Tebsorflow开源实现多GPU训练cifar10数据集：cifar10_multi_gpu_train.py
Tensorflow开源实现cifar10神经网络：cifar10.py
Tensorflow中的并行分为模型并行和数据并行。模型并行需要根据不同模型设计不同的并行方式，其主要原理是将模型中不同计算节点放在不同硬件资源上运算。比较通用且能简便地实现大规模并行的方式是数据并行，同时使用多个硬件资源来计算不同batch的数据梯度，然后汇总梯度进行全局更新。
数据并行几乎适用于所有深度学习模型，总是可以利用多块GPU同时训练多个batch数据，运行在每块GPU上的模型都基于同一个神经网络，网络结构一样，并且共享模型参数。
import os
import re
import time
import numpy as np
import tensorflow as tf
import cifar10_input
import cifar10
batch_size = 128
max_steps = 1000
num_gpus = 1 # gpu数量
# 在scope下生成神经网络并返回scope下的loss
def tower_loss(scope):
images, labels = cifar10.distorted_inputs()
logits = cifar10.inference(images) # 生成神经网络
_ = cifar10.loss(logits, labels) # 不直接返回loss而是放到collection
losses = tf.get_collection('losses', scope) # 获取当前GPU上的loss(通过scope限定范围)
total_loss = tf.add_n(losses, name='total_loss')
return total_loss
'''
外层是不同GPU计算的梯度，内层是某个GPU对应的不同var的值
tower_grads =
[[(grad0_gpu0, var0_gpu0), (grad1_gpu0, var1_gpu0),...],
[(grad0_gpu1, var0_gpu1), (grad1_gpu1, var1_gpu1),...]]
zip(*tower_grads)= 相当于转置了
[[(grad0_gpu0, var0_gpu0), (grad0_gpu1, var0, gpu1),...],
[(grad1_gpu0, var1_gpu0), (grad1_gpu1, var1_gpu1),...]]
'''
def average_gradients(tower_grads):
average_grads = []
for grad_and_vars in zip(*tower_grads):
grads = [tf.concat(tf.expand_dims(g, 0), 0) for g, _ in grad_and_vars]
grad = tf.reduce_mean(grads, 0)
grad_and_var = (grad, grad_and_vars[0][1])
# [(grad0, var0),(grad1, var1),...]
average_grads.append(grad_and_var)
return average_grads
def train():
# 默认的计算设备为CPU
with tf.Graph().as_default(), tf.device('/cpu:0'):
# []表示没有维度，为一个数
# trainable=False,不会加入GraphKeys.TRAINABLE_VARIABLES参与训练
global_step = tf.get_variable('global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
num_batches_per_epoch = cifar10.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / batch_size
decay_steps = int(num_batches_per_epoch * cifar10.NUM_EPOCHS_PER_DECAY)
# https://tensorflow.google.cn/api_docs/python/tf/train/exponential_decay
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
# staircase is True, then global_step / decay_steps is an integer division
lr = tf.train.exponential_decay(cifar10.INITIAL_LEARNING_RATE,
global_step,
decay_steps,
cifar10.LEARNING_RATE_DECAY_FACTOR,
staircase=True)
opt = tf.train.GradientDescentOptimizer(lr)
tower_grads = []
for i in range(num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope:
loss = tower_loss(scope)
# 让神经网络的变量可以重用，所有GPU使用完全相同的参数
# 让下一个tower重用参数
tf.get_variable_scope().reuse_variables()
grads = opt.compute_gradients(loss)
tower_grads.append(grads)
grads = average_gradients(tower_grads)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
saver = tf.train.Saver(tf.all_variables())
init = tf.global_variables_initializer()
# True会自动选择一个存在并且支持的设备来运行
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(init)
tf.train.start_queue_runners(sess=sess)
for step in range(max_steps):
start_time = time.time()
_, loss_value = sess.run([apply_gradient_op, loss])
duration = time.time() - start_time
if step % 10 == 0:
num_examples_per_step = batch_size * num_gpus
examples_per_sec = num_examples_per_step / duration
sec_per_batch = duration / num_gpus
print('step %d, loss=%.2f(%.1f examples/sec;%.3f sec/batch)'
% (step, loss_value, examples_per_sec, sec_per_batch))
if step % 1000 == 0 or (step + 1) == max_steps:
saver.save(sess, 'model.ckpt', global_step=step)
if __name__ == '__main__':
train()