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- #!/usr/bin/env python
- # coding: utf-8
- # %%
- import argparse
- import tensorflow as tf
- import horovod.tensorflow.keras as hvd
- import sys
- import time
- def parse_args():
- parser = argparse.ArgumentParser()
- parser.add_argument("--batch-size", type=int, default=256, help="Batch size")
- args = parser.parse_args()
- return args
- args = parse_args()
- global g_args
- g_args = args
- batch_size = args.batch_size
- # 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(batch_size)
- 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.
- opt = tf.optimizers.Adam(0.001)
- # Horovod: add Horovod DistributedOptimizer.
- opt = hvd.DistributedOptimizer(opt, backward_passes_per_step=1, average_aggregated_gradients=True)
- # 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)
- class PrintLR(tf.keras.callbacks.Callback):
- def __init__(self, total_images=0):
- self.total_images = total_images
- def on_train_begin(self, epoch, logs=None):
- global seconds1
- seconds1 = time.time()
- def on_epoch_begin(self, epoch, logs=None):
- self.epoch_start_time = time.time()
- def on_epoch_end(self, epoch, logs=None):
- if hvd.rank() == 0 :
- epoch_time = time.time() - self.epoch_start_time
- print('Epoch time : {}'.format(epoch_time))
- images_per_sec = round(self.total_images / epoch_time, 2)
- print('Images/sec: {}'.format(images_per_sec))
- 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(),
- #Throughput calculator
- PrintLR(total_images=len(mnist_labels)),
- ]
- # Horovod: write logs on worker 0.
- verbose = 2 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=len(mnist_labels) // (batch_size*hvd.size()), callbacks=callbacks, epochs=6, verbose=verbose)
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