keras.rst

Horovod with Keras

Horovod supports Keras and regular TensorFlow in similar ways. To use Horovod with Keras, make the following modifications to your training script:

1. Run hvd.init().

2. Pin each GPU to a single process.

   With the typical setup of one GPU per process, set this to local rank. The first process on the server will be allocated the first GPU, the second process will be allocated the second GPU, and so forth.

   For TensorFlow v1:

   config = tf.ConfigProto()
   config.gpu_options.visible_device_list = str(hvd.local_rank())
   K.set_session(tf.Session(config=config))

   For TensorFlow v2:

   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')

3. Scale the learning rate by the number of workers.

   Effective batch size in synchronous distributed training is scaled by the number of workers. An increase in learning rate compensates for the increased batch size.

4. Wrap the optimizer in hvd.DistributedOptimizer.

   The distributed optimizer delegates gradient computation to the original optimizer, averages gradients using allreduce or allgather, and then applies those averaged gradients.

   Note: For model parallel usecases there are local variables (layers) that their gradients need not to be synced (by allreduce or allgather). You can register those variables with the returned wrapper optimizer by calling its register_local_var() API. Alternatively, you can use the horovod.keras.PartialDistributedOptimizer API and and pass the local layers to this API in order to register their local variables.

5. Add hvd.callbacks.BroadcastGlobalVariablesCallback(0) to 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.

   Note: For model parallel use cases there are local variables (layers) that their weights need not to be broadcasted. You can pass those local variables to this callback by adding hvd.callbacks.BroadcastGlobalVariablesCallback(0, local_variables=[list of local variables]) instead.

6. Modify your code to save checkpoints only on worker 0 to prevent other workers from corrupting them.

   Accomplish this by guarding model checkpointing code with hvd.rank() != 0.

Note

• Keras 2.0.9 has a known issue that makes each worker allocate all GPUs on the server, instead of the GPU assigned by the local rank. If you have multiple GPUs per server, upgrade to Keras 2.1.2 or downgrade to Keras 2.0.8.
• To use keras bundled with tensorflow you must use from tensorflow import keras instead of import keras and import horovod.tensorflow.keras as hvd instead of import horovod.keras as hvd in the import statements.

See full training simple (shown below) and advanced examples.

from __future__ import print_function
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
import horovod.keras as hvd

# 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, 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'))

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

# Horovod: add Horovod Distributed Optimizer.
opt = hvd.DistributedOptimizer(opt)

model.compile(loss=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(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])

TensorFlow v2 Keras Example (from the MNIST example):

import tensorflow as tf
import horovod.tensorflow.keras as hvd

# Initialize Horovod
hvd.init()

# 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')

# Build model and dataset
dataset = ...
model = ...
opt = tf.optimizers.Adam(0.001 * hvd.size())

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

model.fit(dataset,
          steps_per_epoch=500 // hvd.size(),
          callbacks=callbacks,
          epochs=24,
          verbose=1 if hvd.rank() == 0 else 0)