建议比对
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 = Truesess = 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) 
# 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 (256epochs 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 steps4 Tesla K20m | ~0.10 | ~84% at 30K stepsUsage:Please see the tutorial and website for how to download the CIFAR-10data set, compile the program and train the model.http://tensorflow.org/tutorials/deep_cnn/"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_function from datetime import datetimeimport os.pathimport reimport time import numpy as npfrom six.moves import xrange # pylint: disable=redefined-builtinimport tensorflow as tfimport 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`.