ray 1.9.0rc1

Ray provides a simple, universal API for building distributed applications.

Ray provides a simple, universal API for building distributed applications.

Ray is packaged with the following libraries for accelerating machine learning workloads:

• Tune: Scalable Hyperparameter Tuning

• RLlib: Scalable Reinforcement Learning

• Train: Distributed Deep Learning (beta)

• Datasets: Distributed Data Loading and Compute (beta)

As well as libraries for taking ML and distributed apps to production:

• Serve: Scalable and Programmable Serving

• Workflows: Fast, Durable Application Flows (alpha)

There are also many community integrations with Ray, including Dask, MARS, Modin, Horovod, Hugging Face, Scikit-learn, and others. Check out the full list of Ray distributed libraries here.

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

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

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, PyTorch Lightning, 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]"

This example runs a parallel grid search to optimize an example objective function.

from ray import tune


def objective(step, alpha, beta):
    return (0.1 + alpha * step / 100)**(-1) + beta * 0.1


def training_function(config):
    # Hyperparameters
    alpha, beta = config["alpha"], config["beta"]
    for step in range(10):
        # Iterative training function - can be any arbitrary training procedure.
        intermediate_score = objective(step, alpha, beta)
        # Feed the score back back to Tune.
        tune.report(mean_loss=intermediate_score)


analysis = tune.run(
    training_function,
    config={
        "alpha": tune.grid_search([0.001, 0.01, 0.1]),
        "beta": tune.choice([1, 2, 3])
    })

print("Best config: ", analysis.get_best_config(metric="mean_loss", mode="min"))

# Get a dataframe for analyzing trial results.
df = analysis.results_df

If TensorBoard is installed, automatically visualize all trial results:

tensorboard --logdir ~/ray_results

RLlib Quick Start

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]"

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}})

Ray Serve Quick Start

Ray Serve is a scalable model-serving library built on Ray. It is:

• Framework Agnostic: Use the same toolkit to serve everything from deep learning models built with frameworks like PyTorch or Tensorflow & Keras to Scikit-Learn models or arbitrary business logic.

• Python First: Configure your model serving declaratively in pure Python, without needing YAMLs or JSON configs.

• Performance Oriented: Turn on batching, pipelining, and GPU acceleration to increase the throughput of your model.

• Composition Native: Allow you to create “model pipelines” by composing multiple models together to drive a single prediction.

• Horizontally Scalable: Serve can linearly scale as you add more machines. Enable your ML-powered service to handle growing traffic.

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

$ pip install scikit-learn
$ pip install "ray[serve]"

This example runs serves a scikit-learn gradient boosting classifier.

import pickle
import requests

from sklearn.datasets import load_iris
from sklearn.ensemble import GradientBoostingClassifier

from ray import serve

serve.start()

# Train model.
iris_dataset = load_iris()
model = GradientBoostingClassifier()
model.fit(iris_dataset["data"], iris_dataset["target"])

@serve.deployment(route_prefix="/iris")
class BoostingModel:
    def __init__(self, model):
        self.model = model
        self.label_list = iris_dataset["target_names"].tolist()

    async def __call__(self, request):
        payload = await request.json()["vector"]
        print(f"Received flask request with data {payload}")

        prediction = self.model.predict([payload])[0]
        human_name = self.label_list[prediction]
        return {"result": human_name}


# Deploy model.
BoostingModel.deploy(model)

# Query it!
sample_request_input = {"vector": [1.2, 1.0, 1.1, 0.9]}
response = requests.get("http://localhost:8000/iris", json=sample_request_input)
print(response.text)
# Result:
# {
#  "result": "versicolor"
# }

More Information

• Documentation

• Tutorial

• Blog

• Ray 1.0 Architecture whitepaper (new)

• Ray Design Patterns (new)

• RLlib paper

• RLlib flow paper

• Tune paper

Older documents:

• Ray paper

• Ray HotOS paper

Getting Involved

• Forum: For discussions about development, questions about usage, and feature requests.

• GitHub Issues: For reporting bugs.

• Twitter: Follow updates on Twitter.

• Slack: Join our Slack channel.

• Meetup Group: Join our meetup group.

• StackOverflow: For questions about how to use Ray.