Clean up top level Ray dir (#5404)

doc/examples/hyperopt/hyperopt_simple.py

@@ -0,0 +1,100 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import ray
+import argparse
+
+from tensorflow.examples.tutorials.mnist import input_data
+
+import objective
+
+parser = argparse.ArgumentParser(description="Run the hyperparameter "
+                                             "optimization example.")
+parser.add_argument("--trials", default=2, type=int,
+                    help="The number of random trials to do.")
+parser.add_argument("--steps", default=10, type=int,
+                    help="The number of steps of training to do per network.")
+parser.add_argument("--redis-address", default=None, type=str,
+                    help="The Redis address of the cluster.")
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    ray.init(redis_address=args.redis_address)
+
+    # The number of sets of random hyperparameters to try.
+    trials = args.trials
+    # The number of training passes over the dataset to use for network.
+    steps = args.steps
+
+    # Load the mnist data and turn the data into remote objects.
+    print("Downloading the MNIST dataset. This may take a minute.")
+    mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
+    train_images = ray.put(mnist.train.images)
+    train_labels = ray.put(mnist.train.labels)
+    validation_images = ray.put(mnist.validation.images)
+    validation_labels = ray.put(mnist.validation.labels)
+
+    # Keep track of the best hyperparameters and the best accuracy.
+    best_hyperparameters = None
+    best_accuracy = 0
+    # This list holds the object IDs for all of the experiments that we have
+    # launched and that have not yet been processed.
+    remaining_ids = []
+    # This is a dictionary mapping the object ID of an experiment to the
+    # hyerparameters used for that experiment.
+    hyperparameters_mapping = {}
+
+    # A function for generating random hyperparameters.
+    def generate_hyperparameters():
+        return {"learning_rate": 10 ** np.random.uniform(-5, 5),
+                "batch_size": np.random.randint(1, 100),
+                "dropout": np.random.uniform(0, 1),
+                "stddev": 10 ** np.random.uniform(-5, 5)}
+
+    # Randomly generate some hyperparameters, and launch a task for each set.
+    for i in range(trials):
+        hyperparameters = generate_hyperparameters()
+        accuracy_id = objective.train_cnn_and_compute_accuracy.remote(
+            hyperparameters, steps, train_images, train_labels,
+            validation_images, validation_labels)
+        remaining_ids.append(accuracy_id)
+        # Keep track of which hyperparameters correspond to this experiment.
+        hyperparameters_mapping[accuracy_id] = hyperparameters
+
+    # Fetch and print the results of the tasks in the order that they complete.
+    for i in range(trials):
+        # Use ray.wait to get the object ID of the first task that completes.
+        ready_ids, remaining_ids = ray.wait(remaining_ids)
+        # Process the output of this task.
+        result_id = ready_ids[0]
+        hyperparameters = hyperparameters_mapping[result_id]
+        accuracy, _ = ray.get(result_id)
+        print("""We achieve accuracy {:.3}% with
+            learning_rate: {:.2}
+            batch_size: {}
+            dropout: {:.2}
+            stddev: {:.2}
+          """.format(100 * accuracy,
+                     hyperparameters["learning_rate"],
+                     hyperparameters["batch_size"],
+                     hyperparameters["dropout"],
+                     hyperparameters["stddev"]))
+        if accuracy > best_accuracy:
+            best_hyperparameters = hyperparameters
+            best_accuracy = accuracy
+
+    # Record the best performing set of hyperparameters.
+    print("""Best accuracy over {} trials was {:.3} with
+          learning_rate: {:.2}
+          batch_size: {}
+          dropout: {:.2}
+          stddev: {:.2}
+      """.format(trials, 100 * best_accuracy,
+                 best_hyperparameters["learning_rate"],
+                 best_hyperparameters["batch_size"],
+                 best_hyperparameters["dropout"],
+                 best_hyperparameters["stddev"]))