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* Ray with Docker * cleanup based on comments * rename docker user to ray-user * add examples docker image * working toward reliable Docker devel image * adjust ray-user uid for Linux builds on AWS * update documentation * reduced dependencies for examples * updated Docker documentation * experimental notice on developing with Docker
61 lines
2.2 KiB
Markdown
61 lines
2.2 KiB
Markdown
# Ray
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[](https://travis-ci.org/amplab/ray)
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Ray is an experimental distributed extension of Python. It is under development
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and not ready for general use.
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The goal of Ray is to make it easy to write machine learning applications that
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run on a cluster while providing the development and debugging experience of
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working on a single machine.
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Before jumping into the details, here's a simple Python example for doing a
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Monte Carlo estimation of pi (using multiple cores or potentially multiple
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machines).
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```python
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import ray
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import numpy as np
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# Start a scheduler, an object store, and some workers.
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ray.init(start_ray_local=True, num_workers=10)
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# Define a remote function for estimating pi.
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@ray.remote([int], [float])
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def estimate_pi(n):
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x = np.random.uniform(size=n)
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y = np.random.uniform(size=n)
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return 4 * np.mean(x ** 2 + y ** 2 < 1)
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# Launch 10 tasks, each of which estimates pi.
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result_ids = []
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for _ in range(10):
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result_ids.append(estimate_pi.remote(100))
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# Fetch the results of the tasks and print their average.
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estimate = np.mean([ray.get(result_id) for result_id in result_ids])
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print "Pi is approximately {}.".format(estimate)
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```
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Within the for loop, each call to `estimate_pi.remote(100)` sends a message to
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the scheduler asking it to schedule the task of running `estimate_pi` with the
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argument `100`. This call returns right away without waiting for the actual
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estimation of pi to take place. Instead of returning a float, it returns an
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**object ID**, which represents the eventual output of the computation (this is
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a similar to a Future).
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The call to `ray.get(result_id)` takes an object ID and returns the actual
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estimate of pi (waiting until the computation has finished if necessary).
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## Next Steps
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- Installation on [Ubuntu](doc/install-on-ubuntu.md), [Mac OS X](doc/install-on-macosx.md), [Windows](doc/install-on-windows.md), [Docker](doc/install-on-docker.md)
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- [Tutorial](doc/tutorial.md)
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- [About the System](doc/about-the-system.md)
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- [Using Ray on a Cluster](doc/using-ray-on-a-cluster.md)
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## Example Applications
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- [Hyperparameter Optimization](examples/hyperopt/README.md)
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- [Batch L-BFGS](examples/lbfgs/README.md)
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- [Train AlexNet](examples/alexnet/README.md)
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