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A system for parallel and distributed Python that unifies the ML ecosystem.

Project description

https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png https://travis-ci.com/ray-project/ray.svg?branch=master https://readthedocs.org/projects/ray/badge/?version=latest

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
  • RaySGD: Distributed Training Wrappers

Install Ray with: pip install ray. For nightly wheels, see the Installation page.

NOTE: We are deprecating Python 2 support soon.

Quick Start

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 Quick Start

https://github.com/ray-project/ray/raw/master/doc/source/images/tune-wide.png

Tune is a library for hyperparameter tuning at any scale.

To run this example, you will need to install the following:

$ pip install ray[tune] torch torchvision filelock

This 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 Quick Start

https://github.com/ray-project/ray/raw/master/doc/source/images/rllib-wide.jpg

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}})

Getting Involved

Project details


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