Clean up top level Ray dir (#5404)

doc/examples/parameter_server/sync_parameter_server.py

@@ -0,0 +1,76 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import numpy as np
+
+import ray
+import model
+
+parser = argparse.ArgumentParser(description="Run the synchronous 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, learning_rate):
+        self.net = model.SimpleCNN(learning_rate=learning_rate)
+
+    def apply_gradients(self, *gradients):
+        self.net.apply_gradients(np.mean(gradients, axis=0))
+        return self.net.variables.get_flat()
+
+    def get_weights(self):
+        return self.net.variables.get_flat()
+
+
+@ray.remote
+class Worker(object):
+    def __init__(self, worker_index, batch_size=50):
+        self.worker_index = worker_index
+        self.batch_size = batch_size
+        self.mnist = model.download_mnist_retry(seed=worker_index)
+        self.net = model.SimpleCNN()
+
+    def compute_gradients(self, weights):
+        self.net.variables.set_flat(weights)
+        xs, ys = self.mnist.train.next_batch(self.batch_size)
+        return self.net.compute_gradients(xs, ys)
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    ray.init(redis_address=args.redis_address)
+
+    # Create a parameter server.
+    net = model.SimpleCNN()
+    ps = ParameterServer.remote(1e-4 * args.num_workers)
+
+    # Create workers.
+    workers = [Worker.remote(worker_index)
+               for worker_index in range(args.num_workers)]
+
+    # Download MNIST.
+    mnist = model.download_mnist_retry()
+
+    i = 0
+    current_weights = ps.get_weights.remote()
+    while True:
+        # Compute and apply gradients.
+        gradients = [worker.compute_gradients.remote(current_weights)
+                     for worker in workers]
+        current_weights = ps.apply_gradients.remote(*gradients)
+
+        if i % 10 == 0:
+            # Evaluate the current model.
+            net.variables.set_flat(ray.get(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