Clean up top level Ray dir (#5404)

doc/examples/parameter_server/async_parameter_server.py

@@ -0,0 +1,80 @@
+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)