Ray Examples

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How Ray solves common production challenges for generative AI infrastructure

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Training 175B Parameter Language Models at 1000 GPU scale with Alpa and Ray

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Faster stable diffusion fine-tuning with Ray Train

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How to fine tune and serve LLMs simply, quickly and cost effectively using Ray + DeepSpeed + HuggingFace

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How OpenAI Uses Ray to Train Tools like ChatGPT

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GPT-J-6B Fine-Tuning with Ray Train and DeepSpeed

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Aviary toolkit serving live traffic for LLMs

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Get started with Ray Train from an existing PyTorch codebase

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Distributed training with LightGBM

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Distributed training with XGBoost

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How to use Ray Data to do batch prediction with the Hugging Face Transformers GPT-J model

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How to use Ray Serve to do online serving with the Hugging Face Transformers GPT-J model

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How to fine-tune a DreamBooth text-to-image model with your own images.

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Fine-tune dolly-v2-7b with Ray Train, PyTorch Lightning and FSDP

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Torch Object Detection Example with Ray Train

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Image Classification Batch Inference with PyTorch ResNet152

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How to use Ray Data to do batch prediction with the Stable Diffusion text-to-image model

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Object Detection Batch Inference with PyTorch FasterRCNN_ResNet50

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Image Classification Batch Inference with PyTorch ResNet18

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Image Classification Batch Inference with Huggingface Vision Transformer

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Serving ML models with Ray Serve (Tensorflow, PyTorch, Scikit-Learn, others)

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Batching tutorial for Ray Serve

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Serving RLlib Models with Ray Serve

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Scaling your Gradio app with Ray Serve

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Java tutorial for Ray Serve

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Serving a Stable Diffusion Model

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Serving a Distilbert Model

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Serving a Bert Model on AWS NeuronCore

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Serving an Object Detection Model

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Offline Batch Inference: Comparing Ray, Apache Spark, and SageMaker

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Streaming distributed execution across CPUs and GPUs

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Using Ray Data to parallelize LangChain inference

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Batch Prediction using Ray Data

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Training One Million ML Models in Record Time with Ray

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Many Models Batch Training at Scale with Ray Core

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Batch Training with Ray Core

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Batch Training with Ray Data

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Tune Basic Parallel Experiments

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Batch Training and Tuning using Ray Tune

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Scaling Instacart fulfillment ML on Ray

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Using Aim with Ray Tune For Experiment Management

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Using Comet with Ray Tune For Experiment Management

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Tracking Your Experiment Process Weights & Biases

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Using MLflow Tracking & AutoLogging with Tune

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How To Use Tune With Ax

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How To Use Tune With Dragonfly

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How To Use Tune With HyperOpt

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How To Use Tune With BayesOpt

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How To Use Tune With BlendSearch and CFO

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How To Use Tune With TuneBOHB

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How To Use Tune With Nevergrad

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How To Use Tune With Optuna

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How To Use Tune With SigOpt

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Productionizing ML at Scale with Ray Serve

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Simplify your MLOps with Ray & Ray Serve

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Getting Started with Ray Serve

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Model Composition in Serve

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Getting Started with Ray Tune

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How to distribute hyperparameter tuning with Ray Tune

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Simple Distributed Hyperparameter Optimization

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Hyperparameter Search with 🤗 Transformers

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How To Use Tune With Keras & TF Models

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How To Use Tune With PyTorch Models

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How To Tune PyTorch Lightning Models

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Model Selection & Serving With Ray Serve

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Tuning RL Experiments With Ray Tune & Ray Serve

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A Guide To Tuning XGBoost Parameters With Tune

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A Guide To Tuning LightGBM Parameters With Tune

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A Guide To Tuning Horovod Parameters With Tune

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A Guide To Tuning Huggingface Transformers With Tune

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More Tune use cases on the Blog

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Ray Train, PyTorch, TorchX, and distributed deep learning

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Getting Started with Ray Train

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Fine-tune a 🤗 Transformers model on GLUE benchmark

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PyTorch Fashion MNIST Training Example

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TensorFlow MNIST Training Example

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End-to-end Horovod Training Example

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End-to-end PyTorch Lightning Training Example

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Fine-tune a PyTorch Lightning Text Classifier with Ray Data

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End-to-end Example for Tuning a TensorFlow Model

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End-to-end Example for Tuning a PyTorch Model with PBT

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Applied Reinforcement Learning with RLlib

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Intro to RLlib: Example Environments

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A collection of tuned hyperparameters by RLlib algorithm

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A collection of reasonably optimized Atari and MuJoCo results for RLlib

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RLlib’s trajectory view API and how it enables implementations of GTrXL (attention net) architectures

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A how-to on connecting RLlib with the Unity3D game engine for running visual- and physics-based RL experiments

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How we ported 12 of RLlib’s algorithms from TensorFlow to PyTorch and what we learnt on the way

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This blog post is a brief tutorial on multi-agent RL and its design in RLlib

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Exploration of a functional paradigm for implementing reinforcement learning (RL) algorithms

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Example of defining and registering a gym env and model for use with RLlib

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Example of how to setup an RLlib algorithm against a locally running Unity3D editor

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Rendering and recording of an environment

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Coin game example with RLlib

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Example for how to use a DMLab environment (Watermaze)

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RecSym environment example (for recommender systems) using the SlateQ algorithm

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Example demonstrating how to use the SUMO simulator in connection with RLlib.

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VizDoom example script using RLlib’s auto-attention wrapper

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Example of how to ensure subprocesses spawned by envs are killed when RLlib exits.

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Attention Net (GTrXL) learning the “repeat-after-me” environment

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Example showing how to use the auto-LSTM wrapper for your default- and custom models in RLlib.

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Example of using a custom Keras- or PyTorch RNN model.

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Example of defining and registering a custom model with a supervised loss.

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Example of adding batch norm layers to a custom model.

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Example of how to leverage TensorFlow eager to simplify debugging and design of custom models and policies.

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Example of a “fast” Model learning only one parameter for tf and torch.

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Shows how to define a custom Model API in RLlib, such that it can be used inside certain algorithms.

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An example on how a model can use the trajectory view API to specify its own input.

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Implementations of MobileNetV2 and torch.hub (mobilenet_v2)-wrapping example models.

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Example of DeepMind’s Differentiable Neural Computer for partially-observable environments.

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Example of how to use Tune’s support for custom training functions to implement custom training workflows.

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Example of how to advance the environment through different phases (tasks) over time.

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How to setup a custom Logger object in RLlib.

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Example of how to output custom training metrics to TensorBoard.

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How to setup a custom TFPolicy.

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How to setup a custom TorchPolicy.

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Example of how to use RLlib’s lower-level building blocks to implement a fully customized training workflow.

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Example of how to use the exec. plan of an Algorithm to trin two different policies in parallel (also using multi-agent API).

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How to run a custom Ray Tune experiment with RLlib with custom training- and evaluation phases.

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Example of how to write a custom evaluation function that is called instead of the default behavior, which is running with the evaluation worker set through n episodes.

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Example showing how the evaluation workers and the “normal” rollout workers can run (to some extend) in parallel to speed up training.

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Example showing how to run an offline RL training job using a historic-data json file.

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Example of using Ray Serve to serve RLlib models with HTTP and JSON interface

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This script offers a simple workflow for 1) training a policy with RLlib first, 2) creating a new policy 3) restoring its weights from the trained one and serving the new policy via Ray Serve.

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Example of how to setup n distributed Unity3D (compiled) games in the cloud that function as data collecting clients against a central RLlib Policy server learning how to play the game.

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Example of online serving of predictions for a simple CartPole policy.

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Example of how to externally generate experience batches in RLlib-compatible format.

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Example of how to find a checkpoint after a Tuner.fit() via some custom defined criteria.

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Setup RLlib to run any algorithm in (independent) multi-agent mode against a multi-agent environment.

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Setup RLlib to run any algorithm in (shared-parameter) multi-agent mode against a multi-agent environment.

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Example of different heuristic and learned policies competing against each other in rock-paper-scissors.

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Example of the two-step game from the QMIX paper.

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Example on how to use RLlib to learn in PettingZoo multi-agent environments.

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Example of customizing PPO to leverage a centralized value function.

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A simpler method of implementing a centralized critic by augmentating agent observations with global information.

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Example of running a custom hand-coded policy alongside trainable policies.

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Example of how to define weight-sharing layers between two different policies.

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Example of alternating training between DQN and PPO.

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Example of hierarchical training using the multi-agent API.

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Example of an iterated prisoner’s dilemma environment solved by RLlib.

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Example of how to setup fractional GPUs for learning (driver) and environment rollouts (remote workers).

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Learning in arbitrarily nested action spaces.

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Example of how to handle variable-length or parametric action spaces

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How to filter raw observations coming from the environment for further processing by the Agent’s model(s).

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How to use RLlib’s Repeated space to handle variable length observations.

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Learning with auto-regressive action dependencies (e.g. 2 action components; distribution for 2nd component depends on the 1st component’s actually sampled value).

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A General Evaluation Platform and Building Toolkit for Single/Multi-Agent Intelligence with RLlib-generated baselines.

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Example of training autonomous vehicles with RLlib and CARLA simulator.

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Using Graph Neural Networks and RLlib to train multiple cooperative and adversarial agents to solve the “cover the area”-problem, thereby learning how to best communicate (or - in the adversarial case - how to disturb communication).

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A dense traffic simulating environment with RLlib-generated baselines.

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Example of setting up a multi-agent version of GFootball with RLlib.

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A multiagent AI research environment inspired by Massively Multiplayer Online (MMO) role playing games

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Example of building packet classification trees using RLlib / multi-agent in a bandit-like setting.

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Example of learning optimal LLVM vectorization compiler pragmas for loops in C and C++ codes using RLlib.

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Example of using the multi-agent API to model several social dilemma games.

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Create a custom environment and train a single agent RL using Ray 2.0 with Tune.

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Example of training in StarCraft2 maps with RLlib / multi-agent.

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Example of optimizing mixed-autonomy traffic simulations with RLlib / multi-agent.

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Working with custom Keras models in RLlib

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Getting Started with RLlib

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Deep reinforcement learning at Riot Games

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The Magic of Merlin - Shopify’s New ML Platform

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Large Scale Deep Learning Training and Tuning with Ray

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Griffin: How Instacart’s ML Platform Tripled in a year

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Predibase - A low-code deep learning platform built for scale

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Building a ML Platform with Kubeflow and Ray on GKE

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Ray Summit Panel - ML Platform on Ray

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AutoML for Time Series with Ray

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Highly Available and Scalable Online Applications on Ray at Ant Group

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Ray Forward 2022 Conference: Hyper-scale Ray Application Use Cases

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A new world record on the CloudSort benchmark using Ray

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Speed up your web crawler by parallelizing it with Ray

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Image Classification Batch Inference with Huggingface Vision Transformer

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Image Classification Batch Inference with PyTorch ResNet152

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Object Detection Batch Inference with PyTorch FasterRCNN_ResNet50

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Many Model Training with Ray Data

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Build Batch Prediction Using Ray

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Build a Simple Parameter Server Using Ray

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Simple Parallel Model Selection

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Fault-Tolerant Fairseq Training

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Learning to Play Pong

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A Gentle Introduction to Ray Core by Example

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Using Ray for Highly Parallelizable Tasks

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Running a Simple MapReduce Example with Ray Core

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Benchmark example for the PyTorch data transfer auto pipeline

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How To Use Tune’s Scikit-Learn Adapters?

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Simple example for doing a basic random and grid search.

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Example of using a simple tuning function with AsyncHyperBandScheduler.

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Example of using a Trainable function with HyperBandScheduler. Also uses the AsyncHyperBandScheduler.

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Configuring and running (synchronous) PBT and understanding the underlying algorithm behavior with a simple example.

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Example of using the function API with a PopulationBasedTraining scheduler.

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Example of using the Population-based Bandits (PB2) scheduler.

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Example of custom loggers and custom trial directory naming.

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Basics of using Tune

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Using Search algorithms and Trial Schedulers to optimize your model.

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Using Population-Based Training (PBT).

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Fine-tuning Huggingface Transformers with PBT.

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Logging Tune Runs to Comet ML.

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Using Ray Serve to deploy a chatbot

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Fine-tune vicuna-13b-v1.3 with DeepSpeed, PyTorch Lightning and Ray Train

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Distributed Training with DeepSpeed ZeRO-3 and TorchTrainer

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Distributed Training with Hugging Face Accelelate and TorchTrainer

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RayJob Batch Inference Example on Kubernetes with Ray