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50 lines
1.6 KiB
Markdown
50 lines
1.6 KiB
Markdown
## Introduction
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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 functions # See definition below
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results = []
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for _ in range(10):
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results.append(functions.estimate_pi(100))
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estimate = np.mean([ray.get(ref) for ref in results])
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print "Pi is approximately {}.".format(estimate)
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```
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This assumes that we've defined the file `functions.py` as below.
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```python
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import ray
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import numpy as np
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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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```
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Within the for loop, each call to `functions.estimate_pi(100)` sends a message
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to the scheduler asking it to schedule the task of running
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`functions.estimate_pi` with the argument `100`. This call returns right away
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without waiting for the actual estimation of pi to take place. Instead of
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returning a float, it returns an **object reference**, which represents the
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eventual output of the computation.
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The call to `ray.get(ref)` takes an object reference 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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- [Download and Setup](download-and-setup.md)
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- [Basic Usage](basic-usage.md)
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- [Tutorial](tutorial.md)
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- [About the System](about-the-system.md)
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- [Using Ray on a Cluster](using-ray-on-a-cluster.md)
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