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 osimport reimport timeimport numpy as npimport tensorflow as tfimport cifar10_inputimport cifar10batch_size = 128max_steps = 1000num_gpus = 1 # gpu数量# 在scope下生成神经网络并返回scope下的lossdef tower_loss(scope): # 数据集的路径可以在cifar10.py中的tf.app.flags.DEFINE_string中定义 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.expand_dims(g, 0) for g, _ in grad_and_vars] grads = tf.concat(grads, 0) 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_gradsdef 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) 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 __name__ == '__main__': train()

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