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160 lines
4.7 KiB
ReStructuredText
160 lines
4.7 KiB
ReStructuredText
.. _gentle-intro:
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============================
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A Gentle Introduction to Ray
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============================
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.. include:: basics.rst
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This tutorial will provide a tour of the core features of Ray.
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First, install Ray with: ``pip install ray``, and now we can execute some Python in parallel.
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Parallelizing Python Functions with Ray Tasks
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=============================================
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First, import ray and ``init`` the Ray service.
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Then decorate your function with ``@ray.remote`` to declare that you want to run this function
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remotely. Lastly, call that function with ``.remote()`` instead of calling it normally. This remote call yields a future, or ``ObjectID`` that you can then
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fetch with ``ray.get``.
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.. code-block:: python
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import ray
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ray.init()
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@ray.remote
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def f(x):
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return x * x
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futures = [f.remote(i) for i in range(4)]
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print(ray.get(futures)) # [0, 1, 4, 9]
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In the above code block we defined some Ray Tasks. While these are great for stateless operations, sometimes you
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must maintain the state of your application. You can do that with Ray Actors.
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Parallelizing Python Classes with Ray Actors
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==============================================
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Ray provides actors to allow you to parallelize an instance of a class in Python.
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When you instantiate a class that is a Ray actor, Ray will start a remote instance
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of that class in the cluster. This actor can then execute remote method calls and
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maintain its own internal state.
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.. code-block:: python
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import ray
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ray.init() # Only call this once.
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@ray.remote
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class Counter(object):
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def __init__(self):
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self.n = 0
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def increment(self):
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self.n += 1
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def read(self):
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return self.n
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counters = [Counter.remote() for i in range(4)]
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[c.increment.remote() for c in counters]
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futures = [c.read.remote() for c in counters]
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print(ray.get(futures)) # [1, 1, 1, 1]
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An Overview of the Ray Libraries
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================================
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Ray has a rich ecosystem of libraries and frameworks built on top of it. The main ones being:
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- :ref:`tune-index`
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- :ref:`rllib-index`
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- :ref:`sgd-index`
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- :ref:`rayserve`
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Tune Quick Start
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----------------
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`Tune`_ is a library for hyperparameter tuning at any scale. With Tune, you can launch a multi-node distributed hyperparameter sweep in less than 10 lines of code. Tune supports any deep learning framework, including PyTorch, TensorFlow, and Keras.
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.. note::
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To run this example, you will need to install the following:
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.. code-block:: bash
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$ pip install ray torch torchvision filelock
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This example runs a small grid search to train a CNN using PyTorch and Tune.
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.. literalinclude:: ../../../python/ray/tune/tests/example.py
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:language: python
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:start-after: __quick_start_begin__
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:end-before: __quick_start_end__
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If TensorBoard is installed, automatically visualize all trial results:
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.. code-block:: bash
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tensorboard --logdir ~/ray_results
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.. _`Tune`: tune.html
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RLlib Quick Start
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-----------------
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`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.
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.. code-block:: bash
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pip install tensorflow # or tensorflow-gpu
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pip install ray[rllib] # also recommended: ray[debug]
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.. code-block:: python
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import gym
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from gym.spaces import Discrete, Box
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from ray import tune
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class SimpleCorridor(gym.Env):
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def __init__(self, config):
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self.end_pos = config["corridor_length"]
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self.cur_pos = 0
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self.action_space = Discrete(2)
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self.observation_space = Box(0.0, self.end_pos, shape=(1, ))
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def reset(self):
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self.cur_pos = 0
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return [self.cur_pos]
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def step(self, action):
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if action == 0 and self.cur_pos > 0:
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self.cur_pos -= 1
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elif action == 1:
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self.cur_pos += 1
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done = self.cur_pos >= self.end_pos
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return [self.cur_pos], 1 if done else 0, done, {}
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tune.run(
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"PPO",
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config={
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"env": SimpleCorridor,
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"num_workers": 4,
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"env_config": {"corridor_length": 5}})
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.. _`RLlib`: rllib.html
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Where to go next?
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=================
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Visit the `Walkthrough <walkthrough.html>`_ page a more comprehensive overview of Ray features.
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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 <https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/aws/example-full.yaml>`__, and run:
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``ray submit [CLUSTER.YAML] example.py --start``
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Read more about `:ref:launching clusters <cluster-index>`.
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