Clean up top level Ray dir (#5404)

doc/examples/hyperopt/hyperopt_adaptive.py

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+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+from collections import defaultdict
+import numpy as np
+import ray
+
+from tensorflow.examples.tutorials.mnist import input_data
+
+import objective
+
+parser = argparse.ArgumentParser(description="Run the hyperparameter "
+                                             "optimization example.")
+parser.add_argument("--num-starting-segments", default=5, type=int,
+                    help="The number of training segments to start in "
+                         "parallel.")
+parser.add_argument("--num-segments", default=10, type=int,
+                    help="The number of additional training segments to "
+                         "perform.")
+parser.add_argument("--steps-per-segment", default=20, type=int,
+                    help="The number of steps of training to do per training "
+                         "segment.")
+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 training passes over the dataset to use for network.
+    steps = args.steps_per_segment
+
+    # 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 accuracies that we've seen at different numbers of
+    # iterations.
+    accuracies_by_num_steps = defaultdict(lambda: [])
+
+    # Define a method to determine if an experiment looks promising or not.
+    def is_promising(experiment_info):
+        accuracies = experiment_info["accuracies"]
+        total_num_steps = experiment_info["total_num_steps"]
+        comparable_accuracies = accuracies_by_num_steps[total_num_steps]
+        if len(comparable_accuracies) == 0:
+            if len(accuracies) == 1:
+                # This means that we haven't seen anything finish yet, so keep
+                # running this experiment.
+                return True
+            else:
+                # The experiment is promising if the second half of the
+                # accuracies are better than the first half of the accuracies.
+                return (np.mean(accuracies[:len(accuracies) // 2]) <
+                        np.mean(accuracies[len(accuracies) // 2:]))
+        # Otherwise, continue running the experiment if it is in the top half
+        # of experiments we've seen so far at this point in time.
+        return np.mean(accuracy > np.array(comparable_accuracies)) > 0.5
+
+    # Keep track of all of the experiment segments that we're running. This
+    # dictionary uses the object ID of the experiment as the key.
+    experiment_info = {}
+    # Keep track of the curently running experiment IDs.
+    remaining_ids = []
+
+    # Keep track of the best hyperparameters and the best accuracy.
+    best_hyperparameters = None
+    best_accuracy = 0
+
+    # 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)}
+
+    # Launch some initial experiments.
+    for _ in range(args.num_starting_segments):
+        hyperparameters = generate_hyperparameters()
+        experiment_id = objective.train_cnn_and_compute_accuracy.remote(
+            hyperparameters, steps, train_images, train_labels,
+            validation_images, validation_labels)
+        experiment_info[experiment_id] = {"hyperparameters": hyperparameters,
+                                          "total_num_steps": steps,
+                                          "accuracies": []}
+        remaining_ids.append(experiment_id)
+
+    for _ in range(args.num_segments):
+        # Wait for a segment of an experiment to finish.
+        ready_ids, remaining_ids = ray.wait(remaining_ids, num_returns=1)
+        experiment_id = ready_ids[0]
+        # Get the accuracy and the weights.
+        accuracy, weights = ray.get(experiment_id)
+        # Update the experiment info.
+        previous_info = experiment_info[experiment_id]
+        previous_info["accuracies"].append(accuracy)
+
+        # Update the best accuracy and best hyperparameters.
+        if accuracy > best_accuracy:
+            best_hyperparameters = previous_info["hyperparameters"]
+            best_accuracy = accuracy
+
+        if is_promising(previous_info):
+            # If the experiment still looks promising, then continue running
+            # it.
+            print("Continuing to run the experiment with hyperparameters {}."
+                  .format(previous_info["hyperparameters"]))
+            new_hyperparameters = previous_info["hyperparameters"]
+            new_info = {"hyperparameters": new_hyperparameters,
+                        "total_num_steps": (previous_info["total_num_steps"] +
+                                            steps),
+                        "accuracies": previous_info["accuracies"][:]}
+            starting_weights = weights
+        else:
+            # If the experiment does not look promising, start a new
+            # experiment.
+            print("Ending the experiment with hyperparameters {}."
+                  .format(previous_info["hyperparameters"]))
+            new_hyperparameters = generate_hyperparameters()
+            new_info = {"hyperparameters": new_hyperparameters,
+                        "total_num_steps": steps,
+                        "accuracies": []}
+            starting_weights = None
+
+        # Start running the next segment.
+        new_experiment_id = objective.train_cnn_and_compute_accuracy.remote(
+            new_hyperparameters, steps, train_images, train_labels,
+            validation_images, validation_labels, weights=starting_weights)
+        experiment_info[new_experiment_id] = new_info
+        remaining_ids.append(new_experiment_id)
+
+        # Update the set of all accuracies that we've seen.
+        accuracies_by_num_steps[previous_info["total_num_steps"]].append(
+            accuracy)
+
+    # Record the best performing set of hyperparameters.
+    print("""Best accuracy was {:.3} with
+          learning_rate: {:.2}
+          batch_size: {}
+          dropout: {:.2}
+          stddev: {:.2}
+      """.format(100 * best_accuracy,
+                 best_hyperparameters["learning_rate"],
+                 best_hyperparameters["batch_size"],
+                 best_hyperparameters["dropout"],
+                 best_hyperparameters["stddev"]))

doc/examples/hyperopt/hyperopt_simple.py

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+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"]))

doc/examples/hyperopt/objective.py

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+# Most of the tensorflow code is adapted from Tensorflow's tutorial on using
+# CNNs to train MNIST
+# https://www.tensorflow.org/versions/r0.9/tutorials/mnist/pros/index.html#build-a-multilayer-convolutional-network.  # noqa: E501
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+import ray
+import ray.experimental.tf_utils
+
+
+def get_batch(data, batch_index, batch_size):
+    # This method currently drops data when num_data is not divisible by
+    # batch_size.
+    num_data = data.shape[0]
+    num_batches = num_data // batch_size
+    batch_index %= num_batches
+    return data[(batch_index * batch_size):((batch_index + 1) * batch_size)]
+
+
+def weight(shape, stddev):
+    initial = tf.truncated_normal(shape, stddev=stddev)
+    return tf.Variable(initial)
+
+
+def bias(shape):
+    initial = tf.constant(0.1, shape=shape)
+    return tf.Variable(initial)
+
+
+def conv2d(x, W):
+    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
+
+
+def max_pool_2x2(x):
+    return tf.nn.max_pool(
+        x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
+
+
+def cnn_setup(x, y, keep_prob, lr, stddev):
+    first_hidden = 32
+    second_hidden = 64
+    fc_hidden = 1024
+    W_conv1 = weight([5, 5, 1, first_hidden], stddev)
+    B_conv1 = bias([first_hidden])
+    x_image = tf.reshape(x, [-1, 28, 28, 1])
+    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + B_conv1)
+    h_pool1 = max_pool_2x2(h_conv1)
+    W_conv2 = weight([5, 5, first_hidden, second_hidden], stddev)
+    b_conv2 = bias([second_hidden])
+    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+    h_pool2 = max_pool_2x2(h_conv2)
+    W_fc1 = weight([7 * 7 * second_hidden, fc_hidden], stddev)
+    b_fc1 = bias([fc_hidden])
+    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * second_hidden])
+    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
+    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+    W_fc2 = weight([fc_hidden, 10], stddev)
+    b_fc2 = bias([10])
+    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
+    cross_entropy = tf.reduce_mean(
+        -tf.reduce_sum(y * tf.log(y_conv), reduction_indices=[1]))
+    correct_pred = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y, 1))
+    return (tf.train.AdamOptimizer(lr).minimize(cross_entropy),
+            tf.reduce_mean(tf.cast(correct_pred, tf.float32)), cross_entropy)
+
+
+# Define a remote function that takes a set of hyperparameters as well as the
+# data, consructs and trains a network, and returns the validation accuracy.
+@ray.remote
+def train_cnn_and_compute_accuracy(params,
+                                   steps,
+                                   train_images,
+                                   train_labels,
+                                   validation_images,
+                                   validation_labels,
+                                   weights=None):
+    # Extract the hyperparameters from the params dictionary.
+    learning_rate = params["learning_rate"]
+    batch_size = params["batch_size"]
+    keep = 1 - params["dropout"]
+    stddev = params["stddev"]
+    # Create the network and related variables.
+    with tf.Graph().as_default():
+        # Create the input placeholders for the network.
+        x = tf.placeholder(tf.float32, shape=[None, 784])
+        y = tf.placeholder(tf.float32, shape=[None, 10])
+        keep_prob = tf.placeholder(tf.float32)
+        # Create the network.
+        train_step, accuracy, loss = cnn_setup(x, y, keep_prob, learning_rate,
+                                               stddev)
+        # Do the training and evaluation.
+        with tf.Session() as sess:
+            # Use the TensorFlowVariables utility. This is only necessary if we
+            # want to set and get the weights.
+            variables = ray.experimental.tf_utils.TensorFlowVariables(
+                loss, sess)
+            # Initialize the network weights.
+            sess.run(tf.global_variables_initializer())
+            # If some network weights were passed in, set those.
+            if weights is not None:
+                variables.set_weights(weights)
+            # Do some steps of training.
+            for i in range(1, steps + 1):
+                # Fetch the next batch of data.
+                image_batch = get_batch(train_images, i, batch_size)
+                label_batch = get_batch(train_labels, i, batch_size)
+                # Do one step of training.
+                sess.run(
+                    train_step,
+                    feed_dict={
+                        x: image_batch,
+                        y: label_batch,
+                        keep_prob: keep
+                    })
+            # Training is done, so compute the validation accuracy and the
+            # current weights and return.
+            totalacc = accuracy.eval(feed_dict={
+                x: validation_images,
+                y: validation_labels,
+                keep_prob: 1.0
+            })
+            new_weights = variables.get_weights()
+    return float(totalacc), new_weights