text: # Copyright 2019 Uber Technologies, Inc. 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.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License 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.
  #
  # Script adapted from: https://github.com/horovod/horovod/blob/master/examples/tensorflow2_keras_mnist.py
  # ==============================================================================
  
  import tensorflow as tf
  import horovod.tensorflow.keras as hvd
  
  import os
  import argparse
  
  parser = argparse.ArgumentParser()
  parser.add_argument("--learning-rate", "-lr", type=float, default=0.001)
  parser.add_argument("--epochs", type=int, default=24)
  parser.add_argument("--steps-per-epoch", type=int, default=500)
  args = parser.parse_args()
  
  # Horovod: initialize Horovod.
  hvd.init()
  
  # Horovod: pin GPU to be used to process local rank (one GPU per process)
  gpus = tf.config.experimental.list_physical_devices("GPU")
  for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
  if gpus:
      tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], "GPU")
  
  (mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data(
      path="mnist-%d.npz" % hvd.rank()
  )
  
  dataset = tf.data.Dataset.from_tensor_slices(
      (
          tf.cast(mnist_images[..., tf.newaxis] / 255.0, tf.float32),
          tf.cast(mnist_labels, tf.int64),
      )
  )
  dataset = dataset.repeat().shuffle(10000).batch(128)
  
  mnist_model = tf.keras.Sequential(
      [
          tf.keras.layers.Conv2D(32, [3, 3], activation="relu"),
          tf.keras.layers.Conv2D(64, [3, 3], activation="relu"),
          tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
          tf.keras.layers.Dropout(0.25),
          tf.keras.layers.Flatten(),
          tf.keras.layers.Dense(128, activation="relu"),
          tf.keras.layers.Dropout(0.5),
          tf.keras.layers.Dense(10, activation="softmax"),
      ]
  )
  
  # Horovod: adjust learning rate based on number of GPUs.
  scaled_lr = args.learning_rate * hvd.size()
  opt = tf.optimizers.Adam(scaled_lr)
  
  # Horovod: add Horovod DistributedOptimizer.
  opt = hvd.DistributedOptimizer(opt)
  
  # Horovod: Specify `experimental_run_tf_function=False` to ensure TensorFlow
  # uses hvd.DistributedOptimizer() to compute gradients.
  mnist_model.compile(
      loss=tf.losses.SparseCategoricalCrossentropy(),
      optimizer=opt,
      metrics=["accuracy"],
      experimental_run_tf_function=False,
  )
  
  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: average metrics among workers at the end of every epoch.
      #
      # Note: This callback must be in the list before the ReduceLROnPlateau,
      # TensorBoard or other metrics-based callbacks.
      hvd.callbacks.MetricAverageCallback(),
      # Horovod: using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final
      # accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during
      # the first three epochs. See https://arxiv.org/abs/1706.02677 for details.
      hvd.callbacks.LearningRateWarmupCallback(
          warmup_epochs=3, initial_lr=scaled_lr, verbose=1
      ),
  ]
  
  # Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.
  if hvd.rank() == 0:
      output_dir = "./outputs"
      os.makedirs(output_dir, exist_ok=True)
      callbacks.append(
          tf.keras.callbacks.ModelCheckpoint(
              os.path.join(output_dir, "checkpoint-{epoch}.h5")
          )
      )
  
  # Horovod: write logs on worker 0.
  verbose = 1 if hvd.rank() == 0 else 0
  
  # Train the model.
  # Horovod: adjust number of steps based on number of GPUs.
  mnist_model.fit(
      dataset,
      steps_per_epoch=args.steps_per_epoch // hvd.size(),
      callbacks=callbacks,
      epochs=args.epochs,
      verbose=verbose,
  )