TensorFlow分布式部署【单机多卡】

让TensorFlow飞一会儿

面对大型的深度神经网络训练工程，训练的时间非常重要。训练的时间长短依赖于计算处理器也就是GPU，然而单个GPU的计算能力有限，利用多个GPU进行分布式部署，同时完成一个训练任务是一个很好的办法。对于caffe来说，由于NCCL的存在，可以直接在slover中指定使用的GPU。然而对于Tensorflow，虽然Contrib库中有NCCL，但是我并没有找到相关的例子，所以，还是靠双手成就梦想。

原理简介

TensorFlow支持指定相应的设备来完成相应的操作，所以如何分配任务是很关键的一环。GPU擅长大量计算，所以整个Inference和梯度的计算就交给GPU来做，更新参数的小事情就交给CPU来做。这就比如校长要知道整个年级的平均成绩，就把改卷子的任务分配给每个班的老师，每个班的老师批改完卷子以后，把各自班级的成绩上交给校长，校长计算个平均数就行。在这里，校长就是CPU，每个班级的老师就是GPU。

下面放出一张图来说明问题。

我们可以清楚的看到CPU中保存变量，GPU们计算整个model和gradients，然后把得到的梯度送回CPU中，CPU计算各个GPU送回来梯度的平均值作为本次step的梯度对参数进行更新。从图中我们可以看到只有当所有的GPU完成梯度计算以后，CPU才能求平均值，所以，整个神经网络的迭代速度将取决于最慢的一个GPU，这也就是同步更新。那能不能异步更新呢？当然是可以的把更新参数这个操作也放回到GPU上，但是异步更新会造成训练不稳定，有的快有的慢，你说到底听谁的…

在上图中我们可以看到有几个关键点需要注意：

1. 在CPU上定义变量
2. 在GPU上分别定义model和gradients操作，得到每个GPU中的梯度
3. 又回到CPU中计算平均平均梯度，并进行参数更新

Talk is cheap, show me the code!!

好，下面放代码。

示例代码

示例代码分如下几个部分：

1. 读入数据
2. 在cpu中定义变量
3. 搭建Inference
4. 定义loss
5. 定义训练过程

读入数据

由于是在不同的GPU上进行运算，所以我们采用TF官方的数据格式tfrecords作为输入，tfrecords的MNIST数据集格式可以在网上很轻易的找到。读入数据的时候我们就用标准的tfrecords数据集读入的格式。

def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
        # Defaults are not specified since both keys are required.
        features={
            ‘image_raw‘: tf.FixedLenFeature([], tf.string),
            ‘label‘: tf.FixedLenFeature([], tf.int64),
        })

    image = tf.decode_raw(features[‘image_raw‘], tf.uint8)
    image.set_shape([IMAGE_PIXELS])
    image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
    label = tf.cast(features[‘label‘], tf.int32)
    return image, label

这段函数会返回一个图像和标签，我们需要按照Batch的方式读入

def inputs(train, batch_size, num_epochs):
    if not num_epochs: num_epochs = None
    filename = os.path.join(FLAGS.data_dir,
                            TRAIN_FILE if train else VALIDATION_FILE)

    with tf.name_scope(‘input‘):
        filename_queue = tf.train.string_input_producer(
            [filename], num_epochs=num_epochs)
        image, label = read_and_decode(filename_queue)

        images, sparse_labels = tf.train.shuffle_batch(
            [image, label], batch_size=batch_size, num_threads=2,
            capacity=1000 + 3 * batch_size,
            min_after_dequeue=1000)

        return images, sparse_labels

到这里我们可以读入batch图像和标签。

在CPU中定义变量

我们需要把weight和biases定义在CPU中，以便进行参数的更新。注意

```Python
def _variable_on_cpu(name, shape, initializer):
    """Helper to create a Variable stored on CPU memory.

    Args:
      name: name of the variable
      shape: list of ints
      initializer: initializer for Variable

    Returns:
      Variable Tensor
    """
    with tf.device(‘/cpu:0‘):
        dtype = tf.float32
        var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)
    return var

构建Inference

构建Inference采用的的是卷积神经网络的架构，需要注意的是初始化的时候需要将变量定义在CPU中。

def inference(images):
    """Build the MNIST model.

    Args:
      images: Images returned from MNIST or inputs().

    Returns:
      Logits.
    """
    x_image = tf.reshape(images, [-1, 28, 28, 1])

    # conv1
    with tf.variable_scope(‘conv1‘) as scope:

        kernel = _variable_on_cpu(‘weights‘,shape=[5,5,1,32],
                                initializer = tf.truncated_normal_initializer(stddev=5e-2))
        biases = _variable_on_cpu(‘biases‘, [32], tf.constant_initializer(0.0))
        conv = tf.nn.conv2d(x_image, kernel, strides=[1, 1, 1, 1],
                            padding=‘SAME‘)
        pre_activation = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(pre_activation, name=scope.name)

    # pool1
    pool1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
                           padding=‘SAME‘, name=‘pool1‘)

    # conv2
    with tf.variable_scope(‘conv2‘) as scope:

        kernel = _variable_on_cpu(‘weights‘,shape=[5,5,32,64],
                                initializer = tf.truncated_normal_initializer(stddev=5e-2))
        conv = tf.nn.conv2d(pool1, kernel, strides=[1, 1, 1, 1], padding=‘SAME‘)
        biases = _variable_on_cpu(‘biases‘, [64], tf.constant_initializer(0.1))
        pre_activation = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(pre_activation, name=scope.name)

    # pool2
    pool2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
                           padding=‘SAME‘, name=‘pool2‘)

    # local3
    with tf.variable_scope(‘local3‘) as scope:
        # Move everything into depth so we can perform a single matrix multiply.
        reshape = tf.reshape(pool2, [-1, 7 * 7 * 64])
        dim = reshape.get_shape()[1].value

        weights = _variable_on_cpu(‘weights‘,shape=[dim,1024],
                                    initializer = tf.truncated_normal_initializer(stddev=0.04))
        biases = _variable_on_cpu(‘biases‘, [1024],
                                  tf.constant_initializer(0.1))
        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases,
                            name=scope.name)

    # local4
    with tf.variable_scope(‘local4‘) as scope:
        weights = _variable_on_cpu(‘weight‘,shape=[1024,10],
                                    initializer = tf.truncated_normal_initializer(stddev=0.04))
        biases = _variable_on_cpu(‘biases‘, [10], tf.constant_initializer(0.1))
        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases,
                            name=scope.name)

    # linear layer(WX + b),
    # We don‘t apply softmax here because
    # tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits
    # and performs the softmax internally for efficiency.
    with tf.variable_scope(‘softmax_linear‘) as scope:

        weights = _variable_on_cpu(‘weight‘,[10,10],
                                    initializer = tf.truncated_normal_initializer(stddev=1 / 192.0))
        biases = _variable_on_cpu(‘biases‘, [10],
                                  tf.constant_initializer(0.0))
        softmax_linear = tf.add(tf.matmul(local4, weights), biases,
                                name=scope.name)

    return softmax_linear

定义Loss

定义loss的时候和单GPU的形式不同，因为我们不仅要定义损失函数，还要定义每个GPU的损失函数值和其梯度，最后再计算平均梯度。

def loss(logits, labels):
    """Add L2Loss to all the trainable variables.

    Add summary for "Loss" and "Loss/avg".
    Args:
      logits: Logits from inference().
      labels: Labels from distorted_inputs or inputs(). 1-D tensor
              of shape [batch_size]

    Returns:
      Loss tensor of type float.
    """
    # Calculate the average cross entropy loss across the batch.
    labels = tf.cast(labels, tf.int64)
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
        labels=labels, logits=logits, name=‘cross_entropy_per_example‘)
    cross_entropy_mean = tf.reduce_mean(cross_entropy, name=‘cross_entropy‘)
    tf.add_to_collection(‘losses‘, cross_entropy_mean)

    # The total loss is defined as the cross entropy loss plus all of the weight
    # decay terms (L2 loss).
    return tf.add_n(tf.get_collection(‘losses‘), name=‘total_loss‘)

def tower_loss(scope):
    """Calculate the total loss on a single tower running the MNIST model.

    Args:
      scope: unique prefix string identifying the MNIST tower, e.g. ‘tower_0‘

    Returns:
       Tensor of shape [] containing the total loss for a batch of data
    """
    # Input images and labels.
    images, labels = inputs(train=True, batch_size=FLAGS.batch_size,
                            num_epochs=FLAGS.num_epochs)
    # Build inference Graph.
    logits = inference(images)

    # Build the portion of the Graph calculating the losses. Note that we will
    # assemble the total_loss using a custom function below.
    _ = 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.
    if FLAGS.tb_logging:
        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]*/‘ % TOWER_NAME, ‘‘, l.op.name)
            tf.summary.scalar(loss_name, l)

    return total_loss

def average_gradients(tower_grads):
    """Calculate 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(grads, 0)
        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

定义训练过程

训练过程的需要注意把不同的环节放在不同的devices下面。

def train():
    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)

        # opt = tf.train.MomentumOptimizer(lr,0.9,use_nesterov=True,use_locking=True)
        opt = tf.train.MomentumOptimizer(INITIAL_LEARNING_RATE,0.9,use_nesterov=True,use_locking=True)

        # 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‘ % (TOWER_NAME, i)) as scope:
                        # 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)

                        # 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
                        # MNIST tower.
                        grads = opt.compute_gradients(loss, gate_gradients=0)

                        # 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)

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

        # The op for initializing the variables.
        init_op = tf.group(tf.global_variables_initializer(),
                           tf.local_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_op)

        # Start input enqueue threads.
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)

        try:
            step = 0
            while not coord.should_stop():
                start_time = time.time()

                # Run one step of the model.  The return values are
                # the activations from the `train_op` (which is
                # discarded) and the `loss` op.  To inspect the values
                # of your ops or variables, you may include them in
                # the list passed to sess.run() and the value tensors
                # will be returned in the tuple from the call.
                _, loss_value = sess.run([train_op, loss])

                duration = time.time() - start_time

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

                # Print an overview fairly often.
                if step % 100 == 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))
                step += 1
        except tf.errors.OutOfRangeError:
            print(‘Done training for %d epochs, %d steps.‘ % (
                FLAGS.num_epochs, step))
        finally:
            # When done, ask the threads to stop.
            coord.request_stop()

        # Wait for threads to finish.
        coord.join(threads)
        sess.close()

最后就可以调用Train()函数进行训练了。训练函数分配GPU的时候有for循环，所以可以支持不同数量的GPU。

单机多卡服务器进行深度学习的训练，构建代码比较复杂，并且需要手动分配devices，相比于NCCL的高级库好的一点就是可以针对不同的任务进行定制化的分配，以实现最大程度的优化，工作量比较大，效果也非常好。搭建的时候需要平衡一下效率和开发速度。后续还会尝试多机多卡的情况，目前还在尝试。

来源： https://jinzequn.github.io/2017/12/01/tensorflow-mulit-gpus/

来自为知笔记(Wiz)

原文地址：https://www.cnblogs.com/jins-note/p/9674515.html