NVIDIA-GPU-Bootcamp-Training/ai/Distributed_Deep_Learning/English/python/source_code/N1/cnn_fmnist.py

#!/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=4, verbose=verbose)