keras使用horovod多gpu训练

keras使用horovod多gpu训练

Horovod以类似的方式支持Keras和常规TensorFlow。要使用Horovod，请在程序中添加以下内容。

1. 运行hvd.init()。

2. 使用固定服务器GPU，以供此过程使用config.gpu_options.visible_device_list。

   通过每个进程一个GPU的典型设置，您可以将其设置为local rank。在这种情况下，服务器上的第一个进程将被分配第一GPU，第二个进程将被分配第二GPU，依此类推。

3. 通过工人人数来衡量学习率。

   同步分布式培训中的有效批处理规模是根据工人人数来衡量的。学习率的提高弥补了批量大小的增加。

4. 将优化器包装在中hvd.DistributedOptimizer。

   分布式优化器将梯度计算委派给原始优化器，使用allreduce或allgather对梯度求平均，然后应用这些平均梯度。

5. 添加hvd.callbacks.BroadcastGlobalVariablesCallback(0)到播放初始变量状态从0级到所有其他进程。

   当使用随机权重开始训练或从检查点恢复训练时，这是确保所有工人进行一致初始化的必要步骤。

6. 修改您的代码以仅在工作程序0上保存检查点，以防止其他工作程序破坏它们。

   通过使用来保护模型检查点代码来实现此目的。hvd.rank() != 0

示例代码

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])