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https://github.com/vale981/ray
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e066bcf633
* Synchronous parameter server example. * Added sync parameter server example to documentation index. * Consolidate documentation and minor simplifications. * Fix linting.
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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from tensorflow.examples.tutorials.mnist import input_data
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import time
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import ray
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import model
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parser = argparse.ArgumentParser(description="Run the asynchronous parameter "
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"server example.")
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parser.add_argument("--num-workers", default=4, type=int,
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help="The number of workers to use.")
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parser.add_argument("--redis-address", default=None, type=str,
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help="The Redis address of the cluster.")
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@ray.remote
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class ParameterServer(object):
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def __init__(self, keys, values):
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# These values will be mutated, so we must create a copy that is not
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# backed by the object store.
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values = [value.copy() for value in values]
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self.weights = dict(zip(keys, values))
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def push(self, keys, values):
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for key, value in zip(keys, values):
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self.weights[key] += value
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def pull(self, keys):
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return [self.weights[key] for key in keys]
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@ray.remote
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def worker_task(ps, batch_size=50):
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# Download MNIST.
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mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
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# Initialize the model.
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net = model.SimpleCNN()
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keys = net.get_weights()[0]
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while True:
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# Get the current weights from the parameter server.
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weights = ray.get(ps.pull.remote(keys))
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net.set_weights(keys, weights)
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# Compute an update and push it to the parameter server.
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xs, ys = mnist.train.next_batch(batch_size)
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gradients = net.compute_update(xs, ys)
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ps.push.remote(keys, gradients)
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init(redis_address=args.redis_address)
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# Create a parameter server with some random weights.
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net = model.SimpleCNN()
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all_keys, all_values = net.get_weights()
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ps = ParameterServer.remote(all_keys, all_values)
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# Start some training tasks.
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worker_tasks = [worker_task.remote(ps) for _ in range(args.num_workers)]
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# Download MNIST.
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mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
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i = 0
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while True:
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# Get and evaluate the current model.
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current_weights = ray.get(ps.pull.remote(all_keys))
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net.set_weights(all_keys, current_weights)
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test_xs, test_ys = mnist.test.next_batch(1000)
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accuracy = net.compute_accuracy(test_xs, test_ys)
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print("Iteration {}: accuracy is {}".format(i, accuracy))
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i += 1
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time.sleep(1)
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