TF多GPUcode

# 《TensorFlow实战》09 TensorBoard、多GPU并行及分布式并行
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:sz09.02.py # 多GPU并行


# https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10/cifar10_multi_gpu_train.py
# tensorflow_models\tutorials\image\cifar10\cifar10_multi_gpu_train.py


import os.path
import re
import time
import numpy as np
import tensorflow as tf
import cifar10
batch_size = 128
max_steps = 1000
num_gpus=1 # 具体gpu数量


def tower_loss(scope):
    images, labels = cifar10.distorted_inputs()
    logits = cifar10.inference(images)
    _ = cifar10.loss(logits, labels)
    losses = tf.get_collection('losses', scope)
    total_loss = tf.add_n(losses, name='total_loss')
    return total_loss


def average_gradients(tower_grads):
    average_grads = []
    for grad_and_vars in zip(*tower_grads):
        grads = []
        for g, _ in grad_and_vars:
            expanded_g = tf.expand_dims(g, 0)
            grads.append(expanded_g)


        grad = tf.concat(grads, 0)
        grad = tf.reduce_mean(grad, 0)
        v = grad_and_vars[0][1]
        grad_and_var = (grad, v)
        average_grads.append(grad_and_var)
    return  average_grads


def train():
    with tf.Graph().as_default(), tf.device('/cpu:0'):
        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)
        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)
                    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()
        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


                format_str = ('step %d, loss = %.2f (%.1f examples/sec; %.3f ' 'sec/batch)')
                print(format_str % (step, loss_value, examples_per_sec, sec_per_batch))


            if step % 1000 == 0 or (step + 1) == max_steps:
                saver.save(sess, 'cifar10_train/model.ckpt', global_step=step) # 需要在当前py文件下存在cifar10_train目录


cifar10.maybe_download_and_extract()
train()
'''
step 0, loss = 4.67 (4.6 examples/sec; 27.776 sec/batch)
step 10, loss = 4.64 (839.8 examples/sec; 0.152 sec/batch)
step 20, loss = 4.50 (728.0 examples/sec; 0.176 sec/batch)
...
step 960, loss = 2.39 (467.6 examples/sec; 0.274 sec/batch)
step 970, loss = 2.53 (675.4 examples/sec; 0.190 sec/batch)
step 980, loss = 2.50 (482.5 examples/sec; 0.265 sec/batch)
step 990, loss = 2.48 (709.2 examples/sec; 0.180 sec/batch)
'''