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Awesome Open Source

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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
  • RaySGD <>__: Distributed Training Wrappers
  • Ray Serve_: Scalable and Programmable Serving

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 <>__.

.. _Modin: .. _Hugging Face: .. _MARS: .. _Dask: .. _Horovod: .. _Scikit-learn: joblib.html

Quick Start

Execute Python functions in parallel.

.. code-block:: python

import ray

def f(x):
    return x * x

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

To use Ray's actor model:

.. code-block:: python

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

.. image::

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:

.. code-block:: bash

$ pip install "ray[tune]"

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

.. code-block:: python

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:

.. code-block:: bash

tensorboard --logdir ~/ray_results

.. _Tune: .. _Population Based Training (PBT): .. _Vizier's Median Stopping Rule: .. _HyperBand/ASHA:

RLlib Quick Start

.. image::

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.

.. code-block:: bash

pip install tensorflow # or tensorflow-gpu pip install "ray[rllib]"

.. code-block:: python

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, {}
        "env": SimpleCorridor,
        "num_workers": 4,
        "env_config": {"corridor_length": 5}})

.. _RLlib:

Ray Serve Quick Start

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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 with pure Python code - no more 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:

.. code-block:: bash

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

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

.. code-block:: python

from ray import serve
import pickle
import requests
from sklearn.datasets import load_iris
from sklearn.ensemble import GradientBoostingClassifier

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

# Define Ray Serve model,
class BoostingModel:
    def __init__(self):
        self.model = model
        self.label_list = iris_dataset["target_names"].tolist()

    def __call__(self, flask_request):
        payload = flask_request.json["vector"]
        print("Worker: received flask request with data", payload)

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

# Deploy model
client = serve.start()
client.create_backend("iris:v1", BoostingModel)
client.create_endpoint("iris_classifier", backend="iris:v1", route="/iris")

# 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"
# }

.. _Ray Serve:

More Information

  • Documentation_
  • Tutorial_
  • Blog_
  • Ray 1.0 Architecture whitepaper_ (new)
  • Ray Design Patterns_ (new)
  • RLlib paper_
  • Tune paper_

Older documents:

  • Ray paper_
  • Ray HotOS paper_
  • Blog (old)_

.. _Documentation: .. _Tutorial: .. _Blog (old): .. _Blog: .. _Ray 1.0 Architecture whitepaper: .. _Ray Design Patterns: .. _Ray paper: .. _Ray HotOS paper: .. _RLlib paper: .. _Tune paper:

Getting Involved

  • Forum_: For discussions about development, questions about usage, and feature requests.
  • GitHub Issues_: For reporting bugs.
  • Twitter_: Follow updates on Twitter.
  • Meetup Group_: Join our meetup group.
  • StackOverflow_: For questions about how to use Ray.

.. _Forum: .. _GitHub Issues: .. _StackOverflow: .. _Meetup Group: .. _Twitter:

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