总结-在极客云使用GPU进行深度学习训练

图形处理单元 (GPU) 可显著加快许多深度学习模型的训练过程。用于图片分类、视频分析和自然语言处理等任务的训练模型涉及计算密集型矩阵乘法以及其他可利用 GPU 大规模并行架构的操作。

对于需要对超大数据集执行密集计算任务的深度学习模型，可能需要在单个处理器上运行数日才能完成训练。但是，如果将这些任务分流到一个或多个 GPU，则可以将训练时间从数日缩短至数小时。

但是好的GPU过于昂贵，一般学生（比如我）很难有能力购买，于是我在网上冲浪多次之后发现了一个非常好用，性价比较高的云服务器—极客云 。同阿里云、腾讯云等云服务器相比，其价格更加便宜，更加有针对性。极客云网站打出的标语是：同等算力价格便宜3倍以上。由此，大家可以自行体会一下它的价格。同时使用极客云服务器最大的方便之处是自带很多计算框架。只需要专注于深度学习本身，无需安装任何深度学习环境，零设置开启深度学习之旅（这对于我这种安装软件，配置环境老出现各种各样莫名其妙的问题的人来说，简直是超大福音）。它只需简单几步操作即可测试和训练深度学习模型。

以下为极客云提供的部分GPU截图：

如何使用：

1. 注册（常规操作）

2.创建GPU实例（可以选择你想创建什么型号的GPU主机。我创建了一个GTX 1080Ti虚拟主机（这个能限时免费体验还不需要排队。还需要根据自己的需求选择预装框架（tensorflow、caffe-gpu、fastai等）、预装框架版本、python版本。

3.点击“创建”按钮并等待片刻后，会回到“我的云主机”页面，此时可以看到创建的云主机已经显示在列表界面里面了。现在列表界面有Jupyter Notebook的链接了。点这个链接就可以进入Jupyter Notebook了，使用起来很方便。

极客云的云主机上自带了手写字体识别的python代码，名称为mnist_deep.py。它的位置在Jupyter Notebook的root文件夹下。你可以将其复制粘贴在你新建的python文件中感受一下GPU的速度。这里我将其提供的mnist_deep.py文件放过来，方便观察代码信息：

# 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 deep MNIST classifier using convolutional layers.

See extensive documentation at
https://www.tensorflow.org/get_started/mnist/pros
"""
# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys
import tempfile

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None


def deepnn(x):
  """deepnn builds the graph for a deep net for classifying digits.

  Args:
    x: an input tensor with the dimensions (N_examples, 784), where 784 is the
    number of pixels in a standard MNIST image.

  Returns:
    A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
    equal to the logits of classifying the digit into one of 10 classes (the
    digits 0-9). keep_prob is a scalar placeholder for the probability of
    dropout.
  """
  # Reshape to use within a convolutional neural net.
  # Last dimension is for "features" - there is only one here, since images are
  # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
  with tf.name_scope('reshape'):
    x_image = tf.reshape(x, [-1, 28, 28, 1])

  # First convolutional layer - maps one grayscale image to 32 feature maps.
  with tf.name_scope('conv1'):
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

  # Pooling layer - downsamples by 2X.
  with tf.name_scope('pool1'):
    h_pool1 = max_pool_2x2(h_conv1)

  # Second convolutional layer -- maps 32 feature maps to 64.
  with tf.name_scope('conv2'):
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

  # Second pooling layer.
  with tf.name_scope('pool2'):
    h_pool2 = max_pool_2x2(h_conv2)

  # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
  # is down to 7x7x64 feature maps -- maps this to 1024 features.
  with tf.name_scope('fc1'):
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

  # Dropout - controls the complexity of the model, prevents co-adaptation of
  # features.
  with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

  # Map the 1024 features to 10 classes, one for each digit
  with tf.name_scope('fc2'):
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])

    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
  return y_conv, keep_prob


def conv2d(x, W):
  """conv2d returns a 2d convolution layer with full stride."""
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
  """max_pool_2x2 downsamples a feature map by 2X."""
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')


def weight_variable(shape):
  """weight_variable generates a weight variable of a given shape."""
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)


def bias_variable(shape):
  """bias_variable generates a bias variable of a given shape."""
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)


def main(_):
  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  # Create the model
  x = tf.placeholder(tf.float32, [None, 784])

  # Define loss and optimizer
  y_ = tf.placeholder(tf.float32, [None, 10])

  # Build the graph for the deep net
  y_conv, keep_prob = deepnn(x)

  with tf.name_scope('loss'):
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
                                                            logits=y_conv)
  cross_entropy = tf.reduce_mean(cross_entropy)

  with tf.name_scope('adam_optimizer'):
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

  with tf.name_scope('accuracy'):
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    correct_prediction = tf.cast(correct_prediction, tf.float32)
  accuracy = tf.reduce_mean(correct_prediction)

  graph_location = tempfile.mkdtemp()
  print('Saving graph to: %s' % graph_location)
  train_writer = tf.summary.FileWriter(graph_location)
  train_writer.add_graph(tf.get_default_graph())

  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(20000):
      batch = mnist.train.next_batch(50)
      if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={
            x: batch[0], y_: batch[1], keep_prob: 1.0})
        print('step %d, training accuracy %g' % (i, train_accuracy))
      train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    print('test accuracy %g' % accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument('--data_dir', type=str,
                      default='/tmp/tensorflow/mnist/input_data',
                      help='Directory for storing input data')
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

关于自己的数据集：

可以通过菜单 我的 -> 数据 进入数据页面来上传自己的数据集。

在 我的数据 页面上传的数据，创建的所有云主机都能访问。

当数据集文件数量不大的时候，推荐使用上传目录功能。 如果您的数据集包含大量文件，推荐打成压缩包再上传。可以节省很多上传时间。

上传结束后，您将会在云主机的/data目录下看见所有您上传的文件。

由于 /data 目录是网络存储，读写速度受限于网络，直接在 /data 读取数据进行训练的话，速度会很慢，所以推荐先把数据 从 /data 拷贝到 /input 或 /root 然后再训练。具体做法请参照网站详解。

附极客云网站链接：极客云网站。

另：网上有网友反映说极客云的Gpu如果跑的时间长了的话会自己断开。这个我目前还只是跑了两个小项目，并没有遇到这种情况。大家可以依照自己的情况做取舍。官方目前做出的说明如下：如果训练任务需要跑很长时间（一天以上），强烈建议定时保存checkpoint，即使是欠费停机了，下次开机仍然可以接着上次的进度继续跑，数据也不会丢失。

tensorflow框架保存的方法请参照 ：如何使用Tensorflow加载预训练模型和保存模型。

注：大家不跑项目的时候把你的云主机（实例）给关掉啊啊啊，不然是要一直扣费的。