keras_mnist.py

from __future__ import print_function
import argparse
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import math
import tensorflow as tf
from kungfu.python import current_cluster_size, current_rank
from kungfu.tensorflow.initializer import BroadcastGlobalVariablesCallback
from kungfu.tensorflow.optimizers import (SynchronousAveragingOptimizer,
                                          SynchronousSGDOptimizer,
                                          PairAveragingOptimizer)

parser = argparse.ArgumentParser(description='Keras MNIST example.')
parser.add_argument('--kf-optimizer',
                    type=str,
                    default='sync-sgd',
                    help='kungfu optimizer')
args = parser.parse_args()

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
K.set_session(tf.Session(config=config))

batch_size = 128
num_classes = 10

# KungFu: adjust number of epochs based on number of GPUs.
epochs = int(math.ceil(4.0 / current_cluster_size()))

# Input image dimensions
img_rows, img_cols = 28, 28

# The data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

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 = keras.utils.to_categorical(y_train, num_classes)
y_test = 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'))

# KungFu: adjust learning rate based on number of GPUs.
opt = keras.optimizers.Adadelta(1.0 * current_cluster_size())

# KungFu: wrap distributed optimizers.
if args.kf_optimizer == 'sync-sgd':
    opt = SynchronousSGDOptimizer(opt, with_keras=True)
elif args.kf_optimizer == 'async-sgd':
    opt = PairAveragingOptimizer(opt, with_keras=True)
elif args.kf_optimizer == 'sma':
    opt = SynchronousAveragingOptimizer(opt, with_keras=True)
else:
    raise RuntimeError('unknown optimizer: %s' % name)

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

callbacks = [BroadcastGlobalVariablesCallback(with_keras=True)]

# KungFu: save checkpoints only on worker 0 to prevent other workers from corrupting them.
if current_rank() == 0:
    callbacks.append(
        keras.callbacks.ModelCheckpoint('./checkpoint-{epoch}.h5'))

model.fit(x_train,
          y_train,
          batch_size=batch_size,
          callbacks=callbacks,
          epochs=epochs,
          verbose=1 if current_rank() == 0 else 0,
          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])