mnist_hvd.py

#     Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
#     Licensed under the Apache License, Version 2.0 (the "License").
#     You may not use this file except in compliance with the License.
#     A copy of the License is located at
#
#         https://aws.amazon.com/apache-2-0/
#
#     or in the "license" file accompanying this file. This file is distributed
#     on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
#     express or implied. See the License for the specific language governing
#     permissions and limitations under the License.

from __future__ import print_function

import argparse
import math
import os

import horovod.tensorflow.keras as hvd
import numpy as np
import tensorflow as tf
from tensorflow.keras import backend as K

# import tensorflow.keras as keras
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
from tensorflow.keras.models import Sequential

if __name__ == "__main__":

    num_gpus = int(os.environ["SM_NUM_GPUS"])

    parser = argparse.ArgumentParser()

    # Data, model, and output directories. These are required.
    parser.add_argument("--output-dir", type=str, default=os.environ["SM_OUTPUT_DIR"])
    parser.add_argument("--model_dir", type=str)
    parser.add_argument("--train", type=str, default=os.environ["SM_CHANNEL_TRAIN"])
    parser.add_argument("--test", type=str, default=os.environ["SM_CHANNEL_TEST"])

    args, _ = parser.parse_known_args()

    # Horovod: initialize Horovod.
    hvd.init()

    # Horovod: pin GPU to be used to process local rank (one GPU per process)
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    config.gpu_options.visible_device_list = str(hvd.local_rank())
    K.set_session(tf.Session(config=config))

    batch_size = 128
    num_classes = 10

    # Horovod: adjust number of epochs based on number of GPUs.
    epochs = int(math.ceil(12.0 / hvd.size()))

    # Input image dimensions
    img_rows, img_cols = 28, 28

    # The data, shuffled and split between train and test sets

    x_train = np.load(os.path.join(args.train, "train.npz"))["data"]
    y_train = np.load(os.path.join(args.train, "train.npz"))["labels"]
    print("Train dataset loaded from: {}".format(os.path.join(args.train, "train.npz")))

    x_test = np.load(os.path.join(args.test, "test.npz"))["data"]
    y_test = np.load(os.path.join(args.test, "test.npz"))["labels"]
    print("Test dataset loaded from: {}".format(os.path.join(args.test, "test.npz")))

    if K.image_data_format() == "channels_first":
        x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
        x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
        input_shape = (1, img_rows, img_cols)
    else:
        x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
        x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
        input_shape = (img_rows, img_cols, 1)

    x_train = x_train.astype("float32")
    x_test = x_test.astype("float32")
    x_train /= 255
    x_test /= 255
    print("x_train shape:", x_train.shape)
    print(x_train.shape[0], "train samples")
    print(x_test.shape[0], "test samples")

    # Convert class vectors to binary class matrices
    y_train = tf.keras.utils.to_categorical(y_train, num_classes)
    y_test = tf.keras.utils.to_categorical(y_test, num_classes)

    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation="relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation="relu"))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation="softmax"))

    # Horovod: adjust learning rate based on number of GPUs.
    opt = tf.keras.optimizers.Adadelta(1.0 * hvd.size())

    # Horovod: add Horovod Distributed Optimizer.
    opt = hvd.DistributedOptimizer(opt)

    model.compile(
        loss=tf.keras.losses.categorical_crossentropy, optimizer=opt, metrics=["accuracy"]
    )

    callbacks = [
        # Horovod: broadcast initial variable states from rank 0 to all other processes.
        # This is necessary to ensure consistent initialization of all workers when
        # training is started with random weights or restored from a checkpoint.
        hvd.callbacks.BroadcastGlobalVariablesCallback(0),
    ]

    # Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.
    if hvd.rank() == 0:
        callbacks.append(tf.keras.callbacks.ModelCheckpoint("./checkpoint-{epoch}.h5"))

    model.fit(
        x_train,
        y_train,
        batch_size=batch_size,
        callbacks=callbacks,
        epochs=epochs,
        verbose=1,
        validation_data=(x_test, y_test),
    )
    score = model.evaluate(x_test, y_test, verbose=0)

    print("Test loss:", score[0])
    print("Test accuracy:", score[1])

    # Horovod: Save model only on worker 0 (i.e. master)
    if hvd.rank() == 0:
        model.save(os.path.join(args.model_dir, "model.h5"))