Ray is a fast and simple framework for building and running distributed applications.
Ray is packaged with the following libraries for accelerating machine learning workloads:
- Tune: Scalable Hyperparameter Tuning
- RLlib: Scalable Reinforcement Learning
- Distributed Training
Install Ray with: pip install ray. For nightly wheels, see the
Installation page.
NOTE: We are deprecating Python 2 support soon.
Execute Python functions in parallel.
import ray
ray.init()
@ray.remote
def f(x):
return x * x
futures = [f.remote(i) for i in range(4)]
print(ray.get(futures))To use Ray's actor model:
import ray
ray.init()
@ray.remote
class Counter(object):
def __init__(self):
self.n = 0
def increment(self):
self.n += 1
def read(self):
return self.n
counters = [Counter.remote() for i in range(4)]
[c.increment.remote() for c in counters]
futures = [c.read.remote() for c in counters]
print(ray.get(futures))Ray programs can run on a single machine, and can also seamlessly scale to large clusters. To execute the above Ray script in the cloud, just download this configuration file, and run:
ray submit [CLUSTER.YAML] example.py --start
Read more about launching clusters.
Tune is a library for hyperparameter tuning at any scale.
- Launch a multi-node distributed hyperparameter sweep in less than 10 lines of code.
- Supports any deep learning framework, including PyTorch, TensorFlow, and Keras.
- Visualize results with TensorBoard.
- Choose among scalable SOTA algorithms such as Population Based Training (PBT), Vizier's Median Stopping Rule, HyperBand/ASHA.
- Tune integrates with many optimization libraries such as Facebook Ax, HyperOpt, and Bayesian Optimization and enables you to scale them transparently.
To run this example, you will need to install the following:
$ pip install ray[tune] torch torchvision filelockThis example runs a parallel grid search to train a Convolutional Neural Network using PyTorch.
import torch.optim as optim
from ray import tune
from ray.tune.examples.mnist_pytorch import (
get_data_loaders, ConvNet, train, test)
def train_mnist(config):
train_loader, test_loader = get_data_loaders()
model = ConvNet()
optimizer = optim.SGD(model.parameters(), lr=config["lr"])
for i in range(10):
train(model, optimizer, train_loader)
acc = test(model, test_loader)
tune.track.log(mean_accuracy=acc)
analysis = tune.run(
train_mnist, config={"lr": tune.grid_search([0.001, 0.01, 0.1])})
print("Best config: ", analysis.get_best_config(metric="mean_accuracy"))
# Get a dataframe for analyzing trial results.
df = analysis.dataframe()If TensorBoard is installed, automatically visualize all trial results:
tensorboard --logdir ~/ray_results
RLlib is an open-source library for reinforcement learning built on top of Ray that offers both high scalability and a unified API for a variety of applications.
pip install tensorflow # or tensorflow-gpu
pip install ray[rllib] # also recommended: ray[debug]import gym
from gym.spaces import Discrete, Box
from ray import tune
class SimpleCorridor(gym.Env):
def __init__(self, config):
self.end_pos = config["corridor_length"]
self.cur_pos = 0
self.action_space = Discrete(2)
self.observation_space = Box(0.0, self.end_pos, shape=(1, ))
def reset(self):
self.cur_pos = 0
return [self.cur_pos]
def step(self, action):
if action == 0 and self.cur_pos > 0:
self.cur_pos -= 1
elif action == 1:
self.cur_pos += 1
done = self.cur_pos >= self.end_pos
return [self.cur_pos], 1 if done else 0, done, {}
tune.run(
"PPO",
config={
"env": SimpleCorridor,
"num_workers": 4,
"env_config": {"corridor_length": 5}})- Documentation, in particular Building Ray and Contributing to Ray
- Tutorial
- Blog
- Ray paper
- Ray HotOS paper
- RLlib paper
- Tune paper
- [email protected]: For discussions about development or any general questions.
- StackOverflow: For questions about how to use Ray.
- GitHub Issues: For reporting bugs and feature requests.
- Pull Requests: For submitting code contributions.
- Meetup Group: Join our meetup group.
- Community Slack: Join our Slack workspace.
- Twitter: Follow updates on Twitter.