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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
Data Loading and Preprocessing
Configuring Scale and GPUs
Configuring Persistent Storage
Monitoring and Logging Metrics
Saving and Loading Checkpoints
Experiment Tracking
Inspecting Training Results
Handling Failures and Node Preemption
Reproducibility
Hyperparameter Optimization
Examples
Benchmarks
Ray Train API
Ray Tune
Ray Serve
Ray RLlib
More Libraries
Ray Clusters
Monitoring and Debugging
References
Developer Guides
Security
repository
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Ray Train User Guides
Ray Train User Guides
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Data Loading and Preprocessing
Quickstart
Starting with PyTorch data
Splitting datasets
Random shuffling
Enabling reproducibility
Preprocessing structured data
Performance tips
Configuring Scale and GPUs
Increasing the number of workers
Using GPUs
Setting the resources per worker
Setting the communication backend (PyTorch)
Trainer resources
Configuring Persistent Storage
Cloud storage (AWS S3, Google Cloud Storage)
Shared filesystem (NFS, HDFS)
Local storage
Custom storage
Overview of Ray Train outputs
Advanced configuration
Monitoring and Logging Metrics
How to obtain and aggregate results from different workers?
Saving and Loading Checkpoints
Saving checkpoints during training
Configure checkpointing
Using checkpoints after training
Restore training state from a checkpoint
Experiment Tracking
Getting Started
Examples
Common Errors
Inspecting Training Results
Viewing metrics
Retrieving checkpoints
Accessing storage location
Viewing Errors
Finding results on persistent storage
Handling Failures and Node Preemption
Automatically Recover from Train Worker Failures
Restore a Ray Train Experiment
Reproducibility
Hyperparameter Optimization
Key Concepts
Basic usage
How to configure a Tuner?
Search Space configuration
Train - Tune gotchas
Advanced Tuning