『TensorFlow』分布式训练_其二_单机多GPU并行&GPU模式设定

一、tensorflow GPU设置

GPU指定占用

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)

sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

上面分配给tensorflow的GPU显存大小为：GPU实际显存*0.7。

GPU模式禁用

import os

os.environ["CUDA_VISIBLE_DEVICES"]="-1"

GPU资源申请规则

# 设置 GPU 按需增长

config = tf.ConfigProto()

config.gpu_options.allow_growth = True

sess = tf.Session(config=config)

二、单机多GPU工作原理

以一篇csdn博客（出处见水印）上的图说明多GPU工作原理：

想让 TensorFlow 在多个 GPU 上运行, 需要建立 multi-tower 结构, 在这个结构里每个 tower 分别被指配给不同的 GPU 运行，汇总工作一般交由CPU完成，示意如下，

# 新建一个 graph.

c = []

for d in ['/gpu:2', '/gpu:3']:

  with tf.device(d):

    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])

    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2])

    c.append(tf.matmul(a, b))

with tf.device('/cpu:0'):

  sum = tf.add_n(c)

# 新建session with log_device_placement并设置为True.

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

# 运行这个op.

print sess.run(sum)

三、官方demo

多GPU分布+LR衰减+滑动平均

和MXNet不同，由于TensorFlow使用上下文指定设备，所以数据无需显示的拷贝到指定设备，在目标设备上下文中获取即可（需要调用对应节点于该设备下，如下文中的出队节点）

另一个值得注意的点在于收集来的梯度格式为List of lists of (gradient, variable) tuples，我们计算后返回的是List of (gradient, variable) tuples，variable随便指定一组gpu上的即可，这是因为和MXNet不同，MXNet是得到grad平均值后分发给各个GPU各自更新，TensorFlow实际是各个GPU使用同一套参数（tf.get_variable_scope().reuse_variables()），虽然会被拷贝到各个设备，但是彼此之间是有逻辑关系的，是共享参数，简化示意如下：

#将神经网络的优化过程跑在不同的GPU上

for i in range(N_GPU)：

    with tf.debice('/gpu:%d'%i)

        with tf.name_scope('GPU_%d'%i) as scope:

            cur_loss = get_loss(x,y_regularizer,scope)

            #tf.get_variable的命名空间

            tf.get_variable_scope().reuse_variables()

            #使用当前gpu计算所有变量的梯度

            grads= opt.compute_gradients(cur_loss)

            tower_grads.append(grads)

#计算变量的平均梯度

grads = average_gradients(tower_grads)

#使用平均梯度更新参数

apply_gradient_op = opt.apply_gradients(grads,global_step = global)

models/tutorials/image/cifar10/cifer10_multi_gpu-train.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""A binary to train CIFAR-10 using multiple GPUs with synchronous updates.

Accuracy:

cifar10_multi_gpu_train.py achieves ~86% accuracy after 100K steps (256

epochs of data) as judged by cifar10_eval.py.

Speed: With batch_size 128.

System        | Step Time (sec/batch)  |     Accuracy

--------------------------------------------------------------------

1 Tesla K20m  | 0.35-0.60              | ~86% at 60K steps  (5 hours)

1 Tesla K40m  | 0.25-0.35              | ~86% at 100K steps (4 hours)

2 Tesla K20m  | 0.13-0.20              | ~84% at 30K steps  (2.5 hours)

3 Tesla K20m  | 0.13-0.18              | ~84% at 30K steps

4 Tesla K20m  | ~0.10                  | ~84% at 30K steps

Usage:

Please see the tutorial and website for how to download the CIFAR-10

data set, compile the program and train the model.

http://tensorflow.org/tutorials/deep_cnn/

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

from datetime import datetime

import os.path

import re

import time

import numpy as np

from six.moves import xrange  # pylint: disable=redefined-builtin

import tensorflow as tf

import cifar10

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('train_dir', '/tmp/cifar10_train',

                           """Directory where to write event logs """

                           """and checkpoint.""")

tf.app.flags.DEFINE_integer('max_steps', 1000000,

                            """Number of batches to run.""")

tf.app.flags.DEFINE_integer('num_gpus', 1,

                            """How many GPUs to use.""")

tf.app.flags.DEFINE_boolean('log_device_placement', False,

                            """Whether to log device placement.""")

def tower_loss(scope, images, labels):

  """Calculate the total loss on a single tower running the CIFAR model.

  Args:

    scope: unique prefix string identifying the CIFAR tower, e.g. 'tower_0'

    images: Images. 4D tensor of shape [batch_size, height, width, 3].

    labels: Labels. 1D tensor of shape [batch_size].

  Returns:

     Tensor of shape [] containing the total loss for a batch of data

  """

  # Build inference Graph.

  logits = cifar10.inference(images)

  # Build the portion of the Graph calculating the losses. Note that we will

  # assemble the total_loss using a custom function below.

  _ = cifar10.loss(logits, labels)

  # Assemble all of the losses for the current tower only.

  losses = tf.get_collection('losses', scope)

  # Calculate the total loss for the current tower.

  total_loss = tf.add_n(losses, name='total_loss')

  # Attach a scalar summary to all individual losses and the total loss; do the

  # same for the averaged version of the losses.

  for l in losses + [total_loss]:

    # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training

    # session. This helps the clarity of presentation on tensorboard.

    loss_name = re.sub('%s_[0-9]*/' % cifar10.TOWER_NAME, '', l.op.name)

    tf.summary.scalar(loss_name, l)

  return total_loss

def average_gradients(tower_grads):

  """Calculate the average gradient for each shared variable across all towers.

  Note that this function provides a synchronization point across all towers.

  Args:

    tower_grads: List of lists of (gradient, variable) tuples. The outer list

      is over individual gradients. The inner list is over the gradient

      calculation for each tower.

  Returns:

     List of pairs of (gradient, variable) where the gradient has been averaged

     across all towers.

  """

  average_grads = []

  for grad_and_vars in zip(*tower_grads):

    # Note that each grad_and_vars looks like the following:

    #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))

    grads = []

    for g, _ in grad_and_vars:

      # Add 0 dimension to the gradients to represent the tower.

      expanded_g = tf.expand_dims(g, 0)

      # Append on a 'tower' dimension which we will average over below.

      grads.append(expanded_g)

    # Average over the 'tower' dimension.

    grad = tf.concat(axis=0, values=grads)

    grad = tf.reduce_mean(grad, 0)

    # Keep in mind that the Variables are redundant because they are shared

    # across towers. So .. we will just return the first tower's pointer to

    # the Variable.

    v = grad_and_vars[0][1]

    grad_and_var = (grad, v)

    average_grads.append(grad_and_var)

  return average_grads

def train():

  """Train CIFAR-10 for a number of steps."""

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

    # Create a variable to count the number of train() calls. This equals the

    # number of batches processed * FLAGS.num_gpus.

    global_step = tf.get_variable(

        'global_step', [],

        initializer=tf.constant_initializer(0), trainable=False)

    # Calculate the learning rate schedule.

    num_batches_per_epoch = (cifar10.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /

                             FLAGS.batch_size)

    decay_steps = int(num_batches_per_epoch * cifar10.NUM_EPOCHS_PER_DECAY)

    # Decay the learning rate exponentially based on the number of steps.

    lr = tf.train.exponential_decay(cifar10.INITIAL_LEARNING_RATE,

                                    global_step,

                                    decay_steps,

                                    cifar10.LEARNING_RATE_DECAY_FACTOR,

                                    staircase=True)

    # Create an optimizer that performs gradient descent.

    opt = tf.train.GradientDescentOptimizer(lr)

    # Get images and labels for CIFAR-10.

    images, labels = cifar10.distorted_inputs()

    batch_queue = tf.contrib.slim.prefetch_queue.prefetch_queue(

          [images, labels], capacity=2 * FLAGS.num_gpus)

    # Calculate the gradients for each model tower.

    tower_grads = []

    with tf.variable_scope(tf.get_variable_scope()):

      for i in xrange(FLAGS.num_gpus):

        with tf.device('/gpu:%d' % i):

          with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope:

            # Dequeues one batch for the GPU

            image_batch, label_batch = batch_queue.dequeue()

            # Calculate the loss for one tower of the CIFAR model. This function

            # constructs the entire CIFAR model but shares the variables across

            # all towers.

            loss = tower_loss(scope, image_batch, label_batch)

            # Reuse variables for the next tower.

            tf.get_variable_scope().reuse_variables()

            # Retain the summaries from the final tower.

            summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)

            # Calculate the gradients for the batch of data on this CIFAR tower.

            grads = opt.compute_gradients(loss)

            # Keep track of the gradients across all towers.

            tower_grads.append(grads)

    # We must calculate the mean of each gradient. Note that this is the

    # synchronization point across all towers.

    grads = average_gradients(tower_grads)

    # Add a summary to track the learning rate.

    summaries.append(tf.summary.scalar('learning_rate', lr))

    # Add histograms for gradients.

    for grad, var in grads:

      if grad is not None:

        summaries.append(tf.summary.histogram(var.op.name + '/gradients', grad))

    # Apply the gradients to adjust the shared variables.

    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

    # Add histograms for trainable variables.

    for var in tf.trainable_variables():

      summaries.append(tf.summary.histogram(var.op.name, var))

    # Track the moving averages of all trainable variables.

    variable_averages = tf.train.ExponentialMovingAverage(

        cifar10.MOVING_AVERAGE_DECAY, global_step)

    variables_averages_op = variable_averages.apply(tf.trainable_variables())

    # Group all updates to into a single train op.

    train_op = tf.group(apply_gradient_op, variables_averages_op)

    # Create a saver.

    saver = tf.train.Saver(tf.global_variables())

    # Build the summary operation from the last tower summaries.

    summary_op = tf.summary.merge(summaries)

################################################################################

    # Build an initialization operation to run below.

    init = tf.global_variables_initializer()

    # Start running operations on the Graph. allow_soft_placement must be set to

    # True to build towers on GPU, as some of the ops do not have GPU

    # implementations.

    sess = tf.Session(config=tf.ConfigProto(

        allow_soft_placement=True,

        log_device_placement=FLAGS.log_device_placement))

    sess.run(init)

    # Start the queue runners.

    tf.train.start_queue_runners(sess=sess)

    summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)

    for step in xrange(FLAGS.max_steps):

      start_time = time.time()

      _, loss_value = sess.run([train_op, loss])

      duration = time.time() - start_time

      assert not np.isnan(loss_value), 'Model diverged with loss = NaN'

      if step % 10 == 0:

        num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus

        examples_per_sec = num_examples_per_step / duration

        sec_per_batch = duration / FLAGS.num_gpus

        format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '

                      'sec/batch)')

        print (format_str % (datetime.now(), step, loss_value,

                             examples_per_sec, sec_per_batch))

      if step % 100 == 0:

        summary_str = sess.run(summary_op)

        summary_writer.add_summary(summary_str, step)

      # Save the model checkpoint periodically.

      if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:

        checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')

        saver.save(sess, checkpoint_path, global_step=step)

def main(argv=None):  # pylint: disable=unused-argument

  cifar10.maybe_download_and_extract()

  if tf.gfile.Exists(FLAGS.train_dir):

    tf.gfile.DeleteRecursively(FLAGS.train_dir)

  tf.gfile.MakeDirs(FLAGS.train_dir)

  train()

if __name__ == '__main__':

  tf.app.run()

数据输入函数如下，

def distorted_inputs():

  """Construct distorted input for CIFAR training using the Reader ops.

  Returns:

    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.

    labels: Labels. 1D tensor of [batch_size] size.

  Raises:

    ValueError: If no data_dir

  """

  if not FLAGS.data_dir:

    raise ValueError('Please supply a data_dir')

  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')

  images, labels = cifar10_input.distorted_inputs(data_dir=data_dir,

                                                  batch_size=FLAGS.batch_size)

  if FLAGS.use_fp16:

    images = tf.cast(images, tf.float16)

    labels = tf.cast(labels, tf.float16)

return images, labels

tf.contrib.slim.prefetch_queue.prefetch_queue从介绍来看就是个输入数据队列

Signature: tf.contrib.slim.prefetch_queue.prefetch_queue(tensors, capacity=8, num_threads=1, dynamic_pad=False, shared_name=None, name=None)

Docstring:

Creates a queue to prefetech tensors from `tensors`.

A queue runner for enqueing tensors into the prefetch_queue is automatically

added to the TF QueueRunners collection.

Example:

This is for example useful to pre-assemble input batches read with

`tf.train.batch()` and enqueue the pre-assembled batches.  Ops that dequeue

from the pre-assembled queue will not pay the cost of assembling the batch.

images, labels = tf.train.batch([image, label], batch_size=32, num_threads=4)

batch_queue = prefetch_queue([images, labels])

images, labels = batch_queue.dequeue()

logits = Net(images)

loss = Loss(logits, labels)

Args:

  tensors: A list or dictionary of `Tensors` to enqueue in the buffer.

  capacity: An integer. The maximum number of elements in the queue.

  num_threads: An integer.  Number of threads running the enqueue op.

  dynamic_pad: Boolean.  Whether to allow variable dimensions in input shapes.

  shared_name: (optional). If set, this queue will be shared under the given

    name across multiple sessions.

  name: (Optional) A name for the operations.

Returns:

  A queue from which you can dequeue tensors with the same type and shape

  as `tensors`.