# embeddings_queue

created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870844-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nSimple example for doing a basic random and grid search.\n\n\n\n\n\n\n\nCode example\nExample of using a simple tuning function with AsyncHyperBandScheduler.\n\n\n\n\n\n\n\nCode example\nExample of using a Trainable function with HyperBandScheduler. Also uses the AsyncHyperBandScheduler.\n\n\n\n\n\n\n\nTutorial\nConfiguring and running (synchronous) PBT and understanding the underlying algorithm behavior with a simple example.\n\n\n\n\n\n\n\nTutorial\nExample of using the function API with a PopulationBasedTraining scheduler.\n\n\n\n\n\n\n\nCode example\nExample of using the Population-based Bandits (PB2) scheduler.\n\n\n\n\n\n\n\nCode example\nExample of custom loggers and custom trial directory naming.\n\n\n\n\n\n\n\nCode example\nBasics of using Tune\n\n\n\n\n\n\n\nCode example\nUsing Search algorithms and Trial Schedulers to optimize your model.\n\n\n\n\n\n\n\nCode example\nUsing Population-Based Training (PBT).\n\n\n\n\n\n\n\nCode example\nFine-tuning Huggingface Transformers with PBT.\n\n\n\n\n\n\n\nCode example\nLogging Tune Runs to Comet ML."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987088a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nLogging Tune Runs to Comet ML.\n\n\n\n\n\n\n\nTutorial\nUsing Ray Serve to deploy a chatbot\n\n\n\n\n\n\n\nCode example\nFine-tune vicuna-13b-v1.3 with DeepSpeed, PyTorch Lightning and Ray Train\n\n\n\n\n\n\n\nCode example\nDistributed Training with DeepSpeed ZeRO-3 and TorchTrainer\n\n\n\n\n\n\n\nCode example\nDistributed Training with Hugging Face Accelelate and TorchTrainer\n\n\n\n\n\n\n\nCode example\nRayJob Batch Inference Example on Kubernetes with Ray\n\n\n\n\n\n\n\nBlog\nElastic Distributed Training with XGBoost on Ray\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nRay for ML Infrastructure\n\n\n\n\nnext\nThe Ray Ecosystem\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: b98708da-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-core/walkthrough.html", "title": "What is Ray Core? \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "What is Ray Core? \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\n\n\n\n\n     Key Concepts\n    \n\n\n\n     User Guides\n    \n\n\n\n     Examples\n    \n\n\n\n     Ray Core API\n    \n\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   Getting Started"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870920-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-core/walkthrough.html", "title": "What is Ray Core? \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": ".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   Getting Started\n  \n\n\n\n   Running a Task\n  \n\n\n\n   Calling an Actor\n  \n\n\n\n   Passing an Object\n  \n\n\n\n   Next Steps\n  \n\n\n\n\n\n\n\n\n\nWhat is Ray Core?\n\n\n\n\n Contents \n\n\n\n\n\n   Getting Started\n  \n\n\n\n   Running a Task\n  \n\n\n\n   Calling an Actor\n  \n\n\n\n   Passing an Object\n  \n\n\n\n   Next Steps\n  \n\n\n\n\n\n\n\n\n\nWhat is Ray Core?#\nRay Core provides a small number of core primitives (i.e., tasks, actors, objects) for building and scaling distributed applications. Below we\u2019ll walk through simple examples that show you how to turn your functions and classes easily into Ray tasks and actors, and how to work with Ray objects.\n\nGetting Started#\nTo get started, install Ray via pip install -U ray. See Installing Ray for more installation options. The following few sections will walk through the basics of using Ray Core.\nThe first step is to import and initialize Ray:\nimport ray\n\nray.init()"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870970-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-core/walkthrough.html", "title": "What is Ray Core? \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ray.init()\n\n\n\nNote\nIn recent versions of Ray (>=1.5), ray.init() is automatically called on the first use of a Ray remote API.\n\n\n\nRunning a Task#\nRay lets you run functions as remote tasks in the cluster. To do this, you decorate your function with @ray.remote to declare that you want to run this function remotely.\nThen, you call that function with .remote() instead of calling it normally.\nThis remote call returns a future, a so-called Ray object reference, that you can then fetch with ray.get:\n# Define the square task.\n@ray.remote\ndef square(x):\n    return x * x\n\n# Launch four parallel square tasks.\nfutures = [square.remote(i) for i in range(4)]\n\n# Retrieve results.\nprint(ray.get(futures))\n# -> [0, 1, 4, 9]"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98709b6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-core/walkthrough.html", "title": "What is Ray Core? \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Retrieve results.\nprint(ray.get(futures))\n# -> [0, 1, 4, 9]\n\n\n\n\nCalling an Actor#\nRay provides actors to allow you to parallelize computation across multiple actor instances. When you instantiate a class that is a Ray actor, Ray will start a remote instance of that class in the cluster. This actor can then execute remote method calls and maintain its own internal state:\n# Define the Counter actor.\n@ray.remote\nclass Counter:\n    def __init__(self):\n        self.i = 0\n\n    def get(self):\n        return self.i\n\n    def incr(self, value):\n        self.i += value\n\n# Create a Counter actor.\nc = Counter.remote()\n\n# Submit calls to the actor. These calls run asynchronously but in\n# submission order on the remote actor process.\nfor _ in range(10):\n    c.incr.remote(1)\n\n# Retrieve final actor state.\nprint(ray.get(c.get.remote()))\n# -> 10"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870a06-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-core/walkthrough.html", "title": "What is Ray Core? \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Retrieve final actor state.\nprint(ray.get(c.get.remote()))\n# -> 10\n\n\nThe above covers very basic actor usage. For a more in-depth example, including using both tasks and actors together, check out Monte Carlo Estimation of \u03c0.\n\n\nPassing an Object#\nAs seen above, Ray stores task and actor call results in its distributed object store, returning object references that can be later retrieved. Object references can also be created explicitly via ray.put, and object references can be passed to tasks as substitutes for argument values:\nimport numpy as np\n\n# Define a task that sums the values in a matrix.\n@ray.remote\ndef sum_matrix(matrix):\n    return np.sum(matrix)\n\n# Call the task with a literal argument value.\nprint(ray.get(sum_matrix.remote(np.ones((100, 100)))))\n# -> 10000.0\n\n# Put a large array into the object store.\nmatrix_ref = ray.put(np.ones((1000, 1000)))\n\n# Call the task with the object reference as an argument.\nprint(ray.get(sum_matrix.remote(matrix_ref)))\n# -> 1000000.0"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870a4c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-core/walkthrough.html", "title": "What is Ray Core? \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Next Steps#\n\nTip\nTo check how your application is doing, you can use the Ray dashboard.\n\nRay\u2019s key primitives are simple, but can be composed together to express almost any kind of distributed computation.\nLearn more about Ray\u2019s key concepts with the following user guides:\n\n\n\n\n\n\nUsing remote functions (Tasks)\n\n\n\n\n\n\n\nUsing remote classes (Actors)\n\n\n\n\n\n\n\nWorking with Ray Objects\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nThe Ray Ecosystem\n\n\n\n\nnext\nKey Concepts\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: b9870a9c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Glossary \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\nRay Glossary\n\n\n\n\n\n\n\n\n\nRay Glossary#\nOn this page you find a list of important terminology used throughout the Ray\ndocumentation, sorted alphabetically."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870ae2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Action space#Property of an RL environment. The shape(s) and datatype(s) that actions within\nan RL environment are allowed to have.\nExamples: An RL environment, in which an agent can move up, down, left,\nor right might have an action space of Discrete(4) (integer values\nof 0, 1, 2, or 3).\nAn RL environment, in which an agent can apply a torque between\n-1.0 and 1.0 to a joint, the action space might be\nBox(-1.0, 1.0, (1,), float32) (single float values between -1.0 and 1.0).\n\nActor#A Ray actor is a remote instance of a class, which is\nessentially a stateful service. Learn more about Ray actors.\n\nActor task#An invocation of an Ray actor method. Sometimes we just call it a task.\n\nRay Agent#Daemon process running on each Ray node. It has several functionalities like\ncollecting metrics on the local node and installing runtime environments."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870b96-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Agent#An acting entity inside an RL environment. One RL environment might contain\none (single-agent RL) or more (multi-agent RL) acting agents. Different agents\nwithin the same environment might have different observation- and action-spaces,\ndifferent reward functions, and act at different time-steps.\n\nAlgorithm#A class that holds the who/when/where/how for training one or more RL agent(s).\nThe user interacts with an Algorithm instance directly to train their agents\n(it is the top-most user facing API or RLlib).\n\nAsynchronous execution#An execution model where a later task can begin executing in parallel,\nwithout waiting for an earlier task to finish.\nRay tasks and actor tasks are all executed asynchronously."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870c36-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Asynchronous sampling#Sampling is the process of rolling out (playing) episodes within an RL\nenvironment and thereby collecting the training data (observations, actions\nand rewards). In an asynchronous sampling setup, Ray actors run sampling in the\nbackground and send collected samples back to a main driver script. The driver\nchecks for such \u201cready\u201d data frequently and then triggers central model\nlearning updates. Hence, sampling and learning happen at the same time.\nNote that because of this, the policy/ies used for creating the samples\n(action computations) might be slightly behind the centrally learned policy\nmodel(s), even in an on-policy Algorithm.\n\nAutoscaler#A Ray component that scales up and down the Ray cluster by adding and removing\nRay nodes according to the resources requested by applications running on\nthe cluster.\n\nAutoscaling#The process of scaling up and down the Ray cluster automatically."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870cb8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Autoscaling#The process of scaling up and down the Ray cluster automatically.\n\nBackend#A class containing the initialization and teardown logic for a specific deep\nlearning framework (eg. Torch, TensorFlow), used to set up distributed\ndata-parallel training for Ray Train\u2019s built-in trainers.\n\nBatch format#The way Ray Data represents batches of data.\nSet batch_format in methods like\nDataset.iter_batches() and\nDataset.map_batches() to specify the\nbatch type.\n>>> import ray\n>>> dataset = ray.data.range(10)\n>>> next(iter(dataset.iter_batches(batch_format=\"numpy\", batch_size=5)))\n{'id': array([0, 1, 2, 3, 4])}\n>>> next(iter(dataset.iter_batches(batch_format=\"pandas\", batch_size=5)))\n   id\n0   0\n1   1\n2   2\n3   3\n4   4\n\n\nTo learn more about batch formats, read\nConfiguring batch formats.\n\nBatch size#A batch size in the context of model training is the number of data points used\nto compute and apply one gradient update to the model weights."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870d08-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Block#A processing unit of data. A Dataset consists of a\ncollection of blocks.\nUnder the hood, Ray Data partitions rows into a set of distributed data blocks.\nThis allows it to perform operations in parallel.\nUnlike a batch, which is a user-facing object, a block is an internal abstraction.\n\nPlacement Group Bundle#A collection of resources that must be reserved on a single Ray node.\nLearn more.\n\nCheckpoint#A Ray Train Checkpoint is a common interface for accessing data and models across\ndifferent Ray components and libraries. A Checkpoint can have its data\nrepresented as a directory on local (on-disk) storage, as a directory on an\nexternal storage (e.g., cloud storage), and as an in-memory dictionary.\nLearn more,\n\nRay Client#The Ray Client is an API that connects a Python script to a remote Ray cluster.\nEffectively, it allows you to leverage a remote Ray cluster just like you would\nwith Ray running on your local machine.\nLearn more."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870d58-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Cluster#A Ray cluster is a set of worker nodes connected to a common Ray head node.\nRay clusters can be fixed-size, or they can autoscale up and down according to\nthe resources requested by applications running on the cluster.\n\nConnector#A connector performs transformations on data that comes out of a dataset or an\nRL environment and is about to be passed to a model. Connectors are flexible\ncomponents and can be swapped out such that models are easily reusable and do\nnot have to be retrained for different data transformations.\n\nTune Config#This is the set of hyperparameters corresponding to a Tune trial.\nSampling from a hyperparameter search space will produce a config.\n\nRay Dashboard#Ray\u2019s built-in dashboard is a web interface that provides metrics, charts,\nand other features that help Ray users to understand and debug Ray applications."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870da8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Dataset (object)#A class that produces a sequence of distributed data blocks.\nDataset exposes methods to read, transform, and consume data at scale.\nTo learn more about Datasets and the operations they support, read the Datasets API Reference.\n\nDeployment#A deployment is a group of actors that can handle traffic in Ray Serve.\nDeployments are defined as a single class with a number of options, including\nthe number of \u201creplicas\u201d of the deployment, each of which will map to a Ray\nactor at runtime. Requests to a deployment are load balanced across its replicas.\n\nDeployment graph#A deployment graph is a group of Ray Serve deployments that are bound together\ninto a directed acyclic graph (DAG) to handle requests. This enables model\ncomposition. Each request will be passed through the graph, allowing multiple\nstages of processing. For example, there might be a different deployment for\npreprocessing, inference, and postprocessing."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870df8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ingress Deployment#The \u201cingress\u201d deployment is the one that receives and responds to inbound user\ntraffic. It handles HTTP parsing and response formatting. In the case of a\ndeployment graph, it would also fan out requests to other deployments to do\nthings like a forward pass of an ML model.\n\nDriver#\u201cDriver\u201d is the name of the process running the main script that starts all\nother processes. For Python, this is usually the script you start with\npython ....\n\nTune Driver#The Tune driver is the main event loop that\u2019s happening on the node that\nlaunched the Tune experiment. This event loop schedules trials given the\ncluster resources, executes training on remote Trainable actors, and processes\nresults and checkpoints from those actors."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870e3e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Distributed Data-Parallel#A distributed data-parallel (DDP) training job scales machine learning training\nto happen on multiple nodes, where each node processes one shard of the full\ndataset. Every worker holds a copy of the model weights, and a common strategy\nfor updating weights is a \u201cmirrored strategy\u201d, where each worker will hold the\nexact same weights at all times, and computed gradients are averaged then\napplied across all workers.\nWith N worker nodes and a dataset of size D, each worker is responsible for\nonly D / N datapoints. If each worker node computes the gradient on a batch\nof size B, then the effective batch size of the DDP training is N * B."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870e8e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Environment#The world or simulation, in which one or more reinforcement learning agents\nhave to learn to behave optimally in wrt. a given reward function. An\nenvironment consists of an observation space, a reward function, an action\nspace, a state transition function, and a distribution over initial states\n(after a reset).\nEpisodes consisting of one or more time-steps are played through an\nenvironment in order to generate and collect samples for learning.\nThese samples contain one 4-tuple of\n[observation, action, reward, next observation] per timestep."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870ed4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Episode#A series of subsequent RL environment timesteps, each of which is a\n4-tuple: [observation, action, reward, next observation].\nEpisodes can end with the terminated- or truncated-flags being True.\nAn episode generally spans multiple time-steps for one or more agents.\nThe Episode is an important concept in RL as \u201coptimal agent behavior\u201d is\ndefined as choosing actions that maximize the sum of individual rewards\nover the course of an episode.\n\nTrial Executor#An internal Ray Tune component that manages\nthe resource management and execution of each trial\u2019s corresponding remote\nTrainable actor. The trial  executor\u2019s responsibilities include launching\ntraining, checkpointing, and restoring remote tasks.\n\nExperiment#A Ray Tune or Ray Train experiment is a collection of one or more training jobs\nthat may correspond to different hyperparameter configurations. These\nexperiments are launched via the\nTuner API and the Trainer API."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870f1a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Fault tolerance#Fault tolerance in Ray Train and Tune consists of experiment-level and trial-level\nrestoration. Experiment-level restoration refers to resuming all trials,\nin the event that an experiment is interrupted in the middle of training due\nto a cluster-level failure. Trial-level restoration refers to resuming\nindividual trials, in the event that a trial encounters a runtime\nerror such as OOM.\n\nFramework#The deep-learning framework used for the model(s), loss(es), and optimizer(s)\ninside an RLlib Algorithm. RLlib currently supports PyTorch and TensorFlow.\n\nGCS / Global Control Service#Centralized metadata server for a Ray cluster. It runs on the Ray head node\nand has functions like managing node membership and actor directory.\nIt\u2019s also known as the Global Control Store.\n\nHead node#A node that runs extra cluster-level processes like GCS and API server in\naddition to those processes running on a worker node. A Ray cluster only has\none head node."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870f6a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "HPO#Hyperparameter optimization (HPO) is the process of choosing a set of optimal\nhyperparameters for a learning algorithm. A hyperparameter can be a parameter\nwhose value is used to control the learning process (e.g., learning rate),\ndefine the model architecture (e.g, number of hidden layers), or influence data\npre-processing. In the case of Ray Train, hyperparameters can also include\ncompute processing scale-out parameters such as the number of distributed\ntraining workers.\n\nJob#A ray job is a packaged ray application that can be executed on a\n(remote) Ray cluster. Learn more.\n\nLineage#For Ray objects, this is the set of tasks that was originally executed to\nproduce the object. If an object\u2019s value is lost due to node failure,\nRay may attempt to recover the value by re-executing the object\u2019s lineage."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870fb0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Model#A function approximator with trainable parameters (e.g. a neural network) that\ncan be trained by an algorithm on available data or collected data from an RL\nenvironment. The parameters are usually initialized at random (unlearned state).\nDuring the training process, checkpoints of the model can be created such that -\nafter the learning process is shut down or crashes - training can resume from\nthe latest weights rather than having to re-learn from scratch.\nAfter the training process is completed, models can be deployed into production\nfor inference using Ray Serve."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9871000-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Multi-agent#Denotes an RL environment setup, in which several (more than one) agents act\nin the same environment and learn either the same or different optimal\nbehaviors. The relationship between the different agents in a multi-agent setup\nmight be adversarial (playing against each other), cooperative (trying to reach\na common goal) or neutral (the agents don\u2019t really care about other agents\u2019\nactions). The NN model architectures that can be used for multi-agent training\nrange from \u201cindependent\u201d (each agent trains its own separate model), over\n\u201cpartially shared\u201d (i.e. some agents might share their value function, because\nthey have a common goal), to \u201cidentical\u201d (all agents train on the same model).\n\nNamespace#A namespace is a logical grouping of jobs and named actors. When an actor is\nnamed, its name must be unique within the namespace.\nWhen a namespace is not specified, Ray will place your job in an anonymous\nnamespace."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9871046-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Node#A Ray node is a physical or virtual machine that is part of a Ray cluster.\nSee also Head node.\n\nObject#An application value. These are values that are returned by a task or\ncreated through ray.put.\n\nObject ownership#Ownership is the concept used to decide where metadata for a certain\nObjectRef (and the task that creates the value) should be stored.\nIf a worker calls foo.remote() or ray.put(), it owns the metadata for\nthe returned ObjectRef, e.g., ref count and location information. If an\nobject\u2019s owner dies and another worker tries to get the value,\nit will receive an OwnerDiedError exception.\n\nObject reference#A pointer to an application value, which can be stored anywhere in the cluster.\nCan be created by calling foo.remote() or ray.put().\nIf using foo.remote(), then the returned ObjectRef is also a future.\n\nObject store#A distributed in-memory data store for storing Ray objects."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9871096-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Object store#A distributed in-memory data store for storing Ray objects.\n\nObject spilling#Objects in the object store are spilled to external storage once the capacity\nof the object store is used up. This enables out-of-core data processing for\nmemory-intensive distributed applications. It comes with a performance penalty\nsince data needs to be written to disk."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98710dc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Observation#The full or partial state of an RL environment, which an agent sees\n(has access to) at each timestep. A fully observable environment produces\nobservations that contain all the information to sufficiently infer the current\nunderlying state of the environment. Such states are also called \u201cMarkovian\u201d.\nExamples for environments with Markovian observations are chess or 2D games,\nin which the player can see with each frame the entirety of the game\u2019s state.\nA partially observable (or non-Markovian) environment produces observations\nthat do not contain sufficient information to infer the exact underlying state.\nAn example here would be a robot with a camera on its head facing forward.\nThe robot walks around in a maze, but from a single camera frame might not know\nwhat\u2019s currently behind it."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987112c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Offline data#Data collected in an RL environment up-front and stored in some data format\n(e.g. JSON). Offline data can be used to train an RL agent. The data might have\nbeen generated by a non-RL/ML system, such as a simple decision making script.\nAlso, when training from offline data, the RL algorithm will not be able to\nexplore new actions in new situations as all interactions with the environment\nalready happened in the past (were recorded prior to training).\n\nOffline RL#A sub-field of reinforcement learning (RL), in which specialized offline\nRL Algorithms learn how to compute optimal actions for an agent inside an\nenvironment without the ability to interact live with that environment.\nInstead, the data used for training has already been collected up-front\n(maybe even by a non-RL/ML system). This is very similar to a supervised\nlearning setup. Examples for offline RL algorithms are MARWIL, CQL, and CRR."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9871172-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Off-Policy#A type of RL Algorithm. In an off-policy algorithm, the policy used to compute\nthe actions inside an RL environment (to generate the training data) might be\ndifferent from the one that is being optimized. Examples for off-policy\nAlgorithms are DQN, SAC, and DDPG.\n\nOn-Policy#A type of RL Algorithm. In an on-policy algorithm, the policy used to compute\nthe actions inside an RL environment (to generate the training data) must be the\nexact same (matching NN weights at all times) than the one that is being\noptimized. Examples for on-policy Algorithms are PPO, APPO, and IMPALA.\n\nOOM (Out of Memory)#Ray may run out of memory if the application is using too much memory on a\nsingle node. In this case the Ray OOM killer will kick\nin and kill worker processes to free up memory."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98711c2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Placement group#Placement groups allow users to atomically reserve groups of resources across\nmultiple nodes (i.e., gang scheduling). They can be then used to schedule Ray\ntasks and actors packed as close as possible for locality (PACK), or spread\napart (SPREAD). Placement groups are generally used for gang-scheduling actors,\nbut also support tasks.\nLearn more.\n\nPolicy#A (neural network) model that maps an RL environment observation of some agent\nto its next action inside an RL environment.\n\nPredictor#An interface for performing inference (prediction)\non input data with a trained model.\n\nPreprocessor#An interface used to preprocess a Dataset for\ntraining and inference (prediction). Preprocessors\ncan be stateful, as they can be fitted on the training dataset before being\nused to transform the training and evaluation datasets.\n\nRay application#A collection of Ray tasks, actors, and objects that originate from the\nsame script."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9871212-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Raylet#A system process that runs on each Ray node. It\u2019s responsible for scheduling\nand object management.\n\nReplica#A replica is a single actor that handles requests to a given Serve deployment.\nA deployment may consist of many replicas, either statically-configured via\nnum_replicas or dynamically configured using auto-scaling.\n\nResource (logical and physical)#Ray resources are logical resources (e.g. CPU, GPU) used by tasks and actors.\nIt doesn\u2019t necessarily map 1-to-1 to physical resources of machines on which\nRay cluster runs. Learn more.\n\nReward#A single floating point value that each agent within an RL environment receives\nafter each action taken. An agent is defined to be acting optimally inside the\nRL environment when the sum over all received rewards within an episode is\nmaximized.\nNote that rewards might be delayed (not immediately telling the agent, whether\nan action was good or bad) or sparse (often have a value of zero) making it\nharder for the agent to learn."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9871258-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Rollout#The process of advancing through an episode in an RL environment (with one or\nmore RL agents) by taking sequential actions. During rollouts, the algorithm\nshould collect the environment produced 4-tuples [observations, actions,\nrewards, next observations] in order to (later or simultaneously) learn how to\nbehave more optimally from this data."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98712a8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Rollout Worker#Component within a RLlib Algorithm responsible for advancing and collecting\nobservations and rewards in an RL environment. Actions for the different\nagent(s) within the environment are computed by the Algorithms\u2019 policy models.\nA distributed algorithm might have several replicas of Rollout Workers running\nas Ray actors in order to scale the data collection process for faster RL\ntraining.\nRolloutWorkers are used as @ray.remote actors to collect and return samples\nfrom environments or offline files in parallel. An RLlib\nAlgorithm usually has\nnum_workers RolloutWorker (not @ray.remote) in\nits WorkerSet under self.workers.\nDepending on its evaluation config settings, an additional\nWorkerSet with\nAlgorithm\nunder self.evaluation_workers."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98712ee-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Runtime environment#A runtime environment defines dependencies such as files, packages, environment\nvariables needed for a Python script to run. It is installed dynamically on the\ncluster at runtime, and can be specified for a Ray job, or for specific actors\nand tasks. Learn more.\n\nRemote Function#See Task.\n\nRemote Class#See Actor.\n\n(Ray) Scheduler#A Ray component that assigns execution units (Task/Actor) to Ray nodes.\n\nSearch Space#The definition of the possible values for hyperparameters. Can be composed out\nof constants, discrete values, distributions of functions. This is also\nreferred to as the \u201cparameter space\u201d (param_space in the Tuner)."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9871334-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Search algorithm#Search algorithms suggest new hyperparameter configurations to be evaluated\nby Tune. The default search algorithm is random search, where each new\nconfiguration is independent from the previous one. More sophisticated search\nalgorithms such as ones using Bayesian optimization will fit a model to predict\nthe hyperparameter configuration that will produce the best model, while also\nexploring the space of possible hyperparameters. Many popular search algorithms\nare built into Tune, most of which are integrations with other libraries.\n\nServe application#An application is the unit of upgrade in a Serve cluster.\nAn application consists of one or more deployments. One of these deployments\nis considered the \u201cingress\u201d deployment, which is where all inbound\ntraffic is handled.\nApplications can be called via HTTP at their configured route_prefix."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9871384-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ServeHandle#ServeHandle is the Python API for making requests to Serve deployments. A\nhandle is defined by passing one bound Serve deployment to the constructor of\nanother. Then at runtime that reference can be used to make requests. This is\nused to combine multiple deployments into \u201cdeployment graphs.\u201d\n\nSession#\nA Ray Train/Tune session: Tune session at the experiment execution layer\nand Train session at the Data Parallel training layer\nif running data-parallel distributed training with Ray Train.\nThe session allows access to metadata, such as which trial is being run,\ninformation about the total number of workers, as well as the rank of the\ncurrent worker. The session is also the interface through which an individual\nTrainable can interact with the Tune experiment as a whole. This includes uses\nsuch as reporting an individual trial\u2019s metrics, saving/loading checkpoints,\nand retrieving the corresponding dataset shards for each Train worker."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98713d4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "A Ray cluster: in some cases the session also means a Ray Cluster.\nFor example, logs of a Ray cluster are stored under session_xxx/logs/.\n\n\nSpillback#A task caller schedules a task by first sending a resource request to the\npreferred raylet for that request. If the preferred raylet chooses not to grant\nthe resources locally, it may also \u201cSpillback\u201d and respond to the caller with\nthe address of a remote raylet at which the caller should retry the resource\nrequest.\n\nState#State of the environment an RL agent interacts with.\n\nSynchronous execution#Two tasks A and B are executed synchronously if A must finish before B can\nstart. For example, if you call ray.get immediately after launching a remote\ntask with task.remote(), you\u2019ll be running with synchronous execution,\nsince this will wait for the task to finish before the program continues."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987141a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Synchronous sampling#Sampling workers work in synchronous steps. All of them must finish collecting\na new batch of samples before training can proceed to the next iteration.\n\nTask#A remote function invocation. This is a single function invocation that\nexecutes on a process different from the caller, and potentially on a different\nmachine. A task can be stateless (a @ray.remote function) or stateful (a\nmethod of a @ray.remote class - see Actor below). A task is executed\nasynchronously with the caller: the .remote() call immediately returns\none or more ObjectRefs (futures) that can be used to retrieve the\nreturn value(s). See Actor task."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9871460-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Trainable#A Trainable is the interface that Ray Tune uses to\nperform custom training\nlogic. User-defined Trainables take in a configuration as an input and can\nrun user-defined training code as well as custom metric reporting and\ncheckpointing.\nThere are many types of trainables. Most commonly used is the function\ntrainable API, which is simply a Python function that contains model training\nlogic and metric reporting. Tune also exposes a class trainable API, which\nallows you to implement training, checkpointing, and restoring as different\nmethods.\nRay Tune associates each trial with its own Trainable \u2013 the Trainable is the\none actually doing training. The Trainable is a remote actor that can be placed\non any node in a Ray cluster."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98714b0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Trainer#A Trainer is the top-level API to configure a single distributed training job.\nThere are built-in Trainers for different frameworks,\nlike PyTorch, Tensorflow, and XGBoost. Each trainer shares a common interface\nand otherwise defines framework-specific configurations and entrypoints. The\nmain job of a trainer is to coordinate N distributed training workers and set\nup the communication backends necessary for these workers to communicate\n(e.g., for sharing computed gradients).\n\nTrainer configuration#A Trainer can be configured in various ways. Some\nconfigurations are shared across all trainers, like the RunConfig, which\nconfigures things like the experiment storage, and ScalingConfig, which\nconfigures the number of training workers as well as resources needed per\nworker. Other configurations are specific to the trainer framework."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98714f6-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Training iteration#A partial training pass of input data up to pre-defined yield point\n(e.g., time or data consumed) for checkpointing of long running training jobs.\nA full training epoch can consist of multiple training iterations.\n.. TODO: RLlib\n\nTraining epoch#A full training pass of the input dataset. Typically, model training iterates\nthrough the full dataset in batches of size B, where gradients are calculated\non each batch and then applied as an update to the model weights. Training\njobs can consist of multiple epochs by training through the same dataset\nmultiple times.\n\nTraining step#An RLlib-specific method of the Algorithm class which includes the core logic\nof an RL algorithm. Commonly includes gathering of experiences (either through\nsampling or from offline data), optimization steps, redistribution of learnt\nmodel weights. The particularities of this method are specific to algorithms\nand configurations."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Transition#A tuple of (observation, action, reward, next observation). A transition\nrepresents one step of an agent in an environment.\n\nTrial#One training run within a Ray Tune experiment. If you run multiple trials,\neach trial usually corresponds to a different config (a set of hyperparameters).\n\nTrial scheduler#When running a Ray Tune job, the scheduler will decide how to allocate\nresources to trials. In the most common case, this resource is time - the trial\nscheduler decides which trials to run at what time. Certain built-in schedulers\nlike Asynchronous Hyperband (ASHA) perform early stopping of under-performing\ntrials, while others like Population Based Training (PBT) will make\nunder-performing trials copy the hyperparameter config and model weights of\ntop performing trials and continue training.\n\nTuner#The Tuner is the top level Ray Tune API used to configure and run an\nexperiment with many trials.\n\nWorker process / worker#The process that runs user defined tasks and actors."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987158c-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-references/glossary.html", "title": "Ray Glossary \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Worker process / worker#The process that runs user defined tasks and actors.\n\n\n\n\n\n\n\n\n\n\n\nprevious\nArchitecture Whitepapers\n\n\n\n\nnext\nSecurity\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
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98754b6-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/data/api/api.html", "title": "Ray Data API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Dataset API\nConstructor\nBasic Transformations\nSorting, Shuffling, Repartitioning\nSplitting and Merging Datasets\nGrouped and Global Aggregations\nConsuming Data\nI/O and Conversion\nInspecting Metadata\nExecution\nSerialization\nInternals\n\n\nDataIterator API\nray.data.DataIterator.iter_batches\nray.data.DataIterator.iter_torch_batches\nray.data.DataIterator.to_tf\nray.data.DataIterator.stats\n\n\nExecutionOptions API\nConstructor\nResource Options\n\n\nGroupedData API\nConstructor\nComputations / Descriptive Stats\nFunction Application\nAggregate Function\n\n\nDataContext API\nConstructor\nGet DataContext\n\n\nRandomAccessDataset (experimental)\nConstructor\nFunctions\n\n\nUtility\nray.data.set_progress_bars\n\n\nPreprocessor\nPreprocessor Interface\nGeneric Preprocessors\nCategorical Encoders\nFeature Scalers\nK-Bins Discretizers\n\n\nAPI Guide for Users from Other Data Libraries\nFor Pandas Users\nFor PyArrow Users\nFor PyTorch Dataset & DataLoader Users\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nMany model training with Ray Data\n\n\n\n\nnext\nInput/Output"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98754fc-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/data/api/api.html", "title": "Ray Data API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "previous\nMany model training with Ray Data\n\n\n\n\nnext\nInput/Output\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
id: b98755d8-83c7-11ee-a273-0242ac1c000c
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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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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
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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
id: b9875812-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/cluster/getting-started.html", "title": "Ray Clusters Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Clusters Overview \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\n\n\n\n\n     Key Concepts\n    \n\n\n\n     Deploying on Kubernetes\n    \n\n\n\n     Deploying on VMs\n    \n\n\n\n     Collecting and monitoring metrics\n    \n\n\n\n     Configuring and Managing Ray Dashboard\n    \n\n\n\n     Applications Guide\n    \n\n\n\n     FAQ\n    \n\n\n\n     Ray Cluster Management API\n    \n\n\n\n     Usage Stats Collection\n    \n\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"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9875862-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/cluster/getting-started.html", "title": "Ray Clusters Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "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   Where can I deploy Ray clusters?\n  \n\n\n\n   What\u2019s next?\n  \n\n\n\n\n\n\n\n\n\nRay Clusters Overview\n\n\n\n\n Contents \n\n\n\n\n\n   Where can I deploy Ray clusters?\n  \n\n\n\n   What\u2019s next?\n  \n\n\n\n\n\n\n\n\n\nRay Clusters Overview#\nRay enables seamless scaling of workloads from a laptop to a large cluster. While Ray\nworks out of the box on single machines with just a call to ray.init, to run Ray\napplications on multiple nodes you must first deploy a Ray cluster.\nA Ray cluster is a set of worker nodes connected to a common Ray head node.\nRay clusters can be fixed-size, or they may autoscale up and down according\nto the resources requested by applications running on the cluster."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98758a8-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/cluster/getting-started.html", "title": "Ray Clusters Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Where can I deploy Ray clusters?#\nRay provides native cluster deployment support on the following technology stacks:\n\nOn AWS and GCP. Community-supported Azure, Aliyun and vSphere integrations also exist.\nOn Kubernetes, via the officially supported KubeRay project.\n\nAdvanced users may want to deploy Ray manually\nor onto platforms not listed here.\n\nNote\nMulti-node Ray clusters are only supported on Linux. At your own risk, you\nmay deploy Windows and OSX clusters by setting the environment variable\nRAY_ENABLE_WINDOWS_OR_OSX_CLUSTER=1 during deployment.\n\n\n\nWhat\u2019s next?#\n\n\n\n\n\nI want to learn key Ray cluster concepts\n\n\nUnderstand the key concepts and main ways of interacting with a Ray cluster.\n\n\nLearn Key Concepts\n\n\n\n\n\n\nI want to run Ray on Kubernetes\n\n\nDeploy a Ray application to a Kubernetes cluster. You can run the tutorial on a\nKubernetes cluster or on your laptop via Kind.\n\n\nGet Started with Ray on Kubernetes\n\n\n\n\n\n\nI want to run Ray on a cloud provider"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98758f8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/cluster/getting-started.html", "title": "Ray Clusters Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Get Started with Ray on Kubernetes\n\n\n\n\n\n\nI want to run Ray on a cloud provider\n\n\nTake a sample application designed to run on a laptop and scale it up in the\ncloud. Access to an AWS or GCP account is required.\n\n\nGet Started with Ray on VMs\n\n\n\n\n\n\nI want to run my application on an existing Ray cluster\n\n\nGuide to submitting applications as Jobs to existing Ray clusters.\n\n\nJob Submission\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nray.workflow.cancel\n\n\n\n\nnext\nKey Concepts\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: b987593e-83c7-11ee-a273-0242ac1c000c
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98759ca-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/_static/scripts/pydata-sphinx-theme.js?digest=1999514e3f237ded88cf", "chroma:document": "/**!\n * @fileOverview Kickass library to create and place poppers near their reference elements.\n * @version 1.16.1\n * @license\n * Copyright (c) 2016 Federico Zivolo and contributors\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in all\n * copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,"}


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
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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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
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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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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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987603c-83c7-11ee-a273-0242ac1c000c
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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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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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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
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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
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987646a-83c7-11ee-a273-0242ac1c000c
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98764b0-83c7-11ee-a273-0242ac1c000c
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9876500-83c7-11ee-a273-0242ac1c000c
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9876546-83c7-11ee-a273-0242ac1c000c
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9876596-83c7-11ee-a273-0242ac1c000c
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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
id: b987662c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98766a4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98766f4-83c7-11ee-a273-0242ac1c000c
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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
id: b987678a-83c7-11ee-a273-0242ac1c000c
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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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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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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
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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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
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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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
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9876f78-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-more-libs/index.html", "title": "More Ray ML Libraries \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "More Ray ML Libraries \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\n\n\n\n\n     Distributed Scikit-learn / Joblib\n    \n\n\n\n     Distributed multiprocessing.Pool\n    \n\n\n\n     Ray Collective Communication Lib\n    \n\n\n\n     Using Dask on Ray\n    \n\n\n\n     Using Spark on Ray (RayDP)\n    \n\n\n\n     Using Mars on Ray\n    \n\n\n\n     Using Pandas on Ray (Modin)\n    \n\n\n\n     Ray Workflows (Alpha)\n    \n\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"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9876fc8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/ray-more-libs/index.html", "title": "More Ray ML Libraries \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "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   Ecosystem Map\n  \n\n\n\n\n\n\n\n\n\nMore Ray ML Libraries\n\n\n\n\n Contents \n\n\n\n\n\n   Ecosystem Map\n  \n\n\n\n\n\n\n\n\n\nMore Ray ML Libraries#\nRay has a variety of additional integrations with ecosystem libraries.\n\nDistributed Scikit-learn / Joblib\nDistributed multiprocessing.Pool\nRay Collective Communication Lib\nUsing Dask on Ray\nUsing Spark on Ray (RayDP)\nUsing Mars on Ray\nUsing Pandas on Ray (Modin)\n\n\nEcosystem Map#\nThe following map visualizes the landscape and maturity of Ray components and their integrations. Solid lines denote integrations between Ray components; dotted lines denote integrations with the broader ML ecosystem."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987700e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-more-libs/index.html", "title": "More Ray ML Libraries \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Stable: This component is stable.\nBeta: This component is under development and APIs may be subject to change.\nAlpha: This component is in early development.\nCommunity-Maintained: These integrations are community-maintained and may vary in quality.\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nExternal Application API\n\n\n\n\nnext\nDistributed Scikit-learn / Joblib\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."}


