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

Project description

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

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

def f(x):
    return x * x

futures = [f.remote(i) for i in range(4)]

To use Ray’s actor model:

import ray

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 = [ for c in counters]

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] --start

Read more about launching clusters.

Tune Quick Start

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]"

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.

analysis =
        "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 industry-grade library for reinforcement learning (RL), built on top of Ray. It offers high scalability and unified APIs for a variety of industry- and research applications.

$ pip install "ray[rllib]" tensorflow  # or torch
import gym
from ray.rllib.agents.ppo import PPOTrainer

# Define your problem using python and openAI's gym API:
class SimpleCorridor(gym.Env):
    """Corridor in which an agent must learn to move right to reach the exit.

    | S | 1 | 2 | 3 | G |   S=start; G=goal; corridor_length=5

    Possible actions to chose from are: 0=left; 1=right
    Observations are floats indicating the current field index, e.g. 0.0 for
    starting position, 1.0 for the field next to the starting position, etc..
    Rewards are -0.1 for all steps, except when reaching the goal (+1.0).

    def __init__(self, config):
        self.end_pos = config["corridor_length"]
        self.cur_pos = 0
        self.action_space = gym.spaces.Discrete(2)  # left and right
        self.observation_space = gym.spaces.Box(0.0, self.end_pos, shape=(1,))

    def reset(self):
        """Resets the episode and returns the initial observation of the new one.
        self.cur_pos = 0
        # Return initial observation.
        return [self.cur_pos]

    def step(self, action):
        """Takes a single step in the episode given `action`

            New observation, reward, done-flag, info-dict (empty).
        # Walk left.
        if action == 0 and self.cur_pos > 0:
            self.cur_pos -= 1
        # Walk right.
        elif action == 1:
            self.cur_pos += 1
        # Set `done` flag when end of corridor (goal) reached.
        done = self.cur_pos >= self.end_pos
        # +1 when goal reached, otherwise -1.
        reward = 1.0 if done else -0.1
        return [self.cur_pos], reward, done, {}

# Create an RLlib Trainer instance.
trainer = PPOTrainer(
        # Env class to use (here: our gym.Env sub-class from above).
        "env": SimpleCorridor,
        # Config dict to be passed to our custom env's constructor.
        "env_config": {
            # Use corridor with 20 fields (including S and G).
            "corridor_length": 20
        # Parallelize environment rollouts.
        "num_workers": 3,

# Train for n iterations and report results (mean episode rewards).
# Since we have to move at least 19 times in the env to reach the goal and
# each move gives us -0.1 reward (except the last move at the end: +1.0),
# we can expect to reach an optimal episode reward of -0.1*18 + 1.0 = -0.8
for i in range(5):
    results = trainer.train()
    print(f"Iter: {i}; avg. reward={results['episode_reward_mean']}")

After training, you may want to perform action computations (inference) in your environment. Here is a minimal example on how to do this. Also check out our more detailed examples here (in particular for normal models, LSTMs, and attention nets).

# Perform inference (action computations) based on given env observations.
# Note that we are using a slightly different env here (len 10 instead of 20),
# however, this should still work as the agent has (hopefully) learned
# to "just always walk right!"
env = SimpleCorridor({"corridor_length": 10})
# Get the initial observation (should be: [0.0] for the starting position).
obs = env.reset()
done = False
total_reward = 0.0
# Play one episode.
while not done:
    # Compute a single action, given the current observation
    # from the environment.
    action = trainer.compute_single_action(obs)
    # Apply the computed action in the environment.
    obs, reward, done, info = env.step(action)
    # Sum up rewards for reporting purposes.
    total_reward += reward
# Report results.
print(f"Played 1 episode; total-reward={total_reward}")

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


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

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.

# 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)
# Result:
# {
#  "result": "versicolor"
# }

Getting Involved

Platform Purpose Estimated Response Time Support Level
Discourse Forum For discussions about development and questions about usage. < 1 day Community
GitHub Issues For reporting bugs and filing feature requests. < 2 days Ray OSS Team
Slack For collaborating with other Ray users. < 2 days Community
StackOverflow For asking questions about how to use Ray. 3-5 days Community
Meetup Group For learning about Ray projects and best practices. Monthly Ray DevRel
Twitter For staying up-to-date on new features. Daily Ray DevRel

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