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Welcome to Ray! — Ray 3.0.0.dev0




































































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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  



   Glossary
  



   Security
  







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.md







.pdf















Welcome to Ray!












Welcome to Ray!


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.md







.pdf















Welcome to Ray!












Welcome to Ray!




Welcome to Ray!
Ray is an open-source unified framework for scaling AI and Python applications. 
      It provides the compute layer for parallel processing so that 
      you don’t need to be a distributed systems expert.
      



















Scaling with Ray




            Batch Inference
          

            Model Training
          

            Hyperparameter Tuning
          
            Model Serving
          

            Reinforcement Learning
          






from typing import Dict
import numpy as np

import ray

# Step 1: Create a Ray Dataset from in-memory Numpy arrays.
ds = ray.data.from_numpy(np.asarray(["Complete this", "for me"]))


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# Step 2: Define a Predictor class for inference.
class HuggingFacePredictor:
    def __init__(self):
        from transformers import pipeline
        # Initialize a pre-trained GPT2 Huggingface pipeline.
        self.model = pipeline("text-generation", model="gpt2")

    # Logic for inference on 1 batch of data.
    def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, list]:
        # Get the predictions from the input batch.
        predictions = self.model(
            list(batch["data"]), max_length=20, num_return_sequences=1)
        # `predictions` is a list of length-one lists. For example:
        # [[{'generated_text': 'output_1'}], ..., [{'generated_text': 'output_2'}]]
        # Modify the output to get it into the following format instead:
        # ['output_1', 'output_2']
        batch["output"] = [sequences[0]["generated_text"] for sequences in predictions]
        return batch


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# Use 2 parallel actors for inference. Each actor predicts on a
# different partition of data.
scale = ray.data.ActorPoolStrategy(size=2)
# Step 3: Map the Predictor over the Dataset to get predictions.
predictions = ds.map_batches(HuggingFacePredictor, compute=scale)
# Step 4: Show one prediction output.
predictions.show(limit=1)

            


Learn more  |  API references



 Open in Github
                    





from ray.train import ScalingConfig
from ray.train.torch import TorchTrainer

# Step 1: setup PyTorch model training as you normally would
def train_loop_per_worker():
    model = ...
    train_dataset = ...
    for epoch in range(num_epochs):
        ...  # model training logic

# Step 2: setup Ray's PyTorch Trainer to run on 32 GPUs
trainer = TorchTrainer(
    train_loop_per_worker=train_loop_per_worker,
    scaling_config=ScalingConfig(num_workers=32, use_gpu=True),
    datasets={"train": train_dataset},
)


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# Step 3: run distributed model training on 32 GPUs
result = trainer.fit()
            


Learn more  |  API references



 Open in colab
                    





from ray import tune
from ray.train import ScalingConfig
from ray.train.lightgbm import LightGBMTrainer

train_dataset, eval_dataset = ...

# Step 1: setup Ray's LightGBM Trainer to train on 64 CPUs
trainer = LightGBMTrainer(
    ...
    scaling_config=ScalingConfig(num_workers=64),
    datasets={"train": train_dataset, "eval": eval_dataset},
)

# Step 2: setup Ray Tuner to run 1000 trials
tuner = tune.Tuner(
    trainer=trainer,
    param_space=hyper_param_space,
    tune_config=tune.TuneConfig(num_samples=1000),
)

# Step 3: run distributed HPO with 1000 trials; each trial runs on 64 CPUs
result_grid = tuner.fit()

            


Learn more  |  API references



 Open in Github
                    





import pandas as pd

from ray import serve
from starlette.requests import Request


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import pandas as pd

from ray import serve
from starlette.requests import Request


@serve.deployment(ray_actor_options={"num_gpus": 1})
class PredictDeployment:
    def __init__(self, model_id: str, revision: str = None):
        from transformers import AutoModelForCausalLM, AutoTokenizer
        import torch

        self.model = AutoModelForCausalLM.from_pretrained(
            model_id,
            …
        )
        self.tokenizer = AutoTokenizer.from_pretrained(model_id)

    def generate(self, text: str) -> pd.DataFrame:
        input_ids = self.tokenizer(text, return_tensors="pt").input_ids.to(
            self.model.device
        )

        gen_tokens = self.model.generate(
            input_ids,
            …
        )
        return pd.DataFrame(
            self.tokenizer.batch_decode(gen_tokens), columns=["responses"]
        )


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async def __call__(self, http_request: Request) -> str:
        prompts: list[str] = await http_request.json()["prompts"]
        return self.generate(prompts)


            


Learn more  |  API references



 Open in Github
                    





from ray.rllib.algorithms.ppo import PPOConfig

# Step 1: configure PPO to run 64 parallel workers to collect samples from the env.
ppo_config = (
    PPOConfig()
    .environment(env="Taxi-v3")
    .rollouts(num_rollout_workers=64)
    .framework("torch")
    .training(model=rnn_lage)
)

# Step 2: build the PPO algorithm
ppo_algo = ppo_config.build()

# Step 3: train and evaluate PPO
for _ in range(5):
    print(ppo_algo.train())

ppo_algo.evaluate()
            


Learn more  |  API references



 Open in Github
                    








Getting Started

 


Learn basics

Understand how the Ray framework scales your ML workflows.
Learn more > 
 
 


Install Ray

Installation guide >




Try it out


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Install Ray

Installation guide >




Try it out

Experiment with Ray with an introductory notebook.
Open the notebook>



Beyond the basics





Ray Libraries

Scale the entire ML pipeline from data ingest to model serving with high-level Python APIs that integrate with popular ecosystem frameworks.
Learn more about Ray Libraries>




Ray Core

Scale generic Python code with simple, foundational primitives that enable a high degree of control for building distributed applications or custom platforms.
Learn more about Core >




Ray Clusters

Deploy a Ray cluster on AWS, GCP, Azure or kubernetes from a laptop to a large cluster to seamlessly scale workloads for production
Learn more about clusters >



Getting involved



 Join the community 
 


Attend community events


 


Subscribe to the newsletter


 


Follow us on Twitter




 Get Support 
 


Find community on Slack


 


Ask questions to the forum


 


Open an issue




 Contribute to Ray 
 


Contributor's guide


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Open an issue




 Contribute to Ray 
 


Contributor's guide


 


Create a pull request











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      © Copyright 2023, The Ray Team.


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Developer Guides — Ray 3.0.0.dev0





































































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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  








     API stability
    



     Getting Involved / Contributing
    



     Configuring Ray
    



     Architecture Whitepapers
    





   Glossary
  



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Developer Guides#


API stability
Alpha
Beta
Stable


Getting Involved / Contributing
Contributing to Ray
What can I work on?
Setting up your development environment
Submitting and Merging a Contribution
PR Review Process
Testing
Code Style
Understanding CI test jobs
API compatibility style guide
Becoming a Reviewer
More Resources for Getting Involved


Configuring Ray
Cluster Resources
Logging and Debugging
Ports configurations
TLS Authentication
Java Applications


Architecture Whitepapers











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Ray RLlib API — Ray 3.0.0.dev0





































































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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  








     Getting Started with RLlib
    



     Key Concepts
    



     Environments
    



     Algorithms
    



     User Guides
    



     Examples
    



     Ray RLlib API
    








       Algorithms
      



       Environments
      



       Policy API
      



       Model APIs
      



       Catalog API
      



       RLModule API
      



       Learner API
      



       Sampling the Environment or offline data


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Learner API
      



       Sampling the Environment or offline data
      



       Replay Buffer API
      



       RLlib Utilities
      



       External Application API
      







   More Libraries
  



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   Monitoring and Debugging
  



   Developer Guides
  



   Glossary
  



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Ray RLlib API









Note
From Ray 2.6.0 onwards, RLlib is adopting a new stack for training and model customization,
gradually replacing the ModelV2 API and some convoluted parts of Policy API with the RLModule API.
Click here for details.


Ray RLlib API#

Tip
We’d love to hear your feedback on using RLlib - sign up to our forum and start asking questions!


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This section contains an overview of RLlib’s package- and API reference.
If you think there is anything missing, please open an issue on Github.


Algorithms
Algorithm Configuration API
Building Custom Algorithm Classes
Algorithm API


Environments
Environment API Reference


Policy API
Building Custom Policy Classes
Base Policy classes
Making models
Inference
Computing, processing, and applying gradients
Updating the Policy’s model
Loss, Logging, optimizers, and trajectory processing
Saving and restoring
Connectors
Recurrent Policies
Miscellaneous


Model APIs
Base Model classes
Feed Forward methods
Recurrent Models API
Acessing variables
Customization


Catalog API
Basic usage
Advanced usage


RLModule API
RL Module specifications and configurations
RL Module API
Multi Agent RL Module API


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RLModule API
RL Module specifications and configurations
RL Module API
Multi Agent RL Module API


Learner API
Learner specifications and configurations
Constructor
Performing Updates
Computing Losses
Configuring Optimizers
Gradient Computation
Saving, Loading, Checkpointing, and Restoring States
Adding and Removing Modules
Managing Results


LearnerGroup API
Configuring a LearnerGroup


Sampling the Environment or offline data
RolloutWorker API
WorkerSet API
Sampler API
Policy Map API
Sample batch API
MultiAgent batch API


Replay Buffer API
Replay Buffer Base Classes
Public Methods
Multi Agent Buffers
Utility Methods


RLlib Utilities
Exploration API
Scheduler API
Training Operations Utilities
Framework Utilities


External Application API











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      © Copyright 2023, The Ray Team.


key: chroma:document
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```{title} Welcome to Ray!
```

```{raw} html





Welcome to Ray!




Welcome to Ray!
Ray is an open-source unified framework for scaling AI and Python applications. 
      It provides the compute layer for parallel processing so that 
      you don’t need to be a distributed systems expert.
      



















Scaling with Ray




            Batch Inference
          

            Model Training
          

            Hyperparameter Tuning
          
            Model Serving
          

            Reinforcement Learning
          






from typing import Dict
import numpy as np

import ray

# Step 1: Create a Ray Dataset from in-memory Numpy arrays.
ds = ray.data.from_numpy(np.asarray(["Complete this", "for me"]))


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string_value:
Ray Train: Scalable Model Training — Ray 3.0.0.dev0





































































Toggle navigation sidebar




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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  








     Overview
    



     PyTorch Guide
    



     PyTorch Lightning Guide
    



     Hugging Face Transformers Guide
    



     More Frameworks
    



     User Guides
    



     Examples
    



     Benchmarks
    



     Ray Train API
    





   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  



   Glossary
  



   Security
  







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Glossary
  



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 Contents
    




   Install Ray Train
  



   Get started
  



   Learn more
  









Ray Train: Scalable Model Training




 Contents 





   Install Ray Train
  



   Get started
  



   Learn more
  









Ray Train: Scalable Model Training#









Ray Train is a scalable machine learning library for distributed training and fine-tuning.
Ray 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.
Whether you have large models or large datasets, Ray Train is the simplest solution for distributed training.
Ray Train provides support for many frameworks:






PyTorch Ecosystem
More Frameworks



PyTorch
TensorFlow

PyTorch Lightning
Keras


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PyTorch Ecosystem
More Frameworks



PyTorch
TensorFlow

PyTorch Lightning
Keras

Hugging Face Transformers
Horovod

Hugging Face Accelerate
XGBoost

DeepSpeed
LightGBM




Install Ray Train#
To install Ray Train, run:
$ pip install -U "ray[train]"


To learn more about installing Ray and its libraries, see
Installing Ray.


Get started#





Overview


Understand the key concepts for distributed training with Ray Train.


Learn the basics






PyTorch


Get started on distributed model training with Ray Train and PyTorch.


Try Ray Train with PyTorch






PyTorch Lightning


Get started on distributed model training with Ray Train and Lightning.


Try Ray Train with Lightning






Hugging Face Transformers


Get started on distributed model training with Ray Train and Transformers.


Try Ray Train with Transformers







Learn more#





More Frameworks


Don’t see your framework? See these guides.


Try Ray Train with other frameworks






User Guides


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Try Ray Train with other frameworks






User Guides


Get how-to instructions for common training tasks with Ray Train.


Read how-to guides






Examples


Browse end-to-end code examples for different use cases.


Learn through examples






API


Consult the API Reference for full descriptions of the Ray Train API.


Read the API Reference

















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                    Welcome to Ray!
                




  Ray
 




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   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  



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The Ray Ecosystem — Ray 3.0.0.dev0





































































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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



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The Ray Ecosystem


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.rst







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The Ray Ecosystem









The Ray Ecosystem#
This page lists libraries that have integrations with Ray for distributed execution
in alphabetical order.
It’s easy to add your own integration to this list.
Simply open a pull request with a few lines of text, see the dropdown below for
more information.


Adding Your Integration




To add an integration, simply add an entry to the projects list of our
Gallery YAML on GitHub.
- name: the integration link button text
  section_title: The section title for this integration
  description: A quick description of your library and its integration with Ray
  website: The URL of your website
  repo: The URL of your project on GitHub
  image: The URL of a logo of your project


That’s all!


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That’s all!








Classy 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.


Classy Vision Integration








Dask 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.


Dask Integration








Flambé is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambé’s 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.


Flambé Integration


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Flambé Integration








Flyte 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.


Flyte Integration








Horovod 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.


Horovod Integration








State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0. It integrates with Ray for distributed hyperparameter tuning of transformer models.


Hugging Face Transformers Integration








Analytics Zoo seamlessly scales TensorFlow, Keras and PyTorch to distributed big data (using Spark, Flink & Ray).


Intel Analytics Zoo Integration


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Intel Analytics Zoo Integration








The 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.


NLU Integration








Ludwig 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.


Ludwig Integration








Mars 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.


MARS Integration


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MARS Integration








Scale 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.


Modin Integration








Prefect 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.


Prefect Integration








PyCaret 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.


PyCaret Integration


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PyCaret Integration








RayDP (“Spark on Ray”) 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.


RayDP Integration








Scikit-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.


Scikit Learn Integration


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Scikit Learn Integration








Alibi 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.


Seldon Alibi Integration








Sematic 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.


Sematic Integration








spaCy is a library for advanced Natural Language Processing in Python and Cython. It’s built on the very latest research, and was designed from day one to be used in real products.


spaCy Integration


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spaCy Integration








XGBoost 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.


XGBoost Integration








LightGBM is a high-performance gradient boosting library for classification and regression. It is designed to be distributed and efficient.


LightGBM Integration








Volcano is system for running high-performance workloads on Kubernetes. It features powerful batch scheduling capabilities required by ML and other data-intensive workloads.


Volcano Integration














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Monitoring and Debugging — Ray 3.0.0.dev0





































































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                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  








     Ray Dashboard
    



     Key Concepts
    



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   What is observability
  



   Importance of observability
  



   Monitoring and debugging workflow and tools
  









Monitoring and Debugging




 Contents 





   What is observability
  



   Importance of observability
  



   Monitoring and debugging workflow and tools
  









Monitoring and Debugging#
This section covers how to monitor and debug Ray applications and clusters with Ray’s Observability features.

What is observability#
In general, observability is a measure of how well the internal states of a system can be inferred from knowledge of its external outputs.
In Ray’s context, observability refers to the ability for users to observe and infer Ray applications’ and Ray clusters’ internal states with various external outputs, such as logs, metrics, events, etc.


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Importance of observability#
Debugging 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.



Monitoring and debugging workflow and tools#
Monitoring and debugging Ray applications consist of 4 major steps:

Monitor the clusters and applications.
Identify the surfaced problems or errors.
Debug with various tools and data.
Form a hypothesis, implement a fix, and validate it.

The remainder of this section covers the observability tools that Ray provides to accelerate your monitoring and debugging workflow.












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Security — Ray 3.0.0.dev0




































































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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  



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   Personas
  



   Best practices
  



     Deploy Ray Clusters in a controlled network environment


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Best practices
  



     Deploy Ray Clusters in a controlled network environment
    



     Only execute trusted code within Ray
    



     Enforce isolation outside of Ray with multiple Ray Clusters
    











Security




 Contents 





   Personas
  



   Best practices
  



     Deploy Ray Clusters in a controlled network environment
    



     Only execute trusted code within Ray
    



     Enforce isolation outside of Ray with multiple Ray Clusters


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Security#
Ray 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.
To 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.
Ray 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.


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Personas#
When considering the security responsibilities of running Ray, think about the different personas interacting with Ray.

Ray 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.
Platform providers provide the compute environment on which Developers run Ray.
Users interact with the output of Ray-powered applications.



Best practices#
Security 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.

Deploy Ray Clusters in a controlled network environment#


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Deploy Ray Clusters in a controlled network environment#

Network 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.
gRPC communication can be encrypted with TLS, but it is not a replacement for network isolation.
Platform 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.



Only execute trusted code within Ray#

Ray faithfully executes code that is passed to it – Ray doesn’t differentiate between a tuning experiment, a rootkit install, or an S3 bucket inspection.
Ray developers are responsible for building their applications with this understanding in mind.



Enforce isolation outside of Ray with multiple Ray Clusters#


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Enforce isolation outside of Ray with multiple Ray Clusters#

If workloads require isolation from each other, use separate, isolated Ray Clusters. Ray can schedule multiple distinct Jobs in a single Cluster, but doesn’t attempt to enforce isolation between them. Similarly, Ray doesn’t implement access controls for developers interacting with a given cluster.
Ray developers are responsible for determining which applications need to be separated and platform providers are responsible for providing this isolation.












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Ray Tune API — Ray 3.0.0.dev0





































































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  Ray
 




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   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  








     Getting Started
    



     Key Concepts
    



     User Guides
    



     Ray Tune Examples
    



     Ray Tune FAQ
    



     Ray Tune API
    








       Tune Execution (tune.Tuner)
      



       Tune Experiment Results (tune.ResultGrid)
      



       Training in Tune (tune.Trainable, train.report)
      



       Tune Search Space API
      



       Tune Search Algorithms (tune.search)
      



       Tune Trial Schedulers (tune.schedulers)


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Tune Trial Schedulers (tune.schedulers)
      



       Tune Stopping Mechanisms (tune.stopper)
      



       Tune Console Output (Reporters)
      



       Syncing in Tune (train.SyncConfig)
      



       Tune Loggers (tune.logger)
      



       Tune Callbacks (tune.Callback)
      



       Environment variables used by Ray Tune
      



       External library integrations for Ray Tune
      



       Tune Internals
      



       Tune Client API
      



       Tune CLI (Experimental)
      







   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  



   Glossary
  



   Security
  







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Ray Tune API#


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Ray Tune API









Ray Tune API#

Tip
We’d love to hear your feedback on using Tune - get in touch!

This section contains a reference for the Tune API. If there is anything missing, please open an issue
on Github.


Tune Execution (tune.Tuner)
Tuner
tune.run_experiments


Tune Experiment Results (tune.ResultGrid)
ResultGrid (tune.ResultGrid)
Result (train.Result)
ExperimentAnalysis (tune.ExperimentAnalysis)


Training in Tune (tune.Trainable, train.report)
Function Trainable API
Class Trainable API
Comparing Tune’s Function API and Class API
Advanced Resource Allocation
Function API
Trainable (Class API)
Tune Trainable Utilities


Tune Search Space API
Random Distributions API
Grid Search and Custom Function APIs
References


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Tune Search Space API
Random Distributions API
Grid Search and Custom Function APIs
References


Tune Search Algorithms (tune.search)
Saving and Restoring Tune Search Algorithms
Random search and grid search (tune.search.basic_variant.BasicVariantGenerator)
Ax (tune.search.ax.AxSearch)
Bayesian Optimization (tune.search.bayesopt.BayesOptSearch)
BOHB (tune.search.bohb.TuneBOHB)
BlendSearch (tune.search.flaml.BlendSearch)
CFO (tune.search.flaml.CFO)
Dragonfly (tune.search.dragonfly.DragonflySearch)
HEBO (tune.search.hebo.HEBOSearch)
HyperOpt (tune.search.hyperopt.HyperOptSearch)
Nevergrad (tune.search.nevergrad.NevergradSearch)
Optuna (tune.search.optuna.OptunaSearch)
SigOpt (tune.search.sigopt.SigOptSearch)
Scikit-Optimize (tune.search.skopt.SkOptSearch)
ZOOpt (tune.search.zoopt.ZOOptSearch)
Repeated Evaluations (tune.search.Repeater)
ConcurrencyLimiter (tune.search.ConcurrencyLimiter)
Custom Search Algorithms (tune.search.Searcher)
Shim Instantiation (tune.create_searcher)


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Tune Trial Schedulers (tune.schedulers)
ASHA (tune.schedulers.ASHAScheduler)
HyperBand (tune.schedulers.HyperBandScheduler)
Median Stopping Rule (tune.schedulers.MedianStoppingRule)
Population Based Training (tune.schedulers.PopulationBasedTraining)
Population Based Training Replay (tune.schedulers.PopulationBasedTrainingReplay)
Population Based Bandits (PB2) (tune.schedulers.pb2.PB2)
BOHB (tune.schedulers.HyperBandForBOHB)
ResourceChangingScheduler
FIFOScheduler (Default Scheduler)
TrialScheduler Interface
Shim Instantiation (tune.create_scheduler)


Tune Stopping Mechanisms (tune.stopper)
Stopper Interface (tune.Stopper)
Tune Built-in Stoppers


Tune Console Output (Reporters)
Reporter Interface (tune.ProgressReporter)
Tune Built-in Reporters


Syncing in Tune (train.SyncConfig)
Tune Syncing Configuration


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Syncing in Tune (train.SyncConfig)
Tune Syncing Configuration


Tune Loggers (tune.logger)
LoggerCallback Interface (tune.logger.LoggerCallback)
Tune Built-in Loggers
MLFlow Integration
Wandb Integration
Comet Integration
Aim Integration
Other Integrations


Tune Callbacks (tune.Callback)
Callback Interface


Environment variables used by Ray Tune
External library integrations for Ray Tune
PyTorch Lightning (tune.integration.pytorch_lightning)
XGBoost (tune.integration.xgboost)
LightGBM (tune.integration.lightgbm)


Tune Internals
TunerInternal
Trial
FunctionTrainable
Registry


Tune Client API
Tune CLI (Experimental)











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Getting Involved / Contributing — Ray 3.0.0.dev0





































































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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  








     API stability
    



     Getting Involved / Contributing
    








       Building Ray from Source
      



       Contributing to the Ray Documentation
      



       How to write code snippets
      



       Testing Autoscaling Locally
      



       Tips for testing Ray programs
      



       Debugging for Ray Developers


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Tips for testing Ray programs
      



       Debugging for Ray Developers
      



       Profiling for Ray Developers
      





     Configuring Ray
    



     Architecture Whitepapers
    





   Glossary
  



   Security
  







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   Contributing to Ray
  



   What can I work on?
  



   Setting up your development environment
  



   Submitting and Merging a Contribution
  



   PR Review Process
  



     For contributors who are in the
     

       ray-project
      

     organization:
    



     For contributors who are not in the
     

       ray-project
      

     organization:
    





   Testing
  



     Testing for Python development
    



     Testing for C++ development
    





   Code Style
  



     Lint and Formatting


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Testing for C++ development
    





   Code Style
  



     Lint and Formatting
    





   Understanding CI test jobs
  



   API compatibility style guide
  



   Becoming a Reviewer
  



   More Resources for Getting Involved
  









Getting Involved / Contributing




 Contents 





   Contributing to Ray
  



   What can I work on?
  



   Setting up your development environment
  



   Submitting and Merging a Contribution
  



   PR Review Process
  



     For contributors who are in the
     

       ray-project
      

     organization:
    



     For contributors who are not in the
     

       ray-project
      

     organization:
    





   Testing
  



     Testing for Python development
    



     Testing for C++ development
    





   Code Style
  



     Lint and Formatting
    





   Understanding CI test jobs
  



   API compatibility style guide
  



   Becoming a Reviewer
  



   More Resources for Getting Involved


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Becoming a Reviewer
  



   More Resources for Getting Involved
  









Check your version!
Things can change quickly, and so does this contributor guide.
To make sure you’ve got the most cutting edge version of this guide,
go check out the
latest version.


Getting Involved / Contributing#


Ray is more than a framework for distributed applications but also an active community of developers,
researchers, and folks that love machine learning.

Tip
Ask questions on our forum! The
community is extremely active in helping people succeed in building their
Ray applications.

You can join (and Star!) us on on GitHub.

Contributing to Ray#
We welcome (and encourage!) all forms of contributions to Ray, including and not limited to:


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Code reviewing of patches and PRs.
Pushing patches.
Documentation and examples.
Community participation in forums and issues.
Code readability and code comments to improve readability.
Test cases to make the codebase more robust.
Tutorials, blog posts, talks that promote the project.
Features and major changes via Ray Enhancement Proposals (REP): https://github.com/ray-project/enhancements



What can I work on?#
We use Github to track issues, feature requests, and bugs. Take a look at the
ones labeled “good first issue” for a place to start.


Setting up your development environment#
To edit the Ray source code, you’ll 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.


Submitting and Merging a Contribution#
There are a couple steps to merge a contribution.


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Submitting and Merging a Contribution#
There are a couple steps to merge a contribution.

First merge the most recent version of master into your development branch.
git remote add upstream https://github.com/ray-project/ray.git
git pull . upstream/master


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Make sure all existing tests and linters pass.
Run setup_hooks.sh to create a git hook that will run the linter before you push your changes.
If introducing a new feature or patching a bug, be sure to add new test cases
in the relevant file in ray/python/ray/tests/.
Document the code. Public functions need to be documented, and remember to provide an usage
example if applicable. See doc/README.md for instructions on editing and building public documentation.
Address comments on your PR. During the review
process you may need to address merge conflicts with other changes. To resolve merge conflicts,
run git pull . upstream/master on your branch (please do not use rebase, as it is less
friendly to the GitHub review tool. All commits will be squashed on merge.)
Reviewers will merge and approve the pull request; be sure to ping them if
the pull request is getting stale.



PR Review Process#

For contributors who are in the ray-project organization:#


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PR Review Process#

For contributors who are in the ray-project organization:#

When you first create a PR, add an reviewer to the assignee section.
Assignees will review your PR and add the @author-action-required label if further actions are required.
Address their comments and remove the @author-action-required label from the PR.
Repeat this process until assignees approve your PR.
Once 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.
Committers will merge the PR once the build is passing.



For contributors who are not in the ray-project organization:#

Your PRs will have assignees shortly. Assignees of PRs will be actively engaging with contributors to merge the PR.
Please actively ping assignees after you address your comments!


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Testing#
Even though we have hooks to run unit tests automatically for each pull request,
we recommend you to run unit tests locally beforehand to reduce reviewers’
burden and speedup review process.
If you are running tests for the first time, you can install the required dependencies with:
pip install -c python/requirements_compiled.txt -r python/requirements/test-requirements.txt



Testing for Python development#
The 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:
# Directly calling `pytest -v ...` may lose import paths.
python -m pytest -v -s python/ray/tests/test_basic.py


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This will run all of the tests in the file. To run a specific test, use the following:
# Directly calling `pytest -v ...` may lose import paths.
python -m pytest -v -s test_file.py::name_of_the_test




Testing for C++ development#
To compile and run all C++ tests, you can run:
bazel test $(bazel query 'kind(cc_test, ...)')


Alternatively, you can also run one specific C++ test. You can use:
bazel test $(bazel query 'kind(cc_test, ...)') --test_filter=ClientConnectionTest --test_output=streamed


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Code Style#
In 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.
For Python documentation, we follow a subset of the Google pydoc format. The following code snippets demonstrate the canonical Ray pydoc formatting:
def ray_canonical_doc_style(param1: int, param2: str) -> bool:
    """First sentence MUST be inline with the quotes and fit on one line.

    Additional explanatory text can be added in paragraphs such as this one.
    Do not introduce multi-line first sentences.

    Examples:
        .. doctest::

            >>> # Provide code examples for key use cases, as possible.
            >>> ray_canonical_doc_style(41, "hello")
            True


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>>> # A second example.
            >>> ray_canonical_doc_style(72, "goodbye")
            False

    Args:
        param1: The first parameter. Do not include the types in the
            docstring. They should be defined only in the signature.
            Multi-line parameter docs should be indented by four spaces.
        param2: The second parameter.

    Returns:
        The return value. Do not include types here.
    """


class RayClass:
    """The summary line for a class docstring should fit on one line.

    Additional explanatory text can be added in paragraphs such as this one.
    Do not introduce multi-line first sentences.

    The __init__ method is documented here in the class level docstring.

    All the public methods and attributes should have docstrings.

    Examples:
        .. testcode::

            obj = RayClass(12, "world")
            obj.increment_attr1()


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obj = RayClass(12, "world")
            obj.increment_attr1()

    Args:
        param1: The first parameter. Do not include the types in the
            docstring. They should be defined only in the signature.
            Multi-line parameter docs should be indented by four spaces.
        param2: The second parameter.
    """

    def __init__(self, param1: int, param2: str):
        #: Public attribute is documented here.
        self.attr1 = param1
        #: Public attribute is documented here.
        self.attr2 = param2

    @property
    def attr3(self) -> str:
        """Public property of the class.

        Properties created with the @property decorator
        should be documented here.
        """
        return "hello"

    def increment_attr1(self) -> None:
        """Class methods are similar to regular functions.

        See above about how to document functions.
        """

        self.attr1 = self.attr1 + 1


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self.attr1 = self.attr1 + 1


See this for more details about how to write code snippets in docstrings.

Lint and Formatting#
We also have tests for code formatting and linting that need to pass before merge.

For Python formatting, install the required dependencies first with:

pip install -r python/requirements/lint-requirements.txt



If developing for C++, you will need clang-format version 12 (download this version of Clang from here)

You can run the following locally:
scripts/format.sh


An output like the following indicates failure:
WARNING: clang-format is not installed!  # This is harmless
From https://github.com/ray-project/ray
 * branch                master     -> FETCH_HEAD
python/ray/util/sgd/tf/tf_runner.py:4:1: F401 'numpy as np' imported but unused  # Below is the failure


In addition, there are other formatting and semantic checkers for components like the following (not included in scripts/format.sh):

Python README format:


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Python README format:

cd python
python setup.py check --restructuredtext --strict --metadata



Python & Docs banned words check

./ci/lint/check-banned-words.sh



Bazel format:

./ci/lint/bazel-format.sh



clang-tidy for C++ lint, requires clang and clang-tidy version 12 to be installed:

./ci/lint/check-git-clang-tidy-output.sh


You can run setup_hooks.sh to create a git hook that will run the linter before you push your changes.



Understanding CI test jobs#
The Ray project automatically runs continuous integration (CI) tests once a PR
is opened using Buildkite with
multiple CI test jobs.
The CI test folder contains all integration test scripts and they
invoke other test scripts via pytest, bazel-based test or other bash
scripts. Some of the examples include:


Raylet integration tests commands:
bazel test //:core_worker_test





Bazel test command:
bazel test --build_tests_only //:all


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Bazel test command:
bazel test --build_tests_only //:all





Ray serving test commands:
pytest python/ray/serve/tests
python python/ray/serve/examples/echo_full.py





If a CI build exception doesn’t appear to be related to your change,
please visit this link to
check recent tests known to be flaky.


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API compatibility style guide#
Ray provides stability guarantees for its public APIs in Ray core and libraries, which are described in the API Stability guide.
It’s hard to fully capture the semantics of API compatibility into a single annotation (for example, public APIs may have “experimental” arguments). For more granular stability contracts, those can be noted in the pydoc (e.g., “the random_shuffle option is experimental”). When possible, experimental arguments should also be prefixed by underscores in Python (e.g., _owner=).
Other recommendations:
In 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 “opt1” below, it’s easier with forced kwargs:
def foo_bar(file, *, opt1=x, opt2=y)
    pass


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For callback APIs, consider adding a **kwargs placeholder as a “forward compatibility placeholder” in case more args need to be passed to the callback in the future, e.g.:
def tune_user_callback(model, score, **future_kwargs):
    pass




Becoming a Reviewer#
We identify reviewers from active contributors. Reviewers are individuals who
not only actively contribute to the project and are also willing
to participate in the code review of new contributions.
A pull request to the project has to be reviewed by at least one reviewer in order to be merged.
There is currently no formal process, but active contributors to Ray will be
solicited by current reviewers.


More Resources for Getting Involved#
Ray is more than a framework for distributed applications but also an active community of developers,
researchers, and folks that love machine learning. Here’s a list of tips for getting involved with the Ray community:


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Join our community Slack to discuss Ray!
Star and follow us on on GitHub.
To post questions or feature requests, check out the Discussion Board!
Follow us and spread the word on Twitter!
Join our Meetup Group to connect with others in the community!
Use the [ray] tag on StackOverflow to ask and answer questions about Ray usage


Note
These tips are based off of the TVM contributor guide.











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RLlib: Industry-Grade Reinforcement Learning — Ray 3.0.0.dev0





































































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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  








     Getting Started with RLlib
    



     Key Concepts
    



     Environments
    



     Algorithms
    



     User Guides
    



     Examples
    



     Ray RLlib API
    





   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  



   Glossary
  



   Security
  







            Theme by the Executable Book Project



































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.rst







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 Contents
    




   RLlib in 60 seconds
  



   Feature Overview
  



   Customizing RLlib
  









RLlib: Industry-Grade Reinforcement Learning




 Contents 





   RLlib in 60 seconds
  



   Feature Overview
  



   Customizing RLlib
  









RLlib: Industry-Grade Reinforcement Learning#


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Customizing RLlib
  









RLlib: Industry-Grade Reinforcement Learning#

RLlib is an open-source library for reinforcement learning (RL),
offering support for
production-level, highly distributed RL workloads while maintaining
unified and simple APIs for a large variety of industry applications.
Whether you would like to train your agents in a multi-agent setup,
purely from offline (historic) datasets, or using externally
connected simulators, RLlib offers a simple solution for each of your decision
making needs.
If you either have your problem coded (in python) as an
RL environment
or own lots of pre-recorded, historic behavioral data to learn from, you will be
up and running in only a few days.
RLlib is already used in production by industry leaders in many different verticals,
such as
climate control,
industrial control,
manufacturing and logistics,
finance,
gaming,
automobile,
robotics,
boat design,
and many others.

RLlib in 60 seconds#


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RLlib in 60 seconds#


It only takes a few steps to get your first RLlib workload
up and running on your laptop.
RLlib does not automatically install a deep-learning framework, but supports
TensorFlow (both 1.x with static-graph and 2.x with eager mode) as well as
PyTorch.
Depending on your needs, make sure to install either TensorFlow or
PyTorch (or both, as shown below):

pip install "ray[rllib]" tensorflow torch


For installation on computers running Apple Silicon (such as M1), please follow instructions
here._
To be able to run our Atari examples, you should also install
pip install "gym[atari]" "gym[accept-rom-license]" atari_py.


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This is all you need to start coding against RLlib.
Here is an example of running a PPO Algorithm on the
Taxi domain.
We first create a config for the algorithm, which sets the right environment, and
defines all training parameters we want.
Next, we build the algorithm and train it for a total of 5 iterations.
A training iteration includes parallel sample collection by the environment workers, as well as loss calculation on the collected batch and a model update.
As a last step, we evaluate the trained Algorithm:
from ray.rllib.algorithms.ppo import PPOConfig

config = (  # 1. Configure the algorithm,
    PPOConfig()
    .environment("Taxi-v3")
    .rollouts(num_rollout_workers=2)
    .framework("torch")
    .training(model={"fcnet_hiddens": [64, 64]})
    .evaluation(evaluation_num_workers=1)
)

algo = config.build()  # 2. build the algorithm,

for _ in range(5):
    print(algo.train())  # 3. train it,

algo.evaluate()  # 4. and evaluate it.


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for _ in range(5):
    print(algo.train())  # 3. train it,

algo.evaluate()  # 4. and evaluate it.


Note that you can use any Farama-Foundation Gymnasium environment as env.
In rollouts you can for instance specify the number of parallel workers to collect samples from the environment.
The framework config lets you choose between “tf2”, “tf” and “torch” for execution.
You can also tweak RLlib’s default model config,and set up a separate config for evaluation.
If you want to learn more about the RLlib training API,
you can learn more about it here.
Also, see here for a simple example on how to write an action inference loop after training.
If you want to get a quick preview of which algorithms and environments RLlib supports,
click on the dropdowns below:


RLlib Algorithms





High-throughput architectures

  Importance Weighted Actor-Learner Architecture (IMPALA)
  Asynchronous Proximal Policy Optimization (APPO)


Gradient-based


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Gradient-based

  Deep Q Networks (DQN, Rainbow, Parametric DQN)
  Proximal Policy Optimization (PPO)
  Soft Actor Critic (SAC)


Model-based / Meta-learning / Offline

 DreamerV3


Offline

  Conservative Q-Learning (CQL)
  Advantage Re-Weighted Imitation Learning (MARWIL)






RLlib Environments





RLlib Environments Overview
Farama-Foundation gymnasium
Vectorized
Multi-Agent and Hierarchical
External Agents and Applications

External Application Clients


Advanced Integrations




Feature Overview#





RLlib Key Concepts


Learn more about the core concepts of RLlib, such as environments, algorithms and
policies.


Key Concepts






RLlib Algorithms


Check out the many available RL algorithms of RLlib for model-free and model-based
RL, on-policy and off-policy training, multi-agent RL, and more.


Algorithms






RLlib Environments


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Algorithms






RLlib Environments


Get started with environments supported by RLlib, such as Farama foundation’s Gymnasium, Petting Zoo,
and many custom formats for vectorized and multi-agent environments.


Environments





The following is a summary of RLlib’s most striking features.
Click on the images below to see an example script for each of the listed features:





The most popular deep-learning frameworks: PyTorch and TensorFlow
(tf1.x/2.x static-graph/eager/traced).







Highly distributed learning: Our RLlib algorithms (such as our “PPO” or “IMPALA”)
allow you to set the num_workers config parameter, such that your workloads can run
on 100s of CPUs/nodes thus parallelizing and speeding up learning.


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Vectorized (batched) and remote (parallel) environments: RLlib auto-vectorizes
your gym.Envs via the num_envs_per_worker config. Environment workers can
then batch and thus significantly speedup the action computing forward pass.
On top of that, RLlib offers the remote_worker_envs config to create
single environments (within a vectorized one) as ray Actors,
thus parallelizing even the env stepping process.








Multi-agent RL (MARL): Convert your (custom) gym.Envs into a multi-agent one
via a few simple steps and start training your agents in any of the following fashions:
1) Cooperative with shared or
separate
policies and/or value functions.
2) Adversarial scenarios using self-play
and league-based training.
3) Independent learning
of neutral/co-existing agents.


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External simulators: Don’t have your simulation running as a gym.Env in python?
No problem! RLlib supports an external environment API and comes with a pluggable,
off-the-shelve
client/
server
setup that allows you to run 100s of independent simulators on the “outside”
(e.g. a Windows cloud) connecting to a central RLlib Policy-Server that learns
and serves actions. Alternatively, actions can be computed on the client side
to save on network traffic.







Offline RL and imitation learning/behavior cloning: You don’t have a simulator
for your particular problem, but tons of historic data recorded by a legacy (maybe
non-RL/ML) system? This branch of reinforcement learning is for you!
RLlib’s comes with several offline RL
algorithms (CQL, MARWIL, and DQfD), allowing you to either purely
behavior-clone
your existing system or learn how to further improve over it.


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Customizing RLlib#
RLlib provides simple APIs to customize all aspects of your training- and experimental workflows.
For example, you may code your own environments
in python using Farama-Foundation’s gymnasium or DeepMind’s OpenSpiel, provide custom
TensorFlow/Keras- or ,
Torch models, write your own
policy- and loss definitions, or define
custom exploratory behavior.
Via mapping one or more agents in your environments to (one or more) policies, multi-agent
RL (MARL) becomes an easy-to-use low-level primitive for our users.


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RLlib’s API stack: Built on top of Ray, RLlib offers off-the-shelf, highly distributed
algorithms, policies, loss functions, and default models (including the option to
auto-wrap a neural network with an LSTM or an attention net). Furthermore, our library
comes with a built-in Server/Client setup, allowing you to connect
hundreds of external simulators (clients) via the network to an RLlib server process,
which provides learning functionality and serves action queries. User customizations
are realized via sub-classing the existing abstractions and - by overriding certain
methods in those sub-classes - define custom behavior.#














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Ray Use Cases — Ray 3.0.0.dev0





































































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Toggle in-page Table of Contents












Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  








     Ray for ML Infrastructure
    





   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  



   Glossary
  



   Security
  







            Theme by the Executable Book Project



































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.rst







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 Contents
    




   LLMs and Gen AI
  



   Batch Inference
  



   Many Model Training


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Contents
    




   LLMs and Gen AI
  



   Batch Inference
  



   Many Model Training
  



     How do I do many model training on Ray?
    





   Model Serving
  



   Hyperparameter Tuning
  



   Distributed Training
  



   Reinforcement Learning
  



   ML Platform
  



   End-to-End ML Workflows
  



   Large Scale Workload Orchestration
  









Ray Use Cases




 Contents 





   LLMs and Gen AI
  



   Batch Inference
  



   Many Model Training
  



     How do I do many model training on Ray?
    





   Model Serving
  



   Hyperparameter Tuning
  



   Distributed Training
  



   Reinforcement Learning
  



   ML Platform
  



   End-to-End ML Workflows
  



   Large Scale Workload Orchestration
  









Ray Use Cases#
This page indexes common Ray use cases for scaling ML.
It contains highlighted references to blogs, examples, and tutorials also located
elsewhere in the Ray documentation.


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LLMs and Gen AI#
Large 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.




Explore LLMs and Gen AI examples


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Explore LLMs and Gen AI examples

Batch Inference#
Batch inference is the process of generating model predictions on a large “batch” of input data.
Ray for batch inference works with any cloud provider and ML framework,
and is fast and cheap for modern deep learning applications.
It scales from single machines to large clusters with minimal code changes.
As a Python-first framework, you can easily express and interactively develop your inference workloads in Ray.
To learn more about running batch inference with Ray, see the batch inference guide.




Explore batch inference examples


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Explore batch inference examples

Many Model Training#
Many 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.
The focus is on training many models on subsets of a dataset. This is in contrast to training a single model on the entire dataset.
When 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.



Data parallelism pattern for distributed training on large datasets.#


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Data parallelism pattern for distributed training on large datasets.#



How do I do many model training on Ray?#
To train multiple independent models, use the Ray Tune (Tutorial) library. This is the recommended library for most cases.
You can use Tune with your current data preprocessing pipeline if your data source fits into the memory of a single machine (node).
If you need to scale your data, or you want to plan for future scaling, use the Ray Data library.
Your data must be a supported format, to use Ray Data.
Alternative solutions exist for less common cases:

If 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.
If you have a large preprocessing pipeline, you can use the Ray Data library to train multiple models (Tutorial).


Explore model training examples


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Explore model training examples


Model Serving#
Ray 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.
It 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.



Deployment patterns with Ray Serve. (Click image to enlarge.)#


Learn more about model serving with the following resources.

[Talk] Productionizing ML at Scale with Ray Serve
[Blog] Simplify your MLOps with Ray & Ray Serve
[Guide] Getting Started with Ray Serve
[Guide] Model Composition in Serve
[Gallery] Serve Examples Gallery
[Gallery] More Serve Use Cases on the Blog


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Hyperparameter Tuning#
The Ray Tune library enables any parallel Ray workload to be run under a hyperparameter tuning algorithm.
Running 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.



Distributed tuning with distributed training per trial.#


Learn more about the Tune library with the following talks and user guides.

[Guide] Getting Started with Ray Tune
[Blog] How to distribute hyperparameter tuning with Ray Tune
[Talk] Simple Distributed Hyperparameter Optimization
[Blog] Hyperparameter Search with 🤗 Transformers
[Gallery] Ray Tune Examples Gallery
More Tune use cases on the Blog


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Distributed Training#
The Ray Train library integrates many distributed training frameworks under a simple Trainer API,
providing distributed orchestration and management capabilities out of the box.
In 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.



Model parallelism pattern for distributed large model training.#


Learn more about the Train library with the following talks and user guides.

[Talk] Ray Train, PyTorch, TorchX, and distributed deep learning
[Blog] Elastic Distributed Training with XGBoost on Ray
[Guide] Getting Started with Ray Train
[Example] Fine-tune a 🤗 Transformers model
[Gallery] Ray Train Examples Gallery
[Gallery] More Train Use Cases on the Blog


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Reinforcement Learning#
RLlib is an open-source library for reinforcement learning (RL), offering support for production-level, highly distributed RL workloads while maintaining unified and simple APIs for a large variety of industry applications. RLlib is used by industry leaders in many different verticals, such as climate control, industrial control, manufacturing and logistics, finance, gaming, automobile, robotics, boat design, and many others.



Decentralized distributed proximal polixy optimiation (DD-PPO) architecture.#


Learn more about reinforcement learning with the following resources.

[Course] Applied Reinforcement Learning with RLlib
[Blog] Intro to RLlib: Example Environments
[Guide] Getting Started with RLlib
[Talk] Deep reinforcement learning at Riot Games
[Gallery] RLlib Examples Gallery
[Gallery] More RL Use Cases on the Blog


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ML Platform#
Ray and its AI libraries provide unified compute runtime for teams looking to simplify their ML platform.
Ray’s libraries such as Ray Train, Ray Data, and Ray Serve can be used to compose end-to-end ML workflows, providing features and APIs for
data preprocessing as part of training, and transitioning from training to serving.
Read more about building ML platforms with Ray in this section.


End-to-End ML Workflows#
The following highlights examples utilizing Ray AI libraries to implement end-to-end ML workflows.

[Example] Text classification with Ray
[Example] Object detection with Ray
[Example] Machine learning on tabular data
[Example] AutoML for Time Series with Ray



Large Scale Workload Orchestration#
The following highlights feature projects leveraging Ray Core’s distributed APIs to simplify the orchestration of large scale workloads.


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[Blog] Highly Available and Scalable Online Applications on Ray at Ant Group
[Blog] Ray Forward 2022 Conference: Hyper-scale Ray Application Use Cases
[Blog] A new world record on the CloudSort benchmark using Ray
[Example] Speed up your web crawler by parallelizing it with Ray













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Installing Ray — Ray 3.0.0.dev0






































































Toggle navigation sidebar




Toggle in-page Table of Contents












Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  



   Glossary
  



   Security
  







            Theme by the Executable Book Project



































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.rst







.pdf















 Contents
    




   Official Releases
  



     From Wheels
    





   Daily Releases (Nightlies)
  



   Installing from a specific commit


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Daily Releases (Nightlies)
  



   Installing from a specific commit
  



   Install Ray Java with Maven
  



   Install Ray C++
  



   M1 Mac (Apple Silicon) Support
  



   Windows Support
  



   Installing Ray on Arch Linux
  



   Installing From conda-forge
  



   Building Ray from Source
  



   Docker Source Images
  



     Launch Ray in Docker
    



     Test if the installation succeeded
    



     Installed Python dependencies
    











Installing Ray




 Contents 





   Official Releases
  



     From Wheels
    





   Daily Releases (Nightlies)
  



   Installing from a specific commit
  



   Install Ray Java with Maven
  



   Install Ray C++
  



   M1 Mac (Apple Silicon) Support
  



   Windows Support
  



   Installing Ray on Arch Linux
  



   Installing From conda-forge
  



   Building Ray from Source
  



   Docker Source Images
  



     Launch Ray in Docker
    



     Test if the installation succeeded


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Launch Ray in Docker
    



     Test if the installation succeeded
    



     Installed Python dependencies
    











Installing Ray#
Ray currently officially supports x86_64, aarch64 (ARM) for Linux, and Apple silicon (M1) hardware.
Ray on Windows is currently in beta.

Official Releases#

From Wheels#
You can install the latest official version of Ray from PyPI on Linux, Windows,
and macOS by choosing the option that best matches your use case.



Recommended
For machine learning applications
pip install -U "ray[data,train,tune,serve]"

# For reinforcement learning support, install RLlib instead.
# pip install -U "ray[rllib]"


For general Python applications
pip install -U "ray[default]"

# If you don't want Ray Dashboard or Cluster Launcher, install Ray with minimal dependencies instead.
# pip install -U "ray"





Advanced






Command
Installed components



pip install -U "ray"
Core

pip install -U "ray[default]"
Core, Dashboard, Cluster Launcher


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pip install -U "ray"
Core

pip install -U "ray[default]"
Core, Dashboard, Cluster Launcher

pip install -U "ray[data]"
Core, Data

pip install -U "ray[train]"
Core, Train

pip install -U "ray[tune]"
Core, Tune

pip install -U "ray[serve]"
Core, Dashboard, Cluster Launcher, Serve

pip install -U "ray[serve-grpc]"
Core, Dashboard, Cluster Launcher, Serve with gRPC support

pip install -U "ray[rllib]"
Core, Tune, RLlib

pip install -U "ray[all]"
Core, Dashboard, Cluster Launcher, Data, Train, Tune, Serve, RLlib




Tip
You can combine installation extras.
For example, to install Ray with Dashboard, Cluster Launcher, and Train support, you can run:
pip install -U "ray[default,train]"


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Daily Releases (Nightlies)#
You can install the nightly Ray wheels via the following links. These daily releases are tested via automated tests but do not go through the full release process. To install these wheels, use the following pip command and wheels:
# Clean removal of previous install
pip uninstall -y ray
# Install Ray with support for the dashboard + cluster launcher
pip install -U "ray[default] @ LINK_TO_WHEEL.whl"

# Install Ray with minimal dependencies
# pip install -U LINK_TO_WHEEL.whl





Linux






Linux (x86_64)
Linux (arm64/aarch64)



Linux Python 3.10 (x86_64)
Linux Python 3.10 (aarch64)

Linux Python 3.9 (x86_64)
Linux Python 3.9 (aarch64)

Linux Python 3.8 (x86_64)
Linux Python 3.8 (aarch64)

Linux Python 3.7 (x86_64)
Linux Python 3.7 (aarch64)

Linux Python 3.11 (x86_64) (EXPERIMENTAL)
Linux Python 3.11 (aarch64) (EXPERIMENTAL)






MacOS






MacOS (x86_64)
MacOS (arm64)



MacOS Python 3.10 (x86_64)
MacOS Python 3.10 (arm64)


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MacOS






MacOS (x86_64)
MacOS (arm64)



MacOS Python 3.10 (x86_64)
MacOS Python 3.10 (arm64)

MacOS Python 3.9 (x86_64)
MacOS Python 3.9 (arm64)

MacOS Python 3.8 (x86_64)
MacOS Python 3.8 (arm64)

MacOS Python 3.7 (x86_64)
MacOS Python 3.11 (arm64) (EXPERIMENTAL)

MacOS Python 3.11 (x86_64) (EXPERIMENTAL)







Windows (beta)





Windows (beta)



Windows Python 3.10

Windows Python 3.9

Windows Python 3.8

Windows Python 3.7

Windows Python 3.11 (EXPERIMENTAL)






Note
On Windows, support for multi-node Ray clusters is currently experimental and untested.
If you run into issues please file a report at https://github.com/ray-project/ray/issues.


Note
Usage stats collection is enabled by default (can be disabled) for nightly wheels including both local clusters started via ray.init() and remote clusters via cli.


Note
Python 3.11 support is experimental.


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Note
Python 3.11 support is experimental.



Installing from a specific commit#
You can install the Ray wheels of any particular commit on master with the following template. You need to specify the commit hash, Ray version, Operating System, and Python version:
pip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/{COMMIT_HASH}/ray-{RAY_VERSION}-{PYTHON_VERSION}-{PYTHON_VERSION}-{OS_VERSION}.whl


For example, here are the Ray 3.0.0.dev0 wheels for Python 3.9, MacOS for commit 4f2ec46c3adb6ba9f412f09a9732f436c4a5d0c9:
pip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/4f2ec46c3adb6ba9f412f09a9732f436c4a5d0c9/ray-3.0.0.dev0-cp39-cp39-macosx_10_15_x86_64.whl


There are minor variations to the format of the wheel filename; it’s best to match against the format in the URLs listed in the Nightlies section.
Here’s a summary of the variations:

For MacOS, commits predating August 7, 2021 will have macosx_10_13 in the filename instead of macosx_10_15.


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Install Ray Java with Maven#
Before installing Ray Java with Maven, you should install Ray Python with pip install -U ray . Note that the versions of Ray Java and Ray Python must match.
Note that nightly Ray python wheels are also required if you want to install Ray Java snapshot version.
The latest Ray Java release can be found in central repository. To use the latest Ray Java release in your application, add the following entries in your pom.xml:
<dependency>
  <groupId>io.ray</groupId>
  <artifactId>ray-api</artifactId>
  <version>${ray.version}</version>
</dependency>
<dependency>
  <groupId>io.ray</groupId>
  <artifactId>ray-runtime</artifactId>
  <version>${ray.version}</version>
</dependency>


key: chroma:document
string_value:
The latest Ray Java snapshot can be found in sonatype repository. To use the latest Ray Java snapshot in your application, add the following entries in your pom.xml:
<!-- only needed for snapshot version of ray -->
<repositories>
  <repository>
    <id>sonatype</id>
    <url>https://oss.sonatype.org/content/repositories/snapshots/</url>
    <releases>
      <enabled>false</enabled>
    </releases>
    <snapshots>
      <enabled>true</enabled>
    </snapshots>
  </repository>
</repositories>

<dependencies>
  <dependency>
    <groupId>io.ray</groupId>
    <artifactId>ray-api</artifactId>
    <version>${ray.version}</version>
  </dependency>
  <dependency>
    <groupId>io.ray</groupId>
    <artifactId>ray-runtime</artifactId>
    <version>${ray.version}</version>
  </dependency>
</dependencies>


key: chroma:document
string_value:
Note
When you run pip install to install Ray, Java jars are installed as well. The above dependencies are only used to build your Java code and to run your code in local mode.
If you want to run your Java code in a multi-node Ray cluster, it’s better to exclude Ray jars when packaging your code to avoid jar conficts if the versions (installed Ray with pip install and maven dependencies) don’t match.



Install Ray C++#
You can install and use Ray C++ API as follows.
pip install -U ray[cpp]

# Create a Ray C++ project template to start with.
ray cpp --generate-bazel-project-template-to ray-template



Note
If you build Ray from source, remove the build option build --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" from the file cpp/example/.bazelrc before running your application. The related issue is this.



M1 Mac (Apple Silicon) Support#
Ray supports machines running Apple Silicon (such as M1 macs).
Multi-node clusters are untested. To get started with local Ray development:


key: chroma:document
string_value:
Install miniforge.

wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
bash Miniforge3-MacOSX-arm64.sh
rm Miniforge3-MacOSX-arm64.sh # Cleanup.


Ensure you’re using the miniforge environment (you should see (base) in your terminal).

source ~/.bash_profile
conda activate


Install Ray as you normally would.

pip install ray





Windows Support#
Windows support is in Beta. Ray supports running on Windows with the following caveats (only the first is
Ray-specific, the rest are true anywhere Windows is used):


key: chroma:document
string_value:
Multi-node Ray clusters are untested.
Filenames are tricky on Windows and there still may be a few places where Ray
assumes UNIX filenames rather than Windows ones. This can be true in downstream
packages as well.
Performance on Windows is known to be slower since opening files on Windows
is considerably slower than on other operating systems. This can affect logging.
Windows does not have a copy-on-write forking model, so spinning up new
processes can require more memory.

Submit any issues you encounter to
GitHub.


Installing Ray on Arch Linux#
Note: Installing Ray on Arch Linux is not tested by the Project Ray developers.
Ray is available on Arch Linux via the Arch User Repository (AUR) as
python-ray.
You can manually install the package by following the instructions on the
Arch Wiki or use an AUR helper like yay (recommended for ease of install)
as follows:
yay -S python-ray


key: chroma:document
string_value:
To discuss any issues related to this package refer to the comments section
on the AUR page of python-ray here.


Installing From conda-forge#
Ray can also be installed as a conda package on Linux and Windows.
# also works with mamba
conda create -c conda-forge python=3.9 -n ray
conda activate ray

# Install Ray with support for the dashboard + cluster launcher
conda install -c conda-forge "ray-default"

# Install Ray with minimal dependencies
# conda install -c conda-forge ray


To install Ray libraries, use pip as above or conda/mamba.
conda install -c conda-forge "ray-data"   # installs Ray + dependencies for Ray Data
conda install -c conda-forge "ray-train"  # installs Ray + dependencies for Ray Train
conda install -c conda-forge "ray-tune"   # installs Ray + dependencies for Ray Tune
conda install -c conda-forge "ray-serve"  # installs Ray + dependencies for Ray Serve
conda install -c conda-forge "ray-rllib"  # installs Ray + dependencies for Ray RLlib


key: chroma:document
string_value:
For a complete list of available ray libraries on Conda-forge, have a look
at https://anaconda.org/conda-forge/ray-default

Note
Ray conda packages are maintained by the community, not the Ray team. While
using a conda environment, it is recommended to install Ray from PyPi using
pip install ray in the newly created environment.



Building Ray from Source#
Installing from pip should be sufficient for most Ray users.
However, should you need to build from source, follow these instructions for building Ray.


Docker Source Images#
Most users should pull a Docker image from the Ray Docker Hub.

The rayproject/ray images include Ray and all required dependencies. It comes with anaconda and various versions of Python.
The rayproject/ray-ml images include the above as well as many additional ML libraries.
The rayproject/base-deps and rayproject/ray-deps images are for the Linux and Python dependencies respectively.


key: chroma:document
string_value:
Images are tagged with the format {Ray version}[-{Python version}][-{Platform}]. Ray version tag can be one of the following:






Ray version tag
Description



latest
The most recent Ray release.

x.y.z
A specific Ray release, e.g. 1.12.1

nightly
The most recent Ray development build (a recent commit from Github master)

6 character Git SHA prefix
A specific development build (uses a SHA from the Github master, e.g. 8960af).



The optional Python version tag specifies the Python version in the image. All Python versions supported by Ray are available, e.g. py38, py39 and py310. If unspecified, the tag points to an image using Python 3.8.
The optional Platform tag specifies the platform where the image is intended for:






Platform tag
Description



-cpu
These are based off of an Ubuntu image.

-cuXX
These are based off of an NVIDIA CUDA image with the specified CUDA version. They require the Nvidia Docker Runtime.

-gpu
Aliases to a specific -cuXX tagged image.


key: chroma:document
string_value:
-gpu
Aliases to a specific -cuXX tagged image.

<no tag>
Aliases to -cpu tagged images. For ray-ml image, aliases to -gpu tagged image.



Example: for the nightly image based on Python 3.8 and without GPU support, the tag is nightly-py38-cpu.
If you want to tweak some aspect of these images and build them locally, refer to the following script:
cd ray
./build-docker.sh


Beyond creating the above Docker images, this script can also produce the following two images.

The rayproject/development image has the ray source code included and is setup for development.
The rayproject/examples image adds additional libraries for running examples.

Review images by listing them:
docker images


key: chroma:document
string_value:
Review images by listing them:
docker images


Output should look something like the following:
REPOSITORY                          TAG                 IMAGE ID            CREATED             SIZE
rayproject/ray                      latest              7243a11ac068        2 days ago          1.11 GB
rayproject/ray-deps                 latest              b6b39d979d73        8 days ago          996  MB
rayproject/base-deps                latest              5606591eeab9        8 days ago          512  MB
ubuntu                              focal               1e4467b07108        3 weeks ago         73.9 MB



Launch Ray in Docker#
Start out by launching the deployment container.
docker run --shm-size=<shm-size> -t -i rayproject/ray


key: chroma:document
string_value:
Replace <shm-size> with a limit appropriate for your system, for example
512M or 2G. A good estimate for this is to use roughly 30% of your available memory (this is
what Ray uses internally for its Object Store). The -t and -i options here are required to support
interactive use of the container.
If you use a GPU version Docker image, remember to add --gpus all option. Replace <ray-version> with your target ray version in the following command:
docker run --shm-size=<shm-size> -t -i --gpus all rayproject/ray:<ray-version>-gpu


Note: Ray requires a large amount of shared memory because each object
store keeps all of its objects in shared memory, so the amount of shared memory
will limit the size of the object store.
You should now see a prompt that looks something like:
root@ebc78f68d100:/ray#


key: chroma:document
string_value:
Test if the installation succeeded#
To test if the installation was successful, try running some tests. This assumes
that you’ve cloned the git repository.
python -m pytest -v python/ray/tests/test_mini.py




Installed Python dependencies#
Our docker images are shipped with pre-installed Python dependencies
required for Ray and its libraries.
We publish the dependencies that are installed in our ray and ray-ml
Docker images for Python 3.9.


key: chroma:document
string_value:
ray (Python 3.9)ray-ml (Python 3.9)Ray version: nightly (7b8ec8a)
adal==1.2.7
aiohttp==3.8.6
aiohttp-cors==0.7.0
aiorwlock==1.3.0
aiosignal==1.3.1
anyio==3.7.1
applicationinsights==0.11.10
argcomplete==1.12.3
async-timeout==4.0.3
attrs==21.4.0
azure-cli-core==2.40.0
azure-cli-telemetry==1.0.8
azure-common==1.1.28
azure-core==1.29.5
azure-identity==1.10.0
azure-mgmt-compute==23.1.0
azure-mgmt-core==1.4.0
azure-mgmt-network==19.0.0
azure-mgmt-resource==20.0.0
backoff==1.10.0
bcrypt==4.0.1
blessed==1.20.0
boltons @ file:///croot/boltons_1677628692245/work
boto3==1.26.76
botocore==1.29.165
Brotli @ file:///tmp/abs_ecyw11_7ze/croots/recipe/brotli-split_1659616059936/work
cachetools==5.3.2
certifi @ file:///croot/certifi_1690232220950/work/certifi
cffi @ file:///tmp/abs_98z5h56wf8/croots/recipe/cffi_1659598650955/work
charset-normalizer==3.3.1
click==8.1.7
cloudpickle==2.2.0
colorful==0.5.5
commonmark==0.9.1
conda @ file:///croot/conda_1696257509808/work


key: chroma:document
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cloudpickle==2.2.0
colorful==0.5.5
commonmark==0.9.1
conda @ file:///croot/conda_1696257509808/work
conda-content-trust @ file:///tmp/abs_5952f1c8-355c-4855-ad2e-538535021ba5h26t22e5/croots/recipe/conda-content-trust_1658126371814/work
conda-package-handling @ file:///croot/conda-package-handling_1690999929514/work
conda_package_streaming @ file:///croot/conda-package-streaming_1690987966409/work
cryptography==38.0.1
Cython==0.29.32
distlib==0.3.7
dm-tree==0.1.8
exceptiongroup==1.1.3
Farama-Notifications==0.0.4
fastapi==0.104.0
filelock==3.13.1
flatbuffers==23.5.26
flit_core @ file:///opt/conda/conda-bld/flit-core_1644941570762/work/source/flit_core
frozenlist==1.4.0
fsspec==2023.5.0
google-api-core==2.12.0
google-api-python-client==1.7.8
google-auth==2.23.4
google-auth-httplib2==0.1.1
google-oauth==1.0.1
googleapis-common-protos==1.61.0
gpustat==1.1.1
grpcio==1.59.2
gymnasium==0.28.1
h11==0.12.0
httplib2==0.22.0
httptools==0.6.1
humanfriendly==10.0


key: chroma:document
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grpcio==1.59.2
gymnasium==0.28.1
h11==0.12.0
httplib2==0.22.0
httptools==0.6.1
humanfriendly==10.0
idna @ file:///croot/idna_1666125576474/work
imageio==2.31.1
importlib-metadata==4.10.0
isodate==0.6.1
jax-jumpy==1.0.0
jmespath==1.0.1
jsonpatch @ file:///tmp/build/80754af9/jsonpatch_1615747632069/work
jsonpointer==2.1
jsonschema==4.17.3
knack==0.10.1
lazy_loader==0.3
lz4==4.3.2
msal==1.18.0b1
msal-extensions==1.0.0
msgpack==1.0.7
msrest==0.7.1
msrestazure==0.6.4
multidict==6.0.4
networkx==3.1
numpy==1.26.1
nvidia-ml-py==12.535.108
oauthlib==3.2.2
opencensus==0.11.3
opencensus-context==0.1.3
opentelemetry-api==1.1.0
opentelemetry-exporter-otlp==1.1.0
opentelemetry-exporter-otlp-proto-grpc==1.1.0
opentelemetry-proto==1.1.0
opentelemetry-sdk==1.1.0
opentelemetry-semantic-conventions==0.20b0
packaging==21.3
pandas==1.5.3
paramiko==2.12.0
Pillow==9.2.0
pkginfo==1.9.6
platformdirs==3.11.0
pluggy @ file:///tmp/build/80754af9/pluggy_1648024445381/work
portalocker==2.8.2


key: chroma:document
string_value:
pluggy @ file:///tmp/build/80754af9/pluggy_1648024445381/work
portalocker==2.8.2
prometheus-client==0.18.0
protobuf==3.19.6
psutil==5.9.6
py-spy==0.3.14
pyarrow==12.0.1
pyasn1==0.5.0
pyasn1-modules==0.3.0
pycosat @ file:///croot/pycosat_1696536503704/work
pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work
pydantic==1.10.12
Pygments==2.13.0
PyJWT==2.8.0
PyNaCl==1.5.0
pyOpenSSL==22.1.0
pyparsing==3.1.1
pyrsistent==0.20.0
PySocks @ file:///tmp/build/80754af9/pysocks_1605305812635/work
python-dateutil==2.8.2
python-dotenv==1.0.0
pytz==2022.7.1
PyWavelets==1.4.1
PyYAML==6.0.1
ray @ file:///home/ray/ray-3.0.0.dev0-cp39-cp39-manylinux2014_x86_64.whl#sha256=85cf93d21b91ecef4cf6935194d03ca0c3f907d76267ecd5ad1c6fa6c768717b
redis==3.5.3
requests @ file:///croot/requests_1690400202158/work
requests-oauthlib==1.3.1
rich==12.6.0
rsa==4.7.2
ruamel.yaml @ file:///croot/ruamel.yaml_1666304550667/work
ruamel.yaml.clib @ file:///croot/ruamel.yaml.clib_1666302247304/work


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string_value:
ruamel.yaml.clib @ file:///croot/ruamel.yaml.clib_1666302247304/work
s3transfer==0.6.2
scikit-image==0.21.0
scipy==1.10.1
six==1.16.0
smart-open==6.2.0
sniffio==1.3.0
starlette==0.27.0
tabulate==0.9.0
tensorboardX==2.6
tifffile==2023.7.10
tqdm @ file:///croot/tqdm_1679561862951/work
typer==0.9.0
typing_extensions==4.8.0
uritemplate==3.0.1
urllib3 @ file:///croot/urllib3_1698257533958/work
uvicorn==0.22.0
uvloop==0.19.0
virtualenv==20.21.0
watchfiles==0.19.0
wcwidth==0.2.9
websockets==11.0.3
yarl==1.9.2
zipp==3.17.0
zstandard @ file:///croot/zstandard_1677013143055/work


key: chroma:document
string_value:
Ray version: nightly (7b8ec8a)
about-time==4.2.1
absl-py==1.4.0
accelerate==0.20.3
adal==1.2.7
aim==3.17.5
aim-ui==3.17.5
aimrecords==0.0.7
aimrocks==0.4.0
aioboto3==11.2.0
aiobotocore==2.5.0
aiofiles==22.1.0
aiohttp==3.8.6
aiohttp-cors==0.7.0
aioitertools==0.11.0
aiorwlock==1.3.0
aiosignal==1.3.1
aiosqlite==0.19.0
ale-py==0.8.1
alembic==1.12.1
alive-progress==3.1.4
anyio==3.7.1
applicationinsights==0.11.10
argcomplete==1.12.3
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
array-record==0.4.0
arrow==1.3.0
asttokens==2.4.1
astunparse==1.6.3
async-timeout==4.0.3
attrs==21.4.0
autograd==1.6.2
AutoROM==0.6.1
AutoROM.accept-rom-license==0.6.1
ax-platform==0.3.2
azure-cli-core==2.40.0
azure-cli-telemetry==1.0.8
azure-common==1.1.28
azure-core==1.29.5
azure-identity==1.10.0
azure-mgmt-compute==23.1.0
azure-mgmt-core==1.4.0
azure-mgmt-network==19.0.0
azure-mgmt-resource==20.0.0
Babel==2.13.1
backcall==0.2.0
backoff==1.10.0
base58==2.0.1
bayesian-optimization==1.4.3
bcrypt==4.0.1


key: chroma:document
string_value:
backcall==0.2.0
backoff==1.10.0
base58==2.0.1
bayesian-optimization==1.4.3
bcrypt==4.0.1
beautifulsoup4==4.11.1
bleach==6.1.0
blessed==1.20.0
bokeh==2.4.3
boltons @ file:///croot/boltons_1677628692245/work
boto==2.49.0
boto3==1.26.76
botocore==1.29.76
botorch==0.8.5
Brotli @ file:///tmp/abs_ecyw11_7ze/croots/recipe/brotli-split_1659616059936/work
build==1.0.3
cached-property==1.5.2
cachetools==5.3.2
catboost==1.2.2
certifi @ file:///croot/certifi_1690232220950/work/certifi
cffi==1.16.0
charset-normalizer==3.3.1
chess==1.7.0
chex==0.1.7
click==8.1.7
cloudpickle==2.2.0
cma==3.2.2
cmaes==0.10.0
cmake==3.27.7
colorama==0.4.6
coloredlogs==15.0.1
colorful==0.5.5
colorlog==6.7.0
comet-ml==3.31.9
comm==0.1.4
commonmark==0.9.1
conda @ file:///croot/conda_1696257509808/work
conda-content-trust @ file:///tmp/abs_5952f1c8-355c-4855-ad2e-538535021ba5h26t22e5/croots/recipe/conda-content-trust_1658126371814/work
conda-package-handling @ file:///croot/conda-package-handling_1690999929514/work


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string_value:
conda-package-handling @ file:///croot/conda-package-handling_1690999929514/work
conda_package_streaming @ file:///croot/conda-package-streaming_1690987966409/work
configobj==5.0.8
ConfigSpace==0.7.1
contourpy==1.1.1
crc32c==2.3
crcmod==1.7
cryptography==38.0.1
cycler==0.12.1
Cython==0.29.32
dask==2022.10.1
databricks-cli==0.18.0
datasets==2.0.0
debugpy==1.8.0
decorator==5.1.1
deepspeed==0.8.3
defusedxml==0.7.1
Deprecated==1.2.14
dill==0.3.7
distlib==0.3.7
distributed==2022.10.1
dm-control==1.0.12
dm-env==1.6
dm-tree==0.1.8
docker==6.1.3
docker-pycreds==0.4.0
dopamine-rl==4.0.5
dragonfly-opt==0.1.7
dulwich==0.21.6
entrypoints==0.4
etils==1.3.0
evaluate==0.4.0
everett==3.2.0
exceptiongroup==1.1.3
executing==2.0.1
fairscale==0.4.6
Farama-Notifications==0.0.4
fastapi==0.104.0
fasteners==0.19
fastjsonschema==2.18.1
filelock==3.13.1
FLAML==1.1.1
Flask==2.1.3
Flask-Cors==4.0.0
flatbuffers==2.0.7
flax==0.7.2


key: chroma:document
string_value:
filelock==3.13.1
FLAML==1.1.1
Flask==2.1.3
Flask-Cors==4.0.0
flatbuffers==2.0.7
flax==0.7.2
flit_core @ file:///opt/conda/conda-bld/flit-core_1644941570762/work/source/flit_core
fonttools==4.43.1
fqdn==1.5.1
frozenlist==1.4.0
fsspec==2023.5.0
future==0.18.3
gast==0.4.0
gcs-oauth2-boto-plugin==3.0
gin-config==0.5.0
gitdb==4.0.11
GitPython==3.1.40
glfw==2.6.2
google-api-core==2.12.0
google-api-python-client==1.7.8
google-apitools==0.5.32
google-auth==2.23.4
google-auth-httplib2==0.1.1
google-auth-oauthlib==0.4.6
google-oauth==1.0.1
google-pasta==0.2.0
google-reauth==0.1.1
googleapis-common-protos==1.61.0
gpustat==1.1.1
GPy==1.10.0
gpytorch==1.10
grapheme==0.6.0
graphviz==0.20.1
greenlet==3.0.1
grpcio==1.59.2
gsutil==5.27
gunicorn==20.1.0
gym==0.26.2
gym-notices==0.0.8
gymnasium==0.28.1
h11==0.12.0
h5py==3.7.0
HEBO @ git+https://github.com/huawei-noah/HEBO@9a2a674c22518eed35a8b98e5134576741a95410#subdirectory=HEBO
higher==0.2.1
hjson==3.1.0
hpbandster==0.7.4
httplib2==0.20.4


key: chroma:document
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higher==0.2.1
hjson==3.1.0
hpbandster==0.7.4
httplib2==0.20.4
httptools==0.6.1
huggingface-hub==0.18.0
humanfriendly==10.0
hyperopt==0.2.7
idna @ file:///croot/idna_1666125576474/work
imageio==2.31.1
imageio-ffmpeg==0.4.5
importlib-metadata==4.10.0
importlib-resources==5.13.0
iniconfig==2.0.0
ipykernel==6.26.0
ipython==8.12.3
ipython-genutils==0.2.0
ipywidgets==7.8.1
isodate==0.6.1
isoduration==20.11.0
itsdangerous==2.1.2
jax==0.4.13
jax-jumpy==1.0.0
jaxlib==0.4.13
jedi==0.19.1
Jinja2==3.0.3
jmespath==1.0.1
joblib==1.2.0
json5==0.9.14
jsonpatch @ file:///tmp/build/80754af9/jsonpatch_1615747632069/work
jsonpointer==2.4
jsonschema==4.17.3
jsonschema-specifications==2023.7.1
jupyter-client==7.3.4
jupyter-events==0.6.3
jupyter-server==1.24.0
jupyter-ydoc==0.2.5
jupyter_core==5.5.0
jupyter_server_fileid==0.9.0
jupyter_server_ydoc==0.6.1
jupyterlab==3.6.1
jupyterlab-pygments==0.2.2
jupyterlab-widgets==1.1.7
jupyterlab_server==2.24.0
kaggle-environments==1.7.11
keras==2.11.0


key: chroma:document
string_value:
jupyterlab-widgets==1.1.7
jupyterlab_server==2.24.0
kaggle-environments==1.7.11
keras==2.11.0
kiwisolver==1.4.5
knack==0.10.1
labmaze==1.0.6
lazy_loader==0.3
libclang==16.0.6
lightgbm==3.3.5
lightgbm-ray==0.1.9
linear-operator==0.4.0
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locket==1.0.0
lxml==4.9.1
lz4==4.3.2
Mako==1.2.4
Markdown==3.5.1
markdown-it-py==3.0.0
MarkupSafe==2.1.3
matplotlib==3.7.3
matplotlib-inline==0.1.6
mdurl==0.1.2
minigrid==2.1.1
mistune==0.8.4
ml-dtypes==0.2.0
mlagents-envs==0.28.0
mlflow==2.4.1
modin==0.22.2
monotonic==1.6
more-itertools==10.1.0
mpmath==1.3.0
msal==1.18.0b1
msal-extensions==1.0.0
msgpack==1.0.7
msgpack-numpy==0.4.8
msrest==0.7.1
msrestazure==0.6.4
mujoco==2.3.6
multidict==6.0.4
multipledispatch==1.0.0
multiprocess==0.70.15
nbclassic==1.0.0
nbclient==0.5.13
nbconvert==6.5.4
nbformat==5.9.2
nest-asyncio==1.5.8
netifaces==0.11.0
networkx==3.1
nevergrad==0.4.3.post7
ninja==1.11.1.1
notebook==6.5.6
notebook_shim==0.2.3
numexpr==2.8.4
numpy==1.26.1
nvidia-ml-py==12.535.108


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open-spiel==1.2
opencensus==0.11.3
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opentelemetry-exporter-otlp==1.1.0
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opentelemetry-exporter-otlp-proto-http==1.20.0
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opentelemetry-semantic-conventions==0.20b0
opt-einsum==3.3.0
optax==0.1.7
optuna==3.2.0
orbax-checkpoint==0.2.3
packaging==21.3
pandas==1.5.3
pandocfilters==1.5.0
paramiko==2.12.0
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pathtools==0.1.2
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pickleshare==0.7.5
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platformdirs==3.11.0
plotly==5.18.0
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py==1.11.0
py-cpuinfo==9.0.0
py-spy==0.3.14
py3nvml==0.2.7
py4j==0.10.9.7
pyaml==23.9.7
pyarrow==12.0.1
pyasn1==0.5.0
pyasn1-modules==0.3.0
pycosat @ file:///croot/pycosat_1696536503704/work
pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work
pydantic==1.10.12
pyDeprecate==0.3.2
pygame==2.1.2
pyglet==1.5.15
Pygments==2.13.0
PyJWT==2.8.0
pymars==0.10.0
pymoo==0.6.0.1
pymunk==6.2.1
PyNaCl==1.5.0
PyOpenGL==3.1.7
pyOpenSSL==22.1.0
pyparsing==3.1.1
pypng==0.20220715.0
pyproject_hooks==1.0.0
pyro-api==0.1.2
pyro-ppl==1.8.6
Pyro4==4.82
pyrsistent==0.20.0
PySocks @ file:///tmp/build/80754af9/pysocks_1605305812635/work
pyspark==3.4.1
pytest==7.0.1
pytest-remotedata==0.3.2
python-dateutil==2.8.2
python-dotenv==1.0.0
python-json-logger==2.0.7
pytorch-lightning==1.6.5
pytz==2022.7.1
pyu2f==0.1.5
PyWavelets==1.4.1
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PyYAML==6.0.1
pyzmq==24.0.1
querystring-parser==1.2.4
ray @ file:///home/ray/ray-3.0.0.dev0-cp39-cp39-manylinux2014_x86_64.whl#sha256=9095d2717ebc6220bf25c537edb6d7cbb6d23db5a682525fb2d640258df4c8c7
raydp==1.7.0b20231020.dev0
recsim==0.2.4
redis==3.5.3
referencing==0.30.2
regex==2023.10.3
requests @ file:///croot/requests_1690400202158/work
requests-oauthlib==1.3.1
requests-toolbelt==1.0.0
responses==0.13.4
RestrictedPython==6.2
retry-decorator==1.1.1
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rich==12.6.0
rpds-py==0.10.6
rsa==4.7.2
ruamel.yaml @ file:///croot/ruamel.yaml_1666304550667/work
ruamel.yaml.clib @ file:///croot/ruamel.yaml.clib_1666302247304/work
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s3transfer==0.6.2
safetensors==0.4.0
scikit-image==0.21.0
scikit-learn==1.0.2
scikit-optimize==0.9.0
scipy==1.10.1
segment-analytics-python==2.2.0
semantic-version==2.10.0
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sniffio==1.3.0
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sqlparse==0.4.4
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tensorboard==2.11.2
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorboardX==2.6
tensorflow==2.11.0
tensorflow-datasets==4.9.0
tensorflow-estimator==2.11.0
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tensorflow-metadata==1.13.0
tensorflow-probability==0.19.0
tensorstore==0.1.45
termcolor==2.3.0
terminado==0.17.1
tf-slim==1.1.0
tf2onnx==1.13.0
threadpoolctl==3.1.0
tifffile==2023.7.10
timm==0.9.2
tinycss2==1.2.1
tinyscaler==1.2.6
tokenizers==0.12.1
toml==0.10.2
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torch==2.0.1+cu118
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torch-geometric==2.3.1


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torch-scatter==2.1.1+pt20cu118
torch-sparse==0.6.17+pt20cu118
torch-spline-conv==1.2.2+pt20cu118
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traitlets==5.13.0
transformers==4.19.1
triton==2.0.0
tune-sklearn==0.4.6
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urllib3 @ file:///croot/urllib3_1698257533958/work
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virtualenv==20.21.0
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wrapt==1.15.0
wurlitzer==3.0.3
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zoopt==0.4.1
zstandard @ file:///croot/zstandard_1677013143055/work


key: chroma:document
string_value:
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Ray Tune: Hyperparameter Tuning — Ray 3.0.0.dev0





































































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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  








     Getting Started
    



     Key Concepts
    



     User Guides
    



     Ray Tune Examples
    



     Ray Tune FAQ
    



     Ray Tune API
    





   Ray Serve
  



   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



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   Glossary
  



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.rst







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 Contents
    




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   Learn More About Ray Tune
  



   Citing Tune
  









Ray Tune: Hyperparameter Tuning




 Contents 





   Why choose Tune?
  



   Projects using Tune
  



   Learn More About Ray Tune
  



   Citing Tune
  









Ray Tune: Hyperparameter Tuning#


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Learn More About Ray Tune
  



   Citing Tune
  









Ray Tune: Hyperparameter Tuning#

Tune is a Python library for experiment execution and hyperparameter tuning at any scale.
You can tune your favorite machine learning framework (PyTorch, XGBoost, Scikit-Learn, TensorFlow and Keras, and more) by running state of the art algorithms such as Population Based Training (PBT) and HyperBand/ASHA.
Tune further integrates with a wide range of additional hyperparameter optimization tools, including Ax, BayesOpt, BOHB, Dragonfly, FLAML, Hyperopt, Nevergrad, Optuna and SigOpt.
Click on the following tabs to see code examples for various machine learning frameworks:


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Quickstart
To run this example, install the following: pip install "ray[tune]".
In this quick-start example you minimize a simple function of the form f(x) = a**2 + b, our objective function.
The closer a is to zero and the smaller b is, the smaller the total value of f(x).
We will define a so-called search space for  a and b and let Ray Tune explore the space for good values.
from ray import train, tune


def objective(config):  # ①
    score = config["a"] ** 2 + config["b"]
    return {"score": score}


search_space = {  # ②
    "a": tune.grid_search([0.001, 0.01, 0.1, 1.0]),
    "b": tune.choice([1, 2, 3]),
}

tuner = tune.Tuner(objective, param_space=search_space)  # ③

results = tuner.fit()
print(results.get_best_result(metric="score", mode="min").config)


① Define an objective function.
② Define a search space.
③ Start a Tune run and print the best result.


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Keras+Hyperopt
To tune your Keras models with Hyperopt, you wrap your model in an objective function whose config you
can access for selecting hyperparameters.
In the example below we only tune the activation parameter of the first layer of the model, but you can
tune any parameter of the model you want.
After defining the search space, you can simply initialize the HyperOptSearch object and pass it to run.
It’s important to tell Ray Tune which metric you want to optimize and whether you want to maximize or minimize it.
from ray import tune
from ray.tune.search.hyperopt import HyperOptSearch
import keras


def objective(config):  # ①
    model = keras.models.Sequential()
    model.add(keras.layers.Dense(784, activation=config["activation"]))
    model.add(keras.layers.Dense(10, activation="softmax"))

    model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
    # model.fit(...)
    # loss, accuracy = model.evaluate(...)
    return {"accuracy": accuracy}


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search_space = {"activation": tune.choice(["relu", "tanh"])}  # ②
algo = HyperOptSearch()

tuner = tune.Tuner(  # ③
    objective,
    tune_config=tune.TuneConfig(
        metric="accuracy",
        mode="max",
        search_alg=algo,
    ),
    param_space=search_space,
)
results = tuner.fit()


① Wrap a Keras model in an objective function.
② Define a search space and initialize the search algorithm.
③ Start a Tune run that maximizes accuracy.


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PyTorch+Optuna
To tune your PyTorch models with Optuna, you wrap your model in an objective function whose config you
can access for selecting hyperparameters.
In the example below we only tune the momentum and learning rate (lr) parameters of the model’s optimizer,
but you can tune any other model parameter you want.
After defining the search space, you can simply initialize the OptunaSearch object and pass it to run.
It’s important to tell Ray Tune which metric you want to optimize and whether you want to maximize or minimize it.
We stop tuning this training run after 5 iterations, but you can easily define other stopping rules as well.
import torch
from ray import train, tune
from ray.tune.search.optuna import OptunaSearch


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def objective(config):  # ①
    train_loader, test_loader = load_data()  # Load some data
    model = ConvNet().to("cpu")  # Create a PyTorch conv net
    optimizer = torch.optim.SGD(  # Tune the optimizer
        model.parameters(), lr=config["lr"], momentum=config["momentum"]
    )

    while True:
        train(model, optimizer, train_loader)  # Train the model
        acc = test(model, test_loader)  # Compute test accuracy
        train.report({"mean_accuracy": acc})  # Report to Tune


search_space = {"lr": tune.loguniform(1e-4, 1e-2), "momentum": tune.uniform(0.1, 0.9)}
algo = OptunaSearch()  # ②

tuner = tune.Tuner(  # ③
    objective,
    tune_config=tune.TuneConfig(
        metric="mean_accuracy",
        mode="max",
        search_alg=algo,
    ),
    run_config=train.RunConfig(
        stop={"training_iteration": 5},
    ),
    param_space=search_space,
)
results = tuner.fit()
print("Best config is:", results.get_best_result().config)


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① Wrap a PyTorch model in an objective function.
② Define a search space and initialize the search algorithm.
③ Start a Tune run that maximizes mean accuracy and stops after 5 iterations.


With Tune you can also launch a multi-node distributed hyperparameter sweep
in less than 10 lines of code.
And you can move your models from training to serving on the same infrastructure with Ray Serve.





Getting Started


In our getting started tutorial you will learn how to tune a PyTorch model
effectively with Tune.


Get Started with Tune






Key Concepts


Understand the key concepts behind Ray Tune.
Learn about tune runs, search algorithms, schedulers and other features.


Tune’s Key Concepts






User Guides


Our guides teach you about key features of Tune,
such as distributed training or early stopping.


Learn How To Use Tune






Examples


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Learn How To Use Tune






Examples


In our examples you can find practical tutorials for using frameworks such as
scikit-learn, Keras, TensorFlow, PyTorch, and mlflow, and state of the art search algorithm integrations.


Ray Tune Examples






Ray Tune FAQ


Find answers to commonly asked questions in our detailed FAQ.


Ray Tune FAQ






Ray Tune API


Get more in-depth information about the Ray Tune API, including all about search spaces,
algorithms and training configurations.


Read the API Reference






Why choose Tune?#
There are many other hyperparameter optimization libraries out there.
If you’re new to Tune, you’re probably wondering, “what makes Tune different?”


Cutting-Edge Optimization Algorithms


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Cutting-Edge Optimization Algorithms




As a user, you’re probably looking into hyperparameter optimization because you want to quickly increase your
model performance.
Tune enables you to leverage a variety of these cutting edge optimization algorithms, reducing the cost of tuning
by terminating bad runs early,
choosing better parameters to evaluate, or even
changing the hyperparameters during training to optimize schedules.



First-class Developer Productivity




A key problem with many hyperparameter optimization frameworks is the need to restructure
your code to fit the framework.
With Tune, you can optimize your model just by adding a few code snippets.
Also, Tune removes boilerplate from your code training workflow,
supporting multiple storage options for experiment results (NFS, cloud storage) and
logs results to tools such as MLflow and TensorBoard, while also being highly customizable.



Multi-GPU & Distributed Training Out Of The Box


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Multi-GPU & Distributed Training Out Of The Box




Hyperparameter tuning is known to be highly time-consuming, so it is often necessary to parallelize this process.
Most other tuning frameworks require you to implement your own multi-process framework or build your own
distributed system to speed up hyperparameter tuning.
However, Tune allows you to transparently parallelize across multiple GPUs and multiple nodes.
Tune even has seamless fault tolerance and cloud support, allowing you to scale up
your hyperparameter search by 100x while reducing costs by up to 10x by using cheap preemptible instances.



Coming From Another Hyperparameter Optimization Tool?


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Coming From Another Hyperparameter Optimization Tool?




You might be already using an existing hyperparameter tuning tool such as HyperOpt or Bayesian Optimization.
In this situation, Tune actually allows you to power up your existing workflow.
Tune’s Search Algorithms integrate with a variety of popular hyperparameter tuning
libraries (see examples) and allow you to seamlessly scale up your optimization
process - without sacrificing performance.



Projects using Tune#
Here are some of the popular open source repositories and research projects that leverage Tune.
Feel free to submit a pull-request adding (or requesting a removal!) of a listed project.


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Softlearning: Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
Flambe: An ML framework to accelerate research and its path to production. See flambe.ai.
Population Based Augmentation: Population Based Augmentation (PBA) is a algorithm that quickly and efficiently learns data augmentation functions for neural network training. PBA matches state-of-the-art results on CIFAR with one thousand times less compute.
Fast AutoAugment by Kakao: Fast AutoAugment (Accepted at NeurIPS 2019) learns augmentation policies using a more efficient search strategy based on density matching.
Allentune: Hyperparameter Search for AllenNLP from AllenAI.
machinable: A modular configuration system for machine learning research. See machinable.org.


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machinable: A modular configuration system for machine learning research. See machinable.org.
NeuroCard: NeuroCard (Accepted at VLDB 2021) is a neural cardinality estimator for multi-table join queries. It uses state of the art deep density models to learn correlations across relational database tables.


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Learn More About Ray Tune#
Below you can find blog posts and talks about Ray Tune:

[blog] Tune: a Python library for fast hyperparameter tuning at any scale
[blog] Cutting edge hyperparameter tuning with Ray Tune
[blog] Simple hyperparameter and architecture search in tensorflow with Ray Tune
[slides] Talk given at RISECamp 2019
[video] Talk given at RISECamp 2018
[video] A Guide to Modern Hyperparameter Optimization (PyData LA 2019) (slides)



Citing Tune#
If Tune helps you in your academic research, you are encouraged to cite our paper.
Here is an example bibtex:
@article{liaw2018tune,
    title={Tune: A Research Platform for Distributed Model Selection and Training},
    author={Liaw, Richard and Liang, Eric and Nishihara, Robert
            and Moritz, Philipp and Gonzalez, Joseph E and Stoica, Ion},
    journal={arXiv preprint arXiv:1807.05118},
    year={2018}
}














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Ray Data: Scalable Datasets for ML — Ray 3.0.0.dev0





































































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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




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   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  








     Overview
    



     Key Concepts
    



     User Guides
    



     Ray Data Examples
    



     Ray Data API
    



     Ray Data Internals
    





   Ray Train
  



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   Ray Clusters
  



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Ray Data: Scalable Datasets for ML




 Contents 





   Install Ray Data
  



   Learn more
  









Ray Data: Scalable Datasets for ML#
Ray Data is a scalable data processing library for ML workloads. It provides flexible and performant APIs for scaling Offline batch inference and Data preprocessing and ingest for ML training. Ray Data uses streaming execution to efficiently process large datasets.

Install Ray Data#
To install Ray Data, run:
$ pip install -U 'ray[data]'


To learn more about installing Ray and its libraries, see
Installing Ray.


Learn more#





Ray Data Overview


Get an overview of Ray Data, the workloads that it supports, and how it compares to alternatives.


Ray Data Overview






Key Concepts


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string_value:
Ray Data Overview






Key Concepts


Understand the key concepts behind Ray Data. Learn what
Datasets are and how they’re used.


Learn Key Concepts






User Guides


Learn how to use Ray Data, from basic usage to end-to-end guides.


Learn how to use Ray Data






Examples


Find both simple and scaling-out examples of using Ray Data.


Ray Data Examples






API


Get more in-depth information about the Ray Data API.


Read the API Reference






Ray blogs


Get the latest on engineering updates from the Ray team and how companies are using Ray Data.


Read the Ray blogs

















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Ray Serve: Scalable and Programmable Serving — Ray 3.0.0.dev0





































































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Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  








     Getting Started
    



     Key Concepts
    



     Develop and Deploy an ML Application
    



     Deploy Compositions of Models
    



     Deploy Multiple Applications
    



     Model Multiplexing
    



     Configure Ray Serve deployments
    



     Set Up FastAPI and HTTP
    



     Production Guide
    



     Monitor Your Application
    



     Resource Allocation
    



     Ray Serve Autoscaling
    



     Advanced Guides


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Resource Allocation
    



     Ray Serve Autoscaling
    



     Advanced Guides
    



     Architecture
    



     Examples
    



     Ray Serve API
    





   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  



   Glossary
  



   Security
  







            Theme by the Executable Book Project



































repository







open issue







suggest edit
















.md







.pdf















 Contents
    




   Quickstart
  



   More examples
  



   Why choose Serve?
  



   How can Serve help me as a…
  



   How does Serve compare to …
  



   Learn More
  









Ray Serve: Scalable and Programmable Serving




 Contents 





   Quickstart
  



   More examples
  



   Why choose Serve?
  



   How can Serve help me as a…
  



   How does Serve compare to …
  



   Learn More
  









Ray Serve: Scalable and Programmable Serving#


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Learn More
  









Ray Serve: Scalable and Programmable Serving#

Tip
Get in touch with us if you’re using or considering using Ray Serve.


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Tip
Get in touch with us if you’re using or considering using Ray Serve.


Ray Serve is a scalable model serving library for building online inference APIs.
Serve is framework-agnostic, so you can use a single toolkit to serve everything from deep learning models built with frameworks like PyTorch, TensorFlow, and Keras, to Scikit-Learn models, to arbitrary Python business logic. It has several features and performance optimizations for serving Large Language Models such as response streaming, dynamic request batching, multi-node/multi-GPU serving, etc.
Ray Serve is particularly well suited for model composition and many model serving, enabling you to build a complex inference service consisting of multiple ML models and business logic all in Python code.
Ray Serve is built on top of Ray, so it easily scales to many machines and offers flexible scheduling support such as fractional GPUs so you can share resources and serve many machine learning models at low cost.


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Quickstart#
Install Ray Serve and its dependencies:
pip install "ray[serve]"


Define a simple “hello world” application, run it locally, and query it over HTTP.
import requests
from starlette.requests import Request
from typing import Dict

from ray import serve


# 1: Define a Ray Serve application.
@serve.deployment
class MyModelDeployment:
    def __init__(self, msg: str):
        # Initialize model state: could be very large neural net weights.
        self._msg = msg

    def __call__(self, request: Request) -> Dict:
        return {"result": self._msg}


app = MyModelDeployment.bind(msg="Hello world!")

# 2: Deploy the application locally.
serve.run(app, route_prefix="/")

# 3: Query the application and print the result.
print(requests.get("http://localhost:8000/").json())
# {'result': 'Hello world!'}




More examples#


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More examples#



Model composition
Use Serve’s model composition API to combine multiple deployments into a single application.
import requests
import starlette
from typing import Dict
from ray import serve
from ray.serve.handle import DeploymentHandle


# 1. Define the models in our composition graph and an ingress that calls them.
@serve.deployment
class Adder:
    def __init__(self, increment: int):
        self.increment = increment

    def add(self, inp: int):
        return self.increment + inp


@serve.deployment
class Combiner:
    def average(self, *inputs) -> float:
        return sum(inputs) / len(inputs)


@serve.deployment
class Ingress:
    def __init__(
        self,
        adder1: DeploymentHandle,
        adder2: DeploymentHandle,
        combiner: DeploymentHandle,
    ):
        self._adder1 = adder1
        self._adder2 = adder2
        self._combiner = combiner


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async def __call__(self, request: starlette.requests.Request) -> Dict[str, float]:
        input_json = await request.json()
        final_result = await self._combiner.average.remote(
            self._adder1.add.remote(input_json["val"]),
            self._adder2.add.remote(input_json["val"]),
        )
        return {"result": final_result}


# 2. Build the application consisting of the models and ingress.
app = Ingress.bind(Adder.bind(increment=1), Adder.bind(increment=2), Combiner.bind())
serve.run(app)

# 3: Query the application and print the result.
print(requests.post("http://localhost:8000/", json={"val": 100.0}).json())
# {"result": 101.5}





FastAPI integration
Use Serve’s FastAPI integration to elegantly handle HTTP parsing and validation.
import requests
from fastapi import FastAPI
from ray import serve

# 1: Define a FastAPI app and wrap it in a deployment with a route handler.
app = FastAPI()


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# 1: Define a FastAPI app and wrap it in a deployment with a route handler.
app = FastAPI()


@serve.deployment
@serve.ingress(app)
class FastAPIDeployment:
    # FastAPI will automatically parse the HTTP request for us.
    @app.get("/hello")
    def say_hello(self, name: str) -> str:
        return f"Hello {name}!"


# 2: Deploy the deployment.
serve.run(FastAPIDeployment.bind(), route_prefix="/")

# 3: Query the deployment and print the result.
print(requests.get("http://localhost:8000/hello", params={"name": "Theodore"}).json())
# "Hello Theodore!"





Hugging Face Transformers model
To run this example, install the following: pip install transformers
Serve a pre-trained Hugging Face Transformers model using Ray Serve.
The model we’ll use is a sentiment analysis model: it will take a text string as input and return if the text was “POSITIVE” or “NEGATIVE.”
import requests
from starlette.requests import Request
from typing import Dict

from transformers import pipeline


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from transformers import pipeline

from ray import serve


# 1: Wrap the pretrained sentiment analysis model in a Serve deployment.
@serve.deployment
class SentimentAnalysisDeployment:
    def __init__(self):
        self._model = pipeline("sentiment-analysis")

    def __call__(self, request: Request) -> Dict:
        return self._model(request.query_params["text"])[0]


# 2: Deploy the deployment.
serve.run(SentimentAnalysisDeployment.bind(), route_prefix="/")

# 3: Query the deployment and print the result.
print(
    requests.get(
        "http://localhost:8000/", params={"text": "Ray Serve is great!"}
    ).json()
)
# {'label': 'POSITIVE', 'score': 0.9998476505279541}






Why choose Serve?#


Build end-to-end ML-powered applications


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Why choose Serve?#


Build end-to-end ML-powered applications




Many solutions for ML serving focus on “tensor-in, tensor-out” serving: that is, they wrap ML models behind a predefined, structured endpoint.
However, machine learning isn’t useful in isolation.
It’s often important to combine machine learning with business logic and traditional web serving logic such as database queries.
Ray Serve is unique in that it allows you to build and deploy an end-to-end distributed serving application in a single framework.
You can combine multiple ML models, business logic, and expressive HTTP handling using Serve’s FastAPI integration (see FastAPI HTTP Deployments) to build your entire application as one Python program.



Combine multiple models using a programmable API


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Combine multiple models using a programmable API




Often solving a problem requires more than just a single machine learning model.
For instance, image processing applications typically require a multi-stage pipeline consisting of steps like preprocessing, segmentation, and filtering to achieve their end goal.
In many cases each model may use a different architecture or framework and require different resources (like CPUs vs GPUs).
Many other solutions support defining a static graph in YAML or some other configuration language.
This can be limiting and hard to work with.
Ray Serve, on the other hand, supports multi-model composition using a programmable API where calls to different models look just like function calls.
The models can use different resources and run across different machines in the cluster, but you can write it like a regular program. See Deploy Compositions of Models for more details.



Flexibly scale up and allocate resources


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Flexibly scale up and allocate resources




Machine learning models are compute-intensive and therefore can be very expensive to operate.
A key requirement for any ML serving system is being able to dynamically scale up and down and allocate the right resources for each model to handle the request load while saving cost.
Serve offers a number of built-in primitives to help make your ML serving application efficient.
It supports dynamically scaling the resources for a model up and down by adjusting the number of replicas, batching requests to take advantage of efficient vectorized operations (especially important on GPUs), and a flexible resource allocation model that enables you to serve many models on limited hardware resources.



Avoid framework or vendor lock-in


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Avoid framework or vendor lock-in




Machine learning moves fast, with new libraries and model architectures being released all the time, it’s important to avoid locking yourself into a solution that is tied to a specific framework.
This is particularly important in serving, where making changes to your infrastructure can be time consuming, expensive, and risky.
Additionally, many hosted solutions are limited to a single cloud provider which can be a problem in today’s multi-cloud world.
Ray Serve is not tied to any specific machine learning library or framework, but rather provides a general-purpose scalable serving layer.
Because it’s built on top of Ray, you can run it anywhere Ray can: on your laptop, Kubernetes, any major cloud provider, or even on-premise.



How can Serve help me as a…#


Data scientist


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How can Serve help me as a…#


Data scientist




Serve makes it easy to go from a laptop to a cluster. You can test your models (and your entire deployment graph) on your local machine before deploying it to production on a cluster. You don’t need to know heavyweight Kubernetes concepts or cloud configurations to use Serve.



ML engineer




Serve helps you scale out your deployment and runs them reliably and efficiently to save costs. With Serve’s first-class model composition API, you can combine models together with business logic and build end-to-end user-facing applications. Additionally, Serve runs natively on Kubernetes with minimal operation overhead.



ML platform engineer


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ML platform engineer




Serve specializes in scalable and reliable ML model serving. As such, it can be an important plug-and-play component of your ML platform stack.
Serve supports arbitrary Python code and therefore integrates well with the MLOps ecosystem. You can use it with model optimizers (ONNX, TVM), model monitoring systems (Seldon Alibi, Arize), model registries (MLFlow, Weights and Biases), machine learning frameworks (XGBoost, Scikit-learn), data app UIs (Gradio, Streamlit), and Web API frameworks (FastAPI, gRPC).



LLM developer


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LLM developer




Serve enables you to rapidly prototype, develop, and deploy scalable LLM applications to production. Many large language model (LLM) applications combine prompt preprocessing, vector database lookups, LLM API calls, and response validation. Because Serve supports any arbitrary Python code, you can write all these steps as a single Python module, enabling rapid development and easy testing. You can then quickly deploy your Ray Serve LLM application to production, and each application step can independently autoscale to efficiently accommodate user traffic without wasting resources. In order to improve performance of your LLM applications, Ray Serve has features for batching and can integrate with any model optimization technique. Ray Serve also supports streaming responses, a key feature for chatbot-like applications.



How does Serve compare to …#


TFServing, TorchServe, ONNXRuntime


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How does Serve compare to …#


TFServing, TorchServe, ONNXRuntime




Ray Serve is framework-agnostic, so you can use it alongside any other Python framework or library.
We believe data scientists should not be bound to a particular machine learning framework.
They should be empowered to use the best tool available for the job.
Compared to these framework-specific solutions, Ray Serve doesn’t perform any model-specific optimizations to make your ML model run faster. However, you can still optimize the models yourself
and run them in Ray Serve. For example, you can run a model compiled by
PyTorch JIT or ONNXRuntime.



AWS SageMaker, Azure ML, Google Vertex AI


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AWS SageMaker, Azure ML, Google Vertex AI




As an open-source project, Ray Serve brings the scalability and reliability of these hosted offerings to your own infrastructure.
You can use the Ray cluster launcher to deploy Ray Serve to all major public clouds, K8s, as well as on bare-metal, on-premise machines.
Ray Serve is not a full-fledged ML Platform.
Compared to these other offerings, Ray Serve lacks the functionality for
managing the lifecycle of your models, visualizing their performance, etc. Ray
Serve primarily focuses on model serving and providing the primitives for you to
build your own ML platform on top.



Seldon, KServe, Cortex


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Seldon, KServe, Cortex




You can develop Ray Serve on your laptop, deploy it on a dev box, and scale it out
to multiple machines or a Kubernetes cluster, all with minimal or no changes to code. It’s a lot
easier to get started with when you don’t need to provision and manage a K8s cluster.
When it’s time to deploy, you can use our Kubernetes Operator
to transparently deploy your Ray Serve application to K8s.



BentoML, Comet.ml, MLflow




Many of these tools are focused on serving and scaling models independently.
In contrast, Ray Serve is framework-agnostic and focuses on model composition.
As such, Ray Serve works with any model packaging and registry format.
Ray Serve also provides key features for building production-ready machine learning applications, including best-in-class autoscaling and naturally integrating with business logic.


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We truly believe Serve is unique as it gives you end-to-end control
over your ML application while delivering scalability and high performance. To achieve
Serve’s feature offerings with other tools, you would need to glue together multiple
frameworks like Tensorflow Serving and SageMaker, or even roll your own
micro-batching component to improve throughput.


Learn More#
Check out Getting Started and Key Concepts,
or head over to the Examples to get started building your Ray Serve applications.





Getting Started


Start with our quick start tutorials for deploying a single model locally and how to convert an existing model into a Ray Serve deployment .


Get Started with Ray Serve






Key Concepts


Understand the key concepts behind Ray Serve.
Learn about Deployments, how to query them, and using DeploymentHandles to compose multiple models and business logic together.


Learn Key Concepts






Examples


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Learn Key Concepts






Examples


Follow the tutorials to learn how to integrate Ray Serve with TensorFlow, Scikit-Learn, and RLlib.


Serve Examples






API Reference


Get more in-depth information about the Ray Serve API.


Read the API Reference





For more, see the following blog posts about Ray Serve:

Serving ML Models in Production: Common Patterns by Simon Mo, Edward Oakes, and Michael Galarnyk
The Simplest Way to Serve your NLP Model in Production with Pure Python by Edward Oakes and Bill Chambers
Machine Learning Serving is Broken by Simon Mo
How to Scale Up Your FastAPI Application Using Ray Serve by Archit Kulkarni













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    By The Ray Team
  
      © Copyright 2023, The Ray Team.


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Ray Serve API — Ray 3.0.0.dev0





































































Toggle navigation sidebar




Toggle in-page Table of Contents












Ray 3.0.0.dev0









                    Welcome to Ray!
                




  Ray
 




   Overview
  



   Getting Started
  



   Installation
  



   Use Cases
  



   Example Gallery
  



   Ecosystem
  



   Ray Core
  



   Ray Data
  



   Ray Train
  



   Ray Tune
  



   Ray Serve
  








     Getting Started
    



     Key Concepts
    



     Develop and Deploy an ML Application
    



     Deploy Compositions of Models
    



     Deploy Multiple Applications
    



     Model Multiplexing
    



     Configure Ray Serve deployments
    



     Set Up FastAPI and HTTP
    



     Production Guide
    



     Monitor Your Application
    



     Resource Allocation
    



     Ray Serve Autoscaling
    



     Advanced Guides
    



     Architecture


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Ray Serve Autoscaling
    



     Advanced Guides
    



     Architecture
    



     Examples
    



     Ray Serve API
    








       ray.serve.Deployment
      



       ray.serve.Application
      



       ray.serve.deployment
      



       ray.serve.ingress
      



       ray.serve.batch
      



       ray.serve.multiplexed
      



       ray.serve.handle.DeploymentHandle
      



       ray.serve.handle.DeploymentResponse
      



       ray.serve.handle.DeploymentResponseGenerator
      



       ray.serve.handle.RayServeHandle
      



       ray.serve.handle.RayServeSyncHandle
      



       ray.serve.start
      



       ray.serve.run
      



       ray.serve.delete
      



       ray.serve.status
      



       ray.serve.shutdown
      



       ray.serve.config.ProxyLocation
      



       ray.serve.config.gRPCOptions
      



       ray.serve.config.HTTPOptions
      



       ray.serve.config.AutoscalingConfig


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ray.serve.config.HTTPOptions
      



       ray.serve.config.AutoscalingConfig
      



       ray.serve.get_replica_context
      



       ray.serve.context.ReplicaContext
      



       ray.serve.get_multiplexed_model_id
      



       ray.serve.get_app_handle
      



       ray.serve.get_deployment_handle
      



       ray.serve.schema.ServeDeploySchema
      



       ray.serve.schema.gRPCOptionsSchema
      



       ray.serve.schema.HTTPOptionsSchema
      



       ray.serve.schema.ServeApplicationSchema
      



       ray.serve.schema.DeploymentSchema
      



       ray.serve.schema.RayActorOptionsSchema
      



       ray.serve.schema.ServeInstanceDetails
      



       ray.serve.schema.ApplicationDetails
      



       ray.serve.schema.DeploymentDetails
      



       ray.serve.schema.ReplicaDetails
      



       ray.serve.metrics.Counter
      



       ray.serve.metrics.Histogram
      



       ray.serve.metrics.Gauge


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ray.serve.metrics.Histogram
      



       ray.serve.metrics.Gauge
      







   Ray RLlib
  



   More Libraries
  



   Ray Clusters
  



   Monitoring and Debugging
  



   Developer Guides
  



   Glossary
  



   Security
  







            Theme by the Executable Book Project



































repository







open issue







suggest edit
















.md







.pdf















 Contents
    




   Python API
  



     Writing Applications
    



       Deployment Decorators
      



       Deployment Handles
      





     Running Applications
    



     Configurations
    



       Advanced APIs
      







   Command Line Interface (CLI)
  



     serve
    



       build
      



       config
      



       deploy
      



       run
      



       shutdown
      



       start
      



       status
      







   Serve REST API
  





       PUT
      

       "/api/serve/applications/"


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Serve REST API
  





       PUT
      

       "/api/serve/applications/"
      







       GET
      

       "/api/serve/applications/"
      







       DELETE
      

       "/api/serve/applications/"
      







   Config Schemas
  



   Response Schemas
  



   Metrics API
  









Ray Serve API




 Contents 





   Python API
  



     Writing Applications
    



       Deployment Decorators
      



       Deployment Handles
      





     Running Applications
    



     Configurations
    



       Advanced APIs
      







   Command Line Interface (CLI)
  



     serve
    



       build
      



       config
      



       deploy
      



       run
      



       shutdown
      



       start
      



       status
      







   Serve REST API
  





       PUT
      

       "/api/serve/applications/"
      







       GET
      

       "/api/serve/applications/"
      







       DELETE


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GET
      

       "/api/serve/applications/"
      







       DELETE
      

       "/api/serve/applications/"
      







   Config Schemas
  



   Response Schemas
  



   Metrics API
  









Ray Serve API#

Python API#

Writing Applications#







serve.Deployment
Class (or function) decorated with the @serve.deployment decorator.

serve.Application
One or more deployments bound with arguments that can be deployed together.




Deployment Decorators#






serve.deployment
Decorator that converts a Python class to a Deployment.

serve.ingress
Wrap a deployment class with a FastAPI application for HTTP request parsing.

serve.batch
Converts a function to asynchronously handle batches.

serve.multiplexed
Wrap a callable or method used to load multiplexed models in a replica.





Deployment Handles#


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Deployment Handles#

Note
DeploymentHandle is now the default handle API.
You can continue using the legacy RayServeHandle and RayServeSyncHandle APIs using handle.options(use_new_handle_api=False) or export RAY_SERVE_ENABLE_NEW_HANDLE_API=0, but this support will be removed in a future version.







serve.handle.DeploymentHandle
A handle used to make requests to a deployment at runtime.

serve.handle.DeploymentResponse
A future-like object wrapping the result of a unary deployment handle call.

serve.handle.DeploymentResponseGenerator
A future-like object wrapping the result of a streaming deployment handle call.

serve.handle.RayServeHandle
A handle used to make requests from one deployment to another.

serve.handle.RayServeSyncHandle
A handle used to make requests to the ingress deployment of an application.






Running Applications#






serve.start
Start Serve on the cluster.

serve.run
Run an application and return a handle to its ingress deployment.


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serve.run
Run an application and return a handle to its ingress deployment.

serve.delete
Delete an application by its name.

serve.status
Get the status of Serve on the cluster.

serve.shutdown
Completely shut down Serve on the cluster.





Configurations#






serve.config.ProxyLocation
Config for where to run proxies to receive ingress traffic to the cluster.

serve.config.gRPCOptions
gRPC options for the proxies.

serve.config.HTTPOptions
HTTP options for the proxies.

serve.config.AutoscalingConfig
Config for the Serve Autoscaler.




Advanced APIs#






serve.get_replica_context
Returns the deployment and replica tag from within a replica at runtime.

serve.context.ReplicaContext
Stores runtime context info for replicas.

serve.get_multiplexed_model_id
Get the multiplexed model ID for the current request.

serve.get_app_handle
Get a handle to the application's ingress deployment by name.

serve.get_deployment_handle
Get a handle to a deployment by name.


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serve.get_deployment_handle
Get a handle to a deployment by name.







Command Line Interface (CLI)#

serve#
CLI for managing Serve applications on a Ray cluster.
serve [OPTIONS] COMMAND [ARGS]...



build#
Imports the applications at IMPORT_PATHS and generates a structured, multi-application config for them. If the flag –single-app is set, accepts one application and generates a single-application config. Config outputted from this command can be used by serve deploy or the REST API.
serve build [OPTIONS] IMPORT_PATHS...


Options


-d, --app-dir <app_dir>#
Local directory to look for the IMPORT_PATH (will be inserted into PYTHONPATH). Defaults to ‘.’, meaning that an object in ./main.py can be imported as ‘main.object’. Not relevant if you’re importing from an installed module.



-o, --output-path <output_path>#
Local path where the output config will be written in YAML format. If not provided, the config will be printed to STDOUT.


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--grpc-servicer-functions <grpc_servicer_functions>#
Servicer function for adding the method handler to the gRPC server. Defaults to an empty list and no gRPC server is started.

Arguments


IMPORT_PATHS#
Required argument(s)



config#
Gets the current configs of Serve applications on the cluster.
serve config [OPTIONS]


Options


-a, --address <address>#
Address to use to query the Ray dashboard head (defaults to http://localhost:8265). Can also be specified using the RAY_DASHBOARD_ADDRESS environment variable.



-n, --name <name>#
Name of an application. Only applies to multi-application mode. If set, this will only fetch the config for the specified application.


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deploy#
This supported config format is ServeDeploySchema. This call is async; a successful response only indicates that the request was sent to the Ray cluster successfully. It does not mean the the deployments have been deployed/updated.
Existing deployments with no code changes will not be redeployed.
Use serve config to fetch the current config(s) and serve status to check the status of the application(s) and deployments after deploying.
serve deploy [OPTIONS] CONFIG_FILE_NAME


Options


-a, --address <address>#
Address to use to query the Ray dashboard head (defaults to http://localhost:8265). Can also be specified using the RAY_DASHBOARD_ADDRESS environment variable.

Arguments


CONFIG_FILE_NAME#
Required argument


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Arguments


CONFIG_FILE_NAME#
Required argument



run#
Runs an application from the specified import path (e.g., my_script:app) or application(s) from a YAML config.
If passing an import path, it must point to a Serve Application or a function that returns one. If a function is used, arguments can be passed to it in ‘key=val’ format after the import path, for example:
serve run my_script:app model_path=’/path/to/model.pkl’ num_replicas=5
If passing a YAML config, existing applications with no code changes will not be updated.
By default, this will block and stream logs to the console. If you Ctrl-C the command, it will shut down Serve on the cluster.
serve run [OPTIONS] CONFIG_OR_IMPORT_PATH [ARGUMENTS]...


Options


--runtime-env <runtime_env>#
Path to a local YAML file containing a runtime_env definition. This will be passed to ray.init() as the default for deployments.


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--runtime-env-json <runtime_env_json>#
JSON-serialized runtime_env dictionary. This will be passed to ray.init() as the default for deployments.



--working-dir <working_dir>#
Directory containing files that your application(s) will run in. Can be a local directory or a remote URI to a .zip file (S3, GS, HTTP). This overrides the working_dir in –runtime-env if both are specified. This will be passed to ray.init() as the default for deployments.



-d, --app-dir <app_dir>#
Local directory to look for the IMPORT_PATH (will be inserted into PYTHONPATH). Defaults to ‘.’, meaning that an object in ./main.py can be imported as ‘main.object’. Not relevant if you’re importing from an installed module.



-a, --address <address>#
Address to use for ray.init(). Can also be specified using the RAY_ADDRESS environment variable.



-h, --host <host>#
Host for HTTP server to listen on. Defaults to 127.0.0.1.



-p, --port <port>#
Port for HTTP proxies to listen on. Defaults to 8000.


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-p, --port <port>#
Port for HTTP proxies to listen on. Defaults to 8000.



--blocking, --non-blocking#
Whether or not this command should be blocking. If blocking, it will loop and log status until Ctrl-C’d, then clean up the app.



-r, --reload#
Listens for changes to files in the working directory, –working-dir or the working_dir in the –runtime-env, and automatically redeploys the application. This will block until Ctrl-C’d, then clean up the app.



--route-prefix <route_prefix>#
Route prefix for the application. This should only be used when running an application specified by import path and will be ignored if running a config file.

Arguments


CONFIG_OR_IMPORT_PATH#
Required argument



ARGUMENTS#
Optional argument(s)



shutdown#
Shuts down Serve on the cluster, deleting all applications.
serve shutdown [OPTIONS]


Options


key: chroma:document
string_value:
Options


-a, --address <address>#
Address to use to query the Ray dashboard head (defaults to http://localhost:8265). Can also be specified using the RAY_DASHBOARD_ADDRESS environment variable.



-y, --yes#
Bypass confirmation prompt.



start#
Start Serve on the Ray cluster.
serve start [OPTIONS]


Options


-a, --address <address>#
Address to use for ray.init(). Can also be specified using the RAY_ADDRESS environment variable.



--http-host <http_host>#
Host for HTTP proxies to listen on. Defaults to 127.0.0.1.



--http-port <http_port>#
Port for HTTP proxies to listen on. Defaults to 8000.



--http-location <http_location>#
DEPRECATED: Use --proxy-location instead.

Options
DeploymentMode.NoServer | DeploymentMode.HeadOnly | DeploymentMode.EveryNode





--proxy-location <proxy_location>#
Location of the proxies. Defaults to EveryNode.

Options
ProxyLocation.Disabled | ProxyLocation.HeadOnly | ProxyLocation.EveryNode


key: chroma:document
string_value:
Options
ProxyLocation.Disabled | ProxyLocation.HeadOnly | ProxyLocation.EveryNode





--grpc-port <grpc_port>#
Port for gRPC proxies to listen on. Defaults to 9000.



--grpc-servicer-functions <grpc_servicer_functions>#
Servicer function for adding the method handler to the gRPC server. Defaults to an empty list and no gRPC server is started.



status#
Prints status information about all applications on the cluster.
An application may be:

NOT_STARTED: the application does not exist.
DEPLOYING: the deployments in the application are still deploying and haven’t reached the target number of replicas.
RUNNING: all deployments are healthy.
DEPLOY_FAILED: the application failed to deploy or reach a running state.
DELETING: the application is being deleted, and the deployments in the application are being teared down.

The deployments within each application may be:


key: chroma:document
string_value:
The deployments within each application may be:

HEALTHY: all replicas are acting normally and passing their health checks.
UNHEALTHY: at least one replica is not acting normally and may not be passing its health check.
UPDATING: the deployment is updating.

serve status [OPTIONS]


Options


-a, --address <address>#
Address to use to query the Ray dashboard head (defaults to http://localhost:8265). Can also be specified using the RAY_DASHBOARD_ADDRESS environment variable.



-n, --name <name>#
Name of an application. If set, this will display only the status of the specified application.





Serve REST API#
The Serve REST API is exposed at the same port as the Ray Dashboard. The Dashboard port is 8265 by default. This port can be changed using the --dashboard-port argument when running ray start. All example requests in this section use the default port.


key: chroma:document
string_value:
PUT "/api/serve/applications/"#
Declaratively deploys a list of Serve applications. If Serve is already running on the Ray cluster, removes all applications not listed in the new config. If Serve is not running on the Ray cluster, starts Serve. See multi-app config schema for the request’s JSON schema.
Example Request:
PUT /api/serve/applications/ HTTP/1.1
Host: http://localhost:8265/
Accept: application/json
Content-Type: application/json

{
    "applications": [
        {
            "name": "text_app",
            "route_prefix": "/",
            "import_path": "text_ml:app",
            "runtime_env": {
                "working_dir": "https://github.com/ray-project/serve_config_examples/archive/HEAD.zip"
            },
            "deployments": [
                {"name": "Translator", "user_config": {"language": "french"}},
                {"name": "Summarizer"},
            ]
        },
    ]
}


Example Response
HTTP/1.1 200 OK
Content-Type: application/json


key: chroma:document
string_value:
Example Response
HTTP/1.1 200 OK
Content-Type: application/json




GET "/api/serve/applications/"#
Gets cluster-level info and comprehensive details on all Serve applications deployed on the Ray cluster. See metadata schema for the response’s JSON schema.
GET /api/serve/applications/ HTTP/1.1
Host: http://localhost:8265/
Accept: application/json


Example Response (abridged JSON):
HTTP/1.1 200 OK
Content-Type: application/json


key: chroma:document
string_value:
{
    "controller_info": {
        "node_id": "cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec",
        "node_ip": "10.0.29.214",
        "actor_id": "1d214b7bdf07446ea0ed9d7001000000",
        "actor_name": "SERVE_CONTROLLER_ACTOR",
        "worker_id": "adf416ae436a806ca302d4712e0df163245aba7ab835b0e0f4d85819",
        "log_file_path": "/serve/controller_29778.log"
    },
    "proxy_location": "EveryNode",
    "http_options": {
        "host": "0.0.0.0",
        "port": 8000,
        "root_path": "",
        "request_timeout_s": null,
        "keep_alive_timeout_s": 5
    },
    "grpc_options": {
        "port": 9000,
        "grpc_servicer_functions": []
    },
    "proxies": {
        "cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec": {
            "node_id": "cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec",
            "node_ip": "10.0.29.214",
            "actor_id": "b7a16b8342e1ced620ae638901000000",


key: chroma:document
string_value:
"node_ip": "10.0.29.214",
            "actor_id": "b7a16b8342e1ced620ae638901000000",
            "actor_name": "SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec",
            "worker_id": "206b7fe05b65fac7fdceec3c9af1da5bee82b0e1dbb97f8bf732d530",
            "log_file_path": "/serve/http_proxy_10.0.29.214.log",
            "status": "HEALTHY"
        }
    },
    "deploy_mode": "MULTI_APP",
    "applications": {
        "app1": {
            "name": "app1",
            "route_prefix": "/",
            "docs_path": null,
            "status": "RUNNING",
            "message": "",
            "last_deployed_time_s": 1694042836.1912267,
            "deployed_app_config": {
                "name": "app1",
                "route_prefix": "/",
                "import_path": "src.text-test:app",
                "deployments": [
                    {
                        "name": "Translator",


key: chroma:document
string_value:
{
                        "name": "Translator",
                        "num_replicas": 1,
                        "user_config": {
                            "language": "german"
                        }
                    }
                ]
            },
            "deployments": {
                "Translator": {
                    "name": "Translator",
                    "status": "HEALTHY",
                    "message": "",
                    "deployment_config": {
                        "name": "Translator",
                        "num_replicas": 1,
                        "max_concurrent_queries": 100,
                        "user_config": {
                            "language": "german"
                        },
                        "graceful_shutdown_wait_loop_s": 2.0,
                        "graceful_shutdown_timeout_s": 20.0,
                        "health_check_period_s": 10.0,
                        "health_check_timeout_s": 30.0,


key: chroma:document
string_value:
"health_check_timeout_s": 30.0,
                        "ray_actor_options": {
                            "runtime_env": {
                                "env_vars": {}
                            },
                            "num_cpus": 1.0
                        },
                        "is_driver_deployment": false
                    },
                    "replicas": [
                        {
                            "node_id": "cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec",
                            "node_ip": "10.0.29.214",
                            "actor_id": "4bb8479ad0c9e9087fee651901000000",
                            "actor_name": "SERVE_REPLICA::app1#Translator#oMhRlb",
                            "worker_id": "1624afa1822b62108ead72443ce72ef3c0f280f3075b89dd5c5d5e5f",
                            "log_file_path": "/serve/deployment_Translator_app1#Translator#oMhRlb.log",


key: chroma:document
string_value:
"replica_id": "app1#Translator#oMhRlb",
                            "state": "RUNNING",
                            "pid": 29892,
                            "start_time_s": 1694042840.577496
                        }
                    ]
                },
                "Summarizer": {
                    "name": "Summarizer",
                    "status": "HEALTHY",
                    "message": "",
                    "deployment_config": {
                        "name": "Summarizer",
                        "num_replicas": 1,
                        "max_concurrent_queries": 100,
                        "user_config": null,
                        "graceful_shutdown_wait_loop_s": 2.0,
                        "graceful_shutdown_timeout_s": 20.0,
                        "health_check_period_s": 10.0,
                        "health_check_timeout_s": 30.0,
                        "ray_actor_options": {
                            "runtime_env": {},


key: chroma:document
string_value:
"ray_actor_options": {
                            "runtime_env": {},
                            "num_cpus": 1.0
                        },
                        "is_driver_deployment": false
                    },
                    "replicas": [
                        {
                            "node_id": "cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec",
                            "node_ip": "10.0.29.214",
                            "actor_id": "7118ae807cffc1c99ad5ad2701000000",
                            "actor_name": "SERVE_REPLICA::app1#Summarizer#cwiPXg",
                            "worker_id": "12de2ac83c18ce4a61a443a1f3308294caf5a586f9aa320b29deed92",
                            "log_file_path": "/serve/deployment_Summarizer_app1#Summarizer#cwiPXg.log",
                            "replica_id": "app1#Summarizer#cwiPXg",
                            "state": "RUNNING",
                            "pid": 29893,


key: chroma:document
string_value:
"state": "RUNNING",
                            "pid": 29893,
                            "start_time_s": 1694042840.5789504
                        }
                    ]
                }
            }
        }
    }
}


key: chroma:document
string_value:
DELETE "/api/serve/applications/"#
Shuts down Serve and all applications running on the Ray cluster. Has no effect if Serve is not running on the Ray cluster.
Example Request:
DELETE /api/serve/applications/ HTTP/1.1
Host: http://localhost:8265/
Accept: application/json


Example Response
HTTP/1.1 200 OK
Content-Type: application/json





Config Schemas#






schema.ServeDeploySchema
Multi-application config for deploying a list of Serve applications to the Ray cluster.

schema.gRPCOptionsSchema
Options to start the gRPC Proxy with.

schema.HTTPOptionsSchema
Options to start the HTTP Proxy with.

schema.ServeApplicationSchema
Describes one Serve application, and currently can also be used as a standalone config to deploy a single application to a Ray cluster.

schema.DeploymentSchema
Specifies options for one deployment within a Serve application.

schema.RayActorOptionsSchema
Options with which to start a replica actor.





Response Schemas#


key: chroma:document
string_value:
Response Schemas#






schema.ServeInstanceDetails
Serve metadata with system-level info and details on all applications deployed to the Ray cluster.

schema.ApplicationDetails
Detailed info about a Serve application.

schema.DeploymentDetails
Detailed info about a deployment within a Serve application.

schema.ReplicaDetails
Detailed info about a single deployment replica.





Metrics API#






metrics.Counter
A serve cumulative metric that is monotonically increasing.

metrics.Histogram
Tracks the size and number of events in buckets.

metrics.Gauge
Gauges keep the last recorded value and drop everything before.













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    By The Ray Team
  
      © Copyright 2023, The Ray Team.


key: chroma:document
string_value:
Index — Ray 3.0.0.dev0




































































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Ray 3.0.0.dev0









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Index

Symbols
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 | F
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 | H
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 | N
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Symbols


(Ray) Scheduler


    --address <address>


key: chroma:document
string_value:
Symbols


(Ray) Scheduler


    --address <address>

      
ray-debug command line option

ray-get command line option

ray-job-list command line option

ray-job-logs command line option

ray-job-status command line option

ray-job-stop command line option

ray-job-submit command line option

ray-list command line option

ray-memory command line option

ray-start command line option

ray-status command line option

ray-summary-actors command line option

ray-summary-objects command line option

ray-summary-tasks command line option

ray-timeline command line option

serve-config command line option

serve-deploy command line option

serve-run command line option

serve-shutdown command line option

serve-start command line option

serve-status command line option



    --app-dir <app_dir>

      
serve-build command line option

serve-run command line option



    --args <args>

      
ray-submit command line option



    --autoscaling-config <autoscaling_config>


key: chroma:document
string_value:
ray-submit command line option



    --autoscaling-config <autoscaling_config>

      
ray-start command line option



    --block

      
ray-start command line option



    --blocking

      
serve-run command line option



    --cluster-name <cluster_name>

      
ray-attach command line option

ray-down command line option

ray-exec command line option

ray-get_head_ip command line option

ray-monitor command line option

ray-submit command line option

ray-up command line option



    --dashboard-agent-grpc-port <dashboard_agent_grpc_port>

      
ray-start command line option



    --dashboard-agent-listen-port <dashboard_agent_listen_port>

      
ray-start command line option



    --dashboard-grpc-port <dashboard_grpc_port>

      
ray-start command line option



    --dashboard-host <dashboard_host>

      
ray-start command line option



    --dashboard-port <dashboard_port>

      
ray-start command line option



    --detail


key: chroma:document
string_value:
--dashboard-port <dashboard_port>

      
ray-start command line option



    --detail

      
ray-list command line option



    --disable-usage-stats

      
ray-exec command line option

ray-start command line option

ray-submit command line option

ray-up command line option



    --entrypoint-memory <entrypoint_memory>

      
ray-job-submit command line option



    --entrypoint-num-cpus <entrypoint_num_cpus>

      
ray-job-submit command line option



    --entrypoint-num-gpus <entrypoint_num_gpus>

      
ray-job-submit command line option



    --entrypoint-resources <entrypoint_resources>

      
ray-job-submit command line option



    --extra-screen-args <extra_screen_args>

      
ray-submit command line option



    --filter <filter>

      
ray-list command line option



    --follow

      
ray-job-logs command line option



    --force

      
ray-stop command line option



    --format <format>

      
ray-list command line option


key: chroma:document
string_value:
--format <format>

      
ray-list command line option



    --gcs-server-port <gcs_server_port>

      
ray-start command line option



    --grace-period <grace_period>

      
ray-stop command line option



    --group-by <group_by>

      
ray-memory command line option



    --grpc-port <grpc_port>

      
serve-start command line option



    --grpc-servicer-functions <grpc_servicer_functions>

      
serve-build command line option

serve-start command line option



    --head

      
ray-start command line option



    --headers <headers>

      
ray-job-list command line option

ray-job-logs command line option

ray-job-status command line option

ray-job-stop command line option

ray-job-submit command line option



    --host <host>

      
serve-run command line option



    --http-host <http_host>

      
serve-start command line option



    --http-location <http_location>

      
serve-start command line option



    --http-port <http_port>


key: chroma:document
string_value:
serve-start command line option



    --http-port <http_port>

      
serve-start command line option



    --include-dashboard <include_dashboard>

      
ray-start command line option



    --job-id <job_id>

      
ray-job-submit command line option



    --keep-min-workers

      
ray-down command line option



    --limit <limit>

      
ray-list command line option



    --lines <lines>

      
ray-monitor command line option



    --log-color <log_color>

      
ray-attach command line option

ray-down command line option

ray-exec command line option

ray-job-list command line option

ray-job-logs command line option

ray-job-status command line option

ray-job-stop command line option

ray-job-submit command line option

ray-monitor command line option

ray-start command line option

ray-stop command line option

ray-submit command line option

ray-up command line option



    --log-style <log_style>

      
ray-attach command line option


key: chroma:document
string_value:
ray-up command line option



    --log-style <log_style>

      
ray-attach command line option

ray-down command line option

ray-exec command line option

ray-job-list command line option

ray-job-logs command line option

ray-job-status command line option

ray-job-stop command line option

ray-job-submit command line option

ray-monitor command line option

ray-start command line option

ray-stop command line option

ray-submit command line option

ray-up command line option



    --max-worker-port <max_worker_port>

      
ray-start command line option



    --max-workers <max_workers>

      
ray-up command line option



    --metadata-json <metadata_json>

      
ray-job-submit command line option



    --metrics-export-port <metrics_export_port>

      
ray-start command line option



    --min-worker-port <min_worker_port>

      
ray-start command line option



    --min-workers <min_workers>

      
ray-up command line option



    --n <num_entries>


key: chroma:document
string_value:
--min-workers <min_workers>

      
ray-up command line option



    --n <num_entries>

      
ray-memory command line option



    --name <name>

      
serve-config command line option

serve-status command line option



    --new

      
ray-attach command line option



    --no-config-cache

      
ray-attach command line option

ray-exec command line option

ray-submit command line option

ray-up command line option



    --no-format

      
ray-memory command line option



    --no-redirect-output

      
ray-start command line option



    --no-restart

      
ray-up command line option



    --no-wait

      
ray-job-stop command line option

ray-job-submit command line option



    --node-ip-address <node_ip_address>

      
ray-start command line option



    --node-manager-port <node_manager_port>

      
ray-start command line option



    --non-blocking

      
serve-run command line option





    --num-cpus <num_cpus>


key: chroma:document
string_value:
--non-blocking

      
serve-run command line option





    --num-cpus <num_cpus>

      
ray-start command line option



    --num-entries

      
ray-memory command line option



    --num-gpus <num_gpus>

      
ray-start command line option



    --object-manager-port <object_manager_port>

      
ray-start command line option



    --object-store-memory <object_store_memory>

      
ray-start command line option



    --output-path <output_path>

      
serve-build command line option



    --plasma-directory <plasma_directory>

      
ray-start command line option



    --plasma-store-socket-name <plasma_store_socket_name>

      
ray-start command line option



    --port <port>

      
ray-start command line option

serve-run command line option



    --port-forward <port_forward>

      
ray-attach command line option

ray-exec command line option

ray-submit command line option



    --proxy-location <proxy_location>

      
serve-start command line option


key: chroma:document
string_value:
--proxy-location <proxy_location>

      
serve-start command line option



    --ray-client-server-port <ray_client_server_port>

      
ray-start command line option



    --ray-debugger-external

      
ray-start command line option



    --raylet-socket-name <raylet_socket_name>

      
ray-start command line option



    --redirect-command-output

      
ray-up command line option



    --redis_password <redis_password>

      
ray-memory command line option

ray-status command line option



    --reload

      
serve-run command line option



    --resources <resources>

      
ray-start command line option



    --restart-only

      
ray-up command line option



    --route-prefix <route_prefix>

      
serve-run command line option



    --run-env <run_env>

      
ray-exec command line option



    --runtime-env <runtime_env>

      
ray-job-submit command line option

serve-run command line option



    --runtime-env-agent-port <runtime_env_agent_port>


key: chroma:document
string_value:
serve-run command line option



    --runtime-env-agent-port <runtime_env_agent_port>

      
ray-start command line option



    --runtime-env-json <runtime_env_json>

      
ray-job-submit command line option

serve-run command line option



    --screen

      
ray-attach command line option

ray-exec command line option

ray-submit command line option



    --sort-by <sort_by>

      
ray-memory command line option



    --start

      
ray-attach command line option

ray-exec command line option

ray-submit command line option



    --stats-only

      
ray-memory command line option



    --stop

      
ray-exec command line option

ray-submit command line option



    --storage <storage>

      
ray-start command line option



    --submission-id <submission_id>

      
ray-job-submit command line option



    --temp-dir <temp_dir>

      
ray-start command line option



    --timeout <timeout>

      
ray-get command line option

ray-list command line option


key: chroma:document
string_value:
--timeout <timeout>

      
ray-get command line option

ray-list command line option

ray-summary-actors command line option

ray-summary-objects command line option

ray-summary-tasks command line option



    --tmux

      
ray-attach command line option

ray-exec command line option

ray-submit command line option



    --units <units>

      
ray-memory command line option



    --use-login-shells

      
ray-up command line option



    --use-normal-shells

      
ray-up command line option



    --verbose

      
ray-attach command line option

ray-down command line option

ray-exec command line option

ray-job-list command line option

ray-job-logs command line option

ray-job-status command line option

ray-job-stop command line option

ray-job-submit command line option

ray-monitor command line option

ray-start command line option

ray-stop command line option

ray-submit command line option

ray-up command line option



    --verify <verify>


key: chroma:document
string_value:
ray-submit command line option

ray-up command line option



    --verify <verify>

      
ray-job-list command line option

ray-job-logs command line option

ray-job-status command line option

ray-job-stop command line option

ray-job-submit command line option



    --worker-port-list <worker_port_list>

      
ray-start command line option



    --workers-only

      
ray-down command line option



    --working-dir <working_dir>

      
ray-job-submit command line option

serve-run command line option



    --yes

      
ray-down command line option

ray-up command line option

serve-shutdown command line option



    -a

      
serve-config command line option

serve-deploy command line option

serve-run command line option

serve-shutdown command line option

serve-start command line option

serve-status command line option



    -d

      
serve-build command line option

serve-run command line option



    -f

      
ray-job-logs command line option


key: chroma:document
string_value:
serve-run command line option



    -f

      
ray-job-logs command line option

ray-list command line option

ray-stop command line option



    -g

      
ray-stop command line option



    -h

      
serve-run command line option



    -N

      
ray-attach command line option



    -n

      
ray-attach command line option

ray-down command line option

ray-exec command line option

ray-get_head_ip command line option

ray-monitor command line option

ray-submit command line option

ray-up command line option

serve-config command line option

serve-status command line option



    -o

      
serve-build command line option



    -p

      
ray-attach command line option

ray-exec command line option

ray-submit command line option

serve-run command line option



    -r

      
serve-run command line option



    -v

      
ray-attach command line option

ray-down command line option

ray-exec command line option

ray-job-list command line option


key: chroma:document
string_value:
ray-down command line option

ray-exec command line option

ray-job-list command line option

ray-job-logs command line option

ray-job-status command line option

ray-job-stop command line option

ray-job-submit command line option

ray-monitor command line option

ray-start command line option

ray-stop command line option

ray-submit command line option

ray-up command line option



    -y

      
ray-down command line option

ray-up command line option

serve-shutdown command line option




_


__call__() (ray.rllib.utils.schedules.schedule.Schedule method)

(ray.tune.stopper.Stopper method)


__init__() (ray.data.ActorPoolStrategy method)

(ray.data.aggregate.AggregateFn method)

(ray.data.block.BlockAccessor method)

(ray.data.DataContext method)

(ray.data.Dataset method)

(ray.data.Datasink method)

(ray.data.Datasource method)

(ray.data.datasource.BaseFileMetadataProvider method)

(ray.data.datasource.BlockWritePathProvider method)


key: chroma:document
string_value:
(ray.data.datasource.BlockWritePathProvider method)

(ray.data.datasource.DefaultBlockWritePathProvider method)

(ray.data.datasource.DefaultFileMetadataProvider method)

(ray.data.datasource.DefaultParquetMetadataProvider method)

(ray.data.datasource.FastFileMetadataProvider method)

(ray.data.datasource.FileMetadataProvider method)

(ray.data.datasource.FilenameProvider method)

(ray.data.datasource.ParquetMetadataProvider method)

(ray.data.datasource.PathPartitionFilter method)

(ray.data.datasource.PathPartitionParser method)

(ray.data.datasource.Reader method)

(ray.data.grouped_data.GroupedData method)

(ray.data.preprocessor.Preprocessor method)

(ray.data.random_access_dataset.RandomAccessDataset method)

(ray.rllib.algorithms.algorithm.Algorithm method)

(ray.rllib.core.models.catalog.Catalog method)

(ray.rllib.env.external_env.ExternalEnv method)

(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)

(ray.rllib.env.multi_agent_env.MultiAgentEnv method)


key: chroma:document
string_value:
(ray.rllib.env.multi_agent_env.MultiAgentEnv method)

(ray.rllib.env.vector_env._VectorizedGymEnv method)

(ray.rllib.env.vector_env.VectorEnv method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.evaluation.sampler.AsyncSampler method)

(ray.rllib.evaluation.sampler.SamplerInput method)

(ray.rllib.evaluation.sampler.SyncSampler method)

(ray.rllib.evaluation.worker_set.WorkerSet method)

(ray.rllib.models.modelv2.ModelV2 method)

(ray.rllib.models.tf.recurrent_net.RecurrentNetwork method)

(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)

(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)

(ray.rllib.offline.d4rl_reader.D4RLReader method)

(ray.rllib.offline.input_reader.InputReader method)

(ray.rllib.offline.io_context.IOContext method)

(ray.rllib.offline.json_reader.JsonReader method)

(ray.rllib.offline.mixed_input.MixedInput method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.policy_map.PolicyMap method)


key: chroma:document
string_value:
(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.policy_map.PolicyMap method)

(ray.rllib.policy.sample_batch.MultiAgentBatch method)

(ray.rllib.policy.sample_batch.SampleBatch method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)

(ray.rllib.utils.exploration.curiosity.Curiosity method)

(ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)

(ray.rllib.utils.exploration.exploration.Exploration method)

(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)

(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)

(ray.rllib.utils.exploration.parameter_noise.ParameterNoise method)

(ray.rllib.utils.exploration.random.Random method)

(ray.rllib.utils.exploration.random_encoder.RE3 method)

(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)

(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)


key: chroma:document
string_value:
(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)

(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)

(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)

(ray.rllib.utils.schedules.constant_schedule.ConstantSchedule method)

(ray.rllib.utils.schedules.exponential_schedule.ExponentialSchedule method)

(ray.rllib.utils.schedules.linear_schedule.LinearSchedule method)

(ray.rllib.utils.schedules.piecewise_schedule.PiecewiseSchedule method)

(ray.rllib.utils.schedules.polynomial_schedule.PolynomialSchedule method)

(ray.train.Checkpoint method)

(ray.train.context.TrainContext method)

(ray.train.DataConfig method)

(ray.tune.Callback method)

(ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.ProgressReporter method)

(ray.tune.stopper.Stopper method)


key: chroma:document
string_value:
(ray.tune.ProgressReporter method)

(ray.tune.stopper.Stopper method)

(ray.tune.Trainable method)

(ray.tune.Tuner method)




_check_is_built() (ray.rllib.core.learner.learner.Learner method)

_check_registered_optimizer() (ray.rllib.core.learner.learner.Learner method)

_check_result() (ray.rllib.core.learner.learner.Learner method)

_convert_batch_type() (ray.rllib.core.learner.learner.Learner method)

_determine_components_hook() (ray.rllib.core.models.catalog.Catalog method)

_get_clip_function() (ray.rllib.core.learner.learner.Learner static method)

_get_dist_cls_from_action_space() (ray.rllib.core.models.catalog.Catalog class method)

_get_encoder_config() (ray.rllib.core.models.catalog.Catalog class method)

_get_metadata() (ray.rllib.core.learner.learner.Learner method)

_get_tensor_variable() (ray.rllib.core.learner.learner.Learner method)

_is_module_compatible_with_learner() (ray.rllib.core.learner.learner.Learner method)


key: chroma:document
string_value:
_is_module_compatible_with_learner() (ray.rllib.core.learner.learner.Learner method)

_load_optimizers() (ray.rllib.core.learner.learner.Learner method)

_make_module() (ray.rllib.core.learner.learner.Learner method)

_save_optimizers() (ray.rllib.core.learner.learner.Learner method)

_set_optimizer_lr() (ray.rllib.core.learner.learner.Learner static method)

_update() (ray.rllib.core.learner.learner.Learner method)

_VectorizedGymEnv (class in ray.rllib.env.vector_env)



A


AbsMax (class in ray.data.aggregate)

accelerator_type (ray.serve.schema.RayActorOptionsSchema attribute)

Action space

ACTION_DIST (ray.rllib.policy.sample_batch.SampleBatch attribute)

ACTION_DIST_INPUTS (ray.rllib.policy.sample_batch.SampleBatch attribute)

action_distribution_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


ACTION_LOGP (ray.rllib.policy.sample_batch.SampleBatch attribute)


key: chroma:document
string_value:
ACTION_LOGP (ray.rllib.policy.sample_batch.SampleBatch attribute)

ACTION_PROB (ray.rllib.policy.sample_batch.SampleBatch attribute)

action_sampler_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


action_space (ray.rllib.core.rl_module.rl_module.RLModuleConfig attribute)

(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)

(ray.rllib.env.base_env.BaseEnv property)


action_space_contains() (ray.rllib.env.base_env.BaseEnv method)

action_space_sample() (ray.rllib.env.base_env.BaseEnv method)

ACTIONS (ray.rllib.policy.sample_batch.SampleBatch attribute)

Actor

Actor task

actor_id (ray.runtime_context.RuntimeContext property)

(ray.util.state.common.ActorState attribute)

(ray.util.state.common.TaskState attribute)


actor_locality_enabled (ray.data.ExecutionOptions attribute)

ActorPlacementGroupRemoved

ActorPool (class in ray.util)

ActorPoolStrategy (class in ray.data)


key: chroma:document
string_value:
ActorPlacementGroupRemoved

ActorPool (class in ray.util)

ActorPoolStrategy (class in ray.data)

ActorState (class in ray.util.state.common)

ActorSummaries (class in ray.util.state.common)

ActorSummaryPerClass (class in ray.util.state.common)

ActorUnschedulableError

add() (ray.data.ExecutionResources method)

(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)

(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)

(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)


add_column() (ray.data.Dataset method)

add_configurations() (ray.tune.search.basic_variant.BasicVariantGenerator method)

add_evaluated_point() (ray.tune.search.ax.AxSearch method)

(ray.tune.search.bayesopt.BayesOptSearch method)


key: chroma:document
string_value:
(ray.tune.search.bayesopt.BayesOptSearch method)

(ray.tune.search.bohb.TuneBOHB method)

(ray.tune.search.hyperopt.HyperOptSearch method)

(ray.tune.search.nevergrad.NevergradSearch method)

(ray.tune.search.Repeater method)

(ray.tune.search.Searcher method)

(ray.tune.search.sigopt.SigOptSearch method)

(ray.tune.search.zoopt.ZOOptSearch method)


add_evaluated_trials() (ray.tune.search.ax.AxSearch method)

(ray.tune.search.bayesopt.BayesOptSearch method)

(ray.tune.search.bohb.TuneBOHB method)

(ray.tune.search.ConcurrencyLimiter method)

(ray.tune.search.dragonfly.DragonflySearch method)

(ray.tune.search.hebo.HEBOSearch method)

(ray.tune.search.hyperopt.HyperOptSearch method)

(ray.tune.search.nevergrad.NevergradSearch method)

(ray.tune.search.optuna.OptunaSearch method)

(ray.tune.search.Repeater method)

(ray.tune.search.Searcher method)

(ray.tune.search.sigopt.SigOptSearch method)

(ray.tune.search.skopt.SkOptSearch method)

(ray.tune.search.zoopt.ZOOptSearch method)


key: chroma:document
string_value:
(ray.tune.search.skopt.SkOptSearch method)

(ray.tune.search.zoopt.ZOOptSearch method)


add_metric_column() (ray.tune.CLIReporter method)

(ray.tune.JupyterNotebookReporter method)


add_module() (ray.rllib.core.learner.learner.Learner method)

(ray.rllib.core.learner.learner_group.LearnerGroup method)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)


add_modules() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec method)

add_parameter_column() (ray.tune.CLIReporter method)

(ray.tune.JupyterNotebookReporter method)


add_policy() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.evaluation.worker_set.WorkerSet method)


add_trial() (ray.tune.web_server.TuneClient method)

add_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)

additional_resources_per_worker (ray.train.ScalingConfig property)

additional_update() (ray.rllib.core.learner.learner.Learner method)


key: chroma:document
string_value:
additional_update() (ray.rllib.core.learner.learner.Learner method)

(ray.rllib.core.learner.learner_group.LearnerGroup method)




additional_update_for_module() (ray.rllib.core.learner.learner.Learner method)

Agent

AGENT_INDEX (ray.rllib.policy.sample_batch.SampleBatch attribute)

agent_steps() (ray.rllib.policy.sample_batch.MultiAgentBatch method)

(ray.rllib.policy.sample_batch.SampleBatch method)


aggregate() (ray.data.Dataset method)

(ray.data.grouped_data.GroupedData method)


aggregate_combined_blocks() (ray.data.block.BlockAccessor static method)

AggregateFn (class in ray.data.aggregate)

AimLoggerCallback (class in ray.tune.logger.aim)

Algorithm

(class in ray.rllib.algorithms.algorithm)


AlgorithmConfig (class in ray.rllib.algorithms.algorithm_config)

aligned_array() (in module ray.rllib.utils.numpy)

allgather() (in module ray.util.collective.collective)

allgather_multigpu() (in module ray.util.collective.collective)


key: chroma:document
string_value:
allgather_multigpu() (in module ray.util.collective.collective)

allreduce() (in module ray.util.collective.collective)

allreduce_multigpu() (in module ray.util.collective.collective)

app_name (ray.serve.context.ReplicaContext attribute)

Application (class in ray.serve)

application_details_route_prefix_format() (ray.serve.schema.ApplicationDetails class method)

application_names_nonempty() (ray.serve.schema.ServeDeploySchema class method)

application_names_unique() (ray.serve.schema.ServeDeploySchema class method)

application_routes_unique() (ray.serve.schema.ServeDeploySchema class method)

applications (ray.serve.schema.ServeDeploySchema attribute)

(ray.serve.schema.ServeInstanceDetails attribute)


apply() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


key: chroma:document
string_value:
(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)

(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)

(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)

(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)


apply_grad_clipping() (in module ray.rllib.utils.torch_utils)

apply_gradients() (ray.rllib.core.learner.learner.Learner method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


apply_gradients_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

APPOConfig (class in ray.rllib.algorithms.appo.appo)


key: chroma:document
string_value:
APPOConfig (class in ray.rllib.algorithms.appo.appo)

args (ray.serve.schema.ServeApplicationSchema attribute)


    ARGUMENTS

      
serve-run command line option


as_directory() (ray.train.Checkpoint method)

as_multi_agent() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)

(ray.rllib.policy.sample_batch.MultiAgentBatch method)

(ray.rllib.policy.sample_batch.SampleBatch method)


as_placement_group_factory() (ray.train.ScalingConfig method)

as_trainable() (ray.train.data_parallel_trainer.DataParallelTrainer method)

(ray.train.gbdt_trainer.GBDTTrainer method)

(ray.train.horovod.HorovodTrainer method)

(ray.train.lightgbm.LightGBMTrainer method)

(ray.train.tensorflow.TensorflowTrainer method)

(ray.train.torch.TorchTrainer method)

(ray.train.trainer.BaseTrainer method)

(ray.train.xgboost.XGBoostTrainer method)


ASHAScheduler (in module ray.tune.schedulers)


key: chroma:document
string_value:
(ray.train.xgboost.XGBoostTrainer method)


ASHAScheduler (in module ray.tune.schedulers)

assert_healthy() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

async_update() (ray.rllib.core.learner.learner_group.LearnerGroup method)

Asynchronous execution

Asynchronous sampling

AsyncHyperBandScheduler (class in ray.tune.schedulers)

AsyncioActorExit

AsyncSampler (class in ray.rllib.evaluation.sampler)

attempt_number (ray.util.state.common.TaskState attribute)

ATTENTION_MASKS (ray.rllib.policy.sample_batch.SampleBatch attribute)

Autoscaler

Autoscaling

autoscaling_config (ray.serve.schema.DeploymentSchema attribute)

AutoscalingCluster (class in ray.cluster_utils)

available_resources() (in module ray)

AxSearch (class in ray.tune.search.ax)



B


Backend

(class in ray.train.backend)


backend (ray.train.torch.TorchConfig attribute)

backend_cls (ray.train.horovod.HorovodConfig property)

(ray.train.tensorflow.TensorflowConfig property)


key: chroma:document
string_value:
(ray.train.tensorflow.TensorflowConfig property)

(ray.train.torch.TorchConfig property)


BackendConfig (class in ray.train.backend)

barrier() (in module ray.util.collective.collective)

base_dir (ray.data.datasource.Partitioning attribute)

base_trial_resources (ray.tune.schedulers.ResourceChangingScheduler property)

BaseEnv (class in ray.rllib.env.base_env)

BaseFileMetadataProvider (class in ray.data.datasource)

BaseTrainer (class in ray.train.trainer)

BasicVariantGenerator (class in ray.tune.search.basic_variant)

Batch format

Batch size

batch() (in module ray.serve)

batch_to_block() (ray.data.block.BlockAccessor static method)

BayesOptSearch (class in ray.tune.search.bayesopt)

BCConfig (class in ray.rllib.algorithms.bc.bc)

before_compute_actions() (ray.rllib.utils.exploration.curiosity.Curiosity method)

(ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)

(ray.rllib.utils.exploration.exploration.Exploration method)


key: chroma:document
string_value:
(ray.rllib.utils.exploration.exploration.Exploration method)

(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)

(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)

(ray.rllib.utils.exploration.random.Random method)

(ray.rllib.utils.exploration.random_encoder.RE3 method)

(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)


best_checkpoint (ray.tune.ExperimentAnalysis property)



best_checkpoints (ray.train.Result attribute)

best_config (ray.tune.ExperimentAnalysis property)

best_dataframe (ray.tune.ExperimentAnalysis property)

best_logdir (ray.tune.ExperimentAnalysis property)

best_result (ray.tune.ExperimentAnalysis property)

best_result_df (ray.tune.ExperimentAnalysis property)

best_trial (ray.tune.ExperimentAnalysis property)

bind() (ray.serve.Deployment method)

BlendSearch (in module ray.tune.search.flaml)

Block

(in module ray.data.block)


BlockAccessor (class in ray.data.block)


key: chroma:document
string_value:
Block

(in module ray.data.block)


BlockAccessor (class in ray.data.block)

BlockBasedFileDatasink (class in ray.data)

BlockExecStats (class in ray.data.block)

BlockMetadata (class in ray.data.block)

BlockWritePathProvider (class in ray.data.datasource)

broadcast() (in module ray.util.collective.collective)

broadcast_multigpu() (in module ray.util.collective.collective)

build() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

(ray.rllib.core.learner.learner.Learner method)

(ray.rllib.core.learner.learner.LearnerSpec method)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec method)

(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec method)


build_encoder() (ray.rllib.core.models.catalog.Catalog method)

builder() (ray.data.block.BlockAccessor static method)

bundle_count (ray.util.placement_group.PlacementGroup property)

bundle_specs (ray.util.placement_group.PlacementGroup property)


key: chroma:document
string_value:
bundle_specs (ray.util.placement_group.PlacementGroup property)

bundles (ray.tune.execution.placement_groups.PlacementGroupFactory property)

(ray.util.state.common.PlacementGroupState attribute)




C


call_site (ray.util.state.common.ObjectState attribute)

Callback (class in ray.tune)

callbacks (ray.train.RunConfig attribute)

callbacks() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

callsite_enabled (ray.util.state.common.ObjectSummaries attribute)

can_restore() (ray.train.data_parallel_trainer.DataParallelTrainer class method)

(ray.train.gbdt_trainer.GBDTTrainer class method)

(ray.train.horovod.HorovodTrainer class method)

(ray.train.lightgbm.LightGBMTrainer class method)

(ray.train.tensorflow.TensorflowTrainer class method)

(ray.train.torch.TorchTrainer class method)

(ray.train.trainer.BaseTrainer class method)

(ray.train.xgboost.XGBoostTrainer class method)

(ray.tune.Tuner class method)


cancel() (in module ray)

(in module ray.workflow)


key: chroma:document
string_value:
(ray.tune.Tuner class method)


cancel() (in module ray)

(in module ray.workflow)


Catalog (class in ray.rllib.core.models.catalog)

catalog_class (ray.rllib.core.rl_module.rl_module.RLModuleConfig attribute)

(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)


Categorizer (class in ray.data.preprocessors)

CFO (in module ray.tune.search.flaml)

chdir_to_trial_dir (ray.tune.TuneConfig attribute)

Checkpoint

(class in ray.train)


checkpoint (ray.train.Result attribute)

(ray.tune.experiment.trial.Trial property)


checkpoint_at_end (ray.train.CheckpointConfig attribute)

checkpoint_config (ray.train.RunConfig attribute)

(ray.tune.Experiment property)


checkpoint_dir (ray.tune.Experiment property)

checkpoint_frequency (ray.train.CheckpointConfig attribute)

checkpoint_score_attribute (ray.train.CheckpointConfig attribute)

checkpoint_score_order (ray.train.CheckpointConfig attribute)

CheckpointConfig (class in ray.train)


key: chroma:document
string_value:
CheckpointConfig (class in ray.train)

checkpointing() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

choice() (in module ray.tune)

choose_trial_to_run() (ray.tune.schedulers.HyperBandForBOHB method)

(ray.tune.schedulers.HyperBandScheduler method)

(ray.tune.schedulers.pb2.PB2 method)

(ray.tune.schedulers.PopulationBasedTraining method)

(ray.tune.schedulers.TrialScheduler method)


CKPT_FILE_TMPL (ray.tune.search.ax.AxSearch attribute)

(ray.tune.search.basic_variant.BasicVariantGenerator attribute)

(ray.tune.search.bayesopt.BayesOptSearch attribute)

(ray.tune.search.bohb.TuneBOHB attribute)

(ray.tune.search.ConcurrencyLimiter attribute)

(ray.tune.search.dragonfly.DragonflySearch attribute)

(ray.tune.search.hebo.HEBOSearch attribute)

(ray.tune.search.hyperopt.HyperOptSearch attribute)

(ray.tune.search.nevergrad.NevergradSearch attribute)

(ray.tune.search.optuna.OptunaSearch attribute)

(ray.tune.search.Repeater attribute)


key: chroma:document
string_value:
(ray.tune.search.optuna.OptunaSearch attribute)

(ray.tune.search.Repeater attribute)

(ray.tune.search.Searcher attribute)

(ray.tune.search.sigopt.SigOptSearch attribute)

(ray.tune.search.skopt.SkOptSearch attribute)

(ray.tune.search.zoopt.ZOOptSearch attribute)


class_name (ray.util.state.common.ActorState attribute)

(ray.util.state.common.ActorSummaryPerClass attribute)


cleanup() (ray.tune.Trainable method)

clear() (ray.rllib.policy.policy_map.PolicyMap method)

(ray.rllib.policy.sample_batch.SampleBatch method)

(ray.runtime_env.RuntimeEnv method)

(ray.runtime_env.RuntimeEnvConfig method)


CLIReporter (class in ray.tune)


    CLUSTER_CONFIG_FILE

      
ray-attach command line option

ray-down command line option

ray-exec command line option

ray-get_head_ip command line option

ray-monitor command line option

ray-submit command line option

ray-up command line option


cluster_resources() (in module ray)


    CMD

      
ray-exec command line option


key: chroma:document
string_value:
cluster_resources() (in module ray)


    CMD

      
ray-exec command line option


code_search_path (ray.job_config.JobConfig attribute)

columns() (ray.data.Dataset method)

(ray.rllib.policy.sample_batch.SampleBatch method)


combine() (ray.data.block.BlockAccessor method)

CombinedStopper (class in ray.tune.stopper)

compile_results() (ray.rllib.core.learner.learner.Learner method)

COMPLETE_UPDATE (ray.rllib.core.learner.learner.TorchCompileWhatToCompile attribute)

compress() (ray.rllib.policy.sample_batch.MultiAgentBatch method)

(ray.rllib.policy.sample_batch.SampleBatch method)




compute_actions() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.rllib.policy.policy.Policy method)


compute_actions_from_input_dict() (ray.rllib.policy.policy.Policy method)

compute_gradients() (ray.rllib.core.learner.learner.Learner method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


key: chroma:document
string_value:
(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


compute_gradients_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

compute_log_likelihoods() (ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


compute_loss() (ray.rllib.core.learner.learner.Learner method)

compute_loss_for_module() (ray.rllib.core.learner.learner.Learner method)

compute_single_action() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


concat() (ray.rllib.policy.sample_batch.SampleBatch method)

concat_aligned() (in module ray.rllib.utils.numpy)

concat_multi_gpu_td_errors() (in module ray.rllib.utils.torch_utils)

Concatenator (class in ray.data.preprocessors)

ConcurrencyLimiter (class in ray.tune.search)

config (ray.train.Result property)

(ray.tune.experiment.trial.Trial attribute)


key: chroma:document
string_value:
config (ray.train.Result property)

(ray.tune.experiment.trial.Trial attribute)



    CONFIG_FILE_NAME

      
serve-deploy command line option



    CONFIG_OR_IMPORT_PATH

      
serve-run command line option


configure() (ray.train.DataConfig method)

configure_optimizers() (ray.rllib.core.learner.learner.Learner method)

configure_optimizers_for_module() (ray.rllib.core.learner.learner.Learner method)

connect() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)

Connector

ConstantSchedule (class in ray.rllib.utils.schedules.constant_schedule)

context (ray.data.Dataset property)

context() (ray.rllib.models.modelv2.ModelV2 method)

(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)

(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)


CONTINUE (ray.tune.schedulers.FIFOScheduler attribute)

(ray.tune.schedulers.HyperBandForBOHB attribute)

(ray.tune.schedulers.HyperBandScheduler attribute)

(ray.tune.schedulers.MedianStoppingRule attribute)


key: chroma:document
string_value:
(ray.tune.schedulers.MedianStoppingRule attribute)

(ray.tune.schedulers.pb2.PB2 attribute)

(ray.tune.schedulers.PopulationBasedTraining attribute)

(ray.tune.schedulers.PopulationBasedTrainingReplay attribute)

(ray.tune.schedulers.ResourceChangingScheduler attribute)

(ray.tune.schedulers.TrialScheduler attribute)


controller_info (ray.serve.schema.ServeInstanceDetails attribute)

convert_to_numpy() (in module ray.rllib.utils.numpy)

convert_to_torch_tensor() (in module ray.rllib.utils.torch_utils)

copy() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

(ray.rllib.policy.policy_map.PolicyMap method)

(ray.rllib.policy.sample_batch.MultiAgentBatch method)

(ray.rllib.policy.sample_batch.SampleBatch method)

(ray.runtime_env.RuntimeEnv method)

(ray.runtime_env.RuntimeEnvConfig method)


Count (class in ray.data.aggregate)

count (ray.rllib.policy.sample_batch.MultiAgentBatch attribute)

count() (ray.data.Dataset method)

(ray.data.grouped_data.GroupedData method)


key: chroma:document
string_value:
count() (ray.data.Dataset method)

(ray.data.grouped_data.GroupedData method)


Counter (class in ray.serve.metrics)

(class in ray.util.metrics)


cpu_time_s (ray.data.block.BlockExecStats attribute)

CQLConfig (class in ray.rllib.algorithms.cql.cql)

create_collective_group() (in module ray.util.collective.collective)

(ray.util.collective.collective.GroupManager method)


create_placement_group_factory() (ray.tune.experiment.trial.Trial method)

create_reader() (ray.data.Datasource method)

create_scheduler() (in module ray.tune.schedulers)

create_searcher() (in module ray.tune.search)

creation_args() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

creation_time_ms (ray.util.state.common.RuntimeEnvState attribute)

(ray.util.state.common.TaskState attribute)


creator_job_id (ray.util.state.common.PlacementGroupState attribute)

CrossLanguageError

CSVLoggerCallback (class in ray.tune.logger)

CUR_OBS (ray.rllib.policy.sample_batch.SampleBatch attribute)


key: chroma:document
string_value:
CUR_OBS (ray.rllib.policy.sample_batch.SampleBatch attribute)

Curiosity (class in ray.rllib.utils.exploration.curiosity)

current_actor (ray.runtime_context.RuntimeContext property)

current_placement_group_id (ray.runtime_context.RuntimeContext property)

custom_loss() (ray.rllib.models.modelv2.ModelV2 method)

(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)

(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)


CustomKBinsDiscretizer (class in ray.data.preprocessors)



D


D4RLReader (class in ray.rllib.offline.d4rl_reader)

daemon (ray.rllib.evaluation.sampler.AsyncSampler property)

DataConfig (class in ray.train)

DataContext (class in ray.data)

dataframe() (ray.tune.ExperimentAnalysis method)

DataIterator (class in ray.data)

DataParallelTrainer (class in ray.train.data_parallel_trainer)

Dataset (class in ray.data)

(object)


Datasink (class in ray.data)

Datasource (class in ray.data)

death_cause (ray.util.state.common.ActorState attribute)


key: chroma:document
string_value:
Datasource (class in ray.data)

death_cause (ray.util.state.common.ActorState attribute)

debug_string() (ray.tune.schedulers.AsyncHyperBandScheduler method)

(ray.tune.schedulers.HyperBandForBOHB method)

(ray.tune.schedulers.HyperBandScheduler method)

(ray.tune.schedulers.TrialScheduler method)


debugging() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

decompress_if_needed() (ray.rllib.policy.sample_batch.MultiAgentBatch method)

(ray.rllib.policy.sample_batch.SampleBatch method)


DEFAULT_COLUMNS (ray.tune.CLIReporter attribute)

(ray.tune.JupyterNotebookReporter attribute)


default_ingest_options() (ray.train.DataConfig static method)

default_resource_request() (ray.tune.Trainable class method)

default_sampler_input() (ray.rllib.offline.io_context.IOContext method)

DefaultBlockWritePathProvider (class in ray.data.datasource)

DefaultCallbacks (class in ray.rllib.algorithms.callbacks)

DefaultFileMetadataProvider (class in ray.data.datasource)


key: chroma:document
string_value:
DefaultFileMetadataProvider (class in ray.data.datasource)

DefaultParquetMetadataProvider (class in ray.data.datasource)

delete() (in module ray.serve)

delete_job() (ray.job_submission.JobSubmissionClient method)

deploy_mode (ray.serve.schema.ServeInstanceDetails attribute)

deployed_app_config (ray.serve.schema.ApplicationDetails attribute)

Deployment

(class in ray.serve)


deployment (ray.serve.context.ReplicaContext attribute)

Deployment graph

deployment() (in module ray.serve)

deployment_config (ray.serve.schema.DeploymentDetails attribute)

deployment_names (ray.serve.schema.ServeApplicationSchema property)

deployment_route_prefix_not_set() (ray.serve.schema.DeploymentDetails class method)

deployment_schema_route_prefix_format() (ray.serve.schema.DeploymentSchema class method)

DeploymentHandle (class in ray.serve.handle)

DeploymentResponse (class in ray.serve.handle)

DeploymentResponseGenerator (class in ray.serve.handle)


key: chroma:document
string_value:
DeploymentResponseGenerator (class in ray.serve.handle)

deployments (ray.serve.schema.ApplicationDetails attribute)

(ray.serve.schema.ServeApplicationSchema attribute)


Deprecated() (in module ray.util.annotations)



deserialize() (ray.data.preprocessor.Preprocessor static method)

(ray.data.preprocessors.Categorizer static method)

(ray.data.preprocessors.Concatenator static method)

(ray.data.preprocessors.CustomKBinsDiscretizer static method)

(ray.data.preprocessors.LabelEncoder static method)

(ray.data.preprocessors.MaxAbsScaler static method)

(ray.data.preprocessors.MinMaxScaler static method)

(ray.data.preprocessors.MultiHotEncoder static method)

(ray.data.preprocessors.Normalizer static method)

(ray.data.preprocessors.OneHotEncoder static method)

(ray.data.preprocessors.OrdinalEncoder static method)

(ray.data.preprocessors.PowerTransformer static method)

(ray.data.preprocessors.RobustScaler static method)

(ray.data.preprocessors.SimpleImputer static method)


key: chroma:document
string_value:
(ray.data.preprocessors.SimpleImputer static method)

(ray.data.preprocessors.StandardScaler static method)

(ray.data.preprocessors.UniformKBinsDiscretizer static method)


deserialize_lineage() (ray.data.Dataset static method)

destroy_collective_group() (in module ray.util.collective.collective)

(ray.util.collective.collective.GroupManager method)


DeveloperAPI() (in module ray.util.annotations)

diagnose_serialization() (in module ray.tune.utils)

DIRECTORY (ray.data.datasource.PartitionStyle attribute)

Disabled (ray.serve.config.ProxyLocation attribute)

distributed (ray.rllib.core.learner.learner.Learner property)

Distributed Data-Parallel

distributed_sampler_kwargs (ray.train.lightning.RayDDPStrategy property)

(ray.train.lightning.RayDeepSpeedStrategy property)

(ray.train.lightning.RayFSDPStrategy property)


DistributeResources (class in ray.tune.schedulers.resource_changing_scheduler)


key: chroma:document
string_value:
DistributeResources (class in ray.tune.schedulers.resource_changing_scheduler)

DistributeResourcesToTopJob (class in ray.tune.schedulers.resource_changing_scheduler)

DockerCluster (class in ray.autoscaler._private.fake_multi_node.test_utils)

docs_path (ray.serve.schema.ApplicationDetails attribute)

DONES (ray.rllib.policy.sample_batch.SampleBatch attribute)

downscale_delay_s (ray.serve.config.AutoscalingConfig attribute)

downscale_smoothing_factor (ray.serve.config.AutoscalingConfig attribute)

DQNConfig (class in ray.rllib.algorithms.dqn.dqn)

DragonflySearch (class in ray.tune.search.dragonfly)

Driver

DRIVER (ray.job_submission.JobType attribute)

driver_agent_http_address (ray.job_submission.JobDetails attribute)

(ray.job_submission.JobInfo attribute)


driver_exit_code (ray.job_submission.JobDetails attribute)

(ray.job_submission.JobInfo attribute)


driver_info (ray.job_submission.JobDetails attribute)

driver_node_id (ray.job_submission.JobDetails attribute)


key: chroma:document
string_value:
driver_node_id (ray.job_submission.JobDetails attribute)

(ray.job_submission.JobInfo attribute)


drop_columns() (ray.data.Dataset method)



E


eager_tracing (ray.rllib.core.learner.learner.FrameworkHyperparameters attribute)

EagerTFPolicyV2 (class in ray.rllib.policy.eager_tf_policy_v2)

empty() (ray.util.queue.Queue method)

enable_reproducibility() (in module ray.train.torch)

end_episode() (ray.rllib.env.external_env.ExternalEnv method)

(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)

(ray.rllib.env.policy_client.PolicyClient method)


end_time (ray.job_submission.JobDetails attribute)

(ray.job_submission.JobInfo attribute)


end_time_ms (ray.util.state.common.TaskState attribute)

(ray.util.state.common.WorkerState attribute)



    ENTRYPOINT

      
ray-job-submit command line option


entrypoint (ray.job_submission.JobDetails attribute)

(ray.job_submission.JobInfo attribute)


entrypoint_memory (ray.job_submission.JobInfo attribute)


key: chroma:document
string_value:
(ray.job_submission.JobInfo attribute)


entrypoint_memory (ray.job_submission.JobInfo attribute)

entrypoint_num_cpus (ray.job_submission.JobInfo attribute)

entrypoint_num_gpus (ray.job_submission.JobInfo attribute)

entrypoint_resources (ray.job_submission.JobInfo attribute)

ENV_ID (ray.rllib.policy.sample_batch.SampleBatch attribute)

env_steps() (ray.rllib.policy.sample_batch.MultiAgentBatch method)

(ray.rllib.policy.sample_batch.SampleBatch method)


Environment

environment() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

Episode

EPISODES (ray.rllib.utils.replay_buffers.replay_buffer.StorageUnit attribute)

EPS_ID (ray.rllib.policy.sample_batch.SampleBatch attribute)

EpsilonGreedy (class in ray.rllib.utils.exploration.epsilon_greedy)

error (ray.train.Result attribute)

(ray.util.state.common.RuntimeEnvState attribute)


error_file (ray.tune.experiment.trial.Trial attribute)

error_message (ray.util.state.common.TaskState attribute)


key: chroma:document
string_value:
error_message (ray.util.state.common.TaskState attribute)

error_type (ray.job_submission.JobDetails attribute)

(ray.job_submission.JobInfo attribute)

(ray.util.state.common.TaskState attribute)


errors (ray.tune.ResultGrid property)

estimate_inmemory_data_size() (ray.data.Datasource method)

(ray.data.datasource.Reader method)


evaluate() (ray.rllib.algorithms.algorithm.Algorithm method)

evaluated_params (ray.tune.experiment.trial.Trial attribute)



evaluation() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

events (ray.util.state.common.TaskState attribute)

EveryNode (ray.serve.config.ProxyLocation attribute)

exec_stats (ray.data.block.BlockMetadata attribute)

ExecutionOptions (class in ray.data)

ExecutionResources (class in ray.data)

exit_detail (ray.util.state.common.WorkerState attribute)

exit_type (ray.util.state.common.WorkerState attribute)

expand_paths() (ray.data.datasource.BaseFileMetadataProvider method)

Experiment

(class in ray.tune)


key: chroma:document
string_value:
Experiment

(class in ray.tune)


experiment_path (ray.tune.ExperimentAnalysis property)

(ray.tune.ResultGrid property)


experiment_tag (ray.tune.experiment.trial.Trial attribute)

experimental() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

ExperimentAnalysis (class in ray.tune)

ExperimentPlateauStopper (class in ray.tune.stopper)

explained_variance() (in module ray.rllib.utils.tf_utils)

(in module ray.rllib.utils.torch_utils)


Exploration (class in ray.rllib.utils.exploration.exploration)

exploration() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

ExponentialSchedule (class in ray.rllib.utils.schedules.exponential_schedule)

export_checkpoint() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


export_model() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.rllib.policy.Policy method)


key: chroma:document
string_value:
(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)

(ray.tune.Trainable method)


export_policy_checkpoint() (ray.rllib.algorithms.algorithm.Algorithm method)

export_policy_model() (ray.rllib.algorithms.algorithm.Algorithm method)

extensions_fields (ray.runtime_env.RuntimeEnv attribute)

ExternalEnv (class in ray.rllib.env.external_env)

ExternalMultiAgentEnv (class in ray.rllib.env.external_multi_agent_env)

extra_action_out() (ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)

extra_action_out_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

extra_compute_grad_fetches() (ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)

extra_grad_process() (ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)

extra_learn_fetches_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)



F


fail_fast (ray.train.FailureConfig attribute)


key: chroma:document
string_value:
F


fail_fast (ray.train.FailureConfig attribute)

FAILED (ray.job_submission.JobStatus attribute)

failure_config (ray.train.RunConfig attribute)

FailureConfig (class in ray.train)

FastFileMetadataProvider (class in ray.data.datasource)

Fault tolerance

fault_tolerance() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

fc() (in module ray.rllib.utils.numpy)

fetch_ready_async_reqs() (ray.rllib.evaluation.worker_set.WorkerSet method)

field_names (ray.data.datasource.Partitioning attribute)

FIFOScheduler (class in ray.tune.schedulers)

FileMetadataProvider (class in ray.data.datasource)

FilenameProvider (class in ray.data.datasource)

filesystem (ray.data.datasource.Partitioning attribute)

(ray.train.Checkpoint attribute)

(ray.train.Result property)

(ray.tune.ResultGrid property)


filter() (ray.data.Dataset method)

filter_param_dict_for_optimizer() (ray.rllib.core.learner.learner.Learner method)


key: chroma:document
string_value:
filter_param_dict_for_optimizer() (ray.rllib.core.learner.learner.Learner method)

filterable_columns() (ray.util.state.common.JobState class method)

find_free_port() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

FINISHED (ray.tune.search.ax.AxSearch attribute)

(ray.tune.search.bayesopt.BayesOptSearch attribute)

(ray.tune.search.bohb.TuneBOHB attribute)

(ray.tune.search.ConcurrencyLimiter attribute)

(ray.tune.search.dragonfly.DragonflySearch attribute)

(ray.tune.search.hebo.HEBOSearch attribute)

(ray.tune.search.hyperopt.HyperOptSearch attribute)

(ray.tune.search.nevergrad.NevergradSearch attribute)

(ray.tune.search.optuna.OptunaSearch attribute)

(ray.tune.search.Repeater attribute)

(ray.tune.search.Searcher attribute)

(ray.tune.search.sigopt.SigOptSearch attribute)

(ray.tune.search.skopt.SkOptSearch attribute)

(ray.tune.search.zoopt.ZOOptSearch attribute)


fit() (ray.data.preprocessor.Preprocessor method)

(ray.data.preprocessors.Categorizer method)


key: chroma:document
string_value:
fit() (ray.data.preprocessor.Preprocessor method)

(ray.data.preprocessors.Categorizer method)

(ray.data.preprocessors.Concatenator method)

(ray.data.preprocessors.CustomKBinsDiscretizer method)

(ray.data.preprocessors.LabelEncoder method)

(ray.data.preprocessors.MaxAbsScaler method)

(ray.data.preprocessors.MinMaxScaler method)

(ray.data.preprocessors.MultiHotEncoder method)

(ray.data.preprocessors.Normalizer method)

(ray.data.preprocessors.OneHotEncoder method)

(ray.data.preprocessors.OrdinalEncoder method)

(ray.data.preprocessors.PowerTransformer method)

(ray.data.preprocessors.RobustScaler method)

(ray.data.preprocessors.SimpleImputer method)

(ray.data.preprocessors.StandardScaler method)

(ray.data.preprocessors.UniformKBinsDiscretizer method)

(ray.train.data_parallel_trainer.DataParallelTrainer method)

(ray.train.gbdt_trainer.GBDTTrainer method)

(ray.train.horovod.HorovodTrainer method)

(ray.train.lightgbm.LightGBMTrainer method)


key: chroma:document
string_value:
(ray.train.horovod.HorovodTrainer method)

(ray.train.lightgbm.LightGBMTrainer method)

(ray.train.tensorflow.TensorflowTrainer method)

(ray.train.torch.TorchTrainer method)

(ray.train.trainer.BaseTrainer method)

(ray.train.xgboost.XGBoostTrainer method)

(ray.tune.Tuner method)


fit_transform() (ray.data.preprocessor.Preprocessor method)

(ray.data.preprocessors.Categorizer method)

(ray.data.preprocessors.Concatenator method)

(ray.data.preprocessors.CustomKBinsDiscretizer method)

(ray.data.preprocessors.LabelEncoder method)

(ray.data.preprocessors.MaxAbsScaler method)

(ray.data.preprocessors.MinMaxScaler method)

(ray.data.preprocessors.MultiHotEncoder method)

(ray.data.preprocessors.Normalizer method)

(ray.data.preprocessors.OneHotEncoder method)

(ray.data.preprocessors.OrdinalEncoder method)

(ray.data.preprocessors.PowerTransformer method)

(ray.data.preprocessors.RobustScaler method)

(ray.data.preprocessors.SimpleImputer method)


key: chroma:document
string_value:
(ray.data.preprocessors.RobustScaler method)

(ray.data.preprocessors.SimpleImputer method)

(ray.data.preprocessors.StandardScaler method)

(ray.data.preprocessors.UniformKBinsDiscretizer method)


flat_map() (ray.data.Dataset method)

flatten_inputs_to_1d_tensor() (in module ray.rllib.utils.numpy)

(in module ray.rllib.utils.tf_utils)

(in module ray.rllib.utils.torch_utils)




for_block() (ray.data.block.BlockAccessor static method)

for_policy() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

foreach_env() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.evaluation.worker_set.WorkerSet method)


foreach_env_with_context() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.evaluation.worker_set.WorkerSet method)


foreach_module() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

foreach_policy() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.evaluation.worker_set.WorkerSet method)


key: chroma:document
string_value:
(ray.rllib.evaluation.worker_set.WorkerSet method)


foreach_policy_to_train() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.evaluation.worker_set.WorkerSet method)


foreach_worker() (ray.rllib.evaluation.worker_set.WorkerSet method)

foreach_worker_async() (ray.rllib.evaluation.worker_set.WorkerSet method)

foreach_worker_with_id() (ray.rllib.evaluation.worker_set.WorkerSet method)

forward() (ray.rllib.models.modelv2.ModelV2 method)

(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)

(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)


forward_exploration() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


forward_inference() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


forward_rnn() (ray.rllib.models.tf.recurrent_net.RecurrentNetwork method)


key: chroma:document
string_value:
forward_rnn() (ray.rllib.models.tf.recurrent_net.RecurrentNetwork method)

FORWARD_TRAIN (ray.rllib.core.learner.learner.TorchCompileWhatToCompile attribute)

forward_train() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


FRAGMENTS (ray.rllib.utils.replay_buffers.replay_buffer.StorageUnit attribute)

Framework

framework (ray.rllib.core.learner.learner.Learner attribute)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule attribute)

(ray.rllib.core.rl_module.rl_module.RLModule attribute)


framework() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

framework_hyperparameters (ray.rllib.core.learner.learner.LearnerSpec attribute)

FrameworkHyperparameters (class in ray.rllib.core.learner.learner)

freeze() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

from_arrow() (in module ray.data)

from_arrow_refs() (in module ray.data)


key: chroma:document
string_value:
from_arrow() (in module ray.data)

from_arrow_refs() (in module ray.data)

from_checkpoint() (ray.rllib.algorithms.algorithm.Algorithm static method)

(ray.rllib.core.rl_module.rl_module.RLModule class method)

(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 static method)

(ray.rllib.policy.Policy static method)

(ray.rllib.policy.policy.Policy static method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 static method)


from_dask() (in module ray.data)

from_dict() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig class method)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec class method)

(ray.rllib.core.rl_module.rl_module.RLModuleConfig class method)

(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec class method)


from_directory() (ray.train.Checkpoint class method)

from_huggingface() (in module ray.data)

from_items() (in module ray.data)

from_json() (ray.job_config.JobConfig class method)

(ray.job_submission.JobInfo class method)


key: chroma:document
string_value:
from_json() (ray.job_config.JobConfig class method)

(ray.job_submission.JobInfo class method)

(ray.tune.Experiment class method)


from_mars() (in module ray.data)

from_modin() (in module ray.data)

from_module() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec class method)

from_numpy() (in module ray.data)

from_numpy_refs() (in module ray.data)

from_pandas() (in module ray.data)

from_pandas_refs() (in module ray.data)

from_path() (ray.train.Result class method)

from_placement_group_factory() (ray.train.ScalingConfig class method)

from_spark() (in module ray.data)

from_state() (ray.rllib.algorithms.algorithm.Algorithm static method)

(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 static method)

(ray.rllib.policy.Policy static method)

(ray.rllib.policy.policy.Policy static method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 static method)


from_tf() (in module ray.data)

from_torch() (in module ray.data)


key: chroma:document
string_value:
from_tf() (in module ray.data)

from_torch() (in module ray.data)

fromkeys() (ray.rllib.policy.policy_map.PolicyMap method)

(ray.rllib.policy.sample_batch.SampleBatch method)

(ray.runtime_env.RuntimeEnv method)

(ray.runtime_env.RuntimeEnvConfig method)


full() (ray.util.queue.Queue method)

func_or_class (ray.serve.Deployment property)

func_or_class_name (ray.util.state.common.TaskState attribute)

(ray.util.state.common.TaskSummaryPerFuncOrClassName attribute)


FunctionTrainable (class in ray.tune.trainable.function_trainable)



G


Gauge (class in ray.serve.metrics)

(class in ray.util.metrics)


GaussianNoise (class in ray.rllib.utils.exploration.gaussian_noise)

GBDTTrainer (class in ray.train.gbdt_trainer)

GCS / Global Control Service

gcs_address (ray.runtime_context.RuntimeContext property)

get() (in module ray)

(ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

(ray.rllib.policy.sample_batch.SampleBatch method)


key: chroma:document
string_value:
(ray.rllib.policy.sample_batch.SampleBatch method)

(ray.runtime_context.RuntimeContext method)

(ray.runtime_env.RuntimeEnvConfig method)

(ray.util.queue.Queue method)


get_action() (ray.rllib.env.external_env.ExternalEnv method)

(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)

(ray.rllib.env.policy_client.PolicyClient method)


get_action_dist_cls() (ray.rllib.core.models.catalog.Catalog method)

get_actor() (in module ray)

(in module ray.util.state)


get_actor_id() (ray.runtime_context.RuntimeContext method)

get_actor_name() (ray.runtime_context.RuntimeContext method)

get_address() (ray.job_submission.JobSubmissionClient method)

get_agent_ids() (ray.rllib.env.base_env.BaseEnv method)

get_all_configs() (ray.tune.ExperimentAnalysis method)

get_all_trials() (ray.tune.web_server.TuneClient method)

get_app_handle() (in module ray.serve)

get_assigned_resources() (ray.runtime_context.RuntimeContext method)


key: chroma:document
string_value:
get_assigned_resources() (ray.runtime_context.RuntimeContext method)

get_async() (ray.data.random_access_dataset.RandomAccessDataset method)

(ray.util.queue.Queue method)


get_auto_filled_metrics() (ray.tune.Trainable method)

get_batch_divisibility_req() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


get_best_checkpoint() (ray.train.Result method)

(ray.tune.ExperimentAnalysis method)


get_best_config() (ray.tune.ExperimentAnalysis method)

get_best_result() (ray.tune.ResultGrid method)

get_best_trial() (ray.tune.ExperimentAnalysis method)

get_catalog() (ray.rllib.core.rl_module.rl_module.RLModuleConfig method)

get_checkpoint() (in module ray.train)

get_collective_group_size() (in module ray.util.collective.collective)

get_config() (ray.data.DataContext method)

(ray.rllib.algorithms.algorithm.Algorithm method)

(ray.tune.Trainable method)


key: chroma:document
string_value:
(ray.rllib.algorithms.algorithm.Algorithm method)

(ray.tune.Trainable method)


get_connector_metrics() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


get_context() (in module ray.train)

get_current() (ray.data.DataContext static method)

get_current_placement_group() (in module ray.util)

get_data() (ray.rllib.evaluation.sampler.SamplerInput method)

get_dataframe() (ray.tune.ResultGrid method)

get_dataset_config() (ray.train.data_parallel_trainer.DataParallelTrainer method)

(ray.train.horovod.HorovodTrainer method)

(ray.train.tensorflow.TensorflowTrainer method)

(ray.train.torch.TorchTrainer method)


get_dataset_shard() (in module ray.train)

get_default_learner_class() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

get_default_policy_class() (ray.rllib.algorithms.algorithm.Algorithm class method)


key: chroma:document
string_value:
get_default_policy_class() (ray.rllib.algorithms.algorithm.Algorithm class method)

get_default_rl_module_spec() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

get_deployment_handle() (in module ray.serve)

get_device() (in module ray.rllib.utils.torch_utils)

(in module ray.train.torch)


get_downscale_smoothing_factor() (ray.serve.config.AutoscalingConfig method)

get_empty_schema_dict() (ray.serve.schema.ServeApplicationSchema static method)

(ray.serve.schema.ServeDeploySchema static method)

(ray.serve.schema.ServeInstanceDetails static method)


get_evaluation_config_object() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

get_experiment_name() (ray.train.context.TrainContext method)

get_exploration_action() (ray.rllib.utils.exploration.exploration.Exploration method)

get_exploration_action_dist_cls() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


key: chroma:document
string_value:
(ray.rllib.core.rl_module.rl_module.RLModule method)


get_exploration_optimizer() (ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)

(ray.rllib.utils.exploration.exploration.Exploration method)

(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)

(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)

(ray.rllib.utils.exploration.parameter_noise.ParameterNoise method)

(ray.rllib.utils.exploration.random.Random method)

(ray.rllib.utils.exploration.random_encoder.RE3 method)

(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)


get_exploration_state() (ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


get_extra_batches() (ray.rllib.evaluation.sampler.SamplerInput method)

get_filename_for_block() (ray.data.datasource.FilenameProvider method)

get_filename_for_row() (ray.data.datasource.FilenameProvider method)


key: chroma:document
string_value:
get_filename_for_row() (ray.data.datasource.FilenameProvider method)

get_filters() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

get_global_vars() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

get_gpu_devices() (in module ray.rllib.utils.tf_utils)

get_gpu_ids() (in module ray)

get_group_by_name() (ray.util.collective.collective.GroupManager method)

get_host() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)

(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)

(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)


key: chroma:document
string_value:
(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)

(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)


get_hps_for_module() (ray.rllib.core.learner.learner.LearnerHyperparameters method)

get_in_queue_stats() (ray.rllib.core.learner.learner_group.LearnerGroup method)

get_inference_action_dist_cls() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


get_initial_state() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)

(ray.rllib.models.modelv2.ModelV2 method)

(ray.rllib.models.tf.recurrent_net.RecurrentNetwork method)

(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)

(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)

(ray.rllib.Policy method)

(ray.rllib.policy.policy.Policy method)


key: chroma:document
string_value:
(ray.rllib.Policy method)

(ray.rllib.policy.policy.Policy method)


get_internal_block_refs() (ray.data.Dataset method)

get_job_id() (ray.runtime_context.RuntimeContext method)

get_job_info() (ray.job_submission.JobSubmissionClient method)

get_job_logs() (ray.job_submission.JobSubmissionClient method)

get_job_status() (ray.job_submission.JobSubmissionClient method)

get_last_checkpoint() (ray.tune.ExperimentAnalysis method)

get_learner_hyperparameters() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

get_local_rank() (ray.train.context.TrainContext method)

get_local_world_size() (ray.train.context.TrainContext method)

get_log() (in module ray.util.state)

get_marl_config() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec method)

get_marl_module_spec() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

get_metadata() (in module ray.workflow)

(ray.data.block.BlockAccessor method)

(ray.train.Checkpoint method)


key: chroma:document
string_value:
(ray.data.block.BlockAccessor method)

(ray.train.Checkpoint method)

(ray.train.context.TrainContext method)




get_metrics() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.evaluation.sampler.SamplerInput method)


get_model() (ray.train.lightgbm.LightGBMTrainer static method)

(ray.train.xgboost.XGBoostTrainer static method)


get_module_state() (ray.rllib.core.learner.learner.Learner method)

get_multi_agent_setup() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

get_multiplexed_model_id() (in module ray.serve)

get_name() (ray.data.BlockBasedFileDatasink method)

(ray.data.Datasink method)

(ray.data.Datasource method)

(ray.data.RowBasedFileDatasink method)


get_next() (ray.util.ActorPool method)

get_next_unordered() (ray.util.ActorPool method)

get_node() (in module ray.util.state)

get_node_id() (ray.runtime_context.RuntimeContext method)

get_node_ip() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)


key: chroma:document
string_value:
get_node_ip() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

get_node_rank() (ray.train.context.TrainContext method)

get_nowait() (ray.util.queue.Queue method)

get_nowait_batch() (ray.util.queue.Queue method)

get_num_samples_loaded_into_buffer() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


get_objects() (in module ray.util.state)

get_optimizer() (ray.rllib.core.learner.learner.Learner method)

get_optimizer_state() (ray.rllib.core.learner.learner.Learner method)

get_optimizers_for_module() (ray.rllib.core.learner.learner.Learner method)

get_output() (in module ray.workflow)

get_output_async() (in module ray.workflow)

get_param_ref() (ray.rllib.core.learner.learner.Learner method)

get_parameters() (ray.rllib.core.learner.learner.Learner method)

get_params_dict() (ray.rllib.core.learner.learner.LearnerSpec method)

get_placeholder() (in module ray.rllib.utils.tf_utils)


key: chroma:document
string_value:
get_placeholder() (in module ray.rllib.utils.tf_utils)

get_placement_group() (in module ray.util.state)

get_placement_group_id() (ray.runtime_context.RuntimeContext method)

get_policies_to_train() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

get_policy() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)


get_preprocessor() (ray.rllib.core.models.catalog.Catalog static method)

get_rank() (in module ray.util.collective.collective)

get_read_tasks() (ray.data.Datasource method)

(ray.data.datasource.Reader method)


get_replica_context() (in module ray.serve)

get_resource_ids() (ray.runtime_context.RuntimeContext method)

get_results() (ray.tune.Tuner method)

get_rl_module_config() (ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec method)

get_rollout_fragment_length() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

get_runtime_context() (in module ray.runtime_context)


key: chroma:document
string_value:
get_runtime_context() (in module ray.runtime_context)

get_runtime_env_string() (ray.runtime_context.RuntimeContext method)

get_session() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


get_single_step_input_dict() (ray.rllib.policy.sample_batch.SampleBatch method)

get_state() (ray.rllib.core.learner.learner.Learner method)

(ray.rllib.core.learner.learner_group.LearnerGroup method)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.utils.exploration.curiosity.Curiosity method)

(ray.rllib.utils.exploration.exploration.Exploration method)

(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)


key: chroma:document
string_value:
(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)

(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)

(ray.rllib.utils.exploration.random.Random method)

(ray.rllib.utils.exploration.random_encoder.RE3 method)

(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)

(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)

(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)

(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)

(ray.tune.Callback method)

(ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)


key: chroma:document
string_value:
(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.logger.TBXLoggerCallback method)


get_status() (in module ray.workflow)

get_storage() (ray.train.context.TrainContext method)

get_sub_environments() (ray.rllib.env.base_env.BaseEnv method)

(ray.rllib.env.vector_env._VectorizedGymEnv method)

(ray.rllib.env.vector_env.VectorEnv method)


get_task() (in module ray.util.state)

get_task_id() (ray.runtime_context.RuntimeContext method)

get_tokenizer_config() (ray.rllib.core.models.catalog.Catalog class method)

get_torch_compile_learner_config() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

get_torch_compile_worker_config() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

get_tower_stats() (ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)

get_train_action_dist_cls() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


key: chroma:document
string_value:
(ray.rllib.core.rl_module.rl_module.RLModule method)


get_trainable_name() (ray.tune.Experiment class method)

get_trial() (ray.tune.web_server.TuneClient method)

get_trial_dir() (ray.train.context.TrainContext method)

get_trial_id() (ray.train.context.TrainContext method)

get_trial_name() (ray.train.context.TrainContext method)

get_trial_resources() (ray.train.context.TrainContext method)

get_upscale_smoothing_factor() (ray.serve.config.AutoscalingConfig method)

get_user_configured_option_names() (ray.serve.schema.DeploymentSchema method)

get_version() (ray.job_submission.JobSubmissionClient method)

get_weights() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.rllib.core.learner.learner_group.LearnerGroup method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


get_worker() (in module ray.util.state)

get_worker_id() (ray.runtime_context.RuntimeContext method)


key: chroma:document
string_value:
get_worker_id() (ray.runtime_context.RuntimeContext method)

get_world_rank() (ray.train.context.TrainContext method)

get_world_size() (ray.train.context.TrainContext method)

getName() (ray.rllib.evaluation.sampler.AsyncSampler method)

GetTimeoutError

global_norm() (in module ray.rllib.utils.torch_utils)

graceful_shutdown_timeout_s (ray.serve.schema.DeploymentSchema attribute)

graceful_shutdown_wait_loop_s (ray.serve.schema.DeploymentSchema attribute)

grad_clip (ray.rllib.core.learner.learner.LearnerHyperparameters attribute)

grad_clip_by (ray.rllib.core.learner.learner.LearnerHyperparameters attribute)

grad_stats_fn() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

grid_search() (in module ray.tune)

groupby() (ray.data.Dataset method)

GroupedData (class in ray.data.grouped_data)

GroupManager (class in ray.util.collective.collective)

grpc_options (ray.serve.schema.ServeDeploySchema attribute)

(ray.serve.schema.ServeInstanceDetails attribute)


key: chroma:document
string_value:
(ray.serve.schema.ServeInstanceDetails attribute)


grpc_servicer_func_callable (ray.serve.config.gRPCOptions property)

grpc_servicer_functions (ray.serve.config.gRPCOptions attribute)

(ray.serve.schema.gRPCOptionsSchema attribute)




H


has_checkpoint() (ray.tune.search.basic_variant.BasicVariantGenerator method)

has_free() (ray.util.ActorPool method)

has_next() (ray.util.ActorPool method)

has_serializable_lineage() (ray.data.Dataset method)

Head node

head_bundle_is_empty (ray.tune.execution.placement_groups.PlacementGroupFactory property)

head_cpus (ray.tune.execution.placement_groups.PlacementGroupFactory property)

HeadOnly (ray.serve.config.ProxyLocation attribute)

health_check_period_s (ray.serve.schema.DeploymentSchema attribute)

health_check_timeout_s (ray.serve.schema.DeploymentSchema attribute)

healthy_worker_ids() (ray.rllib.evaluation.worker_set.WorkerSet method)

HEBOSearch (class in ray.tune.search.hebo)

Histogram (class in ray.serve.metrics)


key: chroma:document
string_value:
HEBOSearch (class in ray.tune.search.hebo)

Histogram (class in ray.serve.metrics)

(class in ray.util.metrics)


HIVE (ray.data.datasource.PartitionStyle attribute)



HorovodConfig (class in ray.train.horovod)

HorovodTrainer (class in ray.train.horovod)

host (ray.serve.config.HTTPOptions attribute)

(ray.serve.schema.HTTPOptionsSchema attribute)

(ray.serve.schema.ServeApplicationSchema attribute)


HPO

hps (ray.rllib.core.learner.learner.Learner property)

http_options (ray.serve.schema.ServeDeploySchema attribute)

(ray.serve.schema.ServeInstanceDetails attribute)


huber_loss() (in module ray.rllib.utils.numpy)

(in module ray.rllib.utils.tf_utils)

(in module ray.rllib.utils.torch_utils)


humanify() (ray.util.state.common.JobState class method)

HyperBandForBOHB (class in ray.tune.schedulers)

HyperBandScheduler (class in ray.tune.schedulers)

HyperOptSearch (class in ray.tune.search.hyperopt)



I



    ID

      
ray-get command line option


key: chroma:document
string_value:
I



    ID

      
ray-get command line option


id (ray.job_submission.DriverInfo attribute)

ident (ray.rllib.evaluation.sampler.AsyncSampler property)

ImpalaConfig (class in ray.rllib.algorithms.impala.impala)

import_from_h5() (ray.rllib.models.modelv2.ModelV2 method)

(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)

(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)


import_model() (ray.rllib.algorithms.algorithm.Algorithm method)

import_model_from_h5() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


import_path (ray.serve.schema.ServeApplicationSchema attribute)

import_path_format_valid() (ray.serve.schema.ServeApplicationSchema class method)


    IMPORT_PATHS

      
serve-build command line option


import_policy_model_from_h5() (ray.rllib.algorithms.algorithm.Algorithm method)


key: chroma:document
string_value:
import_policy_model_from_h5() (ray.rllib.algorithms.algorithm.Algorithm method)

inc() (ray.serve.metrics.Counter method)

(ray.util.metrics.Counter method)


info (ray.serve.metrics.Counter property)

(ray.serve.metrics.Gauge property)

(ray.serve.metrics.Histogram property)

(ray.util.metrics.Counter property)

(ray.util.metrics.Gauge property)

(ray.util.metrics.Histogram property)


INFOS (ray.rllib.policy.sample_batch.SampleBatch attribute)

Ingress Deployment

ingress() (in module ray.serve)

init() (in module ray)

init_collective_group() (in module ray.util.collective.collective)

init_local_path() (ray.tune.experiment.trial.Trial method)

init_logdir() (ray.tune.experiment.trial.Trial method)

init_method (ray.train.torch.TorchConfig attribute)

init_view_requirements() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


key: chroma:document
string_value:
(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


initial_replicas (ray.serve.config.AutoscalingConfig attribute)

input_config (ray.rllib.offline.io_context.IOContext property)

input_files (ray.data.block.BlockMetadata attribute)

input_files() (ray.data.Dataset method)

input_specs_exploration() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


input_specs_inference() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


input_specs_train() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)




InputReader (class in ray.rllib.offline.input_reader)

inspect_serializability() (in module ray.util)

inverse_transform() (ray.data.preprocessors.LabelEncoder method)

IOContext (class in ray.rllib.offline.io_context)


key: chroma:document
string_value:
IOContext (class in ray.rllib.offline.io_context)

ip (ray.util.state.common.ObjectState attribute)

(ray.util.state.common.WorkerState attribute)


is_alive (ray.util.state.common.WorkerState attribute)

is_alive() (ray.rllib.evaluation.sampler.AsyncSampler method)

is_atari (ray.rllib.algorithms.algorithm_config.AlgorithmConfig property)

is_detached (ray.util.state.common.ActorState attribute)

(ray.util.state.common.PlacementGroupState attribute)


is_empty (ray.util.placement_group.PlacementGroup property)

is_finished() (ray.tune.search.basic_variant.BasicVariantGenerator method)

is_group_initialized() (in module ray.util.collective.collective)

is_head_node (ray.util.state.common.NodeState attribute)

is_initialized() (in module ray)

is_local (ray.rllib.core.learner.learner_group.LearnerGroup property)

is_multi_agent() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

is_policy_to_train() (ray.rllib.evaluation.worker_set.WorkerSet method)


key: chroma:document
string_value:
is_policy_to_train() (ray.rllib.evaluation.worker_set.WorkerSet method)

is_recurrent() (ray.rllib.Policy method)

(ray.rllib.policy.policy.Policy method)


is_single_trajectory() (ray.rllib.policy.sample_batch.SampleBatch method)

is_stateful() (ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


is_terminal() (ray.job_submission.JobStatus method)

is_terminated_or_truncated() (ray.rllib.policy.sample_batch.SampleBatch method)

is_time_major() (ray.rllib.models.modelv2.ModelV2 method)

(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)

(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)


is_training (ray.rllib.policy.sample_batch.SampleBatch property)

isDaemon() (ray.rllib.evaluation.sampler.AsyncSampler method)

items() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

(ray.rllib.policy.policy_map.PolicyMap method)

(ray.rllib.policy.sample_batch.SampleBatch method)


key: chroma:document
string_value:
(ray.rllib.policy.policy_map.PolicyMap method)

(ray.rllib.policy.sample_batch.SampleBatch method)

(ray.runtime_env.RuntimeEnv method)

(ray.runtime_env.RuntimeEnvConfig method)


iter_batches() (ray.data.DataIterator method)

(ray.data.Dataset method)


iter_rows() (ray.data.block.BlockAccessor method)

(ray.data.Dataset method)


iter_tf_batches() (ray.data.Dataset method)

iter_torch_batches() (ray.data.DataIterator method)

(ray.data.Dataset method)


iteration (ray.rllib.algorithms.algorithm.Algorithm property)

(ray.tune.Trainable property)


iterator() (ray.data.Dataset method)



J


java_actor_class() (in module ray.cross_language)

java_function() (in module ray.cross_language)

Job


    JOB_ID

      
ray-job-logs command line option

ray-job-status command line option

ray-job-stop command line option


job_id (ray.job_submission.JobDetails attribute)

(ray.runtime_context.RuntimeContext property)

(ray.util.state.common.ActorState attribute)


key: chroma:document
string_value:
(ray.runtime_context.RuntimeContext property)

(ray.util.state.common.ActorState attribute)

(ray.util.state.common.TaskState attribute)




JobConfig (class in ray.job_config)

JobInfo (class in ray.job_submission)

JobState (class in ray.util.state.common)

JobStatus (class in ray.job_submission)

JobSubmissionClient (class in ray.job_submission)

JobType (class in ray.job_submission)

join() (ray.rllib.evaluation.sampler.AsyncSampler method)

JsonLoggerCallback (class in ray.tune.logger)

JsonReader (class in ray.rllib.offline.json_reader)

JupyterNotebookReporter (class in ray.tune)

jvm_options (ray.job_config.JobConfig attribute)



K


keep_alive_timeout_s (ray.serve.config.HTTPOptions attribute)

(ray.serve.schema.HTTPOptionsSchema attribute)


key (ray.train.horovod.HorovodConfig attribute)

keys() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.policy.policy_map.PolicyMap method)


key: chroma:document
string_value:
(ray.rllib.policy.policy_map.PolicyMap method)

(ray.rllib.policy.sample_batch.SampleBatch method)

(ray.runtime_env.RuntimeEnv method)

(ray.runtime_env.RuntimeEnvConfig method)




kill() (in module ray)

kill_node() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)

known_fields (ray.runtime_env.RuntimeEnv attribute)

(ray.runtime_env.RuntimeEnvConfig attribute)




L


l2_loss() (in module ray.rllib.utils.numpy)

(in module ray.rllib.utils.tf_utils)

(in module ray.rllib.utils.torch_utils)


LabelEncoder (class in ray.data.preprocessors)

labels (ray.util.state.common.NodeState attribute)

language (ray.util.state.common.TaskState attribute)

last() (ray.rllib.env.base_env.BaseEnv method)

last_deployed_time_s (ray.serve.schema.ApplicationDetails attribute)

last_output() (ray.rllib.models.modelv2.ModelV2 method)

(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)

(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)


key: chroma:document
string_value:
(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)


latent_dims (ray.rllib.core.models.catalog.Catalog property)

learn_on_batch() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


learn_on_batch_from_replay_buffer() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


learn_on_loaded_batch() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


Learner (class in ray.rllib.core.learner.learner)

learner_class (ray.rllib.algorithms.algorithm_config.AlgorithmConfig property)

(ray.rllib.core.learner.learner.LearnerSpec attribute)


learner_group_scaling_config (ray.rllib.core.learner.learner.LearnerSpec attribute)


key: chroma:document
string_value:
learner_group_scaling_config (ray.rllib.core.learner.learner.LearnerSpec attribute)

learner_hyperparameters (ray.rllib.core.learner.learner.LearnerSpec attribute)

LearnerGroup (class in ray.rllib.core.learner.learner_group)

LearnerHyperparameters (class in ray.rllib.core.learner.learner)

LearnerSpec (class in ray.rllib.core.learner.learner)

learning_rate (ray.rllib.core.learner.learner.LearnerHyperparameters attribute)

LightGBMTrainer (class in ray.train.lightgbm)

lightning_module_state_dict() (ray.train.lightning.RayFSDPStrategy method)

limit() (ray.data.Dataset method)

Lineage

LinearSchedule (class in ray.rllib.utils.schedules.linear_schedule)

list_actors() (in module ray.util.state)

list_all() (in module ray.workflow)

list_columns() (ray.util.state.common.JobState class method)

list_jobs() (in module ray.util.state)

(ray.job_submission.JobSubmissionClient method)


list_logs() (in module ray.util.state)

list_nodes() (in module ray.util.state)


key: chroma:document
string_value:
list_logs() (in module ray.util.state)

list_nodes() (in module ray.util.state)

list_objects() (in module ray.util.state)

list_placement_groups() (in module ray.util.state)

list_runtime_envs() (in module ray.util.state)

list_tasks() (in module ray.util.state)

list_workers() (in module ray.util.state)

load_batch_into_buffer() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


load_checkpoint() (ray.tune.Trainable method)

load_module_state() (ray.rllib.core.learner.learner_group.LearnerGroup method)



load_state() (ray.rllib.core.learner.learner.Learner method)

(ray.rllib.core.learner.learner_group.LearnerGroup method)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


load_state_path (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec attribute)


key: chroma:document
string_value:
load_state_path (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec attribute)

(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)


local_dir (ray.train.RunConfig attribute)

(ray.tune.Experiment property)

(ray.tune.experiment.trial.Trial property)


local_path (ray.tune.Experiment property)

(ray.tune.experiment.trial.Trial attribute)


local_worker() (ray.rllib.evaluation.worker_set.WorkerSet method)

locality_with_output (ray.data.ExecutionOptions attribute)

LocalRayletDiedError

location (ray.serve.config.HTTPOptions attribute)

location_backfill_no_server() (ray.serve.config.HTTPOptions class method)

lock() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

log_action() (ray.rllib.env.external_env.ExternalEnv method)

(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)

(ray.rllib.env.policy_client.PolicyClient method)


log_result() (ray.tune.Trainable method)


key: chroma:document
string_value:
(ray.rllib.env.policy_client.PolicyClient method)


log_result() (ray.tune.Trainable method)

log_returns() (ray.rllib.env.external_env.ExternalEnv method)

(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)

(ray.rllib.env.policy_client.PolicyClient method)


log_to_file (ray.train.RunConfig attribute)

log_trial_end() (ray.tune.logger.LoggerCallback method)

log_trial_restore() (ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.logger.TBXLoggerCallback method)


log_trial_result() (ray.tune.logger.LoggerCallback method)

log_trial_save() (ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.logger.TBXLoggerCallback method)


log_trial_start() (ray.tune.logger.CSVLoggerCallback method)


key: chroma:document
string_value:
log_trial_start() (ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.LoggerCallback method)


logdir (ray.rllib.algorithms.algorithm.Algorithm property)

(ray.tune.experiment.trial.Trial property)

(ray.tune.Trainable property)


LoggerCallback (class in ray.tune.logger)

logging_config (ray.serve.schema.DeploymentSchema attribute)

(ray.serve.schema.ServeApplicationSchema attribute)

(ray.serve.schema.ServeDeploySchema attribute)


lograndint() (in module ray.tune)

loguniform() (in module ray.tune)

look_back_period_s (ray.serve.config.AutoscalingConfig attribute)

loss() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


lstm() (in module ray.rllib.utils.numpy)



M


make_action_immutable() (in module ray.rllib.utils.numpy)

make_model() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)


key: chroma:document
string_value:
make_model() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


make_model_and_action_dist() (ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)

make_multi_agent() (in module ray.rllib.env.multi_agent_env)

make_rl_module() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


make_tf_callable() (in module ray.rllib.utils.tf_utils)

map() (ray.data.Dataset method)

(ray.util.ActorPool method)


map_batches() (ray.data.Dataset method)

map_groups() (ray.data.grouped_data.GroupedData method)

map_unordered() (ray.util.ActorPool method)

MARWILConfig (class in ray.rllib.algorithms.marwil.marwil)

materialize() (ray.data.Dataset method)

Max (class in ray.data.aggregate)

max() (ray.data.Dataset method)

(ray.data.grouped_data.GroupedData method)


key: chroma:document
string_value:
max() (ray.data.Dataset method)

(ray.data.grouped_data.GroupedData method)


max_concurrent_queries (ray.serve.Deployment property)

(ray.serve.schema.DeploymentSchema attribute)


max_concurrent_trials (ray.tune.TuneConfig attribute)

max_failures (ray.train.FailureConfig attribute)

max_replicas (ray.serve.config.AutoscalingConfig attribute)

max_replicas_per_node (ray.serve.schema.DeploymentSchema attribute)

MaxAbsScaler (class in ray.data.preprocessors)

MaximumIterationStopper (class in ray.tune.stopper)

maybe_add_time_dimension() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


maybe_remove_time_dimension() (ray.rllib.policy.policy.Policy method)

Mean (class in ray.data.aggregate)

mean() (ray.data.Dataset method)

(ray.data.grouped_data.GroupedData method)


MedianStoppingRule (class in ray.tune.schedulers)


key: chroma:document
string_value:
(ray.data.grouped_data.GroupedData method)


MedianStoppingRule (class in ray.tune.schedulers)

memory (ray.serve.schema.RayActorOptionsSchema attribute)

MemoryTrackingCallbacks (class in ray.rllib.algorithms.callbacks)

merge_algorithm_configs() (ray.rllib.algorithms.algorithm.Algorithm class method)

merge_sorted_blocks() (ray.data.block.BlockAccessor static method)

message (ray.job_submission.JobDetails attribute)

(ray.job_submission.JobInfo attribute)

(ray.serve.schema.ApplicationDetails attribute)

(ray.serve.schema.DeploymentDetails attribute)


metadata (ray.job_config.JobConfig attribute)

(ray.job_submission.JobDetails attribute)

(ray.job_submission.JobInfo attribute)


method() (in module ray)

metric (ray.tune.schedulers.FIFOScheduler property)

(ray.tune.schedulers.HyperBandForBOHB property)

(ray.tune.schedulers.HyperBandScheduler property)

(ray.tune.schedulers.MedianStoppingRule property)

(ray.tune.schedulers.pb2.PB2 property)


key: chroma:document
string_value:
(ray.tune.schedulers.MedianStoppingRule property)

(ray.tune.schedulers.pb2.PB2 property)

(ray.tune.schedulers.PopulationBasedTraining property)

(ray.tune.schedulers.PopulationBasedTrainingReplay property)

(ray.tune.schedulers.ResourceChangingScheduler property)

(ray.tune.schedulers.TrialScheduler property)

(ray.tune.search.ax.AxSearch property)

(ray.tune.search.basic_variant.BasicVariantGenerator property)

(ray.tune.search.bayesopt.BayesOptSearch property)

(ray.tune.search.bohb.TuneBOHB property)

(ray.tune.search.ConcurrencyLimiter property)

(ray.tune.search.dragonfly.DragonflySearch property)

(ray.tune.search.hebo.HEBOSearch property)

(ray.tune.search.hyperopt.HyperOptSearch property)

(ray.tune.search.nevergrad.NevergradSearch property)

(ray.tune.search.optuna.OptunaSearch property)

(ray.tune.search.Repeater property)

(ray.tune.search.Searcher property)

(ray.tune.search.sigopt.SigOptSearch property)

(ray.tune.search.skopt.SkOptSearch property)


key: chroma:document
string_value:
(ray.tune.search.sigopt.SigOptSearch property)

(ray.tune.search.skopt.SkOptSearch property)

(ray.tune.search.zoopt.ZOOptSearch property)

(ray.tune.TuneConfig attribute)




metrics (ray.train.Result attribute)

metrics() (ray.rllib.models.modelv2.ModelV2 method)

(ray.rllib.models.tf.tf_modelv2.TFModelV2 method)

(ray.rllib.models.torch.torch_modelv2.TorchModelV2 method)


metrics_dataframe (ray.train.Result attribute)

metrics_interval_s (ray.serve.config.AutoscalingConfig attribute)

middlewares (ray.serve.config.HTTPOptions attribute)

min() (ray.data.Dataset method)

(ray.data.grouped_data.GroupedData method)


min_replicas (ray.serve.config.AutoscalingConfig attribute)

minimize_and_clip() (in module ray.rllib.utils.tf_utils)

(in module ray.rllib.utils.torch_utils)


MinMaxScaler (class in ray.data.preprocessors)

MixedInput (class in ray.rllib.offline.mixed_input)

mode (ray.tune.search.ax.AxSearch property)

(ray.tune.search.bayesopt.BayesOptSearch property)


key: chroma:document
string_value:
mode (ray.tune.search.ax.AxSearch property)

(ray.tune.search.bayesopt.BayesOptSearch property)

(ray.tune.search.bohb.TuneBOHB property)

(ray.tune.search.ConcurrencyLimiter property)

(ray.tune.search.dragonfly.DragonflySearch property)

(ray.tune.search.hebo.HEBOSearch property)

(ray.tune.search.hyperopt.HyperOptSearch property)

(ray.tune.search.nevergrad.NevergradSearch property)

(ray.tune.search.optuna.OptunaSearch property)

(ray.tune.search.Repeater property)

(ray.tune.search.Searcher property)

(ray.tune.search.sigopt.SigOptSearch property)

(ray.tune.search.skopt.SkOptSearch property)

(ray.tune.search.zoopt.ZOOptSearch property)

(ray.tune.TuneConfig attribute)


Model

model_config_dict (ray.rllib.core.rl_module.rl_module.RLModuleConfig attribute)

(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)


ModelV2 (class in ray.rllib.models.modelv2)


    module

      
ray

ray.rllib.env.multi_agent_env

ray.util.collective.collective


key: chroma:document
string_value:
module

      
ray

ray.rllib.env.multi_agent_env

ray.util.collective.collective


module (ray.rllib.core.learner.learner.Learner property)

(ray.rllib.core.learner.learner.LearnerSpec attribute)


module_class (ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)

module_spec (ray.rllib.core.learner.learner.LearnerSpec attribute)

module_specs (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec attribute)

modules_to_load (ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec attribute)

Multi-agent

multi_agent() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

multi_gpu_train_one_step() (in module ray.rllib.execution.train_ops)

multiagent (ray.rllib.algorithms.algorithm_config.AlgorithmConfig property)

MultiAgentBatch (class in ray.rllib.policy.sample_batch)

MultiAgentEnv (class in ray.rllib.env.multi_agent_env)

MultiAgentPrioritizedReplayBuffer (class in ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer)


key: chroma:document
string_value:
MultiAgentReplayBuffer (class in ray.rllib.utils.replay_buffers.multi_agent_replay_buffer)

MultiAgentRLModule (class in ray.rllib.core.rl_module.marl_module)

MultiAgentRLModuleSpec (class in ray.rllib.core.rl_module.marl_module)

MultiCallbacks (class in ray.rllib.algorithms.callbacks)

multiget() (ray.data.random_access_dataset.RandomAccessDataset method)

MultiHotEncoder (class in ray.data.preprocessors)

multiplexed() (in module ray.serve)



N


name (ray.rllib.evaluation.sampler.AsyncSampler property)

(ray.serve.Deployment property)

(ray.serve.schema.ApplicationDetails attribute)

(ray.serve.schema.DeploymentDetails attribute)

(ray.serve.schema.DeploymentSchema attribute)

(ray.serve.schema.ServeApplicationSchema attribute)

(ray.train.RunConfig attribute)

(ray.util.state.common.ActorState attribute)

(ray.util.state.common.PlacementGroupState attribute)

(ray.util.state.common.TaskState attribute)


Namespace

namespace (ray.runtime_context.RuntimeContext property)


key: chroma:document
string_value:
Namespace

namespace (ray.runtime_context.RuntimeContext property)

native_id (ray.rllib.evaluation.sampler.AsyncSampler property)

nested_host_and_port() (ray.serve.schema.ServeDeploySchema class method)

NevergradSearch (class in ray.tune.search.nevergrad)

next() (ray.rllib.env.policy_server_input.PolicyServerInput method)

(ray.rllib.offline.input_reader.InputReader method)


NEXT_OBS (ray.rllib.policy.sample_batch.SampleBatch attribute)

next_trial() (ray.tune.search.basic_variant.BasicVariantGenerator method)

nics (ray.train.horovod.HorovodConfig attribute)

Node

node_id (ray.data.block.BlockExecStats attribute)

(ray.runtime_context.RuntimeContext property)

(ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy attribute)

(ray.util.state.common.ActorState attribute)

(ray.util.state.common.NodeState attribute)

(ray.util.state.common.RuntimeEnvState attribute)

(ray.util.state.common.TaskState attribute)

(ray.util.state.common.WorkerState attribute)


key: chroma:document
string_value:
(ray.util.state.common.TaskState attribute)

(ray.util.state.common.WorkerState attribute)


node_id_to_summary (ray.util.state.common.StateSummary attribute)

node_ip (ray.util.state.common.NodeState attribute)

node_ip_address (ray.job_submission.DriverInfo attribute)

node_name (ray.util.state.common.NodeState attribute)

NodeAffinitySchedulingStrategy (class in ray.util.scheduling_strategies)

nodes() (in module ray)



NodeState (class in ray.util.state.common)

NOOP (ray.tune.schedulers.FIFOScheduler attribute)

(ray.tune.schedulers.HyperBandForBOHB attribute)

(ray.tune.schedulers.HyperBandScheduler attribute)

(ray.tune.schedulers.MedianStoppingRule attribute)

(ray.tune.schedulers.pb2.PB2 attribute)

(ray.tune.schedulers.PopulationBasedTraining attribute)

(ray.tune.schedulers.PopulationBasedTrainingReplay attribute)

(ray.tune.schedulers.ResourceChangingScheduler attribute)

(ray.tune.schedulers.TrialScheduler attribute)


key: chroma:document
string_value:
(ray.tune.schedulers.TrialScheduler attribute)


normalized_base_dir (ray.data.datasource.Partitioning property)

Normalizer (class in ray.data.preprocessors)

num_blocks() (ray.data.Dataset method)

num_cpus (ray.serve.config.HTTPOptions attribute)

(ray.serve.schema.RayActorOptionsSchema attribute)


num_cpus_per_worker (ray.train.ScalingConfig property)

num_errors (ray.tune.ResultGrid property)

num_gpus (ray.serve.schema.RayActorOptionsSchema attribute)

num_gpus_per_worker (ray.train.ScalingConfig property)

num_healthy_remote_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)

num_healthy_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)

num_in_flight_async_reqs() (ray.rllib.evaluation.worker_set.WorkerSet method)

num_remote_worker_restarts() (ray.rllib.evaluation.worker_set.WorkerSet method)

num_remote_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)

num_replicas (ray.serve.Deployment property)


key: chroma:document
string_value:
num_replicas (ray.serve.Deployment property)

(ray.serve.schema.DeploymentSchema attribute)


num_replicas_and_autoscaling_config_mutually_exclusive() (ray.serve.schema.DeploymentSchema class method)

num_rows (ray.data.block.BlockMetadata attribute)

num_rows() (ray.data.block.BlockAccessor method)

num_samples (ray.tune.TuneConfig attribute)

num_state_tensors() (ray.rllib.Policy method)

(ray.rllib.policy.policy.Policy method)


num_terminated (ray.tune.ResultGrid property)

num_to_keep (ray.train.CheckpointConfig attribute)

num_workers (ray.rllib.algorithms.algorithm_config.AlgorithmConfig property)

(ray.train.ScalingConfig attribute)




O


Object

Object ownership

Object reference

Object spilling

Object store

object_id (ray.util.state.common.ObjectState attribute)

object_size (ray.util.state.common.ObjectState attribute)

object_store_memory (ray.serve.schema.RayActorOptionsSchema attribute)

object_store_memory_str() (ray.data.ExecutionResources method)


key: chroma:document
string_value:
object_store_memory_str() (ray.data.ExecutionResources method)

ObjectFetchTimedOutError

OBJECTIVE_MAP (ray.tune.search.sigopt.SigOptSearch attribute)

ObjectLostError

ObjectReconstructionFailedError

ObjectReconstructionFailedLineageEvictedError

ObjectReconstructionFailedMaxAttemptsExceededError

ObjectState (class in ray.util.state.common)

ObjectStoreFullError

ObjectSummaries (class in ray.util.state.common)

ObjectSummaryPerKey (class in ray.util.state.common)

OBS (ray.rllib.policy.sample_batch.SampleBatch attribute)

OBS_EMBEDS (ray.rllib.policy.sample_batch.SampleBatch attribute)

Observation

observation_space (ray.rllib.core.rl_module.rl_module.RLModuleConfig attribute)

(ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec attribute)

(ray.rllib.env.base_env.BaseEnv property)


observation_space_contains() (ray.rllib.env.base_env.BaseEnv method)

observation_space_sample() (ray.rllib.env.base_env.BaseEnv method)

observe() (ray.serve.metrics.Histogram method)


key: chroma:document
string_value:
observe() (ray.serve.metrics.Histogram method)

(ray.util.metrics.Histogram method)


of() (ray.data.datasource.PathPartitionFilter static method)

(ray.data.datasource.PathPartitionParser static method)


Off-Policy

Offline data

Offline RL

offline_data() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

On-Policy

on_algorithm_init() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_checkpoint() (ray.tune.Callback method)

(ray.tune.experiment.trial.Trial method)

(ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.logger.TBXLoggerCallback method)


on_checkpoint_loaded() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_create_policy() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_episode_created() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)


key: chroma:document
string_value:
on_episode_created() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_episode_end() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

(ray.rllib.utils.exploration.curiosity.Curiosity method)

(ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)

(ray.rllib.utils.exploration.exploration.Exploration method)

(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)

(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)

(ray.rllib.utils.exploration.random.Random method)

(ray.rllib.utils.exploration.random_encoder.RE3 method)

(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)


on_episode_start() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

(ray.rllib.utils.exploration.curiosity.Curiosity method)

(ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)

(ray.rllib.utils.exploration.exploration.Exploration method)


key: chroma:document
string_value:
(ray.rllib.utils.exploration.exploration.Exploration method)

(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)

(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)

(ray.rllib.utils.exploration.random.Random method)

(ray.rllib.utils.exploration.random_encoder.RE3 method)

(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)


on_episode_step() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_evaluate_end() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_evaluate_start() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_experiment_end() (ray.tune.Callback method)

(ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.logger.TBXLoggerCallback method)


on_global_var_update() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)


key: chroma:document
string_value:
on_global_var_update() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


on_learn_on_batch() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_postprocess_trajectory() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_restore() (ray.tune.experiment.trial.Trial method)

on_sample_end() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_save() (ray.train.huggingface.transformers.RayTrainReportCallback method)

on_shutdown() (ray.train.backend.Backend method)

on_start() (ray.train.backend.Backend method)

on_step_begin() (ray.tune.Callback method)

(ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.logger.TBXLoggerCallback method)


on_step_end() (ray.tune.Callback method)


key: chroma:document
string_value:
(ray.tune.logger.TBXLoggerCallback method)


on_step_end() (ray.tune.Callback method)

(ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.logger.TBXLoggerCallback method)




on_sub_environment_created() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_train_result() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_training_start() (ray.train.backend.Backend method)

on_trial_add() (ray.tune.schedulers.AsyncHyperBandScheduler method)

(ray.tune.schedulers.HyperBandForBOHB method)

(ray.tune.schedulers.HyperBandScheduler method)

(ray.tune.schedulers.TrialScheduler method)


on_trial_complete() (ray.tune.Callback method)

(ray.tune.schedulers.AsyncHyperBandScheduler method)

(ray.tune.schedulers.HyperBandForBOHB method)

(ray.tune.schedulers.HyperBandScheduler method)

(ray.tune.schedulers.TrialScheduler method)


key: chroma:document
string_value:
(ray.tune.schedulers.HyperBandScheduler method)

(ray.tune.schedulers.TrialScheduler method)

(ray.tune.search.ax.AxSearch method)

(ray.tune.search.bayesopt.BayesOptSearch method)

(ray.tune.search.dragonfly.DragonflySearch method)

(ray.tune.search.hebo.HEBOSearch method)

(ray.tune.search.hyperopt.HyperOptSearch method)

(ray.tune.search.nevergrad.NevergradSearch method)

(ray.tune.search.Repeater method)

(ray.tune.search.Searcher method)

(ray.tune.search.sigopt.SigOptSearch method)

(ray.tune.search.skopt.SkOptSearch method)

(ray.tune.search.zoopt.ZOOptSearch method)


on_trial_error() (ray.tune.Callback method)

(ray.tune.schedulers.HyperBandForBOHB method)

(ray.tune.schedulers.HyperBandScheduler method)

(ray.tune.schedulers.TrialScheduler method)


on_trial_recover() (ray.tune.Callback method)

(ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)


key: chroma:document
string_value:
(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.logger.TBXLoggerCallback method)


on_trial_remove() (ray.tune.schedulers.AsyncHyperBandScheduler method)

(ray.tune.schedulers.HyperBandForBOHB method)

(ray.tune.schedulers.HyperBandScheduler method)

(ray.tune.schedulers.TrialScheduler method)


on_trial_restore() (ray.tune.Callback method)

on_trial_result() (ray.tune.Callback method)

(ray.tune.schedulers.AsyncHyperBandScheduler method)

(ray.tune.schedulers.HyperBandForBOHB method)

(ray.tune.schedulers.HyperBandScheduler method)

(ray.tune.schedulers.MedianStoppingRule method)

(ray.tune.schedulers.TrialScheduler method)

(ray.tune.search.ax.AxSearch method)

(ray.tune.search.basic_variant.BasicVariantGenerator method)

(ray.tune.search.bayesopt.BayesOptSearch method)

(ray.tune.search.dragonfly.DragonflySearch method)

(ray.tune.search.hebo.HEBOSearch method)

(ray.tune.search.nevergrad.NevergradSearch method)


key: chroma:document
string_value:
(ray.tune.search.hebo.HEBOSearch method)

(ray.tune.search.nevergrad.NevergradSearch method)

(ray.tune.search.Repeater method)

(ray.tune.search.Searcher method)

(ray.tune.search.sigopt.SigOptSearch method)

(ray.tune.search.skopt.SkOptSearch method)

(ray.tune.search.zoopt.ZOOptSearch method)


on_trial_save() (ray.tune.Callback method)

on_trial_start() (ray.tune.Callback method)

on_workers_recreated() (ray.rllib.algorithms.callbacks.DefaultCallbacks method)

on_write_complete() (ray.data.Datasink method)

(ray.data.Datasource method)


on_write_failed() (ray.data.BlockBasedFileDatasink method)

(ray.data.Datasink method)

(ray.data.Datasource method)

(ray.data.RowBasedFileDatasink method)


on_write_start() (ray.data.BlockBasedFileDatasink method)

(ray.data.Datasink method)

(ray.data.Datasource method)

(ray.data.RowBasedFileDatasink method)


one_hot() (in module ray.rllib.utils.numpy)

(in module ray.rllib.utils.tf_utils)

(in module ray.rllib.utils.torch_utils)


key: chroma:document
string_value:
(in module ray.rllib.utils.tf_utils)

(in module ray.rllib.utils.torch_utils)


OneHotEncoder (class in ray.data.preprocessors)

OOM (Out of Memory)

optimizer (ray.tune.search.bayesopt.BayesOptSearch attribute)

(ray.tune.search.zoopt.ZOOptSearch attribute)


optimizer() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


options() (ray.actor.ActorClass method)

(ray.remote_function.RemoteFunction method)

(ray.serve.Deployment method)

(ray.serve.handle.DeploymentHandle method)

(ray.serve.handle.RayServeHandle method)

(ray.serve.handle.RayServeSyncHandle method)


OptunaSearch (class in ray.tune.search.optuna)

order (ray.tune.integration.lightgbm.TuneReportCallback attribute)

(ray.tune.integration.lightgbm.TuneReportCheckpointCallback attribute)


OrdinalEncoder (class in ray.data.preprocessors)

OrnsteinUhlenbeckNoise (class in ray.rllib.utils.exploration.ornstein_uhlenbeck_noise)

OutOfDiskError


key: chroma:document
string_value:
OutOfDiskError

output_config (ray.rllib.offline.io_context.IOContext property)

output_queue (ray.tune.JupyterNotebookReporter property)

output_specs_exploration() (ray.rllib.core.rl_module.rl_module.RLModule method)

output_specs_inference() (ray.rllib.core.rl_module.rl_module.RLModule method)

output_specs_train() (ray.rllib.core.rl_module.rl_module.RLModule method)

overrides() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig class method)

OwnerDiedError



P


par_iter_init() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

par_iter_next() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

par_iter_next_batch() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

par_iter_slice() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

par_iter_slice_batch() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

ParameterNoise (class in ray.rllib.utils.exploration.parameter_noise)


key: chroma:document
string_value:
ParameterNoise (class in ray.rllib.utils.exploration.parameter_noise)

parent_task_id (ray.util.state.common.TaskState attribute)

ParquetMetadataProvider (class in ray.data.datasource)

parser (ray.data.datasource.PathPartitionFilter property)

Partitioning (class in ray.data.datasource)

PartitionStyle (class in ray.data.datasource)

path (ray.train.Checkpoint attribute)

(ray.train.Result property)

(ray.tune.Experiment property)

(ray.tune.experiment.trial.Trial attribute)


PathPartitionFilter (class in ray.data.datasource)

PathPartitionParser (class in ray.data.datasource)

PAUSE (ray.tune.schedulers.FIFOScheduler attribute)

(ray.tune.schedulers.HyperBandForBOHB attribute)

(ray.tune.schedulers.HyperBandScheduler attribute)

(ray.tune.schedulers.MedianStoppingRule attribute)

(ray.tune.schedulers.pb2.PB2 attribute)

(ray.tune.schedulers.PopulationBasedTraining attribute)

(ray.tune.schedulers.PopulationBasedTrainingReplay attribute)


key: chroma:document
string_value:
(ray.tune.schedulers.PopulationBasedTrainingReplay attribute)

(ray.tune.schedulers.ResourceChangingScheduler attribute)

(ray.tune.schedulers.TrialScheduler attribute)


PB2 (class in ray.tune.schedulers.pb2)

PENDING (ray.job_submission.JobStatus attribute)

pid (ray.job_submission.DriverInfo attribute)

(ray.serve.schema.ReplicaDetails attribute)

(ray.util.state.common.ActorState attribute)

(ray.util.state.common.ObjectState attribute)

(ray.util.state.common.WorkerState attribute)


PiecewiseSchedule (class in ray.rllib.utils.schedules.piecewise_schedule)

ping() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)

(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)


key: chroma:document
string_value:
(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)

(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)


Placement group

Placement Group Bundle

placement_group (ray.util.scheduling_strategies.PlacementGroupSchedulingStrategy attribute)

placement_group() (in module ray.util)

placement_group_bundle_index (ray.util.scheduling_strategies.PlacementGroupSchedulingStrategy attribute)

placement_group_bundles (ray.serve.schema.DeploymentSchema attribute)

placement_group_capture_child_tasks (ray.util.scheduling_strategies.PlacementGroupSchedulingStrategy attribute)

placement_group_id (ray.util.state.common.ActorState attribute)

(ray.util.state.common.PlacementGroupState attribute)

(ray.util.state.common.TaskState attribute)


placement_group_strategy (ray.serve.schema.DeploymentSchema attribute)

placement_group_table() (in module ray.util)

placement_group_timeout_s (ray.train.horovod.HorovodConfig attribute)


key: chroma:document
string_value:
placement_group_timeout_s (ray.train.horovod.HorovodConfig attribute)

placement_strategy (ray.train.ScalingConfig attribute)

PlacementGroup (class in ray.util.placement_group)

PlacementGroupFactory (class in ray.tune.execution.placement_groups)

PlacementGroupSchedulingStrategy (class in ray.util.scheduling_strategies)

PlacementGroupState (class in ray.util.state.common)

PlasmaObjectNotAvailable

plugin_uris() (ray.runtime_env.RuntimeEnv method)

Policy

(class in ray.rllib.policy.policy)


policy_batches (ray.rllib.policy.sample_batch.MultiAgentBatch attribute)

PolicyClient (class in ray.rllib.env.policy_client)

PolicyMap (class in ray.rllib.policy.policy_map)

PolicyServerInput (class in ray.rllib.env.policy_server_input)

poll() (ray.rllib.env.base_env.BaseEnv method)

PolynomialSchedule (class in ray.rllib.utils.schedules.polynomial_schedule)

pop() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

(ray.rllib.policy.policy_map.PolicyMap method)


key: chroma:document
string_value:
(ray.rllib.policy.policy_map.PolicyMap method)

(ray.rllib.policy.sample_batch.SampleBatch method)

(ray.runtime_env.RuntimeEnv method)

(ray.runtime_env.RuntimeEnvConfig method)


pop_idle() (ray.util.ActorPool method)

popitem() (ray.rllib.policy.policy_map.PolicyMap method)

(ray.rllib.policy.sample_batch.SampleBatch method)

(ray.runtime_env.RuntimeEnv method)

(ray.runtime_env.RuntimeEnvConfig method)




PopulationBasedTraining (class in ray.tune.schedulers)

PopulationBasedTrainingReplay (class in ray.tune.schedulers)

port (ray.serve.config.gRPCOptions attribute)

(ray.serve.config.HTTPOptions attribute)

(ray.serve.schema.gRPCOptionsSchema attribute)

(ray.serve.schema.HTTPOptionsSchema attribute)

(ray.serve.schema.ServeApplicationSchema attribute)


port_forward (ray.tune.web_server.TuneClient attribute)

postprocess_gradients() (ray.rllib.core.learner.learner.Learner method)

postprocess_gradients_for_module() (ray.rllib.core.learner.learner.Learner method)


key: chroma:document
string_value:
postprocess_gradients_for_module() (ray.rllib.core.learner.learner.Learner method)

postprocess_trajectory() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)

(ray.rllib.utils.exploration.curiosity.Curiosity method)

(ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy method)

(ray.rllib.utils.exploration.exploration.Exploration method)

(ray.rllib.utils.exploration.gaussian_noise.GaussianNoise method)

(ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise method)

(ray.rllib.utils.exploration.random.Random method)

(ray.rllib.utils.exploration.random_encoder.RE3 method)

(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)


PowerTransformer (class in ray.data.preprocessors)

PPOConfig (class in ray.rllib.algorithms.ppo.ppo)

Predictor


key: chroma:document
string_value:
PPOConfig (class in ray.rllib.algorithms.ppo.ppo)

Predictor

preferred_batch_format() (ray.data.preprocessor.Preprocessor class method)

(ray.data.preprocessors.Categorizer class method)

(ray.data.preprocessors.Concatenator class method)

(ray.data.preprocessors.CustomKBinsDiscretizer class method)

(ray.data.preprocessors.LabelEncoder class method)

(ray.data.preprocessors.MaxAbsScaler class method)

(ray.data.preprocessors.MinMaxScaler class method)

(ray.data.preprocessors.MultiHotEncoder class method)

(ray.data.preprocessors.Normalizer class method)

(ray.data.preprocessors.OneHotEncoder class method)

(ray.data.preprocessors.OrdinalEncoder class method)

(ray.data.preprocessors.PowerTransformer class method)

(ray.data.preprocessors.RobustScaler class method)

(ray.data.preprocessors.SimpleImputer class method)

(ray.data.preprocessors.StandardScaler class method)

(ray.data.preprocessors.UniformKBinsDiscretizer class method)


key: chroma:document
string_value:
(ray.data.preprocessors.UniformKBinsDiscretizer class method)


prefetch_file_metadata() (ray.data.datasource.ParquetMetadataProvider method)

prepare_data_loader() (in module ray.train.torch)

prepare_dataset_shard() (in module ray.train.tensorflow)

prepare_model() (in module ray.train.torch)

prepare_read() (ray.data.Datasource method)

prepare_trainer() (in module ray.train.huggingface.transformers)

(in module ray.train.lightning)


preprocess_datasets() (ray.train.gbdt_trainer.GBDTTrainer method)

(ray.train.trainer.BaseTrainer method)


Preprocessor

(class in ray.data.preprocessor)


preserve_order (ray.data.ExecutionOptions attribute)

PREV_ACTIONS (ray.rllib.policy.sample_batch.SampleBatch attribute)

PREV_REWARDS (ray.rllib.policy.sample_batch.SampleBatch attribute)

PrioritizedReplayBuffer (class in ray.rllib.utils.replay_buffers.prioritized_replay_buffer)

probe_unhealthy_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)


key: chroma:document
string_value:
probe_unhealthy_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)

profiling_data (ray.util.state.common.TaskState attribute)

progress_reporter (ray.train.RunConfig attribute)

ProgressReporter (class in ray.tune)

proxies (ray.serve.schema.ServeInstanceDetails attribute)

proxy_location (ray.serve.schema.ServeDeploySchema attribute)

(ray.serve.schema.ServeInstanceDetails attribute)


ProxyLocation (class in ray.serve.config)

PUBLIC_KEYS (ray.tune.Experiment attribute)

public_spec (ray.tune.Experiment property)

PublicAPI() (in module ray.util.annotations)

push() (ray.util.ActorPool method)

put() (in module ray)

(ray.util.queue.Queue method)


put_async() (ray.util.queue.Queue method)

put_nowait() (ray.util.queue.Queue method)

put_nowait_batch() (ray.util.queue.Queue method)

python_environment() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)



Q


qlograndint() (in module ray.tune)

qloguniform() (in module ray.tune)


key: chroma:document
string_value:
Q


qlograndint() (in module ray.tune)

qloguniform() (in module ray.tune)

qrandint() (in module ray.tune)



qrandn() (in module ray.tune)

qsize() (ray.util.queue.Queue method)

Queue (class in ray.util.queue)

quniform() (in module ray.tune)



R


randint() (in module ray.tune)

randn() (in module ray.tune)

Random (class in ray.rllib.utils.exploration.random)

random_sample() (ray.data.Dataset method)

random_shuffle() (ray.data.block.BlockAccessor method)

(ray.data.Dataset method)


RandomAccessDataset (class in ray.data.random_access_dataset)

randomize_block_order() (ray.data.Dataset method)

range() (in module ray.data)

range_tensor() (in module ray.data)


    ray

      
module


Ray Agent

Ray application

Ray Client

Ray Cluster

Ray Dashboard


    ray-attach command line option

      
--cluster-name <cluster_name>

--log-color <log_color>

--log-style <log_style>

--new

--no-config-cache

--port-forward <port_forward>

--screen

--start

--tmux

--verbose

-n


key: chroma:document
string_value:
--new

--no-config-cache

--port-forward <port_forward>

--screen

--start

--tmux

--verbose

-n

-N

-p

-v

CLUSTER_CONFIG_FILE



    ray-debug command line option

      
--address <address>



    ray-down command line option

      
--cluster-name <cluster_name>

--keep-min-workers

--log-color <log_color>

--log-style <log_style>

--verbose

--workers-only

--yes

-n

-v

-y

CLUSTER_CONFIG_FILE



    ray-exec command line option

      
--cluster-name <cluster_name>

--disable-usage-stats

--log-color <log_color>

--log-style <log_style>

--no-config-cache

--port-forward <port_forward>

--run-env <run_env>

--screen

--start

--stop

--tmux

--verbose

-n

-p

-v

CLUSTER_CONFIG_FILE

CMD



    ray-get command line option

      
--address <address>

--timeout <timeout>

ID

RESOURCE



    ray-get_head_ip command line option

      
--cluster-name <cluster_name>

-n

CLUSTER_CONFIG_FILE



    ray-job-list command line option

      
--address <address>


key: chroma:document
string_value:
-n

CLUSTER_CONFIG_FILE



    ray-job-list command line option

      
--address <address>

--headers <headers>

--log-color <log_color>

--log-style <log_style>

--verbose

--verify <verify>

-v



    ray-job-logs command line option

      
--address <address>

--follow

--headers <headers>

--log-color <log_color>

--log-style <log_style>

--verbose

--verify <verify>

-f

-v

JOB_ID



    ray-job-status command line option

      
--address <address>

--headers <headers>

--log-color <log_color>

--log-style <log_style>

--verbose

--verify <verify>

-v

JOB_ID



    ray-job-stop command line option

      
--address <address>

--headers <headers>

--log-color <log_color>

--log-style <log_style>

--no-wait

--verbose

--verify <verify>

-v

JOB_ID



    ray-job-submit command line option

      
--address <address>

--entrypoint-memory <entrypoint_memory>

--entrypoint-num-cpus <entrypoint_num_cpus>

--entrypoint-num-gpus <entrypoint_num_gpus>


key: chroma:document
string_value:
--entrypoint-num-cpus <entrypoint_num_cpus>

--entrypoint-num-gpus <entrypoint_num_gpus>

--entrypoint-resources <entrypoint_resources>

--headers <headers>

--job-id <job_id>

--log-color <log_color>

--log-style <log_style>

--metadata-json <metadata_json>

--no-wait

--runtime-env <runtime_env>

--runtime-env-json <runtime_env_json>

--submission-id <submission_id>

--verbose

--verify <verify>

--working-dir <working_dir>

-v

ENTRYPOINT



    ray-list command line option

      
--address <address>

--detail

--filter <filter>

--format <format>

--limit <limit>

--timeout <timeout>

-f

RESOURCE



    ray-memory command line option

      
--address <address>

--group-by <group_by>

--n <num_entries>

--no-format

--num-entries

--redis_password <redis_password>

--sort-by <sort_by>

--stats-only

--units <units>



    ray-monitor command line option

      
--cluster-name <cluster_name>

--lines <lines>

--log-color <log_color>

--log-style <log_style>

--verbose

-n

-v


key: chroma:document
string_value:
--lines <lines>

--log-color <log_color>

--log-style <log_style>

--verbose

-n

-v

CLUSTER_CONFIG_FILE



    ray-start command line option

      
--address <address>

--autoscaling-config <autoscaling_config>

--block

--dashboard-agent-grpc-port <dashboard_agent_grpc_port>

--dashboard-agent-listen-port <dashboard_agent_listen_port>

--dashboard-grpc-port <dashboard_grpc_port>

--dashboard-host <dashboard_host>

--dashboard-port <dashboard_port>

--disable-usage-stats

--gcs-server-port <gcs_server_port>

--head

--include-dashboard <include_dashboard>

--log-color <log_color>

--log-style <log_style>

--max-worker-port <max_worker_port>

--metrics-export-port <metrics_export_port>

--min-worker-port <min_worker_port>

--no-redirect-output

--node-ip-address <node_ip_address>

--node-manager-port <node_manager_port>

--num-cpus <num_cpus>

--num-gpus <num_gpus>

--object-manager-port <object_manager_port>

--object-store-memory <object_store_memory>


key: chroma:document
string_value:
--object-manager-port <object_manager_port>

--object-store-memory <object_store_memory>

--plasma-directory <plasma_directory>

--plasma-store-socket-name <plasma_store_socket_name>

--port <port>

--ray-client-server-port <ray_client_server_port>

--ray-debugger-external

--raylet-socket-name <raylet_socket_name>

--resources <resources>

--runtime-env-agent-port <runtime_env_agent_port>

--storage <storage>

--temp-dir <temp_dir>

--verbose

--worker-port-list <worker_port_list>

-v



    ray-status command line option

      
--address <address>

--redis_password <redis_password>



    ray-stop command line option

      
--force

--grace-period <grace_period>

--log-color <log_color>

--log-style <log_style>

--verbose

-f

-g

-v



    ray-submit command line option

      
--args <args>

--cluster-name <cluster_name>

--disable-usage-stats

--extra-screen-args <extra_screen_args>

--log-color <log_color>

--log-style <log_style>

--no-config-cache


key: chroma:document
string_value:
--log-color <log_color>

--log-style <log_style>

--no-config-cache

--port-forward <port_forward>

--screen

--start

--stop

--tmux

--verbose

-n

-p

-v

CLUSTER_CONFIG_FILE

SCRIPT

SCRIPT_ARGS



    ray-summary-actors command line option

      
--address <address>

--timeout <timeout>



    ray-summary-objects command line option

      
--address <address>

--timeout <timeout>



    ray-summary-tasks command line option

      
--address <address>

--timeout <timeout>



    ray-timeline command line option

      
--address <address>



    ray-up command line option

      
--cluster-name <cluster_name>

--disable-usage-stats

--log-color <log_color>

--log-style <log_style>

--max-workers <max_workers>

--min-workers <min_workers>

--no-config-cache

--no-restart

--redirect-command-output

--restart-only

--use-login-shells

--use-normal-shells

--verbose

--yes

-n

-v

-y

CLUSTER_CONFIG_FILE





    ray.rllib.env.multi_agent_env

      
module


key: chroma:document
string_value:
--yes

-n

-v

-y

CLUSTER_CONFIG_FILE





    ray.rllib.env.multi_agent_env

      
module



    ray.util.collective.collective

      
module


ray_actor_options (ray.serve.Deployment property)

(ray.serve.schema.DeploymentSchema attribute)


ray_namespace (ray.job_config.JobConfig attribute)

(ray.util.state.common.ActorState attribute)


RayActorError

RayDDPStrategy (class in ray.train.lightning)

RayDeepSpeedStrategy (class in ray.train.lightning)

RayError

RayFSDPStrategy (class in ray.train.lightning)

Raylet

RayLightningEnvironment (class in ray.train.lightning)

RayServeHandle (class in ray.serve.handle)

RayServeSyncHandle (class in ray.serve.handle)

RayStateApiException

RaySystemError

RayTaskError

RayTrainReportCallback (class in ray.train.huggingface.transformers)

(class in ray.train.lightning)


RE3 (class in ray.rllib.utils.exploration.random_encoder)

read_all_files() (ray.rllib.offline.json_reader.JsonReader method)

read_bigquery() (in module ray.data)


key: chroma:document
string_value:
read_bigquery() (in module ray.data)

read_binary_files() (in module ray.data)

read_csv() (in module ray.data)

read_databricks_tables() (in module ray.data)

read_datasource() (in module ray.data)

read_images() (in module ray.data)

read_json() (in module ray.data)

read_mongo() (in module ray.data)

read_numpy() (in module ray.data)

read_parquet() (in module ray.data)

read_parquet_bulk() (in module ray.data)

read_sql() (in module ray.data)

read_text() (in module ray.data)

read_tfrecords() (in module ray.data)

read_webdataset() (in module ray.data)

Reader (class in ray.data.datasource)

ReadTask (class in ray.data)

ready() (ray.util.placement_group.PlacementGroup method)

reallocate_trial_resources_if_needed() (ray.tune.schedulers.ResourceChangingScheduler method)

record() (ray.serve.metrics.Counter method)

(ray.serve.metrics.Gauge method)

(ray.serve.metrics.Histogram method)

(ray.util.metrics.Counter method)

(ray.util.metrics.Gauge method)


key: chroma:document
string_value:
(ray.util.metrics.Counter method)

(ray.util.metrics.Gauge method)

(ray.util.metrics.Histogram method)


RecurrentNetwork (class in ray.rllib.models.tf.recurrent_net)

recv() (in module ray.util.collective.collective)

recv_multigpu() (in module ray.util.collective.collective)

reduce() (in module ray.util.collective.collective)

reduce_mean_ignore_inf() (in module ray.rllib.utils.tf_utils)

(in module ray.rllib.utils.torch_utils)


reduce_multigpu() (in module ray.util.collective.collective)

reducescatter() (in module ray.util.collective.collective)

reducescatter_multigpu() (in module ray.util.collective.collective)

ref_cnt (ray.util.state.common.RuntimeEnvState attribute)

ref_type_counts (ray.util.state.common.ObjectSummaryPerKey attribute)

reference_type (ray.util.state.common.ObjectState attribute)

register_analysis() (ray.tune.search.bayesopt.BayesOptSearch method)

register_env() (in module ray.tune)

register_if_needed() (ray.tune.Experiment class method)


key: chroma:document
string_value:
register_env() (in module ray.tune)

register_if_needed() (ray.tune.Experiment class method)

register_metric() (ray.rllib.core.learner.learner.Learner method)

register_metrics() (ray.rllib.core.learner.learner.Learner method)

register_optimizer() (ray.rllib.core.learner.learner.Learner method)

register_trainable() (in module ray.tune)

register_variables() (ray.rllib.models.tf.tf_modelv2.TFModelV2 method)

relative_logdir (ray.tune.experiment.trial.Trial attribute)

relu() (in module ray.rllib.utils.numpy)

Remote Class

Remote Function

remote() (in module ray)

(ray.serve.handle.DeploymentHandle method)

(ray.serve.handle.RayServeHandle method)

(ray.serve.handle.RayServeSyncHandle method)


remote_execution_api() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)

remote_path (ray.tune.Experiment property)

(ray.tune.experiment.trial.Trial attribute)


remote_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)


key: chroma:document
string_value:
remote_workers() (ray.rllib.evaluation.worker_set.WorkerSet method)

remove_config() (ray.data.DataContext method)

remove_module() (ray.rllib.core.learner.learner.Learner method)

(ray.rllib.core.learner.learner_group.LearnerGroup method)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)


remove_placement_group() (in module ray.util)

remove_policy() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)


render() (ray.rllib.env.multi_agent_env.MultiAgentEnv method)

repartition() (ray.data.Dataset method)

repeat() (ray.data.Dataset method)

Repeater (class in ray.tune.search)

replay() (ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)


ReplayBuffer (class in ray.rllib.utils.replay_buffers.replay_buffer)

Replica


key: chroma:document
string_value:
ReplayBuffer (class in ray.rllib.utils.replay_buffers.replay_buffer)

Replica

replica_id (ray.serve.schema.ReplicaDetails attribute)

replica_tag (ray.serve.context.ReplicaContext attribute)

ReplicaContext (class in ray.serve.context)

replicas (ray.serve.schema.DeploymentDetails attribute)

replicas_settings_valid() (ray.serve.config.AutoscalingConfig class method)

report() (in module ray.train)

(ray.tune.ProgressReporter method)


ReportCheckpointCallback (class in ray.train.tensorflow.keras)

reporting() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

repr_name (ray.util.state.common.ActorState attribute)

request_timeout_s (ray.serve.config.HTTPOptions attribute)

(ray.serve.schema.HTTPOptionsSchema attribute)


required_resources (ray.tune.execution.placement_groups.PlacementGroupFactory property)

(ray.util.state.common.ActorState attribute)

(ray.util.state.common.TaskState attribute)


key: chroma:document
string_value:
(ray.util.state.common.ActorState attribute)

(ray.util.state.common.TaskState attribute)


ReservoirReplayBuffer (class in ray.rllib.utils.replay_buffers.reservoir_replay_buffer)

reset() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.rllib.env.multi_agent_env.MultiAgentEnv method)

(ray.rllib.evaluation.worker_set.WorkerSet method)

(ray.tune.Trainable method)


reset_at() (ray.rllib.env.vector_env._VectorizedGymEnv method)

(ray.rllib.env.vector_env.VectorEnv method)


reset_config() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.tune.Trainable method)


reset_connectors() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


resolved_filesystem (ray.data.datasource.Partitioning property)


    RESOURCE

      
ray-get command line option

ray-list command line option


Resource (logical and physical)


key: chroma:document
string_value:
ray-get command line option

ray-list command line option


Resource (logical and physical)

resource_help() (ray.tune.Trainable class method)

resource_limits (ray.data.ExecutionOptions attribute)

ResourceChangingScheduler (class in ray.tune.schedulers)

resources (ray.serve.schema.RayActorOptionsSchema attribute)

resources() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

resources_per_worker (ray.train.ScalingConfig attribute)

resources_total (ray.util.state.common.NodeState attribute)

restart_at() (ray.rllib.env.vector_env._VectorizedGymEnv method)

(ray.rllib.env.vector_env.VectorEnv method)


restore() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.train.data_parallel_trainer.DataParallelTrainer class method)

(ray.train.gbdt_trainer.GBDTTrainer class method)

(ray.train.horovod.HorovodTrainer class method)

(ray.train.lightgbm.LightGBMTrainer class method)

(ray.train.tensorflow.TensorflowTrainer class method)


key: chroma:document
string_value:
(ray.train.tensorflow.TensorflowTrainer class method)

(ray.train.torch.TorchTrainer class method)

(ray.train.trainer.BaseTrainer class method)

(ray.train.xgboost.XGBoostTrainer class method)

(ray.tune.schedulers.AsyncHyperBandScheduler method)

(ray.tune.schedulers.FIFOScheduler method)

(ray.tune.schedulers.HyperBandForBOHB method)

(ray.tune.schedulers.HyperBandScheduler method)

(ray.tune.schedulers.MedianStoppingRule method)

(ray.tune.schedulers.pb2.PB2 method)

(ray.tune.schedulers.PopulationBasedTraining method)

(ray.tune.schedulers.PopulationBasedTrainingReplay method)

(ray.tune.schedulers.TrialScheduler method)

(ray.tune.search.bayesopt.BayesOptSearch method)

(ray.tune.search.hebo.HEBOSearch method)

(ray.tune.search.Searcher method)

(ray.tune.Trainable method)

(ray.tune.Tuner class method)


restore_connectors() (ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2 method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


key: chroma:document
string_value:
(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.policy.torch_policy_v2.TorchPolicyV2 method)


restore_from_dir() (ray.tune.search.ax.AxSearch method)

(ray.tune.search.basic_variant.BasicVariantGenerator method)

(ray.tune.search.bayesopt.BayesOptSearch method)

(ray.tune.search.bohb.TuneBOHB method)

(ray.tune.search.ConcurrencyLimiter method)

(ray.tune.search.dragonfly.DragonflySearch method)

(ray.tune.search.hebo.HEBOSearch method)

(ray.tune.search.hyperopt.HyperOptSearch method)

(ray.tune.search.nevergrad.NevergradSearch method)

(ray.tune.search.optuna.OptunaSearch method)

(ray.tune.search.Repeater method)

(ray.tune.search.Searcher method)

(ray.tune.search.sigopt.SigOptSearch method)

(ray.tune.search.skopt.SkOptSearch method)

(ray.tune.search.zoopt.ZOOptSearch method)


restore_workers() (ray.rllib.algorithms.algorithm.Algorithm method)

Result (class in ray.train)

result() (ray.serve.handle.DeploymentResponse method)


key: chroma:document
string_value:
Result (class in ray.train)

result() (ray.serve.handle.DeploymentResponse method)

ResultGrid (class in ray.tune)

results (ray.tune.ExperimentAnalysis property)

results_df (ray.tune.ExperimentAnalysis property)

resume() (in module ray.workflow)

resume_all() (in module ray.workflow)

resume_async() (in module ray.workflow)

RETURNS_TO_GO (ray.rllib.policy.sample_batch.SampleBatch attribute)

reuse_actors (ray.tune.TuneConfig attribute)

Reward

REWARDS (ray.rllib.policy.sample_batch.SampleBatch attribute)

right_zero_pad() (ray.rllib.policy.sample_batch.SampleBatch method)

rl_module() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

rl_module_spec (ray.rllib.algorithms.algorithm_config.AlgorithmConfig property)

RLModule (class in ray.rllib.core.rl_module.rl_module)

RLModuleConfig (class in ray.rllib.core.rl_module.rl_module)

RobustScaler (class in ray.data.preprocessors)

Rollout

Rollout Worker


key: chroma:document
string_value:
RobustScaler (class in ray.data.preprocessors)

Rollout

Rollout Worker

rollouts() (ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)

RolloutWorker (class in ray.rllib.evaluation.rollout_worker)

root_device (ray.train.lightning.RayDDPStrategy property)

(ray.train.lightning.RayDeepSpeedStrategy property)

(ray.train.lightning.RayFSDPStrategy property)


root_path (ray.serve.config.HTTPOptions attribute)

(ray.serve.schema.HTTPOptionsSchema attribute)


root_url (ray.serve.config.HTTPOptions attribute)

route_prefix (ray.serve.Deployment property)

(ray.serve.schema.ApplicationDetails attribute)

(ray.serve.schema.DeploymentSchema attribute)

(ray.serve.schema.ServeApplicationSchema attribute)


RowBasedFileDatasink (class in ray.data)

rows() (ray.rllib.policy.sample_batch.SampleBatch method)

run() (in module ray.serve)

(in module ray.workflow)

(ray.rllib.env.external_env.ExternalEnv method)

(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)


key: chroma:document
string_value:
(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)


run_async() (in module ray.workflow)

run_experiments() (in module ray.tune)

run_identifier (ray.tune.Experiment property)

RunConfig (class in ray.train)

RUNNING (ray.job_submission.JobStatus attribute)

Runtime environment

runtime_env (ray.job_submission.JobDetails attribute)

(ray.job_submission.JobInfo attribute)

(ray.runtime_context.RuntimeContext property)

(ray.serve.schema.RayActorOptionsSchema attribute)

(ray.serve.schema.ServeApplicationSchema attribute)

(ray.util.state.common.RuntimeEnvState attribute)


runtime_env_contains_remote_uris() (ray.serve.schema.RayActorOptionsSchema class method)

(ray.serve.schema.ServeApplicationSchema class method)


runtime_env_info (ray.util.state.common.TaskState attribute)

RuntimeContext (class in ray.runtime_context)

RuntimeEnv (class in ray.runtime_env)

RuntimeEnvConfig (class in ray.runtime_env)

RuntimeEnvSetupError


key: chroma:document
string_value:
RuntimeEnvConfig (class in ray.runtime_env)

RuntimeEnvSetupError

RuntimeEnvState (class in ray.util.state.common)



S


SACConfig (class in ray.rllib.algorithms.sac.sac)

sample() (ray.data.block.BlockAccessor method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)

(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)

(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)


sample_and_learn() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

sample_from() (in module ray.tune)

sample_min_n_steps_from_buffer() (in module ray.rllib.utils.replay_buffers.utils)


key: chroma:document
string_value:
sample_min_n_steps_from_buffer() (in module ray.rllib.utils.replay_buffers.utils)

sample_with_count() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

SampleBatch (class in ray.rllib.policy.sample_batch)

SamplerInput (class in ray.rllib.evaluation.sampler)

satisfies_limit() (ray.data.ExecutionResources method)

save() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.tune.schedulers.AsyncHyperBandScheduler method)

(ray.tune.schedulers.FIFOScheduler method)

(ray.tune.schedulers.HyperBandForBOHB method)

(ray.tune.schedulers.HyperBandScheduler method)

(ray.tune.schedulers.MedianStoppingRule method)

(ray.tune.schedulers.pb2.PB2 method)

(ray.tune.schedulers.PopulationBasedTraining method)

(ray.tune.schedulers.PopulationBasedTrainingReplay method)

(ray.tune.schedulers.TrialScheduler method)

(ray.tune.search.bayesopt.BayesOptSearch method)

(ray.tune.search.hebo.HEBOSearch method)

(ray.tune.search.Searcher method)

(ray.tune.Trainable method)


key: chroma:document
string_value:
(ray.tune.search.Searcher method)

(ray.tune.Trainable method)


save_checkpoint() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.tune.Trainable method)


save_state() (ray.rllib.core.learner.learner.Learner method)

(ray.rllib.core.learner.learner_group.LearnerGroup method)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)


save_to_checkpoint() (ray.rllib.core.rl_module.rl_module.RLModule method)

save_to_dir() (ray.tune.search.ax.AxSearch method)

(ray.tune.search.bayesopt.BayesOptSearch method)

(ray.tune.search.bohb.TuneBOHB method)

(ray.tune.search.ConcurrencyLimiter method)

(ray.tune.search.dragonfly.DragonflySearch method)

(ray.tune.search.hebo.HEBOSearch method)

(ray.tune.search.hyperopt.HyperOptSearch method)

(ray.tune.search.nevergrad.NevergradSearch method)

(ray.tune.search.optuna.OptunaSearch method)

(ray.tune.search.Repeater method)

(ray.tune.search.Searcher method)


key: chroma:document
string_value:
(ray.tune.search.Repeater method)

(ray.tune.search.Searcher method)

(ray.tune.search.sigopt.SigOptSearch method)

(ray.tune.search.skopt.SkOptSearch method)

(ray.tune.search.zoopt.ZOOptSearch method)


scale() (ray.data.ExecutionResources method)

ScalingConfig (class in ray.train)

Schedule (class in ray.rllib.utils.schedules.schedule)

scheduler (ray.tune.TuneConfig attribute)

schema (ray.data.block.BlockMetadata attribute)

schema() (ray.data.block.BlockAccessor method)

(ray.data.Dataset method)


scheme (ray.data.datasource.PathPartitionParser property)

scope_vars() (in module ray.rllib.utils.tf_utils)


    SCRIPT

      
ray-submit command line option



    SCRIPT_ARGS

      
ray-submit command line option


Search algorithm

Search Space

search_alg (ray.tune.TuneConfig attribute)

Searcher (class in ray.tune.search)

seed (ray.rllib.core.learner.learner.LearnerHyperparameters attribute)

select() (ray.data.block.BlockAccessor method)


key: chroma:document
string_value:
select() (ray.data.block.BlockAccessor method)

select_columns() (ray.data.Dataset method)

send() (in module ray.util.collective.collective)

send_actions() (ray.rllib.env.base_env.BaseEnv method)

send_multigpu() (in module ray.util.collective.collective)

SEQ_LENS (ray.rllib.policy.sample_batch.SampleBatch attribute)

sequence_mask() (in module ray.rllib.utils.torch_utils)

SEQUENCES (ray.rllib.utils.replay_buffers.replay_buffer.StorageUnit attribute)

serialize() (ray.data.preprocessor.Preprocessor method)

(ray.data.preprocessors.Categorizer method)

(ray.data.preprocessors.Concatenator method)

(ray.data.preprocessors.CustomKBinsDiscretizer method)

(ray.data.preprocessors.LabelEncoder method)

(ray.data.preprocessors.MaxAbsScaler method)

(ray.data.preprocessors.MinMaxScaler method)

(ray.data.preprocessors.MultiHotEncoder method)

(ray.data.preprocessors.Normalizer method)

(ray.data.preprocessors.OneHotEncoder method)

(ray.data.preprocessors.OrdinalEncoder method)


key: chroma:document
string_value:
(ray.data.preprocessors.OneHotEncoder method)

(ray.data.preprocessors.OrdinalEncoder method)

(ray.data.preprocessors.PowerTransformer method)

(ray.data.preprocessors.RobustScaler method)

(ray.data.preprocessors.SimpleImputer method)

(ray.data.preprocessors.StandardScaler method)

(ray.data.preprocessors.UniformKBinsDiscretizer method)

(ray.rllib.algorithms.algorithm_config.AlgorithmConfig method)


serialize_lineage() (ray.data.Dataset method)

serialize_metric() (ray.tune.search.sigopt.SigOptSearch static method)

serialize_result() (ray.tune.search.sigopt.SigOptSearch method)

serialized_runtime_env (ray.util.state.common.ActorState attribute)

servable_object (ray.serve.context.ReplicaContext attribute)

Serve application


    serve-build command line option

      
--app-dir <app_dir>

--grpc-servicer-functions <grpc_servicer_functions>

--output-path <output_path>

-d

-o

IMPORT_PATHS



    serve-config command line option

      
--address <address>

--name <name>

-a


key: chroma:document
string_value:
serve-config command line option

      
--address <address>

--name <name>

-a

-n



    serve-deploy command line option

      
--address <address>

-a

CONFIG_FILE_NAME



    serve-run command line option

      
--address <address>

--app-dir <app_dir>

--blocking

--host <host>

--non-blocking

--port <port>

--reload

--route-prefix <route_prefix>

--runtime-env <runtime_env>

--runtime-env-json <runtime_env_json>

--working-dir <working_dir>

-a

-d

-h

-p

-r

ARGUMENTS

CONFIG_OR_IMPORT_PATH



    serve-shutdown command line option

      
--address <address>

--yes

-a

-y



    serve-start command line option

      
--address <address>

--grpc-port <grpc_port>

--grpc-servicer-functions <grpc_servicer_functions>

--http-host <http_host>

--http-location <http_location>

--http-port <http_port>

--proxy-location <proxy_location>

-a



    serve-status command line option

      
--address <address>

--name <name>

-a

-n


ServeHandle

Session


key: chroma:document
string_value:
--address <address>

--name <name>

-a

-n


ServeHandle

Session

set() (ray.serve.metrics.Gauge method)

(ray.util.metrics.Gauge method)


set_config() (ray.data.DataContext method)

set_default_actor_lifetime() (ray.job_config.JobConfig method)

set_default_tags() (ray.util.metrics.Counter method)

(ray.util.metrics.Gauge method)

(ray.util.metrics.Histogram method)


set_finished() (ray.tune.search.basic_variant.BasicVariantGenerator method)

set_get_interceptor() (ray.rllib.policy.sample_batch.SampleBatch method)

set_global_vars() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

set_is_module_trainable() (ray.rllib.core.learner.learner_group.LearnerGroup method)

set_is_policy_to_train() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

set_location() (ray.tune.experiment.trial.Trial method)

set_max_concurrency() (ray.tune.search.ax.AxSearch method)

(ray.tune.search.bayesopt.BayesOptSearch method)

(ray.tune.search.dragonfly.DragonflySearch method)


key: chroma:document
string_value:
(ray.tune.search.dragonfly.DragonflySearch method)

(ray.tune.search.hyperopt.HyperOptSearch method)

(ray.tune.search.nevergrad.NevergradSearch method)

(ray.tune.search.optuna.OptunaSearch method)

(ray.tune.search.Repeater method)

(ray.tune.search.Searcher method)

(ray.tune.search.skopt.SkOptSearch method)

(ray.tune.search.zoopt.ZOOptSearch method)


set_metadata() (ray.job_config.JobConfig method)

(ray.train.Checkpoint method)


set_module_state() (ray.rllib.core.learner.learner.Learner method)

set_optimizer_state() (ray.rllib.core.learner.learner.Learner method)

set_options() (ray.serve.Deployment method)

set_policy_mapping_fn() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

set_progress_bars() (in module ray.data)

set_ray_namespace() (ray.job_config.JobConfig method)

set_runtime_env() (ray.job_config.JobConfig method)

set_search_properties() (ray.tune.schedulers.AsyncHyperBandScheduler method)

(ray.tune.schedulers.FIFOScheduler method)


key: chroma:document
string_value:
(ray.tune.schedulers.FIFOScheduler method)

(ray.tune.schedulers.PopulationBasedTrainingReplay method)

(ray.tune.schedulers.TrialScheduler method)

(ray.tune.search.basic_variant.BasicVariantGenerator method)

(ray.tune.search.Searcher method)


set_state() (ray.rllib.core.learner.learner.Learner method)

(ray.rllib.core.learner.learner_group.LearnerGroup method)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)

(ray.rllib.utils.exploration.curiosity.Curiosity method)

(ray.rllib.utils.exploration.exploration.Exploration method)

(ray.rllib.utils.exploration.random.Random method)

(ray.rllib.utils.exploration.random_encoder.RE3 method)

(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)


key: chroma:document
string_value:
(ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling method)

(ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer method)

(ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer method)

(ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer method)

(ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer method)

(ray.tune.Callback method)

(ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.logger.TBXLoggerCallback method)




set_status() (ray.tune.experiment.trial.Trial method)

set_storage() (ray.tune.experiment.trial.Trial method)

set_torch_seed() (in module ray.rllib.utils.torch_utils)

set_trace() (in module pdb)


key: chroma:document
string_value:
set_torch_seed() (in module ray.rllib.utils.torch_utils)

set_trace() (in module pdb)

set_training() (ray.rllib.policy.sample_batch.SampleBatch method)

set_trial_resources() (ray.tune.schedulers.ResourceChangingScheduler method)

set_weights() (ray.rllib.algorithms.algorithm.Algorithm method)

(ray.rllib.core.learner.learner_group.LearnerGroup method)

(ray.rllib.evaluation.rollout_worker.RolloutWorker method)

(ray.rllib.policy.Policy method)

(ray.rllib.policy.policy.Policy method)


setDaemon() (ray.rllib.evaluation.sampler.AsyncSampler method)

setdefault() (ray.rllib.policy.policy_map.PolicyMap method)

(ray.rllib.policy.sample_batch.SampleBatch method)

(ray.runtime_env.RuntimeEnv method)

(ray.runtime_env.RuntimeEnvConfig method)


setName() (ray.rllib.evaluation.sampler.AsyncSampler method)

setup() (ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)

(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)


key: chroma:document
string_value:
(ray.rllib.core.rl_module.marl_module.MultiAgentRLModule method)

(ray.rllib.core.rl_module.rl_module.RLModule method)

(ray.train.data_parallel_trainer.DataParallelTrainer method)

(ray.train.gbdt_trainer.GBDTTrainer method)

(ray.train.horovod.HorovodTrainer method)

(ray.train.lightgbm.LightGBMTrainer method)

(ray.train.tensorflow.TensorflowTrainer method)

(ray.train.torch.TorchTrainer method)

(ray.train.trainer.BaseTrainer method)

(ray.train.xgboost.XGBoostTrainer method)

(ray.tune.Callback method)

(ray.tune.logger.aim.AimLoggerCallback method)

(ray.tune.logger.CSVLoggerCallback method)

(ray.tune.logger.JsonLoggerCallback method)

(ray.tune.logger.LoggerCallback method)

(ray.tune.logger.TBXLoggerCallback method)

(ray.tune.ProgressReporter method)

(ray.tune.Trainable method)


setup_create_experiment_checkpoint_dir() (ray.tune.impl.tuner_internal.TunerInternal class method)

setup_ray_cluster() (in module ray.util.spark)


key: chroma:document
string_value:
setup_ray_cluster() (in module ray.util.spark)

setup_torch_data_parallel() (ray.rllib.evaluation.rollout_worker.RolloutWorker method)

share_cuda_visible_devices (ray.train.backend.Backend attribute)

should_capture_child_tasks_in_placement_group (ray.runtime_context.RuntimeContext property)

should_checkpoint() (ray.tune.experiment.trial.Trial method)

should_create_reader (ray.data.Datasource property)

should_recover() (ray.tune.experiment.trial.Trial method)

should_report() (ray.tune.ProgressReporter method)

should_stop() (ray.tune.experiment.trial.Trial method)

show() (ray.data.Dataset method)

shuffle() (ray.rllib.policy.sample_batch.SampleBatch method)

shutdown() (in module ray)

(in module ray.serve)

(ray.cluster_utils.AutoscalingCluster method)

(ray.rllib.core.learner.learner_group.LearnerGroup method)

(ray.util.queue.Queue method)


shutdown_ray_cluster() (in module ray.util.spark)

sigmoid() (in module ray.rllib.utils.numpy)


key: chroma:document
string_value:
shutdown_ray_cluster() (in module ray.util.spark)

sigmoid() (in module ray.rllib.utils.numpy)

SigOptSearch (class in ray.tune.search.sigopt)

SimpleImputer (class in ray.data.preprocessors)

SingleAgentRLModuleSpec (class in ray.rllib.core.rl_module.rl_module)

size() (ray.util.queue.Queue method)

size_bytes (ray.data.block.BlockMetadata attribute)

size_bytes() (ray.data.block.BlockAccessor method)

(ray.data.Dataset method)

(ray.rllib.policy.sample_batch.MultiAgentBatch method)

(ray.rllib.policy.sample_batch.SampleBatch method)


SkOptSearch (class in ray.tune.search.skopt)

slice() (ray.data.block.BlockAccessor method)

(ray.rllib.policy.sample_batch.SampleBatch method)


smoothing_factor (ray.serve.config.AutoscalingConfig attribute)

soft (ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy attribute)

softmax() (in module ray.rllib.utils.numpy)

softmax_cross_entropy_with_logits() (in module ray.rllib.utils.torch_utils)

sort() (ray.data.Dataset method)


key: chroma:document
string_value:
sort() (ray.data.Dataset method)

sort_and_partition() (ray.data.block.BlockAccessor method)

Spillback

split() (ray.data.Dataset method)

split_at_indices() (ray.data.Dataset method)

split_by_episode() (ray.rllib.policy.sample_batch.SampleBatch method)

split_proportionately() (ray.data.Dataset method)

ssh_identity_file (ray.train.horovod.HorovodConfig attribute)

ssh_port (ray.train.horovod.HorovodConfig attribute)

ssh_str (ray.train.horovod.HorovodConfig attribute)

StandardScaler (class in ray.data.preprocessors)

start() (in module ray.serve)

(ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster method)

(ray.cluster_utils.AutoscalingCluster method)

(ray.rllib.evaluation.sampler.AsyncSampler method)


start_episode() (ray.rllib.env.external_env.ExternalEnv method)

(ray.rllib.env.external_multi_agent_env.ExternalMultiAgentEnv method)

(ray.rllib.env.policy_client.PolicyClient method)


start_time (ray.job_submission.JobDetails attribute)


