states-of-entities-by-type",3808"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",3809"https://docs.ray.io/en/master/ray-observability/reference/api.html#list-apis"3810],3811"document_ids": [3812472,3813788,3814673,38154464,38161006,3817673,3818475,3819402,382011575,382114680,38224467,38234623,38244742,3825480,3826488,38276603828],3829"answer": " To list the current Ray actors from Python, you can use the `ray.util.state.list_actors` function. Here is an example:\n\n```python\nimport ray\nfrom ray.util.state import list_actors\n\nactors = list_actors()\nfor actor in actors:\n print(actor)\n```\n\nThis will print out information about all the actors in the Ray cluster.\n\nAlternatively, you can use the `ray list actors` command in the Ray CLI to list the actors:\n\n```\n$ ray list actors\n```\n\nThis will list the IDs, class names, names, PIDs, and states of all the actors in the Ray cluster.",3830"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3831},3832{3833"question": "I want to kill the replica actor from Python. how do I do it?",3834"sources": [3835"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",3836"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3837"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",3838"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3839"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3840"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#resource-management-cpus-gpus",3841"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3842"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3843"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",3844"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",3845"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",3846"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",3847"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",3848"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure",3849"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",3850"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure"3851],3852"document_ids": [385313912,38544859,38554848,38561006,38571005,385813456,38594620,38604613,38614860,38624618,38634579,386413916,38654615,38664577,386713915,3868139053869],3870"answer": " To kill a replica actor from Python, you can use the `ray.kill()` function, passing in the actor handle as an argument. Here is an example:\n```\nimport ray\n\nreplica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")\nray.kill(replica_handle, no_restart=True)\n```\nThis will forcefully terminate the actor, interrupting any running tasks and causing them to fail immediately. If you want to allow pending tasks to finish before killing the actor, you can call `actor.__ray_terminate__.remote()` instead.\n\nNote that if you only have one reference to an actor handle, calling `del actor` may trigger actor deletion. However, if your program has multiple references to the same actor handle, actor termination will not occur until the reference count goes to 0.\n\nAlso, keep in mind that garbage collection for actors has not been implemented yet, so this is the only way to terminate an actor gracefully. The object ref resulting from the task can be waited on to wait for the actor to exit.\n\nFor more information, you can refer to the `ray.kill()` documentation in the Ray documentation.",3871"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3872},3873{3874"question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",3875"sources": [3876"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3877"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#accelerator-types",3878"https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",3879"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3880"https://docs.ray.io/en/master/ray-core/walkthrough.html#running-a-task",3881"https://docs.ray.io/en/master/ray-core/cluster/index.html#what-s-next",3882"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",3883"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3884"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",3885"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-workload-scheduling",3886"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",3887"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote_function.RemoteFunction.options.html#ray-remote-function-remotefunction-options",3888"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",3889"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",3890"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#module-ray.util.collective.collective",3891"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements"3892],3893"document_ids": [38944994,38955006,38964366,38971006,38984485,38994646,39006590,39014996,39024997,39036017,39044697,39054702,39065114,390714767,390814461,390951153910],3911"answer": " To specify that a task should run on a V100 GPU type, you can use the `@ray.remote` decorator with the `accelerator_type` argument set to `NVIDIA_TESLA_V100`. For example:\n```\n@ray.remote(accelerator_type=\"NVIDIA_TESLA_V100\")\ndef train(data):\n return \"This function was run on a node with a Tesla V100 GPU\"\n\nray.get(train.remote(1))\n```\nThis will schedule the task to a node that has a free Tesla V100 GPU and assign the GPU to the task by setting the `CUDA_VISIBLE_DEVICES` environment variable before running the task code.\n\nYou can also specify the number of GPUs required by the task using the `num_gpus` argument. For example:\n```\n@ray.remote(num_gpus=2, accelerator_type=\"NVIDIA_TESLA_V100\")\ndef train(data):\n return \"This function was run on a node with 2 Tesla V100 GPUs\"\n\nray.get(train.remote(1))\n```\nThis will schedule the task to a node that has at least 2 free Tesla V100 GPUs and assign those GPUs to the task.\n\nNote that the `num_gpus` and `accelerator_type` arguments only refer to logical resources used for scheduling purposes. The actual GPU utilization is not controlled or limited by Ray. By default, 0 GPUs are reserved for the entrypoint script.\n\nFor more information, see the Ray documentation on using GPUs in tasks and actors.",3912"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3913},3914{3915"question": "How do I get started?",3916"sources": [3917"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#getting-started",3918"https://docs.ray.io/en/master/ray-overview/getting-started.html#getting-started",3919"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-autoscaler-private-fake-multi-node-test-utils-dockercluster",3920"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3921"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",3922"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",3923"https://docs.ray.io/en/master/serve/index.html#learn-more",3924"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started",3925"https://docs.ray.io/en/master/tune/faq.html#how-can-i-get-started-contributing-to-tune",3926"https://docs.ray.io/en/master/serve/advanced-guides/index.html#advanced-guides",3927"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray",3928"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-1-create-a-kind-cluster",3929"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#getting-started-with-kuberay",3930"https://docs.ray.io/en/master/train/train.html#get-started",3931"https://docs.ray.io/en/master/serve/key-concepts.html#whats-next",3932"https://docs.ray.io/en/master/cluster/vms/index.html#learn-more"3933],3934"document_ids": [39356482,3936220,3937864,39381006,39391005,39402309,394113625,39424483,39431049,394413827,39454185,39466043,39475646,39488244,394913428,395062103951],3952"answer": " To get started with Ray, you should install it via `pip install -U ray`. Then, you can import and initialize Ray by using `import ray` and `ray.init()`. If you're new to Ray Serve, you can start with the Ray Serve Quickstart and refer to the advanced guides for more options and configurations. To learn more about Ray, you can check out the Ray docs, which present all the information you need to start running Ray workloads on VMs.",3953"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3954},3955{3956"question": "How to specify python version in runtime_env?",3957"sources": [3958"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3959"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3960"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3961"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",3962"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",3963"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",3964"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",3965"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",3966"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",3967"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",3968"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",3969"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",3970"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",3971"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.runtime_env.html#ray-job-submission-jobinfo-runtime-env",3972"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",3973"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris"3974],3975"document_ids": [39764330,39774717,39784329,39796565,398066,3981291,39824661,398313856,39844253,39854327,39864283,39874712,39885679,39896665,39904826,399143103992],3993"answer": " To specify the Python version in the runtime environment, you can use the `conda` field in the `runtime_env` dictionary and create a new conda environment with the desired Python version. Here's an example:\n\n```python\nruntime_env = {\n \"conda\": {\n \"name\": \"my_env\",\n \"channels\": [\"conda-forge\"],\n \"dependencies\": [\n \"python=3.9\",\n \"numpy\",\n \"pandas\",\n ],\n },\n \"pip\": [\"my_package\"]\n}\n\nray.init(runtime_env=runtime_env)\n```\n\nIn this example, a new conda environment named `my_env` is created with Python 3.9, along with the NumPy and Pandas packages. The `pip` field is also specified to install `my_package` using pip.\n\nKeep in mind that the `conda` and `pip` fields cannot be specified at the same time within the same `runtime_env` dictionary. If you need to use both, you should create a conda environment with the desired Python version and add your pip dependencies in the `pip` field in your conda environment.yaml file.",3994"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3995},3996{3997"question": "how to create a Actor in a namespace?",3998"sources": [3999"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",4000"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",4001"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",4002"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",4003"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",4004"https://docs.ray.io/en/master/ray-core/actors.html#actors",4005"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",4006"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",4007"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",4008"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",4009"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",4010"https://docs.ray.io/en/master/ray-core/namespaces.html#using-namespaces",4011"https://docs.ray.io/en/master/ray-core/namespaces.html#anonymous-namespaces",4012"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",4013"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor",4014"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors"4015],4016"document_ids": [40174521,40184626,40194625,40205438,40215391,40224469,40234623,40244513,40254504,40264509,40274499,40284516,40294528,40304523,40314871,403246274033],4034"answer": " To create an actor in a specific namespace in Ray, you can use the `options` method on the actor class and set the `name` and `namespace` parameters. Here is an example in Python:\n\n```python\nimport ray\n\n@ray.remote\nclass Actor:\n pass\n\n# Connect to Ray with the desired namespace\nray.init(namespace=\"my_namespace\")\n\n# Create an actor with a specified name and namespace\nactor_handle = Actor.options(name=\"my_actor\", namespace=\"my_namespace\", lifetime=\"detached\").remote()\n```\n\nIn this example, the actor will be created in the \"my\\_namespace\" namespace with the name \"my\\_actor\". You can then use the `actor_handle` to call methods on the actor. Note that you need to connect to the same namespace when creating the actor and when calling methods on the actor.",4035"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4036},4037{4038"question": "Can I specify multiple working directories?",4039"sources": [4040"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",4041"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4042"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",4043"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",4044"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",4045"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#example",4046"https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.PathPartitionParser.html#ray-data-datasource-pathpartitionparser",4047"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4048"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",4049"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",4050"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",4051"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4052"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",4053"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",4054"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4055"https://docs.ray.io/en/master/data/api/doc/ray.data.datasource.PathPartitionParser.html#ray-data-datasource-pathpartitionparser"4056],4057"document_ids": [40581775,40594277,40604301,40611006,40624301,40632264,40647297,40654273,406610170,40671790,406810169,40691066,40704305,40716597,40726632,407372984074],4075"answer": " No, you cannot specify multiple working directories in Ray Tune. The working directory is set per job, not per task or actor, and can be a local directory or a remote URI to a zip file. If a local directory is specified, its contents will be directly accessed as the working directory. If a remote URI is specified, it must be a link directly to a zip file that contains only a single top-level directory, and the contents of this directory will be directly accessed as the working directory. You can use relative paths within the working directory for read-only purposes, but you must make sure that the directory is synced on all nodes if running on multiple machines.",4076"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4077},4078{4079"question": "what if I set num_cpus=0 for tasks",4080"sources": [4081"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources",4082"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",4083"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",4084"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",4085"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",4086"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",4087"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",4088"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",4089"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#code-example",4090"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",4091"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",4092"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.entrypoint_num_cpus.html#ray-job-submission-jobinfo-entrypoint-num-cpus",4093"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.num_cpus_per_worker.html#ray-train-scalingconfig-num-cpus-per-worker",4094"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",4095"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",4096"https://docs.ray.io/en/master/serve/getting_started.html#converting-to-a-ray-serve-application"4097],4098"document_ids": [409911063,4100623,41015936,41021005,41034481,41045125,41054850,41066716,41075234,41084853,41094877,41106703,41118998,41125068,41137639,4114135754115],4116"answer": " If you set `num_cpus=0` for tasks in Ray, your tasks will be built solely on the CPU, even if GPUs are available on the machine. However, keep in mind that if your tasks are very small or have variable durations, you might not see speedups due to Ray's task overhead and the nature of your workload. Also, ensure that your tasks are not too small (less than ten milliseconds) as it may not result in any performance improvement.",4117"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4118},4119{4120"question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",4121"sources": [4122"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",4123"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",4124"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",4125"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",4126"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",4127"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",4128"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#understanding-the-ray-autoscaler-in-the-context-of-kubernetes",4129"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",4130"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",4131"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",4132"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",4133"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",4134"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#overview",4135"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",4136"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",4137"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice"4138],4139"document_ids": [41405953,41415818,41425752,414313447,41445975,41455559,41465969,414713957,41485963,41495656,41505642,41515742,41525956,41535958,41545975,415557884156],4157"answer": " Yes, it is possible to have Ray on K8s without using KubeRay even when autoscaler is enabled. You can deploy a static Ray cluster to Kubernetes without using KubeRay, but it lacks the built-in autoscaling feature that KubeRay provides. Instead, you can utilize Kubernetes cluster autoscaling, which can be provisioned by the Kubernetes Cluster Autoscaler when the Ray autoscaler decides to create a Ray pod. This way, the Ray Autoscaler and the Kubernetes Cluster Autoscaler complement each other. However, you'll need to configure your RayCluster so that only one Ray pod fits per Kubernetes node to ensure Ray Autoscaler pod scaling events correspond roughly one-to-one with cluster autoscaler events.",4158"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4159},4160{4161"question": "how to manually configure and manage Ray cluster on Kubernetes",4162"sources": [4163"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",4164"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",4165"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",4166"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",4167"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",4168"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",4169"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",4170"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",4171"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",4172"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gke-gcs-bucket.html#create-a-minimal-raycluster-yaml-manifest",4173"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",4174"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#quickstart",4175"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide",4176"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-2-deploy-a-ray-cluster-on-kubernetes-with-the-kuberay-operator",4177"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",4178"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-2-deploy-a-ray-cluster-on-kubernetes-with-the-kuberay-operator"4179],4180"document_ids": [41815717,41825918,41835917,418413959,41855717,41865657,41875656,41885642,41895605,41906034,41915953,41925958,41935784,41946161,41956500,419661634197],4198"answer": " To manually configure and manage a Ray cluster on Kubernetes, you can follow these steps:\n\n1. Install and set up `kubectl` and Helm.\n2. Prepare a Kubernetes cluster.\n3. Deploy a KubeRay operator to your Kubernetes cluster.\n4. Create a `RayCluster` custom resource (CR) YAML file, specifying the desired state of the Ray cluster, including the `rayVersion`, `enableInTreeAutoscaling`, and other configurations.\n5. Apply the `RayCluster` CR using `kubectl apply -f raycluster.yaml`.\n6. Monitor your Ray application using the Ray Dashboard.\n7. Learn how to configure and manage the Ray Dashboard, Prometheus, Grafana, and Ray Serve logs for monitoring and observability.\n8. Optionally, configure KubeRay autoscaling, TLS authentication, and GPU support.\n\nFor a minimal RayCluster YAML manifest, you can download it using `curl -LO <https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-cluster.gke-bucket.yaml>`.\n\nFor more advanced configurations, refer to the Ray on Kubernetes documentation, including user guides and experimental features.",4199"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4200},4201{4202"question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",4203"sources": [4204"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",4205"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",4206"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",4207"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",4208"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",4209"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",4210"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance",4211"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#annotations-resources-and-task-options",4212"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",4213"https://docs.ray.io/en/master/serve/api/doc/ray.serve.shutdown.html#ray-serve-shutdown",4214"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#custom-optimization-for-dask-dataframe-shuffling",4215"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",4216"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job",4217"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",4218"https://docs.ray.io/en/master/cluster/key-concepts.html#worker-node",4219"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors"4220],4221"document_ids": [42225129,42234524,42246216,42254503,422611596,422714707,42284519,422914500,42306390,423114043,423214507,423313448,42346694,42354510,42365558,42375554238],4239"answer": " Shutting down a raylet process does not directly kill tasks and workers on that node. However, it may lead to the termination of workers if it's due to memory pressure or other reasons. When a raylet process fails, the corresponding node is marked as dead and treated as a node failure in the Ray cluster.",4240"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4241},4242{4243"question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",4244"sources": [4245"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",4246"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",4247"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",4248"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",4249"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",4250"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",4251"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management",4252"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#what-s-the-out-of-memory-error",4253"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",4254"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",4255"https://docs.ray.io/en/master/serve/monitoring.html#profiling-memory",4256"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",4257"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",4258"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#what-is-the-memory-monitor",4259"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#debugging-using-ray-memory",4260"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#failures-due-to-memory-issues"4261],4262"document_ids": [4263547,4264548,4265559,42661005,42671006,42684464,42695010,4270549,42715118,4272562,427313549,42745016,4275557,42765119,42775024,42786064279],4280"answer": " To debug out-of-memory issues in Ray, you can follow these steps:\n\n1. Familiarize yourself with Ray's Memory Management model.\n2. View the per Task and Actor memory usage graph to locate the source of high memory usage.\n3. Use the CLI command `htop` or `ray memory` to get more details about memory usage.\n4. Refer to the documentation on how to address the out of memory issue: <https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html>.\n\nThe documentation to look for this information is: 'Debugging Out of Memory' and 'Memory Management' in the Ray documentation.",4281"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4282},4283{4284"question": "How to use callback in Trainer?",4285"sources": [4286"https://docs.ray.io/en/master/train/examples/transformers/transformers_torch_trainer_basic.html#ray-train-basic-example-for-huggingface-transformers",4287"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",4288"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.transformers.RayTrainReportCallback.html#ray-train-huggingface-transformers-raytrainreportcallback",4289"https://docs.ray.io/en/master/tune/examples/hpo-frameworks.html#tune-hyperparameter-optimization-framework-examples",4290"https://docs.ray.io/en/master/tune/examples/index.html#tune-hyperparameter-optimization-framework-examples",4291"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",4292"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",4293"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Callback.html#ray-tune-callback",4294"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_end.html#ray-train-lightning-raytrainreportcallback-on-init-end",4295"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_init_start.html#ray-train-lightning-raytrainreportcallback-on-init-start",4296"https://docs.ray.io/en/master/ray-air/examples/dolly_lightning_fsdp_finetuning.html#fine-tune-with-ray-torchtrainer",4297"https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#how-to-work-with-callbacks-in-ray-tune",4298"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_batch_start.html#ray-train-lightning-raytrainreportcallback-on-batch-start",4299"https://docs.ray.io/en/master/tune/api/callbacks.html#callback-initialization-and-setup",4300"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_pretrain_routine_start.html#ray-train-lightning-raytrainreportcallback-on-pretrain-routine-start",4301"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.on_batch_end.html#ray-train-lightning-raytrainreportcallback-on-batch-end"4302],4303"document_ids": [43049553,43059165,43068635,43072429,43083796,4309160,43108206,43111405,43128531,43138760,43149934,43152390,43168999,43171204,43189115,431983564320],4321"answer": " In the context provided, a callback is added to the Trainer using the `add_callback` method. Specifically, `RayTrainReportCallback()` is used. If you want to use a custom callback, you can create a class that inherits from `transformers.trainer_callback.TrainerCallback` and override the necessary methods. For example:\n\n```python\nfrom transformers.trainer_callback import TrainerCallback\n\nclass MyTrainReportCallback(TrainerCallback):\n def __init__(self):\n super().__init__()\n self.metrics = {}\n\n def on_train_end(self, args, state, control, **kwargs):\n # Your code here\n pass\n```\n\nThen, you can add this callback to the Trainer using `add_callback`:\n\n```python\ntrainer = Trainer(\n # ...\n compute_metrics=compute_metrics,\n)\ntrainer.add_callback(MyTrainReportCallback())\n```\n\nThe `on_train_end` method is called at the end of training. You can override other methods to perform actions at different stages of training.",4322"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4323},4324{4325"question": "How to provide current working directory to ray?",4326"sources": [4327"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4328"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4329"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",4330"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4331"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable",4332"https://docs.ray.io/en/master/rllib/user-guides.html#rllib-feature-guides",4333"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",4334"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4335"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",4336"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4337"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4338"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-submit",4339"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4340"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",4341"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4342"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli"4343],4344"document_ids": [43456632,43466633,43474262,43481066,43492371,435010537,43511790,4352511,43536562,43546551,43556552,43566596,43576555,43584264,43594385,436046624361],4362"answer": " To provide the current working directory to Ray, you can specify it in the runtime environment when calling `ray.init()`. Here's an example:\n\n```python\nimport os\nimport ray\n\n# Create a directory with some files\nos.makedirs(\"/tmp/runtime_env_working_dir\", exist_ok=True)\nwith open(\"/tmp/runtime_env_working_dir/data.txt\", \"w\") as f:\n f.write(\"Hello, Ray!\")\n\n# Specify the runtime environment with the working directory\nray.init(runtime_env={\"working_dir\": \"/tmp/runtime_env_working_dir\"})\n\n# Now you can access the files in the working directory using relative paths\nwith open(\"data.txt\", \"r\") as f:\n print(f.read())\n```\n\nIn this example, the `working_dir` key in the `runtime_env` dictionary specifies the current working directory to be uploaded to Ray. Once uploaded, you can access the files in the working directory using relative paths.",4363"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4364},4365{4366"question": "how to create an actor instance with parameter?",4367"sources": [4368"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",4369"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",4370"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",4371"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",4372"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#choose-an-ami-with-the-unified-cloudwatch-agent-pre-installed",4373"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",4374"https://docs.ray.io/en/master/ray-core/actors.html#actors",4375"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",4376"https://docs.ray.io/en/master/rllib/actors.html#actors",4377"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",4378"https://docs.ray.io/en/master/ray-core/api/core.html#actors",4379"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",4380"https://docs.ray.io/en/master/ray-core/api/doc/ray.cross_language.java_actor_class.html#ray-cross-language-java-actor-class",4381"https://docs.ray.io/en/master/ray-core/actors.html#actors",4382"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",4383"https://docs.ray.io/en/master/rllib/actors.html#actors"4384],4385"document_ids": [43864876,43875192,43884629,43895391,43906490,439114724,43924469,439314682,439411576,43955391,43964650,43974435,43984916,43994472,440014686,4401115784402],4403"answer": " To create an actor instance with a parameter, you can use the `.remote()` method with arguments to the constructor of the actor class. Here's an example:\n```python\nimport ray\n\n@ray.remote\nclass MyActor:\n def __init__(self, value):\n self.value = value\n\n def get_value(self):\n return self.value\n\n# Create an instance of MyActor with a parameter value of 42\nmy_actor = MyActor.remote(42)\n\n# Call the get_value method on the actor instance\nvalue = ray.get(my_actor.get_value.remote())\n```\nIn this example, we define a simple actor class `MyActor` with a constructor that takes a single parameter `value`. We then create an instance of this class using the `.remote()` method and passing in the value `42`. Finally, we call the `get_value` method on the actor instance using the `.remote()` method and retrieve the result using the `ray.get()` method.",4404"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4405},4406{4407"question": "how to push a custom module to ray which is using by Actor ?",4408"sources": [4409"https://docs.ray.io/en/master/ray-core/api/core.html#actors",4410"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",4411"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.ActorPool.push.html#ray-util-actorpool-push",4412"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples",4413"https://docs.ray.io/en/master/ray-overview/examples.html#ray-examples",4414"https://docs.ray.io/en/master/ray-overview/examples.html@tags=llm.tmp.html#ray-examples",4415"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#memory-profiling-ray-tasks-and-actors",4416"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#custom-resources-accelerator-types-and-more",4417"https://docs.ray.io/en/master/ray-core/actors.html#actors",4418"https://docs.ray.io/en/master/ray-more-libs/actors.html#actors",4419"https://docs.ray.io/en/master/rllib/actors.html#actors",4420"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",4421"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#defining-the-progress-actor",4422"https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",4423"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",4424"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources"4425],4426"document_ids": [44274650,44285443,44294758,4430169,4431196,4432350,4433568,443413459,44354467,443614680,443711575,443814219,44395387,44404488,44414503,4442147074443],4444"answer": " To push a custom module to Ray which is used by an Actor, you can use the `ray.util.ActorPool.push()` function. Here's an example:\n\n1. Define your custom module (let's call it `MyModule`) with the necessary functions or classes.\n2. Import the `ray` module and decorate your actor class with `@ray.remote`.\n3. Instantiate your actor class and add it to an `ActorPool` using the `push()` method.\n4. When creating the `ActorPool`, you can pass in an existing actor or a list of actors.\n\nHere's some sample code demonstrating this:\n\n```python\nimport ray\nfrom ray.util.actor_pool import ActorPool\n\n@ray.remote\nclass MyActor:\n def my_method(self, arg):\n # Use functions from MyModule here\n from my_module import my_function\n result = my_function(arg)\n return result\n\nmy_actor = MyActor.remote()\n\n# Create an ActorPool and push the actor into it\npool = ActorPool([my_actor])\n```\n\nNow you can use the `ActorPool` to manage your actors and call their methods. When you want to push a new actor into the pool, you can simply call `pool.push(new_actor)`.",4445"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4446},4447{4448"question": "how to print ray working directory?",4449"sources": [4450"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4451"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4452"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4453"https://docs.ray.io/en/master/rllib/user-guides.html#rllib-feature-guides",4454"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",4455"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4456"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#submit-the-workload",4457"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#submit-the-workload",4458"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4459"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",4460"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",4461"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",4462"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",4463"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",4464"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#job-logs",4465"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files"4466],4467"document_ids": [44686632,44696551,4470511,447110537,44724262,44731066,44746179,44756528,44766633,44775732,44786157,44796530,44805757,4481519,44826181,448342644484],4485"answer": " To print the working directory in Ray, you can use the following code:\n```python\nimport ray\nprint(ray.global_state.worker.current_resource_key.working_directory)\n```\nHowever, the context provided does not contain information on how to print the Ray working directory specifically. It mainly discusses Ray clusters, file access, and job submission.",4486"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4487},4488{4489"question": "why I can not see log.info in ray log?",4490"sources": [4491"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4492"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",4493"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4494"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",4495"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/tls.html#step-2-create-separate-private-key-and-self-signed-certificate-for-ray-pods",4496"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#example",4497"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",4498"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4499"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",4500"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",4501"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",4502"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",4503"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",4504"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",4505"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",4506"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence"4507],4508"document_ids": [4509444,4510680,45116631,45121005,45135910,45142264,4515445,4516511,4517662,4518530,4519415,4520493,45216360,4522535,4523477,452463594525],4526"answer": " The logs for `log.info` may not be visible in the Ray dashboard because the logs you are looking for might be the driver logs, which are not captured by default in the Ray logging directory. If you are running the Ray job by executing the driver on the head node directly or connecting via Ray Client, the driver logs will not be accessible from the dashboard. Instead, you can view the driver logs in the terminal or Jupyter Notebook output.\n\nIf you still want to access the driver logs from the Ray dashboard, you can submit the Ray job using the Job API, which will make the job logs available from the dashboard. The log file for the driver follows the following format: `job-driver-<job_submission_id>.log`.\n\nAdditionally, you can use Ray's `list_logs` and `get_log` functions to list and retrieve log files, respectively. You can specify the node ID or node IP to get the logs from a specific node. For example, to get a particular log file from a node using the CLI, you can use the following command:\n```css\nray logs cluster gcs_server.out --node-id <NODE_ID>\n```\nReplace `<NODE_ID>` with the actual node ID of the node you want to retrieve the logs from.",4527"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4528},4529{4530"question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",4531"sources": [4532"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",4533"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4534"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",4535"https://docs.ray.io/en/master/data/performance-tips.html#parquet-column-pruning",4536"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",4537"https://docs.ray.io/en/master/data/examples/custom-datasource.html#implementing-a-custom-datasource",4538"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",4539"https://docs.ray.io/en/master/data/user-guide.html#user-guides",4540"https://docs.ray.io/en/master/data/api/doc/ray.data.read_numpy.html#ray-data-read-numpy",4541"https://docs.ray.io/en/master/data/data-internals.html#reading-files",4542"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",4543"https://docs.ray.io/en/master/data/examples/batch_training.html#transforming-a-dataset-in-parallel-using-custom-functions-a-class-anchor-id-transform-ds-a",4544"https://docs.ray.io/en/master/data/examples/batch_training.html#filtering-a-dataset-on-read-a-class-anchor-id-filter-ds-a",4545"https://docs.ray.io/en/master/data/api/doc/ray.data.read_binary_files.html#ray-data-read-binary-files",4546"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",4547"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition"4548],4549"document_ids": [45509210,45517362,45526835,45536818,45547868,45557920,45567865,45576911,45587495,45596874,45609188,45617875,45627870,45637782,45647360,456573514566],4567"answer": " Yes, you can preserve the order of data when using Ray Dataset to read a file. To do this, you need to enable deterministic execution in Ray Datasets by setting the `preserve_order` flag in the DataContext. Here is an example:\n```python\nctx = ray.data.DataContext.get_current()\nctx.execution_options.preserve_order = True\n```\nThis will ensure that the order of the data is preserved when reading a file using Ray Dataset.",4568"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4569},4570{4571"question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",4572"sources": [4573"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",4574"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",4575"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",4576"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",4577"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",4578"https://docs.ray.io/en/master/workflows/basics.html#error-handling",4579"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",4580"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",4581"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",4582"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4583"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure",4584"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4585"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayTaskError.html#ray-exceptions-raytaskerror",4586"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",4587"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",4588"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure"4589],4590"document_ids": [45914377,45924542,45934535,45944542,45954377,45969672,45974527,45984348,45994546,46004657,46014564,46024658,46034779,46044674,46054660,460645634607],4608"answer": " The statement \"Ray will not retry tasks upon exceptions thrown by application code\" means that if a task fails due to an exception or error in the application's code, Ray will not automatically retry the task by default. This behavior can be changed by setting the `retry_exceptions` option to `True` in the `ray.remote()` or `.options()` methods, which will enable retries upon application-level errors. Without this setting, tasks that fail due to application code errors will not be retried and will result in a failed task.",4609"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4610},4611{4612"question": "how do I specify the log directory when starting Ray?",4613"sources": [4614"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4615"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",4616"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",4617"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4618"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",4619"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",4620"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",4621"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#mounting-the-ray-log-directory",4622"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#configuring-logging",4623"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#redirecting-ray-logs-to-stderr",4624"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4625"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",4626"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",4627"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4628"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-rotation",4629"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging"4630],4631"document_ids": [4632511,46336360,46345861,4635511,46361006,46372309,4638680,46395871,4640510,46415880,46424811,4643530,4644542,46454385,4646543,464743844648],4649"answer": " To specify the log directory when starting Ray, you can use the `--temp-dir` or `--logs-dir` option with the `ray start` or `ray.init()` command. Here's an example:\n```css\nray start --temp-dir /path/to/temp/directory --logs-dir /path/to/log/directory\n```\nor\n```python\nray.init(temp_dir='/path/to/temp/directory', logging_dir='/path/to/log/directory')\n```\nNote that the `temp_dir` option will also set the logging directory to the same path.\n\nAlternatively, you can set the `RAY_Temp_DIR` and `RAY_LOG_DIR` environment variables to specify the temporary and logging directories, respectively. For example:\n```bash\nexport RAY_Temp_DIR=/path/to/temp/directory\nexport RAY_LOG_DIR=/path/to/log/directory\nray start\n```\nor\n```bash\nexport RAY_Temp_DIR=/path/to/temp/directory\nexport RAY_LOG_DIR=/path/to/log/directory\npython -c \"import ray; ray.init()\"\n```\nThis will set the temporary and logging directories for the current Ray session.",4650"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4651},4652{4653"question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",4654"sources": [4655"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4656"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4657"https://docs.ray.io/en/master/cluster/cli.html#ray-up",4658"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4659"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4660"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-resources",4661"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4662"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",4663"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",4664"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",4665"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",4666"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",4667"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",4668"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",4669"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4670"https://docs.ray.io/en/master/ray-core/miscellaneous.html#running-large-ray-clusters"4671],4672"document_ids": [46736337,46746328,46755615,4676264,46776310,46786290,46796203,46806392,46816522,4682851,46834193,46846501,468514483,46866411,46876353,468844554689],4690"answer": " To launch a Ray cluster with 10 nodes without setting the minimum worker count as 10, you can create a cluster configuration file (e.g., `config.yaml`) with the following content:\n\n```yaml\ncluster_name: my-ray-cluster\nmax_workers: 9\n\nray.worker.default:\n resources: {\"CPU\": 2}\n node_config:\n azure_arm_parameters:\n vmSize: Standard_D2s_v3\n imagePublisher: microsoft-dsvm\n\ndocker:\n image: \"rayproject/ray-ml:2.0.0\"\n container_name: \"ray_container\"\n\nprovider:\n type: aws\n region: us-west-2\n availability_zone: us-west-2a\n\nauth:\n ssh_user: ubuntu\n```\n\nThen, you can launch the cluster using the Ray CLI:\n\n```bash\n$ ray up -y config.yaml\n```\n\nThis command will start a Ray cluster with a head node and up to 9 worker nodes. The minimum worker count is not explicitly set, so it will be 0 by default. The Ray autoscaler will manage the number of worker nodes based on the workload.\n\nNote that you can adjust the number of worker nodes by changing the `max_workers` parameter in the configuration file.",4691"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4692},4693{4694"question": "how to use ray api to scale up a cluster",4695"sources": [4696"https://docs.ray.io/en/master/cluster/vms/index.html#overview",4697"https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",4698"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",4699"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",4700"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#scaling-with-ray-data",4701"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",4702"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview",4703"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",4704"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",4705"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",4706"https://docs.ray.io/en/master/cluster/vms/getting-started.html#getting-started",4707"https://docs.ray.io/en/master/cluster/cli.html#ray-up",4708"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#best-practices-for-deploying-large-clusters",4709"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",4710"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-3-scaling-up-to-the-full-dataset-with-ray-data",4711"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/gcp.html#start-ray-with-the-ray-cluster-launcher"4712],4713"document_ids": [47146209,47155604,47164644,47176715,47188040,47197980,472014710,47215600,47226363,47236715,47246189,47255615,47266371,4727368,47287980,472965144730],4731"answer": " To scale up a Ray cluster, you can use the `ray up` command which is part of the Ray Cluster Management CLI. This command allows you to create or update a Ray cluster with specific configuration options such as the minimum and maximum number of worker nodes.\n\nAdditionally, within a Ray program, you can use the `ray.autoscaler.sdk.request_resources` function to command the autoscaler to scale the cluster up to a desired size. This function will bypass normal upscaling speed constraints and immediately attempt to scale to the requested resources.\n\nHere's an example of how to use `ray up`:\n\n```\nray up example-full.yaml\n```\n\nAnd here's an example of how to use `ray.autoscaler.sdk.request_resources`:\n\n```python\nimport ray\n\nray.init()\nray.autoscaler.sdk.request_resources(num_cpus=10)\n```\n\nNote that you may need to configure the Ray cluster launcher and your cloud environment (such as GCP) before using these commands.",4732"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4733},4734{4735"question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",4736"sources": [4737"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4738"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4739"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4740"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",4741"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#launching-an-on-premise-cluster",4742"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",4743"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",4744"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",4745"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",4746"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents",4747"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",4748"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#instructions",4749"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-2-check-ports",4750"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#start-ray-with-the-ray-cluster-launcher",4751"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#launching-a-cluster-ray-up",4752"https://docs.ray.io/en/master/cluster/vms/index.html#overview"4753],4754"document_ids": [47556199,4756273,47576203,47586203,47596499,47604193,47615503,47626458,47636470,47646519,47656467,47666104,47676617,47686471,47696212,477062094771],4772"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\n```yaml\ncluster_name: my-cluster\nprovider:\n type: aws\n region: us-west-2\n subnet_id: subnet-01234567890abcdef0\n```\n\nReplace `subnet-01234567890abcdef0` with the actual ID of your desired subnet.\n\nNote: Make sure to have the necessary permissions to access the specified subnet and configure the security group to allow inbound access to the Ray Client port on the head node.",4773"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4774},4775{4776"question": "where I can find HTTP server error code log for Ray serve",4777"sources": [4778"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",4779"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",4780"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",4781"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",4782"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",4783"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",4784"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",4785"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",4786"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",4787"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",4788"https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally",4789"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4790"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",4791"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",4792"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",4793"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions"4794],4795"document_ids": [479613974,479713541,47986631,479913539,480013541,480114022,48025671,48035747,48044694,48055802,480613437,480713517,480813523,48096183,48106531,4811140254812],4813"answer": " The number of non-200 HTTP responses returned by each deployment can be found in the Ray client logs at `/tmp/ray/session_latest/logs` on the head node. Additionally, you can check the Serve page in the Ray dashboard at `http://localhost:8265/#/serve` to see more details about the deployments and their HTTP responses. The Ray Dashboard is accessible at `localhost:8265` in your browser. 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/`.",4814"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4815},4816{4817"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?",4818"sources": [4819"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",4820"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",4821"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",4822"https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",4823"https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",4824"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",4825"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#configuring-and-managing-ray-dashboard",4826"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",4827"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",4828"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#monitoring-cluster-status-ray-dashboard-status",4829"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",4830"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",4831"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",4832"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#dashboard-host",4833"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",4834"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard"4835],4836"document_ids": [48375530,48385528,4839418,484014606,484114604,48422309,48435526,48446562,48456561,48466223,48475543,484813681,48495531,48505935,4851417,4852135144853],4854"answer": " This issue might be caused by the dashboard port not being publicly exposed in your remote Ray cluster. When you start a remote Ray cluster, the dashboard launches on the head node but the dashboard port may not be publicly exposed. You need to set up port forwarding to access the Ray Dashboard from outside the head node.\n\nYou can customize the port for the Ray Dashboard by starting the cluster explicitly with the `--dashboard-port` argument with `ray start` in the command line or passing the `dashboard_port` keyword argument in your call to `ray.init()`.\n\nIf you are using the Ray Cluster Launcher, you can set up port forwarding automatically using the `ray dashboard` command. Run the following command on your local machine, where `cluster.yaml` is the configuration file you used to launch your cluster:\n\n`ray dashboard cluster.yaml`\n\nThis will forward the dashboard port of the cluster over HTTP to your local machine, allowing you to access the Ray Dashboard from your local browser.",4855"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4856},4857{4858"question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",4859"sources": [4860"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4861"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#available-node-types-node-type-name-node-type-max-workers",4862"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4863"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#disabling-the-dashboard",4864"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",4865"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port",4866"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",4867"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#vm-cluster-setup",4868"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",4869"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",4870"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters",4871"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4872"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4873"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",4874"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster",4875"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"4876],4877"document_ids": [4878256,48796292,48806345,48815538,48824193,48835529,48846392,48856522,48866227,48876366,48886367,48896337,48906328,48915557,48925555,489363114894],4895"answer": " In the Ray cluster launcher YAML, `max_workers` includes only worker nodes, not the head node. This is consistent across different types of Ray clusters, including single-node local clusters, VM clusters, and KubeRay clusters. The head node is typically used for cluster management tasks and may run Ray jobs, but it is not included in the `max_workers` count.",4896"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4897},4898{4899"question": "How to update files in working directory ?",4900"sources": [4901"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4902"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",4903"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",4904"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4905"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",4906"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#reference-your-cloudwatch-json-config-files-in-your-cluster-config-yaml",4907"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersCheckpoint.html#ray-train-huggingface-transformerscheckpoint",4908"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4909"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",4910"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4911"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#outdated-function-definitions",4912"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#synchronizing-files-from-the-cluster-ray-rsync-up-down",4913"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",4914"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#common-workflow-syncing-git-branches",4915"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4916"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"4917],4918"document_ids": [49196632,49201775,49211790,49221066,49231775,49246494,49258894,49261068,49276214,49281066,4929619,49306222,49314273,49326225,49336315,493463314935],4936"answer": " To update files in the working directory, you can use the `ray rsync-up` command to upload files to the cluster head node. Here's the syntax:\n```bash\n$ ray rsync_up cluster.yaml '/local/path' '/path/on/cluster'\n```\nReplace `cluster.yaml` with the configuration file for your Ray cluster, `'/local/path'` with the path to the files you want to upload on your local machine, and `'/path/on/cluster'` with the destination path on the cluster head node.\n\nAdditionally, if you want to sync files continuously, you can set `file_mounts_sync_continuously` to `True` in your Ray cluster configuration YAML file. This will ensure that changes to directories in `file_mounts` or `cluster_synced_files` on the head node are synced to the worker nodes continuously.\n\nNote that if you're using Tune, you can access relative file paths in your training function by setting the `RAY_CHDIR_TO_TRIAL_DIR` environment variable to `0`. This will tell Tune not to change the working directory to the trial directory, giving you access to paths relative to the original working directory.",4937"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4938},4939{4940"question": "How I can update working directory file when ray allready initiated ?",4941"sources": [4942"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4943"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",4944"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",4945"https://docs.ray.io/en/master/tune/faq.html#how-do-i-access-relative-filepaths-in-my-tune-training-function",4946"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4947"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#loading-data-in-a-tune-trainable",4948"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",4949"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig",4950"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",4951"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",4952"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",4953"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",4954"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",4955"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",4956"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",4957"https://docs.ray.io/en/master/workflows/key-concepts.html#workflow-execution"4958],4959"document_ids": [49606632,49614385,49624811,49631066,49646632,49652288,49666555,49671790,49681775,49694263,49706633,4971511,49726214,49736157,49744262,497596444976],4977"answer": " To update the working directory file when Ray is already initiated, you can use the `ray.train.get_context().get_trial_dir()` function to get the trial directory and then write your files there. This is because all workers on the same node share the same working directory. However, if you need to access files in the original working directory, you can set the `RAY_CHDIR_TO_TRIAL_DIR=0` environment variable to disable the behavior of changing the working directory. 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.",4978"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"4979},4980{4981"question": "how can I force ray head node to use custom pem file to ssh worker node?",4982"sources": [4983"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4984"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4985"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4986"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4987"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4988"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4989"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4990"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4991"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4992"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",4993"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",4994"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4995"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",4996"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4997"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",4998"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#alternative-approach-ssh-port-forwarding"4999],5000"document_ids": [50016330,50026307,5003261,5004261,50056307,50066340,50076340,5008272,50096318,50106205,5011268,50126314,50136502,50146324,50156353,501666205017],5018"answer": " To force the Ray head node to use a custom PEM file for SSHing into the worker node, you can specify the `ssh_private_key` field in the `auth` section of your Ray configuration file (e.g., `config.yaml`). This field should point to the path of your custom PEM file on the local machine. Here's an example:\n\n```yaml\nauth:\n ssh_user: ubuntu\n ssh_private_key: /path/to/your/custom/key.pem\n```\n\nMake sure to replace `/path/to/your/custom/key.pem` with the actual path to your custom PEM file. Also, ensure that the corresponding public key (`key.pem.pub`) is present in the authorized keys of the worker node.\n\nAfter updating the configuration file, you can start the Ray cluster with the new SSH key by running `ray start --config config.yaml` on the head node.",5019"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5020},5021{5022"question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",5023"sources": [5024"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",5025"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",5026"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",5027"https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",5028"https://docs.ray.io/en/master/ray-core/configure.html#head-node",5029"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",5030"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",5031"https://docs.ray.io/en/master/ray-core/miscellaneous.html#tuning-operating-system-settings",5032"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",5033"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",5034"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",5035"https://docs.ray.io/en/master/ray-core/configure.html#head-node",5036"https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",5037"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",5038"https://docs.ray.io/en/master/ray-contribute/profiling.html#memory-profiling",5039"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance"5040],5041"document_ids": [504214740,50434508,50446375,5045937,50464390,50475709,5048563,50494457,505013870,50514522,50525011,50534390,5054940,505513928,5056939,505758305058],5059"answer": " The GCS server, or Global Control Service, is a centralized metadata server that runs on the head node of a Ray cluster. It manages node membership and actor directory, and provides cluster-level operations like actor, placement groups, and node management. However, it does not have fault tolerance by default, meaning that if the GCS server fails, the entire Ray cluster fails. To make the GCS server fault tolerant, HA Redis can be used.\n\nThe reason why your GCS server might be taking up so much memory on the head node is because it is a memory-demanding system component that runs on the head node. The head node also has additional system processes running on it, such as GCS or the dashboard, which can contribute to increased memory usage. Additionally, if you are running tasks and actors from the head node, it can easily have out-of-memory problems if the head node has the same memory capacity as worker nodes.\n\nTo alleviate memory pressure on the head node, you can consider tuning operating system settings to support a large number of network connections, or adjusting the hardware specification of the head node based on metrics such as CPU usage, memory usage, and network bandwidth usage. You can also consider running tasks and actors on worker nodes instead of the head node.\n\nIf you want to run memory profiling on Ray core components, you can use Jemalloc and override LD\\_PRELOAD on core components. For example, to profile the gcs\\_server, you can set the RAY\\_JEMALLOC\\_LIB\\_PATH and RAY\\_JEMALLOC\\_CONF environment variables.",5060"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5061},5062{5063"question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",5064"sources": [5065"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",5066"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-fake-cluster-1-ray-start",5067"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",5068"https://docs.ray.io/en/master/tune/faq.html#how-can-i-use-tune-with-kubernetes",5069"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",5070"https://docs.ray.io/en/master/tune/faq.html#how-can-i-run-multiple-ray-tune-jobs-on-the-same-cluster-at-the-same-time-multi-tenancy",5071"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",5072"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",5073"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-3-deploy-a-raycluster-custom-resource",5074"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",5075"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",5076"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",5077"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",5078"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",5079"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#troubleshooting",5080"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#solution-1-container-command-recommended"5081],5082"document_ids": [5083467,5084852,50856147,50861061,50876411,50881070,50899510,50906215,50915724,5092254,509310208,50949898,5095466,509610218,50972428,509858475099],5100"answer": " If you're encountering nodes that are \"pending\" for a long time when starting a cluster with `ray up`, you can debug this issue by following these steps:\n\n1. Run `ray status` to view the detailed cluster status. This command will show you the status of each node in the cluster, including any pending nodes or recent failures.\n\n2. Check the autoscaler monitor log (`cat /tmp/ray/session_latest/logs/monitor.*`) for any error messages or warnings that might help explain why the nodes are pending.\n\n3. Ensure that your Kubernetes resources are properly configured and that there are no errors in your YAML file. You can check the status of your pods using `kubectl describe pod <pod-name>` and ensure that your Docker resource limits are set high enough.\n\n4. If you're using Ray Tune with Kubernetes, make sure that you're using shared storage for checkpoints to avoid performance bottlenecks.\n\n5. If you're running multiple Ray Tune jobs on the same cluster at the same time, be aware that this is not officially supported and may cause resource contention.\n\n6. If you're using the Ray Dataset API, be aware that it can consume cluster resources, including CPUs. Make sure that you're not exceeding the available resources in your cluster.\n\n7. If none of the above steps help, you can try restarting the cluster using `ray up --restart-only` to reset the cluster without running any of the installation commands.",5101"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5102},5103{5104"question": "how to install Ray 2.5.1 from github or wheel?",5105"sources": [5106"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",5107"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",5108"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",5109"https://docs.ray.io/en/master/ray-contribute/writing-code-snippets.html#how-to-locally-test-examples",5110"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",5111"https://docs.ray.io/en/master/cluster/vms/getting-started.html#setup",5112"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",5113"https://docs.ray.io/en/master/ray-more-libs/installation.html#installing-from-a-specific-commit",5114"https://docs.ray.io/en/master/ray-overview/installation.html#installing-from-a-specific-commit",5115"https://docs.ray.io/en/master/installation.html#installing-from-a-specific-commit",5116"https://docs.ray.io/en/master/ray-overview/installation.html#from-wheels",5117"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",5118"https://docs.ray.io/en/master/installation.html#from-wheels",5119"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",5120"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#install-ray",5121"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only"5122],5123"document_ids": [5124288,512514545,512663,5127849,51284307,51296192,51306335,513114551,5132291,513366,5134286,513514541,513661,513710168,51385412,51398835140],5141"answer": " To install Ray 2.5.1 from a GitHub commit, first find the commit hash for the desired version on Ray's GitHub repository. Then, use the following pip command to install the Ray wheel for that commit:\n\n```\npip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/{COMMIT_HASH}/ray-2.5.1-{PYTHON_VERSION}-{PYTHON_VERSION}-{OS_VERSION}.whl\n```\n\nReplace `{COMMIT_HASH}` with the actual commit hash, and specify the appropriate Python version (`cp36`, `cp37`, `cp38`, or `cp39`) and OS version (`manylinux1_x86_64`, `manylinux2010_x86_64`, `manylinux2014_x86_64`, or `macosx_10_15_x86_64`) for your system.\n\nNote that Ray 2.5.1 is not available as a pre-built wheel on PyPI, so you cannot install it using `pip install ray==2.5.1`. You must use a GitHub commit or build Ray from source to install version 2.5.1.",5142"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5143},5144{5145"question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",5146"sources": [5147"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",5148"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.__init__.html#ray-rllib-evaluation-worker-set-workerset-init",5149"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",5150"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5151"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-6-runtime-env",5152"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5153"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",5154"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",5155"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#worker-setup-commands",5156"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",5157"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",5158"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",5159"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",5160"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#callbacks-and-custom-metrics",5161"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",5162"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference"5163],5164"document_ids": [5165542,516612971,51674287,51681006,51695684,51701005,517110762,51724720,51736259,51746202,51754288,51764881,517713199,517810768,51799342,518042725181],5182"answer": " To use `worker_setup_hook` in a runtime environment to set up on worker node creation, you can specify it in the `runtime_env` argument when initializing Ray as follows:\n```python\nimport ray\n\ndef logging_setup_func():\n # setup logging here\n\nray.init(runtime_env={\"worker_process_setup_hook\": logging_setup_func})\n```\nThe `worker_process_setup_hook` is an experimental feature that allows you to specify a setup hook that is called after workers start and before tasks and actors are scheduled. The value should be a callable when passed to the Job, Task, or Actor.\n\nNote that this hook is called only once per worker process, so if you need to set up something for each task or actor, you should do it inside the task or actor function instead.\n\nAlso, keep in mind that this feature is experimental, so it may change in future versions of Ray.",5183"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5184},5185{5186"question": "how to use Ray dataset on aws",5187"sources": [5188"https://docs.ray.io/en/master/data/key-concepts.html#loading-data",5189"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",5190"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",5191"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#ingress",5192"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",5193"https://docs.ray.io/en/master/data/overview.html#how-does-ray-data-compare-to-x-for-offline-inference",5194"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",5195"https://docs.ray.io/en/master/data/examples/batch_training.html#creating-a-dataset-a-class-anchor-id-create-ds-a",5196"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",5197"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",5198"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",5199"https://docs.ray.io/en/master/data/key-concepts.html#datasets",5200"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes",5201"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",5202"https://docs.ray.io/en/master/ray-core/using-ray-with-jupyter.html#working-with-jupyter-notebooks-jupyterlab",5203"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images"5204],5205"document_ids": [52066732,52078006,52086839,52096100,52106467,52116842,52129188,52137865,52146935,52156835,52166467,52176731,52186809,52196779,52204419,522173625222],5223"answer": " To use Ray dataset on AWS, you can read data from an S3 bucket using the `ray.data.read_csv` function. Here's an example:\n```python\nimport ray\n\nds = ray.data.read_csv(\"s3://my-bucket/my-data.csv\")\n```\nYou can also use other file formats supported by Arrow, such as Parquet, by specifying the corresponding function, like `ray.data.read_parquet`.\n\nBefore running the above code, make sure you have started a Ray cluster on AWS using the Ray cluster launcher with the AWS Python SDK. You can follow the guide on \"Launching Ray Clusters on AWS\" to set up the cluster.\n\nOnce you have created the dataset, you can perform various operations on it, such as data manipulation, ML model training, and inference. Ray Datasets support many different data sources and formats, and they can represent data collections that exceed a single machine's memory.",5224"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5225},5226{5227"question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",5228"sources": [5229"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5230"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling",5231"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",5232"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5233"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5234"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",5235"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",5236"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",5237"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",5238"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",5239"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",5240"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.repartition.html#ray-data-dataset-repartition",5241"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-data",5242"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",5243"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5244"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling"5245],5246"document_ids": [52477081,52486864,52497395,52501006,52511005,52529209,52539207,52546795,52558046,525610017,52576876,52587352,52596859,52607796,52617085,526292095263],5264"answer": " To avoid shuffling of your dataset during `ray.data.map_batches()`, you can set `local_shuffle_buffer_size` to a value that achieves sufficient randomness for your use case. This will shuffle a subset of rows during iteration, which is more performant than a full global shuffle.\n\nHere's an example:\n\n```python\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/sms_spam_collection_subset.txt\")\n\n# Set local_shuffle_buffer_size to a value that achieves sufficient randomness\nds = ds.map_batches(preprocess_function, batch_format=\"pandas\", local_shuffle_buffer_size=10000)\n```\n\nThis will shuffle up to 10,000 rows during iteration, which should be faster than a full global shuffle. Note that this is not a true global shuffle, but it can still provide sufficient randomness for many use cases.",5265"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5266},5267{5268"question": "Is the order of the input data preserved after a map_batches operation?",5269"sources": [5270"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",5271"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5272"https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms",5273"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",5274"https://docs.ray.io/en/master/data/performance-tips.html#deterministic-execution",5275"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait",5276"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5277"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",5278"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",5279"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5280"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",5281"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5282"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference",5283"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5284"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",5285"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#model-inference"5286],5287"document_ids": [52887640,52897081,52906813,52917289,52926833,52934748,52947091,52957800,52967738,52977085,52987737,52997088,53007988,53017082,530210266,530380705304],5305"answer": " Yes, the order of the input data is preserved after a `map_batches` operation. This is stated in the context: \"The order of the blocks in the datasets is preserved, as is the relative ordering between the datasets passed in the argument list.\"",5306"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5307},5308{5309"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?",5310"sources": [5311"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",5312"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",5313"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",5314"https://docs.ray.io/en/master/serve/tutorials/gradio-integration.html#scaling-your-gradio-app-with-ray-serve",5315"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging",5316"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#verify-and-save-results",5317"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",5318"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",5319"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",5320"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",5321"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",5322"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",5323"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",5324"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",5325"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#rayservice-troubleshooting",5326"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-2-check-rayservice-cr-status"5327],5328"document_ids": [53294349,53304527,533113517,533214427,5333585,53347963,53354348,53365671,53374660,533813974,5339595,53404694,53414658,534213469,53435661,534456645345],5346"answer": " To get more detailed errors or logs when Ray Serve returns a generic internal service error due to an internal failure, you can:\n\n1. Check Python-level exceptions: Ray surfaces application-level failures as Python-level exceptions. When a task on a remote worker or actor fails due to a Python-level exception, Ray wraps the original exception in a RayTaskError and stores this as the task\u2019s return value. This wrapped exception will be thrown to any worker that tries to get the result.\n\n2. Use Ray logging: Ray uses Python\u2019s standard logging module with a logger named \"ray.serve\". By default, logs are emitted from actors both to stderr and on disk on each node at /tmp/ray/session_latest/logs/serve/. In development, logs are streamed to the driver Ray program, so it\u2019s convenient to keep the driver running while debugging.\n\n3. Enable detailed progress reporting: For Ray Data, you can run `ray.data.DataContext.get_current().execution_options.verbose_progress = True` for detailed progress reporting.\n\n4. Check the RayServe script: Ensure that your Ray Serve script is correct to avoid any issues. It is recommended to test your Ray Serve script locally or in a RayCluster before deploying it to a RayService.\n\n5. Fault tolerance: Ray provides mechanisms to handle application-level failures, such as catching errors, retrying failed code, and handling misbehaving code. Ray also provides mechanisms to automatically recover from internal system-level failures like node failures.\n\n6. RayService troubleshooting: If the issue pertains to the data plane, specifically your Ray Serve scripts or Ray Serve configurations, you can check the status and events of the RayService CR to see if there are any errors using `kubectl describe rayservice $RAYSERVICE_NAME -n $YOUR_NAMESPACE`.",5347"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5348},5349{5350"question": "how do i track an uncaught exception in ray serve",5351"sources": [5352"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",5353"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",5354"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",5355"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#post-mortem-debugging",5356"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5357"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5358"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",5359"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",5360"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.exception.RayStateApiException.html#ray-util-state-exception-raystateapiexception",5361"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",5362"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",5363"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",5364"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",5365"https://docs.ray.io/en/master/ray-observability/getting-started.html#ray-dashboard",5366"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_placement_group.html#ray-util-state-get-placement-group",5367"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect"5368],5369"document_ids": [537013974,53714694,53724527,5373585,53741006,53751005,53764674,53775747,5378778,53794658,53804660,53814675,5382696,5383416,5384701,538556715386],5387"answer": " To track an uncaught exception in Ray Serve, you can use the RayTaskError mechanism to catch and handle application-level failures. When a task on a remote worker or actor fails due to a Python-level exception, Ray wraps the original exception in a RayTaskError. This wrapped exception will be thrown to any worker that tries to get the result.\n\nAdditionally, Ray provides a web-based dashboard for monitoring and debugging Ray applications, including Ray Serve. The dashboard allows you to track the performance of applications and troubleshoot issues, which can help you identify and handle uncaught exceptions.\n\nHere's an example of how to catch and handle a RayTaskError:\n\n```python\nimport ray\n\n@ray.remote\ndef f():\n raise Exception(\"the real error\")\n\ntry:\n result = ray.get(f.remote())\nexcept ray.exceptions.RayTaskError as e:\n print(\"An uncaught exception occurred:\", e.original_exception)\n```\n\nAnd here's how to access the Ray dashboard:\n\n```bash\nkubectl port-forward svc/rayservice-sample-head-svc --address 0.0.0.0 8265:8265\n```\n\nThen open a web browser and navigate to `http://localhost:8265/#/serve`.",5388"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5389},5390{5391"question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",5392"sources": [5393"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",5394"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",5395"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",5396"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",5397"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",5398"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",5399"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#debugging",5400"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",5401"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#redirecting-worker-logs-to-the-driver",5402"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",5403"https://docs.ray.io/en/master/serve/monitoring.html#json-logging-format",5404"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",5405"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",5406"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",5407"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",5408"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3"5409],5410"document_ids": [541113517,5412680,5413445,541413517,541513547,5416445,54174323,541813524,5419519,5420444,542113525,5422493,542313523,5424415,5425411,54265335427],5428"answer": " To view the logs emitted by your Ray Serve endpoint in the Ray cluster, you can look for them in the logs directory on each node at `/tmp/ray/session_latest/logs/serve/`. This includes both system-level logs and access logs produced from within deployment replicas. In development, logs are streamed to the driver Ray program. If you're running the driver script on the head node or using Ray Client, you can see the logs in the terminal or Jupyter Notebook output. Task and Actor logs are also accessible from the Task and Actor table view in the Ray dashboard by clicking the \"Log\" button.",5429"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5430},5431{5432"question": "where can I see logs for a failed ray serve deployment",5433"sources": [5434"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",5435"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",5436"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",5437"https://docs.ray.io/en/master/serve/monitoring.html#filtering-logs-with-loki",5438"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",5439"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",5440"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#inspect-deeper",5441"https://docs.ray.io/en/master/serve/develop-and-deploy.html#monitor-ray-serve",5442"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",5443"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",5444"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",5445"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",5446"https://docs.ray.io/en/master/serve/monitoring.html#ray-dashboard",5447"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",5448"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",5449"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods"5450],5451"document_ids": [545213523,54536631,5454444,545513536,545613523,545713960,545813658,545913442,546013517,546113960,546213537,546313521,546413514,546513959,5466445,546756655468],5469"answer": " The logs for a failed Ray serve deployment can be found in the following locations:\n\n1. For the Kuberay operator logs, run `kubectl logs -f <kuberay-operator-pod-name>`.\n2. For the controller log and deployment log, check `/tmp/ray/session_latest/logs/serve/` in both the head node pod and worker node pod.\n3. You can also access the logs directly by executing `kubectl exec -it $RAY_POD -n $YOUR_NAMESPACE -- bash` and checking the logs under `/tmp/ray/session_latest/logs/serve/`.\n\nAdditionally, you can view the Ray Serve dashboard at `http://localhost:8265/#/serve` for more details on the deployments and replicas.",5470"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5471},5472{5473"question": "How to take a subset of a Ray Dataset?",5474"sources": [5475"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart",5476"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples",5477"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",5478"https://docs.ray.io/en/master/tune/getting-started.html#getting-started-with-ray-tune",5479"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#random-shuffling",5480"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-aware-scheduling",5481"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",5482"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.TransformersTrainer.html#ray-train-huggingface-transformerstrainer",5483"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#splitting-datasets",5484"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#loading-data",5485"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-rows",5486"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray",5487"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-ml-libraries",5488"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",5489"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-preprocess-data-for-training",5490"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images"5491],5492"document_ids": [54939180,54948006,54956835,5496986,54979209,54985042,549910017,55008610,55019198,55029188,55036901,5504143,55056756,550610013,55078155,550873625509],5510"answer": " To take a subset of a Ray Dataset, you can use the `ray.data.Dataset.take()` method, which returns the first n rows of the dataset. Here's an example:\n```python\nsubset_dataset = train_dataset.take(100) # Returns the first 100 rows of the train_dataset\n```\nAlternatively, if you want to take a random subset of the dataset, you can use the `ray.data.Dataset.random_shuffle()` method to shuffle the dataset and then use `take()` to get the first n rows.\n```python\ntrain_dataset = train_dataset.random_shuffle()\nsubset_dataset = train_dataset.take(100) # Returns a random subset of 100 rows from the train_dataset\n```\nNote that `take()` returns a new dataset object, and does not modify the original dataset.",5511"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5512},5513{5514"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.",5515"sources": [5516"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",5517"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#function-api-checkpointing",5518"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",5519"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5520"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",5521"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",5522"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints",5523"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#getting-data-out-of-tune-using-checkpoints-other-artifacts",5524"https://docs.ray.io/en/master/tune/tutorials/overview.html#tune-feature-guides",5525"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#tunecontroller",5526"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment",5527"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",5528"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",5529"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#example-running-tune-with-cloud-storage",5530"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",5531"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example"5532],5533"document_ids": [55342226,55352217,55362253,55371006,55382309,55392350,55402212,55412299,55422270,55432243,55442255,55452348,55462350,55472347,55482310,554924395550],5551"answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `Tuner.restore()` function. This function can restore an experiment from a local directory or a cloud storage location. If you used cloud checkpointing, you can restore from the S3 bucket path.\n\nHere's an example of how to restore an experiment from a cloud storage location:\n\n```python\nfrom ray import tune\n\ntuner = tune.Tuner.restore(\n \"s3://my-checkpoints-bucket/path/my-tune-exp\",\n trainable=my_trainable,\n resume_errored=True\n)\ntuner.fit()\n```\n\nReplace `my_trainable` with your actual trainable function and update the S3 bucket path with your own path. The `resume_errored=True` argument allows you to resume an experiment that was previously stopped due to an error.\n\nAfter restoring the experiment, you can access the results and checkpoints by calling `Tuner.get_results()` to receive the `ResultGrid` object, and then proceed as needed.",5552"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5553},5554{5555"question": "How can I kill a \"detached\" Actor ?",5556"sources": [5557"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",5558"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",5559"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",5560"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",5561"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill",5562"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5563"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",5564"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",5565"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",5566"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",5567"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",5568"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-fault-tolerance",5569"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",5570"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",5571"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.cleanup.html#ray-tune-trainable-cleanup",5572"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-creator-failure"5573],5574"document_ids": [55754633,55764859,55774631,55782157,55794859,55801006,55814615,55824613,55834579,55844620,55854848,55864561,55874618,55884614,55892157,559045775591],5592"answer": " To kill a detached actor in Ray, you can use the `ray.kill()` function, passing the actor's handle as an argument. For example:\n\n```python\nray.kill(detached_actor)\n```\n\nThis will forcefully terminate the actor, interrupting any running tasks and not running any atexit handlers installed in the actor. If you want to let pending tasks finish before killing the actor, you can call `actor.__ray_terminate__.remote()` instead.\n\nKeep in mind that after calling `ray.kill()`, subsequent calls to get the actor's handle via `ray.get_actor()` will fail. Also, note that detached actors are not garbage-collected by Ray, so you must manually destroy them once they are no longer needed.",5593"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5594},5595{5596"question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",5597"sources": [5598"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",5599"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",5600"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5601"https://docs.ray.io/en/master/tune/faq.html#how-can-i-continue-training-a-completed-tune-experiment-for-longer-and-with-new-configurations-iterative-experimentation",5602"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5603"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5604"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",5605"https://docs.ray.io/en/master/serve/api/index.html#serve-run",5606"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",5607"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",5608"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#set-up-ray-a-name-setup-a",5609"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.init_method.html#ray-train-torch-torchconfig-init-method",5610"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#dependencies-per-deployment",5611"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",5612"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",5613"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv"5614],5615"document_ids": [56166614,56179343,56184811,56191073,56201005,56211006,56224285,562313990,5624542,56254253,562610295,56279118,562813850,562913684,56306613,563147125632],5633"answer": " To set environment variables in `ray.init()`, you can include the `env_vars` option in the `runtime_env` argument. Here's an example:\n\n```python\nray.init(\n runtime_env={\n \"pip\": [\"toolz\", \"requests\"],\n \"env_vars\": {\n \"foo\": \"foo\"\n }\n }\n)\n```\n\nIn this example, the environment variable `foo` is set to the value `\"foo\"` when initializing Ray. You can replace `\"foo\": \"foo\"` with any other environment variable you'd like to set.",5634"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5635},5636{5637"question": "What is the rest api for getting the head node id?",5638"sources": [5639"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",5640"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.node_id.html#ray-runtime-context-runtimecontext-node-id",5641"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_node.html#ray-util-state-get-node",5642"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",5643"https://docs.ray.io/en/master/cluster/running-applications/job-submission/rest.html#ray-jobs-rest-api",5644"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",5645"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",5646"https://docs.ray.io/en/master/ray-core/miscellaneous.html#node-information",5647"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",5648"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",5649"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",5650"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobDetails.html#ray-job-submission-jobdetails",5651"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",5652"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",5653"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",5654"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions"5655],5656"document_ids": [5657721,56584731,5659754,56606407,56616571,56626341,56634909,56644450,566514324,566614202,5667492,56686657,5669493,56706407,5671720,5672140225673],5674"answer": " The REST API for getting the head node ID is not explicitly stated in the provided context. However, you can use the `ray.util.state.get_node` function, which takes a node ID as a parameter, to get a node by its ID. To get the head node ID specifically, you can use the function `ray.util.state.list_nodes` to get a list of nodes and then select the head node from the list.\n\nHere is an example of how you can use these functions to get the head node ID:\n\n```\nnodes = ray.util.state.list_nodes()\nfor node in nodes:\n if node.is_head_node:\n head_node_id = node.node_id\n break\n\nhead_node = ray.util.state.get_node(head_node_id)\n```\n\nAlternatively, you can use the `ray.runtime_context.RuntimeContext.get_node_id` function to get the current node ID for the worker or driver. If the current node is the head node, this will return the head node ID.\n\n```\nhead_node_id = ray.runtime_context.RuntimeContext.get_node_id()\nhead_node = ray.util.state.get_node(head_node_id)\n```\n\nNote that the `ray.runtime_context.RuntimeContext.get_node_id` function is deprecated and may be removed in future Ray releases. It is recommended to use the `ray.util.state.get_node` function instead.",5675"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5676},5677{5678"question": "how to rerun a canceled ray task",5679"sources": [5680"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",5681"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",5682"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",5683"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",5684"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",5685"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",5686"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",5687"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",5688"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",5689"https://docs.ray.io/en/master/ray-more-libs/actors.html#cancelling-actor-tasks",5690"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks",5691"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",5692"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",5693"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",5694"https://docs.ray.io/en/master/rllib/actors.html#cancelling-actor-tasks",5695"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks"5696],5697"document_ids": [56984897,56994375,57004550,57015427,57024535,57034492,570414702,57054494,570611591,570714703,57084497,570911592,57104894,57114542,571211590,571344925714],5715"answer": " You cannot rerun a canceled Ray task as cancelled tasks aren't retried in Ray. Instead, you can create a new task with the same function and parameters. When trying to get the result of a cancelled task using `ray.get`, it raises a `TaskCancelledError`. Cancellation in Ray is best-effort and doesn't guarantee immediate termination of the task.",5716"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5717},5718{5719"question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",5720"sources": [5721"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",5722"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.sigopt.SigOptSearch.html#ray-tune-search-sigopt-sigoptsearch",5723"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.hyperopt.HyperOptSearch.set_max_concurrency.html#ray-tune-search-hyperopt-hyperoptsearch-set-max-concurrency",5724"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#how-to-limit-concurrency-in-tune",5725"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5726"https://docs.ray.io/en/master/ray-core/actors/async_api.html#setting-concurrency-in-async-actors",5727"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.zoopt.ZOOptSearch.set_max_concurrency.html#ray-tune-search-zoopt-zooptsearch-set-max-concurrency",5728"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ConcurrencyLimiter.html#ray-tune-search-concurrencylimiter",5729"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.html#ray-tune-search-optuna-optunasearch",5730"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.skopt.SkOptSearch.set_max_concurrency.html#ray-tune-search-skopt-skoptsearch-set-max-concurrency",5731"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.bayesopt.BayesOptSearch.set_max_concurrency.html#ray-tune-search-bayesopt-bayesoptsearch-set-max-concurrency",5732"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.optuna.OptunaSearch.set_max_concurrency.html#ray-tune-search-optuna-optunasearch-set-max-concurrency",5733"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.ax.AxSearch.set_max_concurrency.html#ray-tune-search-ax-axsearch-set-max-concurrency",5734"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.dragonfly.DragonflySearch.set_max_concurrency.html#ray-tune-search-dragonfly-dragonflysearch-set-max-concurrency",5735"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.Searcher.set_max_concurrency.html#ray-tune-search-searcher-set-max-concurrency",5736"https://docs.ray.io/en/master/serve/api/doc/ray.serve.batch.html#ray-serve-batch"5737],5738"document_ids": [57397738,57402072,57411654,57422406,57431005,57444606,57451340,57461464,57471867,57481547,57491597,57501610,57511543,57521434,57531712,5754140875755],5756"answer": " To set the maximum number of parallel concurrent scheduled tasks in `map_batches`, you can use the `ConcurrencyLimiter` class provided by Ray Tune. This class allows you to limit the number of trials that can run concurrently in a search algorithm.\n\nHere's an example of how you can use `ConcurrencyLimiter` to set the maximum number of concurrent tasks in `map_batches`:\n\n```python\nfrom ray.tune.search import ConcurrencyLimiter, HyperOptSearch\n\n# Create the search algorithm\nsearch_alg = HyperOptSearch()\n\n# Set the maximum number of concurrent tasks\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=<your_max_concurrent_trials>)\n\n# Use the search algorithm with map_batches\nds.map_batches(lambda batch: ..., search_alg=search_alg)\n```\n\nIn this example, replace `<your_max_concurrent_trials>` with the maximum number of concurrent tasks you want to allow.\n\nNote that the `max_concurrent` parameter of `ConcurrencyLimiter` specifies the maximum number of concurrent trials that can be run, not the maximum number of tasks that can be executed in parallel. However, since each trial in `map_batches` corresponds to a single task, you can use `max_concurrent` to limit the number of tasks that can run concurrently.",5757"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5758},5759{5760"question": "How do I get the number of cpus from ray cluster?",5761"sources": [5762"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",5763"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#num-cpus",5764"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#step-6-profile-using-ray-dashboard",5765"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",5766"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",5767"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",5768"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",5769"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",5770"https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",5771"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",5772"https://docs.ray.io/en/master/data/performance-tips.html#tuning-read-parallelism",5773"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",5774"https://docs.ray.io/en/master/tune/api/doc/ray.tune.execution.placement_groups.PlacementGroupFactory.head_cpus.html#ray-tune-execution-placement-groups-placementgroupfactory-head-cpus",5775"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",5776"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5777"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-2-sharing-the-ray-cluster-across-tests-if-possible"5778],5779"document_ids": [578010003,57815936,57826049,57836411,5784622,57851006,57866504,57875413,57884380,57896013,57906815,57915693,57922112,5793614,57944818,579553995796],5797"answer": " To get the number of CPUs in a Ray cluster, you can use the `ray.cluster_resources()` function in Python. This function returns a dictionary containing the resources of the cluster, including the number of CPUs, which can be accessed using the key 'CPU'. For example:\n\n`num_cpus = ray.cluster_resources()['CPU']`\n\nThis will give you the total number of CPUs in the Ray cluster.",5798"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5799},5800{5801"question": "How to use the exclude option to the runtime_env",5802"sources": [5803"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",5804"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",5805"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",5806"https://docs.ray.io/en/master/data/api/doc/ray.data.preprocessors.Concatenator.html#ray-data-preprocessors-concatenator",5807"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",5808"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",5809"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",5810"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.get_runtime_env_string.html#ray-runtime-context-runtimecontext-get-runtime-env-string",5811"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-6-runtime-env",5812"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.runtime_env.html#ray-job-submission-jobinfo-runtime-env",5813"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",5814"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",5815"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.runtime_env.html#ray-runtime-context-runtimecontext-runtime-env",5816"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",5817"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",5818"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_runtime_envs.html#ray-util-state-list-runtime-envs"5819],5820"document_ids": [58214661,58224826,582313856,58247445,5825269,58266315,58274712,58284912,58295684,58306665,58314291,58324764,58334770,58344319,58354308,58367075837],5838"answer": " To use the `exclude` option with the runtime environment in Ray, you can create a `RuntimeEnv` object and modify its configuration using the `set_runtime_env` method in the `JobConfig` class. However, the `exclude` option is not explicitly mentioned in the provided context.\n\nThe `exclude` option is used in the context of the `concat_columns` function, not the `RuntimeEnv` configuration. It is used to exclude specific columns from concatenation.\n\nIf you want to exclude certain packages or directories when using `RuntimeEnv`, you can use the `rsync_exclude` option to specify patterns for files to exclude when running rsync up or rsync down.\n\nHere's an example of using `rsync_exclude`:\n\n```python\nray.runtime_env.RuntimeEnv(\n rsync_exclude=[\n \"**/__pycache__\",\n \"**/.pytest_cache\",\n ],\n ...\n)\n```\n\nIn this example, the `rsync_exclude` option is used to exclude `__pycache__` and `.pytest_cache` directories when syncing files between nodes.",5839"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5840},5841{5842"question": "show a map batch example with batch_format",5843"sources": [5844"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5845"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.policy.sample_batch.SampleBatch.split_by_episode.html#ray-rllib-policy-sample-batch-samplebatch-split-by-episode",5846"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",5847"https://docs.ray.io/en/master/data/working-with-images.html#performing-inference-on-images",5848"https://docs.ray.io/en/master/data/batch_inference.html#quickstart",5849"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",5850"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",5851"https://docs.ray.io/en/master/data/batch_inference.html#configuring-batch-size",5852"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.flat_map.html#ray-data-dataset-flat-map",5853"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5854"https://docs.ray.io/en/master/rllib/rllib-offline.html#example-converting-external-experiences-to-batch-format",5855"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",5856"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",5857"https://docs.ray.io/en/master/data/api/doc/ray.data.block.BlockAccessor.to_batch_format.html#ray-data-block-blockaccessor-to-batch-format",5858"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.default_batch_format.html#ray-data-dataset-default-batch-format",5859"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference"5860],5861"document_ids": [58627081,586313299,586410266,58656727,58666941,58676981,586814727,58696956,58707640,58717091,587210622,58736790,58747088,58757384,58767791,587779885878],5879"answer": " Here's an example of using `map_batches()` with `batch_format` set to \"pandas\":\n\n```python\nimport pandas as pd\nimport ray\n\nclass PredictCallable:\n def __call__(self, batch):\n # Input validation and preprocessing\n # ...\n\n # Perform predictions\n preds = model.predict(batch)\n\n # Postprocessing and result formatting\n result = preds.to_dict('records')\n return pd.DataFrame(result)\n\nds = ray.data.Dataset(...) # Your dataset here\n\npreds = ds.repartition(100).map_batches(\n PredictCallable(),\n batch_size=4,\n batch_format=\"pandas\",\n compute=ray.data.ActorPoolStrategy(),\n num_gpus=1,\n)\n\n# View the generated text after map_batches is done\npreds.take_all()\n```\n\nThis example defines a `PredictCallable` class that takes a batch, preprocesses it, performs predictions, and then postprocesses the results. The `map_batches()` function is then used to apply this class to the dataset, with `batch_format` set to \"pandas\". After the function is done, you can view the generated text using `take_all()`.",5880"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5881},5882{5883"question": "how to find local ray address",5884"sources": [5885"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",5886"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5887"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#ray-dashboard",5888"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",5889"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5890"https://docs.ray.io/en/master/tune/examples/ml-frameworks.html#examples-using-ray-tune-with-ml-frameworks",5891"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#ray-dashboard",5892"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",5893"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",5894"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy",5895"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",5896"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-core",5897"https://docs.ray.io/en/master/ray-core/configure.html#driver-options",5898"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",5899"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_placement_groups.html#ray-util-state-list-placement-groups",5900"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_objects.html#ray-util-state-list-objects"5901],5902"document_ids": [59036552,59044817,59056531,59066552,59074817,59082799,59096183,59104187,59114818,591213988,591313974,59145413,59154409,5916739,5917772,59187575919],5920"answer": " To find the local Ray address, you can look for the Ray dashboard address, which is displayed in the output when you start a local Ray cluster. The default address for the Ray dashboard is <http://127.0.0.1:8265>. Alternatively, you can find the Ray cluster address by checking the environment variable RAY\\_ADDRESS, or by checking the address of the latest cluster started. If a concrete address is not provided, Ray will try to find an existing Ray instance to connect to.\n\nYou can also set the RAY\\_ADDRESS environment variable to the address of your Ray cluster to tell the Ray Jobs CLI how to find your Ray Cluster. For example:\n\n$ export RAY\\_ADDRESS=\"http://127.0.0.1:8265\"\n\nAlternatively, you can pass the --address flag explicitly to each Ray Jobs CLI command, or prepend each command with RAY\\_ADDRESS=<address>.\n\nFor example:\n\n$ RAY\\_ADDRESS=http://127.0.0.1:8265 ray job submit ...\n\nor\n\n$ ray job submit --address=http://127.0.0.1:8265 ...\n\nI hope this helps! Let me know if you have any other questions.",5921"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5922},5923{5924"question": "How to start ray cluster on multiple node via CLI?",5925"sources": [5926"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider",5927"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",5928"https://docs.ray.io/en/master/cluster/cli.html#cluster-management-cli",5929"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",5930"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",5931"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#launching-an-on-premise-cluster",5932"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-the-head-node",5933"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",5934"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#manually-set-up-a-ray-cluster",5935"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",5936"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",5937"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",5938"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-ray-with-the-ray-cluster-launcher",5939"https://docs.ray.io/en/master/ray-core/cluster/index.html#ray-clusters-overview",5940"https://docs.ray.io/en/master/cluster/getting-started.html#ray-clusters-overview",5941"https://docs.ray.io/en/master/ray-more-libs/cluster/index.html#ray-clusters-overview"5942],5943"document_ids": [59446203,59456616,59465605,59474186,594813677,59496499,59506501,59514193,59526500,59536510,59546622,59556206,59566509,59574644,59585600,5959147105960],5961"answer": " To start a Ray cluster on multiple nodes via CLI, you can use the `ray up` command with a configuration file that specifies the head node and worker nodes. Here's an example of how to do this:\n\n1. Create a configuration file (e.g. `config.yaml`) that specifies the head node and worker nodes. Here's an example of what this file might look like:\n```\nhead_node:\n # Replace these placeholders with the actual IP address and SSH user of your head node\n address: <head_node_ip>\n",5962"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"5963},5964{5965"question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",5966"sources": [5967"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",5968"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",5969"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#fine-tune-the-model",5970"https://docs.ray.io/en/master/tune/api/trainable.html#advanced-resource-allocation",5971"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",5972"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",5973"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#model-fine-tuning",5974"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",5975"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#no-speedup",5976"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",5977"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",5978"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",5979"https://docs.ray.io/en/master/ray-air/examples/lightgbm_example.html#training-a-model-with-distributed-lightgbm",5980"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",5981"https://docs.ray.io/en/master/ray-air/examples/xgboost_example.html#training-a-model-with-distributed-xgboost",5982"https://docs.ray.io/en/master/tune/faq.html#what-are-all-these-extra-outputs"5983],5984"document_ids": [59855068,5986625,598710218,59881159,5989468,59905068,59919397,59926879,5993622,59949901,59951113,5996468,59979898,599810384,59999882,600010226001],6002"answer": " The Ray tuner shows \"running\" but CPU usage is almost 0% because Ray actors and tasks occupy 0 CPU after they are created, and Ray Tune tries to fully utilize cluster CPUs by default. 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.",6003"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6004},6005{6006"question": "should the Ray head node and all workers have the same object store memory size allocated?",6007"sources": [6008"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",6009"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",6010"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",6011"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#head-node-commands",6012"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#head-node-commands",6013"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",6014"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",6015"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",6016"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",6017"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster",6018"https://docs.ray.io/en/master/cluster/key-concepts.html#head-node",6019"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",6020"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",6021"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",6022"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#head-node-commands",6023"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts"6024],6025"document_ids": [60266394,60275109,6028563,60296441,60306444,60316446,60325013,60336437,60346446,6035855,60365557,603714483,6038560,60396375,60406441,604150146042],6043"answer": " Yes, it is recommended to have the same object store memory size allocated for the Ray head node and all worker nodes. This is because the Ray head node has more memory-demanding system components and can easily have out-of-memory problems if the same number of tasks and actors are executed from a head node. The default value for object store memory is 0.3 \\* physical memory of the node, but it can be manually overridden based on application needs.",6044"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6045},6046{6047"question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",6048"sources": [6049"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",6050"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",6051"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",6052"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#launching-an-on-premise-cluster",6053"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",6054"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#servicetype-ingresses",6055"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",6056"https://docs.ray.io/en/master/serve/api/index.html#serve-start",6057"https://docs.ray.io/en/master/serve/architecture.html#how-does-serve-ensure-horizontal-scalability-and-availability",6058"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",6059"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",6060"https://docs.ray.io/en/master/serve/develop-and-deploy.html#build-serve-config-files-for-production-deployment",6061"https://docs.ray.io/en/master/serve/architecture.html#high-level-view",6062"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.ProxyLocation.HeadOnly.html#ray-serve-config-proxylocation-headonly",6063"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",6064"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout"6065],6066"document_ids": [606714022,606814055,606914025,60706499,60715543,60725944,607314288,607413994,607513476,607613958,607714287,607813439,607913465,608014163,608114045,6082137956083],6084"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* `Disabled`: don't run proxies at all\n* `HeadOnly`: only run a single proxy on the head node\n* `EveryNode`: run a proxy on every node in the cluster that has at least one replica actor (this is the default)\n\nYou can set this configuration using the `ray.serve.config.ProxyLocation.HeadOnly` or `ray.serve.config.ProxyLocation.EveryNode` in your Ray Serve configuration.",6085"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6086},6087{6088"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?",6089"sources": [6090"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",6091"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",6092"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",6093"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",6094"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",6095"https://docs.ray.io/en/master/serve/tutorials/batch.html#deploy-the-deployment",6096"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-grafana-host-location",6097"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embedding-grafana-visualizations-into-ray-dashboard",6098"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-11-embed-grafana-panels-in-ray-dashboard",6099"https://docs.ray.io/en/master/cluster/metrics.html#simplist-setting-up-grafana-with-ray-provided-configurations",6100"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",6101"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-10-access-grafana",6102"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster",6103"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-prometheus-host-location",6104"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#dashboard-host",6105"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#step-4-install-a-raycluster"6106],6107"document_ids": [61085544,61096061,61105543,61115543,61125544,611314606,61145547,61155545,61166086,61175597,61186084,61196085,61206058,61215546,61225935,612360626124],6125"answer": " Given that Prometheus and Grafana are not running on your Head node and you are using KubeRay, you should set the `RAY_PROMETHEUS_HOST` to the address that the head node can use to access Prometheus, and `RAY_GRAFANA_HOST` to the external URL of the Grafana ingress if both Grafana and the Ray Cluster are on the same Kubernetes cluster.\n\nTo make these settings more intelligent and avoid hardcoding the head node IP, which is changing every time you reconfigure your cluster, you can use environment variables or configuration files that are dynamically updated with the current head node IP.\n\nRegarding `RAY_GRAFANA_IFRAME_HOST`, if it is not set, Ray Dashboard uses the value of `RAY_GRAFANA_HOST`. So, you can set it to the same value as `RAY_GRAFANA_HOST` or provide a different address that the user's browsers can use to access Grafana and embed visualizations.\n\nIn summary, set `RAY_PROMETHEUS_HOST` to the address that the head node can use to access Prometheus, and `RAY_GRAFANA_HOST` to the external URL of the Grafana ingress if both Grafana and the Ray Cluster are on the same Kubernetes cluster. Use environment variables or configuration files to dynamically update these settings and avoid hardcoding the head node IP.",6126"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6127},6128{6129"question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",6130"sources": [6131"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",6132"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",6133"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",6134"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",6135"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#pod-templates",6136"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",6137"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",6138"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",6139"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kubernetes-infrastructure-setup-on-gcp",6140"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#deploy-a-ray-cluster",6141"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",6142"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",6143"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#experiment-results-kuberay-v0-6-0",6144"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",6145"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#step-1-create-a-new-kubernetes-cluster",6146"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#worker-init-container"6147],6148"document_ids": [614913957,615013956,61515975,615213870,61535925,61544515,61555953,61565972,61576170,61586175,61595973,616013902,61615779,61625963,61635772,616457086165],6166"answer": " When using KubeRay autoscaling with GCS (Global Control Store) in Kubernetes, the Ray Autoscaler and the Kubernetes Cluster Autoscaler work together to manage Ray pods and Kubernetes nodes. After the Ray autoscaler decides to create or delete a Ray pod, the Kubernetes Cluster Autoscaler provisions or cleans up Kubernetes nodes as needed. It is recommended to configure your RayCluster so that only one Ray pod fits per Kubernetes node.\n\nFor GCS fault tolerance, KubeRay is required. When GCS restarts, it recovers the state by reading from Redis. When the GCS is recovering from its failed state, the raylet tries to reconnect to the GCS. If the raylet fails to reconnect to the GCS for more than 60 seconds, the raylet exits and the corresponding node fails.\n\nTo enable autoscaling in a KubeRay Cluster, you need to set enableInTreeAutoscaling to True in the Serve config. The Ray autoscaler adjusts the number of nodes (Ray pods) in the cluster based on the resources required by tasks, actors, or placement groups. The KubeRay operator then deletes that pod when the Ray Autoscaler decides to scale down a pod.",6167"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6168},6169{6170"question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",6171"sources": [6172"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",6173"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",6174"https://docs.ray.io/en/master/serve/api/doc/ray.serve.config.HTTPOptions.html#ray-serve-config-httpoptions",6175"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",6176"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",6177"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serves-recovery-procedures",6178"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",6179"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",6180"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",6181"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s",6182"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",6183"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s",6184"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.HTTPOptionsSchema.html#ray-serve-schema-httpoptionsschema",6185"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",6186"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",6187"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests"6188],6189"document_ids": [619013854,619113795,619214020,619313832,619413677,619513880,61964287,619714044,619814288,61998481,62004765,62018655,620214045,620313852,620414055,6205137166206],6207"answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you can include it in the `http_options` field of the config file as follows:\n```yaml\nhttp_options:\n request_timeout_s: <value_in_seconds>\n```\nFor example:\n```yaml\nhttp_options:\n request_timeout_s: 30\n```\nThis will set the end-to-end timeout for HTTP requests to 30 seconds before terminating and retrying at another replica. The `http_options` field is global to your Ray cluster and cannot be updated during runtime.",6208"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6209},6210{6211"question": "How do I make the GPU available on my M1 laptop to ray?",6212"sources": [6213"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",6214"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#using-gpus-in-tasks-and-actors",6215"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",6216"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",6217"https://docs.ray.io/en/master/ray-contribute/docs.html#building-docs-for-apple-silicon-m1",6218"https://docs.ray.io/en/master/ray-core/cluster/index.html#what-s-next",6219"https://docs.ray.io/en/master/installation.html#m1-mac-apple-silicon-support",6220"https://docs.ray.io/en/master/ray-more-libs/installation.html#m1-mac-apple-silicon-support",6221"https://docs.ray.io/en/master/ray-overview/installation.html#m1-mac-apple-silicon-support",6222"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#requesting-gpus",6223"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",6224"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#configuring-ray-pods-for-gpu-usage",6225"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",6226"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gcp-gke-gpu-cluster.html#step-2-create-a-gpu-node-pool",6227"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#starting-ray-nodes-with-gpus",6228"https://docs.ray.io/en/master/tune/api/doc/ray.tune.utils.wait_for_gpu.html#ray-tune-utils-wait-for-gpu"6229],6230"document_ids": [62314997,62324995,62334818,62341006,6235803,62364646,623773,623814571,6239298,62406020,62412966,62426012,6243417,62445950,62454992,624614876247],6248"answer": " To make the GPU available for Ray on your M1 laptop, you need to ensure that you have the GPU version of TensorFlow installed. Then, you can define a Ray remote function with `num_gpus=1` and use TensorFlow within the function to make use of the GPU. Here is an example:\n\n```python\n@ray.remote(num_gpus=1)\ndef use_gpu():\n import tensorflow as tf\n # Use TensorFlow to utilize the GPU\n```\n\nTo start a new local Ray instance, you can use `ray.init()`. Make sure that you have installed Ray on your M1 laptop following the instructions provided in the context:\n\n1. Install miniforge.\n2. Activate the miniforge environment.\n3. Install Ray as you normally would using `pip install ray`.\n\nAfter setting up Ray and TensorFlow with GPU support, you can use the `use_gpu()` function defined above in your Ray program to access the GPU.",6249"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6250},6251{6252"question": "How can I add a timeout for the Ray job?",6253"sources": [6254"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",6255"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",6256"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#example-run-a-simple-ray-job-with-rayjob",6257"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",6258"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",6259"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",6260"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",6261"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",6262"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.GetTimeoutError.html#ray-exceptions-gettimeouterror",6263"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-job-stop",6264"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",6265"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management",6266"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnvConfig.html#ray-runtime-env-runtimeenvconfig",6267"https://docs.ray.io/en/master/train/api/doc/ray.train.horovod.HorovodConfig.timeout_s.html#ray-train-horovod-horovodconfig-timeout-s",6268"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchConfig.timeout_s.html#ray-train-torch-torchconfig-timeout-s",6269"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"6270],6271"document_ids": [627213795,62736556,62745757,627513698,62761006,62774417,62784416,62796554,62804796,62816602,62826558,62836569,62844765,62858655,62868481,6287138546288],6289"answer": " To add a timeout for a Ray job, you can use the `--no-wait` flag when submitting the job with the Ray CLI. This will cause the job submission server to return immediately, without waiting for the job to finish. You can then use other CLI commands, such as `ray job status` and `ray job logs`, to check the status and logs of the job.\n\nIf you want to set a timeout for a specific task or function within a Ray job, you can use the `ray.get()` function with a specified timeout. For example, `ray.get(object_ref, timeout=4)` will return the result of the task or function associated with `object_ref` within 4 seconds, or raise a `ray.exceptions.GetTimeoutError` if the result is not available within that time.\n\nNote that the `request_timeout_s` field in the `http_options` of the Serve config is a global setting that applies to all HTTP requests in the Ray cluster, and cannot be updated during runtime. It sets the end-to-end timeout for a request before terminating and retrying at another replica. By default, there is no request timeout.",6290"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6291},6292{6293"question": "how do I set custom /tmp directory for remote cluster?",6294"sources": [6295"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#api-reference",6296"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",6297"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",6298"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",6299"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",6300"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",6301"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",6302"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",6303"https://docs.ray.io/en/master/cluster/cli.html#ray-submit",6304"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.restore.html#ray-tune-trainable-restore",6305"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.restore.html#ray-rllib-algorithms-algorithm-algorithm-restore",6306"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",6307"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",6308"https://docs.ray.io/en/master/train/user-guides/persistent-storage.html#persistent-storage",6309"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",6310"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster"6311],6312"document_ids": [63134273,63146561,6315872,63161005,63171006,63182309,631913680,632013718,63215625,63222081,632312465,63246205,632513719,63269150,63279160,632865626329],6330"answer": " To set a custom `/tmp` directory for a remote cluster in Ray, you can use the `RAY_TEMPDIR` environment variable when initializing the Ray cluster. This variable defines where the temporary directory for the cluster files should be created, and it should be accessible by the host, the outer container, and the inner container.\n\nHere is an example of how to set the `RAY_TEMPDIR` variable in a cluster configuration file:\n```\nRAY_TEMPDIR=\"/ray-mount\"\n```\nYou can then use this configuration file with the `ray init` or `ray start` command to start the Ray cluster with the custom `/tmp` directory.\n\nNote: The `RAY_TEMPDIR` variable can only be set per-job, not per-task or per-actor. It is also important to ensure that the directory specified by `RAY_TEMPDIR` is accessible by all nodes in the cluster.",6331"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6332},6333{6334"question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",6335"sources": [6336"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6337"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",6338"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",6339"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6340"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6341"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6342"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",6343"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",6344"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",6345"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",6346"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints",6347"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.html#ray-tune-trainable",6348"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",6349"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",6350"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",6351"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init"6352],6353"document_ids": [63544960,63554385,63564959,63574960,63584963,63594961,6360511,63616632,6362872,63636396,63649160,63651775,63664384,63674961,63686360,636948236370],6371"answer": " Yes, if you set the `--temp-dir` flag to a different directory, Ray object spilling will use that custom directory instead of the default `/tmp`. However, to configure the directory for object spilling specifically, you should use the `object_spilling_config` parameter in `ray.init()` as shown in the context. This will ensure that objects are spilled to the desired directory when the object store is full.",6372"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6373},6374{6375"question": "can you give me an example for *`--runtime-env-json`*",6376"sources": [6377"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#remote-uris",6378"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#option-2-manually-create-url-slower-to-implement-but-recommended-for-production-environments",6379"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments",6380"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",6381"https://docs.ray.io/en/master/rllib/rllib-examples.html#custom-and-complex-models",6382"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#setting-up-logging-sidecars-with-fluent-bit",6383"https://docs.ray.io/en/master/ray-core/api/runtime-env.html#runtime-env-api",6384"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",6385"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",6386"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",6387"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",6388"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",6389"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",6390"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance",6391"https://docs.ray.io/en/master/serve/api/index.html#serve-run",6392"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build"6393],6394"document_ids": [63954308,63964319,63974253,639811458,639910636,64005866,64014661,640213720,64034712,64044716,640514217,64064713,640714236,64084292,640913990,6410138566411],6412"answer": " The `--runtime-env-json` option allows you to specify a JSON-serialized runtime environment dictionary that will be passed to `ray.init()` as the default for deployments. Here's an example:\n\n```json\nserve run --address=ray://<head-node-ip-address>:10001 --runtime-env-json='{\"env_vars\": {\"MY_ENV_VAR\": \"my-value\"}, \"working_dir\": \"./project/src\", \"pip\": [\"requests\", \"chess\"]}' local_dev:graph\n```\n\nIn this example, the runtime environment includes some environment variables (`env_vars`), a working directory (`working_dir`), and a list of Python packages (`pip`).",6413"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6414},6415{6416"question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",6417"sources": [6418"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",6419"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",6420"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",6421"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",6422"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",6423"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#pod-configuration-headgroupspec-and-workergroupspecs",6424"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",6425"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",6426"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-config-parameters",6427"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",6428"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",6429"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",6430"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-autoscaler",6431"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",6432"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#configuring-the-autoscaler-sidecar-container",6433"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#programmatic-scaling"6434],6435"document_ids": [643613449,643714066,643814225,643914141,644014261,64415924,644214187,644314303,64445963,644513450,644614141,644713451,64486377,644914098,64505967,645163706452],6453"answer": " The value of `maxConcurrentReplicas` should not be specified in the autoscaling configuration. Instead, it is specified as `max_replicas` in the deployment configuration, which sets the maximum number of replicas for the deployment. The autoscaling algorithm will then adjust the number of replicas within the range specified by `min_replicas` and `max_replicas` based on the deployment's resource usage and workload.",6454"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6455},6456{6457"question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",6458"sources": [6459"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",6460"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",6461"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",6462"https://docs.ray.io/en/master/rllib/package_ref/learner.html#torchlearner-configurations",6463"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#docker-container-name",6464"https://docs.ray.io/en/master/rllib/package_ref/algorithm.html#algorithms",6465"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",6466"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",6467"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#overriding-deployment-settings",6468"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",6469"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",6470"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",6471"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema",6472"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",6473"https://docs.ray.io/en/master/tune/api/doc/ray.tune.search.zoopt.ZOOptSearch.set_max_concurrency.html#ray-tune-search-zoopt-zooptsearch-set-max-concurrency",6474"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"6475],6476"document_ids": [647713451,647814303,647914066,648011140,64816265,648211164,648314187,648414225,648513561,648613450,648714302,648814186,648914065,649014224,64911340,6492141286493],6494"answer": " The value of `max_concurrent_queries` should be specified by the user and it represents the maximum number of pending queries in a single replica. It uses a default value of 1 if not specified. It is not directly related to the `target_num_ongoing_requests_per_replica` value.",6495"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6496},6497{6498"question": "what is a `smoothing_factor`",6499"sources": [6500"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",6501"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",6502"https://docs.ray.io/en/master/tune/examples/includes/pb2_example.html#pb2-example",6503"https://docs.ray.io/en/master/tune/faq.html#what-are-hyperparameters",6504"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner.FrameworkHyperparameters.what_to_compile.html#ray-rllib-core-learner-learner-frameworkhyperparameters-what-to-compile",6505"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#how-does-tune-work",6506"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",6507"https://docs.ray.io/en/master/rllib/package_ref/utils.html#tensorflow-utilities",6508"https://docs.ray.io/en/master/rllib/package_ref/utils.html#numpy-utilities",6509"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.numpy.fc.html#ray-rllib-utils-numpy-fc",6510"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#twin-delayed-ddpg-td3",6511"https://docs.ray.io/en/master/rllib/rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg",6512"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.AsyncSampler.html#ray-rllib-evaluation-sampler-asyncsampler",6513"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.AsyncHyperBandScheduler.html#ray-tune-schedulers-asynchyperbandscheduler",6514"https://docs.ray.io/en/master/data/examples/batch_training.html#run-batch-training-using-map-groups",6515"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.utils.exploration.gaussian_noise.GaussianNoise.html#ray-rllib-utils-exploration-gaussian-noise-gaussiannoise"6516],6517"document_ids": [651813454,651913455,65203991,65211007,652213155,65232232,65241920,652511114,652611120,652713324,652811867,652911849,653012379,65311735,65327897,6533126756534],6535"answer": " A `smoothing_factor` is a multiplicative factor that speeds up or slows down each upscaling or downscaling decision made by an autoscaling algorithm. It can be used to make the application more or less sensitive to changes in traffic volume. For example, increasing the `upscale_smoothing_factor` will cause the application to scale up its resources more quickly in response to high traffic volume.",6536"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6537},6538{6539"question": "What is the reason actors change their state to unhealthy?",6540"sources": [6541"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",6542"https://docs.ray.io/en/master/serve/api/index.html#get-api-serve-deployments-status",6543"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",6544"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",6545"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.rl_module.marl_module.MultiAgentRLModule.set_state.html#ray-rllib-core-rl-module-marl-module-multiagentrlmodule-set-state",6546"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",6547"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",6548"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",6549"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",6550"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",6551"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",6552"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#other-possible-scenarios-that-trigger-a-new-raycluster-preparation",6553"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema",6554"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.probe_unhealthy_workers.html#ray-rllib-evaluation-worker-set-workerset-probe-unhealthy-workers",6555"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#other-possible-scenarios-that-trigger-a-new-raycluster-preparation",6556"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails"6557],6558"document_ids": [655913836,656014006,656114294,6562656,656313335,65645191,656514708,65664505,656711597,656813935,6569747,65705814,657114131,657212306,65735813,6574143066575],6576"answer": " Actors in the context of Ray, a distributed computing framework, can become unhealthy due to errors in their deployment constructor, a crashed replica, or general system or machine errors. This information is provided in the context when discussing the different states of a Ray deployment, including healthy and unhealthy.",6577"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6578},6579{6580"question": "How do I access logs for a dead node?",6581"sources": [6582"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",6583"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",6584"https://docs.ray.io/en/master/ray-observability/reference/api.html#log-apis",6585"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",6586"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",6587"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",6588"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",6589"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",6590"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",6591"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs",6592"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",6593"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",6594"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-logs",6595"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",6596"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",6597"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#error-message-1-connect-connection-refused"6598],6599"document_ids": [6600492,6601493,6602662,66031006,6604492,66052309,6606739,6607740,6608512,66096631,661013894,6611695,6612681,6613445,6614680,661556826616],6617"answer": " You cannot access logs from a dead node, as stated in the context. However, you can access logs from a head node using Ray's State API or CLI. To get all retrievable log file names from a head node in a cluster, you can use the following command:\n```css\nray logs cluster --node-id <HEAD_NODE_ID>\n```\nReplace `<HEAD_NODE_ID>` with the actual head node ID. You can get the node ID/node IP from `ray list nodes`.\n\nIf you want to get a particular log file from a node, you can use the following command:\n```bash\nray logs gcs_server.out --node-id <NODE_ID>\n```\nReplace `gcs_server.out` with the actual log file name and `<NODE_ID>` with the actual node ID.\n\nNote that these commands will only work for alive nodes.",6618"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6619},6620{6621"question": "What are the reasons for a node to change it\u2019s status to dead?",6622"sources": [6623"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",6624"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6625"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-recovery",6626"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#configuring-iam-role-and-ec2-instance-profile",6627"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#rewriting-trajectories",6628"https://docs.ray.io/en/master/tune/examples/tune-xgboost.html#minimum-child-weight",6629"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#rescheduling-bundles-on-a-dead-node",6630"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure",6631"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.PlacementGroupState.html#ray-util-state-common-placementgroupstate",6632"https://docs.ray.io/en/master/ray-core/scheduling/index.html#nodeaffinityschedulingstrategy",6633"https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#handling-failures-and-node-preemption",6634"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#provider-cache-stopped-nodes",6635"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#ray-autoscaler-with-kubernetes-cluster-autoscaler",6636"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",6637"https://docs.ray.io/en/master/ray-core/api/doc/ray.util.placement_group.html#ray-util-placement-group",6638"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure"6639],6640"document_ids": [66414524,6642554,664313869,66446475,664510846,66462661,66475098,664813898,6649724,66505147,66519228,66526283,66535976,6654467,66554790,6656138946657],6658"answer": " A node in a Ray cluster can change its status to dead due to:\n\n1. Raylet process failure: When the raylet process fails, the corresponding node is marked as dead.\n2. Task being killed due to the node running low on memory.\n3. Worker node failure: When a worker node fails, the actors running on it also fail, and KubeRay detects that the node itself has failed, it attempts to restart the worker pod on another running node.\n4. If nodes that contain some bundles of a placement group die, all the bundles are rescheduled on different nodes by GCS.\n5. Simulating a head node failure by killing the head pod or the head node.\n\nAdditionally, the node status can change to dead during cluster scaling events, depending on the configuration of provider.cache\\_stopped\\_nodes.",6659"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6660},6661{6662"question": "What are the reasons for spikes in node CPU utilization",6663"sources": [6664"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",6665"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#adding-cpu-only-nodes-to-your-cluster",6666"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6667"https://docs.ray.io/en/master/ray-observability/getting-started.html#view-the-resource-utilization",6668"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#system-metrics",6669"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",6670"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",6671"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",6672"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",6673"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",6674"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6675"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",6676"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",6677"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",6678"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training",6679"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#configuring-and-running-population-based-training"6680],6681"document_ids": [6682468,66839227,6684554,6685442,6686651,66875112,66886375,66896364,66903015,66913008,6692555,66933002,66943037,66955118,66963029,669730226698],6699"answer": " Spikes in node CPU utilization can be caused by:\n\n1. Increased workload: An increase in the number of tasks or actors, or more computationally intensive tasks, can lead to higher CPU usage.\n2. Inefficient resource utilization: If tasks or actors are not optimized to use resources efficiently, they may consume more CPU than necessary, leading to spikes in utilization.\n3. Autoscaling: When the autoscaler adds nodes to satisfy resource demands, it can lead to a temporary spike in CPU utilization as new nodes are launched and configured.\n4. System processes: Additional system processes running on the head node, such as GCS, can also consume CPU resources and contribute to spikes in utilization.\n\nIt's important to monitor CPU utilization and other resource metrics to identify the cause of any spikes and take appropriate action, such as optimizing resource usage, adding more nodes, or adjusting autoscaling settings.",6700"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6701},6702{6703"question": "What AWS machine type is recommended to deploy a RayService on EKS?",6704"sources": [6705"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-aks-microsoft-azure",6706"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",6707"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",6708"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",6709"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",6710"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",6711"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",6712"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",6713"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",6714"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",6715"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node",6716"https://docs.ray.io/en/master/cluster/kubernetes/user-guides.html#user-guides",6717"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#setting-up-an-eks-cluster-aws",6718"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",6719"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/k8s-cluster-setup.html#managed-kubernetes-services",6720"https://docs.ray.io/en/master/ray-core/cluster/index.html#where-can-i-deploy-ray-clusters"6721],6722"document_ids": [67235887,6724372,672513622,672613930,67275636,672813931,672913962,673013931,67315656,673213930,67336376,67345642,67355886,67366458,67375884,673846456739],6740"answer": " The recommended way to deploy a RayService on Amazon EKS is to use the KubeRay operator, which provides the best of both worlds: the user experience and scalable compute of Ray Serve and the operational benefits of Kubernetes. This also allows you to integrate with existing applications that may be running on Kubernetes. When deploying on Kubernetes, it is recommended to use the RayService controller provided as part of KubeRay, which handles important production requirements such as health checking, status reporting, failure recovery, and upgrades.",6741"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6742},6743{6744"question": "Is there a way to configure the session name generated by ray?",6745"sources": [6746"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",6747"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",6748"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",6749"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",6750"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",6751"https://docs.ray.io/en/master/tune/tutorials/tune_get_data_in_and_out.html#how-do-i-access-tune-results-after-i-am-finished",6752"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments",6753"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",6754"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",6755"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",6756"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",6757"https://docs.ray.io/en/master/cluster/cli.html#ray-attach",6758"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#developing-ray-serve-python-scripts-on-a-raycluster",6759"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",6760"https://docs.ray.io/en/master/ray-core/configure.html#configuring-ray",6761"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client-logs"6762],6763"document_ids": [67644384,67654811,676613974,67674384,676813854,67692309,67706613,67714514,6772511,67734823,67744662,67755628,67765979,67776615,67784379,677966316780],6781"answer": " Yes, you can configure the session name generated by Ray by using the `namespace` argument in the `ray.init()` function or when using the Ray Client. The session name will be based on the provided namespace. By default, the session name is in the format of `session_{timestamp}_{pid}`.",6782"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6783},6784{6785"question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",6786"sources": [6787"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#python-sdk-overview",6788"https://docs.ray.io/en/master/cluster/running-applications/job-submission/jobs-package-ref.html#python-sdk-api-reference",6789"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#quickstart-using-the-ray-jobs-cli",6790"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",6791"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#ray-state-cli-and-sdk",6792"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",6793"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",6794"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",6795"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.get_job_info.html#ray-job-submission-jobsubmissionclient-get-job-info",6796"https://docs.ray.io/en/master/cluster/running-applications/job-submission/rest.html#ray-jobs-rest-api",6797"https://docs.ray.io/en/master/ray-overview/getting-started.html#debugging-and-monitoring-quickstart",6798"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#submitting-a-ray-job",6799"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",6800"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#observe-and-debug-placement-groups",6801"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",6802"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.list_jobs.html#ray-job-submission-jobsubmissionclient-list-jobs"6803],6804"document_ids": [68056575,68066539,68076547,68085729,6809469,681063,68116640,68126637,68136671,68146571,6815275,68166578,68176551,68185085,6819415,682066776821],6822"answer": " No, the Python SDK does not provide a direct method to get a link to the Ray dashboard for a given job. The Ray Dashboard can be accessed through the default URL, <http://localhost:8265>, and the state APIs allow users to access the current state of Ray through the CLI or Python SDK. However, these APIs do not provide a link to the dashboard. You can find the Ray Cluster's HTTP address for job submission in the same address used by the Ray Dashboard, typically <http://127.0.0.1:8265> for a local Ray Cluster.",6823"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6824},6825{6826"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).",6827"sources": [6828"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",6829"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",6830"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6831"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6832"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6833"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#add-metadata-to-structured-logs",6834"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.LocalRayletDiedError.html#ray-exceptions-localrayletdiederror",6835"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.TaskUnschedulableError.html#ray-exceptions-taskunschedulableerror",6836"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6837"https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#understanding-objectlosterrors",6838"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.WorkerCrashedError.html#ray-exceptions-workercrashederror",6839"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",6840"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#detecting-out-of-memory-errors",6841"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",6842"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",6843"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#node-fault-tolerance"6844],6845"document_ids": [68465129,68474658,6848554,6849555,6850554,6851538,68524858,68534830,6854557,68554559,68564906,68574524,6858555,68595118,6860597,686145196862],6863"answer": " The node where the task was running crashed unexpectedly possibly due to:\n\n1. The instance where the node was running failed.\n2. Raylet crashed unexpectedly, which could be due to Out-Of-Memory (OOM) error, preempted node, etc.\n3. The node ran out of memory, causing Ray to kill the worker.",6864"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6865},6866{6867"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.",6868"sources": [6869"https://docs.ray.io/en/master/cluster/faq.html#what-does-a-worker-node-failure-to-connect-look-like",6870"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#troubleshooting",6871"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",6872"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",6873"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",6874"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#replica-health-checking",6875"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",6876"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#head-node-failure",6877"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-recovery-ray-gcs-fault-tolerance",6878"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",6879"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",6880"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",6881"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#getting-the-status-of-the-application",6882"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",6883"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#cli-command-ray-status",6884"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-9-upgrade-from-ray-serves-single-application-api-to-its-multi-application-api-without-downtime"6885],6886"document_ids": [68875638,68886505,688913870,689013553,68911005,689213866,68934508,68944522,689513871,68965709,68974515,689814740,689913947,690013889,6901467,690257056903],6904"answer": " The error message \"Health check failed for node\" typically indicates that the worker node is unable to connect to the GCS (Global Control Service) running on the head node. This could be due to several reasons such as:\n\n* The head node is not running or is inaccessible at the specified address.\n* A different version of Ray is running at the specified address.\n* The specified address is wrong.\n* There are firewall settings preventing access.\n\nTo troubleshoot this issue, you can use tools such as `nmap` or `nc` to check whether each port can be reached from a node. Additionally, you can customize the health-check frequency and timeout using the `health_check_period_s` and `health_check_timeout_s` options in the deployment configuration.\n\nIf the head node has failed, you can make the GCS fault tolerant by using HA Redis, which allows the GCS to recover its state when it restarts. This can prevent the Ray cluster from crashing if the head node goes down.\n\nIf you are using Kubernetes but not KubeRay, you can refer to the Kubernetes documentation for instructions on how to make the GCS fault tolerant. In this case, when the GCS restarts, it will recover the state by reading from Redis. If the raylet fails to reconnect to the GCS for more than 60 seconds, the raylet will exit and the corresponding node will fail. This timeout threshold can be tuned by the OS environment variable `RAY_gcs_rpc_server_reconnect_timeout_s`. You can also set the OS environment variable `RAY_external_storage_namespace` to isolate the data stored in Redis.",6905"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6906},6907{6908"question": "Do you need the DAGDriver to deploy a serve application using RayServe?",6909"sources": [6910"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",6911"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-the-applications",6912"https://docs.ray.io/en/master/serve/key-concepts.html#deployment-graph",6913"https://docs.ray.io/en/master/serve/production-guide/index.html#working-example-fruitstand-application",6914"https://docs.ray.io/en/master/serve/production-guide/index.html#working-example-fruitstand-application",6915"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",6916"https://docs.ray.io/en/master/serve/http-guide.html#serve-deployment-graph-dagdriver",6917"https://docs.ray.io/en/master/serve/tutorials/gradio-dag-visualization.html#requirement-driver",6918"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",6919"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",6920"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",6921"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",6922"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#deploy-multiple-applications",6923"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#drivers-and-http-adapters",6924"https://docs.ray.io/en/master/serve/api/doc/ray.serve.run.html#ray-serve-run",6925"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to"6926],6927"document_ids": [692813741,692913655,693013426,693113971,693213972,693313677,693413502,693514524,693613859,693713652,693813650,693914732,694013647,694113744,694214238,6943136216944],6945"answer": " No, you don't need the DAGDriver to deploy a Ray Serve application. The DAGDriver is used to route HTTP requests through your call graph and can be useful when working with deployment graphs, but it is not a requirement for deploying a Ray Serve application. You can deploy an application using the `serve deploy` command and a configuration file, as shown in the context.",6946"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6947},6948{6949"question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",6950"sources": [6951"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",6952"https://docs.ray.io/en/master/workflows/basics.html#sub-task-results",6953"https://docs.ray.io/en/master/serve/api/index.html#serve-run",6954"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-ray-train-logger-callbacks",6955"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.write_numpy.html#ray-data-dataset-write-numpy",6956"https://docs.ray.io/en/master/ray-core/configure.html#code-search-path",6957"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",6958"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path",6959"https://docs.ray.io/en/master/serve/api/doc/ray.serve.Application.html#ray-serve-application",6960"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#defining-an-application-builder",6961"https://docs.ray.io/en/master/serve/tutorials/batch.html#define-the-deployment",6962"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#walkthrough",6963"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",6964"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#step-0-preparation",6965"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",6966"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayserve-dev-doc.html#step-9-create-a-rayservice-yaml-file"6967],6968"document_ids": [696913844,69709670,697113990,69729141,69737248,69744406,69755789,69765678,697714037,697813813,697914586,69808078,698110281,698210168,698313846,698459946985],6986"answer": " To provide the import path for a simple RayServe deployment, you need to ensure that the deployment graph is importable by Serve at runtime. When running on a cluster, you can build the code into the cluster's container image or use a runtime environment with a remote URI that hosts the code in remote storage. For example, you can use a config file hosted on GitHub to deploy the FruitStand deployment graph to your Ray cluster even if you don't have the code locally.\n\nIn the context provided, the import path for the fruit stand application is defined in the deployment\\_graph variable in fruit.py in the test\\_dag repo. The import\\_path in the config points to this variable to tell Serve from where to import the application.\n\nSo, the import path for the fruit stand application would be:\n\ntest\\_dag.deployment\\_graph:fruit\n\nNote: You may need to adjust the import path based on the location of your deployment graph and the name of your application.",6987"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"6988},6989{6990"question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",6991"sources": [6992"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",6993"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-3-install-a-rayjob",6994"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#prerequisites",6995"https://docs.ray.io/en/master/ray-contribute/docs.html#what-to-contribute",6996"https://docs.ray.io/en/master/data/examples/custom-datasource.html#implementing-a-custom-datasource",6997"https://docs.ray.io/en/master/cluster/usage-stats.html#how-does-it-work",6998"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",6999"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",7000"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",7001"https://docs.ray.io/en/master/serve/develop-and-deploy.html#deploy-ray-serve-in-production",7002"https://docs.ray.io/en/master/serve/production-guide/docker.html#using-custom-docker-images-in-kuberay",7003"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#step-4-verify-the-kubernetes-cluster-status",7004"https://docs.ray.io/en/master/cluster/kubernetes/index.html#about-kuberay",7005"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#step-2-deploy-a-ray-cluster-on-kubernetes-with-the-kuberay-operator",7006"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-3-install-a-rayservice",7007"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide"7008],7009"document_ids": [70105751,70115760,70125752,7013809,70147924,70155568,70166144,70175647,70185658,701913441,702013926,70215763,70225660,70236162,70245742,702557547026],7027"answer": " Yes, the context includes an example of using RayJob in Kuberay. You can download the RayJob configuration file using this command:\n\ncurl -LO [https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray\\_v1alpha1\\_rayjob.yaml](https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray_v1alpha1_rayjob.yaml)\n\nThen, you can create the RayJob by running:\n\nkubectl apply -f ray\\_v1alpha1\\_rayjob.yaml\n\nThis is covered in Step 3 of the Quickstart section in the context.",7028"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"7029},7030{7031"question": "Does Ray metrics have to be exported via an actor?",7032"sources": [7033"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",7034"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency",7035"https://docs.ray.io/en/master/ray-observability/getting-started.html#metrics-view",7036"https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",7037"https://docs.ray.io/en/master/tune/api/doc/ray.tune.Trainable.export_model.html#ray-tune-trainable-export-model",7038"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.algorithms.algorithm.Algorithm.export_model.html#ray-rllib-algorithms-algorithm-algorithm-export-model",7039"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",7040"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",7041"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",7042"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",7043"https://docs.ray.io/en/master/ray-observability/key-concepts.html#metrics",7044"https://docs.ray.io/en/master/cluster/metrics.html#scraping-metrics",7045"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",7046"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#summary",7047"https://docs.ray.io/en/master/ray-core/key-concepts.html#actors",7048"https://docs.ray.io/en/master/rllib/actors.html#actors"7049],7050"document_ids": [7051503,7052656,7053438,7054403,70551781,705612157,70575573,705811596,70594503,706014707,7061403,70625581,706314723,70645443,70654200,7066115757067],7068"answer": " No, Ray metrics do not have to be exported via an actor. Metrics can be defined and exported using the `ray.util.metrics` module, as shown in the example provided. However, actors can also export metrics if they are designed to do so. The metrics are then exported to a Prometheus format and can be scraped from each node's metrics agent endpoint.",7069"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"7070},7071{7072"question": "how can I avoid objects not getting spilled?",7073"sources": [7074"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",7075"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",7076"https://docs.ray.io/en/master/ray-core/objects.html#more-about-ray-objects",7077"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",7078"https://docs.ray.io/en/master/tune/api/search_space.html#tune-search-space-api",7079"https://docs.ray.io/en/master/tune/tutorials/tune-search-spaces.html#working-with-tune-search-spaces",7080"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",7081"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",7082"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",7083"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#out-of-core-data-processing",7084"https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",7085"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",7086"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#cluster-mode",7087"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#stats",7088"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",7089"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node"7090],7091"document_ids": [70926374,70934959,70944447,70951006,70961101,70972317,709814748,70994960,71004963,710114485,71021247,71034962,71044968,71054969,71064223,710749617108],7109"answer": " To avoid objects not getting spilled and improve performance when using Ray, consider the following steps:\n\n1. Use high-performance storage such as NVMe SSDs.\n2. Adjust the `object_spilling_config` to configure the directory where objects are spilled to and increase the `buffer_size` for better performance.\n3. Prevent running out of disk space by adjusting the `local_fs_capacity_threshold`.\n4. If using an HDD, specify a large buffer size (> 1MB) to reduce IO requests during spilling.\n5. For memory-intensive workloads, use an SSD instead of an HDD when using object spilling.\n6. Avoid passing large objects as values in the search space, as it incurs a performance overhead. Use `tune.with_parameters` to pass large objects in or load them from disk or cloud storage.\n7. In multi-node clusters, enable object spilling by specifying the `object_spilling_config` when starting the head node.\n\nThese steps can help you manage object spilling effectively and optimize performance in Ray.",7110"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"7111},7112{7113"question": "what\u2019s ray core",7114"sources": [7115"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",7116"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",7117"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",7118"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner.LearnerSpec.html#ray-rllib-core-learner-learner-learnerspec",7119"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.core.learner.learner.Learner.html#ray-rllib-core-learner-learner-learner",7120"https://docs.ray.io/en/master/installation.html#from-wheels",7121"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",7122"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#want-to-learn-more",7123"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",7124"https://docs.ray.io/en/master/data/overview.html#ray-data-overview",7125"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",7126"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",7127"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",7128"https://docs.ray.io/en/master/ray-core/examples/overview.html#basic-examples",7129"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-for-developers",7130"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework"7131],7132"document_ids": [71334662,71344481,71354673,713612726,713712511,713862,71394811,71405444,71415656,71426835,7143367,714413974,7145246,71465383,7147646,71483717149],7150"answer": " Ray Core is a distributed computing library that provides primitives such as tasks, actors, and objects for building and scaling distributed applications. It is the foundation of Ray's unified compute framework, which includes a set of open-source Python libraries for machine learning applications. Ray Core allows developers to parallelize functions and scale Python applications, and it can be used to build custom distributed systems. It can be installed using pip and has support for various features such as data, train, tune, serve, and RLlib through installation extras.",7151"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"7152},7153{7154"question": "Does ray support cron job",7155"sources": [7156"https://docs.ray.io/en/master/serve/api/index.html#ray-serve-api",7157"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",7158"https://docs.ray.io/en/master/workflows/management.html#recurring-workflows",7159"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide",7160"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.tail_job_logs.html#ray-job-submission-jobsubmissionclient-tail-job-logs",7161"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.get_job_info.html#ray-job-submission-jobsubmissionclient-get-job-info",7162"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",7163"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",7164"https://docs.ray.io/en/master/cluster/vms/index.html#ray-on-cloud-vms",7165"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-api-server",7166"https://docs.ray.io/en/master/cluster/faq.html#do-ray-clusters-support-multi-tenancy",7167"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",7168"https://docs.ray.io/en/master/cluster/kubernetes/index.html#ray-on-kubernetes",7169"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide",7170"https://docs.ray.io/en/master/cluster/vms/examples/ml-example.html#job-logs",7171"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr"7172],7173"document_ids": [717413974,71754662,71769634,71775754,71786689,71796672,71806637,71814811,71826208,718313474,71845635,71856638,71865656,71875784,71886530,71894107190],7191"answer": " No, Ray does not directly support cron jobs. However, you can use an external job scheduler to interact with your Ray cluster and trigger workflow runs. Ray Workflows does not have a built-in job scheduler, but you can use Kubernetes' native support for Ray clusters and jobs to define a Ray cluster and job using a Kubernetes config.\n\nFor job logs, you can use the `ray job logs` command with the Ray Job's submission ID to follow the job's logs. Ray directs stdout and stderr from all Tasks and Actors to the Worker log files, including any log messages generated by the Worker.",7192"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"7193},7194{7195"question": "can you give me the dependencies list for api read_images?",7196"sources": [7197"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",7198"https://docs.ray.io/en/master/data/api/doc/ray.data.read_images.html#ray-data-read-images",7199"https://docs.ray.io/en/master/data/working-with-images.html#reading-images",7200"https://docs.ray.io/en/master/serve/deploy-many-models/model-multiplexing.html#model-multiplexing",7201"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",7202"https://docs.ray.io/en/master/ray-core/key-concepts.html#environment-dependencies",7203"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#load-images-into-memory",7204"https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",7205"https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",7206"https://docs.ray.io/en/master/installation.html#docker-source-images",7207"https://docs.ray.io/en/master/serve/tutorials/object-detection.html#serving-an-object-detection-model",7208"https://docs.ray.io/en/master/serve/http-guide.html#list-of-built-in-adapters",7209"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset",7210"https://docs.ray.io/en/master/data/working-with-tensors.html#saving-tensor-data",7211"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning",7212"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data"7213],7214"document_ids": [72157361,72167360,72176720,721813666,72194267,72204203,722110191,722214587,7223306,722481,722514472,722613511,72278041,72286779,72296011,723079097231],7232"answer": " The dependencies for the `api read_images` function, as provided in the context, are primarily related to the `ray.data.read_images` function used to load image data. These dependencies include:\n\n1. `pyarrow.fs`: This is a required dependency for handling file systems when reading images.\n2. `parallelism`: An optional parameter to specify the degree of parallelism when reading images.\n3. `meta_provider`: An optional parameter to specify a metadata provider for the images.\n4. `arrow_open_file_args`: An optional parameter to specify additional arguments for opening files using Arrow.\n5. `partition_filter`: An optional parameter to specify a filter for partitioning the image data based on file extensions.\n6. `partitioning`: An optional parameter to specify the partitioning strategy for the image data.\n7. `size`: An optional parameter to specify the size of the images to be read.\n8. `mode`: An optional parameter to specify the mode for reading images.\n9. `include_paths`: An optional parameter to specify whether to include the file paths when reading images.\n\nAdditionally, the `read_images` function requires the `numpy` and `PIL` libraries for processing the image data.\n\nNote that these dependencies are required for loading and processing the image data, and may not necessarily be specific to the `api read_images` function.",7233"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"7234},7235{7236"question": "how do I kill a specific serve replica",7237"sources": [7238"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",7239"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",7240"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#local-development-with-http-requests",7241"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",7242"https://docs.ray.io/en/master/tune/faq.html#ray-tune-faq",7243"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#replica-health-checking",7244"https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-deployments",7245"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure",7246"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",7247"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#worker-node-failure",7248"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",7249"https://docs.ray.io/en/master/serve/api/index.html#delete-api-serve-applications",7250"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#replica-health-checking",7251"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#httpproxy-failure",7252"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",7253"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#head-node-failure"7254],7255"document_ids": [725613912,725713911,725813717,725913912,72601006,726113867,726214007,726313908,726413448,726513892,726613915,726714015,726813865,726913916,727013905,7271139017272],7273"answer": " To kill a specific serve replica, you can use the following steps:\n\n1. Get a handle to the replica using its name: `replica_handle = ray.get_actor(\"SERVE_REPLICA::SleepyPid#RlRptP\", namespace=\"serve\")`\n2. Kill the replica using the handle: `ray.kill(replica_handle, no_restart=True)`\n\nThis will stop the replica without restarting it. You can check the status of the replica using the `ray list actors` command.",7274"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"7275}7276]7277}7278