text: #     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 os
  
  import numpy as np
  
  # import tensorflow.keras as keras
  from tensorflow.keras.datasets import mnist
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense, Dropout, Flatten
  from tensorflow.keras.layers import Conv2D, MaxPooling2D
  from tensorflow.keras import backend as K
  import math
  import tensorflow as tf
  import horovod.tensorflow.keras as hvd
  
  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')) # change this parameter to scale up the neural network
      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:
          saved_model_path = tf.contrib.saved_model.save_keras_model(model, args.model_dir)
          print("Model successfully saved at: {}".format(saved_model_path))
  