.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png

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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
- `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 `__.

.. _`Modin`: https://github.com/modin-project/modin
.. _`Hugging Face`: https://huggingface.co/transformers/main_classes/trainer.html#transformers.Trainer.hyperparameter_search
.. _`MARS`: https://docs.ray.io/en/latest/data/mars-on-ray.html
.. _`Dask`: https://docs.ray.io/en/latest/data/dask-on-ray.html
.. _`Horovod`: https://horovod.readthedocs.io/en/stable/ray_include.html
.. _`Scikit-learn`: https://docs.ray.io/en/master/joblib.html
.. _`Serve`: https://docs.ray.io/en/master/serve/index.html
.. _`Datasets`: https://docs.ray.io/en/master/data/dataset.html
.. _`Workflows`: https://docs.ray.io/en/master/workflows/concepts.html
.. _`Train`: https://docs.ray.io/en/master/train/train.html


Quick Start
-----------

Execute Python functions in parallel.

.. code-block:: python

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:

.. code-block:: python


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
----------------

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

`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.
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:

.. code-block:: bash

tensorboard --logdir ~/ray_results

.. _`Tune`: https://docs.ray.io/en/master/tune.html
.. _`Population Based Training (PBT)`: https://docs.ray.io/en/master/tune/api_docs/schedulers.html#population-based-training-tune-schedulers-populationbasedtraining
.. _`Vizier's Median Stopping Rule`: https://docs.ray.io/en/master/tune/api_docs/schedulers.html#median-stopping-rule-tune-schedulers-medianstoppingrule
.. _`HyperBand/ASHA`: https://docs.ray.io/en/master/tune/api_docs/schedulers.html#asha-tune-schedulers-ashascheduler

RLlib Quick Start
-----------------

.. image:: https://github.com/ray-project/ray/raw/master/doc/source/rllib/images/rllib-logo.png

`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 `_.

.. code-block:: bash

$ pip install "ray[rllib]" tensorflow # or torch


.. Do NOT edit the following code directly in this README! Instead, edit
the ray/rllib/examples/documentation/rllib_on_ray_readme.py script and then
copy the new code in here:

.. code-block:: python

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`

Returns:
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(
config={
# 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 `_).

.. code-block:: python

# 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}")


.. _`RLlib`: https://docs.ray.io/en/master/rllib/index.html


Ray Serve Quick Start
---------------------

.. image:: https://raw.githubusercontent.com/ray-project/ray/master/doc/source/serve/logo.svg
:width: 400

`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:

.. code-block:: bash

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

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

.. code-block:: python

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"
# }


.. _`Ray Serve`: https://docs.ray.io/en/master/serve/index.html

More Information
----------------

- `Documentation`_
- `Tutorial`_
- `Blog`_
- `Ray 1.0 Architecture whitepaper`_ **(new)**
- `Exoshuffle: large-scale data shuffle in Ray`_ **(new)**
- `RLlib paper`_
- `RLlib flow paper`_
- `Tune paper`_

*Older documents:*

- `Ray paper`_
- `Ray HotOS paper`_

.. _`Documentation`: http://docs.ray.io/en/master/index.html
.. _`Tutorial`: https://github.com/ray-project/tutorial
.. _`Blog`: https://medium.com/distributed-computing-with-ray
.. _`Ray 1.0 Architecture whitepaper`: https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview
.. _`Exoshuffle: large-scale data shuffle in Ray`: https://arxiv.org/abs/2203.05072
.. _`Ray paper`: https://arxiv.org/abs/1712.05889
.. _`Ray HotOS paper`: https://arxiv.org/abs/1703.03924
.. _`RLlib paper`: https://arxiv.org/abs/1712.09381
.. _`RLlib flow paper`: https://arxiv.org/abs/2011.12719
.. _`Tune paper`: https://arxiv.org/abs/1807.05118

Getting Involved
----------------

.. list-table::
:widths: 25 50 25 25
:header-rows: 1

* - 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

.. _`Discourse Forum`: https://discuss.ray.io/
.. _`GitHub Issues`: https://github.com/ray-project/ray/issues
.. _`StackOverflow`: https://stackoverflow.com/questions/tagged/ray
.. _`Meetup Group`: https://www.meetup.com/Bay-Area-Ray-Meetup/
.. _`Twitter`: https://twitter.com/raydistributed
.. _`Slack`: https://forms.gle/9TSdDYUgxYs8SA9e8