# embeddings_queue

created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986599e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/use-cases.html", "title": "Ray Use Cases \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Reinforcement Learning#\nRLlib is an open-source library for reinforcement learning (RL), offering support for production-level, highly distributed RL workloads while maintaining unified and simple APIs for a large variety of industry applications. RLlib is used by industry leaders in many different verticals, such as climate control, industrial control, manufacturing and logistics, finance, gaming, automobile, robotics, boat design, and many others.\n\n\n\nDecentralized distributed proximal polixy optimiation (DD-PPO) architecture.#\n\n\nLearn more about reinforcement learning with the following resources.\n\n[Course] Applied Reinforcement Learning with RLlib\n[Blog] Intro to RLlib: Example Environments\n[Guide] Getting Started with RLlib\n[Talk] Deep reinforcement learning at Riot Games\n[Gallery] RLlib Examples Gallery\n[Gallery] More RL Use Cases on the Blog"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98659ee-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/use-cases.html", "title": "Ray Use Cases \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ML Platform#\nRay and its AI libraries provide unified compute runtime for teams looking to simplify their ML platform.\nRay\u2019s libraries such as Ray Train, Ray Data, and Ray Serve can be used to compose end-to-end ML workflows, providing features and APIs for\ndata preprocessing as part of training, and transitioning from training to serving.\nRead more about building ML platforms with Ray in this section.\n\n\nEnd-to-End ML Workflows#\nThe following highlights examples utilizing Ray AI libraries to implement end-to-end ML workflows.\n\n[Example] Text classification with Ray\n[Example] Object detection with Ray\n[Example] Machine learning on tabular data\n[Example] AutoML for Time Series with Ray\n\n\n\nLarge Scale Workload Orchestration#\nThe following highlights feature projects leveraging Ray Core\u2019s distributed APIs to simplify the orchestration of large scale workloads."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865a3e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/use-cases.html", "title": "Ray Use Cases \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "[Blog] Highly Available and Scalable Online Applications on Ray at Ant Group\n[Blog] Ray Forward 2022 Conference: Hyper-scale Ray Application Use Cases\n[Blog] A new world record on the CloudSort benchmark using Ray\n[Example] Speed up your web crawler by parallelizing it with Ray\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nInstalling Ray\n\n\n\n\nnext\nRay for ML Infrastructure\n\n\n\n\n\n\n\n\n\n\n\nThanks for the feedback!\n\n\nWas this helpful?\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\nFeedback\n\nSubmit\n\n\n  \n    By The Ray Team\n  \n      \u00a9 Copyright 2023, The Ray Team."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865a84-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Installing Ray \u2014 Ray 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nToggle navigation sidebar\n\n\n\n\nToggle in-page Table of Contents\n\n\n\n\n\n\n\n\n\n\n\n\nRay 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n                    Welcome to Ray!\n                \n\n\n\n\n  Ray\n \n\n\n\n\n   Overview\n  \n\n\n\n   Getting Started\n  \n\n\n\n   Installation\n  \n\n\n\n   Use Cases\n  \n\n\n\n   Example Gallery\n  \n\n\n\n   Ecosystem\n  \n\n\n\n   Ray Core\n  \n\n\n\n   Ray Data\n  \n\n\n\n   Ray Train\n  \n\n\n\n   Ray Tune\n  \n\n\n\n   Ray Serve\n  \n\n\n\n   Ray RLlib\n  \n\n\n\n   More Libraries\n  \n\n\n\n   Ray Clusters\n  \n\n\n\n   Monitoring and Debugging\n  \n\n\n\n   Developer Guides\n  \n\n\n\n   Glossary\n  \n\n\n\n   Security\n  \n\n\n\n\n\n\n\n            Theme by the Executable Book Project\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nrepository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.rst\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Contents\n    \n\n\n\n\n   Official Releases\n  \n\n\n\n     From Wheels\n    \n\n\n\n\n\n   Daily Releases (Nightlies)\n  \n\n\n\n   Installing from a specific commit"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865ad4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Daily Releases (Nightlies)\n  \n\n\n\n   Installing from a specific commit\n  \n\n\n\n   Install Ray Java with Maven\n  \n\n\n\n   Install Ray C++\n  \n\n\n\n   M1 Mac (Apple Silicon) Support\n  \n\n\n\n   Windows Support\n  \n\n\n\n   Installing Ray on Arch Linux\n  \n\n\n\n   Installing From conda-forge\n  \n\n\n\n   Building Ray from Source\n  \n\n\n\n   Docker Source Images\n  \n\n\n\n     Launch Ray in Docker\n    \n\n\n\n     Test if the installation succeeded\n    \n\n\n\n     Installed Python dependencies\n    \n\n\n\n\n\n\n\n\n\n\n\nInstalling Ray\n\n\n\n\n Contents \n\n\n\n\n\n   Official Releases\n  \n\n\n\n     From Wheels\n    \n\n\n\n\n\n   Daily Releases (Nightlies)\n  \n\n\n\n   Installing from a specific commit\n  \n\n\n\n   Install Ray Java with Maven\n  \n\n\n\n   Install Ray C++\n  \n\n\n\n   M1 Mac (Apple Silicon) Support\n  \n\n\n\n   Windows Support\n  \n\n\n\n   Installing Ray on Arch Linux\n  \n\n\n\n   Installing From conda-forge\n  \n\n\n\n   Building Ray from Source\n  \n\n\n\n   Docker Source Images\n  \n\n\n\n     Launch Ray in Docker\n    \n\n\n\n     Test if the installation succeeded"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865b2e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Launch Ray in Docker\n    \n\n\n\n     Test if the installation succeeded\n    \n\n\n\n     Installed Python dependencies\n    \n\n\n\n\n\n\n\n\n\n\n\nInstalling Ray#\nRay currently officially supports x86_64, aarch64 (ARM) for Linux, and Apple silicon (M1) hardware.\nRay on Windows is currently in beta.\n\nOfficial Releases#\n\nFrom Wheels#\nYou can install the latest official version of Ray from PyPI on Linux, Windows,\nand macOS by choosing the option that best matches your use case.\n\n\n\nRecommended\nFor machine learning applications\npip install -U \"ray[data,train,tune,serve]\"\n\n# For reinforcement learning support, install RLlib instead.\n# pip install -U \"ray[rllib]\"\n\n\nFor general Python applications\npip install -U \"ray[default]\"\n\n# If you don't want Ray Dashboard or Cluster Launcher, install Ray with minimal dependencies instead.\n# pip install -U \"ray\"\n\n\n\n\n\nAdvanced\n\n\n\n\n\n\nCommand\nInstalled components\n\n\n\npip install -U \"ray\"\nCore\n\npip install -U \"ray[default]\"\nCore, Dashboard, Cluster Launcher"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865bb0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "pip install -U \"ray\"\nCore\n\npip install -U \"ray[default]\"\nCore, Dashboard, Cluster Launcher\n\npip install -U \"ray[data]\"\nCore, Data\n\npip install -U \"ray[train]\"\nCore, Train\n\npip install -U \"ray[tune]\"\nCore, Tune\n\npip install -U \"ray[serve]\"\nCore, Dashboard, Cluster Launcher, Serve\n\npip install -U \"ray[serve-grpc]\"\nCore, Dashboard, Cluster Launcher, Serve with gRPC support\n\npip install -U \"ray[rllib]\"\nCore, Tune, RLlib\n\npip install -U \"ray[all]\"\nCore, Dashboard, Cluster Launcher, Data, Train, Tune, Serve, RLlib\n\n\n\n\nTip\nYou can combine installation extras.\nFor example, to install Ray with Dashboard, Cluster Launcher, and Train support, you can run:\npip install -U \"ray[default,train]\""}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865c00-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Daily Releases (Nightlies)#\nYou can install the nightly Ray wheels via the following links. These daily releases are tested via automated tests but do not go through the full release process. To install these wheels, use the following pip command and wheels:\n# Clean removal of previous install\npip uninstall -y ray\n# Install Ray with support for the dashboard + cluster launcher\npip install -U \"ray[default] @ LINK_TO_WHEEL.whl\"\n\n# Install Ray with minimal dependencies\n# pip install -U LINK_TO_WHEEL.whl\n\n\n\n\n\nLinux\n\n\n\n\n\n\nLinux (x86_64)\nLinux (arm64/aarch64)\n\n\n\nLinux Python 3.10 (x86_64)\nLinux Python 3.10 (aarch64)\n\nLinux Python 3.9 (x86_64)\nLinux Python 3.9 (aarch64)\n\nLinux Python 3.8 (x86_64)\nLinux Python 3.8 (aarch64)\n\nLinux Python 3.7 (x86_64)\nLinux Python 3.7 (aarch64)\n\nLinux Python 3.11 (x86_64) (EXPERIMENTAL)\nLinux Python 3.11 (aarch64) (EXPERIMENTAL)\n\n\n\n\n\n\nMacOS\n\n\n\n\n\n\nMacOS (x86_64)\nMacOS (arm64)\n\n\n\nMacOS Python 3.10 (x86_64)\nMacOS Python 3.10 (arm64)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865c46-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "MacOS\n\n\n\n\n\n\nMacOS (x86_64)\nMacOS (arm64)\n\n\n\nMacOS Python 3.10 (x86_64)\nMacOS Python 3.10 (arm64)\n\nMacOS Python 3.9 (x86_64)\nMacOS Python 3.9 (arm64)\n\nMacOS Python 3.8 (x86_64)\nMacOS Python 3.8 (arm64)\n\nMacOS Python 3.7 (x86_64)\nMacOS Python 3.11 (arm64) (EXPERIMENTAL)\n\nMacOS Python 3.11 (x86_64) (EXPERIMENTAL)\n\n\n\n\n\n\n\nWindows (beta)\n\n\n\n\n\nWindows (beta)\n\n\n\nWindows Python 3.10\n\nWindows Python 3.9\n\nWindows Python 3.8\n\nWindows Python 3.7\n\nWindows Python 3.11 (EXPERIMENTAL)\n\n\n\n\n\n\nNote\nOn Windows, support for multi-node Ray clusters is currently experimental and untested.\nIf you run into issues please file a report at https://github.com/ray-project/ray/issues.\n\n\nNote\nUsage stats collection is enabled by default (can be disabled) for nightly wheels including both local clusters started via ray.init() and remote clusters via cli.\n\n\nNote\nPython 3.11 support is experimental."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865caa-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Note\nPython 3.11 support is experimental.\n\n\n\nInstalling from a specific commit#\nYou can install the Ray wheels of any particular commit on master with the following template. You need to specify the commit hash, Ray version, Operating System, and Python version:\npip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/{COMMIT_HASH}/ray-{RAY_VERSION}-{PYTHON_VERSION}-{PYTHON_VERSION}-{OS_VERSION}.whl\n\n\nFor example, here are the Ray 3.0.0.dev0 wheels for Python 3.9, MacOS for commit 4f2ec46c3adb6ba9f412f09a9732f436c4a5d0c9:\npip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/4f2ec46c3adb6ba9f412f09a9732f436c4a5d0c9/ray-3.0.0.dev0-cp39-cp39-macosx_10_15_x86_64.whl\n\n\nThere are minor variations to the format of the wheel filename; it\u2019s best to match against the format in the URLs listed in the Nightlies section.\nHere\u2019s a summary of the variations:\n\nFor MacOS, commits predating August 7, 2021 will have macosx_10_13 in the filename instead of macosx_10_15."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865d36-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Install Ray Java with Maven#\nBefore installing Ray Java with Maven, you should install Ray Python with pip install -U ray . Note that the versions of Ray Java and Ray Python must match.\nNote that nightly Ray python wheels are also required if you want to install Ray Java snapshot version.\nThe latest Ray Java release can be found in central repository. To use the latest Ray Java release in your application, add the following entries in your pom.xml:\n<dependency>\n  <groupId>io.ray</groupId>\n  <artifactId>ray-api</artifactId>\n  <version>${ray.version}</version>\n</dependency>\n<dependency>\n  <groupId>io.ray</groupId>\n  <artifactId>ray-runtime</artifactId>\n  <version>${ray.version}</version>\n</dependency>"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865db8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "The latest Ray Java snapshot can be found in sonatype repository. To use the latest Ray Java snapshot in your application, add the following entries in your pom.xml:\n<!-- only needed for snapshot version of ray -->\n<repositories>\n  <repository>\n    <id>sonatype</id>\n    <url>https://oss.sonatype.org/content/repositories/snapshots/</url>\n    <releases>\n      <enabled>false</enabled>\n    </releases>\n    <snapshots>\n      <enabled>true</enabled>\n    </snapshots>\n  </repository>\n</repositories>\n\n<dependencies>\n  <dependency>\n    <groupId>io.ray</groupId>\n    <artifactId>ray-api</artifactId>\n    <version>${ray.version}</version>\n  </dependency>\n  <dependency>\n    <groupId>io.ray</groupId>\n    <artifactId>ray-runtime</artifactId>\n    <version>${ray.version}</version>\n  </dependency>\n</dependencies>"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865e44-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Note\nWhen you run pip install to install Ray, Java jars are installed as well. The above dependencies are only used to build your Java code and to run your code in local mode.\nIf you want to run your Java code in a multi-node Ray cluster, it\u2019s better to exclude Ray jars when packaging your code to avoid jar conficts if the versions (installed Ray with pip install and maven dependencies) don\u2019t match.\n\n\n\nInstall Ray C++#\nYou can install and use Ray C++ API as follows.\npip install -U ray[cpp]\n\n# Create a Ray C++ project template to start with.\nray cpp --generate-bazel-project-template-to ray-template\n\n\n\nNote\nIf you build Ray from source, remove the build option build --cxxopt=\"-D_GLIBCXX_USE_CXX11_ABI=0\" from the file cpp/example/.bazelrc before running your application. The related issue is this.\n\n\n\nM1 Mac (Apple Silicon) Support#\nRay supports machines running Apple Silicon (such as M1 macs).\nMulti-node clusters are untested. To get started with local Ray development:"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865ec6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Install miniforge.\n\nwget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh\nbash Miniforge3-MacOSX-arm64.sh\nrm Miniforge3-MacOSX-arm64.sh # Cleanup.\n\n\nEnsure you\u2019re using the miniforge environment (you should see (base) in your terminal).\n\nsource ~/.bash_profile\nconda activate\n\n\nInstall Ray as you normally would.\n\npip install ray\n\n\n\n\n\nWindows Support#\nWindows support is in Beta. Ray supports running on Windows with the following caveats (only the first is\nRay-specific, the rest are true anywhere Windows is used):"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865f48-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Multi-node Ray clusters are untested.\nFilenames are tricky on Windows and there still may be a few places where Ray\nassumes UNIX filenames rather than Windows ones. This can be true in downstream\npackages as well.\nPerformance on Windows is known to be slower since opening files on Windows\nis considerably slower than on other operating systems. This can affect logging.\nWindows does not have a copy-on-write forking model, so spinning up new\nprocesses can require more memory.\n\nSubmit any issues you encounter to\nGitHub.\n\n\nInstalling Ray on Arch Linux#\nNote: Installing Ray on Arch Linux is not tested by the Project Ray developers.\nRay is available on Arch Linux via the Arch User Repository (AUR) as\npython-ray.\nYou can manually install the package by following the instructions on the\nArch Wiki or use an AUR helper like yay (recommended for ease of install)\nas follows:\nyay -S python-ray"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865fd4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "To discuss any issues related to this package refer to the comments section\non the AUR page of python-ray here.\n\n\nInstalling From conda-forge#\nRay can also be installed as a conda package on Linux and Windows.\n# also works with mamba\nconda create -c conda-forge python=3.9 -n ray\nconda activate ray\n\n# Install Ray with support for the dashboard + cluster launcher\nconda install -c conda-forge \"ray-default\"\n\n# Install Ray with minimal dependencies\n# conda install -c conda-forge ray\n\n\nTo install Ray libraries, use pip as above or conda/mamba.\nconda install -c conda-forge \"ray-data\"   # installs Ray + dependencies for Ray Data\nconda install -c conda-forge \"ray-train\"  # installs Ray + dependencies for Ray Train\nconda install -c conda-forge \"ray-tune\"   # installs Ray + dependencies for Ray Tune\nconda install -c conda-forge \"ray-serve\"  # installs Ray + dependencies for Ray Serve\nconda install -c conda-forge \"ray-rllib\"  # installs Ray + dependencies for Ray RLlib"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866060-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "For a complete list of available ray libraries on Conda-forge, have a look\nat https://anaconda.org/conda-forge/ray-default\n\nNote\nRay conda packages are maintained by the community, not the Ray team. While\nusing a conda environment, it is recommended to install Ray from PyPi using\npip install ray in the newly created environment.\n\n\n\nBuilding Ray from Source#\nInstalling from pip should be sufficient for most Ray users.\nHowever, should you need to build from source, follow these instructions for building Ray.\n\n\nDocker Source Images#\nMost users should pull a Docker image from the Ray Docker Hub.\n\nThe rayproject/ray images include Ray and all required dependencies. It comes with anaconda and various versions of Python.\nThe rayproject/ray-ml images include the above as well as many additional ML libraries.\nThe rayproject/base-deps and rayproject/ray-deps images are for the Linux and Python dependencies respectively."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98660ec-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Images are tagged with the format {Ray version}[-{Python version}][-{Platform}]. Ray version tag can be one of the following:\n\n\n\n\n\n\nRay version tag\nDescription\n\n\n\nlatest\nThe most recent Ray release.\n\nx.y.z\nA specific Ray release, e.g. 1.12.1\n\nnightly\nThe most recent Ray development build (a recent commit from Github master)\n\n6 character Git SHA prefix\nA specific development build (uses a SHA from the Github master, e.g. 8960af).\n\n\n\nThe optional Python version tag specifies the Python version in the image. All Python versions supported by Ray are available, e.g. py38, py39 and py310. If unspecified, the tag points to an image using Python 3.8.\nThe optional Platform tag specifies the platform where the image is intended for:\n\n\n\n\n\n\nPlatform tag\nDescription\n\n\n\n-cpu\nThese are based off of an Ubuntu image.\n\n-cuXX\nThese are based off of an NVIDIA CUDA image with the specified CUDA version. They require the Nvidia Docker Runtime.\n\n-gpu\nAliases to a specific -cuXX tagged image."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866178-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "-gpu\nAliases to a specific -cuXX tagged image.\n\n<no tag>\nAliases to -cpu tagged images. For ray-ml image, aliases to -gpu tagged image.\n\n\n\nExample: for the nightly image based on Python 3.8 and without GPU support, the tag is nightly-py38-cpu.\nIf you want to tweak some aspect of these images and build them locally, refer to the following script:\ncd ray\n./build-docker.sh\n\n\nBeyond creating the above Docker images, this script can also produce the following two images.\n\nThe rayproject/development image has the ray source code included and is setup for development.\nThe rayproject/examples image adds additional libraries for running examples.\n\nReview images by listing them:\ndocker images"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866204-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Review images by listing them:\ndocker images\n\n\nOutput should look something like the following:\nREPOSITORY                          TAG                 IMAGE ID            CREATED             SIZE\nrayproject/ray                      latest              7243a11ac068        2 days ago          1.11 GB\nrayproject/ray-deps                 latest              b6b39d979d73        8 days ago          996  MB\nrayproject/base-deps                latest              5606591eeab9        8 days ago          512  MB\nubuntu                              focal               1e4467b07108        3 weeks ago         73.9 MB\n\n\n\nLaunch Ray in Docker#\nStart out by launching the deployment container.\ndocker run --shm-size=<shm-size> -t -i rayproject/ray"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866290-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Replace <shm-size> with a limit appropriate for your system, for example\n512M or 2G. A good estimate for this is to use roughly 30% of your available memory (this is\nwhat Ray uses internally for its Object Store). The -t and -i options here are required to support\ninteractive use of the container.\nIf you use a GPU version Docker image, remember to add --gpus all option. Replace <ray-version> with your target ray version in the following command:\ndocker run --shm-size=<shm-size> -t -i --gpus all rayproject/ray:<ray-version>-gpu\n\n\nNote: Ray requires a large amount of shared memory because each object\nstore keeps all of its objects in shared memory, so the amount of shared memory\nwill limit the size of the object store.\nYou should now see a prompt that looks something like:\nroot@ebc78f68d100:/ray#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866312-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Test if the installation succeeded#\nTo test if the installation was successful, try running some tests. This assumes\nthat you\u2019ve cloned the git repository.\npython -m pytest -v python/ray/tests/test_mini.py\n\n\n\n\nInstalled Python dependencies#\nOur docker images are shipped with pre-installed Python dependencies\nrequired for Ray and its libraries.\nWe publish the dependencies that are installed in our ray and ray-ml\nDocker images for Python 3.9."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ray (Python 3.9)ray-ml (Python 3.9)Ray version: nightly (7b8ec8a)\nadal==1.2.7\naiohttp==3.8.6\naiohttp-cors==0.7.0\naiorwlock==1.3.0\naiosignal==1.3.1\nanyio==3.7.1\napplicationinsights==0.11.10\nargcomplete==1.12.3\nasync-timeout==4.0.3\nattrs==21.4.0\nazure-cli-core==2.40.0\nazure-cli-telemetry==1.0.8\nazure-common==1.1.28\nazure-core==1.29.5\nazure-identity==1.10.0\nazure-mgmt-compute==23.1.0\nazure-mgmt-core==1.4.0\nazure-mgmt-network==19.0.0\nazure-mgmt-resource==20.0.0\nbackoff==1.10.0\nbcrypt==4.0.1\nblessed==1.20.0\nboltons @ file:///croot/boltons_1677628692245/work\nboto3==1.26.76\nbotocore==1.29.165\nBrotli @ file:///tmp/abs_ecyw11_7ze/croots/recipe/brotli-split_1659616059936/work\ncachetools==5.3.2\ncertifi @ file:///croot/certifi_1690232220950/work/certifi\ncffi @ file:///tmp/abs_98z5h56wf8/croots/recipe/cffi_1659598650955/work\ncharset-normalizer==3.3.1\nclick==8.1.7\ncloudpickle==2.2.0\ncolorful==0.5.5\ncommonmark==0.9.1\nconda @ file:///croot/conda_1696257509808/work"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866420-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "cloudpickle==2.2.0\ncolorful==0.5.5\ncommonmark==0.9.1\nconda @ file:///croot/conda_1696257509808/work\nconda-content-trust @ file:///tmp/abs_5952f1c8-355c-4855-ad2e-538535021ba5h26t22e5/croots/recipe/conda-content-trust_1658126371814/work\nconda-package-handling @ file:///croot/conda-package-handling_1690999929514/work\nconda_package_streaming @ file:///croot/conda-package-streaming_1690987966409/work\ncryptography==38.0.1\nCython==0.29.32\ndistlib==0.3.7\ndm-tree==0.1.8\nexceptiongroup==1.1.3\nFarama-Notifications==0.0.4\nfastapi==0.104.0\nfilelock==3.13.1\nflatbuffers==23.5.26\nflit_core @ file:///opt/conda/conda-bld/flit-core_1644941570762/work/source/flit_core\nfrozenlist==1.4.0\nfsspec==2023.5.0\ngoogle-api-core==2.12.0\ngoogle-api-python-client==1.7.8\ngoogle-auth==2.23.4\ngoogle-auth-httplib2==0.1.1\ngoogle-oauth==1.0.1\ngoogleapis-common-protos==1.61.0\ngpustat==1.1.1\ngrpcio==1.59.2\ngymnasium==0.28.1\nh11==0.12.0\nhttplib2==0.22.0\nhttptools==0.6.1\nhumanfriendly==10.0"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98664ac-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "grpcio==1.59.2\ngymnasium==0.28.1\nh11==0.12.0\nhttplib2==0.22.0\nhttptools==0.6.1\nhumanfriendly==10.0\nidna @ file:///croot/idna_1666125576474/work\nimageio==2.31.1\nimportlib-metadata==4.10.0\nisodate==0.6.1\njax-jumpy==1.0.0\njmespath==1.0.1\njsonpatch @ file:///tmp/build/80754af9/jsonpatch_1615747632069/work\njsonpointer==2.1\njsonschema==4.17.3\nknack==0.10.1\nlazy_loader==0.3\nlz4==4.3.2\nmsal==1.18.0b1\nmsal-extensions==1.0.0\nmsgpack==1.0.7\nmsrest==0.7.1\nmsrestazure==0.6.4\nmultidict==6.0.4\nnetworkx==3.1\nnumpy==1.26.1\nnvidia-ml-py==12.535.108\noauthlib==3.2.2\nopencensus==0.11.3\nopencensus-context==0.1.3\nopentelemetry-api==1.1.0\nopentelemetry-exporter-otlp==1.1.0\nopentelemetry-exporter-otlp-proto-grpc==1.1.0\nopentelemetry-proto==1.1.0\nopentelemetry-sdk==1.1.0\nopentelemetry-semantic-conventions==0.20b0\npackaging==21.3\npandas==1.5.3\nparamiko==2.12.0\nPillow==9.2.0\npkginfo==1.9.6\nplatformdirs==3.11.0\npluggy @ file:///tmp/build/80754af9/pluggy_1648024445381/work\nportalocker==2.8.2"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866574-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "pluggy @ file:///tmp/build/80754af9/pluggy_1648024445381/work\nportalocker==2.8.2\nprometheus-client==0.18.0\nprotobuf==3.19.6\npsutil==5.9.6\npy-spy==0.3.14\npyarrow==12.0.1\npyasn1==0.5.0\npyasn1-modules==0.3.0\npycosat @ file:///croot/pycosat_1696536503704/work\npycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work\npydantic==1.10.12\nPygments==2.13.0\nPyJWT==2.8.0\nPyNaCl==1.5.0\npyOpenSSL==22.1.0\npyparsing==3.1.1\npyrsistent==0.20.0\nPySocks @ file:///tmp/build/80754af9/pysocks_1605305812635/work\npython-dateutil==2.8.2\npython-dotenv==1.0.0\npytz==2022.7.1\nPyWavelets==1.4.1\nPyYAML==6.0.1\nray @ file:///home/ray/ray-3.0.0.dev0-cp39-cp39-manylinux2014_x86_64.whl#sha256=85cf93d21b91ecef4cf6935194d03ca0c3f907d76267ecd5ad1c6fa6c768717b\nredis==3.5.3\nrequests @ file:///croot/requests_1690400202158/work\nrequests-oauthlib==1.3.1\nrich==12.6.0\nrsa==4.7.2\nruamel.yaml @ file:///croot/ruamel.yaml_1666304550667/work\nruamel.yaml.clib @ file:///croot/ruamel.yaml.clib_1666302247304/work"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866600-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ruamel.yaml.clib @ file:///croot/ruamel.yaml.clib_1666302247304/work\ns3transfer==0.6.2\nscikit-image==0.21.0\nscipy==1.10.1\nsix==1.16.0\nsmart-open==6.2.0\nsniffio==1.3.0\nstarlette==0.27.0\ntabulate==0.9.0\ntensorboardX==2.6\ntifffile==2023.7.10\ntqdm @ file:///croot/tqdm_1679561862951/work\ntyper==0.9.0\ntyping_extensions==4.8.0\nuritemplate==3.0.1\nurllib3 @ file:///croot/urllib3_1698257533958/work\nuvicorn==0.22.0\nuvloop==0.19.0\nvirtualenv==20.21.0\nwatchfiles==0.19.0\nwcwidth==0.2.9\nwebsockets==11.0.3\nyarl==1.9.2\nzipp==3.17.0\nzstandard @ file:///croot/zstandard_1677013143055/work"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986668c-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray version: nightly (7b8ec8a)\nabout-time==4.2.1\nabsl-py==1.4.0\naccelerate==0.20.3\nadal==1.2.7\naim==3.17.5\naim-ui==3.17.5\naimrecords==0.0.7\naimrocks==0.4.0\naioboto3==11.2.0\naiobotocore==2.5.0\naiofiles==22.1.0\naiohttp==3.8.6\naiohttp-cors==0.7.0\naioitertools==0.11.0\naiorwlock==1.3.0\naiosignal==1.3.1\naiosqlite==0.19.0\nale-py==0.8.1\nalembic==1.12.1\nalive-progress==3.1.4\nanyio==3.7.1\napplicationinsights==0.11.10\nargcomplete==1.12.3\nargon2-cffi==23.1.0\nargon2-cffi-bindings==21.2.0\narray-record==0.4.0\narrow==1.3.0\nasttokens==2.4.1\nastunparse==1.6.3\nasync-timeout==4.0.3\nattrs==21.4.0\nautograd==1.6.2\nAutoROM==0.6.1\nAutoROM.accept-rom-license==0.6.1\nax-platform==0.3.2\nazure-cli-core==2.40.0\nazure-cli-telemetry==1.0.8\nazure-common==1.1.28\nazure-core==1.29.5\nazure-identity==1.10.0\nazure-mgmt-compute==23.1.0\nazure-mgmt-core==1.4.0\nazure-mgmt-network==19.0.0\nazure-mgmt-resource==20.0.0\nBabel==2.13.1\nbackcall==0.2.0\nbackoff==1.10.0\nbase58==2.0.1\nbayesian-optimization==1.4.3\nbcrypt==4.0.1"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986670e-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "backcall==0.2.0\nbackoff==1.10.0\nbase58==2.0.1\nbayesian-optimization==1.4.3\nbcrypt==4.0.1\nbeautifulsoup4==4.11.1\nbleach==6.1.0\nblessed==1.20.0\nbokeh==2.4.3\nboltons @ file:///croot/boltons_1677628692245/work\nboto==2.49.0\nboto3==1.26.76\nbotocore==1.29.76\nbotorch==0.8.5\nBrotli @ file:///tmp/abs_ecyw11_7ze/croots/recipe/brotli-split_1659616059936/work\nbuild==1.0.3\ncached-property==1.5.2\ncachetools==5.3.2\ncatboost==1.2.2\ncertifi @ file:///croot/certifi_1690232220950/work/certifi\ncffi==1.16.0\ncharset-normalizer==3.3.1\nchess==1.7.0\nchex==0.1.7\nclick==8.1.7\ncloudpickle==2.2.0\ncma==3.2.2\ncmaes==0.10.0\ncmake==3.27.7\ncolorama==0.4.6\ncoloredlogs==15.0.1\ncolorful==0.5.5\ncolorlog==6.7.0\ncomet-ml==3.31.9\ncomm==0.1.4\ncommonmark==0.9.1\nconda @ file:///croot/conda_1696257509808/work\nconda-content-trust @ file:///tmp/abs_5952f1c8-355c-4855-ad2e-538535021ba5h26t22e5/croots/recipe/conda-content-trust_1658126371814/work\nconda-package-handling @ file:///croot/conda-package-handling_1690999929514/work"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98667c2-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "conda-package-handling @ file:///croot/conda-package-handling_1690999929514/work\nconda_package_streaming @ file:///croot/conda-package-streaming_1690987966409/work\nconfigobj==5.0.8\nConfigSpace==0.7.1\ncontourpy==1.1.1\ncrc32c==2.3\ncrcmod==1.7\ncryptography==38.0.1\ncycler==0.12.1\nCython==0.29.32\ndask==2022.10.1\ndatabricks-cli==0.18.0\ndatasets==2.0.0\ndebugpy==1.8.0\ndecorator==5.1.1\ndeepspeed==0.8.3\ndefusedxml==0.7.1\nDeprecated==1.2.14\ndill==0.3.7\ndistlib==0.3.7\ndistributed==2022.10.1\ndm-control==1.0.12\ndm-env==1.6\ndm-tree==0.1.8\ndocker==6.1.3\ndocker-pycreds==0.4.0\ndopamine-rl==4.0.5\ndragonfly-opt==0.1.7\ndulwich==0.21.6\nentrypoints==0.4\netils==1.3.0\nevaluate==0.4.0\neverett==3.2.0\nexceptiongroup==1.1.3\nexecuting==2.0.1\nfairscale==0.4.6\nFarama-Notifications==0.0.4\nfastapi==0.104.0\nfasteners==0.19\nfastjsonschema==2.18.1\nfilelock==3.13.1\nFLAML==1.1.1\nFlask==2.1.3\nFlask-Cors==4.0.0\nflatbuffers==2.0.7\nflax==0.7.2"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "filelock==3.13.1\nFLAML==1.1.1\nFlask==2.1.3\nFlask-Cors==4.0.0\nflatbuffers==2.0.7\nflax==0.7.2\nflit_core @ file:///opt/conda/conda-bld/flit-core_1644941570762/work/source/flit_core\nfonttools==4.43.1\nfqdn==1.5.1\nfrozenlist==1.4.0\nfsspec==2023.5.0\nfuture==0.18.3\ngast==0.4.0\ngcs-oauth2-boto-plugin==3.0\ngin-config==0.5.0\ngitdb==4.0.11\nGitPython==3.1.40\nglfw==2.6.2\ngoogle-api-core==2.12.0\ngoogle-api-python-client==1.7.8\ngoogle-apitools==0.5.32\ngoogle-auth==2.23.4\ngoogle-auth-httplib2==0.1.1\ngoogle-auth-oauthlib==0.4.6\ngoogle-oauth==1.0.1\ngoogle-pasta==0.2.0\ngoogle-reauth==0.1.1\ngoogleapis-common-protos==1.61.0\ngpustat==1.1.1\nGPy==1.10.0\ngpytorch==1.10\ngrapheme==0.6.0\ngraphviz==0.20.1\ngreenlet==3.0.1\ngrpcio==1.59.2\ngsutil==5.27\ngunicorn==20.1.0\ngym==0.26.2\ngym-notices==0.0.8\ngymnasium==0.28.1\nh11==0.12.0\nh5py==3.7.0\nHEBO @ git+https://github.com/huawei-noah/HEBO@9a2a674c22518eed35a8b98e5134576741a95410#subdirectory=HEBO\nhigher==0.2.1\nhjson==3.1.0\nhpbandster==0.7.4\nhttplib2==0.20.4"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98668d0-83c7-11ee-a273-0242ac1c000c
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created_at: 2023-11-15 15:00:56
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "jupyterlab-widgets==1.1.7\njupyterlab_server==2.24.0\nkaggle-environments==1.7.11\nkeras==2.11.0\nkiwisolver==1.4.5\nknack==0.10.1\nlabmaze==1.0.6\nlazy_loader==0.3\nlibclang==16.0.6\nlightgbm==3.3.5\nlightgbm-ray==0.1.9\nlinear-operator==0.4.0\nlit==17.0.4\nlocket==1.0.0\nlxml==4.9.1\nlz4==4.3.2\nMako==1.2.4\nMarkdown==3.5.1\nmarkdown-it-py==3.0.0\nMarkupSafe==2.1.3\nmatplotlib==3.7.3\nmatplotlib-inline==0.1.6\nmdurl==0.1.2\nminigrid==2.1.1\nmistune==0.8.4\nml-dtypes==0.2.0\nmlagents-envs==0.28.0\nmlflow==2.4.1\nmodin==0.22.2\nmonotonic==1.6\nmore-itertools==10.1.0\nmpmath==1.3.0\nmsal==1.18.0b1\nmsal-extensions==1.0.0\nmsgpack==1.0.7\nmsgpack-numpy==0.4.8\nmsrest==0.7.1\nmsrestazure==0.6.4\nmujoco==2.3.6\nmultidict==6.0.4\nmultipledispatch==1.0.0\nmultiprocess==0.70.15\nnbclassic==1.0.0\nnbclient==0.5.13\nnbconvert==6.5.4\nnbformat==5.9.2\nnest-asyncio==1.5.8\nnetifaces==0.11.0\nnetworkx==3.1\nnevergrad==0.4.3.post7\nninja==1.11.1.1\nnotebook==6.5.6\nnotebook_shim==0.2.3\nnumexpr==2.8.4\nnumpy==1.26.1\nnvidia-ml-py==12.535.108"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "notebook==6.5.6\nnotebook_shim==0.2.3\nnumexpr==2.8.4\nnumpy==1.26.1\nnvidia-ml-py==12.535.108\noauth2client==4.1.3\noauthlib==3.2.2\nonnx==1.12.0\nonnxruntime==1.14.1\nopen-spiel==1.2\nopencensus==0.11.3\nopencensus-context==0.1.3\nopencv-python==4.8.1.78\nopentelemetry-api==1.1.0\nopentelemetry-exporter-otlp==1.1.0\nopentelemetry-exporter-otlp-proto-common==1.20.0\nopentelemetry-exporter-otlp-proto-grpc==1.1.0\nopentelemetry-exporter-otlp-proto-http==1.20.0\nopentelemetry-proto==1.1.0\nopentelemetry-sdk==1.1.0\nopentelemetry-semantic-conventions==0.20b0\nopt-einsum==3.3.0\noptax==0.1.7\noptuna==3.2.0\norbax-checkpoint==0.2.3\npackaging==21.3\npandas==1.5.3\npandocfilters==1.5.0\nparamiko==2.12.0\nparamz==0.9.5\nparso==0.8.3\npartd==1.4.1\npathtools==0.1.2\npatsy==0.5.3\npettingzoo==1.23.1\npexpect==4.8.0\npickleshare==0.7.5\nPillow==9.2.0\npip-tools==7.3.0\npkginfo==1.9.6\nplatformdirs==3.11.0\nplotly==5.18.0\npluggy==1.3.0\nportalocker==2.8.2\nprometheus-client==0.18.0\npromise==2.3\nprompt-toolkit==3.0.39\nprotobuf==3.19.6"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "portalocker==2.8.2\nprometheus-client==0.18.0\npromise==2.3\nprompt-toolkit==3.0.39\nprotobuf==3.19.6\npsutil==5.9.6\nptyprocess==0.7.0\npure-eval==0.2.2\npy==1.11.0\npy-cpuinfo==9.0.0\npy-spy==0.3.14\npy3nvml==0.2.7\npy4j==0.10.9.7\npyaml==23.9.7\npyarrow==12.0.1\npyasn1==0.5.0\npyasn1-modules==0.3.0\npycosat @ file:///croot/pycosat_1696536503704/work\npycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work\npydantic==1.10.12\npyDeprecate==0.3.2\npygame==2.1.2\npyglet==1.5.15\nPygments==2.13.0\nPyJWT==2.8.0\npymars==0.10.0\npymoo==0.6.0.1\npymunk==6.2.1\nPyNaCl==1.5.0\nPyOpenGL==3.1.7\npyOpenSSL==22.1.0\npyparsing==3.1.1\npypng==0.20220715.0\npyproject_hooks==1.0.0\npyro-api==0.1.2\npyro-ppl==1.8.6\nPyro4==4.82\npyrsistent==0.20.0\nPySocks @ file:///tmp/build/80754af9/pysocks_1605305812635/work\npyspark==3.4.1\npytest==7.0.1\npytest-remotedata==0.3.2\npython-dateutil==2.8.2\npython-dotenv==1.0.0\npython-json-logger==2.0.7\npytorch-lightning==1.6.5\npytz==2022.7.1\npyu2f==0.1.5\nPyWavelets==1.4.1\nPyYAML==6.0.1"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866af6-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "pytorch-lightning==1.6.5\npytz==2022.7.1\npyu2f==0.1.5\nPyWavelets==1.4.1\nPyYAML==6.0.1\npyzmq==24.0.1\nquerystring-parser==1.2.4\nray @ file:///home/ray/ray-3.0.0.dev0-cp39-cp39-manylinux2014_x86_64.whl#sha256=9095d2717ebc6220bf25c537edb6d7cbb6d23db5a682525fb2d640258df4c8c7\nraydp==1.7.0b20231020.dev0\nrecsim==0.2.4\nredis==3.5.3\nreferencing==0.30.2\nregex==2023.10.3\nrequests @ file:///croot/requests_1690400202158/work\nrequests-oauthlib==1.3.1\nrequests-toolbelt==1.0.0\nresponses==0.13.4\nRestrictedPython==6.2\nretry-decorator==1.1.1\nrfc3339-validator==0.1.4\nrfc3986-validator==0.1.1\nrich==12.6.0\nrpds-py==0.10.6\nrsa==4.7.2\nruamel.yaml @ file:///croot/ruamel.yaml_1666304550667/work\nruamel.yaml.clib @ file:///croot/ruamel.yaml.clib_1666302247304/work\ns3fs==2023.5.0\ns3transfer==0.6.2\nsafetensors==0.4.0\nscikit-image==0.21.0\nscikit-learn==1.0.2\nscikit-optimize==0.9.0\nscipy==1.10.1\nsegment-analytics-python==2.2.0\nsemantic-version==2.10.0\nSend2Trash==1.8.2\nsentencepiece==0.1.96\nsentry-sdk==1.33.1"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "semantic-version==2.10.0\nSend2Trash==1.8.2\nsentencepiece==0.1.96\nsentry-sdk==1.33.1\nserpent==1.41\nsetproctitle==1.3.3\nShimmy==1.3.0\nshortuuid==1.0.1\nsigopt==7.5.0\nsix==1.16.0\nsmart-open==6.2.0\nsmmap==5.0.1\nsniffio==1.3.0\nsortedcontainers==2.4.0\nsoupsieve==2.5\nSQLAlchemy==1.4.17\nsqlparse==0.4.4\nstack-data==0.6.3\nstarlette==0.27.0\nstatsmodels==0.14.0\nSuperSuit==3.8.0\nsympy==1.12\ntabulate==0.9.0\ntblib==3.0.0\ntenacity==8.2.3\ntensorboard==2.11.2\ntensorboard-data-server==0.6.1\ntensorboard-plugin-wit==1.8.1\ntensorboardX==2.6\ntensorflow==2.11.0\ntensorflow-datasets==4.9.0\ntensorflow-estimator==2.11.0\ntensorflow-io-gcs-filesystem==0.34.0\ntensorflow-metadata==1.13.0\ntensorflow-probability==0.19.0\ntensorstore==0.1.45\ntermcolor==2.3.0\nterminado==0.17.1\ntf-slim==1.1.0\ntf2onnx==1.13.0\nthreadpoolctl==3.1.0\ntifffile==2023.7.10\ntimm==0.9.2\ntinycss2==1.2.1\ntinyscaler==1.2.6\ntokenizers==0.12.1\ntoml==0.10.2\ntomli==2.0.1\ntoolz==0.12.0\ntorch==2.0.1+cu118\ntorch-cluster==1.6.1+pt20cu118\ntorch-geometric==2.3.1"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "tomli==2.0.1\ntoolz==0.12.0\ntorch==2.0.1+cu118\ntorch-cluster==1.6.1+pt20cu118\ntorch-geometric==2.3.1\ntorch-scatter==2.1.1+pt20cu118\ntorch-sparse==0.6.17+pt20cu118\ntorch-spline-conv==1.2.2+pt20cu118\ntorchdata==0.6.1\ntorchmetrics==0.10.3\ntorchtext==0.15.2+cpu\ntorchvision==0.15.2+cu118\ntornado==6.1\ntqdm==4.64.1\ntraitlets==5.13.0\ntransformers==4.19.1\ntriton==2.0.0\ntune-sklearn==0.4.6\ntypeguard==2.13.3\ntyper==0.9.0\ntypes-python-dateutil==2.8.19.14\ntyping_extensions==4.8.0\ntzdata==2023.3\nuri-template==1.3.0\nuritemplate==3.0.1\nurllib3 @ file:///croot/urllib3_1698257533958/work\nuvicorn==0.22.0\nuvloop==0.19.0\nvirtualenv==20.21.0\nwandb==0.13.4\nwatchfiles==0.19.0\nwcwidth==0.2.9\nwebcolors==1.13\nwebencodings==0.5.1\nwebsocket-client==1.6.4\nwebsockets==11.0.3\nWerkzeug==2.1.2\nwidgetsnbextension==3.6.6\nwrapt==1.15.0\nwurlitzer==3.0.3\nxgboost==1.7.6\nxgboost-ray==0.1.18\nxmltodict==0.13.0\nxxhash==3.4.1\ny-py==0.6.2\nyarl==1.9.2\nypy-websocket==0.8.4\nzict==3.0.0\nzipp==3.17.0\nzoopt==0.4.1"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866e5c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "xxhash==3.4.1\ny-py==0.6.2\nyarl==1.9.2\nypy-websocket==0.8.4\nzict==3.0.0\nzipp==3.17.0\nzoopt==0.4.1\nzstandard @ file:///croot/zstandard_1677013143055/work"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866eb6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/installation.html", "title": "Installing Ray \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "previous\nGetting Started\n\n\n\n\nnext\nRay Use Cases\n\n\n\n\n\n\n\n\n\n\n\nThanks for the feedback!\n\n\nWas this helpful?\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\nFeedback\n\nSubmit\n\n\n  \n    By The Ray Team\n  \n      \u00a9 Copyright 2023, The Ray Team."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9866f88-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nToggle navigation sidebar\n\n\n\n\nToggle in-page Table of Contents\n\n\n\n\n\n\n\n\n\n\n\n\nRay 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n                    Welcome to Ray!\n                \n\n\n\n\n  Ray\n \n\n\n\n\n   Overview\n  \n\n\n\n   Getting Started\n  \n\n\n\n   Installation\n  \n\n\n\n   Use Cases\n  \n\n\n\n   Example Gallery\n  \n\n\n\n   Ecosystem\n  \n\n\n\n   Ray Core\n  \n\n\n\n   Ray Data\n  \n\n\n\n   Ray Train\n  \n\n\n\n   Ray Tune\n  \n\n\n\n\n\n\n\n\n     Getting Started\n    \n\n\n\n     Key Concepts\n    \n\n\n\n     User Guides\n    \n\n\n\n     Ray Tune Examples\n    \n\n\n\n     Ray Tune FAQ\n    \n\n\n\n     Ray Tune API\n    \n\n\n\n\n\n   Ray Serve\n  \n\n\n\n   Ray RLlib\n  \n\n\n\n   More Libraries\n  \n\n\n\n   Ray Clusters\n  \n\n\n\n   Monitoring and Debugging\n  \n\n\n\n   Developer Guides\n  \n\n\n\n   Glossary\n  \n\n\n\n   Security\n  \n\n\n\n\n\n\n\n            Theme by the Executable Book Project\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nrepository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.rst"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986701e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "repository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.rst\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Contents\n    \n\n\n\n\n   Why choose Tune?\n  \n\n\n\n   Projects using Tune\n  \n\n\n\n   Learn More About Ray Tune\n  \n\n\n\n   Citing Tune\n  \n\n\n\n\n\n\n\n\n\nRay Tune: Hyperparameter Tuning\n\n\n\n\n Contents \n\n\n\n\n\n   Why choose Tune?\n  \n\n\n\n   Projects using Tune\n  \n\n\n\n   Learn More About Ray Tune\n  \n\n\n\n   Citing Tune\n  \n\n\n\n\n\n\n\n\n\nRay Tune: Hyperparameter Tuning#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98670aa-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Learn More About Ray Tune\n  \n\n\n\n   Citing Tune\n  \n\n\n\n\n\n\n\n\n\nRay Tune: Hyperparameter Tuning#\n\nTune is a Python library for experiment execution and hyperparameter tuning at any scale.\nYou can tune your favorite machine learning framework (PyTorch, XGBoost, Scikit-Learn, TensorFlow and Keras, and more) by running state of the art algorithms such as Population Based Training (PBT) and HyperBand/ASHA.\nTune further integrates with a wide range of additional hyperparameter optimization tools, including Ax, BayesOpt, BOHB, Dragonfly, FLAML, Hyperopt, Nevergrad, Optuna and SigOpt.\nClick on the following tabs to see code examples for various machine learning frameworks:"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986712c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Quickstart\nTo run this example, install the following: pip install \"ray[tune]\".\nIn this quick-start example you minimize a simple function of the form f(x) = a**2 + b, our objective function.\nThe closer a is to zero and the smaller b is, the smaller the total value of f(x).\nWe will define a so-called search space for  a and b and let Ray Tune explore the space for good values.\nfrom ray import train, tune\n\n\ndef objective(config):  # \u2460\n    score = config[\"a\"] ** 2 + config[\"b\"]\n    return {\"score\": score}\n\n\nsearch_space = {  # \u2461\n    \"a\": tune.grid_search([0.001, 0.01, 0.1, 1.0]),\n    \"b\": tune.choice([1, 2, 3]),\n}\n\ntuner = tune.Tuner(objective, param_space=search_space)  # \u2462\n\nresults = tuner.fit()\nprint(results.get_best_result(metric=\"score\", mode=\"min\").config)\n\n\n\u2460 Define an objective function.\n\u2461 Define a search space.\n\u2462 Start a Tune run and print the best result."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98671a4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Keras+Hyperopt\nTo tune your Keras models with Hyperopt, you wrap your model in an objective function whose config you\ncan access for selecting hyperparameters.\nIn the example below we only tune the activation parameter of the first layer of the model, but you can\ntune any parameter of the model you want.\nAfter defining the search space, you can simply initialize the HyperOptSearch object and pass it to run.\nIt\u2019s important to tell Ray Tune which metric you want to optimize and whether you want to maximize or minimize it.\nfrom ray import tune\nfrom ray.tune.search.hyperopt import HyperOptSearch\nimport keras\n\n\ndef objective(config):  # \u2460\n    model = keras.models.Sequential()\n    model.add(keras.layers.Dense(784, activation=config[\"activation\"]))\n    model.add(keras.layers.Dense(10, activation=\"softmax\"))\n\n    model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n    # model.fit(...)\n    # loss, accuracy = model.evaluate(...)\n    return {\"accuracy\": accuracy}"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98671f4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "search_space = {\"activation\": tune.choice([\"relu\", \"tanh\"])}  # \u2461\nalgo = HyperOptSearch()\n\ntuner = tune.Tuner(  # \u2462\n    objective,\n    tune_config=tune.TuneConfig(\n        metric=\"accuracy\",\n        mode=\"max\",\n        search_alg=algo,\n    ),\n    param_space=search_space,\n)\nresults = tuner.fit()\n\n\n\u2460 Wrap a Keras model in an objective function.\n\u2461 Define a search space and initialize the search algorithm.\n\u2462 Start a Tune run that maximizes accuracy."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867244-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "PyTorch+Optuna\nTo tune your PyTorch models with Optuna, you wrap your model in an objective function whose config you\ncan access for selecting hyperparameters.\nIn the example below we only tune the momentum and learning rate (lr) parameters of the model\u2019s optimizer,\nbut you can tune any other model parameter you want.\nAfter defining the search space, you can simply initialize the OptunaSearch object and pass it to run.\nIt\u2019s important to tell Ray Tune which metric you want to optimize and whether you want to maximize or minimize it.\nWe stop tuning this training run after 5 iterations, but you can easily define other stopping rules as well.\nimport torch\nfrom ray import train, tune\nfrom ray.tune.search.optuna import OptunaSearch"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867294-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "def objective(config):  # \u2460\n    train_loader, test_loader = load_data()  # Load some data\n    model = ConvNet().to(\"cpu\")  # Create a PyTorch conv net\n    optimizer = torch.optim.SGD(  # Tune the optimizer\n        model.parameters(), lr=config[\"lr\"], momentum=config[\"momentum\"]\n    )\n\n    while True:\n        train(model, optimizer, train_loader)  # Train the model\n        acc = test(model, test_loader)  # Compute test accuracy\n        train.report({\"mean_accuracy\": acc})  # Report to Tune\n\n\nsearch_space = {\"lr\": tune.loguniform(1e-4, 1e-2), \"momentum\": tune.uniform(0.1, 0.9)}\nalgo = OptunaSearch()  # \u2461\n\ntuner = tune.Tuner(  # \u2462\n    objective,\n    tune_config=tune.TuneConfig(\n        metric=\"mean_accuracy\",\n        mode=\"max\",\n        search_alg=algo,\n    ),\n    run_config=train.RunConfig(\n        stop={\"training_iteration\": 5},\n    ),\n    param_space=search_space,\n)\nresults = tuner.fit()\nprint(\"Best config is:\", results.get_best_result().config)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98672da-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "\u2460 Wrap a PyTorch model in an objective function.\n\u2461 Define a search space and initialize the search algorithm.\n\u2462 Start a Tune run that maximizes mean accuracy and stops after 5 iterations.\n\n\nWith Tune you can also launch a multi-node distributed hyperparameter sweep\nin less than 10 lines of code.\nAnd you can move your models from training to serving on the same infrastructure with Ray Serve.\n\n\n\n\n\nGetting Started\n\n\nIn our getting started tutorial you will learn how to tune a PyTorch model\neffectively with Tune.\n\n\nGet Started with Tune\n\n\n\n\n\n\nKey Concepts\n\n\nUnderstand the key concepts behind Ray Tune.\nLearn about tune runs, search algorithms, schedulers and other features.\n\n\nTune\u2019s Key Concepts\n\n\n\n\n\n\nUser Guides\n\n\nOur guides teach you about key features of Tune,\nsuch as distributed training or early stopping.\n\n\nLearn How To Use Tune\n\n\n\n\n\n\nExamples"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986732a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Learn How To Use Tune\n\n\n\n\n\n\nExamples\n\n\nIn our examples you can find practical tutorials for using frameworks such as\nscikit-learn, Keras, TensorFlow, PyTorch, and mlflow, and state of the art search algorithm integrations.\n\n\nRay Tune Examples\n\n\n\n\n\n\nRay Tune FAQ\n\n\nFind answers to commonly asked questions in our detailed FAQ.\n\n\nRay Tune FAQ\n\n\n\n\n\n\nRay Tune API\n\n\nGet more in-depth information about the Ray Tune API, including all about search spaces,\nalgorithms and training configurations.\n\n\nRead the API Reference\n\n\n\n\n\n\nWhy choose Tune?#\nThere are many other hyperparameter optimization libraries out there.\nIf you\u2019re new to Tune, you\u2019re probably wondering, \u201cwhat makes Tune different?\u201d\n\n\nCutting-Edge Optimization Algorithms"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867384-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Cutting-Edge Optimization Algorithms\n\n\n\n\nAs a user, you\u2019re probably looking into hyperparameter optimization because you want to quickly increase your\nmodel performance.\nTune enables you to leverage a variety of these cutting edge optimization algorithms, reducing the cost of tuning\nby terminating bad runs early,\nchoosing better parameters to evaluate, or even\nchanging the hyperparameters during training to optimize schedules.\n\n\n\nFirst-class Developer Productivity\n\n\n\n\nA key problem with many hyperparameter optimization frameworks is the need to restructure\nyour code to fit the framework.\nWith Tune, you can optimize your model just by adding a few code snippets.\nAlso, Tune removes boilerplate from your code training workflow,\nsupporting multiple storage options for experiment results (NFS, cloud storage) and\nlogs results to tools such as MLflow and TensorBoard, while also being highly customizable.\n\n\n\nMulti-GPU & Distributed Training Out Of The Box"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98673d4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Multi-GPU & Distributed Training Out Of The Box\n\n\n\n\nHyperparameter tuning is known to be highly time-consuming, so it is often necessary to parallelize this process.\nMost other tuning frameworks require you to implement your own multi-process framework or build your own\ndistributed system to speed up hyperparameter tuning.\nHowever, Tune allows you to transparently parallelize across multiple GPUs and multiple nodes.\nTune even has seamless fault tolerance and cloud support, allowing you to scale up\nyour hyperparameter search by 100x while reducing costs by up to 10x by using cheap preemptible instances.\n\n\n\nComing From Another Hyperparameter Optimization Tool?"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867424-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Coming From Another Hyperparameter Optimization Tool?\n\n\n\n\nYou might be already using an existing hyperparameter tuning tool such as HyperOpt or Bayesian Optimization.\nIn this situation, Tune actually allows you to power up your existing workflow.\nTune\u2019s Search Algorithms integrate with a variety of popular hyperparameter tuning\nlibraries (see examples) and allow you to seamlessly scale up your optimization\nprocess - without sacrificing performance.\n\n\n\nProjects using Tune#\nHere are some of the popular open source repositories and research projects that leverage Tune.\nFeel free to submit a pull-request adding (or requesting a removal!) of a listed project."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867474-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Softlearning: Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.\nFlambe: An ML framework to accelerate research and its path to production. See flambe.ai.\nPopulation Based Augmentation: Population Based Augmentation (PBA) is a algorithm that quickly and efficiently learns data augmentation functions for neural network training. PBA matches state-of-the-art results on CIFAR with one thousand times less compute.\nFast AutoAugment by Kakao: Fast AutoAugment (Accepted at NeurIPS 2019) learns augmentation policies using a more efficient search strategy based on density matching.\nAllentune: Hyperparameter Search for AllenNLP from AllenAI.\nmachinable: A modular configuration system for machine learning research. See machinable.org."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98674ba-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "machinable: A modular configuration system for machine learning research. See machinable.org.\nNeuroCard: NeuroCard (Accepted at VLDB 2021) is a neural cardinality estimator for multi-table join queries. It uses state of the art deep density models to learn correlations across relational database tables."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986750a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Learn More About Ray Tune#\nBelow you can find blog posts and talks about Ray Tune:\n\n[blog] Tune: a Python library for fast hyperparameter tuning at any scale\n[blog] Cutting edge hyperparameter tuning with Ray Tune\n[blog] Simple hyperparameter and architecture search in tensorflow with Ray Tune\n[slides] Talk given at RISECamp 2019\n[video] Talk given at RISECamp 2018\n[video] A Guide to Modern Hyperparameter Optimization (PyData LA 2019) (slides)\n\n\n\nCiting Tune#\nIf Tune helps you in your academic research, you are encouraged to cite our paper.\nHere is an example bibtex:\n@article{liaw2018tune,\n    title={Tune: A Research Platform for Distributed Model Selection and Training},\n    author={Liaw, Richard and Liang, Eric and Nishihara, Robert\n            and Moritz, Philipp and Gonzalez, Joseph E and Stoica, Ion},\n    journal={arXiv preprint arXiv:1807.05118},\n    year={2018}\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nray.train.backend.BackendConfig\n\n\n\n\nnext\nGetting Started with Ray Tune"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986755a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/index.html", "title": "Ray Tune: Hyperparameter Tuning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "previous\nray.train.backend.BackendConfig\n\n\n\n\nnext\nGetting Started with Ray Tune\n\n\n\n\n\n\n\n\n\n\n\nThanks for the feedback!\n\n\nWas this helpful?\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\nFeedback\n\nSubmit\n\n\n  \n    By The Ray Team\n  \n      \u00a9 Copyright 2023, The Ray Team."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98675aa-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/data/data.html", "title": "Ray Data: Scalable Datasets for ML \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Data: Scalable Datasets for ML \u2014 Ray 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nToggle navigation sidebar\n\n\n\n\nToggle in-page Table of Contents\n\n\n\n\n\n\n\n\n\n\n\n\nRay 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n                    Welcome to Ray!\n                \n\n\n\n\n  Ray\n \n\n\n\n\n   Overview\n  \n\n\n\n   Getting Started\n  \n\n\n\n   Installation\n  \n\n\n\n   Use Cases\n  \n\n\n\n   Example Gallery\n  \n\n\n\n   Ecosystem\n  \n\n\n\n   Ray Core\n  \n\n\n\n   Ray Data\n  \n\n\n\n\n\n\n\n\n     Overview\n    \n\n\n\n     Key Concepts\n    \n\n\n\n     User Guides\n    \n\n\n\n     Ray Data Examples\n    \n\n\n\n     Ray Data API\n    \n\n\n\n     Ray Data Internals\n    \n\n\n\n\n\n   Ray Train\n  \n\n\n\n   Ray Tune\n  \n\n\n\n   Ray Serve\n  \n\n\n\n   Ray RLlib\n  \n\n\n\n   More Libraries\n  \n\n\n\n   Ray Clusters\n  \n\n\n\n   Monitoring and Debugging\n  \n\n\n\n   Developer Guides\n  \n\n\n\n   Glossary\n  \n\n\n\n   Security\n  \n\n\n\n\n\n\n\n            Theme by the Executable Book Project\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nrepository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867604-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/data/data.html", "title": "Ray Data: Scalable Datasets for ML \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "repository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.rst\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Contents\n    \n\n\n\n\n   Install Ray Data\n  \n\n\n\n   Learn more\n  \n\n\n\n\n\n\n\n\n\nRay Data: Scalable Datasets for ML\n\n\n\n\n Contents \n\n\n\n\n\n   Install Ray Data\n  \n\n\n\n   Learn more\n  \n\n\n\n\n\n\n\n\n\nRay Data: Scalable Datasets for ML#\nRay Data is a scalable data processing library for ML workloads. It provides flexible and performant APIs for scaling Offline batch inference and Data preprocessing and ingest for ML training. Ray Data uses streaming execution to efficiently process large datasets.\n\nInstall Ray Data#\nTo install Ray Data, run:\n$ pip install -U 'ray[data]'\n\n\nTo learn more about installing Ray and its libraries, see\nInstalling Ray.\n\n\nLearn more#\n\n\n\n\n\nRay Data Overview\n\n\nGet an overview of Ray Data, the workloads that it supports, and how it compares to alternatives.\n\n\nRay Data Overview\n\n\n\n\n\n\nKey Concepts"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986764a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/data/data.html", "title": "Ray Data: Scalable Datasets for ML \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Data Overview\n\n\n\n\n\n\nKey Concepts\n\n\nUnderstand the key concepts behind Ray Data. Learn what\nDatasets are and how they\u2019re used.\n\n\nLearn Key Concepts\n\n\n\n\n\n\nUser Guides\n\n\nLearn how to use Ray Data, from basic usage to end-to-end guides.\n\n\nLearn how to use Ray Data\n\n\n\n\n\n\nExamples\n\n\nFind both simple and scaling-out examples of using Ray Data.\n\n\nRay Data Examples\n\n\n\n\n\n\nAPI\n\n\nGet more in-depth information about the Ray Data API.\n\n\nRead the API Reference\n\n\n\n\n\n\nRay blogs\n\n\nGet the latest on engineering updates from the Ray team and how companies are using Ray Data.\n\n\nRead the Ray blogs\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nray.util.state.exception.RayStateApiException\n\n\n\n\nnext\nRay Data Overview\n\n\n\n\n\n\n\n\n\n\n\nThanks for the feedback!\n\n\nWas this helpful?\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\nFeedback\n\nSubmit\n\n\n  \n    By The Ray Team\n  \n      \u00a9 Copyright 2023, The Ray Team."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986769a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nToggle navigation sidebar\n\n\n\n\nToggle in-page Table of Contents\n\n\n\n\n\n\n\n\n\n\n\n\nRay 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n                    Welcome to Ray!\n                \n\n\n\n\n  Ray\n \n\n\n\n\n   Overview\n  \n\n\n\n   Getting Started\n  \n\n\n\n   Installation\n  \n\n\n\n   Use Cases\n  \n\n\n\n   Example Gallery\n  \n\n\n\n   Ecosystem\n  \n\n\n\n   Ray Core\n  \n\n\n\n   Ray Data\n  \n\n\n\n   Ray Train\n  \n\n\n\n   Ray Tune\n  \n\n\n\n   Ray Serve\n  \n\n\n\n\n\n\n\n\n     Getting Started\n    \n\n\n\n     Key Concepts\n    \n\n\n\n     Develop and Deploy an ML Application\n    \n\n\n\n     Deploy Compositions of Models\n    \n\n\n\n     Deploy Multiple Applications\n    \n\n\n\n     Model Multiplexing\n    \n\n\n\n     Configure Ray Serve deployments\n    \n\n\n\n     Set Up FastAPI and HTTP\n    \n\n\n\n     Production Guide\n    \n\n\n\n     Monitor Your Application\n    \n\n\n\n     Resource Allocation\n    \n\n\n\n     Ray Serve Autoscaling\n    \n\n\n\n     Advanced Guides"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98676ea-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Resource Allocation\n    \n\n\n\n     Ray Serve Autoscaling\n    \n\n\n\n     Advanced Guides\n    \n\n\n\n     Architecture\n    \n\n\n\n     Examples\n    \n\n\n\n     Ray Serve API\n    \n\n\n\n\n\n   Ray RLlib\n  \n\n\n\n   More Libraries\n  \n\n\n\n   Ray Clusters\n  \n\n\n\n   Monitoring and Debugging\n  \n\n\n\n   Developer Guides\n  \n\n\n\n   Glossary\n  \n\n\n\n   Security\n  \n\n\n\n\n\n\n\n            Theme by the Executable Book Project\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nrepository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.md\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Contents\n    \n\n\n\n\n   Quickstart\n  \n\n\n\n   More examples\n  \n\n\n\n   Why choose Serve?\n  \n\n\n\n   How can Serve help me as a\u2026\n  \n\n\n\n   How does Serve compare to \u2026\n  \n\n\n\n   Learn More\n  \n\n\n\n\n\n\n\n\n\nRay Serve: Scalable and Programmable Serving\n\n\n\n\n Contents \n\n\n\n\n\n   Quickstart\n  \n\n\n\n   More examples\n  \n\n\n\n   Why choose Serve?\n  \n\n\n\n   How can Serve help me as a\u2026\n  \n\n\n\n   How does Serve compare to \u2026\n  \n\n\n\n   Learn More\n  \n\n\n\n\n\n\n\n\n\nRay Serve: Scalable and Programmable Serving#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867730-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Learn More\n  \n\n\n\n\n\n\n\n\n\nRay Serve: Scalable and Programmable Serving#\n\nTip\nGet in touch with us if you\u2019re using or considering using Ray Serve."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98677a8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Tip\nGet in touch with us if you\u2019re using or considering using Ray Serve.\n\n\nRay Serve is a scalable model serving library for building online inference APIs.\nServe is framework-agnostic, so you can use a single toolkit to serve everything from deep learning models built with frameworks like PyTorch, TensorFlow, and Keras, to Scikit-Learn models, to arbitrary Python business logic. It has several features and performance optimizations for serving Large Language Models such as response streaming, dynamic request batching, multi-node/multi-GPU serving, etc.\nRay Serve is particularly well suited for model composition and many model serving, enabling you to build a complex inference service consisting of multiple ML models and business logic all in Python code.\nRay Serve is built on top of Ray, so it easily scales to many machines and offers flexible scheduling support such as fractional GPUs so you can share resources and serve many machine learning models at low cost."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98677f8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Quickstart#\nInstall Ray Serve and its dependencies:\npip install \"ray[serve]\"\n\n\nDefine a simple \u201chello world\u201d application, run it locally, and query it over HTTP.\nimport requests\nfrom starlette.requests import Request\nfrom typing import Dict\n\nfrom ray import serve\n\n\n# 1: Define a Ray Serve application.\n@serve.deployment\nclass MyModelDeployment:\n    def __init__(self, msg: str):\n        # Initialize model state: could be very large neural net weights.\n        self._msg = msg\n\n    def __call__(self, request: Request) -> Dict:\n        return {\"result\": self._msg}\n\n\napp = MyModelDeployment.bind(msg=\"Hello world!\")\n\n# 2: Deploy the application locally.\nserve.run(app, route_prefix=\"/\")\n\n# 3: Query the application and print the result.\nprint(requests.get(\"http://localhost:8000/\").json())\n# {'result': 'Hello world!'}\n\n\n\n\nMore examples#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986783e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "More examples#\n\n\n\nModel composition\nUse Serve\u2019s model composition API to combine multiple deployments into a single application.\nimport requests\nimport starlette\nfrom typing import Dict\nfrom ray import serve\nfrom ray.serve.handle import DeploymentHandle\n\n\n# 1. Define the models in our composition graph and an ingress that calls them.\n@serve.deployment\nclass Adder:\n    def __init__(self, increment: int):\n        self.increment = increment\n\n    def add(self, inp: int):\n        return self.increment + inp\n\n\n@serve.deployment\nclass Combiner:\n    def average(self, *inputs) -> float:\n        return sum(inputs) / len(inputs)\n\n\n@serve.deployment\nclass Ingress:\n    def __init__(\n        self,\n        adder1: DeploymentHandle,\n        adder2: DeploymentHandle,\n        combiner: DeploymentHandle,\n    ):\n        self._adder1 = adder1\n        self._adder2 = adder2\n        self._combiner = combiner"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986788e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "async def __call__(self, request: starlette.requests.Request) -> Dict[str, float]:\n        input_json = await request.json()\n        final_result = await self._combiner.average.remote(\n            self._adder1.add.remote(input_json[\"val\"]),\n            self._adder2.add.remote(input_json[\"val\"]),\n        )\n        return {\"result\": final_result}\n\n\n# 2. Build the application consisting of the models and ingress.\napp = Ingress.bind(Adder.bind(increment=1), Adder.bind(increment=2), Combiner.bind())\nserve.run(app)\n\n# 3: Query the application and print the result.\nprint(requests.post(\"http://localhost:8000/\", json={\"val\": 100.0}).json())\n# {\"result\": 101.5}\n\n\n\n\n\nFastAPI integration\nUse Serve\u2019s FastAPI integration to elegantly handle HTTP parsing and validation.\nimport requests\nfrom fastapi import FastAPI\nfrom ray import serve\n\n# 1: Define a FastAPI app and wrap it in a deployment with a route handler.\napp = FastAPI()"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98678de-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# 1: Define a FastAPI app and wrap it in a deployment with a route handler.\napp = FastAPI()\n\n\n@serve.deployment\n@serve.ingress(app)\nclass FastAPIDeployment:\n    # FastAPI will automatically parse the HTTP request for us.\n    @app.get(\"/hello\")\n    def say_hello(self, name: str) -> str:\n        return f\"Hello {name}!\"\n\n\n# 2: Deploy the deployment.\nserve.run(FastAPIDeployment.bind(), route_prefix=\"/\")\n\n# 3: Query the deployment and print the result.\nprint(requests.get(\"http://localhost:8000/hello\", params={\"name\": \"Theodore\"}).json())\n# \"Hello Theodore!\"\n\n\n\n\n\nHugging Face Transformers model\nTo run this example, install the following: pip install transformers\nServe a pre-trained Hugging Face Transformers model using Ray Serve.\nThe model we\u2019ll use is a sentiment analysis model: it will take a text string as input and return if the text was \u201cPOSITIVE\u201d or \u201cNEGATIVE.\u201d\nimport requests\nfrom starlette.requests import Request\nfrom typing import Dict\n\nfrom transformers import pipeline"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986792e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "from transformers import pipeline\n\nfrom ray import serve\n\n\n# 1: Wrap the pretrained sentiment analysis model in a Serve deployment.\n@serve.deployment\nclass SentimentAnalysisDeployment:\n    def __init__(self):\n        self._model = pipeline(\"sentiment-analysis\")\n\n    def __call__(self, request: Request) -> Dict:\n        return self._model(request.query_params[\"text\"])[0]\n\n\n# 2: Deploy the deployment.\nserve.run(SentimentAnalysisDeployment.bind(), route_prefix=\"/\")\n\n# 3: Query the deployment and print the result.\nprint(\n    requests.get(\n        \"http://localhost:8000/\", params={\"text\": \"Ray Serve is great!\"}\n    ).json()\n)\n# {'label': 'POSITIVE', 'score': 0.9998476505279541}\n\n\n\n\n\n\nWhy choose Serve?#\n\n\nBuild end-to-end ML-powered applications"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867974-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Why choose Serve?#\n\n\nBuild end-to-end ML-powered applications\n\n\n\n\nMany solutions for ML serving focus on \u201ctensor-in, tensor-out\u201d serving: that is, they wrap ML models behind a predefined, structured endpoint.\nHowever, machine learning isn\u2019t useful in isolation.\nIt\u2019s often important to combine machine learning with business logic and traditional web serving logic such as database queries.\nRay Serve is unique in that it allows you to build and deploy an end-to-end distributed serving application in a single framework.\nYou can combine multiple ML models, business logic, and expressive HTTP handling using Serve\u2019s FastAPI integration (see FastAPI HTTP Deployments) to build your entire application as one Python program.\n\n\n\nCombine multiple models using a programmable API"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98679c4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Combine multiple models using a programmable API\n\n\n\n\nOften solving a problem requires more than just a single machine learning model.\nFor instance, image processing applications typically require a multi-stage pipeline consisting of steps like preprocessing, segmentation, and filtering to achieve their end goal.\nIn many cases each model may use a different architecture or framework and require different resources (like CPUs vs GPUs).\nMany other solutions support defining a static graph in YAML or some other configuration language.\nThis can be limiting and hard to work with.\nRay Serve, on the other hand, supports multi-model composition using a programmable API where calls to different models look just like function calls.\nThe models can use different resources and run across different machines in the cluster, but you can write it like a regular program. See Deploy Compositions of Models for more details.\n\n\n\nFlexibly scale up and allocate resources"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867a14-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Flexibly scale up and allocate resources\n\n\n\n\nMachine learning models are compute-intensive and therefore can be very expensive to operate.\nA key requirement for any ML serving system is being able to dynamically scale up and down and allocate the right resources for each model to handle the request load while saving cost.\nServe offers a number of built-in primitives to help make your ML serving application efficient.\nIt supports dynamically scaling the resources for a model up and down by adjusting the number of replicas, batching requests to take advantage of efficient vectorized operations (especially important on GPUs), and a flexible resource allocation model that enables you to serve many models on limited hardware resources.\n\n\n\nAvoid framework or vendor lock-in"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867a5a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Avoid framework or vendor lock-in\n\n\n\n\nMachine learning moves fast, with new libraries and model architectures being released all the time, it\u2019s important to avoid locking yourself into a solution that is tied to a specific framework.\nThis is particularly important in serving, where making changes to your infrastructure can be time consuming, expensive, and risky.\nAdditionally, many hosted solutions are limited to a single cloud provider which can be a problem in today\u2019s multi-cloud world.\nRay Serve is not tied to any specific machine learning library or framework, but rather provides a general-purpose scalable serving layer.\nBecause it\u2019s built on top of Ray, you can run it anywhere Ray can: on your laptop, Kubernetes, any major cloud provider, or even on-premise.\n\n\n\nHow can Serve help me as a\u2026#\n\n\nData scientist"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867aaa-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "How can Serve help me as a\u2026#\n\n\nData scientist\n\n\n\n\nServe makes it easy to go from a laptop to a cluster. You can test your models (and your entire deployment graph) on your local machine before deploying it to production on a cluster. You don\u2019t need to know heavyweight Kubernetes concepts or cloud configurations to use Serve.\n\n\n\nML engineer\n\n\n\n\nServe helps you scale out your deployment and runs them reliably and efficiently to save costs. With Serve\u2019s first-class model composition API, you can combine models together with business logic and build end-to-end user-facing applications. Additionally, Serve runs natively on Kubernetes with minimal operation overhead.\n\n\n\nML platform engineer"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867afa-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ML platform engineer\n\n\n\n\nServe specializes in scalable and reliable ML model serving. As such, it can be an important plug-and-play component of your ML platform stack.\nServe supports arbitrary Python code and therefore integrates well with the MLOps ecosystem. You can use it with model optimizers (ONNX, TVM), model monitoring systems (Seldon Alibi, Arize), model registries (MLFlow, Weights and Biases), machine learning frameworks (XGBoost, Scikit-learn), data app UIs (Gradio, Streamlit), and Web API frameworks (FastAPI, gRPC).\n\n\n\nLLM developer"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867b4a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "LLM developer\n\n\n\n\nServe enables you to rapidly prototype, develop, and deploy scalable LLM applications to production. Many large language model (LLM) applications combine prompt preprocessing, vector database lookups, LLM API calls, and response validation. Because Serve supports any arbitrary Python code, you can write all these steps as a single Python module, enabling rapid development and easy testing. You can then quickly deploy your Ray Serve LLM application to production, and each application step can independently autoscale to efficiently accommodate user traffic without wasting resources. In order to improve performance of your LLM applications, Ray Serve has features for batching and can integrate with any model optimization technique. Ray Serve also supports streaming responses, a key feature for chatbot-like applications.\n\n\n\nHow does Serve compare to \u2026#\n\n\nTFServing, TorchServe, ONNXRuntime"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867b90-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "How does Serve compare to \u2026#\n\n\nTFServing, TorchServe, ONNXRuntime\n\n\n\n\nRay Serve is framework-agnostic, so you can use it alongside any other Python framework or library.\nWe believe data scientists should not be bound to a particular machine learning framework.\nThey should be empowered to use the best tool available for the job.\nCompared to these framework-specific solutions, Ray Serve doesn\u2019t perform any model-specific optimizations to make your ML model run faster. However, you can still optimize the models yourself\nand run them in Ray Serve. For example, you can run a model compiled by\nPyTorch JIT or ONNXRuntime.\n\n\n\nAWS SageMaker, Azure ML, Google Vertex AI"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867be0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "AWS SageMaker, Azure ML, Google Vertex AI\n\n\n\n\nAs an open-source project, Ray Serve brings the scalability and reliability of these hosted offerings to your own infrastructure.\nYou can use the Ray cluster launcher to deploy Ray Serve to all major public clouds, K8s, as well as on bare-metal, on-premise machines.\nRay Serve is not a full-fledged ML Platform.\nCompared to these other offerings, Ray Serve lacks the functionality for\nmanaging the lifecycle of your models, visualizing their performance, etc. Ray\nServe primarily focuses on model serving and providing the primitives for you to\nbuild your own ML platform on top.\n\n\n\nSeldon, KServe, Cortex"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867c58-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Seldon, KServe, Cortex\n\n\n\n\nYou can develop Ray Serve on your laptop, deploy it on a dev box, and scale it out\nto multiple machines or a Kubernetes cluster, all with minimal or no changes to code. It\u2019s a lot\neasier to get started with when you don\u2019t need to provision and manage a K8s cluster.\nWhen it\u2019s time to deploy, you can use our Kubernetes Operator\nto transparently deploy your Ray Serve application to K8s.\n\n\n\nBentoML, Comet.ml, MLflow\n\n\n\n\nMany of these tools are focused on serving and scaling models independently.\nIn contrast, Ray Serve is framework-agnostic and focuses on model composition.\nAs such, Ray Serve works with any model packaging and registry format.\nRay Serve also provides key features for building production-ready machine learning applications, including best-in-class autoscaling and naturally integrating with business logic."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867cb2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "We truly believe Serve is unique as it gives you end-to-end control\nover your ML application while delivering scalability and high performance. To achieve\nServe\u2019s feature offerings with other tools, you would need to glue together multiple\nframeworks like Tensorflow Serving and SageMaker, or even roll your own\nmicro-batching component to improve throughput.\n\n\nLearn More#\nCheck out Getting Started and Key Concepts,\nor head over to the Examples to get started building your Ray Serve applications.\n\n\n\n\n\nGetting Started\n\n\nStart with our quick start tutorials for deploying a single model locally and how to convert an existing model into a Ray Serve deployment .\n\n\nGet Started with Ray Serve\n\n\n\n\n\n\nKey Concepts\n\n\nUnderstand the key concepts behind Ray Serve.\nLearn about Deployments, how to query them, and using DeploymentHandles to compose multiple models and business logic together.\n\n\nLearn Key Concepts\n\n\n\n\n\n\nExamples"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867d02-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/index.html", "title": "Ray Serve: Scalable and Programmable Serving \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Learn Key Concepts\n\n\n\n\n\n\nExamples\n\n\nFollow the tutorials to learn how to integrate Ray Serve with TensorFlow, Scikit-Learn, and RLlib.\n\n\nServe Examples\n\n\n\n\n\n\nAPI Reference\n\n\nGet more in-depth information about the Ray Serve API.\n\n\nRead the API Reference\n\n\n\n\n\nFor more, see the following blog posts about Ray Serve:\n\nServing ML Models in Production: Common Patterns by Simon Mo, Edward Oakes, and Michael Galarnyk\nThe Simplest Way to Serve your NLP Model in Production with Pure Python by Edward Oakes and Bill Chambers\nMachine Learning Serving is Broken by Simon Mo\nHow to Scale Up Your FastAPI Application Using Ray Serve by Archit Kulkarni\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nTune CLI (Experimental)\n\n\n\n\nnext\nGetting Started\n\n\n\n\n\n\n\n\n\n\n\nThanks for the feedback!\n\n\nWas this helpful?\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\nFeedback\n\nSubmit\n\n\n  \n    By The Ray Team\n  \n      \u00a9 Copyright 2023, The Ray Team."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867d52-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Serve API \u2014 Ray 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nToggle navigation sidebar\n\n\n\n\nToggle in-page Table of Contents\n\n\n\n\n\n\n\n\n\n\n\n\nRay 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n                    Welcome to Ray!\n                \n\n\n\n\n  Ray\n \n\n\n\n\n   Overview\n  \n\n\n\n   Getting Started\n  \n\n\n\n   Installation\n  \n\n\n\n   Use Cases\n  \n\n\n\n   Example Gallery\n  \n\n\n\n   Ecosystem\n  \n\n\n\n   Ray Core\n  \n\n\n\n   Ray Data\n  \n\n\n\n   Ray Train\n  \n\n\n\n   Ray Tune\n  \n\n\n\n   Ray Serve\n  \n\n\n\n\n\n\n\n\n     Getting Started\n    \n\n\n\n     Key Concepts\n    \n\n\n\n     Develop and Deploy an ML Application\n    \n\n\n\n     Deploy Compositions of Models\n    \n\n\n\n     Deploy Multiple Applications\n    \n\n\n\n     Model Multiplexing\n    \n\n\n\n     Configure Ray Serve deployments\n    \n\n\n\n     Set Up FastAPI and HTTP\n    \n\n\n\n     Production Guide\n    \n\n\n\n     Monitor Your Application\n    \n\n\n\n     Resource Allocation\n    \n\n\n\n     Ray Serve Autoscaling\n    \n\n\n\n     Advanced Guides\n    \n\n\n\n     Architecture"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867dca-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Serve Autoscaling\n    \n\n\n\n     Advanced Guides\n    \n\n\n\n     Architecture\n    \n\n\n\n     Examples\n    \n\n\n\n     Ray Serve API\n    \n\n\n\n\n\n\n\n\n       ray.serve.Deployment\n      \n\n\n\n       ray.serve.Application\n      \n\n\n\n       ray.serve.deployment\n      \n\n\n\n       ray.serve.ingress\n      \n\n\n\n       ray.serve.batch\n      \n\n\n\n       ray.serve.multiplexed\n      \n\n\n\n       ray.serve.handle.DeploymentHandle\n      \n\n\n\n       ray.serve.handle.DeploymentResponse\n      \n\n\n\n       ray.serve.handle.DeploymentResponseGenerator\n      \n\n\n\n       ray.serve.handle.RayServeHandle\n      \n\n\n\n       ray.serve.handle.RayServeSyncHandle\n      \n\n\n\n       ray.serve.start\n      \n\n\n\n       ray.serve.run\n      \n\n\n\n       ray.serve.delete\n      \n\n\n\n       ray.serve.status\n      \n\n\n\n       ray.serve.shutdown\n      \n\n\n\n       ray.serve.config.ProxyLocation\n      \n\n\n\n       ray.serve.config.gRPCOptions\n      \n\n\n\n       ray.serve.config.HTTPOptions\n      \n\n\n\n       ray.serve.config.AutoscalingConfig"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867e1a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ray.serve.config.HTTPOptions\n      \n\n\n\n       ray.serve.config.AutoscalingConfig\n      \n\n\n\n       ray.serve.get_replica_context\n      \n\n\n\n       ray.serve.context.ReplicaContext\n      \n\n\n\n       ray.serve.get_multiplexed_model_id\n      \n\n\n\n       ray.serve.get_app_handle\n      \n\n\n\n       ray.serve.get_deployment_handle\n      \n\n\n\n       ray.serve.schema.ServeDeploySchema\n      \n\n\n\n       ray.serve.schema.gRPCOptionsSchema\n      \n\n\n\n       ray.serve.schema.HTTPOptionsSchema\n      \n\n\n\n       ray.serve.schema.ServeApplicationSchema\n      \n\n\n\n       ray.serve.schema.DeploymentSchema\n      \n\n\n\n       ray.serve.schema.RayActorOptionsSchema\n      \n\n\n\n       ray.serve.schema.ServeInstanceDetails\n      \n\n\n\n       ray.serve.schema.ApplicationDetails\n      \n\n\n\n       ray.serve.schema.DeploymentDetails\n      \n\n\n\n       ray.serve.schema.ReplicaDetails\n      \n\n\n\n       ray.serve.metrics.Counter\n      \n\n\n\n       ray.serve.metrics.Histogram\n      \n\n\n\n       ray.serve.metrics.Gauge"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867e6a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ray.serve.metrics.Histogram\n      \n\n\n\n       ray.serve.metrics.Gauge\n      \n\n\n\n\n\n\n\n   Ray RLlib\n  \n\n\n\n   More Libraries\n  \n\n\n\n   Ray Clusters\n  \n\n\n\n   Monitoring and Debugging\n  \n\n\n\n   Developer Guides\n  \n\n\n\n   Glossary\n  \n\n\n\n   Security\n  \n\n\n\n\n\n\n\n            Theme by the Executable Book Project\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nrepository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.md\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Contents\n    \n\n\n\n\n   Python API\n  \n\n\n\n     Writing Applications\n    \n\n\n\n       Deployment Decorators\n      \n\n\n\n       Deployment Handles\n      \n\n\n\n\n\n     Running Applications\n    \n\n\n\n     Configurations\n    \n\n\n\n       Advanced APIs\n      \n\n\n\n\n\n\n\n   Command Line Interface (CLI)\n  \n\n\n\n     serve\n    \n\n\n\n       build\n      \n\n\n\n       config\n      \n\n\n\n       deploy\n      \n\n\n\n       run\n      \n\n\n\n       shutdown\n      \n\n\n\n       start\n      \n\n\n\n       status\n      \n\n\n\n\n\n\n\n   Serve REST API\n  \n\n\n\n\n\n       PUT\n      \n\n       \"/api/serve/applications/\""}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867eba-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Serve REST API\n  \n\n\n\n\n\n       PUT\n      \n\n       \"/api/serve/applications/\"\n      \n\n\n\n\n\n\n\n       GET\n      \n\n       \"/api/serve/applications/\"\n      \n\n\n\n\n\n\n\n       DELETE\n      \n\n       \"/api/serve/applications/\"\n      \n\n\n\n\n\n\n\n   Config Schemas\n  \n\n\n\n   Response Schemas\n  \n\n\n\n   Metrics API\n  \n\n\n\n\n\n\n\n\n\nRay Serve API\n\n\n\n\n Contents \n\n\n\n\n\n   Python API\n  \n\n\n\n     Writing Applications\n    \n\n\n\n       Deployment Decorators\n      \n\n\n\n       Deployment Handles\n      \n\n\n\n\n\n     Running Applications\n    \n\n\n\n     Configurations\n    \n\n\n\n       Advanced APIs\n      \n\n\n\n\n\n\n\n   Command Line Interface (CLI)\n  \n\n\n\n     serve\n    \n\n\n\n       build\n      \n\n\n\n       config\n      \n\n\n\n       deploy\n      \n\n\n\n       run\n      \n\n\n\n       shutdown\n      \n\n\n\n       start\n      \n\n\n\n       status\n      \n\n\n\n\n\n\n\n   Serve REST API\n  \n\n\n\n\n\n       PUT\n      \n\n       \"/api/serve/applications/\"\n      \n\n\n\n\n\n\n\n       GET\n      \n\n       \"/api/serve/applications/\"\n      \n\n\n\n\n\n\n\n       DELETE"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867f00-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "GET\n      \n\n       \"/api/serve/applications/\"\n      \n\n\n\n\n\n\n\n       DELETE\n      \n\n       \"/api/serve/applications/\"\n      \n\n\n\n\n\n\n\n   Config Schemas\n  \n\n\n\n   Response Schemas\n  \n\n\n\n   Metrics API\n  \n\n\n\n\n\n\n\n\n\nRay Serve API#\n\nPython API#\n\nWriting Applications#\n\n\n\n\n\n\n\nserve.Deployment\nClass (or function) decorated with the @serve.deployment decorator.\n\nserve.Application\nOne or more deployments bound with arguments that can be deployed together.\n\n\n\n\nDeployment Decorators#\n\n\n\n\n\n\nserve.deployment\nDecorator that converts a Python class to a Deployment.\n\nserve.ingress\nWrap a deployment class with a FastAPI application for HTTP request parsing.\n\nserve.batch\nConverts a function to asynchronously handle batches.\n\nserve.multiplexed\nWrap a callable or method used to load multiplexed models in a replica.\n\n\n\n\n\nDeployment Handles#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867f50-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Deployment Handles#\n\nNote\nDeploymentHandle is now the default handle API.\nYou can continue using the legacy RayServeHandle and RayServeSyncHandle APIs using handle.options(use_new_handle_api=False) or export RAY_SERVE_ENABLE_NEW_HANDLE_API=0, but this support will be removed in a future version.\n\n\n\n\n\n\n\nserve.handle.DeploymentHandle\nA handle used to make requests to a deployment at runtime.\n\nserve.handle.DeploymentResponse\nA future-like object wrapping the result of a unary deployment handle call.\n\nserve.handle.DeploymentResponseGenerator\nA future-like object wrapping the result of a streaming deployment handle call.\n\nserve.handle.RayServeHandle\nA handle used to make requests from one deployment to another.\n\nserve.handle.RayServeSyncHandle\nA handle used to make requests to the ingress deployment of an application.\n\n\n\n\n\n\nRunning Applications#\n\n\n\n\n\n\nserve.start\nStart Serve on the cluster.\n\nserve.run\nRun an application and return a handle to its ingress deployment."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867fa0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "serve.run\nRun an application and return a handle to its ingress deployment.\n\nserve.delete\nDelete an application by its name.\n\nserve.status\nGet the status of Serve on the cluster.\n\nserve.shutdown\nCompletely shut down Serve on the cluster.\n\n\n\n\n\nConfigurations#\n\n\n\n\n\n\nserve.config.ProxyLocation\nConfig for where to run proxies to receive ingress traffic to the cluster.\n\nserve.config.gRPCOptions\ngRPC options for the proxies.\n\nserve.config.HTTPOptions\nHTTP options for the proxies.\n\nserve.config.AutoscalingConfig\nConfig for the Serve Autoscaler.\n\n\n\n\nAdvanced APIs#\n\n\n\n\n\n\nserve.get_replica_context\nReturns the deployment and replica tag from within a replica at runtime.\n\nserve.context.ReplicaContext\nStores runtime context info for replicas.\n\nserve.get_multiplexed_model_id\nGet the multiplexed model ID for the current request.\n\nserve.get_app_handle\nGet a handle to the application's ingress deployment by name.\n\nserve.get_deployment_handle\nGet a handle to a deployment by name."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9867ff0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "serve.get_deployment_handle\nGet a handle to a deployment by name.\n\n\n\n\n\n\n\nCommand Line Interface (CLI)#\n\nserve#\nCLI for managing Serve applications on a Ray cluster.\nserve [OPTIONS] COMMAND [ARGS]...\n\n\n\nbuild#\nImports the applications at IMPORT_PATHS and generates a structured, multi-application config for them. If the flag \u2013single-app is set, accepts one application and generates a single-application config. Config outputted from this command can be used by serve deploy or the REST API.\nserve build [OPTIONS] IMPORT_PATHS...\n\n\nOptions\n\n\n-d, --app-dir <app_dir>#\nLocal directory to look for the IMPORT_PATH (will be inserted into PYTHONPATH). Defaults to \u2018.\u2019, meaning that an object in ./main.py can be imported as \u2018main.object\u2019. Not relevant if you\u2019re importing from an installed module.\n\n\n\n-o, --output-path <output_path>#\nLocal path where the output config will be written in YAML format. If not provided, the config will be printed to STDOUT."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868040-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--grpc-servicer-functions <grpc_servicer_functions>#\nServicer function for adding the method handler to the gRPC server. Defaults to an empty list and no gRPC server is started.\n\nArguments\n\n\nIMPORT_PATHS#\nRequired argument(s)\n\n\n\nconfig#\nGets the current configs of Serve applications on the cluster.\nserve config [OPTIONS]\n\n\nOptions\n\n\n-a, --address <address>#\nAddress to use to query the Ray dashboard head (defaults to http://localhost:8265). Can also be specified using the RAY_DASHBOARD_ADDRESS environment variable.\n\n\n\n-n, --name <name>#\nName of an application. Only applies to multi-application mode. If set, this will only fetch the config for the specified application."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868090-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "deploy#\nThis supported config format is ServeDeploySchema. This call is async; a successful response only indicates that the request was sent to the Ray cluster successfully. It does not mean the the deployments have been deployed/updated.\nExisting deployments with no code changes will not be redeployed.\nUse serve config to fetch the current config(s) and serve status to check the status of the application(s) and deployments after deploying.\nserve deploy [OPTIONS] CONFIG_FILE_NAME\n\n\nOptions\n\n\n-a, --address <address>#\nAddress to use to query the Ray dashboard head (defaults to http://localhost:8265). Can also be specified using the RAY_DASHBOARD_ADDRESS environment variable.\n\nArguments\n\n\nCONFIG_FILE_NAME#\nRequired argument"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98680d6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Arguments\n\n\nCONFIG_FILE_NAME#\nRequired argument\n\n\n\nrun#\nRuns an application from the specified import path (e.g., my_script:app) or application(s) from a YAML config.\nIf passing an import path, it must point to a Serve Application or a function that returns one. If a function is used, arguments can be passed to it in \u2018key=val\u2019 format after the import path, for example:\nserve run my_script:app model_path=\u2019/path/to/model.pkl\u2019 num_replicas=5\nIf passing a YAML config, existing applications with no code changes will not be updated.\nBy default, this will block and stream logs to the console. If you Ctrl-C the command, it will shut down Serve on the cluster.\nserve run [OPTIONS] CONFIG_OR_IMPORT_PATH [ARGUMENTS]...\n\n\nOptions\n\n\n--runtime-env <runtime_env>#\nPath to a local YAML file containing a runtime_env definition. This will be passed to ray.init() as the default for deployments."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868130-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--runtime-env-json <runtime_env_json>#\nJSON-serialized runtime_env dictionary. This will be passed to ray.init() as the default for deployments.\n\n\n\n--working-dir <working_dir>#\nDirectory containing files that your application(s) will run in. Can be a local directory or a remote URI to a .zip file (S3, GS, HTTP). This overrides the working_dir in \u2013runtime-env if both are specified. This will be passed to ray.init() as the default for deployments.\n\n\n\n-d, --app-dir <app_dir>#\nLocal directory to look for the IMPORT_PATH (will be inserted into PYTHONPATH). Defaults to \u2018.\u2019, meaning that an object in ./main.py can be imported as \u2018main.object\u2019. Not relevant if you\u2019re importing from an installed module.\n\n\n\n-a, --address <address>#\nAddress to use for ray.init(). Can also be specified using the RAY_ADDRESS environment variable.\n\n\n\n-h, --host <host>#\nHost for HTTP server to listen on. Defaults to 127.0.0.1.\n\n\n\n-p, --port <port>#\nPort for HTTP proxies to listen on. Defaults to 8000."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868180-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "-p, --port <port>#\nPort for HTTP proxies to listen on. Defaults to 8000.\n\n\n\n--blocking, --non-blocking#\nWhether or not this command should be blocking. If blocking, it will loop and log status until Ctrl-C\u2019d, then clean up the app.\n\n\n\n-r, --reload#\nListens for changes to files in the working directory, \u2013working-dir or the working_dir in the \u2013runtime-env, and automatically redeploys the application. This will block until Ctrl-C\u2019d, then clean up the app.\n\n\n\n--route-prefix <route_prefix>#\nRoute prefix for the application. This should only be used when running an application specified by import path and will be ignored if running a config file.\n\nArguments\n\n\nCONFIG_OR_IMPORT_PATH#\nRequired argument\n\n\n\nARGUMENTS#\nOptional argument(s)\n\n\n\nshutdown#\nShuts down Serve on the cluster, deleting all applications.\nserve shutdown [OPTIONS]\n\n\nOptions"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98681d0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Options\n\n\n-a, --address <address>#\nAddress to use to query the Ray dashboard head (defaults to http://localhost:8265). Can also be specified using the RAY_DASHBOARD_ADDRESS environment variable.\n\n\n\n-y, --yes#\nBypass confirmation prompt.\n\n\n\nstart#\nStart Serve on the Ray cluster.\nserve start [OPTIONS]\n\n\nOptions\n\n\n-a, --address <address>#\nAddress to use for ray.init(). Can also be specified using the RAY_ADDRESS environment variable.\n\n\n\n--http-host <http_host>#\nHost for HTTP proxies to listen on. Defaults to 127.0.0.1.\n\n\n\n--http-port <http_port>#\nPort for HTTP proxies to listen on. Defaults to 8000.\n\n\n\n--http-location <http_location>#\nDEPRECATED: Use --proxy-location instead.\n\nOptions\nDeploymentMode.NoServer | DeploymentMode.HeadOnly | DeploymentMode.EveryNode\n\n\n\n\n\n--proxy-location <proxy_location>#\nLocation of the proxies. Defaults to EveryNode.\n\nOptions\nProxyLocation.Disabled | ProxyLocation.HeadOnly | ProxyLocation.EveryNode"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868216-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Options\nProxyLocation.Disabled | ProxyLocation.HeadOnly | ProxyLocation.EveryNode\n\n\n\n\n\n--grpc-port <grpc_port>#\nPort for gRPC proxies to listen on. Defaults to 9000.\n\n\n\n--grpc-servicer-functions <grpc_servicer_functions>#\nServicer function for adding the method handler to the gRPC server. Defaults to an empty list and no gRPC server is started.\n\n\n\nstatus#\nPrints status information about all applications on the cluster.\nAn application may be:\n\nNOT_STARTED: the application does not exist.\nDEPLOYING: the deployments in the application are still deploying and haven\u2019t reached the target number of replicas.\nRUNNING: all deployments are healthy.\nDEPLOY_FAILED: the application failed to deploy or reach a running state.\nDELETING: the application is being deleted, and the deployments in the application are being teared down.\n\nThe deployments within each application may be:"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868266-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "The deployments within each application may be:\n\nHEALTHY: all replicas are acting normally and passing their health checks.\nUNHEALTHY: at least one replica is not acting normally and may not be passing its health check.\nUPDATING: the deployment is updating.\n\nserve status [OPTIONS]\n\n\nOptions\n\n\n-a, --address <address>#\nAddress to use to query the Ray dashboard head (defaults to http://localhost:8265). Can also be specified using the RAY_DASHBOARD_ADDRESS environment variable.\n\n\n\n-n, --name <name>#\nName of an application. If set, this will display only the status of the specified application.\n\n\n\n\n\nServe REST API#\nThe Serve REST API is exposed at the same port as the Ray Dashboard. The Dashboard port is 8265 by default. This port can be changed using the --dashboard-port argument when running ray start. All example requests in this section use the default port."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98682b6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "PUT \"/api/serve/applications/\"#\nDeclaratively deploys a list of Serve applications. If Serve is already running on the Ray cluster, removes all applications not listed in the new config. If Serve is not running on the Ray cluster, starts Serve. See multi-app config schema for the request\u2019s JSON schema.\nExample Request:\nPUT /api/serve/applications/ HTTP/1.1\nHost: http://localhost:8265/\nAccept: application/json\nContent-Type: application/json\n\n{\n    \"applications\": [\n        {\n            \"name\": \"text_app\",\n            \"route_prefix\": \"/\",\n            \"import_path\": \"text_ml:app\",\n            \"runtime_env\": {\n                \"working_dir\": \"https://github.com/ray-project/serve_config_examples/archive/HEAD.zip\"\n            },\n            \"deployments\": [\n                {\"name\": \"Translator\", \"user_config\": {\"language\": \"french\"}},\n                {\"name\": \"Summarizer\"},\n            ]\n        },\n    ]\n}\n\n\nExample Response\nHTTP/1.1 200 OK\nContent-Type: application/json"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98682fc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Example Response\nHTTP/1.1 200 OK\nContent-Type: application/json\n\n\n\n\nGET \"/api/serve/applications/\"#\nGets cluster-level info and comprehensive details on all Serve applications deployed on the Ray cluster. See metadata schema for the response\u2019s JSON schema.\nGET /api/serve/applications/ HTTP/1.1\nHost: http://localhost:8265/\nAccept: application/json\n\n\nExample Response (abridged JSON):\nHTTP/1.1 200 OK\nContent-Type: application/json"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986834c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "{\n    \"controller_info\": {\n        \"node_id\": \"cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec\",\n        \"node_ip\": \"10.0.29.214\",\n        \"actor_id\": \"1d214b7bdf07446ea0ed9d7001000000\",\n        \"actor_name\": \"SERVE_CONTROLLER_ACTOR\",\n        \"worker_id\": \"adf416ae436a806ca302d4712e0df163245aba7ab835b0e0f4d85819\",\n        \"log_file_path\": \"/serve/controller_29778.log\"\n    },\n    \"proxy_location\": \"EveryNode\",\n    \"http_options\": {\n        \"host\": \"0.0.0.0\",\n        \"port\": 8000,\n        \"root_path\": \"\",\n        \"request_timeout_s\": null,\n        \"keep_alive_timeout_s\": 5\n    },\n    \"grpc_options\": {\n        \"port\": 9000,\n        \"grpc_servicer_functions\": []\n    },\n    \"proxies\": {\n        \"cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec\": {\n            \"node_id\": \"cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec\",\n            \"node_ip\": \"10.0.29.214\",\n            \"actor_id\": \"b7a16b8342e1ced620ae638901000000\","}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98683ba-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "\"node_ip\": \"10.0.29.214\",\n            \"actor_id\": \"b7a16b8342e1ced620ae638901000000\",\n            \"actor_name\": \"SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec\",\n            \"worker_id\": \"206b7fe05b65fac7fdceec3c9af1da5bee82b0e1dbb97f8bf732d530\",\n            \"log_file_path\": \"/serve/http_proxy_10.0.29.214.log\",\n            \"status\": \"HEALTHY\"\n        }\n    },\n    \"deploy_mode\": \"MULTI_APP\",\n    \"applications\": {\n        \"app1\": {\n            \"name\": \"app1\",\n            \"route_prefix\": \"/\",\n            \"docs_path\": null,\n            \"status\": \"RUNNING\",\n            \"message\": \"\",\n            \"last_deployed_time_s\": 1694042836.1912267,\n            \"deployed_app_config\": {\n                \"name\": \"app1\",\n                \"route_prefix\": \"/\",\n                \"import_path\": \"src.text-test:app\",\n                \"deployments\": [\n                    {\n                        \"name\": \"Translator\","}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986840a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "{\n                        \"name\": \"Translator\",\n                        \"num_replicas\": 1,\n                        \"user_config\": {\n                            \"language\": \"german\"\n                        }\n                    }\n                ]\n            },\n            \"deployments\": {\n                \"Translator\": {\n                    \"name\": \"Translator\",\n                    \"status\": \"HEALTHY\",\n                    \"message\": \"\",\n                    \"deployment_config\": {\n                        \"name\": \"Translator\",\n                        \"num_replicas\": 1,\n                        \"max_concurrent_queries\": 100,\n                        \"user_config\": {\n                            \"language\": \"german\"\n                        },\n                        \"graceful_shutdown_wait_loop_s\": 2.0,\n                        \"graceful_shutdown_timeout_s\": 20.0,\n                        \"health_check_period_s\": 10.0,\n                        \"health_check_timeout_s\": 30.0,"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868450-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "\"health_check_timeout_s\": 30.0,\n                        \"ray_actor_options\": {\n                            \"runtime_env\": {\n                                \"env_vars\": {}\n                            },\n                            \"num_cpus\": 1.0\n                        },\n                        \"is_driver_deployment\": false\n                    },\n                    \"replicas\": [\n                        {\n                            \"node_id\": \"cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec\",\n                            \"node_ip\": \"10.0.29.214\",\n                            \"actor_id\": \"4bb8479ad0c9e9087fee651901000000\",\n                            \"actor_name\": \"SERVE_REPLICA::app1#Translator#oMhRlb\",\n                            \"worker_id\": \"1624afa1822b62108ead72443ce72ef3c0f280f3075b89dd5c5d5e5f\",\n                            \"log_file_path\": \"/serve/deployment_Translator_app1#Translator#oMhRlb.log\","}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98684aa-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "\"replica_id\": \"app1#Translator#oMhRlb\",\n                            \"state\": \"RUNNING\",\n                            \"pid\": 29892,\n                            \"start_time_s\": 1694042840.577496\n                        }\n                    ]\n                },\n                \"Summarizer\": {\n                    \"name\": \"Summarizer\",\n                    \"status\": \"HEALTHY\",\n                    \"message\": \"\",\n                    \"deployment_config\": {\n                        \"name\": \"Summarizer\",\n                        \"num_replicas\": 1,\n                        \"max_concurrent_queries\": 100,\n                        \"user_config\": null,\n                        \"graceful_shutdown_wait_loop_s\": 2.0,\n                        \"graceful_shutdown_timeout_s\": 20.0,\n                        \"health_check_period_s\": 10.0,\n                        \"health_check_timeout_s\": 30.0,\n                        \"ray_actor_options\": {\n                            \"runtime_env\": {},"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98684f0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "\"ray_actor_options\": {\n                            \"runtime_env\": {},\n                            \"num_cpus\": 1.0\n                        },\n                        \"is_driver_deployment\": false\n                    },\n                    \"replicas\": [\n                        {\n                            \"node_id\": \"cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec\",\n                            \"node_ip\": \"10.0.29.214\",\n                            \"actor_id\": \"7118ae807cffc1c99ad5ad2701000000\",\n                            \"actor_name\": \"SERVE_REPLICA::app1#Summarizer#cwiPXg\",\n                            \"worker_id\": \"12de2ac83c18ce4a61a443a1f3308294caf5a586f9aa320b29deed92\",\n                            \"log_file_path\": \"/serve/deployment_Summarizer_app1#Summarizer#cwiPXg.log\",\n                            \"replica_id\": \"app1#Summarizer#cwiPXg\",\n                            \"state\": \"RUNNING\",\n                            \"pid\": 29893,"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868540-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "\"state\": \"RUNNING\",\n                            \"pid\": 29893,\n                            \"start_time_s\": 1694042840.5789504\n                        }\n                    ]\n                }\n            }\n        }\n    }\n}"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868590-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "DELETE \"/api/serve/applications/\"#\nShuts down Serve and all applications running on the Ray cluster. Has no effect if Serve is not running on the Ray cluster.\nExample Request:\nDELETE /api/serve/applications/ HTTP/1.1\nHost: http://localhost:8265/\nAccept: application/json\n\n\nExample Response\nHTTP/1.1 200 OK\nContent-Type: application/json\n\n\n\n\n\nConfig Schemas#\n\n\n\n\n\n\nschema.ServeDeploySchema\nMulti-application config for deploying a list of Serve applications to the Ray cluster.\n\nschema.gRPCOptionsSchema\nOptions to start the gRPC Proxy with.\n\nschema.HTTPOptionsSchema\nOptions to start the HTTP Proxy with.\n\nschema.ServeApplicationSchema\nDescribes one Serve application, and currently can also be used as a standalone config to deploy a single application to a Ray cluster.\n\nschema.DeploymentSchema\nSpecifies options for one deployment within a Serve application.\n\nschema.RayActorOptionsSchema\nOptions with which to start a replica actor.\n\n\n\n\n\nResponse Schemas#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98685e0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/serve/api/index.html", "title": "Ray Serve API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Response Schemas#\n\n\n\n\n\n\nschema.ServeInstanceDetails\nServe metadata with system-level info and details on all applications deployed to the Ray cluster.\n\nschema.ApplicationDetails\nDetailed info about a Serve application.\n\nschema.DeploymentDetails\nDetailed info about a deployment within a Serve application.\n\nschema.ReplicaDetails\nDetailed info about a single deployment replica.\n\n\n\n\n\nMetrics API#\n\n\n\n\n\n\nmetrics.Counter\nA serve cumulative metric that is monotonically increasing.\n\nmetrics.Histogram\nTracks the size and number of events in buckets.\n\nmetrics.Gauge\nGauges keep the last recorded value and drop everything before.\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nJava Tutorial\n\n\n\n\nnext\nray.serve.Deployment\n\n\n\n\n\n\n\n\n\n\n\nThanks for the feedback!\n\n\nWas this helpful?\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\nFeedback\n\nSubmit\n\n\n  \n    By The Ray Team\n  \n      \u00a9 Copyright 2023, The Ray Team."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868630-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Index \u2014 Ray 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nToggle navigation sidebar\n\n\n\n\nToggle in-page Table of Contents\n\n\n\n\n\n\n\n\n\n\n\n\nRay 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n                    Welcome to Ray!\n                \n\n\n\n\n  Ray\n \n\n\n\n\n   Overview\n  \n\n\n\n   Getting Started\n  \n\n\n\n   Installation\n  \n\n\n\n   Use Cases\n  \n\n\n\n   Example Gallery\n  \n\n\n\n   Ecosystem\n  \n\n\n\n   Ray Core\n  \n\n\n\n   Ray Data\n  \n\n\n\n   Ray Train\n  \n\n\n\n   Ray Tune\n  \n\n\n\n   Ray Serve\n  \n\n\n\n   Ray RLlib\n  \n\n\n\n   More Libraries\n  \n\n\n\n   Ray Clusters\n  \n\n\n\n   Monitoring and Debugging\n  \n\n\n\n   Developer Guides\n  \n\n\n\n   Glossary\n  \n\n\n\n   Security\n  \n\n\n\n\n\n\n\n            Theme by the Executable Book Project\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nrepository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIndex\n\nSymbols\n | _\n | A\n | B\n | C\n | D\n | E\n | F\n | G\n | H\n | I\n | J\n | K\n | L\n | M\n | N\n | O\n | P\n | Q\n | R\n | S\n | T\n | U\n | V\n | W\n | X\n | Z\n\nSymbols\n\n\n(Ray) Scheduler\n\n\n    --address <address>"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868680-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Symbols\n\n\n(Ray) Scheduler\n\n\n    --address <address>\n\n      \nray-debug command line option\n\nray-get command line option\n\nray-job-list command line option\n\nray-job-logs command line option\n\nray-job-status command line option\n\nray-job-stop command line option\n\nray-job-submit command line option\n\nray-list command line option\n\nray-memory command line option\n\nray-start command line option\n\nray-status command line option\n\nray-summary-actors command line option\n\nray-summary-objects command line option\n\nray-summary-tasks command line option\n\nray-timeline command line option\n\nserve-config command line option\n\nserve-deploy command line option\n\nserve-run command line option\n\nserve-shutdown command line option\n\nserve-start command line option\n\nserve-status command line option\n\n\n\n    --app-dir <app_dir>\n\n      \nserve-build command line option\n\nserve-run command line option\n\n\n\n    --args <args>\n\n      \nray-submit command line option\n\n\n\n    --autoscaling-config <autoscaling_config>"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98686c6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ray-submit command line option\n\n\n\n    --autoscaling-config <autoscaling_config>\n\n      \nray-start command line option\n\n\n\n    --block\n\n      \nray-start command line option\n\n\n\n    --blocking\n\n      \nserve-run command line option\n\n\n\n    --cluster-name <cluster_name>\n\n      \nray-attach command line option\n\nray-down command line option\n\nray-exec command line option\n\nray-get_head_ip command line option\n\nray-monitor command line option\n\nray-submit command line option\n\nray-up command line option\n\n\n\n    --dashboard-agent-grpc-port <dashboard_agent_grpc_port>\n\n      \nray-start command line option\n\n\n\n    --dashboard-agent-listen-port <dashboard_agent_listen_port>\n\n      \nray-start command line option\n\n\n\n    --dashboard-grpc-port <dashboard_grpc_port>\n\n      \nray-start command line option\n\n\n\n    --dashboard-host <dashboard_host>\n\n      \nray-start command line option\n\n\n\n    --dashboard-port <dashboard_port>\n\n      \nray-start command line option\n\n\n\n    --detail"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868716-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--dashboard-port <dashboard_port>\n\n      \nray-start command line option\n\n\n\n    --detail\n\n      \nray-list command line option\n\n\n\n    --disable-usage-stats\n\n      \nray-exec command line option\n\nray-start command line option\n\nray-submit command line option\n\nray-up command line option\n\n\n\n    --entrypoint-memory <entrypoint_memory>\n\n      \nray-job-submit command line option\n\n\n\n    --entrypoint-num-cpus <entrypoint_num_cpus>\n\n      \nray-job-submit command line option\n\n\n\n    --entrypoint-num-gpus <entrypoint_num_gpus>\n\n      \nray-job-submit command line option\n\n\n\n    --entrypoint-resources <entrypoint_resources>\n\n      \nray-job-submit command line option\n\n\n\n    --extra-screen-args <extra_screen_args>\n\n      \nray-submit command line option\n\n\n\n    --filter <filter>\n\n      \nray-list command line option\n\n\n\n    --follow\n\n      \nray-job-logs command line option\n\n\n\n    --force\n\n      \nray-stop command line option\n\n\n\n    --format <format>\n\n      \nray-list command line option"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868766-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--format <format>\n\n      \nray-list command line option\n\n\n\n    --gcs-server-port <gcs_server_port>\n\n      \nray-start command line option\n\n\n\n    --grace-period <grace_period>\n\n      \nray-stop command line option\n\n\n\n    --group-by <group_by>\n\n      \nray-memory command line option\n\n\n\n    --grpc-port <grpc_port>\n\n      \nserve-start command line option\n\n\n\n    --grpc-servicer-functions <grpc_servicer_functions>\n\n      \nserve-build command line option\n\nserve-start command line option\n\n\n\n    --head\n\n      \nray-start command line option\n\n\n\n    --headers <headers>\n\n      \nray-job-list command line option\n\nray-job-logs command line option\n\nray-job-status command line option\n\nray-job-stop command line option\n\nray-job-submit command line option\n\n\n\n    --host <host>\n\n      \nserve-run command line option\n\n\n\n    --http-host <http_host>\n\n      \nserve-start command line option\n\n\n\n    --http-location <http_location>\n\n      \nserve-start command line option\n\n\n\n    --http-port <http_port>"}


created_at: 2023-11-15 15:00:56
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "serve-start command line option\n\n\n\n    --http-port <http_port>\n\n      \nserve-start command line option\n\n\n\n    --include-dashboard <include_dashboard>\n\n      \nray-start command line option\n\n\n\n    --job-id <job_id>\n\n      \nray-job-submit command line option\n\n\n\n    --keep-min-workers\n\n      \nray-down command line option\n\n\n\n    --limit <limit>\n\n      \nray-list command line option\n\n\n\n    --lines <lines>\n\n      \nray-monitor command line option\n\n\n\n    --log-color <log_color>\n\n      \nray-attach command line option\n\nray-down command line option\n\nray-exec command line option\n\nray-job-list command line option\n\nray-job-logs command line option\n\nray-job-status command line option\n\nray-job-stop command line option\n\nray-job-submit command line option\n\nray-monitor command line option\n\nray-start command line option\n\nray-stop command line option\n\nray-submit command line option\n\nray-up command line option\n\n\n\n    --log-style <log_style>\n\n      \nray-attach command line option"}


created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--min-workers <min_workers>\n\n      \nray-up command line option\n\n\n\n    --n <num_entries>\n\n      \nray-memory command line option\n\n\n\n    --name <name>\n\n      \nserve-config command line option\n\nserve-status command line option\n\n\n\n    --new\n\n      \nray-attach command line option\n\n\n\n    --no-config-cache\n\n      \nray-attach command line option\n\nray-exec command line option\n\nray-submit command line option\n\nray-up command line option\n\n\n\n    --no-format\n\n      \nray-memory command line option\n\n\n\n    --no-redirect-output\n\n      \nray-start command line option\n\n\n\n    --no-restart\n\n      \nray-up command line option\n\n\n\n    --no-wait\n\n      \nray-job-stop command line option\n\nray-job-submit command line option\n\n\n\n    --node-ip-address <node_ip_address>\n\n      \nray-start command line option\n\n\n\n    --node-manager-port <node_manager_port>\n\n      \nray-start command line option\n\n\n\n    --non-blocking\n\n      \nserve-run command line option\n\n\n\n\n\n    --num-cpus <num_cpus>"}


created_at: 2023-11-15 15:00:56
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--non-blocking\n\n      \nserve-run command line option\n\n\n\n\n\n    --num-cpus <num_cpus>\n\n      \nray-start command line option\n\n\n\n    --num-entries\n\n      \nray-memory command line option\n\n\n\n    --num-gpus <num_gpus>\n\n      \nray-start command line option\n\n\n\n    --object-manager-port <object_manager_port>\n\n      \nray-start command line option\n\n\n\n    --object-store-memory <object_store_memory>\n\n      \nray-start command line option\n\n\n\n    --output-path <output_path>\n\n      \nserve-build command line option\n\n\n\n    --plasma-directory <plasma_directory>\n\n      \nray-start command line option\n\n\n\n    --plasma-store-socket-name <plasma_store_socket_name>\n\n      \nray-start command line option\n\n\n\n    --port <port>\n\n      \nray-start command line option\n\nserve-run command line option\n\n\n\n    --port-forward <port_forward>\n\n      \nray-attach command line option\n\nray-exec command line option\n\nray-submit command line option\n\n\n\n    --proxy-location <proxy_location>\n\n      \nserve-start command line option"}


created_at: 2023-11-15 15:00:56
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--proxy-location <proxy_location>\n\n      \nserve-start command line option\n\n\n\n    --ray-client-server-port <ray_client_server_port>\n\n      \nray-start command line option\n\n\n\n    --ray-debugger-external\n\n      \nray-start command line option\n\n\n\n    --raylet-socket-name <raylet_socket_name>\n\n      \nray-start command line option\n\n\n\n    --redirect-command-output\n\n      \nray-up command line option\n\n\n\n    --redis_password <redis_password>\n\n      \nray-memory command line option\n\nray-status command line option\n\n\n\n    --reload\n\n      \nserve-run command line option\n\n\n\n    --resources <resources>\n\n      \nray-start command line option\n\n\n\n    --restart-only\n\n      \nray-up command line option\n\n\n\n    --route-prefix <route_prefix>\n\n      \nserve-run command line option\n\n\n\n    --run-env <run_env>\n\n      \nray-exec command line option\n\n\n\n    --runtime-env <runtime_env>\n\n      \nray-job-submit command line option\n\nserve-run command line option\n\n\n\n    --runtime-env-agent-port <runtime_env_agent_port>"}


created_at: 2023-11-15 15:00:56
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "serve-run command line option\n\n\n\n    --runtime-env-agent-port <runtime_env_agent_port>\n\n      \nray-start command line option\n\n\n\n    --runtime-env-json <runtime_env_json>\n\n      \nray-job-submit command line option\n\nserve-run command line option\n\n\n\n    --screen\n\n      \nray-attach command line option\n\nray-exec command line option\n\nray-submit command line option\n\n\n\n    --sort-by <sort_by>\n\n      \nray-memory command line option\n\n\n\n    --start\n\n      \nray-attach command line option\n\nray-exec command line option\n\nray-submit command line option\n\n\n\n    --stats-only\n\n      \nray-memory command line option\n\n\n\n    --stop\n\n      \nray-exec command line option\n\nray-submit command line option\n\n\n\n    --storage <storage>\n\n      \nray-start command line option\n\n\n\n    --submission-id <submission_id>\n\n      \nray-job-submit command line option\n\n\n\n    --temp-dir <temp_dir>\n\n      \nray-start command line option\n\n\n\n    --timeout <timeout>\n\n      \nray-get command line option\n\nray-list command line option"}


created_at: 2023-11-15 15:00:56
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--timeout <timeout>\n\n      \nray-get command line option\n\nray-list command line option\n\nray-summary-actors command line option\n\nray-summary-objects command line option\n\nray-summary-tasks command line option\n\n\n\n    --tmux\n\n      \nray-attach command line option\n\nray-exec command line option\n\nray-submit command line option\n\n\n\n    --units <units>\n\n      \nray-memory command line option\n\n\n\n    --use-login-shells\n\n      \nray-up command line option\n\n\n\n    --use-normal-shells\n\n      \nray-up command line option\n\n\n\n    --verbose\n\n      \nray-attach command line option\n\nray-down command line option\n\nray-exec command line option\n\nray-job-list command line option\n\nray-job-logs command line option\n\nray-job-status command line option\n\nray-job-stop command line option\n\nray-job-submit command line option\n\nray-monitor command line option\n\nray-start command line option\n\nray-stop command line option\n\nray-submit command line option\n\nray-up command line option\n\n\n\n    --verify <verify>"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ray-submit command line option\n\nray-up command line option\n\n\n\n    --verify <verify>\n\n      \nray-job-list command line option\n\nray-job-logs command line option\n\nray-job-status command line option\n\nray-job-stop command line option\n\nray-job-submit command line option\n\n\n\n    --worker-port-list <worker_port_list>\n\n      \nray-start command line option\n\n\n\n    --workers-only\n\n      \nray-down command line option\n\n\n\n    --working-dir <working_dir>\n\n      \nray-job-submit command line option\n\nserve-run command line option\n\n\n\n    --yes\n\n      \nray-down command line option\n\nray-up command line option\n\nserve-shutdown command line option\n\n\n\n    -a\n\n      \nserve-config command line option\n\nserve-deploy command line option\n\nserve-run command line option\n\nserve-shutdown command line option\n\nserve-start command line option\n\nserve-status command line option\n\n\n\n    -d\n\n      \nserve-build command line option\n\nserve-run command line option\n\n\n\n    -f\n\n      \nray-job-logs command line option"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868a4a-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "serve-run command line option\n\n\n\n    -f\n\n      \nray-job-logs command line option\n\nray-list command line option\n\nray-stop command line option\n\n\n\n    -g\n\n      \nray-stop command line option\n\n\n\n    -h\n\n      \nserve-run command line option\n\n\n\n    -N\n\n      \nray-attach command line option\n\n\n\n    -n\n\n      \nray-attach command line option\n\nray-down command line option\n\nray-exec command line option\n\nray-get_head_ip command line option\n\nray-monitor command line option\n\nray-submit command line option\n\nray-up command line option\n\nserve-config command line option\n\nserve-status command line option\n\n\n\n    -o\n\n      \nserve-build command line option\n\n\n\n    -p\n\n      \nray-attach command line option\n\nray-exec command line option\n\nray-submit command line option\n\nserve-run command line option\n\n\n\n    -r\n\n      \nserve-run command line option\n\n\n\n    -v\n\n      \nray-attach command line option\n\nray-down command line option\n\nray-exec command line option\n\nray-job-list command line option"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868aea-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.datasource.BlockWritePathProvider method)\n\n(ray.data.datasource.DefaultBlockWritePathProvider method)\n\n(ray.data.datasource.DefaultFileMetadataProvider method)\n\n(ray.data.datasource.DefaultParquetMetadataProvider method)\n\n(ray.data.datasource.FastFileMetadataProvider method)\n\n(ray.data.datasource.FileMetadataProvider method)\n\n(ray.data.datasource.FilenameProvider method)\n\n(ray.data.datasource.ParquetMetadataProvider method)\n\n(ray.data.datasource.PathPartitionFilter method)\n\n(ray.data.datasource.PathPartitionParser method)\n\n(ray.data.datasource.Reader method)\n\n(ray.data.grouped_data.GroupedData method)\n\n(ray.data.preprocessor.Preprocessor method)\n\n(ray.data.random_access_dataset.RandomAccessDataset method)\n\n(ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.core.models.catalog.Catalog method)\n\n(ray.rllib.env.external_env.ExternalEnv method)\n\n(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)\n\n(ray.rllib.env.multi_agent_env.MultiAgentEnv method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868b3a-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.env.multi_agent_env.MultiAgentEnv method)\n\n(ray.rllib.env.vector_env._VectorizedGymEnv method)\n\n(ray.rllib.env.vector_env.VectorEnv method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.evaluation.sampler.AsyncSampler method)\n\n(ray.rllib.evaluation.sampler.SamplerInput method)\n\n(ray.rllib.evaluation.sampler.SyncSampler method)\n\n(ray.rllib.evaluation.worker_set.WorkerSet method)\n\n(ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.models.tf.recurrent_net.RecurrentNetwork method)\n\n(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\n(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)\n\n(ray.rllib.offline.d4rl_reader.D4RLReader method)\n\n(ray.rllib.offline.input_reader.InputReader method)\n\n(ray.rllib.offline.io_context.IOContext method)\n\n(ray.rllib.offline.json_reader.JsonReader method)\n\n(ray.rllib.offline.mixed_input.MixedInput method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.policy_map.PolicyMap method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868b8a-83c7-11ee-a273-0242ac1c000c
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metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.MultiAgentBatch method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n(ray.rllib.utils.exploration.curiosity.Curiosity method)\n\n(ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)\n\n(ray.rllib.utils.exploration.exploration.Exploration method)\n\n(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)\n\n(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)\n\n(ray.rllib.utils.exploration.parameter_noise.ParameterNoise method)\n\n(ray.rllib.utils.exploration.random.Random method)\n\n(ray.rllib.utils.exploration.random_encoder.RE3 method)\n\n(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868bd0-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)\n\n(ray.rllib.utils.schedules.constant_schedule.ConstantSchedule method)\n\n(ray.rllib.utils.schedules.exponential_schedule.ExponentialSchedule method)\n\n(ray.rllib.utils.schedules.linear_schedule.LinearSchedule method)\n\n(ray.rllib.utils.schedules.piecewise_schedule.PiecewiseSchedule method)\n\n(ray.rllib.utils.schedules.polynomial_schedule.PolynomialSchedule method)\n\n(ray.train.Checkpoint method)\n\n(ray.train.context.TrainContext method)\n\n(ray.train.DataConfig method)\n\n(ray.tune.Callback method)\n\n(ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.ProgressReporter method)\n\n(ray.tune.stopper.Stopper method)"}


created_at: 2023-11-15 15:00:56
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encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.ProgressReporter method)\n\n(ray.tune.stopper.Stopper method)\n\n(ray.tune.Trainable method)\n\n(ray.tune.Tuner method)\n\n\n\n\n_check_is_built() (ray.rllib.core.learner.learner.Learner method)\n\n_check_registered_optimizer() (ray.rllib.core.learner.learner.Learner method)\n\n_check_result() (ray.rllib.core.learner.learner.Learner method)\n\n_convert_batch_type() (ray.rllib.core.learner.learner.Learner method)\n\n_determine_components_hook() (ray.rllib.core.models.catalog.Catalog method)\n\n_get_clip_function() (ray.rllib.core.learner.learner.Learner static method)\n\n_get_dist_cls_from_action_space() (ray.rllib.core.models.catalog.Catalog class method)\n\n_get_encoder_config() (ray.rllib.core.models.catalog.Catalog class method)\n\n_get_metadata() (ray.rllib.core.learner.learner.Learner method)\n\n_get_tensor_variable() (ray.rllib.core.learner.learner.Learner method)\n\n_is_module_compatible_with_learner() (ray.rllib.core.learner.learner.Learner method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868c70-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "_is_module_compatible_with_learner() (ray.rllib.core.learner.learner.Learner method)\n\n_load_optimizers() (ray.rllib.core.learner.learner.Learner method)\n\n_make_module() (ray.rllib.core.learner.learner.Learner method)\n\n_save_optimizers() (ray.rllib.core.learner.learner.Learner method)\n\n_set_optimizer_lr() (ray.rllib.core.learner.learner.Learner static method)\n\n_update() (ray.rllib.core.learner.learner.Learner method)\n\n_VectorizedGymEnv (class in ray.rllib.env.vector_env)\n\n\n\nA\n\n\nAbsMax (class in ray.data.aggregate)\n\naccelerator_type (ray.serve.schema.RayActorOptionsSchema attribute)\n\nAction space\n\nACTION_DIST (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nACTION_DIST_INPUTS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\naction_distribution_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nACTION_LOGP (ray.rllib.policy.sample_batch.SampleBatch attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868cc0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ACTION_LOGP (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nACTION_PROB (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\naction_sampler_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\naction_space (ray.rllib.core.rl_module.rl_module.RLModuleConfig attribute)\n\n(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)\n\n(ray.rllib.env.base_env.BaseEnv property)\n\n\naction_space_contains() (ray.rllib.env.base_env.BaseEnv method)\n\naction_space_sample() (ray.rllib.env.base_env.BaseEnv method)\n\nACTIONS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nActor\n\nActor task\n\nactor_id (ray.runtime_context.RuntimeContext property)\n\n(ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n\nactor_locality_enabled (ray.data.ExecutionOptions attribute)\n\nActorPlacementGroupRemoved\n\nActorPool (class in ray.util)\n\nActorPoolStrategy (class in ray.data)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868d06-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ActorPlacementGroupRemoved\n\nActorPool (class in ray.util)\n\nActorPoolStrategy (class in ray.data)\n\nActorState (class in ray.util.state.common)\n\nActorSummaries (class in ray.util.state.common)\n\nActorSummaryPerClass (class in ray.util.state.common)\n\nActorUnschedulableError\n\nadd() (ray.data.ExecutionResources method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)\n\n\nadd_column() (ray.data.Dataset method)\n\nadd_configurations() (ray.tune.search.basic_variant.BasicVariantGenerator method)\n\nadd_evaluated_point() (ray.tune.search.ax.AxSearch method)\n\n(ray.tune.search.bayesopt.BayesOptSearch method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868d60-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.search.bayesopt.BayesOptSearch method)\n\n(ray.tune.search.bohb.TuneBOHB method)\n\n(ray.tune.search.hyperopt.HyperOptSearch method)\n\n(ray.tune.search.nevergrad.NevergradSearch method)\n\n(ray.tune.search.Repeater method)\n\n(ray.tune.search.Searcher method)\n\n(ray.tune.search.sigopt.SigOptSearch method)\n\n(ray.tune.search.zoopt.ZOOptSearch method)\n\n\nadd_evaluated_trials() (ray.tune.search.ax.AxSearch method)\n\n(ray.tune.search.bayesopt.BayesOptSearch method)\n\n(ray.tune.search.bohb.TuneBOHB method)\n\n(ray.tune.search.ConcurrencyLimiter method)\n\n(ray.tune.search.dragonfly.DragonflySearch method)\n\n(ray.tune.search.hebo.HEBOSearch method)\n\n(ray.tune.search.hyperopt.HyperOptSearch method)\n\n(ray.tune.search.nevergrad.NevergradSearch method)\n\n(ray.tune.search.optuna.OptunaSearch method)\n\n(ray.tune.search.Repeater method)\n\n(ray.tune.search.Searcher method)\n\n(ray.tune.search.sigopt.SigOptSearch method)\n\n(ray.tune.search.skopt.SkOptSearch method)\n\n(ray.tune.search.zoopt.ZOOptSearch method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868db0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.search.skopt.SkOptSearch method)\n\n(ray.tune.search.zoopt.ZOOptSearch method)\n\n\nadd_metric_column() (ray.tune.CLIReporter method)\n\n(ray.tune.JupyterNotebookReporter method)\n\n\nadd_module() (ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n\nadd_modules() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec method)\n\nadd_parameter_column() (ray.tune.CLIReporter method)\n\n(ray.tune.JupyterNotebookReporter method)\n\n\nadd_policy() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.evaluation.worker_set.WorkerSet method)\n\n\nadd_trial() (ray.tune.web_server.TuneClient method)\n\nadd_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nadditional_resources_per_worker (ray.train.ScalingConfig property)\n\nadditional_update() (ray.rllib.core.learner.learner.Learner method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868df6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "additional_update() (ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n\n\n\nadditional_update_for_module() (ray.rllib.core.learner.learner.Learner method)\n\nAgent\n\nAGENT_INDEX (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nagent_steps() (ray.rllib.policy.sample_batch.MultiAgentBatch method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n\naggregate() (ray.data.Dataset method)\n\n(ray.data.grouped_data.GroupedData method)\n\n\naggregate_combined_blocks() (ray.data.block.BlockAccessor static method)\n\nAggregateFn (class in ray.data.aggregate)\n\nAimLoggerCallback (class in ray.tune.logger.aim)\n\nAlgorithm\n\n(class in ray.rllib.algorithms.algorithm)\n\n\nAlgorithmConfig (class in ray.rllib.algorithms.algorithm_config)\n\naligned_array() (in module ray.rllib.utils.numpy)\n\nallgather() (in module ray.util.collective.collective)\n\nallgather_multigpu() (in module ray.util.collective.collective)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868e46-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "allgather_multigpu() (in module ray.util.collective.collective)\n\nallreduce() (in module ray.util.collective.collective)\n\nallreduce_multigpu() (in module ray.util.collective.collective)\n\napp_name (ray.serve.context.ReplicaContext attribute)\n\nApplication (class in ray.serve)\n\napplication_details_route_prefix_format() (ray.serve.schema.ApplicationDetails class method)\n\napplication_names_nonempty() (ray.serve.schema.ServeDeploySchema class method)\n\napplication_names_unique() (ray.serve.schema.ServeDeploySchema class method)\n\napplication_routes_unique() (ray.serve.schema.ServeDeploySchema class method)\n\napplications (ray.serve.schema.ServeDeploySchema attribute)\n\n(ray.serve.schema.ServeInstanceDetails attribute)\n\n\napply() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868e96-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)\n\n\napply_grad_clipping() (in module ray.rllib.utils.torch_utils)\n\napply_gradients() (ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\napply_gradients_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\nAPPOConfig (class in ray.rllib.algorithms.appo.appo)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868edc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "APPOConfig (class in ray.rllib.algorithms.appo.appo)\n\nargs (ray.serve.schema.ServeApplicationSchema attribute)\n\n\n    ARGUMENTS\n\n      \nserve-run command line option\n\n\nas_directory() (ray.train.Checkpoint method)\n\nas_multi_agent() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n(ray.rllib.policy.sample_batch.MultiAgentBatch method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n\nas_placement_group_factory() (ray.train.ScalingConfig method)\n\nas_trainable() (ray.train.data_parallel_trainer.DataParallelTrainer method)\n\n(ray.train.gbdt_trainer.GBDTTrainer method)\n\n(ray.train.horovod.HorovodTrainer method)\n\n(ray.train.lightgbm.LightGBMTrainer method)\n\n(ray.train.tensorflow.TensorflowTrainer method)\n\n(ray.train.torch.TorchTrainer method)\n\n(ray.train.trainer.BaseTrainer method)\n\n(ray.train.xgboost.XGBoostTrainer method)\n\n\nASHAScheduler (in module ray.tune.schedulers)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868f2c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.train.xgboost.XGBoostTrainer method)\n\n\nASHAScheduler (in module ray.tune.schedulers)\n\nassert_healthy() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nasync_update() (ray.rllib.core.learner.learner_group.LearnerGroup method)\n\nAsynchronous execution\n\nAsynchronous sampling\n\nAsyncHyperBandScheduler (class in ray.tune.schedulers)\n\nAsyncioActorExit\n\nAsyncSampler (class in ray.rllib.evaluation.sampler)\n\nattempt_number (ray.util.state.common.TaskState attribute)\n\nATTENTION_MASKS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nAutoscaler\n\nAutoscaling\n\nautoscaling_config (ray.serve.schema.DeploymentSchema attribute)\n\nAutoscalingCluster (class in ray.cluster_utils)\n\navailable_resources() (in module ray)\n\nAxSearch (class in ray.tune.search.ax)\n\n\n\nB\n\n\nBackend\n\n(class in ray.train.backend)\n\n\nbackend (ray.train.torch.TorchConfig attribute)\n\nbackend_cls (ray.train.horovod.HorovodConfig property)\n\n(ray.train.tensorflow.TensorflowConfig property)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868fa4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.train.tensorflow.TensorflowConfig property)\n\n(ray.train.torch.TorchConfig property)\n\n\nBackendConfig (class in ray.train.backend)\n\nbarrier() (in module ray.util.collective.collective)\n\nbase_dir (ray.data.datasource.Partitioning attribute)\n\nbase_trial_resources (ray.tune.schedulers.ResourceChangingScheduler property)\n\nBaseEnv (class in ray.rllib.env.base_env)\n\nBaseFileMetadataProvider (class in ray.data.datasource)\n\nBaseTrainer (class in ray.train.trainer)\n\nBasicVariantGenerator (class in ray.tune.search.basic_variant)\n\nBatch format\n\nBatch size\n\nbatch() (in module ray.serve)\n\nbatch_to_block() (ray.data.block.BlockAccessor static method)\n\nBayesOptSearch (class in ray.tune.search.bayesopt)\n\nBCConfig (class in ray.rllib.algorithms.bc.bc)\n\nbefore_compute_actions() (ray.rllib.utils.exploration.curiosity.Curiosity method)\n\n(ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)\n\n(ray.rllib.utils.exploration.exploration.Exploration method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9868ff4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.utils.exploration.exploration.Exploration method)\n\n(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)\n\n(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)\n\n(ray.rllib.utils.exploration.random.Random method)\n\n(ray.rllib.utils.exploration.random_encoder.RE3 method)\n\n(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)\n\n\nbest_checkpoint (ray.tune.ExperimentAnalysis property)\n\n\n\nbest_checkpoints (ray.train.Result attribute)\n\nbest_config (ray.tune.ExperimentAnalysis property)\n\nbest_dataframe (ray.tune.ExperimentAnalysis property)\n\nbest_logdir (ray.tune.ExperimentAnalysis property)\n\nbest_result (ray.tune.ExperimentAnalysis property)\n\nbest_result_df (ray.tune.ExperimentAnalysis property)\n\nbest_trial (ray.tune.ExperimentAnalysis property)\n\nbind() (ray.serve.Deployment method)\n\nBlendSearch (in module ray.tune.search.flaml)\n\nBlock\n\n(in module ray.data.block)\n\n\nBlockAccessor (class in ray.data.block)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869044-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Block\n\n(in module ray.data.block)\n\n\nBlockAccessor (class in ray.data.block)\n\nBlockBasedFileDatasink (class in ray.data)\n\nBlockExecStats (class in ray.data.block)\n\nBlockMetadata (class in ray.data.block)\n\nBlockWritePathProvider (class in ray.data.datasource)\n\nbroadcast() (in module ray.util.collective.collective)\n\nbroadcast_multigpu() (in module ray.util.collective.collective)\n\nbuild() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n(ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.core.learner.learner.LearnerSpec method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec method)\n\n(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec method)\n\n\nbuild_encoder() (ray.rllib.core.models.catalog.Catalog method)\n\nbuilder() (ray.data.block.BlockAccessor static method)\n\nbundle_count (ray.util.placement_group.PlacementGroup property)\n\nbundle_specs (ray.util.placement_group.PlacementGroup property)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869094-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "bundle_specs (ray.util.placement_group.PlacementGroup property)\n\nbundles (ray.tune.execution.placement_groups.PlacementGroupFactory property)\n\n(ray.util.state.common.PlacementGroupState attribute)\n\n\n\n\nC\n\n\ncall_site (ray.util.state.common.ObjectState attribute)\n\nCallback (class in ray.tune)\n\ncallbacks (ray.train.RunConfig attribute)\n\ncallbacks() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\ncallsite_enabled (ray.util.state.common.ObjectSummaries attribute)\n\ncan_restore() (ray.train.data_parallel_trainer.DataParallelTrainer class method)\n\n(ray.train.gbdt_trainer.GBDTTrainer class method)\n\n(ray.train.horovod.HorovodTrainer class method)\n\n(ray.train.lightgbm.LightGBMTrainer class method)\n\n(ray.train.tensorflow.TensorflowTrainer class method)\n\n(ray.train.torch.TorchTrainer class method)\n\n(ray.train.trainer.BaseTrainer class method)\n\n(ray.train.xgboost.XGBoostTrainer class method)\n\n(ray.tune.Tuner class method)\n\n\ncancel() (in module ray)\n\n(in module ray.workflow)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98690da-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.Tuner class method)\n\n\ncancel() (in module ray)\n\n(in module ray.workflow)\n\n\nCatalog (class in ray.rllib.core.models.catalog)\n\ncatalog_class (ray.rllib.core.rl_module.rl_module.RLModuleConfig attribute)\n\n(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)\n\n\nCategorizer (class in ray.data.preprocessors)\n\nCFO (in module ray.tune.search.flaml)\n\nchdir_to_trial_dir (ray.tune.TuneConfig attribute)\n\nCheckpoint\n\n(class in ray.train)\n\n\ncheckpoint (ray.train.Result attribute)\n\n(ray.tune.experiment.trial.Trial property)\n\n\ncheckpoint_at_end (ray.train.CheckpointConfig attribute)\n\ncheckpoint_config (ray.train.RunConfig attribute)\n\n(ray.tune.Experiment property)\n\n\ncheckpoint_dir (ray.tune.Experiment property)\n\ncheckpoint_frequency (ray.train.CheckpointConfig attribute)\n\ncheckpoint_score_attribute (ray.train.CheckpointConfig attribute)\n\ncheckpoint_score_order (ray.train.CheckpointConfig attribute)\n\nCheckpointConfig (class in ray.train)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986912a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "CheckpointConfig (class in ray.train)\n\ncheckpointing() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nchoice() (in module ray.tune)\n\nchoose_trial_to_run() (ray.tune.schedulers.HyperBandForBOHB method)\n\n(ray.tune.schedulers.HyperBandScheduler method)\n\n(ray.tune.schedulers.pb2.PB2 method)\n\n(ray.tune.schedulers.PopulationBasedTraining method)\n\n(ray.tune.schedulers.TrialScheduler method)\n\n\nCKPT_FILE_TMPL (ray.tune.search.ax.AxSearch attribute)\n\n(ray.tune.search.basic_variant.BasicVariantGenerator attribute)\n\n(ray.tune.search.bayesopt.BayesOptSearch attribute)\n\n(ray.tune.search.bohb.TuneBOHB attribute)\n\n(ray.tune.search.ConcurrencyLimiter attribute)\n\n(ray.tune.search.dragonfly.DragonflySearch attribute)\n\n(ray.tune.search.hebo.HEBOSearch attribute)\n\n(ray.tune.search.hyperopt.HyperOptSearch attribute)\n\n(ray.tune.search.nevergrad.NevergradSearch attribute)\n\n(ray.tune.search.optuna.OptunaSearch attribute)\n\n(ray.tune.search.Repeater attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986917a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.search.optuna.OptunaSearch attribute)\n\n(ray.tune.search.Repeater attribute)\n\n(ray.tune.search.Searcher attribute)\n\n(ray.tune.search.sigopt.SigOptSearch attribute)\n\n(ray.tune.search.skopt.SkOptSearch attribute)\n\n(ray.tune.search.zoopt.ZOOptSearch attribute)\n\n\nclass_name (ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.ActorSummaryPerClass attribute)\n\n\ncleanup() (ray.tune.Trainable method)\n\nclear() (ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_env.RuntimeEnv method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n\nCLIReporter (class in ray.tune)\n\n\n    CLUSTER_CONFIG_FILE\n\n      \nray-attach command line option\n\nray-down command line option\n\nray-exec command line option\n\nray-get_head_ip command line option\n\nray-monitor command line option\n\nray-submit command line option\n\nray-up command line option\n\n\ncluster_resources() (in module ray)\n\n\n    CMD\n\n      \nray-exec command line option"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98691ca-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "cluster_resources() (in module ray)\n\n\n    CMD\n\n      \nray-exec command line option\n\n\ncode_search_path (ray.job_config.JobConfig attribute)\n\ncolumns() (ray.data.Dataset method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n\ncombine() (ray.data.block.BlockAccessor method)\n\nCombinedStopper (class in ray.tune.stopper)\n\ncompile_results() (ray.rllib.core.learner.learner.Learner method)\n\nCOMPLETE_UPDATE (ray.rllib.core.learner.learner.TorchCompileWhatToCompile attribute)\n\ncompress() (ray.rllib.policy.sample_batch.MultiAgentBatch method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n\n\n\ncompute_actions() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\ncompute_actions_from_input_dict() (ray.rllib.policy.policy.Policy method)\n\ncompute_gradients() (ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986921a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\ncompute_gradients_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\ncompute_log_likelihoods() (ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\ncompute_loss() (ray.rllib.core.learner.learner.Learner method)\n\ncompute_loss_for_module() (ray.rllib.core.learner.learner.Learner method)\n\ncompute_single_action() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nconcat() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nconcat_aligned() (in module ray.rllib.utils.numpy)\n\nconcat_multi_gpu_td_errors() (in module ray.rllib.utils.torch_utils)\n\nConcatenator (class in ray.data.preprocessors)\n\nConcurrencyLimiter (class in ray.tune.search)\n\nconfig (ray.train.Result property)\n\n(ray.tune.experiment.trial.Trial attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869260-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "config (ray.train.Result property)\n\n(ray.tune.experiment.trial.Trial attribute)\n\n\n\n    CONFIG_FILE_NAME\n\n      \nserve-deploy command line option\n\n\n\n    CONFIG_OR_IMPORT_PATH\n\n      \nserve-run command line option\n\n\nconfigure() (ray.train.DataConfig method)\n\nconfigure_optimizers() (ray.rllib.core.learner.learner.Learner method)\n\nconfigure_optimizers_for_module() (ray.rllib.core.learner.learner.Learner method)\n\nconnect() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)\n\nConnector\n\nConstantSchedule (class in ray.rllib.utils.schedules.constant_schedule)\n\ncontext (ray.data.Dataset property)\n\ncontext() (ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\n(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)\n\n\nCONTINUE (ray.tune.schedulers.FIFOScheduler attribute)\n\n(ray.tune.schedulers.HyperBandForBOHB attribute)\n\n(ray.tune.schedulers.HyperBandScheduler attribute)\n\n(ray.tune.schedulers.MedianStoppingRule attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98692b0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.schedulers.MedianStoppingRule attribute)\n\n(ray.tune.schedulers.pb2.PB2 attribute)\n\n(ray.tune.schedulers.PopulationBasedTraining attribute)\n\n(ray.tune.schedulers.PopulationBasedTrainingReplay attribute)\n\n(ray.tune.schedulers.ResourceChangingScheduler attribute)\n\n(ray.tune.schedulers.TrialScheduler attribute)\n\n\ncontroller_info (ray.serve.schema.ServeInstanceDetails attribute)\n\nconvert_to_numpy() (in module ray.rllib.utils.numpy)\n\nconvert_to_torch_tensor() (in module ray.rllib.utils.torch_utils)\n\ncopy() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n(ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.MultiAgentBatch method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_env.RuntimeEnv method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n\nCount (class in ray.data.aggregate)\n\ncount (ray.rllib.policy.sample_batch.MultiAgentBatch attribute)\n\ncount() (ray.data.Dataset method)\n\n(ray.data.grouped_data.GroupedData method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869300-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "count() (ray.data.Dataset method)\n\n(ray.data.grouped_data.GroupedData method)\n\n\nCounter (class in ray.serve.metrics)\n\n(class in ray.util.metrics)\n\n\ncpu_time_s (ray.data.block.BlockExecStats attribute)\n\nCQLConfig (class in ray.rllib.algorithms.cql.cql)\n\ncreate_collective_group() (in module ray.util.collective.collective)\n\n(ray.util.collective.collective.GroupManager method)\n\n\ncreate_placement_group_factory() (ray.tune.experiment.trial.Trial method)\n\ncreate_reader() (ray.data.Datasource method)\n\ncreate_scheduler() (in module ray.tune.schedulers)\n\ncreate_searcher() (in module ray.tune.search)\n\ncreation_args() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\ncreation_time_ms (ray.util.state.common.RuntimeEnvState attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n\ncreator_job_id (ray.util.state.common.PlacementGroupState attribute)\n\nCrossLanguageError\n\nCSVLoggerCallback (class in ray.tune.logger)\n\nCUR_OBS (ray.rllib.policy.sample_batch.SampleBatch attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869350-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "CUR_OBS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nCuriosity (class in ray.rllib.utils.exploration.curiosity)\n\ncurrent_actor (ray.runtime_context.RuntimeContext property)\n\ncurrent_placement_group_id (ray.runtime_context.RuntimeContext property)\n\ncustom_loss() (ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\n(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)\n\n\nCustomKBinsDiscretizer (class in ray.data.preprocessors)\n\n\n\nD\n\n\nD4RLReader (class in ray.rllib.offline.d4rl_reader)\n\ndaemon (ray.rllib.evaluation.sampler.AsyncSampler property)\n\nDataConfig (class in ray.train)\n\nDataContext (class in ray.data)\n\ndataframe() (ray.tune.ExperimentAnalysis method)\n\nDataIterator (class in ray.data)\n\nDataParallelTrainer (class in ray.train.data_parallel_trainer)\n\nDataset (class in ray.data)\n\n(object)\n\n\nDatasink (class in ray.data)\n\nDatasource (class in ray.data)\n\ndeath_cause (ray.util.state.common.ActorState attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98693a0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Datasource (class in ray.data)\n\ndeath_cause (ray.util.state.common.ActorState attribute)\n\ndebug_string() (ray.tune.schedulers.AsyncHyperBandScheduler method)\n\n(ray.tune.schedulers.HyperBandForBOHB method)\n\n(ray.tune.schedulers.HyperBandScheduler method)\n\n(ray.tune.schedulers.TrialScheduler method)\n\n\ndebugging() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\ndecompress_if_needed() (ray.rllib.policy.sample_batch.MultiAgentBatch method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n\nDEFAULT_COLUMNS (ray.tune.CLIReporter attribute)\n\n(ray.tune.JupyterNotebookReporter attribute)\n\n\ndefault_ingest_options() (ray.train.DataConfig static method)\n\ndefault_resource_request() (ray.tune.Trainable class method)\n\ndefault_sampler_input() (ray.rllib.offline.io_context.IOContext method)\n\nDefaultBlockWritePathProvider (class in ray.data.datasource)\n\nDefaultCallbacks (class in ray.rllib.algorithms.callbacks)\n\nDefaultFileMetadataProvider (class in ray.data.datasource)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98693e6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "DefaultFileMetadataProvider (class in ray.data.datasource)\n\nDefaultParquetMetadataProvider (class in ray.data.datasource)\n\ndelete() (in module ray.serve)\n\ndelete_job() (ray.job_submission.JobSubmissionClient method)\n\ndeploy_mode (ray.serve.schema.ServeInstanceDetails attribute)\n\ndeployed_app_config (ray.serve.schema.ApplicationDetails attribute)\n\nDeployment\n\n(class in ray.serve)\n\n\ndeployment (ray.serve.context.ReplicaContext attribute)\n\nDeployment graph\n\ndeployment() (in module ray.serve)\n\ndeployment_config (ray.serve.schema.DeploymentDetails attribute)\n\ndeployment_names (ray.serve.schema.ServeApplicationSchema property)\n\ndeployment_route_prefix_not_set() (ray.serve.schema.DeploymentDetails class method)\n\ndeployment_schema_route_prefix_format() (ray.serve.schema.DeploymentSchema class method)\n\nDeploymentHandle (class in ray.serve.handle)\n\nDeploymentResponse (class in ray.serve.handle)\n\nDeploymentResponseGenerator (class in ray.serve.handle)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869436-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "DeploymentResponseGenerator (class in ray.serve.handle)\n\ndeployments (ray.serve.schema.ApplicationDetails attribute)\n\n(ray.serve.schema.ServeApplicationSchema attribute)\n\n\nDeprecated() (in module ray.util.annotations)\n\n\n\ndeserialize() (ray.data.preprocessor.Preprocessor static method)\n\n(ray.data.preprocessors.Categorizer static method)\n\n(ray.data.preprocessors.Concatenator static method)\n\n(ray.data.preprocessors.CustomKBinsDiscretizer static method)\n\n(ray.data.preprocessors.LabelEncoder static method)\n\n(ray.data.preprocessors.MaxAbsScaler static method)\n\n(ray.data.preprocessors.MinMaxScaler static method)\n\n(ray.data.preprocessors.MultiHotEncoder static method)\n\n(ray.data.preprocessors.Normalizer static method)\n\n(ray.data.preprocessors.OneHotEncoder static method)\n\n(ray.data.preprocessors.OrdinalEncoder static method)\n\n(ray.data.preprocessors.PowerTransformer static method)\n\n(ray.data.preprocessors.RobustScaler static method)\n\n(ray.data.preprocessors.SimpleImputer static method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869486-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.preprocessors.SimpleImputer static method)\n\n(ray.data.preprocessors.StandardScaler static method)\n\n(ray.data.preprocessors.UniformKBinsDiscretizer static method)\n\n\ndeserialize_lineage() (ray.data.Dataset static method)\n\ndestroy_collective_group() (in module ray.util.collective.collective)\n\n(ray.util.collective.collective.GroupManager method)\n\n\nDeveloperAPI() (in module ray.util.annotations)\n\ndiagnose_serialization() (in module ray.tune.utils)\n\nDIRECTORY (ray.data.datasource.PartitionStyle attribute)\n\nDisabled (ray.serve.config.ProxyLocation attribute)\n\ndistributed (ray.rllib.core.learner.learner.Learner property)\n\nDistributed Data-Parallel\n\ndistributed_sampler_kwargs (ray.train.lightning.RayDDPStrategy property)\n\n(ray.train.lightning.RayDeepSpeedStrategy property)\n\n(ray.train.lightning.RayFSDPStrategy property)\n\n\nDistributeResources (class in ray.tune.schedulers.resource_changing_scheduler)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98694cc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "DistributeResources (class in ray.tune.schedulers.resource_changing_scheduler)\n\nDistributeResourcesToTopJob (class in ray.tune.schedulers.resource_changing_scheduler)\n\nDockerCluster (class in ray.autoscaler._private.fake_multi_node.test_utils)\n\ndocs_path (ray.serve.schema.ApplicationDetails attribute)\n\nDONES (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\ndownscale_delay_s (ray.serve.config.AutoscalingConfig attribute)\n\ndownscale_smoothing_factor (ray.serve.config.AutoscalingConfig attribute)\n\nDQNConfig (class in ray.rllib.algorithms.dqn.dqn)\n\nDragonflySearch (class in ray.tune.search.dragonfly)\n\nDriver\n\nDRIVER (ray.job_submission.JobType attribute)\n\ndriver_agent_http_address (ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n\ndriver_exit_code (ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n\ndriver_info (ray.job_submission.JobDetails attribute)\n\ndriver_node_id (ray.job_submission.JobDetails attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869526-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "driver_node_id (ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n\ndrop_columns() (ray.data.Dataset method)\n\n\n\nE\n\n\neager_tracing (ray.rllib.core.learner.learner.FrameworkHyperparameters attribute)\n\nEagerTFPolicyV2 (class in ray.rllib.policy.eager_tf_policy_v2)\n\nempty() (ray.util.queue.Queue method)\n\nenable_reproducibility() (in module ray.train.torch)\n\nend_episode() (ray.rllib.env.external_env.ExternalEnv method)\n\n(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)\n\n(ray.rllib.env.policy_client.PolicyClient method)\n\n\nend_time (ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n\nend_time_ms (ray.util.state.common.TaskState attribute)\n\n(ray.util.state.common.WorkerState attribute)\n\n\n\n    ENTRYPOINT\n\n      \nray-job-submit command line option\n\n\nentrypoint (ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n\nentrypoint_memory (ray.job_submission.JobInfo attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869594-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.job_submission.JobInfo attribute)\n\n\nentrypoint_memory (ray.job_submission.JobInfo attribute)\n\nentrypoint_num_cpus (ray.job_submission.JobInfo attribute)\n\nentrypoint_num_gpus (ray.job_submission.JobInfo attribute)\n\nentrypoint_resources (ray.job_submission.JobInfo attribute)\n\nENV_ID (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nenv_steps() (ray.rllib.policy.sample_batch.MultiAgentBatch method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n\nEnvironment\n\nenvironment() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nEpisode\n\nEPISODES (ray.rllib.utils.replay_buffers.replay_buffer.StorageUnit attribute)\n\nEPS_ID (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nEpsilonGreedy (class in ray.rllib.utils.exploration.epsilon_greedy)\n\nerror (ray.train.Result attribute)\n\n(ray.util.state.common.RuntimeEnvState attribute)\n\n\nerror_file (ray.tune.experiment.trial.Trial attribute)\n\nerror_message (ray.util.state.common.TaskState attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98695e4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "error_message (ray.util.state.common.TaskState attribute)\n\nerror_type (ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n\nerrors (ray.tune.ResultGrid property)\n\nestimate_inmemory_data_size() (ray.data.Datasource method)\n\n(ray.data.datasource.Reader method)\n\n\nevaluate() (ray.rllib.algorithms.algorithm.Algorithm method)\n\nevaluated_params (ray.tune.experiment.trial.Trial attribute)\n\n\n\nevaluation() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nevents (ray.util.state.common.TaskState attribute)\n\nEveryNode (ray.serve.config.ProxyLocation attribute)\n\nexec_stats (ray.data.block.BlockMetadata attribute)\n\nExecutionOptions (class in ray.data)\n\nExecutionResources (class in ray.data)\n\nexit_detail (ray.util.state.common.WorkerState attribute)\n\nexit_type (ray.util.state.common.WorkerState attribute)\n\nexpand_paths() (ray.data.datasource.BaseFileMetadataProvider method)\n\nExperiment\n\n(class in ray.tune)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869a62-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Experiment\n\n(class in ray.tune)\n\n\nexperiment_path (ray.tune.ExperimentAnalysis property)\n\n(ray.tune.ResultGrid property)\n\n\nexperiment_tag (ray.tune.experiment.trial.Trial attribute)\n\nexperimental() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nExperimentAnalysis (class in ray.tune)\n\nExperimentPlateauStopper (class in ray.tune.stopper)\n\nexplained_variance() (in module ray.rllib.utils.tf_utils)\n\n(in module ray.rllib.utils.torch_utils)\n\n\nExploration (class in ray.rllib.utils.exploration.exploration)\n\nexploration() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nExponentialSchedule (class in ray.rllib.utils.schedules.exponential_schedule)\n\nexport_checkpoint() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nexport_model() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.policy.Policy method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869ada-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n(ray.tune.Trainable method)\n\n\nexport_policy_checkpoint() (ray.rllib.algorithms.algorithm.Algorithm method)\n\nexport_policy_model() (ray.rllib.algorithms.algorithm.Algorithm method)\n\nextensions_fields (ray.runtime_env.RuntimeEnv attribute)\n\nExternalEnv (class in ray.rllib.env.external_env)\n\nExternalMultiAgentEnv (class in ray.rllib.env.external_multi_agent_env)\n\nextra_action_out() (ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\nextra_action_out_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\nextra_compute_grad_fetches() (ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\nextra_grad_process() (ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\nextra_learn_fetches_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n\n\nF\n\n\nfail_fast (ray.train.FailureConfig attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869b2a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "F\n\n\nfail_fast (ray.train.FailureConfig attribute)\n\nFAILED (ray.job_submission.JobStatus attribute)\n\nfailure_config (ray.train.RunConfig attribute)\n\nFailureConfig (class in ray.train)\n\nFastFileMetadataProvider (class in ray.data.datasource)\n\nFault tolerance\n\nfault_tolerance() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nfc() (in module ray.rllib.utils.numpy)\n\nfetch_ready_async_reqs() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nfield_names (ray.data.datasource.Partitioning attribute)\n\nFIFOScheduler (class in ray.tune.schedulers)\n\nFileMetadataProvider (class in ray.data.datasource)\n\nFilenameProvider (class in ray.data.datasource)\n\nfilesystem (ray.data.datasource.Partitioning attribute)\n\n(ray.train.Checkpoint attribute)\n\n(ray.train.Result property)\n\n(ray.tune.ResultGrid property)\n\n\nfilter() (ray.data.Dataset method)\n\nfilter_param_dict_for_optimizer() (ray.rllib.core.learner.learner.Learner method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869b7a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "filter_param_dict_for_optimizer() (ray.rllib.core.learner.learner.Learner method)\n\nfilterable_columns() (ray.util.state.common.JobState class method)\n\nfind_free_port() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nFINISHED (ray.tune.search.ax.AxSearch attribute)\n\n(ray.tune.search.bayesopt.BayesOptSearch attribute)\n\n(ray.tune.search.bohb.TuneBOHB attribute)\n\n(ray.tune.search.ConcurrencyLimiter attribute)\n\n(ray.tune.search.dragonfly.DragonflySearch attribute)\n\n(ray.tune.search.hebo.HEBOSearch attribute)\n\n(ray.tune.search.hyperopt.HyperOptSearch attribute)\n\n(ray.tune.search.nevergrad.NevergradSearch attribute)\n\n(ray.tune.search.optuna.OptunaSearch attribute)\n\n(ray.tune.search.Repeater attribute)\n\n(ray.tune.search.Searcher attribute)\n\n(ray.tune.search.sigopt.SigOptSearch attribute)\n\n(ray.tune.search.skopt.SkOptSearch attribute)\n\n(ray.tune.search.zoopt.ZOOptSearch attribute)\n\n\nfit() (ray.data.preprocessor.Preprocessor method)\n\n(ray.data.preprocessors.Categorizer method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869bc0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "fit() (ray.data.preprocessor.Preprocessor method)\n\n(ray.data.preprocessors.Categorizer method)\n\n(ray.data.preprocessors.Concatenator method)\n\n(ray.data.preprocessors.CustomKBinsDiscretizer method)\n\n(ray.data.preprocessors.LabelEncoder method)\n\n(ray.data.preprocessors.MaxAbsScaler method)\n\n(ray.data.preprocessors.MinMaxScaler method)\n\n(ray.data.preprocessors.MultiHotEncoder method)\n\n(ray.data.preprocessors.Normalizer method)\n\n(ray.data.preprocessors.OneHotEncoder method)\n\n(ray.data.preprocessors.OrdinalEncoder method)\n\n(ray.data.preprocessors.PowerTransformer method)\n\n(ray.data.preprocessors.RobustScaler method)\n\n(ray.data.preprocessors.SimpleImputer method)\n\n(ray.data.preprocessors.StandardScaler method)\n\n(ray.data.preprocessors.UniformKBinsDiscretizer method)\n\n(ray.train.data_parallel_trainer.DataParallelTrainer method)\n\n(ray.train.gbdt_trainer.GBDTTrainer method)\n\n(ray.train.horovod.HorovodTrainer method)\n\n(ray.train.lightgbm.LightGBMTrainer method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869c10-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.train.horovod.HorovodTrainer method)\n\n(ray.train.lightgbm.LightGBMTrainer method)\n\n(ray.train.tensorflow.TensorflowTrainer method)\n\n(ray.train.torch.TorchTrainer method)\n\n(ray.train.trainer.BaseTrainer method)\n\n(ray.train.xgboost.XGBoostTrainer method)\n\n(ray.tune.Tuner method)\n\n\nfit_transform() (ray.data.preprocessor.Preprocessor method)\n\n(ray.data.preprocessors.Categorizer method)\n\n(ray.data.preprocessors.Concatenator method)\n\n(ray.data.preprocessors.CustomKBinsDiscretizer method)\n\n(ray.data.preprocessors.LabelEncoder method)\n\n(ray.data.preprocessors.MaxAbsScaler method)\n\n(ray.data.preprocessors.MinMaxScaler method)\n\n(ray.data.preprocessors.MultiHotEncoder method)\n\n(ray.data.preprocessors.Normalizer method)\n\n(ray.data.preprocessors.OneHotEncoder method)\n\n(ray.data.preprocessors.OrdinalEncoder method)\n\n(ray.data.preprocessors.PowerTransformer method)\n\n(ray.data.preprocessors.RobustScaler method)\n\n(ray.data.preprocessors.SimpleImputer method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869c60-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.preprocessors.RobustScaler method)\n\n(ray.data.preprocessors.SimpleImputer method)\n\n(ray.data.preprocessors.StandardScaler method)\n\n(ray.data.preprocessors.UniformKBinsDiscretizer method)\n\n\nflat_map() (ray.data.Dataset method)\n\nflatten_inputs_to_1d_tensor() (in module ray.rllib.utils.numpy)\n\n(in module ray.rllib.utils.tf_utils)\n\n(in module ray.rllib.utils.torch_utils)\n\n\n\n\nfor_block() (ray.data.block.BlockAccessor static method)\n\nfor_policy() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nforeach_env() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.evaluation.worker_set.WorkerSet method)\n\n\nforeach_env_with_context() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.evaluation.worker_set.WorkerSet method)\n\n\nforeach_module() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\nforeach_policy() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.evaluation.worker_set.WorkerSet method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869cb0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.evaluation.worker_set.WorkerSet method)\n\n\nforeach_policy_to_train() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.evaluation.worker_set.WorkerSet method)\n\n\nforeach_worker() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nforeach_worker_async() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nforeach_worker_with_id() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nforward() (ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\n(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)\n\n\nforward_exploration() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nforward_inference() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nforward_rnn() (ray.rllib.models.tf.recurrent_net.RecurrentNetwork method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869cf6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "forward_rnn() (ray.rllib.models.tf.recurrent_net.RecurrentNetwork method)\n\nFORWARD_TRAIN (ray.rllib.core.learner.learner.TorchCompileWhatToCompile attribute)\n\nforward_train() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nFRAGMENTS (ray.rllib.utils.replay_buffers.replay_buffer.StorageUnit attribute)\n\nFramework\n\nframework (ray.rllib.core.learner.learner.Learner attribute)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule attribute)\n\n(ray.rllib.core.rl_module.rl_module.RLModule attribute)\n\n\nframework() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nframework_hyperparameters (ray.rllib.core.learner.learner.LearnerSpec attribute)\n\nFrameworkHyperparameters (class in ray.rllib.core.learner.learner)\n\nfreeze() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nfrom_arrow() (in module ray.data)\n\nfrom_arrow_refs() (in module ray.data)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869d46-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "from_arrow() (in module ray.data)\n\nfrom_arrow_refs() (in module ray.data)\n\nfrom_checkpoint() (ray.rllib.algorithms.algorithm.Algorithm static method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule class method)\n\n(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 static method)\n\n(ray.rllib.policy.Policy static method)\n\n(ray.rllib.policy.policy.Policy static method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 static method)\n\n\nfrom_dask() (in module ray.data)\n\nfrom_dict() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig class method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec class method)\n\n(ray.rllib.core.rl_module.rl_module.RLModuleConfig class method)\n\n(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec class method)\n\n\nfrom_directory() (ray.train.Checkpoint class method)\n\nfrom_huggingface() (in module ray.data)\n\nfrom_items() (in module ray.data)\n\nfrom_json() (ray.job_config.JobConfig class method)\n\n(ray.job_submission.JobInfo class method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869d96-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "from_json() (ray.job_config.JobConfig class method)\n\n(ray.job_submission.JobInfo class method)\n\n(ray.tune.Experiment class method)\n\n\nfrom_mars() (in module ray.data)\n\nfrom_modin() (in module ray.data)\n\nfrom_module() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec class method)\n\nfrom_numpy() (in module ray.data)\n\nfrom_numpy_refs() (in module ray.data)\n\nfrom_pandas() (in module ray.data)\n\nfrom_pandas_refs() (in module ray.data)\n\nfrom_path() (ray.train.Result class method)\n\nfrom_placement_group_factory() (ray.train.ScalingConfig class method)\n\nfrom_spark() (in module ray.data)\n\nfrom_state() (ray.rllib.algorithms.algorithm.Algorithm static method)\n\n(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 static method)\n\n(ray.rllib.policy.Policy static method)\n\n(ray.rllib.policy.policy.Policy static method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 static method)\n\n\nfrom_tf() (in module ray.data)\n\nfrom_torch() (in module ray.data)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869de6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "from_tf() (in module ray.data)\n\nfrom_torch() (in module ray.data)\n\nfromkeys() (ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_env.RuntimeEnv method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n\nfull() (ray.util.queue.Queue method)\n\nfunc_or_class (ray.serve.Deployment property)\n\nfunc_or_class_name (ray.util.state.common.TaskState attribute)\n\n(ray.util.state.common.TaskSummaryPerFuncOrClassName attribute)\n\n\nFunctionTrainable (class in ray.tune.trainable.function_trainable)\n\n\n\nG\n\n\nGauge (class in ray.serve.metrics)\n\n(class in ray.util.metrics)\n\n\nGaussianNoise (class in ray.rllib.utils.exploration.gaussian_noise)\n\nGBDTTrainer (class in ray.train.gbdt_trainer)\n\nGCS / Global Control Service\n\ngcs_address (ray.runtime_context.RuntimeContext property)\n\nget() (in module ray)\n\n(ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869e2c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_context.RuntimeContext method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n(ray.util.queue.Queue method)\n\n\nget_action() (ray.rllib.env.external_env.ExternalEnv method)\n\n(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)\n\n(ray.rllib.env.policy_client.PolicyClient method)\n\n\nget_action_dist_cls() (ray.rllib.core.models.catalog.Catalog method)\n\nget_actor() (in module ray)\n\n(in module ray.util.state)\n\n\nget_actor_id() (ray.runtime_context.RuntimeContext method)\n\nget_actor_name() (ray.runtime_context.RuntimeContext method)\n\nget_address() (ray.job_submission.JobSubmissionClient method)\n\nget_agent_ids() (ray.rllib.env.base_env.BaseEnv method)\n\nget_all_configs() (ray.tune.ExperimentAnalysis method)\n\nget_all_trials() (ray.tune.web_server.TuneClient method)\n\nget_app_handle() (in module ray.serve)\n\nget_assigned_resources() (ray.runtime_context.RuntimeContext method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869e7c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "get_assigned_resources() (ray.runtime_context.RuntimeContext method)\n\nget_async() (ray.data.random_access_dataset.RandomAccessDataset method)\n\n(ray.util.queue.Queue method)\n\n\nget_auto_filled_metrics() (ray.tune.Trainable method)\n\nget_batch_divisibility_req() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nget_best_checkpoint() (ray.train.Result method)\n\n(ray.tune.ExperimentAnalysis method)\n\n\nget_best_config() (ray.tune.ExperimentAnalysis method)\n\nget_best_result() (ray.tune.ResultGrid method)\n\nget_best_trial() (ray.tune.ExperimentAnalysis method)\n\nget_catalog() (ray.rllib.core.rl_module.rl_module.RLModuleConfig method)\n\nget_checkpoint() (in module ray.train)\n\nget_collective_group_size() (in module ray.util.collective.collective)\n\nget_config() (ray.data.DataContext method)\n\n(ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.tune.Trainable method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869ecc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.tune.Trainable method)\n\n\nget_connector_metrics() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nget_context() (in module ray.train)\n\nget_current() (ray.data.DataContext static method)\n\nget_current_placement_group() (in module ray.util)\n\nget_data() (ray.rllib.evaluation.sampler.SamplerInput method)\n\nget_dataframe() (ray.tune.ResultGrid method)\n\nget_dataset_config() (ray.train.data_parallel_trainer.DataParallelTrainer method)\n\n(ray.train.horovod.HorovodTrainer method)\n\n(ray.train.tensorflow.TensorflowTrainer method)\n\n(ray.train.torch.TorchTrainer method)\n\n\nget_dataset_shard() (in module ray.train)\n\nget_default_learner_class() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nget_default_policy_class() (ray.rllib.algorithms.algorithm.Algorithm class method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869f12-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "get_default_policy_class() (ray.rllib.algorithms.algorithm.Algorithm class method)\n\nget_default_rl_module_spec() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nget_deployment_handle() (in module ray.serve)\n\nget_device() (in module ray.rllib.utils.torch_utils)\n\n(in module ray.train.torch)\n\n\nget_downscale_smoothing_factor() (ray.serve.config.AutoscalingConfig method)\n\nget_empty_schema_dict() (ray.serve.schema.ServeApplicationSchema static method)\n\n(ray.serve.schema.ServeDeploySchema static method)\n\n(ray.serve.schema.ServeInstanceDetails static method)\n\n\nget_evaluation_config_object() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nget_experiment_name() (ray.train.context.TrainContext method)\n\nget_exploration_action() (ray.rllib.utils.exploration.exploration.Exploration method)\n\nget_exploration_action_dist_cls() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869f62-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nget_exploration_optimizer() (ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)\n\n(ray.rllib.utils.exploration.exploration.Exploration method)\n\n(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)\n\n(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)\n\n(ray.rllib.utils.exploration.parameter_noise.ParameterNoise method)\n\n(ray.rllib.utils.exploration.random.Random method)\n\n(ray.rllib.utils.exploration.random_encoder.RE3 method)\n\n(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)\n\n\nget_exploration_state() (ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nget_extra_batches() (ray.rllib.evaluation.sampler.SamplerInput method)\n\nget_filename_for_block() (ray.data.datasource.FilenameProvider method)\n\nget_filename_for_row() (ray.data.datasource.FilenameProvider method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9869fb2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "get_filename_for_row() (ray.data.datasource.FilenameProvider method)\n\nget_filters() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nget_global_vars() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nget_gpu_devices() (in module ray.rllib.utils.tf_utils)\n\nget_gpu_ids() (in module ray)\n\nget_group_by_name() (ray.util.collective.collective.GroupManager method)\n\nget_host() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a002-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)\n\n\nget_hps_for_module() (ray.rllib.core.learner.learner.LearnerHyperparameters method)\n\nget_in_queue_stats() (ray.rllib.core.learner.learner_group.LearnerGroup method)\n\nget_inference_action_dist_cls() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nget_initial_state() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n(ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.models.tf.recurrent_net.RecurrentNetwork method)\n\n(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\n(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)\n\n(ray.rllib.Policy method)\n\n(ray.rllib.policy.policy.Policy method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a048-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\nget_internal_block_refs() (ray.data.Dataset method)\n\nget_job_id() (ray.runtime_context.RuntimeContext method)\n\nget_job_info() (ray.job_submission.JobSubmissionClient method)\n\nget_job_logs() (ray.job_submission.JobSubmissionClient method)\n\nget_job_status() (ray.job_submission.JobSubmissionClient method)\n\nget_last_checkpoint() (ray.tune.ExperimentAnalysis method)\n\nget_learner_hyperparameters() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nget_local_rank() (ray.train.context.TrainContext method)\n\nget_local_world_size() (ray.train.context.TrainContext method)\n\nget_log() (in module ray.util.state)\n\nget_marl_config() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec method)\n\nget_marl_module_spec() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nget_metadata() (in module ray.workflow)\n\n(ray.data.block.BlockAccessor method)\n\n(ray.train.Checkpoint method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a098-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.block.BlockAccessor method)\n\n(ray.train.Checkpoint method)\n\n(ray.train.context.TrainContext method)\n\n\n\n\nget_metrics() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.evaluation.sampler.SamplerInput method)\n\n\nget_model() (ray.train.lightgbm.LightGBMTrainer static method)\n\n(ray.train.xgboost.XGBoostTrainer static method)\n\n\nget_module_state() (ray.rllib.core.learner.learner.Learner method)\n\nget_multi_agent_setup() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nget_multiplexed_model_id() (in module ray.serve)\n\nget_name() (ray.data.BlockBasedFileDatasink method)\n\n(ray.data.Datasink method)\n\n(ray.data.Datasource method)\n\n(ray.data.RowBasedFileDatasink method)\n\n\nget_next() (ray.util.ActorPool method)\n\nget_next_unordered() (ray.util.ActorPool method)\n\nget_node() (in module ray.util.state)\n\nget_node_id() (ray.runtime_context.RuntimeContext method)\n\nget_node_ip() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a0e8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "get_node_ip() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nget_node_rank() (ray.train.context.TrainContext method)\n\nget_nowait() (ray.util.queue.Queue method)\n\nget_nowait_batch() (ray.util.queue.Queue method)\n\nget_num_samples_loaded_into_buffer() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\nget_objects() (in module ray.util.state)\n\nget_optimizer() (ray.rllib.core.learner.learner.Learner method)\n\nget_optimizer_state() (ray.rllib.core.learner.learner.Learner method)\n\nget_optimizers_for_module() (ray.rllib.core.learner.learner.Learner method)\n\nget_output() (in module ray.workflow)\n\nget_output_async() (in module ray.workflow)\n\nget_param_ref() (ray.rllib.core.learner.learner.Learner method)\n\nget_parameters() (ray.rllib.core.learner.learner.Learner method)\n\nget_params_dict() (ray.rllib.core.learner.learner.LearnerSpec method)\n\nget_placeholder() (in module ray.rllib.utils.tf_utils)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a138-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "get_placeholder() (in module ray.rllib.utils.tf_utils)\n\nget_placement_group() (in module ray.util.state)\n\nget_placement_group_id() (ray.runtime_context.RuntimeContext method)\n\nget_policies_to_train() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nget_policy() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n\nget_preprocessor() (ray.rllib.core.models.catalog.Catalog static method)\n\nget_rank() (in module ray.util.collective.collective)\n\nget_read_tasks() (ray.data.Datasource method)\n\n(ray.data.datasource.Reader method)\n\n\nget_replica_context() (in module ray.serve)\n\nget_resource_ids() (ray.runtime_context.RuntimeContext method)\n\nget_results() (ray.tune.Tuner method)\n\nget_rl_module_config() (ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec method)\n\nget_rollout_fragment_length() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nget_runtime_context() (in module ray.runtime_context)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a17e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "get_runtime_context() (in module ray.runtime_context)\n\nget_runtime_env_string() (ray.runtime_context.RuntimeContext method)\n\nget_session() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nget_single_step_input_dict() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nget_state() (ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.utils.exploration.curiosity.Curiosity method)\n\n(ray.rllib.utils.exploration.exploration.Exploration method)\n\n(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a1ce-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)\n\n(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)\n\n(ray.rllib.utils.exploration.random.Random method)\n\n(ray.rllib.utils.exploration.random_encoder.RE3 method)\n\n(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)\n\n(ray.tune.Callback method)\n\n(ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a21e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.logger.TBXLoggerCallback method)\n\n\nget_status() (in module ray.workflow)\n\nget_storage() (ray.train.context.TrainContext method)\n\nget_sub_environments() (ray.rllib.env.base_env.BaseEnv method)\n\n(ray.rllib.env.vector_env._VectorizedGymEnv method)\n\n(ray.rllib.env.vector_env.VectorEnv method)\n\n\nget_task() (in module ray.util.state)\n\nget_task_id() (ray.runtime_context.RuntimeContext method)\n\nget_tokenizer_config() (ray.rllib.core.models.catalog.Catalog class method)\n\nget_torch_compile_learner_config() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nget_torch_compile_worker_config() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nget_tower_stats() (ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\nget_train_action_dist_cls() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a264-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nget_trainable_name() (ray.tune.Experiment class method)\n\nget_trial() (ray.tune.web_server.TuneClient method)\n\nget_trial_dir() (ray.train.context.TrainContext method)\n\nget_trial_id() (ray.train.context.TrainContext method)\n\nget_trial_name() (ray.train.context.TrainContext method)\n\nget_trial_resources() (ray.train.context.TrainContext method)\n\nget_upscale_smoothing_factor() (ray.serve.config.AutoscalingConfig method)\n\nget_user_configured_option_names() (ray.serve.schema.DeploymentSchema method)\n\nget_version() (ray.job_submission.JobSubmissionClient method)\n\nget_weights() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\nget_worker() (in module ray.util.state)\n\nget_worker_id() (ray.runtime_context.RuntimeContext method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a2b4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "get_worker_id() (ray.runtime_context.RuntimeContext method)\n\nget_world_rank() (ray.train.context.TrainContext method)\n\nget_world_size() (ray.train.context.TrainContext method)\n\ngetName() (ray.rllib.evaluation.sampler.AsyncSampler method)\n\nGetTimeoutError\n\nglobal_norm() (in module ray.rllib.utils.torch_utils)\n\ngraceful_shutdown_timeout_s (ray.serve.schema.DeploymentSchema attribute)\n\ngraceful_shutdown_wait_loop_s (ray.serve.schema.DeploymentSchema attribute)\n\ngrad_clip (ray.rllib.core.learner.learner.LearnerHyperparameters attribute)\n\ngrad_clip_by (ray.rllib.core.learner.learner.LearnerHyperparameters attribute)\n\ngrad_stats_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\ngrid_search() (in module ray.tune)\n\ngroupby() (ray.data.Dataset method)\n\nGroupedData (class in ray.data.grouped_data)\n\nGroupManager (class in ray.util.collective.collective)\n\ngrpc_options (ray.serve.schema.ServeDeploySchema attribute)\n\n(ray.serve.schema.ServeInstanceDetails attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a304-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.serve.schema.ServeInstanceDetails attribute)\n\n\ngrpc_servicer_func_callable (ray.serve.config.gRPCOptions property)\n\ngrpc_servicer_functions (ray.serve.config.gRPCOptions attribute)\n\n(ray.serve.schema.gRPCOptionsSchema attribute)\n\n\n\n\nH\n\n\nhas_checkpoint() (ray.tune.search.basic_variant.BasicVariantGenerator method)\n\nhas_free() (ray.util.ActorPool method)\n\nhas_next() (ray.util.ActorPool method)\n\nhas_serializable_lineage() (ray.data.Dataset method)\n\nHead node\n\nhead_bundle_is_empty (ray.tune.execution.placement_groups.PlacementGroupFactory property)\n\nhead_cpus (ray.tune.execution.placement_groups.PlacementGroupFactory property)\n\nHeadOnly (ray.serve.config.ProxyLocation attribute)\n\nhealth_check_period_s (ray.serve.schema.DeploymentSchema attribute)\n\nhealth_check_timeout_s (ray.serve.schema.DeploymentSchema attribute)\n\nhealthy_worker_ids() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nHEBOSearch (class in ray.tune.search.hebo)\n\nHistogram (class in ray.serve.metrics)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a390-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "HEBOSearch (class in ray.tune.search.hebo)\n\nHistogram (class in ray.serve.metrics)\n\n(class in ray.util.metrics)\n\n\nHIVE (ray.data.datasource.PartitionStyle attribute)\n\n\n\nHorovodConfig (class in ray.train.horovod)\n\nHorovodTrainer (class in ray.train.horovod)\n\nhost (ray.serve.config.HTTPOptions attribute)\n\n(ray.serve.schema.HTTPOptionsSchema attribute)\n\n(ray.serve.schema.ServeApplicationSchema attribute)\n\n\nHPO\n\nhps (ray.rllib.core.learner.learner.Learner property)\n\nhttp_options (ray.serve.schema.ServeDeploySchema attribute)\n\n(ray.serve.schema.ServeInstanceDetails attribute)\n\n\nhuber_loss() (in module ray.rllib.utils.numpy)\n\n(in module ray.rllib.utils.tf_utils)\n\n(in module ray.rllib.utils.torch_utils)\n\n\nhumanify() (ray.util.state.common.JobState class method)\n\nHyperBandForBOHB (class in ray.tune.schedulers)\n\nHyperBandScheduler (class in ray.tune.schedulers)\n\nHyperOptSearch (class in ray.tune.search.hyperopt)\n\n\n\nI\n\n\n\n    ID\n\n      \nray-get command line option"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a3e0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "I\n\n\n\n    ID\n\n      \nray-get command line option\n\n\nid (ray.job_submission.DriverInfo attribute)\n\nident (ray.rllib.evaluation.sampler.AsyncSampler property)\n\nImpalaConfig (class in ray.rllib.algorithms.impala.impala)\n\nimport_from_h5() (ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\n(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)\n\n\nimport_model() (ray.rllib.algorithms.algorithm.Algorithm method)\n\nimport_model_from_h5() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nimport_path (ray.serve.schema.ServeApplicationSchema attribute)\n\nimport_path_format_valid() (ray.serve.schema.ServeApplicationSchema class method)\n\n\n    IMPORT_PATHS\n\n      \nserve-build command line option\n\n\nimport_policy_model_from_h5() (ray.rllib.algorithms.algorithm.Algorithm method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a426-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "import_policy_model_from_h5() (ray.rllib.algorithms.algorithm.Algorithm method)\n\ninc() (ray.serve.metrics.Counter method)\n\n(ray.util.metrics.Counter method)\n\n\ninfo (ray.serve.metrics.Counter property)\n\n(ray.serve.metrics.Gauge property)\n\n(ray.serve.metrics.Histogram property)\n\n(ray.util.metrics.Counter property)\n\n(ray.util.metrics.Gauge property)\n\n(ray.util.metrics.Histogram property)\n\n\nINFOS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nIngress Deployment\n\ningress() (in module ray.serve)\n\ninit() (in module ray)\n\ninit_collective_group() (in module ray.util.collective.collective)\n\ninit_local_path() (ray.tune.experiment.trial.Trial method)\n\ninit_logdir() (ray.tune.experiment.trial.Trial method)\n\ninit_method (ray.train.torch.TorchConfig attribute)\n\ninit_view_requirements() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a476-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\ninitial_replicas (ray.serve.config.AutoscalingConfig attribute)\n\ninput_config (ray.rllib.offline.io_context.IOContext property)\n\ninput_files (ray.data.block.BlockMetadata attribute)\n\ninput_files() (ray.data.Dataset method)\n\ninput_specs_exploration() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\ninput_specs_inference() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\ninput_specs_train() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\n\n\nInputReader (class in ray.rllib.offline.input_reader)\n\ninspect_serializability() (in module ray.util)\n\ninverse_transform() (ray.data.preprocessors.LabelEncoder method)\n\nIOContext (class in ray.rllib.offline.io_context)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a4bc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "IOContext (class in ray.rllib.offline.io_context)\n\nip (ray.util.state.common.ObjectState attribute)\n\n(ray.util.state.common.WorkerState attribute)\n\n\nis_alive (ray.util.state.common.WorkerState attribute)\n\nis_alive() (ray.rllib.evaluation.sampler.AsyncSampler method)\n\nis_atari (ray.rllib.algorithms.algorithm_config.AlgorithmConfig property)\n\nis_detached (ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.PlacementGroupState attribute)\n\n\nis_empty (ray.util.placement_group.PlacementGroup property)\n\nis_finished() (ray.tune.search.basic_variant.BasicVariantGenerator method)\n\nis_group_initialized() (in module ray.util.collective.collective)\n\nis_head_node (ray.util.state.common.NodeState attribute)\n\nis_initialized() (in module ray)\n\nis_local (ray.rllib.core.learner.learner_group.LearnerGroup property)\n\nis_multi_agent() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nis_policy_to_train() (ray.rllib.evaluation.worker_set.WorkerSet method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a50c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "is_policy_to_train() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nis_recurrent() (ray.rllib.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\nis_single_trajectory() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nis_stateful() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nis_terminal() (ray.job_submission.JobStatus method)\n\nis_terminated_or_truncated() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nis_time_major() (ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\n(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)\n\n\nis_training (ray.rllib.policy.sample_batch.SampleBatch property)\n\nisDaemon() (ray.rllib.evaluation.sampler.AsyncSampler method)\n\nitems() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n(ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a55c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_env.RuntimeEnv method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n\niter_batches() (ray.data.DataIterator method)\n\n(ray.data.Dataset method)\n\n\niter_rows() (ray.data.block.BlockAccessor method)\n\n(ray.data.Dataset method)\n\n\niter_tf_batches() (ray.data.Dataset method)\n\niter_torch_batches() (ray.data.DataIterator method)\n\n(ray.data.Dataset method)\n\n\niteration (ray.rllib.algorithms.algorithm.Algorithm property)\n\n(ray.tune.Trainable property)\n\n\niterator() (ray.data.Dataset method)\n\n\n\nJ\n\n\njava_actor_class() (in module ray.cross_language)\n\njava_function() (in module ray.cross_language)\n\nJob\n\n\n    JOB_ID\n\n      \nray-job-logs command line option\n\nray-job-status command line option\n\nray-job-stop command line option\n\n\njob_id (ray.job_submission.JobDetails attribute)\n\n(ray.runtime_context.RuntimeContext property)\n\n(ray.util.state.common.ActorState attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a5a2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.runtime_context.RuntimeContext property)\n\n(ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n\n\n\nJobConfig (class in ray.job_config)\n\nJobInfo (class in ray.job_submission)\n\nJobState (class in ray.util.state.common)\n\nJobStatus (class in ray.job_submission)\n\nJobSubmissionClient (class in ray.job_submission)\n\nJobType (class in ray.job_submission)\n\njoin() (ray.rllib.evaluation.sampler.AsyncSampler method)\n\nJsonLoggerCallback (class in ray.tune.logger)\n\nJsonReader (class in ray.rllib.offline.json_reader)\n\nJupyterNotebookReporter (class in ray.tune)\n\njvm_options (ray.job_config.JobConfig attribute)\n\n\n\nK\n\n\nkeep_alive_timeout_s (ray.serve.config.HTTPOptions attribute)\n\n(ray.serve.schema.HTTPOptionsSchema attribute)\n\n\nkey (ray.train.horovod.HorovodConfig attribute)\n\nkeys() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.policy.policy_map.PolicyMap method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a5f2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_env.RuntimeEnv method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n\n\n\nkill() (in module ray)\n\nkill_node() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)\n\nknown_fields (ray.runtime_env.RuntimeEnv attribute)\n\n(ray.runtime_env.RuntimeEnvConfig attribute)\n\n\n\n\nL\n\n\nl2_loss() (in module ray.rllib.utils.numpy)\n\n(in module ray.rllib.utils.tf_utils)\n\n(in module ray.rllib.utils.torch_utils)\n\n\nLabelEncoder (class in ray.data.preprocessors)\n\nlabels (ray.util.state.common.NodeState attribute)\n\nlanguage (ray.util.state.common.TaskState attribute)\n\nlast() (ray.rllib.env.base_env.BaseEnv method)\n\nlast_deployed_time_s (ray.serve.schema.ApplicationDetails attribute)\n\nlast_output() (ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\n(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a638-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)\n\n\nlatent_dims (ray.rllib.core.models.catalog.Catalog property)\n\nlearn_on_batch() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\nlearn_on_batch_from_replay_buffer() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nlearn_on_loaded_batch() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\nLearner (class in ray.rllib.core.learner.learner)\n\nlearner_class (ray.rllib.algorithms.algorithm_config.AlgorithmConfig property)\n\n(ray.rllib.core.learner.learner.LearnerSpec attribute)\n\n\nlearner_group_scaling_config (ray.rllib.core.learner.learner.LearnerSpec attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a688-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "learner_group_scaling_config (ray.rllib.core.learner.learner.LearnerSpec attribute)\n\nlearner_hyperparameters (ray.rllib.core.learner.learner.LearnerSpec attribute)\n\nLearnerGroup (class in ray.rllib.core.learner.learner_group)\n\nLearnerHyperparameters (class in ray.rllib.core.learner.learner)\n\nLearnerSpec (class in ray.rllib.core.learner.learner)\n\nlearning_rate (ray.rllib.core.learner.learner.LearnerHyperparameters attribute)\n\nLightGBMTrainer (class in ray.train.lightgbm)\n\nlightning_module_state_dict() (ray.train.lightning.RayFSDPStrategy method)\n\nlimit() (ray.data.Dataset method)\n\nLineage\n\nLinearSchedule (class in ray.rllib.utils.schedules.linear_schedule)\n\nlist_actors() (in module ray.util.state)\n\nlist_all() (in module ray.workflow)\n\nlist_columns() (ray.util.state.common.JobState class method)\n\nlist_jobs() (in module ray.util.state)\n\n(ray.job_submission.JobSubmissionClient method)\n\n\nlist_logs() (in module ray.util.state)\n\nlist_nodes() (in module ray.util.state)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a6ce-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "list_logs() (in module ray.util.state)\n\nlist_nodes() (in module ray.util.state)\n\nlist_objects() (in module ray.util.state)\n\nlist_placement_groups() (in module ray.util.state)\n\nlist_runtime_envs() (in module ray.util.state)\n\nlist_tasks() (in module ray.util.state)\n\nlist_workers() (in module ray.util.state)\n\nload_batch_into_buffer() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\nload_checkpoint() (ray.tune.Trainable method)\n\nload_module_state() (ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n\n\nload_state() (ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nload_state_path (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a71e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "load_state_path (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec attribute)\n\n(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)\n\n\nlocal_dir (ray.train.RunConfig attribute)\n\n(ray.tune.Experiment property)\n\n(ray.tune.experiment.trial.Trial property)\n\n\nlocal_path (ray.tune.Experiment property)\n\n(ray.tune.experiment.trial.Trial attribute)\n\n\nlocal_worker() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nlocality_with_output (ray.data.ExecutionOptions attribute)\n\nLocalRayletDiedError\n\nlocation (ray.serve.config.HTTPOptions attribute)\n\nlocation_backfill_no_server() (ray.serve.config.HTTPOptions class method)\n\nlock() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nlog_action() (ray.rllib.env.external_env.ExternalEnv method)\n\n(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)\n\n(ray.rllib.env.policy_client.PolicyClient method)\n\n\nlog_result() (ray.tune.Trainable method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a76e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.env.policy_client.PolicyClient method)\n\n\nlog_result() (ray.tune.Trainable method)\n\nlog_returns() (ray.rllib.env.external_env.ExternalEnv method)\n\n(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)\n\n(ray.rllib.env.policy_client.PolicyClient method)\n\n\nlog_to_file (ray.train.RunConfig attribute)\n\nlog_trial_end() (ray.tune.logger.LoggerCallback method)\n\nlog_trial_restore() (ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.logger.TBXLoggerCallback method)\n\n\nlog_trial_result() (ray.tune.logger.LoggerCallback method)\n\nlog_trial_save() (ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.logger.TBXLoggerCallback method)\n\n\nlog_trial_start() (ray.tune.logger.CSVLoggerCallback method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a7be-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "log_trial_start() (ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n\nlogdir (ray.rllib.algorithms.algorithm.Algorithm property)\n\n(ray.tune.experiment.trial.Trial property)\n\n(ray.tune.Trainable property)\n\n\nLoggerCallback (class in ray.tune.logger)\n\nlogging_config (ray.serve.schema.DeploymentSchema attribute)\n\n(ray.serve.schema.ServeApplicationSchema attribute)\n\n(ray.serve.schema.ServeDeploySchema attribute)\n\n\nlograndint() (in module ray.tune)\n\nloguniform() (in module ray.tune)\n\nlook_back_period_s (ray.serve.config.AutoscalingConfig attribute)\n\nloss() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nlstm() (in module ray.rllib.utils.numpy)\n\n\n\nM\n\n\nmake_action_immutable() (in module ray.rllib.utils.numpy)\n\nmake_model() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a80e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "make_model() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nmake_model_and_action_dist() (ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\nmake_multi_agent() (in module ray.rllib.env.multi_agent_env)\n\nmake_rl_module() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nmake_tf_callable() (in module ray.rllib.utils.tf_utils)\n\nmap() (ray.data.Dataset method)\n\n(ray.util.ActorPool method)\n\n\nmap_batches() (ray.data.Dataset method)\n\nmap_groups() (ray.data.grouped_data.GroupedData method)\n\nmap_unordered() (ray.util.ActorPool method)\n\nMARWILConfig (class in ray.rllib.algorithms.marwil.marwil)\n\nmaterialize() (ray.data.Dataset method)\n\nMax (class in ray.data.aggregate)\n\nmax() (ray.data.Dataset method)\n\n(ray.data.grouped_data.GroupedData method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a85e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "max() (ray.data.Dataset method)\n\n(ray.data.grouped_data.GroupedData method)\n\n\nmax_concurrent_queries (ray.serve.Deployment property)\n\n(ray.serve.schema.DeploymentSchema attribute)\n\n\nmax_concurrent_trials (ray.tune.TuneConfig attribute)\n\nmax_failures (ray.train.FailureConfig attribute)\n\nmax_replicas (ray.serve.config.AutoscalingConfig attribute)\n\nmax_replicas_per_node (ray.serve.schema.DeploymentSchema attribute)\n\nMaxAbsScaler (class in ray.data.preprocessors)\n\nMaximumIterationStopper (class in ray.tune.stopper)\n\nmaybe_add_time_dimension() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nmaybe_remove_time_dimension() (ray.rllib.policy.policy.Policy method)\n\nMean (class in ray.data.aggregate)\n\nmean() (ray.data.Dataset method)\n\n(ray.data.grouped_data.GroupedData method)\n\n\nMedianStoppingRule (class in ray.tune.schedulers)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a8ae-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.grouped_data.GroupedData method)\n\n\nMedianStoppingRule (class in ray.tune.schedulers)\n\nmemory (ray.serve.schema.RayActorOptionsSchema attribute)\n\nMemoryTrackingCallbacks (class in ray.rllib.algorithms.callbacks)\n\nmerge_algorithm_configs() (ray.rllib.algorithms.algorithm.Algorithm class method)\n\nmerge_sorted_blocks() (ray.data.block.BlockAccessor static method)\n\nmessage (ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n(ray.serve.schema.ApplicationDetails attribute)\n\n(ray.serve.schema.DeploymentDetails attribute)\n\n\nmetadata (ray.job_config.JobConfig attribute)\n\n(ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n\nmethod() (in module ray)\n\nmetric (ray.tune.schedulers.FIFOScheduler property)\n\n(ray.tune.schedulers.HyperBandForBOHB property)\n\n(ray.tune.schedulers.HyperBandScheduler property)\n\n(ray.tune.schedulers.MedianStoppingRule property)\n\n(ray.tune.schedulers.pb2.PB2 property)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a8f4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.schedulers.MedianStoppingRule property)\n\n(ray.tune.schedulers.pb2.PB2 property)\n\n(ray.tune.schedulers.PopulationBasedTraining property)\n\n(ray.tune.schedulers.PopulationBasedTrainingReplay property)\n\n(ray.tune.schedulers.ResourceChangingScheduler property)\n\n(ray.tune.schedulers.TrialScheduler property)\n\n(ray.tune.search.ax.AxSearch property)\n\n(ray.tune.search.basic_variant.BasicVariantGenerator property)\n\n(ray.tune.search.bayesopt.BayesOptSearch property)\n\n(ray.tune.search.bohb.TuneBOHB property)\n\n(ray.tune.search.ConcurrencyLimiter property)\n\n(ray.tune.search.dragonfly.DragonflySearch property)\n\n(ray.tune.search.hebo.HEBOSearch property)\n\n(ray.tune.search.hyperopt.HyperOptSearch property)\n\n(ray.tune.search.nevergrad.NevergradSearch property)\n\n(ray.tune.search.optuna.OptunaSearch property)\n\n(ray.tune.search.Repeater property)\n\n(ray.tune.search.Searcher property)\n\n(ray.tune.search.sigopt.SigOptSearch property)\n\n(ray.tune.search.skopt.SkOptSearch property)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a944-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.search.sigopt.SigOptSearch property)\n\n(ray.tune.search.skopt.SkOptSearch property)\n\n(ray.tune.search.zoopt.ZOOptSearch property)\n\n(ray.tune.TuneConfig attribute)\n\n\n\n\nmetrics (ray.train.Result attribute)\n\nmetrics() (ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\n(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)\n\n\nmetrics_dataframe (ray.train.Result attribute)\n\nmetrics_interval_s (ray.serve.config.AutoscalingConfig attribute)\n\nmiddlewares (ray.serve.config.HTTPOptions attribute)\n\nmin() (ray.data.Dataset method)\n\n(ray.data.grouped_data.GroupedData method)\n\n\nmin_replicas (ray.serve.config.AutoscalingConfig attribute)\n\nminimize_and_clip() (in module ray.rllib.utils.tf_utils)\n\n(in module ray.rllib.utils.torch_utils)\n\n\nMinMaxScaler (class in ray.data.preprocessors)\n\nMixedInput (class in ray.rllib.offline.mixed_input)\n\nmode (ray.tune.search.ax.AxSearch property)\n\n(ray.tune.search.bayesopt.BayesOptSearch property)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a994-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "mode (ray.tune.search.ax.AxSearch property)\n\n(ray.tune.search.bayesopt.BayesOptSearch property)\n\n(ray.tune.search.bohb.TuneBOHB property)\n\n(ray.tune.search.ConcurrencyLimiter property)\n\n(ray.tune.search.dragonfly.DragonflySearch property)\n\n(ray.tune.search.hebo.HEBOSearch property)\n\n(ray.tune.search.hyperopt.HyperOptSearch property)\n\n(ray.tune.search.nevergrad.NevergradSearch property)\n\n(ray.tune.search.optuna.OptunaSearch property)\n\n(ray.tune.search.Repeater property)\n\n(ray.tune.search.Searcher property)\n\n(ray.tune.search.sigopt.SigOptSearch property)\n\n(ray.tune.search.skopt.SkOptSearch property)\n\n(ray.tune.search.zoopt.ZOOptSearch property)\n\n(ray.tune.TuneConfig attribute)\n\n\nModel\n\nmodel_config_dict (ray.rllib.core.rl_module.rl_module.RLModuleConfig attribute)\n\n(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)\n\n\nModelV2 (class in ray.rllib.models.modelv2)\n\n\n    module\n\n      \nray\n\nray.rllib.env.multi_agent_env\n\nray.util.collective.collective"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986a9da-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "module\n\n      \nray\n\nray.rllib.env.multi_agent_env\n\nray.util.collective.collective\n\n\nmodule (ray.rllib.core.learner.learner.Learner property)\n\n(ray.rllib.core.learner.learner.LearnerSpec attribute)\n\n\nmodule_class (ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)\n\nmodule_spec (ray.rllib.core.learner.learner.LearnerSpec attribute)\n\nmodule_specs (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec attribute)\n\nmodules_to_load (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec attribute)\n\nMulti-agent\n\nmulti_agent() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nmulti_gpu_train_one_step() (in module ray.rllib.execution.train_ops)\n\nmultiagent (ray.rllib.algorithms.algorithm_config.AlgorithmConfig property)\n\nMultiAgentBatch (class in ray.rllib.policy.sample_batch)\n\nMultiAgentEnv (class in ray.rllib.env.multi_agent_env)\n\nMultiAgentPrioritizedReplayBuffer (class in ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986aa2a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "MultiAgentReplayBuffer (class in ray.rllib.utils.replay_buffers.multi_agent_replay_buffer)\n\nMultiAgentRLModule (class in ray.rllib.core.rl_module.marl_module)\n\nMultiAgentRLModuleSpec (class in ray.rllib.core.rl_module.marl_module)\n\nMultiCallbacks (class in ray.rllib.algorithms.callbacks)\n\nmultiget() (ray.data.random_access_dataset.RandomAccessDataset method)\n\nMultiHotEncoder (class in ray.data.preprocessors)\n\nmultiplexed() (in module ray.serve)\n\n\n\nN\n\n\nname (ray.rllib.evaluation.sampler.AsyncSampler property)\n\n(ray.serve.Deployment property)\n\n(ray.serve.schema.ApplicationDetails attribute)\n\n(ray.serve.schema.DeploymentDetails attribute)\n\n(ray.serve.schema.DeploymentSchema attribute)\n\n(ray.serve.schema.ServeApplicationSchema attribute)\n\n(ray.train.RunConfig attribute)\n\n(ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.PlacementGroupState attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n\nNamespace\n\nnamespace (ray.runtime_context.RuntimeContext property)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986aa7a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Namespace\n\nnamespace (ray.runtime_context.RuntimeContext property)\n\nnative_id (ray.rllib.evaluation.sampler.AsyncSampler property)\n\nnested_host_and_port() (ray.serve.schema.ServeDeploySchema class method)\n\nNevergradSearch (class in ray.tune.search.nevergrad)\n\nnext() (ray.rllib.env.policy_server_input.PolicyServerInput method)\n\n(ray.rllib.offline.input_reader.InputReader method)\n\n\nNEXT_OBS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nnext_trial() (ray.tune.search.basic_variant.BasicVariantGenerator method)\n\nnics (ray.train.horovod.HorovodConfig attribute)\n\nNode\n\nnode_id (ray.data.block.BlockExecStats attribute)\n\n(ray.runtime_context.RuntimeContext property)\n\n(ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy attribute)\n\n(ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.NodeState attribute)\n\n(ray.util.state.common.RuntimeEnvState attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n(ray.util.state.common.WorkerState attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986aac0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.util.state.common.TaskState attribute)\n\n(ray.util.state.common.WorkerState attribute)\n\n\nnode_id_to_summary (ray.util.state.common.StateSummary attribute)\n\nnode_ip (ray.util.state.common.NodeState attribute)\n\nnode_ip_address (ray.job_submission.DriverInfo attribute)\n\nnode_name (ray.util.state.common.NodeState attribute)\n\nNodeAffinitySchedulingStrategy (class in ray.util.scheduling_strategies)\n\nnodes() (in module ray)\n\n\n\nNodeState (class in ray.util.state.common)\n\nNOOP (ray.tune.schedulers.FIFOScheduler attribute)\n\n(ray.tune.schedulers.HyperBandForBOHB attribute)\n\n(ray.tune.schedulers.HyperBandScheduler attribute)\n\n(ray.tune.schedulers.MedianStoppingRule attribute)\n\n(ray.tune.schedulers.pb2.PB2 attribute)\n\n(ray.tune.schedulers.PopulationBasedTraining attribute)\n\n(ray.tune.schedulers.PopulationBasedTrainingReplay attribute)\n\n(ray.tune.schedulers.ResourceChangingScheduler attribute)\n\n(ray.tune.schedulers.TrialScheduler attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ab10-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.schedulers.TrialScheduler attribute)\n\n\nnormalized_base_dir (ray.data.datasource.Partitioning property)\n\nNormalizer (class in ray.data.preprocessors)\n\nnum_blocks() (ray.data.Dataset method)\n\nnum_cpus (ray.serve.config.HTTPOptions attribute)\n\n(ray.serve.schema.RayActorOptionsSchema attribute)\n\n\nnum_cpus_per_worker (ray.train.ScalingConfig property)\n\nnum_errors (ray.tune.ResultGrid property)\n\nnum_gpus (ray.serve.schema.RayActorOptionsSchema attribute)\n\nnum_gpus_per_worker (ray.train.ScalingConfig property)\n\nnum_healthy_remote_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nnum_healthy_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nnum_in_flight_async_reqs() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nnum_remote_worker_restarts() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nnum_remote_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nnum_replicas (ray.serve.Deployment property)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ab60-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "num_replicas (ray.serve.Deployment property)\n\n(ray.serve.schema.DeploymentSchema attribute)\n\n\nnum_replicas_and_autoscaling_config_mutually_exclusive() (ray.serve.schema.DeploymentSchema class method)\n\nnum_rows (ray.data.block.BlockMetadata attribute)\n\nnum_rows() (ray.data.block.BlockAccessor method)\n\nnum_samples (ray.tune.TuneConfig attribute)\n\nnum_state_tensors() (ray.rllib.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\nnum_terminated (ray.tune.ResultGrid property)\n\nnum_to_keep (ray.train.CheckpointConfig attribute)\n\nnum_workers (ray.rllib.algorithms.algorithm_config.AlgorithmConfig property)\n\n(ray.train.ScalingConfig attribute)\n\n\n\n\nO\n\n\nObject\n\nObject ownership\n\nObject reference\n\nObject spilling\n\nObject store\n\nobject_id (ray.util.state.common.ObjectState attribute)\n\nobject_size (ray.util.state.common.ObjectState attribute)\n\nobject_store_memory (ray.serve.schema.RayActorOptionsSchema attribute)\n\nobject_store_memory_str() (ray.data.ExecutionResources method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986aba6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "object_store_memory_str() (ray.data.ExecutionResources method)\n\nObjectFetchTimedOutError\n\nOBJECTIVE_MAP (ray.tune.search.sigopt.SigOptSearch attribute)\n\nObjectLostError\n\nObjectReconstructionFailedError\n\nObjectReconstructionFailedLineageEvictedError\n\nObjectReconstructionFailedMaxAttemptsExceededError\n\nObjectState (class in ray.util.state.common)\n\nObjectStoreFullError\n\nObjectSummaries (class in ray.util.state.common)\n\nObjectSummaryPerKey (class in ray.util.state.common)\n\nOBS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nOBS_EMBEDS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nObservation\n\nobservation_space (ray.rllib.core.rl_module.rl_module.RLModuleConfig attribute)\n\n(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)\n\n(ray.rllib.env.base_env.BaseEnv property)\n\n\nobservation_space_contains() (ray.rllib.env.base_env.BaseEnv method)\n\nobservation_space_sample() (ray.rllib.env.base_env.BaseEnv method)\n\nobserve() (ray.serve.metrics.Histogram method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986abf6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "observe() (ray.serve.metrics.Histogram method)\n\n(ray.util.metrics.Histogram method)\n\n\nof() (ray.data.datasource.PathPartitionFilter static method)\n\n(ray.data.datasource.PathPartitionParser static method)\n\n\nOff-Policy\n\nOffline data\n\nOffline RL\n\noffline_data() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nOn-Policy\n\non_algorithm_init() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_checkpoint() (ray.tune.Callback method)\n\n(ray.tune.experiment.trial.Trial method)\n\n(ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.logger.TBXLoggerCallback method)\n\n\non_checkpoint_loaded() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_create_policy() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_episode_created() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ac3c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "on_episode_created() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_episode_end() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\n(ray.rllib.utils.exploration.curiosity.Curiosity method)\n\n(ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)\n\n(ray.rllib.utils.exploration.exploration.Exploration method)\n\n(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)\n\n(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)\n\n(ray.rllib.utils.exploration.random.Random method)\n\n(ray.rllib.utils.exploration.random_encoder.RE3 method)\n\n(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)\n\n\non_episode_start() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\n(ray.rllib.utils.exploration.curiosity.Curiosity method)\n\n(ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)\n\n(ray.rllib.utils.exploration.exploration.Exploration method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ac8c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.utils.exploration.exploration.Exploration method)\n\n(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)\n\n(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)\n\n(ray.rllib.utils.exploration.random.Random method)\n\n(ray.rllib.utils.exploration.random_encoder.RE3 method)\n\n(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)\n\n\non_episode_step() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_evaluate_end() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_evaluate_start() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_experiment_end() (ray.tune.Callback method)\n\n(ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.logger.TBXLoggerCallback method)\n\n\non_global_var_update() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986acdc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "on_global_var_update() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\non_learn_on_batch() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_postprocess_trajectory() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_restore() (ray.tune.experiment.trial.Trial method)\n\non_sample_end() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_save() (ray.train.huggingface.transformers.RayTrainReportCallback method)\n\non_shutdown() (ray.train.backend.Backend method)\n\non_start() (ray.train.backend.Backend method)\n\non_step_begin() (ray.tune.Callback method)\n\n(ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.logger.TBXLoggerCallback method)\n\n\non_step_end() (ray.tune.Callback method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ad22-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.logger.TBXLoggerCallback method)\n\n\non_step_end() (ray.tune.Callback method)\n\n(ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.logger.TBXLoggerCallback method)\n\n\n\n\non_sub_environment_created() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_train_result() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_training_start() (ray.train.backend.Backend method)\n\non_trial_add() (ray.tune.schedulers.AsyncHyperBandScheduler method)\n\n(ray.tune.schedulers.HyperBandForBOHB method)\n\n(ray.tune.schedulers.HyperBandScheduler method)\n\n(ray.tune.schedulers.TrialScheduler method)\n\n\non_trial_complete() (ray.tune.Callback method)\n\n(ray.tune.schedulers.AsyncHyperBandScheduler method)\n\n(ray.tune.schedulers.HyperBandForBOHB method)\n\n(ray.tune.schedulers.HyperBandScheduler method)\n\n(ray.tune.schedulers.TrialScheduler method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ad72-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.schedulers.HyperBandScheduler method)\n\n(ray.tune.schedulers.TrialScheduler method)\n\n(ray.tune.search.ax.AxSearch method)\n\n(ray.tune.search.bayesopt.BayesOptSearch method)\n\n(ray.tune.search.dragonfly.DragonflySearch method)\n\n(ray.tune.search.hebo.HEBOSearch method)\n\n(ray.tune.search.hyperopt.HyperOptSearch method)\n\n(ray.tune.search.nevergrad.NevergradSearch method)\n\n(ray.tune.search.Repeater method)\n\n(ray.tune.search.Searcher method)\n\n(ray.tune.search.sigopt.SigOptSearch method)\n\n(ray.tune.search.skopt.SkOptSearch method)\n\n(ray.tune.search.zoopt.ZOOptSearch method)\n\n\non_trial_error() (ray.tune.Callback method)\n\n(ray.tune.schedulers.HyperBandForBOHB method)\n\n(ray.tune.schedulers.HyperBandScheduler method)\n\n(ray.tune.schedulers.TrialScheduler method)\n\n\non_trial_recover() (ray.tune.Callback method)\n\n(ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986adc2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.logger.TBXLoggerCallback method)\n\n\non_trial_remove() (ray.tune.schedulers.AsyncHyperBandScheduler method)\n\n(ray.tune.schedulers.HyperBandForBOHB method)\n\n(ray.tune.schedulers.HyperBandScheduler method)\n\n(ray.tune.schedulers.TrialScheduler method)\n\n\non_trial_restore() (ray.tune.Callback method)\n\non_trial_result() (ray.tune.Callback method)\n\n(ray.tune.schedulers.AsyncHyperBandScheduler method)\n\n(ray.tune.schedulers.HyperBandForBOHB method)\n\n(ray.tune.schedulers.HyperBandScheduler method)\n\n(ray.tune.schedulers.MedianStoppingRule method)\n\n(ray.tune.schedulers.TrialScheduler method)\n\n(ray.tune.search.ax.AxSearch method)\n\n(ray.tune.search.basic_variant.BasicVariantGenerator method)\n\n(ray.tune.search.bayesopt.BayesOptSearch method)\n\n(ray.tune.search.dragonfly.DragonflySearch method)\n\n(ray.tune.search.hebo.HEBOSearch method)\n\n(ray.tune.search.nevergrad.NevergradSearch method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ae12-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.search.hebo.HEBOSearch method)\n\n(ray.tune.search.nevergrad.NevergradSearch method)\n\n(ray.tune.search.Repeater method)\n\n(ray.tune.search.Searcher method)\n\n(ray.tune.search.sigopt.SigOptSearch method)\n\n(ray.tune.search.skopt.SkOptSearch method)\n\n(ray.tune.search.zoopt.ZOOptSearch method)\n\n\non_trial_save() (ray.tune.Callback method)\n\non_trial_start() (ray.tune.Callback method)\n\non_workers_recreated() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)\n\non_write_complete() (ray.data.Datasink method)\n\n(ray.data.Datasource method)\n\n\non_write_failed() (ray.data.BlockBasedFileDatasink method)\n\n(ray.data.Datasink method)\n\n(ray.data.Datasource method)\n\n(ray.data.RowBasedFileDatasink method)\n\n\non_write_start() (ray.data.BlockBasedFileDatasink method)\n\n(ray.data.Datasink method)\n\n(ray.data.Datasource method)\n\n(ray.data.RowBasedFileDatasink method)\n\n\none_hot() (in module ray.rllib.utils.numpy)\n\n(in module ray.rllib.utils.tf_utils)\n\n(in module ray.rllib.utils.torch_utils)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ae62-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(in module ray.rllib.utils.tf_utils)\n\n(in module ray.rllib.utils.torch_utils)\n\n\nOneHotEncoder (class in ray.data.preprocessors)\n\nOOM (Out of Memory)\n\noptimizer (ray.tune.search.bayesopt.BayesOptSearch attribute)\n\n(ray.tune.search.zoopt.ZOOptSearch attribute)\n\n\noptimizer() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\noptions() (ray.actor.ActorClass method)\n\n(ray.remote_function.RemoteFunction method)\n\n(ray.serve.Deployment method)\n\n(ray.serve.handle.DeploymentHandle method)\n\n(ray.serve.handle.RayServeHandle method)\n\n(ray.serve.handle.RayServeSyncHandle method)\n\n\nOptunaSearch (class in ray.tune.search.optuna)\n\norder (ray.tune.integration.lightgbm.TuneReportCallback attribute)\n\n(ray.tune.integration.lightgbm.TuneReportCheckpointCallback attribute)\n\n\nOrdinalEncoder (class in ray.data.preprocessors)\n\nOrnsteinUhlenbeckNoise (class in ray.rllib.utils.exploration.ornstein_uhlenbeck_noise)\n\nOutOfDiskError"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986aeb2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "OutOfDiskError\n\noutput_config (ray.rllib.offline.io_context.IOContext property)\n\noutput_queue (ray.tune.JupyterNotebookReporter property)\n\noutput_specs_exploration() (ray.rllib.core.rl_module.rl_module.RLModule method)\n\noutput_specs_inference() (ray.rllib.core.rl_module.rl_module.RLModule method)\n\noutput_specs_train() (ray.rllib.core.rl_module.rl_module.RLModule method)\n\noverrides() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig class method)\n\nOwnerDiedError\n\n\n\nP\n\n\npar_iter_init() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\npar_iter_next() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\npar_iter_next_batch() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\npar_iter_slice() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\npar_iter_slice_batch() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nParameterNoise (class in ray.rllib.utils.exploration.parameter_noise)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986aef8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ParameterNoise (class in ray.rllib.utils.exploration.parameter_noise)\n\nparent_task_id (ray.util.state.common.TaskState attribute)\n\nParquetMetadataProvider (class in ray.data.datasource)\n\nparser (ray.data.datasource.PathPartitionFilter property)\n\nPartitioning (class in ray.data.datasource)\n\nPartitionStyle (class in ray.data.datasource)\n\npath (ray.train.Checkpoint attribute)\n\n(ray.train.Result property)\n\n(ray.tune.Experiment property)\n\n(ray.tune.experiment.trial.Trial attribute)\n\n\nPathPartitionFilter (class in ray.data.datasource)\n\nPathPartitionParser (class in ray.data.datasource)\n\nPAUSE (ray.tune.schedulers.FIFOScheduler attribute)\n\n(ray.tune.schedulers.HyperBandForBOHB attribute)\n\n(ray.tune.schedulers.HyperBandScheduler attribute)\n\n(ray.tune.schedulers.MedianStoppingRule attribute)\n\n(ray.tune.schedulers.pb2.PB2 attribute)\n\n(ray.tune.schedulers.PopulationBasedTraining attribute)\n\n(ray.tune.schedulers.PopulationBasedTrainingReplay attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986af48-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.schedulers.PopulationBasedTrainingReplay attribute)\n\n(ray.tune.schedulers.ResourceChangingScheduler attribute)\n\n(ray.tune.schedulers.TrialScheduler attribute)\n\n\nPB2 (class in ray.tune.schedulers.pb2)\n\nPENDING (ray.job_submission.JobStatus attribute)\n\npid (ray.job_submission.DriverInfo attribute)\n\n(ray.serve.schema.ReplicaDetails attribute)\n\n(ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.ObjectState attribute)\n\n(ray.util.state.common.WorkerState attribute)\n\n\nPiecewiseSchedule (class in ray.rllib.utils.schedules.piecewise_schedule)\n\nping() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986af98-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)\n\n\nPlacement group\n\nPlacement Group Bundle\n\nplacement_group (ray.util.scheduling_strategies.PlacementGroupSchedulingStrategy attribute)\n\nplacement_group() (in module ray.util)\n\nplacement_group_bundle_index (ray.util.scheduling_strategies.PlacementGroupSchedulingStrategy attribute)\n\nplacement_group_bundles (ray.serve.schema.DeploymentSchema attribute)\n\nplacement_group_capture_child_tasks (ray.util.scheduling_strategies.PlacementGroupSchedulingStrategy attribute)\n\nplacement_group_id (ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.PlacementGroupState attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n\nplacement_group_strategy (ray.serve.schema.DeploymentSchema attribute)\n\nplacement_group_table() (in module ray.util)\n\nplacement_group_timeout_s (ray.train.horovod.HorovodConfig attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986afe8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "placement_group_timeout_s (ray.train.horovod.HorovodConfig attribute)\n\nplacement_strategy (ray.train.ScalingConfig attribute)\n\nPlacementGroup (class in ray.util.placement_group)\n\nPlacementGroupFactory (class in ray.tune.execution.placement_groups)\n\nPlacementGroupSchedulingStrategy (class in ray.util.scheduling_strategies)\n\nPlacementGroupState (class in ray.util.state.common)\n\nPlasmaObjectNotAvailable\n\nplugin_uris() (ray.runtime_env.RuntimeEnv method)\n\nPolicy\n\n(class in ray.rllib.policy.policy)\n\n\npolicy_batches (ray.rllib.policy.sample_batch.MultiAgentBatch attribute)\n\nPolicyClient (class in ray.rllib.env.policy_client)\n\nPolicyMap (class in ray.rllib.policy.policy_map)\n\nPolicyServerInput (class in ray.rllib.env.policy_server_input)\n\npoll() (ray.rllib.env.base_env.BaseEnv method)\n\nPolynomialSchedule (class in ray.rllib.utils.schedules.polynomial_schedule)\n\npop() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n(ray.rllib.policy.policy_map.PolicyMap method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b038-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_env.RuntimeEnv method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n\npop_idle() (ray.util.ActorPool method)\n\npopitem() (ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_env.RuntimeEnv method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n\n\n\nPopulationBasedTraining (class in ray.tune.schedulers)\n\nPopulationBasedTrainingReplay (class in ray.tune.schedulers)\n\nport (ray.serve.config.gRPCOptions attribute)\n\n(ray.serve.config.HTTPOptions attribute)\n\n(ray.serve.schema.gRPCOptionsSchema attribute)\n\n(ray.serve.schema.HTTPOptionsSchema attribute)\n\n(ray.serve.schema.ServeApplicationSchema attribute)\n\n\nport_forward (ray.tune.web_server.TuneClient attribute)\n\npostprocess_gradients() (ray.rllib.core.learner.learner.Learner method)\n\npostprocess_gradients_for_module() (ray.rllib.core.learner.learner.Learner method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b07e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "postprocess_gradients_for_module() (ray.rllib.core.learner.learner.Learner method)\n\npostprocess_trajectory() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n(ray.rllib.utils.exploration.curiosity.Curiosity method)\n\n(ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)\n\n(ray.rllib.utils.exploration.exploration.Exploration method)\n\n(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)\n\n(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)\n\n(ray.rllib.utils.exploration.random.Random method)\n\n(ray.rllib.utils.exploration.random_encoder.RE3 method)\n\n(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)\n\n\nPowerTransformer (class in ray.data.preprocessors)\n\nPPOConfig (class in ray.rllib.algorithms.ppo.ppo)\n\nPredictor"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b0ce-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "PPOConfig (class in ray.rllib.algorithms.ppo.ppo)\n\nPredictor\n\npreferred_batch_format() (ray.data.preprocessor.Preprocessor class method)\n\n(ray.data.preprocessors.Categorizer class method)\n\n(ray.data.preprocessors.Concatenator class method)\n\n(ray.data.preprocessors.CustomKBinsDiscretizer class method)\n\n(ray.data.preprocessors.LabelEncoder class method)\n\n(ray.data.preprocessors.MaxAbsScaler class method)\n\n(ray.data.preprocessors.MinMaxScaler class method)\n\n(ray.data.preprocessors.MultiHotEncoder class method)\n\n(ray.data.preprocessors.Normalizer class method)\n\n(ray.data.preprocessors.OneHotEncoder class method)\n\n(ray.data.preprocessors.OrdinalEncoder class method)\n\n(ray.data.preprocessors.PowerTransformer class method)\n\n(ray.data.preprocessors.RobustScaler class method)\n\n(ray.data.preprocessors.SimpleImputer class method)\n\n(ray.data.preprocessors.StandardScaler class method)\n\n(ray.data.preprocessors.UniformKBinsDiscretizer class method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b11e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.preprocessors.UniformKBinsDiscretizer class method)\n\n\nprefetch_file_metadata() (ray.data.datasource.ParquetMetadataProvider method)\n\nprepare_data_loader() (in module ray.train.torch)\n\nprepare_dataset_shard() (in module ray.train.tensorflow)\n\nprepare_model() (in module ray.train.torch)\n\nprepare_read() (ray.data.Datasource method)\n\nprepare_trainer() (in module ray.train.huggingface.transformers)\n\n(in module ray.train.lightning)\n\n\npreprocess_datasets() (ray.train.gbdt_trainer.GBDTTrainer method)\n\n(ray.train.trainer.BaseTrainer method)\n\n\nPreprocessor\n\n(class in ray.data.preprocessor)\n\n\npreserve_order (ray.data.ExecutionOptions attribute)\n\nPREV_ACTIONS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nPREV_REWARDS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nPrioritizedReplayBuffer (class in ray.rllib.utils.replay_buffers.prioritized_replay_buffer)\n\nprobe_unhealthy_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b16e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "probe_unhealthy_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nprofiling_data (ray.util.state.common.TaskState attribute)\n\nprogress_reporter (ray.train.RunConfig attribute)\n\nProgressReporter (class in ray.tune)\n\nproxies (ray.serve.schema.ServeInstanceDetails attribute)\n\nproxy_location (ray.serve.schema.ServeDeploySchema attribute)\n\n(ray.serve.schema.ServeInstanceDetails attribute)\n\n\nProxyLocation (class in ray.serve.config)\n\nPUBLIC_KEYS (ray.tune.Experiment attribute)\n\npublic_spec (ray.tune.Experiment property)\n\nPublicAPI() (in module ray.util.annotations)\n\npush() (ray.util.ActorPool method)\n\nput() (in module ray)\n\n(ray.util.queue.Queue method)\n\n\nput_async() (ray.util.queue.Queue method)\n\nput_nowait() (ray.util.queue.Queue method)\n\nput_nowait_batch() (ray.util.queue.Queue method)\n\npython_environment() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n\n\nQ\n\n\nqlograndint() (in module ray.tune)\n\nqloguniform() (in module ray.tune)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b1b4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Q\n\n\nqlograndint() (in module ray.tune)\n\nqloguniform() (in module ray.tune)\n\nqrandint() (in module ray.tune)\n\n\n\nqrandn() (in module ray.tune)\n\nqsize() (ray.util.queue.Queue method)\n\nQueue (class in ray.util.queue)\n\nquniform() (in module ray.tune)\n\n\n\nR\n\n\nrandint() (in module ray.tune)\n\nrandn() (in module ray.tune)\n\nRandom (class in ray.rllib.utils.exploration.random)\n\nrandom_sample() (ray.data.Dataset method)\n\nrandom_shuffle() (ray.data.block.BlockAccessor method)\n\n(ray.data.Dataset method)\n\n\nRandomAccessDataset (class in ray.data.random_access_dataset)\n\nrandomize_block_order() (ray.data.Dataset method)\n\nrange() (in module ray.data)\n\nrange_tensor() (in module ray.data)\n\n\n    ray\n\n      \nmodule\n\n\nRay Agent\n\nRay application\n\nRay Client\n\nRay Cluster\n\nRay Dashboard\n\n\n    ray-attach command line option\n\n      \n--cluster-name <cluster_name>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--new\n\n--no-config-cache\n\n--port-forward <port_forward>\n\n--screen\n\n--start\n\n--tmux\n\n--verbose\n\n-n"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b204-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--new\n\n--no-config-cache\n\n--port-forward <port_forward>\n\n--screen\n\n--start\n\n--tmux\n\n--verbose\n\n-n\n\n-N\n\n-p\n\n-v\n\nCLUSTER_CONFIG_FILE\n\n\n\n    ray-debug command line option\n\n      \n--address <address>\n\n\n\n    ray-down command line option\n\n      \n--cluster-name <cluster_name>\n\n--keep-min-workers\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--verbose\n\n--workers-only\n\n--yes\n\n-n\n\n-v\n\n-y\n\nCLUSTER_CONFIG_FILE\n\n\n\n    ray-exec command line option\n\n      \n--cluster-name <cluster_name>\n\n--disable-usage-stats\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--no-config-cache\n\n--port-forward <port_forward>\n\n--run-env <run_env>\n\n--screen\n\n--start\n\n--stop\n\n--tmux\n\n--verbose\n\n-n\n\n-p\n\n-v\n\nCLUSTER_CONFIG_FILE\n\nCMD\n\n\n\n    ray-get command line option\n\n      \n--address <address>\n\n--timeout <timeout>\n\nID\n\nRESOURCE\n\n\n\n    ray-get_head_ip command line option\n\n      \n--cluster-name <cluster_name>\n\n-n\n\nCLUSTER_CONFIG_FILE\n\n\n\n    ray-job-list command line option\n\n      \n--address <address>"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b254-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "-n\n\nCLUSTER_CONFIG_FILE\n\n\n\n    ray-job-list command line option\n\n      \n--address <address>\n\n--headers <headers>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--verbose\n\n--verify <verify>\n\n-v\n\n\n\n    ray-job-logs command line option\n\n      \n--address <address>\n\n--follow\n\n--headers <headers>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--verbose\n\n--verify <verify>\n\n-f\n\n-v\n\nJOB_ID\n\n\n\n    ray-job-status command line option\n\n      \n--address <address>\n\n--headers <headers>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--verbose\n\n--verify <verify>\n\n-v\n\nJOB_ID\n\n\n\n    ray-job-stop command line option\n\n      \n--address <address>\n\n--headers <headers>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--no-wait\n\n--verbose\n\n--verify <verify>\n\n-v\n\nJOB_ID\n\n\n\n    ray-job-submit command line option\n\n      \n--address <address>\n\n--entrypoint-memory <entrypoint_memory>\n\n--entrypoint-num-cpus <entrypoint_num_cpus>\n\n--entrypoint-num-gpus <entrypoint_num_gpus>"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b2a4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--entrypoint-num-cpus <entrypoint_num_cpus>\n\n--entrypoint-num-gpus <entrypoint_num_gpus>\n\n--entrypoint-resources <entrypoint_resources>\n\n--headers <headers>\n\n--job-id <job_id>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--metadata-json <metadata_json>\n\n--no-wait\n\n--runtime-env <runtime_env>\n\n--runtime-env-json <runtime_env_json>\n\n--submission-id <submission_id>\n\n--verbose\n\n--verify <verify>\n\n--working-dir <working_dir>\n\n-v\n\nENTRYPOINT\n\n\n\n    ray-list command line option\n\n      \n--address <address>\n\n--detail\n\n--filter <filter>\n\n--format <format>\n\n--limit <limit>\n\n--timeout <timeout>\n\n-f\n\nRESOURCE\n\n\n\n    ray-memory command line option\n\n      \n--address <address>\n\n--group-by <group_by>\n\n--n <num_entries>\n\n--no-format\n\n--num-entries\n\n--redis_password <redis_password>\n\n--sort-by <sort_by>\n\n--stats-only\n\n--units <units>\n\n\n\n    ray-monitor command line option\n\n      \n--cluster-name <cluster_name>\n\n--lines <lines>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--verbose\n\n-n\n\n-v"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b2f4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--lines <lines>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--verbose\n\n-n\n\n-v\n\nCLUSTER_CONFIG_FILE\n\n\n\n    ray-start command line option\n\n      \n--address <address>\n\n--autoscaling-config <autoscaling_config>\n\n--block\n\n--dashboard-agent-grpc-port <dashboard_agent_grpc_port>\n\n--dashboard-agent-listen-port <dashboard_agent_listen_port>\n\n--dashboard-grpc-port <dashboard_grpc_port>\n\n--dashboard-host <dashboard_host>\n\n--dashboard-port <dashboard_port>\n\n--disable-usage-stats\n\n--gcs-server-port <gcs_server_port>\n\n--head\n\n--include-dashboard <include_dashboard>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--max-worker-port <max_worker_port>\n\n--metrics-export-port <metrics_export_port>\n\n--min-worker-port <min_worker_port>\n\n--no-redirect-output\n\n--node-ip-address <node_ip_address>\n\n--node-manager-port <node_manager_port>\n\n--num-cpus <num_cpus>\n\n--num-gpus <num_gpus>\n\n--object-manager-port <object_manager_port>\n\n--object-store-memory <object_store_memory>"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b344-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--object-manager-port <object_manager_port>\n\n--object-store-memory <object_store_memory>\n\n--plasma-directory <plasma_directory>\n\n--plasma-store-socket-name <plasma_store_socket_name>\n\n--port <port>\n\n--ray-client-server-port <ray_client_server_port>\n\n--ray-debugger-external\n\n--raylet-socket-name <raylet_socket_name>\n\n--resources <resources>\n\n--runtime-env-agent-port <runtime_env_agent_port>\n\n--storage <storage>\n\n--temp-dir <temp_dir>\n\n--verbose\n\n--worker-port-list <worker_port_list>\n\n-v\n\n\n\n    ray-status command line option\n\n      \n--address <address>\n\n--redis_password <redis_password>\n\n\n\n    ray-stop command line option\n\n      \n--force\n\n--grace-period <grace_period>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--verbose\n\n-f\n\n-g\n\n-v\n\n\n\n    ray-submit command line option\n\n      \n--args <args>\n\n--cluster-name <cluster_name>\n\n--disable-usage-stats\n\n--extra-screen-args <extra_screen_args>\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--no-config-cache"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b38a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--log-color <log_color>\n\n--log-style <log_style>\n\n--no-config-cache\n\n--port-forward <port_forward>\n\n--screen\n\n--start\n\n--stop\n\n--tmux\n\n--verbose\n\n-n\n\n-p\n\n-v\n\nCLUSTER_CONFIG_FILE\n\nSCRIPT\n\nSCRIPT_ARGS\n\n\n\n    ray-summary-actors command line option\n\n      \n--address <address>\n\n--timeout <timeout>\n\n\n\n    ray-summary-objects command line option\n\n      \n--address <address>\n\n--timeout <timeout>\n\n\n\n    ray-summary-tasks command line option\n\n      \n--address <address>\n\n--timeout <timeout>\n\n\n\n    ray-timeline command line option\n\n      \n--address <address>\n\n\n\n    ray-up command line option\n\n      \n--cluster-name <cluster_name>\n\n--disable-usage-stats\n\n--log-color <log_color>\n\n--log-style <log_style>\n\n--max-workers <max_workers>\n\n--min-workers <min_workers>\n\n--no-config-cache\n\n--no-restart\n\n--redirect-command-output\n\n--restart-only\n\n--use-login-shells\n\n--use-normal-shells\n\n--verbose\n\n--yes\n\n-n\n\n-v\n\n-y\n\nCLUSTER_CONFIG_FILE\n\n\n\n\n\n    ray.rllib.env.multi_agent_env\n\n      \nmodule"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b3da-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--yes\n\n-n\n\n-v\n\n-y\n\nCLUSTER_CONFIG_FILE\n\n\n\n\n\n    ray.rllib.env.multi_agent_env\n\n      \nmodule\n\n\n\n    ray.util.collective.collective\n\n      \nmodule\n\n\nray_actor_options (ray.serve.Deployment property)\n\n(ray.serve.schema.DeploymentSchema attribute)\n\n\nray_namespace (ray.job_config.JobConfig attribute)\n\n(ray.util.state.common.ActorState attribute)\n\n\nRayActorError\n\nRayDDPStrategy (class in ray.train.lightning)\n\nRayDeepSpeedStrategy (class in ray.train.lightning)\n\nRayError\n\nRayFSDPStrategy (class in ray.train.lightning)\n\nRaylet\n\nRayLightningEnvironment (class in ray.train.lightning)\n\nRayServeHandle (class in ray.serve.handle)\n\nRayServeSyncHandle (class in ray.serve.handle)\n\nRayStateApiException\n\nRaySystemError\n\nRayTaskError\n\nRayTrainReportCallback (class in ray.train.huggingface.transformers)\n\n(class in ray.train.lightning)\n\n\nRE3 (class in ray.rllib.utils.exploration.random_encoder)\n\nread_all_files() (ray.rllib.offline.json_reader.JsonReader method)\n\nread_bigquery() (in module ray.data)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b42a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "read_bigquery() (in module ray.data)\n\nread_binary_files() (in module ray.data)\n\nread_csv() (in module ray.data)\n\nread_databricks_tables() (in module ray.data)\n\nread_datasource() (in module ray.data)\n\nread_images() (in module ray.data)\n\nread_json() (in module ray.data)\n\nread_mongo() (in module ray.data)\n\nread_numpy() (in module ray.data)\n\nread_parquet() (in module ray.data)\n\nread_parquet_bulk() (in module ray.data)\n\nread_sql() (in module ray.data)\n\nread_text() (in module ray.data)\n\nread_tfrecords() (in module ray.data)\n\nread_webdataset() (in module ray.data)\n\nReader (class in ray.data.datasource)\n\nReadTask (class in ray.data)\n\nready() (ray.util.placement_group.PlacementGroup method)\n\nreallocate_trial_resources_if_needed() (ray.tune.schedulers.ResourceChangingScheduler method)\n\nrecord() (ray.serve.metrics.Counter method)\n\n(ray.serve.metrics.Gauge method)\n\n(ray.serve.metrics.Histogram method)\n\n(ray.util.metrics.Counter method)\n\n(ray.util.metrics.Gauge method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b47a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.util.metrics.Counter method)\n\n(ray.util.metrics.Gauge method)\n\n(ray.util.metrics.Histogram method)\n\n\nRecurrentNetwork (class in ray.rllib.models.tf.recurrent_net)\n\nrecv() (in module ray.util.collective.collective)\n\nrecv_multigpu() (in module ray.util.collective.collective)\n\nreduce() (in module ray.util.collective.collective)\n\nreduce_mean_ignore_inf() (in module ray.rllib.utils.tf_utils)\n\n(in module ray.rllib.utils.torch_utils)\n\n\nreduce_multigpu() (in module ray.util.collective.collective)\n\nreducescatter() (in module ray.util.collective.collective)\n\nreducescatter_multigpu() (in module ray.util.collective.collective)\n\nref_cnt (ray.util.state.common.RuntimeEnvState attribute)\n\nref_type_counts (ray.util.state.common.ObjectSummaryPerKey attribute)\n\nreference_type (ray.util.state.common.ObjectState attribute)\n\nregister_analysis() (ray.tune.search.bayesopt.BayesOptSearch method)\n\nregister_env() (in module ray.tune)\n\nregister_if_needed() (ray.tune.Experiment class method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b4c0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "register_env() (in module ray.tune)\n\nregister_if_needed() (ray.tune.Experiment class method)\n\nregister_metric() (ray.rllib.core.learner.learner.Learner method)\n\nregister_metrics() (ray.rllib.core.learner.learner.Learner method)\n\nregister_optimizer() (ray.rllib.core.learner.learner.Learner method)\n\nregister_trainable() (in module ray.tune)\n\nregister_variables() (ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\nrelative_logdir (ray.tune.experiment.trial.Trial attribute)\n\nrelu() (in module ray.rllib.utils.numpy)\n\nRemote Class\n\nRemote Function\n\nremote() (in module ray)\n\n(ray.serve.handle.DeploymentHandle method)\n\n(ray.serve.handle.RayServeHandle method)\n\n(ray.serve.handle.RayServeSyncHandle method)\n\n\nremote_execution_api() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)\n\nremote_path (ray.tune.Experiment property)\n\n(ray.tune.experiment.trial.Trial attribute)\n\n\nremote_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b510-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "remote_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nremove_config() (ray.data.DataContext method)\n\nremove_module() (ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n\nremove_placement_group() (in module ray.util)\n\nremove_policy() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n\nrender() (ray.rllib.env.multi_agent_env.MultiAgentEnv method)\n\nrepartition() (ray.data.Dataset method)\n\nrepeat() (ray.data.Dataset method)\n\nRepeater (class in ray.tune.search)\n\nreplay() (ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)\n\n\nReplayBuffer (class in ray.rllib.utils.replay_buffers.replay_buffer)\n\nReplica"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b560-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ReplayBuffer (class in ray.rllib.utils.replay_buffers.replay_buffer)\n\nReplica\n\nreplica_id (ray.serve.schema.ReplicaDetails attribute)\n\nreplica_tag (ray.serve.context.ReplicaContext attribute)\n\nReplicaContext (class in ray.serve.context)\n\nreplicas (ray.serve.schema.DeploymentDetails attribute)\n\nreplicas_settings_valid() (ray.serve.config.AutoscalingConfig class method)\n\nreport() (in module ray.train)\n\n(ray.tune.ProgressReporter method)\n\n\nReportCheckpointCallback (class in ray.train.tensorflow.keras)\n\nreporting() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nrepr_name (ray.util.state.common.ActorState attribute)\n\nrequest_timeout_s (ray.serve.config.HTTPOptions attribute)\n\n(ray.serve.schema.HTTPOptionsSchema attribute)\n\n\nrequired_resources (ray.tune.execution.placement_groups.PlacementGroupFactory property)\n\n(ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.TaskState attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b5b0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n\nReservoirReplayBuffer (class in ray.rllib.utils.replay_buffers.reservoir_replay_buffer)\n\nreset() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.env.multi_agent_env.MultiAgentEnv method)\n\n(ray.rllib.evaluation.worker_set.WorkerSet method)\n\n(ray.tune.Trainable method)\n\n\nreset_at() (ray.rllib.env.vector_env._VectorizedGymEnv method)\n\n(ray.rllib.env.vector_env.VectorEnv method)\n\n\nreset_config() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.tune.Trainable method)\n\n\nreset_connectors() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nresolved_filesystem (ray.data.datasource.Partitioning property)\n\n\n    RESOURCE\n\n      \nray-get command line option\n\nray-list command line option\n\n\nResource (logical and physical)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b5f6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ray-get command line option\n\nray-list command line option\n\n\nResource (logical and physical)\n\nresource_help() (ray.tune.Trainable class method)\n\nresource_limits (ray.data.ExecutionOptions attribute)\n\nResourceChangingScheduler (class in ray.tune.schedulers)\n\nresources (ray.serve.schema.RayActorOptionsSchema attribute)\n\nresources() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nresources_per_worker (ray.train.ScalingConfig attribute)\n\nresources_total (ray.util.state.common.NodeState attribute)\n\nrestart_at() (ray.rllib.env.vector_env._VectorizedGymEnv method)\n\n(ray.rllib.env.vector_env.VectorEnv method)\n\n\nrestore() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.train.data_parallel_trainer.DataParallelTrainer class method)\n\n(ray.train.gbdt_trainer.GBDTTrainer class method)\n\n(ray.train.horovod.HorovodTrainer class method)\n\n(ray.train.lightgbm.LightGBMTrainer class method)\n\n(ray.train.tensorflow.TensorflowTrainer class method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b646-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.train.tensorflow.TensorflowTrainer class method)\n\n(ray.train.torch.TorchTrainer class method)\n\n(ray.train.trainer.BaseTrainer class method)\n\n(ray.train.xgboost.XGBoostTrainer class method)\n\n(ray.tune.schedulers.AsyncHyperBandScheduler method)\n\n(ray.tune.schedulers.FIFOScheduler method)\n\n(ray.tune.schedulers.HyperBandForBOHB method)\n\n(ray.tune.schedulers.HyperBandScheduler method)\n\n(ray.tune.schedulers.MedianStoppingRule method)\n\n(ray.tune.schedulers.pb2.PB2 method)\n\n(ray.tune.schedulers.PopulationBasedTraining method)\n\n(ray.tune.schedulers.PopulationBasedTrainingReplay method)\n\n(ray.tune.schedulers.TrialScheduler method)\n\n(ray.tune.search.bayesopt.BayesOptSearch method)\n\n(ray.tune.search.hebo.HEBOSearch method)\n\n(ray.tune.search.Searcher method)\n\n(ray.tune.Trainable method)\n\n(ray.tune.Tuner class method)\n\n\nrestore_connectors() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b696-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nrestore_from_dir() (ray.tune.search.ax.AxSearch method)\n\n(ray.tune.search.basic_variant.BasicVariantGenerator method)\n\n(ray.tune.search.bayesopt.BayesOptSearch method)\n\n(ray.tune.search.bohb.TuneBOHB method)\n\n(ray.tune.search.ConcurrencyLimiter method)\n\n(ray.tune.search.dragonfly.DragonflySearch method)\n\n(ray.tune.search.hebo.HEBOSearch method)\n\n(ray.tune.search.hyperopt.HyperOptSearch method)\n\n(ray.tune.search.nevergrad.NevergradSearch method)\n\n(ray.tune.search.optuna.OptunaSearch method)\n\n(ray.tune.search.Repeater method)\n\n(ray.tune.search.Searcher method)\n\n(ray.tune.search.sigopt.SigOptSearch method)\n\n(ray.tune.search.skopt.SkOptSearch method)\n\n(ray.tune.search.zoopt.ZOOptSearch method)\n\n\nrestore_workers() (ray.rllib.algorithms.algorithm.Algorithm method)\n\nResult (class in ray.train)\n\nresult() (ray.serve.handle.DeploymentResponse method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b6e6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Result (class in ray.train)\n\nresult() (ray.serve.handle.DeploymentResponse method)\n\nResultGrid (class in ray.tune)\n\nresults (ray.tune.ExperimentAnalysis property)\n\nresults_df (ray.tune.ExperimentAnalysis property)\n\nresume() (in module ray.workflow)\n\nresume_all() (in module ray.workflow)\n\nresume_async() (in module ray.workflow)\n\nRETURNS_TO_GO (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nreuse_actors (ray.tune.TuneConfig attribute)\n\nReward\n\nREWARDS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nright_zero_pad() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nrl_module() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nrl_module_spec (ray.rllib.algorithms.algorithm_config.AlgorithmConfig property)\n\nRLModule (class in ray.rllib.core.rl_module.rl_module)\n\nRLModuleConfig (class in ray.rllib.core.rl_module.rl_module)\n\nRobustScaler (class in ray.data.preprocessors)\n\nRollout\n\nRollout Worker"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b72c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "RobustScaler (class in ray.data.preprocessors)\n\nRollout\n\nRollout Worker\n\nrollouts() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nRolloutWorker (class in ray.rllib.evaluation.rollout_worker)\n\nroot_device (ray.train.lightning.RayDDPStrategy property)\n\n(ray.train.lightning.RayDeepSpeedStrategy property)\n\n(ray.train.lightning.RayFSDPStrategy property)\n\n\nroot_path (ray.serve.config.HTTPOptions attribute)\n\n(ray.serve.schema.HTTPOptionsSchema attribute)\n\n\nroot_url (ray.serve.config.HTTPOptions attribute)\n\nroute_prefix (ray.serve.Deployment property)\n\n(ray.serve.schema.ApplicationDetails attribute)\n\n(ray.serve.schema.DeploymentSchema attribute)\n\n(ray.serve.schema.ServeApplicationSchema attribute)\n\n\nRowBasedFileDatasink (class in ray.data)\n\nrows() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nrun() (in module ray.serve)\n\n(in module ray.workflow)\n\n(ray.rllib.env.external_env.ExternalEnv method)\n\n(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b77c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)\n\n\nrun_async() (in module ray.workflow)\n\nrun_experiments() (in module ray.tune)\n\nrun_identifier (ray.tune.Experiment property)\n\nRunConfig (class in ray.train)\n\nRUNNING (ray.job_submission.JobStatus attribute)\n\nRuntime environment\n\nruntime_env (ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n(ray.runtime_context.RuntimeContext property)\n\n(ray.serve.schema.RayActorOptionsSchema attribute)\n\n(ray.serve.schema.ServeApplicationSchema attribute)\n\n(ray.util.state.common.RuntimeEnvState attribute)\n\n\nruntime_env_contains_remote_uris() (ray.serve.schema.RayActorOptionsSchema class method)\n\n(ray.serve.schema.ServeApplicationSchema class method)\n\n\nruntime_env_info (ray.util.state.common.TaskState attribute)\n\nRuntimeContext (class in ray.runtime_context)\n\nRuntimeEnv (class in ray.runtime_env)\n\nRuntimeEnvConfig (class in ray.runtime_env)\n\nRuntimeEnvSetupError"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b7cc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "RuntimeEnvConfig (class in ray.runtime_env)\n\nRuntimeEnvSetupError\n\nRuntimeEnvState (class in ray.util.state.common)\n\n\n\nS\n\n\nSACConfig (class in ray.rllib.algorithms.sac.sac)\n\nsample() (ray.data.block.BlockAccessor method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)\n\n\nsample_and_learn() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nsample_from() (in module ray.tune)\n\nsample_min_n_steps_from_buffer() (in module ray.rllib.utils.replay_buffers.utils)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b81c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "sample_min_n_steps_from_buffer() (in module ray.rllib.utils.replay_buffers.utils)\n\nsample_with_count() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nSampleBatch (class in ray.rllib.policy.sample_batch)\n\nSamplerInput (class in ray.rllib.evaluation.sampler)\n\nsatisfies_limit() (ray.data.ExecutionResources method)\n\nsave() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.tune.schedulers.AsyncHyperBandScheduler method)\n\n(ray.tune.schedulers.FIFOScheduler method)\n\n(ray.tune.schedulers.HyperBandForBOHB method)\n\n(ray.tune.schedulers.HyperBandScheduler method)\n\n(ray.tune.schedulers.MedianStoppingRule method)\n\n(ray.tune.schedulers.pb2.PB2 method)\n\n(ray.tune.schedulers.PopulationBasedTraining method)\n\n(ray.tune.schedulers.PopulationBasedTrainingReplay method)\n\n(ray.tune.schedulers.TrialScheduler method)\n\n(ray.tune.search.bayesopt.BayesOptSearch method)\n\n(ray.tune.search.hebo.HEBOSearch method)\n\n(ray.tune.search.Searcher method)\n\n(ray.tune.Trainable method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b862-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.search.Searcher method)\n\n(ray.tune.Trainable method)\n\n\nsave_checkpoint() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.tune.Trainable method)\n\n\nsave_state() (ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nsave_to_checkpoint() (ray.rllib.core.rl_module.rl_module.RLModule method)\n\nsave_to_dir() (ray.tune.search.ax.AxSearch method)\n\n(ray.tune.search.bayesopt.BayesOptSearch method)\n\n(ray.tune.search.bohb.TuneBOHB method)\n\n(ray.tune.search.ConcurrencyLimiter method)\n\n(ray.tune.search.dragonfly.DragonflySearch method)\n\n(ray.tune.search.hebo.HEBOSearch method)\n\n(ray.tune.search.hyperopt.HyperOptSearch method)\n\n(ray.tune.search.nevergrad.NevergradSearch method)\n\n(ray.tune.search.optuna.OptunaSearch method)\n\n(ray.tune.search.Repeater method)\n\n(ray.tune.search.Searcher method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b8b2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.search.Repeater method)\n\n(ray.tune.search.Searcher method)\n\n(ray.tune.search.sigopt.SigOptSearch method)\n\n(ray.tune.search.skopt.SkOptSearch method)\n\n(ray.tune.search.zoopt.ZOOptSearch method)\n\n\nscale() (ray.data.ExecutionResources method)\n\nScalingConfig (class in ray.train)\n\nSchedule (class in ray.rllib.utils.schedules.schedule)\n\nscheduler (ray.tune.TuneConfig attribute)\n\nschema (ray.data.block.BlockMetadata attribute)\n\nschema() (ray.data.block.BlockAccessor method)\n\n(ray.data.Dataset method)\n\n\nscheme (ray.data.datasource.PathPartitionParser property)\n\nscope_vars() (in module ray.rllib.utils.tf_utils)\n\n\n    SCRIPT\n\n      \nray-submit command line option\n\n\n\n    SCRIPT_ARGS\n\n      \nray-submit command line option\n\n\nSearch algorithm\n\nSearch Space\n\nsearch_alg (ray.tune.TuneConfig attribute)\n\nSearcher (class in ray.tune.search)\n\nseed (ray.rllib.core.learner.learner.LearnerHyperparameters attribute)\n\nselect() (ray.data.block.BlockAccessor method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b8f8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "select() (ray.data.block.BlockAccessor method)\n\nselect_columns() (ray.data.Dataset method)\n\nsend() (in module ray.util.collective.collective)\n\nsend_actions() (ray.rllib.env.base_env.BaseEnv method)\n\nsend_multigpu() (in module ray.util.collective.collective)\n\nSEQ_LENS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nsequence_mask() (in module ray.rllib.utils.torch_utils)\n\nSEQUENCES (ray.rllib.utils.replay_buffers.replay_buffer.StorageUnit attribute)\n\nserialize() (ray.data.preprocessor.Preprocessor method)\n\n(ray.data.preprocessors.Categorizer method)\n\n(ray.data.preprocessors.Concatenator method)\n\n(ray.data.preprocessors.CustomKBinsDiscretizer method)\n\n(ray.data.preprocessors.LabelEncoder method)\n\n(ray.data.preprocessors.MaxAbsScaler method)\n\n(ray.data.preprocessors.MinMaxScaler method)\n\n(ray.data.preprocessors.MultiHotEncoder method)\n\n(ray.data.preprocessors.Normalizer method)\n\n(ray.data.preprocessors.OneHotEncoder method)\n\n(ray.data.preprocessors.OrdinalEncoder method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b948-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.preprocessors.OneHotEncoder method)\n\n(ray.data.preprocessors.OrdinalEncoder method)\n\n(ray.data.preprocessors.PowerTransformer method)\n\n(ray.data.preprocessors.RobustScaler method)\n\n(ray.data.preprocessors.SimpleImputer method)\n\n(ray.data.preprocessors.StandardScaler method)\n\n(ray.data.preprocessors.UniformKBinsDiscretizer method)\n\n(ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n\nserialize_lineage() (ray.data.Dataset method)\n\nserialize_metric() (ray.tune.search.sigopt.SigOptSearch static method)\n\nserialize_result() (ray.tune.search.sigopt.SigOptSearch method)\n\nserialized_runtime_env (ray.util.state.common.ActorState attribute)\n\nservable_object (ray.serve.context.ReplicaContext attribute)\n\nServe application\n\n\n    serve-build command line option\n\n      \n--app-dir <app_dir>\n\n--grpc-servicer-functions <grpc_servicer_functions>\n\n--output-path <output_path>\n\n-d\n\n-o\n\nIMPORT_PATHS\n\n\n\n    serve-config command line option\n\n      \n--address <address>\n\n--name <name>\n\n-a"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b998-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "serve-config command line option\n\n      \n--address <address>\n\n--name <name>\n\n-a\n\n-n\n\n\n\n    serve-deploy command line option\n\n      \n--address <address>\n\n-a\n\nCONFIG_FILE_NAME\n\n\n\n    serve-run command line option\n\n      \n--address <address>\n\n--app-dir <app_dir>\n\n--blocking\n\n--host <host>\n\n--non-blocking\n\n--port <port>\n\n--reload\n\n--route-prefix <route_prefix>\n\n--runtime-env <runtime_env>\n\n--runtime-env-json <runtime_env_json>\n\n--working-dir <working_dir>\n\n-a\n\n-d\n\n-h\n\n-p\n\n-r\n\nARGUMENTS\n\nCONFIG_OR_IMPORT_PATH\n\n\n\n    serve-shutdown command line option\n\n      \n--address <address>\n\n--yes\n\n-a\n\n-y\n\n\n\n    serve-start command line option\n\n      \n--address <address>\n\n--grpc-port <grpc_port>\n\n--grpc-servicer-functions <grpc_servicer_functions>\n\n--http-host <http_host>\n\n--http-location <http_location>\n\n--http-port <http_port>\n\n--proxy-location <proxy_location>\n\n-a\n\n\n\n    serve-status command line option\n\n      \n--address <address>\n\n--name <name>\n\n-a\n\n-n\n\n\nServeHandle\n\nSession"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986b9e8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "--address <address>\n\n--name <name>\n\n-a\n\n-n\n\n\nServeHandle\n\nSession\n\nset() (ray.serve.metrics.Gauge method)\n\n(ray.util.metrics.Gauge method)\n\n\nset_config() (ray.data.DataContext method)\n\nset_default_actor_lifetime() (ray.job_config.JobConfig method)\n\nset_default_tags() (ray.util.metrics.Counter method)\n\n(ray.util.metrics.Gauge method)\n\n(ray.util.metrics.Histogram method)\n\n\nset_finished() (ray.tune.search.basic_variant.BasicVariantGenerator method)\n\nset_get_interceptor() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nset_global_vars() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nset_is_module_trainable() (ray.rllib.core.learner.learner_group.LearnerGroup method)\n\nset_is_policy_to_train() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nset_location() (ray.tune.experiment.trial.Trial method)\n\nset_max_concurrency() (ray.tune.search.ax.AxSearch method)\n\n(ray.tune.search.bayesopt.BayesOptSearch method)\n\n(ray.tune.search.dragonfly.DragonflySearch method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ba2e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.search.dragonfly.DragonflySearch method)\n\n(ray.tune.search.hyperopt.HyperOptSearch method)\n\n(ray.tune.search.nevergrad.NevergradSearch method)\n\n(ray.tune.search.optuna.OptunaSearch method)\n\n(ray.tune.search.Repeater method)\n\n(ray.tune.search.Searcher method)\n\n(ray.tune.search.skopt.SkOptSearch method)\n\n(ray.tune.search.zoopt.ZOOptSearch method)\n\n\nset_metadata() (ray.job_config.JobConfig method)\n\n(ray.train.Checkpoint method)\n\n\nset_module_state() (ray.rllib.core.learner.learner.Learner method)\n\nset_optimizer_state() (ray.rllib.core.learner.learner.Learner method)\n\nset_options() (ray.serve.Deployment method)\n\nset_policy_mapping_fn() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nset_progress_bars() (in module ray.data)\n\nset_ray_namespace() (ray.job_config.JobConfig method)\n\nset_runtime_env() (ray.job_config.JobConfig method)\n\nset_search_properties() (ray.tune.schedulers.AsyncHyperBandScheduler method)\n\n(ray.tune.schedulers.FIFOScheduler method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ba7e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.tune.schedulers.FIFOScheduler method)\n\n(ray.tune.schedulers.PopulationBasedTrainingReplay method)\n\n(ray.tune.schedulers.TrialScheduler method)\n\n(ray.tune.search.basic_variant.BasicVariantGenerator method)\n\n(ray.tune.search.Searcher method)\n\n\nset_state() (ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n(ray.rllib.utils.exploration.curiosity.Curiosity method)\n\n(ray.rllib.utils.exploration.exploration.Exploration method)\n\n(ray.rllib.utils.exploration.random.Random method)\n\n(ray.rllib.utils.exploration.random_encoder.RE3 method)\n\n(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bace-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)\n\n(ray.tune.Callback method)\n\n(ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.logger.TBXLoggerCallback method)\n\n\n\n\nset_status() (ray.tune.experiment.trial.Trial method)\n\nset_storage() (ray.tune.experiment.trial.Trial method)\n\nset_torch_seed() (in module ray.rllib.utils.torch_utils)\n\nset_trace() (in module pdb)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bb14-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "set_torch_seed() (in module ray.rllib.utils.torch_utils)\n\nset_trace() (in module pdb)\n\nset_training() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nset_trial_resources() (ray.tune.schedulers.ResourceChangingScheduler method)\n\nset_weights() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.policy.Policy method)\n\n(ray.rllib.policy.policy.Policy method)\n\n\nsetDaemon() (ray.rllib.evaluation.sampler.AsyncSampler method)\n\nsetdefault() (ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_env.RuntimeEnv method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n\nsetName() (ray.rllib.evaluation.sampler.AsyncSampler method)\n\nsetup() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bb64-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n(ray.train.data_parallel_trainer.DataParallelTrainer method)\n\n(ray.train.gbdt_trainer.GBDTTrainer method)\n\n(ray.train.horovod.HorovodTrainer method)\n\n(ray.train.lightgbm.LightGBMTrainer method)\n\n(ray.train.tensorflow.TensorflowTrainer method)\n\n(ray.train.torch.TorchTrainer method)\n\n(ray.train.trainer.BaseTrainer method)\n\n(ray.train.xgboost.XGBoostTrainer method)\n\n(ray.tune.Callback method)\n\n(ray.tune.logger.aim.AimLoggerCallback method)\n\n(ray.tune.logger.CSVLoggerCallback method)\n\n(ray.tune.logger.JsonLoggerCallback method)\n\n(ray.tune.logger.LoggerCallback method)\n\n(ray.tune.logger.TBXLoggerCallback method)\n\n(ray.tune.ProgressReporter method)\n\n(ray.tune.Trainable method)\n\n\nsetup_create_experiment_checkpoint_dir() (ray.tune.impl.tuner_internal.TunerInternal class method)\n\nsetup_ray_cluster() (in module ray.util.spark)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bbaa-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "setup_ray_cluster() (in module ray.util.spark)\n\nsetup_torch_data_parallel() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nshare_cuda_visible_devices (ray.train.backend.Backend attribute)\n\nshould_capture_child_tasks_in_placement_group (ray.runtime_context.RuntimeContext property)\n\nshould_checkpoint() (ray.tune.experiment.trial.Trial method)\n\nshould_create_reader (ray.data.Datasource property)\n\nshould_recover() (ray.tune.experiment.trial.Trial method)\n\nshould_report() (ray.tune.ProgressReporter method)\n\nshould_stop() (ray.tune.experiment.trial.Trial method)\n\nshow() (ray.data.Dataset method)\n\nshuffle() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nshutdown() (in module ray)\n\n(in module ray.serve)\n\n(ray.cluster_utils.AutoscalingCluster method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n(ray.util.queue.Queue method)\n\n\nshutdown_ray_cluster() (in module ray.util.spark)\n\nsigmoid() (in module ray.rllib.utils.numpy)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bbfa-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "shutdown_ray_cluster() (in module ray.util.spark)\n\nsigmoid() (in module ray.rllib.utils.numpy)\n\nSigOptSearch (class in ray.tune.search.sigopt)\n\nSimpleImputer (class in ray.data.preprocessors)\n\nSingleAgentRLModuleSpec (class in ray.rllib.core.rl_module.rl_module)\n\nsize() (ray.util.queue.Queue method)\n\nsize_bytes (ray.data.block.BlockMetadata attribute)\n\nsize_bytes() (ray.data.block.BlockAccessor method)\n\n(ray.data.Dataset method)\n\n(ray.rllib.policy.sample_batch.MultiAgentBatch method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n\nSkOptSearch (class in ray.tune.search.skopt)\n\nslice() (ray.data.block.BlockAccessor method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n\nsmoothing_factor (ray.serve.config.AutoscalingConfig attribute)\n\nsoft (ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy attribute)\n\nsoftmax() (in module ray.rllib.utils.numpy)\n\nsoftmax_cross_entropy_with_logits() (in module ray.rllib.utils.torch_utils)\n\nsort() (ray.data.Dataset method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bc40-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "sort() (ray.data.Dataset method)\n\nsort_and_partition() (ray.data.block.BlockAccessor method)\n\nSpillback\n\nsplit() (ray.data.Dataset method)\n\nsplit_at_indices() (ray.data.Dataset method)\n\nsplit_by_episode() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nsplit_proportionately() (ray.data.Dataset method)\n\nssh_identity_file (ray.train.horovod.HorovodConfig attribute)\n\nssh_port (ray.train.horovod.HorovodConfig attribute)\n\nssh_str (ray.train.horovod.HorovodConfig attribute)\n\nStandardScaler (class in ray.data.preprocessors)\n\nstart() (in module ray.serve)\n\n(ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)\n\n(ray.cluster_utils.AutoscalingCluster method)\n\n(ray.rllib.evaluation.sampler.AsyncSampler method)\n\n\nstart_episode() (ray.rllib.env.external_env.ExternalEnv method)\n\n(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)\n\n(ray.rllib.env.policy_client.PolicyClient method)\n\n\nstart_time (ray.job_submission.JobDetails attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bc90-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "start_time (ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n\nstart_time_ms (ray.util.state.common.NodeState attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n(ray.util.state.common.WorkerState attribute)\n\n\nstart_time_s (ray.serve.schema.ReplicaDetails attribute)\n\nstart_timeout (ray.train.horovod.HorovodConfig property)\n\nState\n\nstate (ray.serve.schema.ReplicaDetails attribute)\n\n(ray.util.state.common.ActorState attribute)\n\n(ray.util.state.common.NodeState attribute)\n\n(ray.util.state.common.PlacementGroupState attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n\nstate_counts (ray.util.state.common.ActorSummaryPerClass attribute)\n\n(ray.util.state.common.TaskSummaryPerFuncOrClassName attribute)\n\n\nStateSummary (class in ray.util.state.common)\n\nstats (ray.util.state.common.PlacementGroupState attribute)\n\nstats() (ray.data.DataIterator method)\n\n(ray.data.Dataset method)\n\n(ray.data.random_access_dataset.RandomAccessDataset method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bcd6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.Dataset method)\n\n(ray.data.random_access_dataset.RandomAccessDataset method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)\n\n\nstats_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)\n\n\nstatus (ray.job_submission.JobDetails attribute)\n\n(ray.job_submission.JobInfo attribute)\n\n(ray.serve.schema.ApplicationDetails attribute)\n\n(ray.serve.schema.DeploymentDetails attribute)\n\n(ray.tune.experiment.trial.Trial attribute)\n\n\nstatus() (in module ray.serve)\n\nStd (class in ray.data.aggregate)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bda8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "status() (in module ray.serve)\n\nStd (class in ray.data.aggregate)\n\nstd() (ray.data.Dataset method)\n\n(ray.data.grouped_data.GroupedData method)\n\n\nstep() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.env.multi_agent_env.MultiAgentEnv method)\n\n(ray.tune.Trainable method)\n\n\nStochasticSampling (class in ray.rllib.utils.exploration.stochastic_sampling)\n\nstop (ray.train.RunConfig attribute)\n\nSTOP (ray.tune.schedulers.FIFOScheduler attribute)\n\n(ray.tune.schedulers.HyperBandForBOHB attribute)\n\n(ray.tune.schedulers.HyperBandScheduler attribute)\n\n(ray.tune.schedulers.MedianStoppingRule attribute)\n\n(ray.tune.schedulers.pb2.PB2 attribute)\n\n(ray.tune.schedulers.PopulationBasedTraining attribute)\n\n(ray.tune.schedulers.PopulationBasedTrainingReplay attribute)\n\n(ray.tune.schedulers.ResourceChangingScheduler attribute)\n\n(ray.tune.schedulers.TrialScheduler attribute)\n\n\nstop() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986be48-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "stop() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)\n\n(ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.rllib.env.base_env.BaseEnv method)\n\n(ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\n(ray.rllib.evaluation.worker_set.WorkerSet method)\n\n(ray.tune.Trainable method)\n\n\nstop_all() (ray.tune.stopper.ExperimentPlateauStopper method)\n\n(ray.tune.stopper.Stopper method)\n\n\nstop_experiment() (ray.tune.web_server.TuneClient method)\n\nstop_job() (ray.job_submission.JobSubmissionClient method)\n\nstop_trial() (ray.tune.web_server.TuneClient method)\n\nSTOPPED (ray.job_submission.JobStatus attribute)\n\nStopper (class in ray.tune.stopper)\n\nstopper (ray.tune.Experiment property)\n\nstorage_filesystem (ray.train.RunConfig attribute)\n\nstorage_path (ray.train.RunConfig attribute)\n\nStorageUnit (class in ray.rllib.utils.replay_buffers.replay_buffer)\n\nstrategy (ray.tune.execution.placement_groups.PlacementGroupFactory property)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986beac-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "strategy (ray.tune.execution.placement_groups.PlacementGroupFactory property)\n\nstreaming_split() (ray.data.Dataset method)\n\nstyle (ray.data.datasource.Partitioning attribute)\n\nSUBMISSION (ray.job_submission.JobType attribute)\n\nsubmission_id (ray.job_submission.JobDetails attribute)\n\nsubmit() (ray.util.ActorPool method)\n\nsubmit_job() (ray.job_submission.JobSubmissionClient method)\n\nSUCCEEDED (ray.job_submission.JobStatus attribute)\n\nsuccess (ray.util.state.common.RuntimeEnvState attribute)\n\nsuggest() (ray.tune.search.bayesopt.BayesOptSearch method)\n\n(ray.tune.search.Searcher method)\n\n\nSum (class in ray.data.aggregate)\n\nsum() (ray.data.Dataset method)\n\n(ray.data.grouped_data.GroupedData method)\n\n\nsummarize_actors() (in module ray.util.state)\n\nsummarize_objects() (in module ray.util.state)\n\nsummarize_tasks() (in module ray.util.state)\n\nsummary (ray.util.state.common.ActorSummaries attribute)\n\n(ray.util.state.common.ObjectSummaries attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bf06-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.util.state.common.ObjectSummaries attribute)\n\n\nsupports_buffered_results (ray.tune.schedulers.FIFOScheduler property)\n\n(ray.tune.schedulers.HyperBandForBOHB property)\n\n(ray.tune.schedulers.HyperBandScheduler property)\n\n(ray.tune.schedulers.MedianStoppingRule property)\n\n(ray.tune.schedulers.pb2.PB2 property)\n\n(ray.tune.schedulers.PopulationBasedTraining property)\n\n(ray.tune.schedulers.PopulationBasedTrainingReplay property)\n\n(ray.tune.schedulers.ResourceChangingScheduler property)\n\n(ray.tune.schedulers.TrialScheduler property)\n\n\nsupports_distributed_reads (ray.data.Datasource property)\n\nsupports_distributed_writes (ray.data.BlockBasedFileDatasink property)\n\n(ray.data.Datasink property)\n\n(ray.data.RowBasedFileDatasink property)\n\n\nsync_artifacts (ray.train.SyncConfig attribute)\n\nsync_artifacts_on_checkpoint (ray.train.SyncConfig attribute)\n\nsync_config (ray.train.RunConfig attribute)\n\nsync_filters() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bf4c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "sync_filters() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nsync_on_checkpoint (ray.train.SyncConfig attribute)\n\nsync_period (ray.train.SyncConfig attribute)\n\nsync_timeout (ray.train.SyncConfig attribute)\n\nsync_weights() (ray.rllib.evaluation.worker_set.WorkerSet method)\n\nSyncConfig (class in ray.train)\n\nsyncer (ray.train.SyncConfig attribute)\n\nsynchronize() (in module ray.util.collective.collective)\n\nSynchronous execution\n\nSynchronous sampling\n\nSyncSampler (class in ray.rllib.evaluation.sampler)\n\n\n\nT\n\n\nT (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\ntail_job_logs() (ray.job_submission.JobSubmissionClient method)\n\ntake() (ray.data.block.BlockAccessor method)\n\n(ray.data.Dataset method)\n\n\ntake_all() (ray.data.Dataset method)\n\ntake_batch() (ray.data.Dataset method)\n\ntarget_capacity (ray.serve.schema.ServeDeploySchema attribute)\n\n(ray.serve.schema.ServeInstanceDetails attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bfa6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.serve.schema.ServeInstanceDetails attribute)\n\n\ntarget_num_ongoing_requests_per_replica (ray.serve.config.AutoscalingConfig attribute)\n\nTask\n\ntask_id (ray.runtime_context.RuntimeContext property)\n\n(ray.util.state.common.TaskState attribute)\n\n\ntask_log_info (ray.util.state.common.TaskState attribute)\n\ntask_state_counts (ray.util.state.common.ObjectSummaryPerKey attribute)\n\ntask_status (ray.util.state.common.ObjectState attribute)\n\nTaskCancelledError\n\nTaskPlacementGroupRemoved\n\nTaskState (class in ray.util.state.common)\n\nTaskSummaries (class in ray.util.state.common)\n\nTaskSummaryPerFuncOrClassName (class in ray.util.state.common)\n\nTaskUnschedulableError\n\nTBXLoggerCallback (class in ray.tune.logger)\n\nteardown() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)\n\nTensorflowConfig (class in ray.train.tensorflow)\n\nTensorflowTrainer (class in ray.train.tensorflow)\n\nTERMINATEDS (ray.rllib.policy.sample_batch.SampleBatch attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986bff6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "TERMINATEDS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\ntf_input_ops() (ray.rllib.evaluation.sampler.AsyncSampler method)\n\n(ray.rllib.evaluation.sampler.SamplerInput method)\n\n(ray.rllib.evaluation.sampler.SyncSampler method)\n\n(ray.rllib.offline.d4rl_reader.D4RLReader method)\n\n(ray.rllib.offline.input_reader.InputReader method)\n\n(ray.rllib.offline.json_reader.JsonReader method)\n\n(ray.rllib.offline.mixed_input.MixedInput method)\n\n\nTFModelV2 (class in ray.rllib.models.tf.tf_modelv2)\n\ntime_budget_s (ray.tune.TuneConfig attribute)\n\ntimeline() (in module ray)\n\ntimeout_s (ray.train.horovod.HorovodConfig attribute)\n\n(ray.train.torch.TorchConfig attribute)\n\n\nTimeoutStopper (class in ray.tune.stopper)\n\ntimeslices() (ray.rllib.policy.sample_batch.MultiAgentBatch method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n\nTIMESTEPS (ray.rllib.utils.replay_buffers.replay_buffer.StorageUnit attribute)\n\nto_arrow() (ray.data.block.BlockAccessor method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c046-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "to_arrow() (ray.data.block.BlockAccessor method)\n\nto_arrow_refs() (ray.data.Dataset method)\n\nto_base_env() (ray.rllib.env.base_env.BaseEnv method)\n\n(ray.rllib.env.external_env.ExternalEnv method)\n\n(ray.rllib.env.vector_env.VectorEnv method)\n\n\nto_batch_format() (ray.data.block.BlockAccessor method)\n\nto_block() (ray.data.block.BlockAccessor method)\n\nto_dask() (ray.data.Dataset method)\n\nto_default() (ray.data.block.BlockAccessor method)\n\nto_device() (ray.rllib.policy.sample_batch.SampleBatch method)\n\nto_dict() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec method)\n\n(ray.rllib.core.rl_module.rl_module.RLModuleConfig method)\n\n(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec method)\n\n\nto_directory() (ray.train.Checkpoint method)\n\nto_json() (ray.job_submission.JobInfo method)\n\nto_mars() (ray.data.Dataset method)\n\nto_modin() (ray.data.Dataset method)\n\nto_numpy() (ray.data.block.BlockAccessor method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c08c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "to_modin() (ray.data.Dataset method)\n\nto_numpy() (ray.data.block.BlockAccessor method)\n\nto_numpy_refs() (ray.data.Dataset method)\n\nto_pandas() (ray.data.block.BlockAccessor method)\n\n(ray.data.Dataset method)\n\n\nto_pandas_refs() (ray.data.Dataset method)\n\nto_random_access_dataset() (ray.data.Dataset method)\n\nto_spark() (ray.data.Dataset method)\n\nto_summary_by_lineage() (ray.util.state.common.TaskSummaries class method)\n\nto_tf() (ray.data.DataIterator method)\n\n(ray.data.Dataset method)\n\n\nto_torch() (ray.data.Dataset method)\n\ntorch_compile (ray.rllib.core.learner.learner.FrameworkHyperparameters attribute)\n\ntorch_compile_cfg (ray.rllib.core.learner.learner.FrameworkHyperparameters attribute)\n\nTorchCompileWhatToCompile (class in ray.rllib.core.learner.learner)\n\nTorchConfig (class in ray.train.torch)\n\nTorchModelV2 (class in ray.rllib.models.torch.torch_modelv2)\n\nTorchPolicyV2 (class in ray.rllib.policy.torch_policy_v2)\n\nTorchTrainer (class in ray.train.torch)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c0dc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "TorchPolicyV2 (class in ray.rllib.policy.torch_policy_v2)\n\nTorchTrainer (class in ray.train.torch)\n\ntotal_actor_scheduled (ray.util.state.common.TaskSummaries attribute)\n\ntotal_actor_tasks (ray.util.state.common.TaskSummaries attribute)\n\ntotal_actors (ray.util.state.common.ActorSummaries attribute)\n\nTOTAL_LOSS_KEY (ray.rllib.core.learner.learner.Learner attribute)\n\ntotal_num_nodes (ray.util.state.common.ObjectSummaryPerKey attribute)\n\ntotal_num_workers (ray.util.state.common.ObjectSummaryPerKey attribute)\n\ntotal_objects (ray.util.state.common.ObjectSummaries attribute)\n\n(ray.util.state.common.ObjectSummaryPerKey attribute)\n\n\ntotal_resources (ray.train.ScalingConfig property)\n\ntotal_samples (ray.tune.search.basic_variant.BasicVariantGenerator property)\n\ntotal_size_mb (ray.util.state.common.ObjectSummaries attribute)\n\n(ray.util.state.common.ObjectSummaryPerKey attribute)\n\n\ntotal_tasks (ray.util.state.common.TaskSummaries attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c12c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "total_tasks (ray.util.state.common.TaskSummaries attribute)\n\ntrain() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.tune.Trainable method)\n\n\ntrain_buffered() (ray.rllib.algorithms.algorithm.Algorithm method)\n\n(ray.tune.Trainable method)\n\n\ntrain_one_step() (in module ray.rllib.execution.train_ops)\n\ntrain_test_split() (ray.data.Dataset method)\n\nTrainable\n\n(class in ray.tune)\n\n\ntrainable_name (ray.tune.experiment.trial.Trial attribute)\n\ntrainable_variables() (ray.rllib.models.modelv2.ModelV2 method)\n\nTrainContext (class in ray.train.context)\n\nTrainer\n\nTrainer configuration\n\ntrainer_resources (ray.train.ScalingConfig attribute)\n\nTraining epoch\n\nTraining iteration\n\n\n\nTraining step\n\ntraining() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n(ray.rllib.algorithms.appo.appo.APPOConfig method)\n\n(ray.rllib.algorithms.bc.bc.BCConfig method)\n\n(ray.rllib.algorithms.cql.cql.CQLConfig method)\n\n(ray.rllib.algorithms.dqn.dqn.DQNConfig method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c172-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.algorithms.cql.cql.CQLConfig method)\n\n(ray.rllib.algorithms.dqn.dqn.DQNConfig method)\n\n(ray.rllib.algorithms.impala.impala.ImpalaConfig method)\n\n(ray.rllib.algorithms.marwil.marwil.MARWILConfig method)\n\n(ray.rllib.algorithms.ppo.ppo.PPOConfig method)\n\n(ray.rllib.algorithms.sac.sac.SACConfig method)\n\n\ntraining_iteration (ray.rllib.algorithms.algorithm.Algorithm property)\n\n(ray.tune.Trainable property)\n\n\ntraining_loop() (ray.train.trainer.BaseTrainer method)\n\ntraining_step() (ray.rllib.algorithms.algorithm.Algorithm method)\n\ntransform() (ray.data.preprocessor.Preprocessor method)\n\n(ray.data.preprocessors.Categorizer method)\n\n(ray.data.preprocessors.Concatenator method)\n\n(ray.data.preprocessors.CustomKBinsDiscretizer method)\n\n(ray.data.preprocessors.LabelEncoder method)\n\n(ray.data.preprocessors.MaxAbsScaler method)\n\n(ray.data.preprocessors.MinMaxScaler method)\n\n(ray.data.preprocessors.MultiHotEncoder method)\n\n(ray.data.preprocessors.Normalizer method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c1c2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.preprocessors.MultiHotEncoder method)\n\n(ray.data.preprocessors.Normalizer method)\n\n(ray.data.preprocessors.OneHotEncoder method)\n\n(ray.data.preprocessors.OrdinalEncoder method)\n\n(ray.data.preprocessors.PowerTransformer method)\n\n(ray.data.preprocessors.RobustScaler method)\n\n(ray.data.preprocessors.SimpleImputer method)\n\n(ray.data.preprocessors.StandardScaler method)\n\n(ray.data.preprocessors.UniformKBinsDiscretizer method)\n\n\ntransform_batch() (ray.data.preprocessor.Preprocessor method)\n\n(ray.data.preprocessors.Categorizer method)\n\n(ray.data.preprocessors.Concatenator method)\n\n(ray.data.preprocessors.CustomKBinsDiscretizer method)\n\n(ray.data.preprocessors.LabelEncoder method)\n\n(ray.data.preprocessors.MaxAbsScaler method)\n\n(ray.data.preprocessors.MinMaxScaler method)\n\n(ray.data.preprocessors.MultiHotEncoder method)\n\n(ray.data.preprocessors.Normalizer method)\n\n(ray.data.preprocessors.OneHotEncoder method)\n\n(ray.data.preprocessors.OrdinalEncoder method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c208-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.preprocessors.OneHotEncoder method)\n\n(ray.data.preprocessors.OrdinalEncoder method)\n\n(ray.data.preprocessors.PowerTransformer method)\n\n(ray.data.preprocessors.RobustScaler method)\n\n(ray.data.preprocessors.SimpleImputer method)\n\n(ray.data.preprocessors.StandardScaler method)\n\n(ray.data.preprocessors.UniformKBinsDiscretizer method)\n\n\ntransform_stats() (ray.data.preprocessor.Preprocessor method)\n\n(ray.data.preprocessors.Categorizer method)\n\n(ray.data.preprocessors.Concatenator method)\n\n(ray.data.preprocessors.CustomKBinsDiscretizer method)\n\n(ray.data.preprocessors.LabelEncoder method)\n\n(ray.data.preprocessors.MaxAbsScaler method)\n\n(ray.data.preprocessors.MinMaxScaler method)\n\n(ray.data.preprocessors.MultiHotEncoder method)\n\n(ray.data.preprocessors.Normalizer method)\n\n(ray.data.preprocessors.OneHotEncoder method)\n\n(ray.data.preprocessors.OrdinalEncoder method)\n\n(ray.data.preprocessors.PowerTransformer method)\n\n(ray.data.preprocessors.RobustScaler method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c258-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.preprocessors.PowerTransformer method)\n\n(ray.data.preprocessors.RobustScaler method)\n\n(ray.data.preprocessors.SimpleImputer method)\n\n(ray.data.preprocessors.StandardScaler method)\n\n(ray.data.preprocessors.UniformKBinsDiscretizer method)\n\n\nTransition\n\nTrial\n\n(class in ray.tune.experiment.trial)\n\n\nTrial Executor\n\nTrial scheduler\n\ntrial_dataframes (ray.tune.ExperimentAnalysis property)\n\ntrial_dirname_creator (ray.tune.TuneConfig attribute)\n\ntrial_id (ray.rllib.algorithms.algorithm.Algorithm property)\n\n(ray.tune.experiment.trial.Trial attribute)\n\n(ray.tune.Trainable property)\n\n\ntrial_name (ray.rllib.algorithms.algorithm.Algorithm property)\n\n(ray.tune.Trainable property)\n\n\ntrial_name_creator (ray.tune.TuneConfig attribute)\n\ntrial_resources (ray.rllib.algorithms.algorithm.Algorithm property)\n\n(ray.tune.Trainable property)\n\n\nTrialPlateauStopper (class in ray.tune.stopper)\n\nTrialScheduler (class in ray.tune.schedulers)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c2a8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "TrialPlateauStopper (class in ray.tune.stopper)\n\nTrialScheduler (class in ray.tune.schedulers)\n\nTRUNCATEDS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\ntry_import_tf() (in module ray.rllib.utils.framework)\n\ntry_import_tfp() (in module ray.rllib.utils.framework)\n\ntry_import_torch() (in module ray.rllib.utils.framework)\n\ntry_render() (ray.rllib.env.base_env.BaseEnv method)\n\ntry_render_at() (ray.rllib.env.vector_env._VectorizedGymEnv method)\n\n(ray.rllib.env.vector_env.VectorEnv method)\n\n\ntry_reset() (ray.rllib.env.base_env.BaseEnv method)\n\ntry_restart() (ray.rllib.env.base_env.BaseEnv method)\n\nTune Config\n\nTune Driver\n\ntune_address (ray.tune.web_server.TuneClient attribute)\n\nTuneBOHB (class in ray.tune.search.bohb)\n\nTuneClient (class in ray.tune.web_server)\n\nTuneConfig (class in ray.tune)\n\nTuner\n\n(class in ray.tune)\n\n\nTuneReportCallback (class in ray.tune.integration.lightgbm)\n\n(class in ray.tune.integration.pytorch_lightning)\n\n(class in ray.tune.integration.xgboost)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c2ee-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(class in ray.tune.integration.pytorch_lightning)\n\n(class in ray.tune.integration.xgboost)\n\n\nTuneReportCheckpointCallback (class in ray.tune.integration.lightgbm)\n\n(class in ray.tune.integration.pytorch_lightning)\n\n(class in ray.tune.integration.xgboost)\n\n\nTunerInternal (class in ray.tune.impl.tuner_internal)\n\ntype (ray.job_submission.JobDetails attribute)\n\n(ray.util.state.common.ObjectState attribute)\n\n(ray.util.state.common.TaskState attribute)\n\n(ray.util.state.common.TaskSummaryPerFuncOrClassName attribute)\n\n\n\n\nU\n\n\nuniform() (in module ray.tune)\n\nUniformKBinsDiscretizer (class in ray.data.preprocessors)\n\nunion() (ray.data.Dataset method)\n\nunique() (ray.data.Dataset method)\n\nunlock() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)\n\nUNROLL_ID (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nunwrapped() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c33e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nupdate() (ray.rllib.core.learner.learner.Learner method)\n\n(ray.rllib.core.learner.learner_group.LearnerGroup method)\n\n(ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec method)\n\n(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_env.RuntimeEnv method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n\nupdate_config() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)\n\n\n\nupdate_default_view_requirements() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)\n\n(ray.rllib.core.rl_module.rl_module.RLModule method)\n\n\nupdate_from_dict() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nupdate_metadata() (ray.train.Checkpoint method)\n\nupdate_ops() (ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\nupdate_policy_weights() (ray.rllib.env.policy_client.PolicyClient method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c384-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "update_policy_weights() (ray.rllib.env.policy_client.PolicyClient method)\n\nupdate_priorities() (ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)\n\n(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)\n\n\nupdate_priorities_in_replay_buffer() (in module ray.rllib.utils.replay_buffers.utils)\n\nupdate_resources() (ray.tune.experiment.trial.Trial method)\n\nupload_dir (ray.train.SyncConfig attribute)\n\nupscale_delay_s (ray.serve.config.AutoscalingConfig attribute)\n\nupscale_smoothing_factor (ray.serve.config.AutoscalingConfig attribute)\n\nuse_gpu (ray.train.ScalingConfig attribute)\n\nuser_config (ray.serve.Deployment property)\n\n(ray.serve.schema.DeploymentSchema attribute)\n\n\n\n\nV\n\n\nVALID_HPARAMS (ray.tune.logger.aim.AimLoggerCallback attribute)\n\n(ray.tune.logger.TBXLoggerCallback attribute)\n\n\nVALID_NP_HPARAMS (ray.tune.logger.aim.AimLoggerCallback attribute)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c3d4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "VALID_NP_HPARAMS (ray.tune.logger.aim.AimLoggerCallback attribute)\n\n(ray.tune.logger.TBXLoggerCallback attribute)\n\n\nVALID_SUMMARY_TYPES (ray.tune.CLIReporter attribute)\n\n(ray.tune.JupyterNotebookReporter attribute)\n\n\nvalidate() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nvalidate_env() (ray.rllib.algorithms.algorithm.Algorithm static method)\n\nvalidate_save_restore() (in module ray.tune.utils)\n\nvalidate_train_batch_size_vs_rollout_fragment_length() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\nvalue() (ray.rllib.utils.schedules.constant_schedule.ConstantSchedule method)\n\n(ray.rllib.utils.schedules.exponential_schedule.ExponentialSchedule method)\n\n(ray.rllib.utils.schedules.linear_schedule.LinearSchedule method)\n\n(ray.rllib.utils.schedules.piecewise_schedule.PiecewiseSchedule method)\n\n(ray.rllib.utils.schedules.polynomial_schedule.PolynomialSchedule method)\n\n(ray.rllib.utils.schedules.schedule.Schedule method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c41a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.utils.schedules.schedule.Schedule method)\n\n\nvalue_function() (ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)\n\n(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)\n\n\n\n\nvalues() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)\n\n(ray.rllib.policy.policy_map.PolicyMap method)\n\n(ray.rllib.policy.sample_batch.SampleBatch method)\n\n(ray.runtime_env.RuntimeEnv method)\n\n(ray.runtime_env.RuntimeEnvConfig method)\n\n\nVALUES_BOOTSTRAPPED (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\nvariables() (ray.rllib.models.modelv2.ModelV2 method)\n\n(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)\n\n\nvector_reset() (ray.rllib.env.vector_env._VectorizedGymEnv method)\n\n(ray.rllib.env.vector_env.VectorEnv method)\n\n\nvector_step() (ray.rllib.env.vector_env._VectorizedGymEnv method)\n\n(ray.rllib.env.vector_env.VectorEnv method)\n\n\nVectorEnv (class in ray.rllib.env.vector_env)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c46a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.rllib.env.vector_env.VectorEnv method)\n\n\nVectorEnv (class in ray.rllib.env.vector_env)\n\nvectorize_gym_envs() (ray.rllib.env.vector_env.VectorEnv static method)\n\nverbose (ray.train.horovod.HorovodConfig attribute)\n\n(ray.train.RunConfig attribute)\n\n\nverbose_progress (ray.data.ExecutionOptions attribute)\n\nVF_PREDS (ray.rllib.policy.sample_batch.SampleBatch attribute)\n\n\n\nW\n\n\nwait() (in module ray)\n\n(ray.util.placement_group.PlacementGroup method)\n\n\nwait_for_gpu() (in module ray.tune.utils)\n\nwait_for_resources() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster static method)\n\nwall_time_s (ray.data.block.BlockExecStats attribute)\n\nwarn_for_middlewares() (ray.serve.config.HTTPOptions class method)\n\nwarn_for_num_cpus() (ray.serve.config.HTTPOptions class method)\n\nwarn_if_infinite_kl_divergence() (in module ray.rllib.utils.tf_utils)\n\n(in module ray.rllib.utils.torch_utils)\n\n\nwas_current_actor_reconstructed (ray.runtime_context.RuntimeContext property)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c4b0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "was_current_actor_reconstructed (ray.runtime_context.RuntimeContext property)\n\nwhat_to_compile (ray.rllib.core.learner.learner.FrameworkHyperparameters attribute)\n\nwindow() (ray.data.Dataset method)\n\nwith_agent_groups() (ray.rllib.env.multi_agent_env.MultiAgentEnv method)\n\nwith_parameters() (in module ray.tune)\n\nwith_resources() (in module ray.tune)\n\nWorker process / worker\n\nworker_id (ray.util.state.common.TaskState attribute)\n\n(ray.util.state.common.WorkerState attribute)\n\n\nworker_launch_time_ms (ray.util.state.common.WorkerState attribute)\n\nworker_launched_time_ms (ray.util.state.common.WorkerState attribute)\n\n\n\nworker_pid (ray.util.state.common.TaskState attribute)\n\nworker_type (ray.util.state.common.WorkerState attribute)\n\nWorkerCrashedError\n\nWorkerSet (class in ray.rllib.evaluation.worker_set)\n\nWorkerState (class in ray.util.state.common)\n\nwrap_as_needed() (ray.rllib.policy.sample_batch.MultiAgentBatch static method)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c500-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "wrap_as_needed() (ray.rllib.policy.sample_batch.MultiAgentBatch static method)\n\nwrap_function() (in module ray.tune.trainable.function_trainable)\n\nwrite() (ray.data.Datasink method)\n\n(ray.data.Datasource method)\n\n\nwrite_bigquery() (ray.data.Dataset method)\n\nwrite_csv() (ray.data.Dataset method)\n\nwrite_datasink() (ray.data.Dataset method)\n\nwrite_datasource() (ray.data.Dataset method)\n\nwrite_images() (ray.data.Dataset method)\n\nwrite_json() (ray.data.Dataset method)\n\nwrite_mongo() (ray.data.Dataset method)\n\nwrite_numpy() (ray.data.Dataset method)\n\nwrite_parquet() (ray.data.Dataset method)\n\nwrite_sql() (ray.data.Dataset method)\n\nwrite_tfrecords() (ray.data.Dataset method)\n\nwrite_webdataset() (ray.data.Dataset method)\n\n\n\nX\n\n\nXGBoostTrainer (class in ray.train.xgboost)\n\n\n\nZ\n\n\nzero_logps_from_actions() (in module ray.rllib.utils.tf_utils)\n\nzip() (ray.data.block.BlockAccessor method)\n\n(ray.data.Dataset method)\n\n\n\n\nZOOptSearch (class in ray.tune.search.zoopt)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c550-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/genindex.html", "title": "Index \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "(ray.data.Dataset method)\n\n\n\n\nZOOptSearch (class in ray.tune.search.zoopt)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThanks for the feedback!\n\n\nWas this helpful?\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\nFeedback\n\nSubmit\n\n\n  \n    By The Ray Team\n  \n      \u00a9 Copyright 2023, The Ray Team."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c596-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Getting Started \u2014 Ray 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nToggle navigation sidebar\n\n\n\n\nToggle in-page Table of Contents\n\n\n\n\n\n\n\n\n\n\n\n\nRay 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n                    Welcome to Ray!\n                \n\n\n\n\n  Ray\n \n\n\n\n\n   Overview\n  \n\n\n\n   Getting Started\n  \n\n\n\n   Installation\n  \n\n\n\n   Use Cases\n  \n\n\n\n   Example Gallery\n  \n\n\n\n   Ecosystem\n  \n\n\n\n   Ray Core\n  \n\n\n\n   Ray Data\n  \n\n\n\n   Ray Train\n  \n\n\n\n   Ray Tune\n  \n\n\n\n   Ray Serve\n  \n\n\n\n   Ray RLlib\n  \n\n\n\n   More Libraries\n  \n\n\n\n   Ray Clusters\n  \n\n\n\n   Monitoring and Debugging\n  \n\n\n\n   Developer Guides\n  \n\n\n\n   Glossary\n  \n\n\n\n   Security\n  \n\n\n\n\n\n\n\n            Theme by the Executable Book Project\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nrepository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.md\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Contents\n    \n\n\n\n\n   Getting Started\n  \n\n\n\n     Ray AI Libraries Quickstart\n    \n\n\n\n     Ray Core Quickstart\n    \n\n\n\n     Ray Cluster Quickstart"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c5e6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Core Quickstart\n    \n\n\n\n     Ray Cluster Quickstart\n    \n\n\n\n     Debugging and Monitoring Quickstart\n    \n\n\n\n\n\n   Learn More\n  \n\n\n\n     Blog and Press\n    \n\n\n\n     Talks (Videos)\n    \n\n\n\n     Slides\n    \n\n\n\n     Papers\n    \n\n\n\n\n\n\n\n\n\n\n\nGetting Started\n\n\n\n\n Contents \n\n\n\n\n\n   Getting Started\n  \n\n\n\n     Ray AI Libraries Quickstart\n    \n\n\n\n     Ray Core Quickstart\n    \n\n\n\n     Ray Cluster Quickstart\n    \n\n\n\n     Debugging and Monitoring Quickstart\n    \n\n\n\n\n\n   Learn More\n  \n\n\n\n     Blog and Press\n    \n\n\n\n     Talks (Videos)\n    \n\n\n\n     Slides\n    \n\n\n\n     Papers\n    \n\n\n\n\n\n\n\n\n\n\n\nGetting Started#\nUse Ray to scale applications on your laptop or the cloud. Choose the right guide for your task.\n\nScale ML workloads: Ray Libraries Quickstart\nScale general Python applications: Ray Core Quickstart\nDeploy to the cloud: Ray Clusters Quickstart\nDebug and monitor applications: Debugging and Monitoring Quickstart"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c636-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray AI Libraries Quickstart#\nUse individual libraries for ML workloads. Click on the dropdowns for your workload below.\n\n\n Data: Scalable Datasets for ML\n\n\n\n\nScale offline inference and training ingest with Ray Data \u2013\na data processing library designed for ML.\nTo learn more, see Offline batch inference and\nData preprocessing and ingest for ML training.\n\nNote\nTo run this example, install Ray Data:\npip install -U \"ray[data]\"\n\n\n\nfrom typing import Dict\nimport numpy as np\nimport ray\n\n# Create datasets from on-disk files, Python objects, and cloud storage like S3.\nds = ray.data.read_csv(\"s3://anonymous@ray-example-data/iris.csv\")\n\n# Apply functions to transform data. Ray Data executes transformations in parallel.\ndef compute_area(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:\n    length = batch[\"petal length (cm)\"]\n    width = batch[\"petal width (cm)\"]\n    batch[\"petal area (cm^2)\"] = length * width\n    return batch\n\ntransformed_ds = ds.map_batches(compute_area)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c6ae-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "transformed_ds = ds.map_batches(compute_area)\n\n# Iterate over batches of data.\nfor batch in transformed_ds.iter_batches(batch_size=4):\n    print(batch)\n\n# Save dataset contents to on-disk files or cloud storage.\ntransformed_ds.write_parquet(\"local:///tmp/iris/\")\n\n\nLearn more about Ray Data\n\n\n\n Train: Distributed Model Training\n\n\n\n\nRay Train abstracts away the complexity of setting up a distributed training\nsystem.\n\n\n\nPyTorch\nThis example shows how you can use Ray Train with PyTorch.\nTo run this example install Ray Train and PyTorch packages:\n\nNote\npip install -U \"ray[train]\" torch torchvision\n\n\n\nSet up your dataset and model.\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torchvision.transforms import ToTensor\n\ndef get_dataset():\n    return datasets.FashionMNIST(\n        root=\"/tmp/data\",\n        train=True,\n        download=True,\n        transform=ToTensor(),\n    )"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c708-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "class NeuralNetwork(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.flatten = nn.Flatten()\n        self.linear_relu_stack = nn.Sequential(\n            nn.Linear(28 * 28, 512),\n            nn.ReLU(),\n            nn.Linear(512, 512),\n            nn.ReLU(),\n            nn.Linear(512, 10),\n        )\n\n    def forward(self, inputs):\n        inputs = self.flatten(inputs)\n        logits = self.linear_relu_stack(inputs)\n        return logits\n\n\nNow define your single-worker PyTorch training function.\ndef train_func():\n    num_epochs = 3\n    batch_size = 64\n\n    dataset = get_dataset()\n    dataloader = DataLoader(dataset, batch_size=batch_size)\n\n    model = NeuralNetwork()\n\n    criterion = nn.CrossEntropyLoss()\n    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c74e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "criterion = nn.CrossEntropyLoss()\n    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n\n    for epoch in range(num_epochs):\n        for inputs, labels in dataloader:\n            optimizer.zero_grad()\n            pred = model(inputs)\n            loss = criterion(pred, labels)\n            loss.backward()\n            optimizer.step()\n        print(f\"epoch: {epoch}, loss: {loss.item()}\")\n\n\nThis training function can be executed with:\ntrain_func()\n\n\nConvert this to a distributed multi-worker training function.\nUse the ray.train.torch.prepare_model and\nray.train.torch.prepare_data_loader utility functions to\nset up your model and data for distributed training.\nThis automatically wraps the model with DistributedDataParallel\nand places it on the right device, and adds DistributedSampler to the DataLoaders.\nfrom ray import train\n\ndef train_func_distributed():\n    num_epochs = 3\n    batch_size = 64"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c79e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "def train_func_distributed():\n    num_epochs = 3\n    batch_size = 64\n\n    dataset = get_dataset()\n    dataloader = DataLoader(dataset, batch_size=batch_size)\n    dataloader = train.torch.prepare_data_loader(dataloader)\n\n    model = NeuralNetwork()\n    model = train.torch.prepare_model(model)\n\n    criterion = nn.CrossEntropyLoss()\n    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n\n    for epoch in range(num_epochs):\n        for inputs, labels in dataloader:\n            optimizer.zero_grad()\n            pred = model(inputs)\n            loss = criterion(pred, labels)\n            loss.backward()\n            optimizer.step()\n        print(f\"epoch: {epoch}, loss: {loss.item()}\")\n\n\nInstantiate a TorchTrainer\nwith 4 workers, and use it to run the new training function.\nfrom ray.train.torch import TorchTrainer\nfrom ray.train import ScalingConfig\n\n# For GPU Training, set `use_gpu` to True.\nuse_gpu = False"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c7e4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# For GPU Training, set `use_gpu` to True.\nuse_gpu = False\n\ntrainer = TorchTrainer(\n    train_func_distributed,\n    scaling_config=ScalingConfig(num_workers=4, use_gpu=use_gpu)\n)\n\nresults = trainer.fit()\n\n\n\n\n\nTensorFlow\nThis example shows how you can use Ray Train to set up Multi-worker training\nwith Keras.\nTo run this example install Ray Train and Tensorflow packages:\n\nNote\npip install -U \"ray[train]\" tensorflow\n\n\n\nSet up your dataset and model.\nimport numpy as np\nimport tensorflow as tf\n\ndef mnist_dataset(batch_size):\n    (x_train, y_train), _ = tf.keras.datasets.mnist.load_data()\n    # The `x` arrays are in uint8 and have values in the [0, 255] range.\n    # You need to convert them to float32 with values in the [0, 1] range.\n    x_train = x_train / np.float32(255)\n    y_train = y_train.astype(np.int64)\n    train_dataset = tf.data.Dataset.from_tensor_slices(\n        (x_train, y_train)).shuffle(60000).repeat().batch(batch_size)\n    return train_dataset"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c834-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "def build_and_compile_cnn_model():\n    model = tf.keras.Sequential([\n        tf.keras.layers.InputLayer(input_shape=(28, 28)),\n        tf.keras.layers.Reshape(target_shape=(28, 28, 1)),\n        tf.keras.layers.Conv2D(32, 3, activation='relu'),\n        tf.keras.layers.Flatten(),\n        tf.keras.layers.Dense(128, activation='relu'),\n        tf.keras.layers.Dense(10)\n    ])\n    model.compile(\n        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n        optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),\n        metrics=['accuracy'])\n    return model\n\n\nNow define your single-worker TensorFlow training function.\ndef train_func():\n    batch_size = 64\n    single_worker_dataset = mnist_dataset(batch_size)\n    single_worker_model = build_and_compile_cnn_model()\n    single_worker_model.fit(single_worker_dataset, epochs=3, steps_per_epoch=70)\n\n\nThis training function can be executed with:\ntrain_func()"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c87a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "This training function can be executed with:\ntrain_func()\n\n\nNow convert this to a distributed multi-worker training function.\n\nSet the global batch size - each worker processes the same size\nbatch as in the single-worker code.\nChoose your TensorFlow distributed training strategy. This examples\nuses the MultiWorkerMirroredStrategy.\n\nimport json\nimport os\n\ndef train_func_distributed():\n    per_worker_batch_size = 64\n    # This environment variable will be set by Ray Train.\n    tf_config = json.loads(os.environ['TF_CONFIG'])\n    num_workers = len(tf_config['cluster']['worker'])\n\n    strategy = tf.distribute.MultiWorkerMirroredStrategy()\n\n    global_batch_size = per_worker_batch_size * num_workers\n    multi_worker_dataset = mnist_dataset(global_batch_size)\n\n    with strategy.scope():\n        # Model building/compiling need to be within `strategy.scope()`.\n        multi_worker_model = build_and_compile_cnn_model()"}


