Browse runnable examples organized by Ray library. Each example links to source code and a walkthrough.Documentation Index
Fetch the complete documentation index at: https://ray-preview.mintlify.app/llms.txt
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Ray Core
Parallel Python with tasks
Convert sequential Python functions into a parallel pipeline.
Build a stateful service with actors
Maintain state across calls with Ray actors.
Distributed object passing
Share large arrays across tasks with the object store.
Resource scheduling
Place tasks on specific resources with placement groups.
Ray Data
Image classification batch inference
Run a vision model over millions of images.
Streaming data pipeline
Stream and transform data with
map_batches.LLM batch inference
Score prompts at scale using a vLLM-backed pipeline.
Working with tensors
Read, write, and transform tensor columns.
Ray Train
Distributed PyTorch training
Scale PyTorch training across multiple GPUs.
PyTorch Lightning
Distributed training with the Lightning trainer.
Hugging Face Transformers
Distributed fine-tuning of LLMs and vision transformers.
Fault-tolerant training
Recover from worker failures automatically.
Ray Tune
ASHA scheduler
Early-stop poor trials with asynchronous successive halving.
Optuna search
Bayesian optimization with Optuna.
Distributed hyperparameter tuning
Run trials across the entire cluster.
Result analysis
Inspect and compare trial outcomes.
Ray Serve
FastAPI integration
Combine Ray Serve with FastAPI for HTTP serving.
Model composition
Compose multi-model pipelines with deployment graphs.
Autoscaling
Scale replicas in response to traffic.
LLM serving
Serve LLMs with vLLM or custom backends.
RLlib
PPO on CartPole
Train PPO on a classic control task.
Custom environments
Wrap your own simulator or game.
Custom RL modules
Build a custom policy network.
Offline RL
Train from logged data with MARWIL and CQL.