mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
e96acc26f7
* Fix MNIST downloading problems in parameter server examples. * Improve seeding. * Fixes.
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import argparse
|
|
import time
|
|
|
|
import ray
|
|
import model
|
|
|
|
parser = argparse.ArgumentParser(description="Run the asynchronous parameter "
|
|
"server example.")
|
|
parser.add_argument("--num-workers", default=4, type=int,
|
|
help="The number of workers to use.")
|
|
parser.add_argument("--redis-address", default=None, type=str,
|
|
help="The Redis address of the cluster.")
|
|
|
|
|
|
@ray.remote
|
|
class ParameterServer(object):
|
|
def __init__(self, keys, values):
|
|
# These values will be mutated, so we must create a copy that is not
|
|
# backed by the object store.
|
|
values = [value.copy() for value in values]
|
|
self.weights = dict(zip(keys, values))
|
|
|
|
def push(self, keys, values):
|
|
for key, value in zip(keys, values):
|
|
self.weights[key] += value
|
|
|
|
def pull(self, keys):
|
|
return [self.weights[key] for key in keys]
|
|
|
|
|
|
@ray.remote
|
|
def worker_task(ps, worker_index, batch_size=50):
|
|
# Download MNIST.
|
|
mnist = model.download_mnist_retry(seed=worker_index)
|
|
|
|
# Initialize the model.
|
|
net = model.SimpleCNN()
|
|
keys = net.get_weights()[0]
|
|
|
|
while True:
|
|
# Get the current weights from the parameter server.
|
|
weights = ray.get(ps.pull.remote(keys))
|
|
net.set_weights(keys, weights)
|
|
|
|
# Compute an update and push it to the parameter server.
|
|
xs, ys = mnist.train.next_batch(batch_size)
|
|
gradients = net.compute_update(xs, ys)
|
|
ps.push.remote(keys, gradients)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parser.parse_args()
|
|
|
|
ray.init(redis_address=args.redis_address)
|
|
|
|
# Create a parameter server with some random weights.
|
|
net = model.SimpleCNN()
|
|
all_keys, all_values = net.get_weights()
|
|
ps = ParameterServer.remote(all_keys, all_values)
|
|
|
|
# Start some training tasks.
|
|
worker_tasks = [worker_task.remote(ps, i) for i in range(args.num_workers)]
|
|
|
|
# Download MNIST.
|
|
mnist = model.download_mnist_retry()
|
|
|
|
i = 0
|
|
while True:
|
|
# Get and evaluate the current model.
|
|
current_weights = ray.get(ps.pull.remote(all_keys))
|
|
net.set_weights(all_keys, current_weights)
|
|
test_xs, test_ys = mnist.test.next_batch(1000)
|
|
accuracy = net.compute_accuracy(test_xs, test_ys)
|
|
print("Iteration {}: accuracy is {}".format(i, accuracy))
|
|
i += 1
|
|
time.sleep(1)
|