# migrations

dir: embeddings_queue
filename: 00001-embeddings.sqlite.sql
hash: d3755dfd232be8e8301f4d7fcfb3a486
sql:
CREATE TABLE embeddings_queue (
    seq_id INTEGER PRIMARY KEY,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    operation INTEGER NOT NULL,
    topic TEXT NOT NULL,
    id TEXT NOT NULL,
    vector BLOB,
    encoding TEXT,
    metadata TEXT
);


dir: sysdb
filename: 00001-collections.sqlite.sql
hash: 38352d725ad1c16074fac420b22b4633
sql:
CREATE TABLE collections (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    topic TEXT NOT NULL,
    UNIQUE (name)
);

CREATE TABLE collection_metadata (
    collection_id TEXT REFERENCES collections(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (collection_id, key)
);


dir: sysdb
filename: 00002-segments.sqlite.sql
hash: 2913cb6a503055a95f625448037e8912
sql:
CREATE TABLE segments (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    topic TEXT,
    collection TEXT REFERENCES collection(id)
);

CREATE TABLE segment_metadata (
    segment_id TEXT  REFERENCES segments(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (segment_id, key)
);


dir: sysdb
filename: 00004-tenants-databases.sqlite.sql
hash: 048867ce8fcdefe4023c7110e4433591
sql:
CREATE TABLE IF NOT EXISTS tenants (
    id TEXT PRIMARY KEY,
    UNIQUE (id)
);

CREATE TABLE IF NOT EXISTS databases (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per tenant
    tenant_id TEXT NOT NULL REFERENCES tenants(id) ON DELETE CASCADE,
    UNIQUE (tenant_id, name) -- Ensure that a tenant has only one database with a given name
);

CREATE TABLE IF NOT EXISTS collections_tmp (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per database
    topic TEXT NOT NULL,
    dimension INTEGER,
    database_id TEXT NOT NULL REFERENCES databases(id) ON DELETE CASCADE,
    UNIQUE (name, database_id)
);

-- Create default tenant and database
INSERT OR REPLACE INTO tenants (id) VALUES ('default_tenant'); -- The default tenant id is 'default_tenant' others are UUIDs
INSERT OR REPLACE INTO databases (id, name, tenant_id) VALUES ('00000000-0000-0000-0000-000000000000', 'default_database', 'default_tenant');

INSERT OR REPLACE INTO collections_tmp (id, name, topic, dimension, database_id)
    SELECT id, name, topic, dimension, '00000000-0000-0000-0000-000000000000' FROM collections;
DROP TABLE collections;
ALTER TABLE collections_tmp RENAME TO collections;


dir: metadb
filename: 00001-embedding-metadata.sqlite.sql
hash: 2b4cf52c4bb2676e21d6860a4409f856
sql:
CREATE TABLE embeddings (
    id INTEGER PRIMARY KEY,
    segment_id TEXT NOT NULL,
    embedding_id TEXT NOT NULL,
    seq_id BLOB NOT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    UNIQUE (segment_id, embedding_id)
);

CREATE TABLE embedding_metadata (
    id INTEGER REFERENCES embeddings(id),
    key TEXT NOT NULL,
    string_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (id, key)
);

CREATE TABLE max_seq_id (
    segment_id TEXT PRIMARY KEY,
    seq_id BLOB NOT NULL
);

CREATE VIRTUAL TABLE embedding_fulltext USING fts5(id, string_value);


dir: metadb
filename: 00002-embedding-metadata.sqlite.sql
hash: 12a570f7121b3a8ce750a2a7c36da20f
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE embedding_metadata ADD COLUMN bool_value INTEGER


dir: metadb
filename: 00003-full-text-tokenize.sqlite.sql
hash: f97ad6334aeaa8f419f01110b648b97a
sql:
CREATE VIRTUAL TABLE embedding_fulltext_search USING fts5(string_value, tokenize='trigram');
INSERT INTO embedding_fulltext_search (rowid, string_value) SELECT rowid, string_value FROM embedding_metadata;
DROP TABLE embedding_fulltext;


# embeddings_queue

created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9860da4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/", "title": "Welcome to Ray! \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Welcome to 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\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\nWelcome to Ray!\n\n\n\n\n\n\n\n\n\n\n\n\nWelcome to Ray!"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9860e94-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/", "title": "Welcome to Ray! \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": ".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\nWelcome to Ray!\n\n\n\n\n\n\n\n\n\n\n\n\nWelcome to Ray!\n\n\n\n\nWelcome to Ray!\nRay is an open-source unified framework for scaling AI and Python applications. \n      It provides the compute layer for parallel processing so that \n      you don\u2019t need to be a distributed systems expert.\n      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nScaling with Ray\n\n\n\n\n            Batch Inference\n          \n\n            Model Training\n          \n\n            Hyperparameter Tuning\n          \n            Model Serving\n          \n\n            Reinforcement Learning\n          \n\n\n\n\n\n\nfrom typing import Dict\nimport numpy as np\n\nimport ray\n\n# Step 1: Create a Ray Dataset from in-memory Numpy arrays.\nds = ray.data.from_numpy(np.asarray([\"Complete this\", \"for me\"]))"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9860f0c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/", "title": "Welcome to Ray! \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Step 2: Define a Predictor class for inference.\nclass HuggingFacePredictor:\n    def __init__(self):\n        from transformers import pipeline\n        # Initialize a pre-trained GPT2 Huggingface pipeline.\n        self.model = pipeline(\"text-generation\", model=\"gpt2\")\n\n    # Logic for inference on 1 batch of data.\n    def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, list]:\n        # Get the predictions from the input batch.\n        predictions = self.model(\n            list(batch[\"data\"]), max_length=20, num_return_sequences=1)\n        # `predictions` is a list of length-one lists. For example:\n        # [[{'generated_text': 'output_1'}], ..., [{'generated_text': 'output_2'}]]\n        # Modify the output to get it into the following format instead:\n        # ['output_1', 'output_2']\n        batch[\"output\"] = [sequences[0][\"generated_text\"] for sequences in predictions]\n        return batch"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9860f66-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/", "title": "Welcome to Ray! \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Use 2 parallel actors for inference. Each actor predicts on a\n# different partition of data.\nscale = ray.data.ActorPoolStrategy(size=2)\n# Step 3: Map the Predictor over the Dataset to get predictions.\npredictions = ds.map_batches(HuggingFacePredictor, compute=scale)\n# Step 4: Show one prediction output.\npredictions.show(limit=1)\n\n            \n\n\nLearn more  |  API references\n\n\n\n Open in Github\n                    \n\n\n\n\n\nfrom ray.train import ScalingConfig\nfrom ray.train.torch import TorchTrainer\n\n# Step 1: setup PyTorch model training as you normally would\ndef train_loop_per_worker():\n    model = ...\n    train_dataset = ...\n    for epoch in range(num_epochs):\n        ...  # model training logic\n\n# Step 2: setup Ray's PyTorch Trainer to run on 32 GPUs\ntrainer = TorchTrainer(\n    train_loop_per_worker=train_loop_per_worker,\n    scaling_config=ScalingConfig(num_workers=32, use_gpu=True),\n    datasets={\"train\": train_dataset},\n)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9860fb6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/", "title": "Welcome to Ray! \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Step 3: run distributed model training on 32 GPUs\nresult = trainer.fit()\n            \n\n\nLearn more  |  API references\n\n\n\n Open in colab\n                    \n\n\n\n\n\nfrom ray import tune\nfrom ray.train import ScalingConfig\nfrom ray.train.lightgbm import LightGBMTrainer\n\ntrain_dataset, eval_dataset = ...\n\n# Step 1: setup Ray's LightGBM Trainer to train on 64 CPUs\ntrainer = LightGBMTrainer(\n    ...\n    scaling_config=ScalingConfig(num_workers=64),\n    datasets={\"train\": train_dataset, \"eval\": eval_dataset},\n)\n\n# Step 2: setup Ray Tuner to run 1000 trials\ntuner = tune.Tuner(\n    trainer=trainer,\n    param_space=hyper_param_space,\n    tune_config=tune.TuneConfig(num_samples=1000),\n)\n\n# Step 3: run distributed HPO with 1000 trials; each trial runs on 64 CPUs\nresult_grid = tuner.fit()\n\n            \n\n\nLearn more  |  API references\n\n\n\n Open in Github\n                    \n\n\n\n\n\nimport pandas as pd\n\nfrom ray import serve\nfrom starlette.requests import Request"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861010-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/", "title": "Welcome to Ray! \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "import pandas as pd\n\nfrom ray import serve\nfrom starlette.requests import Request\n\n\n@serve.deployment(ray_actor_options={\"num_gpus\": 1})\nclass PredictDeployment:\n    def __init__(self, model_id: str, revision: str = None):\n        from transformers import AutoModelForCausalLM, AutoTokenizer\n        import torch\n\n        self.model = AutoModelForCausalLM.from_pretrained(\n            model_id,\n            \u2026\n        )\n        self.tokenizer = AutoTokenizer.from_pretrained(model_id)\n\n    def generate(self, text: str) -> pd.DataFrame:\n        input_ids = self.tokenizer(text, return_tensors=\"pt\").input_ids.to(\n            self.model.device\n        )\n\n        gen_tokens = self.model.generate(\n            input_ids,\n            \u2026\n        )\n        return pd.DataFrame(\n            self.tokenizer.batch_decode(gen_tokens), columns=[\"responses\"]\n        )"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986106a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/", "title": "Welcome to Ray! \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "async def __call__(self, http_request: Request) -> str:\n        prompts: list[str] = await http_request.json()[\"prompts\"]\n        return self.generate(prompts)\n\n\n            \n\n\nLearn more  |  API references\n\n\n\n Open in Github\n                    \n\n\n\n\n\nfrom ray.rllib.algorithms.ppo import PPOConfig\n\n# Step 1: configure PPO to run 64 parallel workers to collect samples from the env.\nppo_config = (\n    PPOConfig()\n    .environment(env=\"Taxi-v3\")\n    .rollouts(num_rollout_workers=64)\n    .framework(\"torch\")\n    .training(model=rnn_lage)\n)\n\n# Step 2: build the PPO algorithm\nppo_algo = ppo_config.build()\n\n# Step 3: train and evaluate PPO\nfor _ in range(5):\n    print(ppo_algo.train())\n\nppo_algo.evaluate()\n            \n\n\nLearn more  |  API references\n\n\n\n Open in Github\n                    \n\n\n\n\n\n\n\n\nGetting Started\n\n \n\n\nLearn basics\n\nUnderstand how the Ray framework scales your ML workflows.\nLearn more > \n \n \n\n\nInstall Ray\n\nInstallation guide >\n\n\n\n\nTry it out"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98610ba-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/", "title": "Welcome to Ray! \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Install Ray\n\nInstallation guide >\n\n\n\n\nTry it out\n\nExperiment with Ray with an introductory notebook.\nOpen the notebook>\n\n\n\nBeyond the basics\n\n\n\n\n\nRay Libraries\n\nScale the entire ML pipeline from data ingest to model serving with high-level Python APIs that integrate with popular ecosystem frameworks.\nLearn more about Ray Libraries>\n\n\n\n\nRay Core\n\nScale generic Python code with simple, foundational primitives that enable a high degree of control for building distributed applications or custom platforms.\nLearn more about Core >\n\n\n\n\nRay Clusters\n\nDeploy a Ray cluster on AWS, GCP, Azure or kubernetes from a laptop to a large cluster to seamlessly scale workloads for production\nLearn more about clusters >\n\n\n\nGetting involved\n\n\n\n Join the community \n \n\n\nAttend community events\n\n\n \n\n\nSubscribe to the newsletter\n\n\n \n\n\nFollow us on Twitter\n\n\n\n\n Get Support \n \n\n\nFind community on Slack\n\n\n \n\n\nAsk questions to the forum\n\n\n \n\n\nOpen an issue\n\n\n\n\n Contribute to Ray \n \n\n\nContributor's guide"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861100-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/", "title": "Welcome to Ray! \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Open an issue\n\n\n\n\n Contribute to Ray \n \n\n\nContributor's guide\n\n\n \n\n\nCreate a pull request\n\n\n\n\n\n\n\n\n\n\n\nnext\nOverview\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: b9861150-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/index.html", "title": "Developer Guides \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Developer Guides \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\n\n\n\n\n     API stability\n    \n\n\n\n     Getting Involved / Contributing\n    \n\n\n\n     Configuring Ray\n    \n\n\n\n     Architecture Whitepapers\n    \n\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"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98611aa-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/index.html", "title": "Developer Guides \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\nDeveloper Guides\n\n\n\n\n\n\n\n\n\nDeveloper Guides#\n\n\nAPI stability\nAlpha\nBeta\nStable\n\n\nGetting Involved / Contributing\nContributing to Ray\nWhat can I work on?\nSetting up your development environment\nSubmitting and Merging a Contribution\nPR Review Process\nTesting\nCode Style\nUnderstanding CI test jobs\nAPI compatibility style guide\nBecoming a Reviewer\nMore Resources for Getting Involved\n\n\nConfiguring Ray\nCluster Resources\nLogging and Debugging\nPorts configurations\nTLS Authentication\nJava Applications\n\n\nArchitecture Whitepapers\n\n\n\n\n\n\n\n\n\n\n\nprevious\nSystem Metrics\n\n\n\n\nnext\nAPI stability\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: b98611f0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/package_ref/index.html", "title": "Ray RLlib API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray RLlib 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   Ray RLlib\n  \n\n\n\n\n\n\n\n\n     Getting Started with RLlib\n    \n\n\n\n     Key Concepts\n    \n\n\n\n     Environments\n    \n\n\n\n     Algorithms\n    \n\n\n\n     User Guides\n    \n\n\n\n     Examples\n    \n\n\n\n     Ray RLlib API\n    \n\n\n\n\n\n\n\n\n       Algorithms\n      \n\n\n\n       Environments\n      \n\n\n\n       Policy API\n      \n\n\n\n       Model APIs\n      \n\n\n\n       Catalog API\n      \n\n\n\n       RLModule API\n      \n\n\n\n       Learner API\n      \n\n\n\n       Sampling the Environment or offline data"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861240-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/package_ref/index.html", "title": "Ray RLlib API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Learner API\n      \n\n\n\n       Sampling the Environment or offline data\n      \n\n\n\n       Replay Buffer API\n      \n\n\n\n       RLlib Utilities\n      \n\n\n\n       External Application API\n      \n\n\n\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\nRay RLlib API\n\n\n\n\n\n\n\n\n\nNote\nFrom Ray 2.6.0 onwards, RLlib is adopting a new stack for training and model customization,\ngradually replacing the ModelV2 API and some convoluted parts of Policy API with the RLModule API.\nClick here for details.\n\n\nRay RLlib API#\n\nTip\nWe\u2019d love to hear your feedback on using RLlib - sign up to our forum and start asking questions!"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861290-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/package_ref/index.html", "title": "Ray RLlib API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "This section contains an overview of RLlib\u2019s package- and API reference.\nIf you think there is anything missing, please open an issue on Github.\n\n\nAlgorithms\nAlgorithm Configuration API\nBuilding Custom Algorithm Classes\nAlgorithm API\n\n\nEnvironments\nEnvironment API Reference\n\n\nPolicy API\nBuilding Custom Policy Classes\nBase Policy classes\nMaking models\nInference\nComputing, processing, and applying gradients\nUpdating the Policy\u2019s model\nLoss, Logging, optimizers, and trajectory processing\nSaving and restoring\nConnectors\nRecurrent Policies\nMiscellaneous\n\n\nModel APIs\nBase Model classes\nFeed Forward methods\nRecurrent Models API\nAcessing variables\nCustomization\n\n\nCatalog API\nBasic usage\nAdvanced usage\n\n\nRLModule API\nRL Module specifications and configurations\nRL Module API\nMulti Agent RL Module API"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98612ea-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/package_ref/index.html", "title": "Ray RLlib API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "RLModule API\nRL Module specifications and configurations\nRL Module API\nMulti Agent RL Module API\n\n\nLearner API\nLearner specifications and configurations\nConstructor\nPerforming Updates\nComputing Losses\nConfiguring Optimizers\nGradient Computation\nSaving, Loading, Checkpointing, and Restoring States\nAdding and Removing Modules\nManaging Results\n\n\nLearnerGroup API\nConfiguring a LearnerGroup\n\n\nSampling the Environment or offline data\nRolloutWorker API\nWorkerSet API\nSampler API\nPolicy Map API\nSample batch API\nMultiAgent batch API\n\n\nReplay Buffer API\nReplay Buffer Base Classes\nPublic Methods\nMulti Agent Buffers\nUtility Methods\n\n\nRLlib Utilities\nExploration API\nScheduler API\nTraining Operations Utilities\nFramework Utilities\n\n\nExternal Application API\n\n\n\n\n\n\n\n\n\n\n\nprevious\nExamples\n\n\n\n\nnext\nAlgorithms\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: b986133a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/_sources/index.md.txt", "title": "Welcome to Ray!", "chroma:document": "```{title} Welcome to Ray!\n```\n\n```{raw} html\n\n\n\n\n\nWelcome to Ray!\n\n\n\n\nWelcome to Ray!\nRay is an open-source unified framework for scaling AI and Python applications. \n      It provides the compute layer for parallel processing so that \n      you don\u2019t need to be a distributed systems expert.\n      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nScaling with Ray\n\n\n\n\n            Batch Inference\n          \n\n            Model Training\n          \n\n            Hyperparameter Tuning\n          \n            Model Serving\n          \n\n            Reinforcement Learning\n          \n\n\n\n\n\n\nfrom typing import Dict\nimport numpy as np\n\nimport ray\n\n# Step 1: Create a Ray Dataset from in-memory Numpy arrays.\nds = ray.data.from_numpy(np.asarray([\"Complete this\", \"for me\"]))"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986138a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/_sources/index.md.txt", "title": "Welcome to Ray!", "chroma:document": "# Step 2: Define a Predictor class for inference.\nclass HuggingFacePredictor:\n    def __init__(self):\n        from transformers import pipeline\n        # Initialize a pre-trained GPT2 Huggingface pipeline.\n        self.model = pipeline(\"text-generation\", model=\"gpt2\")\n\n    # Logic for inference on 1 batch of data.\n    def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, list]:\n        # Get the predictions from the input batch.\n        predictions = self.model(\n            list(batch[\"data\"]), max_length=20, num_return_sequences=1)\n        # `predictions` is a list of length-one lists. For example:\n        # [[{'generated_text': 'output_1'}], ..., [{'generated_text': 'output_2'}]]\n        # Modify the output to get it into the following format instead:\n        # ['output_1', 'output_2']\n        batch[\"output\"] = [sequences[0][\"generated_text\"] for sequences in predictions]\n        return batch"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98613e4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/_sources/index.md.txt", "title": "Welcome to Ray!", "chroma:document": "# Use 2 parallel actors for inference. Each actor predicts on a\n# different partition of data.\nscale = ray.data.ActorPoolStrategy(size=2)\n# Step 3: Map the Predictor over the Dataset to get predictions.\npredictions = ds.map_batches(HuggingFacePredictor, compute=scale)\n# Step 4: Show one prediction output.\npredictions.show(limit=1)\n\n            \n\n\nLearn more  |  API references\n\n\n\n Open in Github\n                    \n\n\n\n\n\nfrom ray.train import ScalingConfig\nfrom ray.train.torch import TorchTrainer\n\n# Step 1: setup PyTorch model training as you normally would\ndef train_loop_per_worker():\n    model = ...\n    train_dataset = ...\n    for epoch in range(num_epochs):\n        ...  # model training logic\n\n# Step 2: setup Ray's PyTorch Trainer to run on 32 GPUs\ntrainer = TorchTrainer(\n    train_loop_per_worker=train_loop_per_worker,\n    scaling_config=ScalingConfig(num_workers=32, use_gpu=True),\n    datasets={\"train\": train_dataset},\n)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861434-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/_sources/index.md.txt", "title": "Welcome to Ray!", "chroma:document": "# Step 3: run distributed model training on 32 GPUs\nresult = trainer.fit()\n            \n\n\nLearn more  |  API references\n\n\n\n Open in colab\n                    \n\n\n\n\n\nfrom ray import tune\nfrom ray.train import ScalingConfig\nfrom ray.train.lightgbm import LightGBMTrainer\n\ntrain_dataset, eval_dataset = ...\n\n# Step 1: setup Ray's LightGBM Trainer to train on 64 CPUs\ntrainer = LightGBMTrainer(\n    ...\n    scaling_config=ScalingConfig(num_workers=64),\n    datasets={\"train\": train_dataset, \"eval\": eval_dataset},\n)\n\n# Step 2: setup Ray Tuner to run 1000 trials\ntuner = tune.Tuner(\n    trainer=trainer,\n    param_space=hyper_param_space,\n    tune_config=tune.TuneConfig(num_samples=1000),\n)\n\n# Step 3: run distributed HPO with 1000 trials; each trial runs on 64 CPUs\nresult_grid = tuner.fit()\n\n            \n\n\nLearn more  |  API references\n\n\n\n Open in Github\n                    \n\n\n\n\n\nimport pandas as pd\n\nfrom ray import serve\nfrom starlette.requests import Request"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861484-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/_sources/index.md.txt", "title": "Welcome to Ray!", "chroma:document": "import pandas as pd\n\nfrom ray import serve\nfrom starlette.requests import Request\n\n\n@serve.deployment(ray_actor_options={\"num_gpus\": 1})\nclass PredictDeployment:\n    def __init__(self, model_id: str, revision: str = None):\n        from transformers import AutoModelForCausalLM, AutoTokenizer\n        import torch\n\n        self.model = AutoModelForCausalLM.from_pretrained(\n            model_id,\n            \u2026\n        )\n        self.tokenizer = AutoTokenizer.from_pretrained(model_id)\n\n    def generate(self, text: str) -> pd.DataFrame:\n        input_ids = self.tokenizer(text, return_tensors=\"pt\").input_ids.to(\n            self.model.device\n        )\n\n        gen_tokens = self.model.generate(\n            input_ids,\n            \u2026\n        )\n        return pd.DataFrame(\n            self.tokenizer.batch_decode(gen_tokens), columns=[\"responses\"]\n        )"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98614d4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/_sources/index.md.txt", "title": "Welcome to Ray!", "chroma:document": "async def __call__(self, http_request: Request) -> str:\n        prompts: list[str] = await http_request.json()[\"prompts\"]\n        return self.generate(prompts)\n\n\n            \n\n\nLearn more  |  API references\n\n\n\n Open in Github\n                    \n\n\n\n\n\nfrom ray.rllib.algorithms.ppo import PPOConfig\n\n# Step 1: configure PPO to run 64 parallel workers to collect samples from the env.\nppo_config = (\n    PPOConfig()\n    .environment(env=\"Taxi-v3\")\n    .rollouts(num_rollout_workers=64)\n    .framework(\"torch\")\n    .training(model=rnn_lage)\n)\n\n# Step 2: build the PPO algorithm\nppo_algo = ppo_config.build()\n\n# Step 3: train and evaluate PPO\nfor _ in range(5):\n    print(ppo_algo.train())\n\nppo_algo.evaluate()\n            \n\n\nLearn more  |  API references\n\n\n\n Open in Github\n                    \n\n\n\n\n\n\n\n\nGetting Started\n\n \n\n\nLearn basics\n\nUnderstand how the Ray framework scales your ML workflows.\nLearn more > \n \n \n\n\nInstall Ray\n\nInstallation guide >\n\n\n\n\nTry it out"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861538-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/_sources/index.md.txt", "title": "Welcome to Ray!", "chroma:document": "Install Ray\n\nInstallation guide >\n\n\n\n\nTry it out\n\nExperiment with Ray with an introductory notebook.\nOpen the notebook>\n\n\n\nBeyond the basics\n\n\n\n\n\nRay Libraries\n\nScale the entire ML pipeline from data ingest to model serving with high-level Python APIs that integrate with popular ecosystem frameworks.\nLearn more about Ray Libraries>\n\n\n\n\nRay Core\n\nScale generic Python code with simple, foundational primitives that enable a high degree of control for building distributed applications or custom platforms.\nLearn more about Core >\n\n\n\n\nRay Clusters\n\nDeploy a Ray cluster on AWS, GCP, Azure or kubernetes from a laptop to a large cluster to seamlessly scale workloads for production\nLearn more about clusters >\n\n\n\nGetting involved\n\n\n\n Join the community \n \n\n\nAttend community events\n\n\n \n\n\nSubscribe to the newsletter\n\n\n \n\n\nFollow us on Twitter\n\n\n\n\n Get Support \n \n\n\nFind community on Slack\n\n\n \n\n\nAsk questions to the forum\n\n\n \n\n\nOpen an issue\n\n\n\n\n Contribute to Ray \n \n\n\nContributor's guide"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861592-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/_sources/index.md.txt", "title": "Welcome to Ray!", "chroma:document": "Open an issue\n\n\n\n\n Contribute to Ray \n \n\n\nContributor's guide\n\n\n \n\n\nCreate a pull request\n\n\n\n\n```"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98615ec-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/train/train.html", "title": "Ray Train: Scalable Model Training \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Train: Scalable Model Training \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\n\n\n\n\n     Overview\n    \n\n\n\n     PyTorch Guide\n    \n\n\n\n     PyTorch Lightning Guide\n    \n\n\n\n     Hugging Face Transformers Guide\n    \n\n\n\n     More Frameworks\n    \n\n\n\n     User Guides\n    \n\n\n\n     Examples\n    \n\n\n\n     Benchmarks\n    \n\n\n\n     Ray Train API\n    \n\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"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986163c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/train/train.html", "title": "Ray Train: Scalable Model Training \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "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   Install Ray Train\n  \n\n\n\n   Get started\n  \n\n\n\n   Learn more\n  \n\n\n\n\n\n\n\n\n\nRay Train: Scalable Model Training\n\n\n\n\n Contents \n\n\n\n\n\n   Install Ray Train\n  \n\n\n\n   Get started\n  \n\n\n\n   Learn more\n  \n\n\n\n\n\n\n\n\n\nRay Train: Scalable Model Training#\n\n\n\n\n\n\n\n\n\nRay Train is a scalable machine learning library for distributed training and fine-tuning.\nRay Train allows you to scale model training code from a single machine to a cluster of machines in the cloud, and abstracts away the complexities of distributed computing.\nWhether you have large models or large datasets, Ray Train is the simplest solution for distributed training.\nRay Train provides support for many frameworks:\n\n\n\n\n\n\nPyTorch Ecosystem\nMore Frameworks\n\n\n\nPyTorch\nTensorFlow\n\nPyTorch Lightning\nKeras"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98616be-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/train/train.html", "title": "Ray Train: Scalable Model Training \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "PyTorch Ecosystem\nMore Frameworks\n\n\n\nPyTorch\nTensorFlow\n\nPyTorch Lightning\nKeras\n\nHugging Face Transformers\nHorovod\n\nHugging Face Accelerate\nXGBoost\n\nDeepSpeed\nLightGBM\n\n\n\n\nInstall Ray Train#\nTo install Ray Train, run:\n$ pip install -U \"ray[train]\"\n\n\nTo learn more about installing Ray and its libraries, see\nInstalling Ray.\n\n\nGet started#\n\n\n\n\n\nOverview\n\n\nUnderstand the key concepts for distributed training with Ray Train.\n\n\nLearn the basics\n\n\n\n\n\n\nPyTorch\n\n\nGet started on distributed model training with Ray Train and PyTorch.\n\n\nTry Ray Train with PyTorch\n\n\n\n\n\n\nPyTorch Lightning\n\n\nGet started on distributed model training with Ray Train and Lightning.\n\n\nTry Ray Train with Lightning\n\n\n\n\n\n\nHugging Face Transformers\n\n\nGet started on distributed model training with Ray Train and Transformers.\n\n\nTry Ray Train with Transformers\n\n\n\n\n\n\n\nLearn more#\n\n\n\n\n\nMore Frameworks\n\n\nDon\u2019t see your framework? See these guides.\n\n\nTry Ray Train with other frameworks\n\n\n\n\n\n\nUser Guides"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986170e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/train/train.html", "title": "Ray Train: Scalable Model Training \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Try Ray Train with other frameworks\n\n\n\n\n\n\nUser Guides\n\n\nGet how-to instructions for common training tasks with Ray Train.\n\n\nRead how-to guides\n\n\n\n\n\n\nExamples\n\n\nBrowse end-to-end code examples for different use cases.\n\n\nLearn through examples\n\n\n\n\n\n\nAPI\n\n\nConsult the API Reference for full descriptions of the Ray Train API.\n\n\nRead the API Reference\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nRay Data Internals\n\n\n\n\nnext\nRay Train 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: b9861768-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/search.html", "title": "Search \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Search \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\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\nSearch\n\n\n\n    Please activate JavaScript to enable the search\n    functionality.\n  \n\n\n\n    Searching for multiple words only shows matches that contain\n    all words."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98617b8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/search.html", "title": "Search \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Thanks 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: b9861808-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/ray-libraries.html", "title": "The Ray Ecosystem \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "The Ray Ecosystem \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.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\nThe Ray Ecosystem"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861858-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/ray-overview/ray-libraries.html", "title": "The Ray Ecosystem \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "suggest 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\nThe Ray Ecosystem\n\n\n\n\n\n\n\n\n\nThe Ray Ecosystem#\nThis page lists libraries that have integrations with Ray for distributed execution\nin alphabetical order.\nIt\u2019s easy to add your own integration to this list.\nSimply open a pull request with a few lines of text, see the dropdown below for\nmore information.\n\n\nAdding Your Integration\n\n\n\n\nTo add an integration, simply add an entry to the projects list of our\nGallery YAML on GitHub.\n- name: the integration link button text\n  section_title: The section title for this integration\n  description: A quick description of your library and its integration with Ray\n  website: The URL of your website\n  repo: The URL of your project on GitHub\n  image: The URL of a logo of your project\n\n\nThat\u2019s all!"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98618a8-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/ray-libraries.html", "title": "The Ray Ecosystem \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "That\u2019s all!\n\n\n\n\n\n\n\n\nClassy Vision is a new end-to-end, PyTorch-based framework for large-scale training of state-of-the-art image and video classification models. The library features a modular, flexible design that allows anyone to train machine learning models on top of PyTorch using very simple abstractions.\n\n\nClassy Vision Integration\n\n\n\n\n\n\n\n\nDask provides advanced parallelism for analytics, enabling performance at scale for the tools you love. Dask uses existing Python APIs and data structures to make it easy to switch between Numpy, Pandas, Scikit-learn to their Dask-powered equivalents.\n\n\nDask Integration\n\n\n\n\n\n\n\n\nFlamb\u00e9 is a machine learning experimentation framework built to accelerate the entire research life cycle. Flamb\u00e9\u2019s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference.\n\n\nFlamb\u00e9 Integration"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98618f8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/ray-overview/ray-libraries.html", "title": "The Ray Ecosystem \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Flamb\u00e9 Integration\n\n\n\n\n\n\n\n\nFlyte is a Kubernetes-native workflow automation platform for complex, mission-critical data and ML processes at scale. It has been battle-tested at Lyft, Spotify, Freenome, and others and is truly open-source.\n\n\nFlyte Integration\n\n\n\n\n\n\n\n\nHorovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use.\n\n\nHorovod Integration\n\n\n\n\n\n\n\n\nState-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0. It integrates with Ray for distributed hyperparameter tuning of transformer models.\n\n\nHugging Face Transformers Integration\n\n\n\n\n\n\n\n\nAnalytics Zoo seamlessly scales TensorFlow, Keras and PyTorch to distributed big data (using Spark, Flink & Ray).\n\n\nIntel Analytics Zoo Integration"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861952-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/ray-libraries.html", "title": "The Ray Ecosystem \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Intel Analytics Zoo Integration\n\n\n\n\n\n\n\n\nThe power of 350+ pre-trained NLP models, 100+ Word Embeddings, 50+ Sentence Embeddings, and 50+ Classifiers in 46 languages with 1 line of Python code.\n\n\nNLU Integration\n\n\n\n\n\n\n\n\nLudwig is a toolbox that allows users to train and test deep learning models without the need to write code. With Ludwig, you can train a deep learning model on Ray in zero lines of code, automatically leveraging Dask on Ray for data preprocessing, Horovod on Ray for distributed training, and Ray Tune for hyperparameter optimization.\n\n\nLudwig Integration\n\n\n\n\n\n\n\n\nMars is a tensor-based unified framework for large-scale data computation which scales Numpy, Pandas and Scikit-learn. Mars can scale in to a single machine, and scale out to a cluster with thousands of machines.\n\n\nMARS Integration"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98619a2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/ray-overview/ray-libraries.html", "title": "The Ray Ecosystem \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "MARS Integration\n\n\n\n\n\n\n\n\nScale your pandas workflows by changing one line of code. Modin transparently distributes the data and computation so that all you need to do is continue using the pandas API as you were before installing Modin.\n\n\nModin Integration\n\n\n\n\n\n\n\n\nPrefect is an open source workflow orchestration platform in Python. It allows you to easily define, track and schedule workflows in Python. This integration makes it easy to run a Prefect workflow on a Ray cluster in a distributed way.\n\n\nPrefect Integration\n\n\n\n\n\n\n\n\nPyCaret is an open source low-code machine learning library in Python that aims to reduce the hypothesis to insights cycle time in a ML experiment. It enables data scientists to perform end-to-end experiments quickly and efficiently.\n\n\nPyCaret Integration"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98619f2-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/ray-libraries.html", "title": "The Ray Ecosystem \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "PyCaret Integration\n\n\n\n\n\n\n\n\nRayDP (\u201cSpark on Ray\u201d) enables you to easily use Spark inside a Ray program. You can use Spark to read the input data, process the data using SQL, Spark DataFrame, or Pandas (via Koalas) API, extract and transform features using Spark MLLib, and use RayDP Estimator API for distributed training on the preprocessed dataset.\n\n\nRayDP Integration\n\n\n\n\n\n\n\n\nScikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.\n\n\nScikit Learn Integration"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861a42-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/ray-libraries.html", "title": "The Ray Ecosystem \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Scikit Learn Integration\n\n\n\n\n\n\n\n\nAlibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.\n\n\nSeldon Alibi Integration\n\n\n\n\n\n\n\n\nSematic is an open-source ML pipelining tool written in Python. It enables users to write end-to-end pipelines that can seamlessly transition between your laptop and the cloud, with rich visualizations, traceability, reproducibility, and usability as first-class citizens. This integration enables dynamic allocation of Ray clusters within Sematic pipelines.\n\n\nSematic Integration\n\n\n\n\n\n\n\n\nspaCy is a library for advanced Natural Language Processing in Python and Cython. It\u2019s built on the very latest research, and was designed from day one to be used in real products.\n\n\nspaCy Integration"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9861a92-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/ray-libraries.html", "title": "The Ray Ecosystem \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "spaCy Integration\n\n\n\n\n\n\n\n\nXGBoost is a popular gradient boosting library for classification and regression. It is one of the most popular tools in data science and workhorse of many top-performing Kaggle kernels.\n\n\nXGBoost Integration\n\n\n\n\n\n\n\n\nLightGBM is a high-performance gradient boosting library for classification and regression. It is designed to be distributed and efficient.\n\n\nLightGBM Integration\n\n\n\n\n\n\n\n\nVolcano is system for running high-performance workloads on Kubernetes. It features powerful batch scheduling capabilities required by ML and other data-intensive workloads.\n\n\nVolcano Integration\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nRay Examples\n\n\n\n\nnext\nWhat is Ray Core?\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
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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
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metadata:
{"source": "https://docs.ray.io/en/master/ray-observability/index.html", "title": "Monitoring and Debugging \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Monitoring and Debugging \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\n\n\n\n\n     Ray Dashboard\n    \n\n\n\n     Key Concepts\n    \n\n\n\n     User Guides\n    \n\n\n\n     Reference\n    \n\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"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98646c0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-observability/index.html", "title": "Monitoring and Debugging \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "open 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   What is observability\n  \n\n\n\n   Importance of observability\n  \n\n\n\n   Monitoring and debugging workflow and tools\n  \n\n\n\n\n\n\n\n\n\nMonitoring and Debugging\n\n\n\n\n Contents \n\n\n\n\n\n   What is observability\n  \n\n\n\n   Importance of observability\n  \n\n\n\n   Monitoring and debugging workflow and tools\n  \n\n\n\n\n\n\n\n\n\nMonitoring and Debugging#\nThis section covers how to monitor and debug Ray applications and clusters with Ray\u2019s Observability features.\n\nWhat is observability#\nIn general, observability is a measure of how well the internal states of a system can be inferred from knowledge of its external outputs.\nIn Ray\u2019s context, observability refers to the ability for users to observe and infer Ray applications\u2019 and Ray clusters\u2019 internal states with various external outputs, such as logs, metrics, events, etc."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864710-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-observability/index.html", "title": "Monitoring and Debugging \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Importance of observability#\nDebugging a distributed system can be challenging due to the large scale and complexity. Good observability is important for Ray users to be able to easily monitor and debug their Ray applications and clusters.\n\n\n\nMonitoring and debugging workflow and tools#\nMonitoring and debugging Ray applications consist of 4 major steps:\n\nMonitor the clusters and applications.\nIdentify the surfaced problems or errors.\nDebug with various tools and data.\nForm a hypothesis, implement a fix, and validate it.\n\nThe remainder of this section covers the observability tools that Ray provides to accelerate your monitoring and debugging workflow.\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nUsage Stats Collection\n\n\n\n\nnext\nRay Dashboard\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: b9864756-83c7-11ee-a273-0242ac1c000c
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metadata:
{"source": "https://docs.ray.io/en/master/ray-security/index.html", "title": "Security \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Security \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\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   Personas\n  \n\n\n\n   Best practices\n  \n\n\n\n     Deploy Ray Clusters in a controlled network environment"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864896-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-security/index.html", "title": "Security \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Best practices\n  \n\n\n\n     Deploy Ray Clusters in a controlled network environment\n    \n\n\n\n     Only execute trusted code within Ray\n    \n\n\n\n     Enforce isolation outside of Ray with multiple Ray Clusters\n    \n\n\n\n\n\n\n\n\n\n\n\nSecurity\n\n\n\n\n Contents \n\n\n\n\n\n   Personas\n  \n\n\n\n   Best practices\n  \n\n\n\n     Deploy Ray Clusters in a controlled network environment\n    \n\n\n\n     Only execute trusted code within Ray\n    \n\n\n\n     Enforce isolation outside of Ray with multiple Ray Clusters"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986492c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-security/index.html", "title": "Security \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Security#\nRay is an easy-to-use framework to run arbitrary code across one or more nodes in a Ray Cluster. Ray provides fault-tolerance, optimized scheduling, task orchestration, and auto-scaling to run a given workload.\nTo achieve performant and distributed workloads, Ray components require intra-cluster communication. This communication includes central tenets like distributed memory and node-heartbeats, as well as auxiliary functions like metrics and logs. Ray leverages gRPC for a majority of this communication.\nRay offers additional services to improve the developer experience. These services include Ray Dashboard (to allow for cluster introspection and debugging), Ray Jobs (hosted alongside the Dashboard, which services Ray Job submissions), and Ray Client (to allow for local, interactive development with a remote cluster). These services provide complete access to the Ray Cluster and the underlying compute resources."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98649a4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-security/index.html", "title": "Security \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Personas#\nWhen considering the security responsibilities of running Ray, think about the different personas interacting with Ray.\n\nRay Developers write code that relies on Ray. They either run a single-node Ray Cluster locally or multi-node Clusters remotely on provided compute infrastructure.\nPlatform providers provide the compute environment on which Developers run Ray.\nUsers interact with the output of Ray-powered applications.\n\n\n\nBest practices#\nSecurity and isolation must be enforced outside of the Ray Cluster. Ray expects to run in a safe network environment and to act upon trusted code. Developers and platform providers must maintain the following invariants to ensure the safe operation of Ray Clusters.\n\nDeploy Ray Clusters in a controlled network environment#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864a1c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-security/index.html", "title": "Security \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Deploy Ray Clusters in a controlled network environment#\n\nNetwork traffic between core Ray components and additional Ray components should always be in a controlled, isolated network. Access to additional services should be gated with strict network controls and/or external authentication/authorization proxies.\ngRPC communication can be encrypted with TLS, but it is not a replacement for network isolation.\nPlatform providers are responsible for ensuring that Ray runs in sufficiently controlled network environments and that developers can access features like Ray Dashboard in a secure manner.\n\n\n\nOnly execute trusted code within Ray#\n\nRay faithfully executes code that is passed to it \u2013 Ray doesn\u2019t differentiate between a tuning experiment, a rootkit install, or an S3 bucket inspection.\nRay developers are responsible for building their applications with this understanding in mind.\n\n\n\nEnforce isolation outside of Ray with multiple Ray Clusters#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864a6c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-security/index.html", "title": "Security \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Enforce isolation outside of Ray with multiple Ray Clusters#\n\nIf workloads require isolation from each other, use separate, isolated Ray Clusters. Ray can schedule multiple distinct Jobs in a single Cluster, but doesn\u2019t attempt to enforce isolation between them. Similarly, Ray doesn\u2019t implement access controls for developers interacting with a given cluster.\nRay developers are responsible for determining which applications need to be separated and platform providers are responsible for providing this isolation.\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nRay Glossary\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: b9864abc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/api/api.html", "title": "Ray Tune API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Tune 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\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\n\n\n       Tune Execution (tune.Tuner)\n      \n\n\n\n       Tune Experiment Results (tune.ResultGrid)\n      \n\n\n\n       Training in Tune (tune.Trainable, train.report)\n      \n\n\n\n       Tune Search Space API\n      \n\n\n\n       Tune Search Algorithms (tune.search)\n      \n\n\n\n       Tune Trial Schedulers (tune.schedulers)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864b0c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/api/api.html", "title": "Ray Tune API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Tune Trial Schedulers (tune.schedulers)\n      \n\n\n\n       Tune Stopping Mechanisms (tune.stopper)\n      \n\n\n\n       Tune Console Output (Reporters)\n      \n\n\n\n       Syncing in Tune (train.SyncConfig)\n      \n\n\n\n       Tune Loggers (tune.logger)\n      \n\n\n\n       Tune Callbacks (tune.Callback)\n      \n\n\n\n       Environment variables used by Ray Tune\n      \n\n\n\n       External library integrations for Ray Tune\n      \n\n\n\n       Tune Internals\n      \n\n\n\n       Tune Client API\n      \n\n\n\n       Tune CLI (Experimental)\n      \n\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\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRay Tune API\n\n\n\n\n\n\n\n\n\nRay Tune API#"}


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/tune/api/api.html", "title": "Ray Tune API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "suggest 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\nRay Tune API\n\n\n\n\n\n\n\n\n\nRay Tune API#\n\nTip\nWe\u2019d love to hear your feedback on using Tune - get in touch!\n\nThis section contains a reference for the Tune API. If there is anything missing, please open an issue\non Github.\n\n\nTune Execution (tune.Tuner)\nTuner\ntune.run_experiments\n\n\nTune Experiment Results (tune.ResultGrid)\nResultGrid (tune.ResultGrid)\nResult (train.Result)\nExperimentAnalysis (tune.ExperimentAnalysis)\n\n\nTraining in Tune (tune.Trainable, train.report)\nFunction Trainable API\nClass Trainable API\nComparing Tune\u2019s Function API and Class API\nAdvanced Resource Allocation\nFunction API\nTrainable (Class API)\nTune Trainable Utilities\n\n\nTune Search Space API\nRandom Distributions API\nGrid Search and Custom Function APIs\nReferences"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864bac-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/api/api.html", "title": "Ray Tune API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Tune Search Space API\nRandom Distributions API\nGrid Search and Custom Function APIs\nReferences\n\n\nTune Search Algorithms (tune.search)\nSaving and Restoring Tune Search Algorithms\nRandom search and grid search (tune.search.basic_variant.BasicVariantGenerator)\nAx (tune.search.ax.AxSearch)\nBayesian Optimization (tune.search.bayesopt.BayesOptSearch)\nBOHB (tune.search.bohb.TuneBOHB)\nBlendSearch (tune.search.flaml.BlendSearch)\nCFO (tune.search.flaml.CFO)\nDragonfly (tune.search.dragonfly.DragonflySearch)\nHEBO (tune.search.hebo.HEBOSearch)\nHyperOpt (tune.search.hyperopt.HyperOptSearch)\nNevergrad (tune.search.nevergrad.NevergradSearch)\nOptuna (tune.search.optuna.OptunaSearch)\nSigOpt (tune.search.sigopt.SigOptSearch)\nScikit-Optimize (tune.search.skopt.SkOptSearch)\nZOOpt (tune.search.zoopt.ZOOptSearch)\nRepeated Evaluations (tune.search.Repeater)\nConcurrencyLimiter (tune.search.ConcurrencyLimiter)\nCustom Search Algorithms (tune.search.Searcher)\nShim Instantiation (tune.create_searcher)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864bfc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/api/api.html", "title": "Ray Tune API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Tune Trial Schedulers (tune.schedulers)\nASHA (tune.schedulers.ASHAScheduler)\nHyperBand (tune.schedulers.HyperBandScheduler)\nMedian Stopping Rule (tune.schedulers.MedianStoppingRule)\nPopulation Based Training (tune.schedulers.PopulationBasedTraining)\nPopulation Based Training Replay (tune.schedulers.PopulationBasedTrainingReplay)\nPopulation Based Bandits (PB2) (tune.schedulers.pb2.PB2)\nBOHB (tune.schedulers.HyperBandForBOHB)\nResourceChangingScheduler\nFIFOScheduler (Default Scheduler)\nTrialScheduler Interface\nShim Instantiation (tune.create_scheduler)\n\n\nTune Stopping Mechanisms (tune.stopper)\nStopper Interface (tune.Stopper)\nTune Built-in Stoppers\n\n\nTune Console Output (Reporters)\nReporter Interface (tune.ProgressReporter)\nTune Built-in Reporters\n\n\nSyncing in Tune (train.SyncConfig)\nTune Syncing Configuration"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864c4c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/tune/api/api.html", "title": "Ray Tune API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Syncing in Tune (train.SyncConfig)\nTune Syncing Configuration\n\n\nTune Loggers (tune.logger)\nLoggerCallback Interface (tune.logger.LoggerCallback)\nTune Built-in Loggers\nMLFlow Integration\nWandb Integration\nComet Integration\nAim Integration\nOther Integrations\n\n\nTune Callbacks (tune.Callback)\nCallback Interface\n\n\nEnvironment variables used by Ray Tune\nExternal library integrations for Ray Tune\nPyTorch Lightning (tune.integration.pytorch_lightning)\nXGBoost (tune.integration.xgboost)\nLightGBM (tune.integration.lightgbm)\n\n\nTune Internals\nTunerInternal\nTrial\nFunctionTrainable\nRegistry\n\n\nTune Client API\nTune CLI (Experimental)\n\n\n\n\n\n\n\n\n\n\n\nprevious\nRay Tune FAQ\n\n\n\n\nnext\nTune Execution (tune.Tuner)\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: b9864c9c-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Getting Involved / Contributing \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\n\n\n\n\n     API stability\n    \n\n\n\n     Getting Involved / Contributing\n    \n\n\n\n\n\n\n\n\n       Building Ray from Source\n      \n\n\n\n       Contributing to the Ray Documentation\n      \n\n\n\n       How to write code snippets\n      \n\n\n\n       Testing Autoscaling Locally\n      \n\n\n\n       Tips for testing Ray programs\n      \n\n\n\n       Debugging for Ray Developers"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864cec-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Tips for testing Ray programs\n      \n\n\n\n       Debugging for Ray Developers\n      \n\n\n\n       Profiling for Ray Developers\n      \n\n\n\n\n\n     Configuring Ray\n    \n\n\n\n     Architecture Whitepapers\n    \n\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   Contributing to Ray\n  \n\n\n\n   What can I work on?\n  \n\n\n\n   Setting up your development environment\n  \n\n\n\n   Submitting and Merging a Contribution\n  \n\n\n\n   PR Review Process\n  \n\n\n\n     For contributors who are in the\n     \n\n       ray-project\n      \n\n     organization:\n    \n\n\n\n     For contributors who are not in the\n     \n\n       ray-project\n      \n\n     organization:\n    \n\n\n\n\n\n   Testing\n  \n\n\n\n     Testing for Python development\n    \n\n\n\n     Testing for C++ development\n    \n\n\n\n\n\n   Code Style\n  \n\n\n\n     Lint and Formatting"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864d3c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Testing for C++ development\n    \n\n\n\n\n\n   Code Style\n  \n\n\n\n     Lint and Formatting\n    \n\n\n\n\n\n   Understanding CI test jobs\n  \n\n\n\n   API compatibility style guide\n  \n\n\n\n   Becoming a Reviewer\n  \n\n\n\n   More Resources for Getting Involved\n  \n\n\n\n\n\n\n\n\n\nGetting Involved / Contributing\n\n\n\n\n Contents \n\n\n\n\n\n   Contributing to Ray\n  \n\n\n\n   What can I work on?\n  \n\n\n\n   Setting up your development environment\n  \n\n\n\n   Submitting and Merging a Contribution\n  \n\n\n\n   PR Review Process\n  \n\n\n\n     For contributors who are in the\n     \n\n       ray-project\n      \n\n     organization:\n    \n\n\n\n     For contributors who are not in the\n     \n\n       ray-project\n      \n\n     organization:\n    \n\n\n\n\n\n   Testing\n  \n\n\n\n     Testing for Python development\n    \n\n\n\n     Testing for C++ development\n    \n\n\n\n\n\n   Code Style\n  \n\n\n\n     Lint and Formatting\n    \n\n\n\n\n\n   Understanding CI test jobs\n  \n\n\n\n   API compatibility style guide\n  \n\n\n\n   Becoming a Reviewer\n  \n\n\n\n   More Resources for Getting Involved"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864d8c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Becoming a Reviewer\n  \n\n\n\n   More Resources for Getting Involved\n  \n\n\n\n\n\n\n\n\n\nCheck your version!\nThings can change quickly, and so does this contributor guide.\nTo make sure you\u2019ve got the most cutting edge version of this guide,\ngo check out the\nlatest version.\n\n\nGetting Involved / Contributing#\n\n\nRay is more than a framework for distributed applications but also an active community of developers,\nresearchers, and folks that love machine learning.\n\nTip\nAsk questions on our forum! The\ncommunity is extremely active in helping people succeed in building their\nRay applications.\n\nYou can join (and Star!) us on on GitHub.\n\nContributing to Ray#\nWe welcome (and encourage!) all forms of contributions to Ray, including and not limited to:"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864ddc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code reviewing of patches and PRs.\nPushing patches.\nDocumentation and examples.\nCommunity participation in forums and issues.\nCode readability and code comments to improve readability.\nTest cases to make the codebase more robust.\nTutorials, blog posts, talks that promote the project.\nFeatures and major changes via Ray Enhancement Proposals (REP): https://github.com/ray-project/enhancements\n\n\n\nWhat can I work on?#\nWe use Github to track issues, feature requests, and bugs. Take a look at the\nones labeled \u201cgood first issue\u201d for a place to start.\n\n\nSetting up your development environment#\nTo edit the Ray source code, you\u2019ll want to checkout the repository and also build Ray from source. Follow these instructions for building a local copy of Ray to easily make changes.\n\n\nSubmitting and Merging a Contribution#\nThere are a couple steps to merge a contribution."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864e2c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Submitting and Merging a Contribution#\nThere are a couple steps to merge a contribution.\n\nFirst merge the most recent version of master into your development branch.\ngit remote add upstream https://github.com/ray-project/ray.git\ngit pull . upstream/master"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864e7c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Make sure all existing tests and linters pass.\nRun setup_hooks.sh to create a git hook that will run the linter before you push your changes.\nIf introducing a new feature or patching a bug, be sure to add new test cases\nin the relevant file in ray/python/ray/tests/.\nDocument the code. Public functions need to be documented, and remember to provide an usage\nexample if applicable. See doc/README.md for instructions on editing and building public documentation.\nAddress comments on your PR. During the review\nprocess you may need to address merge conflicts with other changes. To resolve merge conflicts,\nrun git pull . upstream/master on your branch (please do not use rebase, as it is less\nfriendly to the GitHub review tool. All commits will be squashed on merge.)\nReviewers will merge and approve the pull request; be sure to ping them if\nthe pull request is getting stale.\n\n\n\nPR Review Process#\n\nFor contributors who are in the ray-project organization:#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864ec2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "PR Review Process#\n\nFor contributors who are in the ray-project organization:#\n\nWhen you first create a PR, add an reviewer to the assignee section.\nAssignees will review your PR and add the @author-action-required label if further actions are required.\nAddress their comments and remove the @author-action-required label from the PR.\nRepeat this process until assignees approve your PR.\nOnce the PR is approved, the author is in charge of ensuring the PR passes the build. Add the test-ok label if the build succeeds.\nCommitters will merge the PR once the build is passing.\n\n\n\nFor contributors who are not in the ray-project organization:#\n\nYour PRs will have assignees shortly. Assignees of PRs will be actively engaging with contributors to merge the PR.\nPlease actively ping assignees after you address your comments!"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864f3a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Testing#\nEven though we have hooks to run unit tests automatically for each pull request,\nwe recommend you to run unit tests locally beforehand to reduce reviewers\u2019\nburden and speedup review process.\nIf you are running tests for the first time, you can install the required dependencies with:\npip install -c python/requirements_compiled.txt -r python/requirements/test-requirements.txt\n\n\n\nTesting for Python development#\nThe full suite of tests is too large to run on a single machine. However, you can run individual relevant Python test files. Suppose that one of the tests in a file of tests, e.g., python/ray/tests/test_basic.py, is failing. You can run just that test file locally as follows:\n# Directly calling `pytest -v ...` may lose import paths.\npython -m pytest -v -s python/ray/tests/test_basic.py"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864f8a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "This will run all of the tests in the file. To run a specific test, use the following:\n# Directly calling `pytest -v ...` may lose import paths.\npython -m pytest -v -s test_file.py::name_of_the_test\n\n\n\n\nTesting for C++ development#\nTo compile and run all C++ tests, you can run:\nbazel test $(bazel query 'kind(cc_test, ...)')\n\n\nAlternatively, you can also run one specific C++ test. You can use:\nbazel test $(bazel query 'kind(cc_test, ...)') --test_filter=ClientConnectionTest --test_output=streamed"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9864fda-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code Style#\nIn general, we follow the Google style guide for C++ code and the Black code style for Python code. Python imports follow PEP8 style. However, it is more important for code to be in a locally consistent style than to strictly follow guidelines. Whenever in doubt, follow the local code style of the component.\nFor Python documentation, we follow a subset of the Google pydoc format. The following code snippets demonstrate the canonical Ray pydoc formatting:\ndef ray_canonical_doc_style(param1: int, param2: str) -> bool:\n    \"\"\"First sentence MUST be inline with the quotes and fit on one line.\n\n    Additional explanatory text can be added in paragraphs such as this one.\n    Do not introduce multi-line first sentences.\n\n    Examples:\n        .. doctest::\n\n            >>> # Provide code examples for key use cases, as possible.\n            >>> ray_canonical_doc_style(41, \"hello\")\n            True"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986502a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": ">>> # A second example.\n            >>> ray_canonical_doc_style(72, \"goodbye\")\n            False\n\n    Args:\n        param1: The first parameter. Do not include the types in the\n            docstring. They should be defined only in the signature.\n            Multi-line parameter docs should be indented by four spaces.\n        param2: The second parameter.\n\n    Returns:\n        The return value. Do not include types here.\n    \"\"\"\n\n\nclass RayClass:\n    \"\"\"The summary line for a class docstring should fit on one line.\n\n    Additional explanatory text can be added in paragraphs such as this one.\n    Do not introduce multi-line first sentences.\n\n    The __init__ method is documented here in the class level docstring.\n\n    All the public methods and attributes should have docstrings.\n\n    Examples:\n        .. testcode::\n\n            obj = RayClass(12, \"world\")\n            obj.increment_attr1()"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986507a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "obj = RayClass(12, \"world\")\n            obj.increment_attr1()\n\n    Args:\n        param1: The first parameter. Do not include the types in the\n            docstring. They should be defined only in the signature.\n            Multi-line parameter docs should be indented by four spaces.\n        param2: The second parameter.\n    \"\"\"\n\n    def __init__(self, param1: int, param2: str):\n        #: Public attribute is documented here.\n        self.attr1 = param1\n        #: Public attribute is documented here.\n        self.attr2 = param2\n\n    @property\n    def attr3(self) -> str:\n        \"\"\"Public property of the class.\n\n        Properties created with the @property decorator\n        should be documented here.\n        \"\"\"\n        return \"hello\"\n\n    def increment_attr1(self) -> None:\n        \"\"\"Class methods are similar to regular functions.\n\n        See above about how to document functions.\n        \"\"\"\n\n        self.attr1 = self.attr1 + 1"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98650c0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "self.attr1 = self.attr1 + 1\n\n\nSee this for more details about how to write code snippets in docstrings.\n\nLint and Formatting#\nWe also have tests for code formatting and linting that need to pass before merge.\n\nFor Python formatting, install the required dependencies first with:\n\npip install -r python/requirements/lint-requirements.txt\n\n\n\nIf developing for C++, you will need clang-format version 12 (download this version of Clang from here)\n\nYou can run the following locally:\nscripts/format.sh\n\n\nAn output like the following indicates failure:\nWARNING: clang-format is not installed!  # This is harmless\nFrom https://github.com/ray-project/ray\n * branch                master     -> FETCH_HEAD\npython/ray/util/sgd/tf/tf_runner.py:4:1: F401 'numpy as np' imported but unused  # Below is the failure\n\n\nIn addition, there are other formatting and semantic checkers for components like the following (not included in scripts/format.sh):\n\nPython README format:"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986511a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Python README format:\n\ncd python\npython setup.py check --restructuredtext --strict --metadata\n\n\n\nPython & Docs banned words check\n\n./ci/lint/check-banned-words.sh\n\n\n\nBazel format:\n\n./ci/lint/bazel-format.sh\n\n\n\nclang-tidy for C++ lint, requires clang and clang-tidy version 12 to be installed:\n\n./ci/lint/check-git-clang-tidy-output.sh\n\n\nYou can run setup_hooks.sh to create a git hook that will run the linter before you push your changes.\n\n\n\nUnderstanding CI test jobs#\nThe Ray project automatically runs continuous integration (CI) tests once a PR\nis opened using Buildkite with\nmultiple CI test jobs.\nThe CI test folder contains all integration test scripts and they\ninvoke other test scripts via pytest, bazel-based test or other bash\nscripts. Some of the examples include:\n\n\nRaylet integration tests commands:\nbazel test //:core_worker_test\n\n\n\n\n\nBazel test command:\nbazel test --build_tests_only //:all"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865160-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Bazel test command:\nbazel test --build_tests_only //:all\n\n\n\n\n\nRay serving test commands:\npytest python/ray/serve/tests\npython python/ray/serve/examples/echo_full.py\n\n\n\n\n\nIf a CI build exception doesn\u2019t appear to be related to your change,\nplease visit this link to\ncheck recent tests known to be flaky."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98651b0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "API compatibility style guide#\nRay provides stability guarantees for its public APIs in Ray core and libraries, which are described in the API Stability guide.\nIt\u2019s hard to fully capture the semantics of API compatibility into a single annotation (for example, public APIs may have \u201cexperimental\u201d arguments). For more granular stability contracts, those can be noted in the pydoc (e.g., \u201cthe random_shuffle option is experimental\u201d). When possible, experimental arguments should also be prefixed by underscores in Python (e.g., _owner=).\nOther recommendations:\nIn Python APIs, consider forcing the use of kwargs instead of positional arguments (with the * operator). Kwargs are easier to keep backwards compatible than positional arguments, e.g. imagine if you needed to deprecate \u201copt1\u201d below, it\u2019s easier with forced kwargs:\ndef foo_bar(file, *, opt1=x, opt2=y)\n    pass"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865200-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "For callback APIs, consider adding a **kwargs placeholder as a \u201cforward compatibility placeholder\u201d in case more args need to be passed to the callback in the future, e.g.:\ndef tune_user_callback(model, score, **future_kwargs):\n    pass\n\n\n\n\nBecoming a Reviewer#\nWe identify reviewers from active contributors. Reviewers are individuals who\nnot only actively contribute to the project and are also willing\nto participate in the code review of new contributions.\nA pull request to the project has to be reviewed by at least one reviewer in order to be merged.\nThere is currently no formal process, but active contributors to Ray will be\nsolicited by current reviewers.\n\n\nMore Resources for Getting Involved#\nRay is more than a framework for distributed applications but also an active community of developers,\nresearchers, and folks that love machine learning. Here\u2019s a list of tips for getting involved with the Ray community:"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865250-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-contribute/getting-involved.html", "title": "Getting Involved / Contributing \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Join our community Slack to discuss Ray!\nStar and follow us on on GitHub.\nTo post questions or feature requests, check out the Discussion Board!\nFollow us and spread the word on Twitter!\nJoin our Meetup Group to connect with others in the community!\nUse the [ray] tag on StackOverflow to ask and answer questions about Ray usage\n\n\nNote\nThese tips are based off of the TVM contributor guide.\n\n\n\n\n\n\n\n\n\n\n\nprevious\nAPI stability\n\n\n\n\nnext\nBuilding Ray from Source\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: b9865296-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "RLlib: Industry-Grade Reinforcement Learning \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\n\n\n\n\n     Getting Started with RLlib\n    \n\n\n\n     Key Concepts\n    \n\n\n\n     Environments\n    \n\n\n\n     Algorithms\n    \n\n\n\n     User Guides\n    \n\n\n\n     Examples\n    \n\n\n\n     Ray RLlib API\n    \n\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"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98652e6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \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   RLlib in 60 seconds\n  \n\n\n\n   Feature Overview\n  \n\n\n\n   Customizing RLlib\n  \n\n\n\n\n\n\n\n\n\nRLlib: Industry-Grade Reinforcement Learning\n\n\n\n\n Contents \n\n\n\n\n\n   RLlib in 60 seconds\n  \n\n\n\n   Feature Overview\n  \n\n\n\n   Customizing RLlib\n  \n\n\n\n\n\n\n\n\n\nRLlib: Industry-Grade Reinforcement Learning#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865340-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Customizing RLlib\n  \n\n\n\n\n\n\n\n\n\nRLlib: Industry-Grade Reinforcement Learning#\n\nRLlib is an open-source library for reinforcement learning (RL),\noffering support for\nproduction-level, highly distributed RL workloads while maintaining\nunified and simple APIs for a large variety of industry applications.\nWhether you would like to train your agents in a multi-agent setup,\npurely from offline (historic) datasets, or using externally\nconnected simulators, RLlib offers a simple solution for each of your decision\nmaking needs.\nIf you either have your problem coded (in python) as an\nRL environment\nor own lots of pre-recorded, historic behavioral data to learn from, you will be\nup and running in only a few days.\nRLlib is already used in production by industry leaders in many different verticals,\nsuch as\nclimate control,\nindustrial control,\nmanufacturing and logistics,\nfinance,\ngaming,\nautomobile,\nrobotics,\nboat design,\nand many others.\n\nRLlib in 60 seconds#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865390-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "RLlib in 60 seconds#\n\n\nIt only takes a few steps to get your first RLlib workload\nup and running on your laptop.\nRLlib does not automatically install a deep-learning framework, but supports\nTensorFlow (both 1.x with static-graph and 2.x with eager mode) as well as\nPyTorch.\nDepending on your needs, make sure to install either TensorFlow or\nPyTorch (or both, as shown below):\n\npip install \"ray[rllib]\" tensorflow torch\n\n\nFor installation on computers running Apple Silicon (such as M1), please follow instructions\nhere._\nTo be able to run our Atari examples, you should also install\npip install \"gym[atari]\" \"gym[accept-rom-license]\" atari_py."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98653ea-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "This is all you need to start coding against RLlib.\nHere is an example of running a PPO Algorithm on the\nTaxi domain.\nWe first create a config for the algorithm, which sets the right environment, and\ndefines all training parameters we want.\nNext, we build the algorithm and train it for a total of 5 iterations.\nA training iteration includes parallel sample collection by the environment workers, as well as loss calculation on the collected batch and a model update.\nAs a last step, we evaluate the trained Algorithm:\nfrom ray.rllib.algorithms.ppo import PPOConfig\n\nconfig = (  # 1. Configure the algorithm,\n    PPOConfig()\n    .environment(\"Taxi-v3\")\n    .rollouts(num_rollout_workers=2)\n    .framework(\"torch\")\n    .training(model={\"fcnet_hiddens\": [64, 64]})\n    .evaluation(evaluation_num_workers=1)\n)\n\nalgo = config.build()  # 2. build the algorithm,\n\nfor _ in range(5):\n    print(algo.train())  # 3. train it,\n\nalgo.evaluate()  # 4. and evaluate it."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986543a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "for _ in range(5):\n    print(algo.train())  # 3. train it,\n\nalgo.evaluate()  # 4. and evaluate it.\n\n\nNote that you can use any Farama-Foundation Gymnasium environment as env.\nIn rollouts you can for instance specify the number of parallel workers to collect samples from the environment.\nThe framework config lets you choose between \u201ctf2\u201d, \u201ctf\u201d and \u201ctorch\u201d for execution.\nYou can also tweak RLlib\u2019s default model config,and set up a separate config for evaluation.\nIf you want to learn more about the RLlib training API,\nyou can learn more about it here.\nAlso, see here for a simple example on how to write an action inference loop after training.\nIf you want to get a quick preview of which algorithms and environments RLlib supports,\nclick on the dropdowns below:\n\n\nRLlib Algorithms\n\n\n\n\n\nHigh-throughput architectures\n\n  Importance Weighted Actor-Learner Architecture (IMPALA)\n  Asynchronous Proximal Policy Optimization (APPO)\n\n\nGradient-based"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986548a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Gradient-based\n\n  Deep Q Networks (DQN, Rainbow, Parametric DQN)\n  Proximal Policy Optimization (PPO)\n  Soft Actor Critic (SAC)\n\n\nModel-based / Meta-learning / Offline\n\n DreamerV3\n\n\nOffline\n\n  Conservative Q-Learning (CQL)\n  Advantage Re-Weighted Imitation Learning (MARWIL)\n\n\n\n\n\n\nRLlib Environments\n\n\n\n\n\nRLlib Environments Overview\nFarama-Foundation gymnasium\nVectorized\nMulti-Agent and Hierarchical\nExternal Agents and Applications\n\nExternal Application Clients\n\n\nAdvanced Integrations\n\n\n\n\nFeature Overview#\n\n\n\n\n\nRLlib Key Concepts\n\n\nLearn more about the core concepts of RLlib, such as environments, algorithms and\npolicies.\n\n\nKey Concepts\n\n\n\n\n\n\nRLlib Algorithms\n\n\nCheck out the many available RL algorithms of RLlib for model-free and model-based\nRL, on-policy and off-policy training, multi-agent RL, and more.\n\n\nAlgorithms\n\n\n\n\n\n\nRLlib Environments"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98654da-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Algorithms\n\n\n\n\n\n\nRLlib Environments\n\n\nGet started with environments supported by RLlib, such as Farama foundation\u2019s Gymnasium, Petting Zoo,\nand many custom formats for vectorized and multi-agent environments.\n\n\nEnvironments\n\n\n\n\n\nThe following is a summary of RLlib\u2019s most striking features.\nClick on the images below to see an example script for each of the listed features:\n\n\n\n\n\nThe most popular deep-learning frameworks: PyTorch and TensorFlow\n(tf1.x/2.x static-graph/eager/traced).\n\n\n\n\n\n\n\nHighly distributed learning: Our RLlib algorithms (such as our \u201cPPO\u201d or \u201cIMPALA\u201d)\nallow you to set the num_workers config parameter, such that your workloads can run\non 100s of CPUs/nodes thus parallelizing and speeding up learning."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865584-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Vectorized (batched) and remote (parallel) environments: RLlib auto-vectorizes\nyour gym.Envs via the num_envs_per_worker config. Environment workers can\nthen batch and thus significantly speedup the action computing forward pass.\nOn top of that, RLlib offers the remote_worker_envs config to create\nsingle environments (within a vectorized one) as ray Actors,\nthus parallelizing even the env stepping process.\n\n\n\n\n\n\n\n\nMulti-agent RL (MARL): Convert your (custom) gym.Envs into a multi-agent one\nvia a few simple steps and start training your agents in any of the following fashions:\n1) Cooperative with shared or\nseparate\npolicies and/or value functions.\n2) Adversarial scenarios using self-play\nand league-based training.\n3) Independent learning\nof neutral/co-existing agents."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98655d4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "External simulators: Don\u2019t have your simulation running as a gym.Env in python?\nNo problem! RLlib supports an external environment API and comes with a pluggable,\noff-the-shelve\nclient/\nserver\nsetup that allows you to run 100s of independent simulators on the \u201coutside\u201d\n(e.g. a Windows cloud) connecting to a central RLlib Policy-Server that learns\nand serves actions. Alternatively, actions can be computed on the client side\nto save on network traffic.\n\n\n\n\n\n\n\nOffline RL and imitation learning/behavior cloning: You don\u2019t have a simulator\nfor your particular problem, but tons of historic data recorded by a legacy (maybe\nnon-RL/ML) system? This branch of reinforcement learning is for you!\nRLlib\u2019s comes with several offline RL\nalgorithms (CQL, MARWIL, and DQfD), allowing you to either purely\nbehavior-clone\nyour existing system or learn how to further improve over it."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865624-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Customizing RLlib#\nRLlib provides simple APIs to customize all aspects of your training- and experimental workflows.\nFor example, you may code your own environments\nin python using Farama-Foundation\u2019s gymnasium or DeepMind\u2019s OpenSpiel, provide custom\nTensorFlow/Keras- or ,\nTorch models, write your own\npolicy- and loss definitions, or define\ncustom exploratory behavior.\nVia mapping one or more agents in your environments to (one or more) policies, multi-agent\nRL (MARL) becomes an easy-to-use low-level primitive for our users."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865674-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/rllib/index.html", "title": "RLlib: Industry-Grade Reinforcement Learning \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "RLlib\u2019s API stack: Built on top of Ray, RLlib offers off-the-shelf, highly distributed\nalgorithms, policies, loss functions, and default models (including the option to\nauto-wrap a neural network with an LSTM or an attention net). Furthermore, our library\ncomes with a built-in Server/Client setup, allowing you to connect\nhundreds of external simulators (clients) via the network to an RLlib server process,\nwhich provides learning functionality and serves action queries. User customizations\nare realized via sub-classing the existing abstractions and - by overriding certain\nmethods in those sub-classes - define custom behavior.#\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nray.serve.metrics.Gauge.info\n\n\n\n\nnext\nGetting Started with RLlib\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: b98656c4-83c7-11ee-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": "Ray Use Cases \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\n\n\n\n\n     Ray for ML Infrastructure\n    \n\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   LLMs and Gen AI\n  \n\n\n\n   Batch Inference\n  \n\n\n\n   Many Model Training"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865714-83c7-11ee-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": "Contents\n    \n\n\n\n\n   LLMs and Gen AI\n  \n\n\n\n   Batch Inference\n  \n\n\n\n   Many Model Training\n  \n\n\n\n     How do I do many model training on Ray?\n    \n\n\n\n\n\n   Model Serving\n  \n\n\n\n   Hyperparameter Tuning\n  \n\n\n\n   Distributed Training\n  \n\n\n\n   Reinforcement Learning\n  \n\n\n\n   ML Platform\n  \n\n\n\n   End-to-End ML Workflows\n  \n\n\n\n   Large Scale Workload Orchestration\n  \n\n\n\n\n\n\n\n\n\nRay Use Cases\n\n\n\n\n Contents \n\n\n\n\n\n   LLMs and Gen AI\n  \n\n\n\n   Batch Inference\n  \n\n\n\n   Many Model Training\n  \n\n\n\n     How do I do many model training on Ray?\n    \n\n\n\n\n\n   Model Serving\n  \n\n\n\n   Hyperparameter Tuning\n  \n\n\n\n   Distributed Training\n  \n\n\n\n   Reinforcement Learning\n  \n\n\n\n   ML Platform\n  \n\n\n\n   End-to-End ML Workflows\n  \n\n\n\n   Large Scale Workload Orchestration\n  \n\n\n\n\n\n\n\n\n\nRay Use Cases#\nThis page indexes common Ray use cases for scaling ML.\nIt contains highlighted references to blogs, examples, and tutorials also located\nelsewhere in the Ray documentation."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865764-83c7-11ee-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": "LLMs and Gen AI#\nLarge language models (LLMs) and generative AI are rapidly changing industries, and demand compute at an astonishing pace. Ray provides a distributed compute framework for scaling these models, allowing developers to train and deploy models faster and more efficiently. With specialized libraries for data streaming, training, fine-tuning, hyperparameter tuning, and serving, Ray simplifies the process of developing and deploying large-scale AI models.\n\n\n\n\nExplore LLMs and Gen AI examples"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98657b4-83c7-11ee-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": "Explore LLMs and Gen AI examples\n\nBatch Inference#\nBatch inference is the process of generating model predictions on a large \u201cbatch\u201d of input data.\nRay for batch inference works with any cloud provider and ML framework,\nand is fast and cheap for modern deep learning applications.\nIt scales from single machines to large clusters with minimal code changes.\nAs a Python-first framework, you can easily express and interactively develop your inference workloads in Ray.\nTo learn more about running batch inference with Ray, see the batch inference guide.\n\n\n\n\nExplore batch inference examples"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865804-83c7-11ee-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": "Explore batch inference examples\n\nMany Model Training#\nMany model training is common in ML use cases such as time series forecasting, which require fitting of models on multiple data batches corresponding to locations, products, etc.\nThe focus is on training many models on subsets of a dataset. This is in contrast to training a single model on the entire dataset.\nWhen any given model you want to train can fit on a single GPU, Ray can assign each training run to a separate Ray Task. In this way, all available workers are utilized to run independent remote training rather than one worker running jobs sequentially.\n\n\n\nData parallelism pattern for distributed training on large datasets.#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9865854-83c7-11ee-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": "Data parallelism pattern for distributed training on large datasets.#\n\n\n\nHow do I do many model training on Ray?#\nTo train multiple independent models, use the Ray Tune (Tutorial) library. This is the recommended library for most cases.\nYou can use Tune with your current data preprocessing pipeline if your data source fits into the memory of a single machine (node).\nIf you need to scale your data, or you want to plan for future scaling, use the Ray Data library.\nYour data must be a supported format, to use Ray Data.\nAlternative solutions exist for less common cases:\n\nIf your data is not in a supported format, use Ray Core (Tutorial) for custom applications. This is an advanced option and requires and understanding of design patterns and anti-patterns.\nIf you have a large preprocessing pipeline, you can use the Ray Data library to train multiple models (Tutorial).\n\n\nExplore model training examples"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98658a4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"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": "Explore model training examples\n\n\nModel Serving#\nRay Serve is well suited for model composition, enabling you to build a complex inference service consisting of multiple ML models and business logic all in Python code.\nIt supports complex model deployment patterns requiring the orchestration of multiple Ray actors, where different actors provide inference for different models. Serve handles both batch and online inference and can scale to thousands of models in production.\n\n\n\nDeployment patterns with Ray Serve. (Click image to enlarge.)#\n\n\nLearn more about model serving with the following resources.\n\n[Talk] Productionizing ML at Scale with Ray Serve\n[Blog] Simplify your MLOps with Ray & Ray Serve\n[Guide] Getting Started with Ray Serve\n[Guide] Model Composition in Serve\n[Gallery] Serve Examples Gallery\n[Gallery] More Serve Use Cases on the Blog"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98658f4-83c7-11ee-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": "Hyperparameter Tuning#\nThe Ray Tune library enables any parallel Ray workload to be run under a hyperparameter tuning algorithm.\nRunning multiple hyperparameter tuning experiments is a pattern apt for distributed computing because each experiment is independent of one another. Ray Tune handles the hard bit of distributing hyperparameter optimization and makes available key features such as checkpointing the best result, optimizing scheduling, and specifying search patterns.\n\n\n\nDistributed tuning with distributed training per trial.#\n\n\nLearn more about the Tune library with the following talks and user guides.\n\n[Guide] Getting Started with Ray Tune\n[Blog] How to distribute hyperparameter tuning with Ray Tune\n[Talk] Simple Distributed Hyperparameter Optimization\n[Blog] Hyperparameter Search with \ud83e\udd17 Transformers\n[Gallery] Ray Tune Examples Gallery\nMore Tune use cases on the Blog"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986594e-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": "Distributed Training#\nThe Ray Train library integrates many distributed training frameworks under a simple Trainer API,\nproviding distributed orchestration and management capabilities out of the box.\nIn contrast to training many models, model parallelism partitions a large model across many machines for training. Ray Train has built-in abstractions for distributing shards of models and running training in parallel.\n\n\n\nModel parallelism pattern for distributed large model training.#\n\n\nLearn more about the Train library with the following talks and user guides.\n\n[Talk] Ray Train, PyTorch, TorchX, and distributed deep learning\n[Blog] Elastic Distributed Training with XGBoost on Ray\n[Guide] Getting Started with Ray Train\n[Example] Fine-tune a \ud83e\udd17 Transformers model\n[Gallery] Ray Train Examples Gallery\n[Gallery] More Train Use Cases on the Blog"}


