docker与tensorflow结合使用

最近这段时间一直在学习docker的使用,以及如何在docker中使用tensorflow.今天就把在docker中如何使用tensorflow记录一下.

docker安装

我是把docker安装在centos 7.4操作系统上面,在vmware中装的centos,vmware中安装centos很简单.具体的网络配置可以参考vmware nat配置.docker安装很简单,找到docker官网,直接按照上面的步骤安装即可.运行docker version查看版本如下

因为docker 采用的是客户端/服务端的结构,所以这里可以看到client以及server,它们分别都有版本号.

tensorflow

在docker中运行tensorflow的第一步就是要找到自己需要的镜像,我们可以去docker hub找到自己需要的tensorflow镜像.tensorflow的镜像主要分两类,一种是在CPU上面跑的,还有一种是在GPU上面跑的,如果需要GPU的,那么还需要安装nvidia-docker.这里我使用的是CPU版本的.当然我们还需要选择具体的tensorflow版本.这里我拉取的命令如下:

docker pull tensorflow/tensorflow:1.9.0-devel-py3

拉取成功之后,运行docker images可以看到有tensorflow镜像.

tensorflow在docker中使用

docker run -it -p 8888:8888 --name tf-1.9 tensorflow/tensorflow:1.9.0-devel-py3

运行上面的命令,在容器中启动镜像.-p表示指定端口映射,即将本机的8888端口映射到容器的8888端口.--name用来指定容器的名字为tf-1.9.因为这里采用的镜像是devel模式的,所以默认不启动jupyter.如果想使用默认启动jupyter的镜像,那么直接拉取不带devel的镜像就可以.即拉取最近的镜像docker pull tensorflow/tensorflow
启动之后,我们就进入了容器,ls / 查看容器根目录内容,可以看到有run_jupyter.sh文件.运行此文件,即在根目录下执行./run_jupyter.sh --allow-root,--allow-root参数是因为jupyter启动不推荐使用root,这里是主动允许使用root.然后在浏览器中就可以访问jupyter的内容了.

创建自己的镜像

上面仅仅是跑了一个什么都没有的镜像,如果我们需要在镜像里面跑我们的深度学习程序怎么办呢?这首先做的第一步就是要制作我们自己的镜像.这里我们跑一个简单的mnist数据集,程序可以直接去tensorflow上面找一个例子程序.这里我的程序如下:

# 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.
# ==============================================================================

"""Simple, end-to-end, LeNet-5-like convolutional MNIST model example.

This should achieve a test error of 0.7%. Please keep this model as simple and
linear as possible, it is meant as a tutorial for simple convolutional models.
Run with --self_test on the command line to execute a short self-test.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import gzip
import os
import sys
import time
import logging

import numpy
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
# 如果WORK_DIRECTORY中没有需要的数据,则从此地址下载数据
SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'
# 训练数据位置
# WORK_DIRECTORY = 'data'
WORK_DIRECTORY = './MNIST-data'
IMAGE_SIZE = 28
NUM_CHANNELS = 1
PIXEL_DEPTH = 255
NUM_LABELS = 10
VALIDATION_SIZE = 5000  # Size of the validation set.
SEED = 66478  # Set to None for random seed.
BATCH_SIZE = 64
NUM_EPOCHS = 10
EVAL_BATCH_SIZE = 64
EVAL_FREQUENCY = 100  # Number of steps between evaluations.

FLAGS = None

# 打印信息设置
# logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s',
#                     level=logging.DEBUG)
logging.basicConfig(level=logging.DEBUG,  # 控制台打印的日志级别
                    filename='cnn_mnist.log',
                    filemode='a',  # 模式，有w和a，w就是写模式，每次都会重新写日志，覆盖之前的日志
                    # a是追加模式，默认如果不写的话，就是追加模式
                    format=
                    '%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
                    # 日志格式
                    )


def data_type():
    """Return the type of the activations, weights, and placeholder variables."""
    if FLAGS.use_fp16:
        return tf.float16
    else:
        return tf.float32


def maybe_download(filename):
    """Download the data from Yann's website, unless it's already here."""
    if not tf.gfile.Exists(WORK_DIRECTORY):
        tf.gfile.MakeDirs(WORK_DIRECTORY)
    filepath = os.path.join(WORK_DIRECTORY, filename)
    if not tf.gfile.Exists(filepath):
        filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
        with tf.gfile.GFile(filepath) as f:
            size = f.size()
        print('Successfully downloaded', filename, size, 'bytes.')
    return filepath


def extract_data(filename, num_images):
    """Extract the images into a 4D tensor [image index, y, x, channels].

    Values are rescaled from [0, 255] down to [-0.5, 0.5].
    """
    logging.info('Extracting' + filename)
    print('Extracting', filename)
    with gzip.open(filename) as bytestream:
        bytestream.read(16)
        buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)
        data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)
        data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH
        data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)
        return data


def extract_labels(filename, num_images):
    """Extract the labels into a vector of int64 label IDs."""
    logging.info('Extracting' + filename)
    print('Extracting', filename)
    with gzip.open(filename) as bytestream:
        bytestream.read(8)
        buf = bytestream.read(1 * num_images)
        labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)
    return labels


def fake_data(num_images):
    """Generate a fake dataset that matches the dimensions of MNIST."""
    data = numpy.ndarray(
        shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),
        dtype=numpy.float32)
    labels = numpy.zeros(shape=(num_images,), dtype=numpy.int64)
    for image in xrange(num_images):
        label = image % 2
        data[image, :, :, 0] = label - 0.5
        labels[image] = label
    return data, labels


def error_rate(predictions, labels):
    """Return the error rate based on dense predictions and sparse labels."""
    return 100.0 - (
            100.0 *
            numpy.sum(numpy.argmax(predictions, 1) == labels) /
            predictions.shape[0])


def main(_):
    if FLAGS.self_test:
        logging.info('Running self-test.')
        print('Running self-test.')
        train_data, train_labels = fake_data(256)
        validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)
        test_data, test_labels = fake_data(EVAL_BATCH_SIZE)
        num_epochs = 1
    else:
        # Get the data.
        train_data_filename = maybe_download('train-images-idx3-ubyte.gz')
        train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')
        test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')
        test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')

        # Extract it into numpy arrays.
        train_data = extract_data(train_data_filename, 60000)
        train_labels = extract_labels(train_labels_filename, 60000)
        test_data = extract_data(test_data_filename, 10000)
        test_labels = extract_labels(test_labels_filename, 10000)

        # Generate a validation set.
        validation_data = train_data[:VALIDATION_SIZE, ...]
        validation_labels = train_labels[:VALIDATION_SIZE]
        train_data = train_data[VALIDATION_SIZE:, ...]
        train_labels = train_labels[VALIDATION_SIZE:]
        num_epochs = NUM_EPOCHS
    train_size = train_labels.shape[0]

    # This is where training samples and labels are fed to the graph.
    # These placeholder nodes will be fed a batch of training data at each
    # training step using the {feed_dict} argument to the Run() call below.
    train_data_node = tf.placeholder(
        data_type(),
        shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
    train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))
    eval_data = tf.placeholder(
        data_type(),
        shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))

    # The variables below hold all the trainable weights. They are passed an
    # initial value which will be assigned when we call:
    # {tf.global_variables_initializer().run()}
    conv1_weights = tf.Variable(
        tf.truncated_normal([5, 5, NUM_CHANNELS, 32],  # 5x5 filter, depth 32.
                            stddev=0.1,
                            seed=SEED, dtype=data_type()))
    conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type()))
    conv2_weights = tf.Variable(tf.truncated_normal(
        [5, 5, 32, 64], stddev=0.1,
        seed=SEED, dtype=data_type()))
    conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type()))
    fc1_weights = tf.Variable(  # fully connected, depth 512.
        tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],
                            stddev=0.1,
                            seed=SEED,
                            dtype=data_type()))
    fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type()))
    fc2_weights = tf.Variable(tf.truncated_normal([512, NUM_LABELS],
                                                  stddev=0.1,
                                                  seed=SEED,
                                                  dtype=data_type()))
    fc2_biases = tf.Variable(tf.constant(
        0.1, shape=[NUM_LABELS], dtype=data_type()))

    # We will replicate the model structure for the training subgraph, as well
    # as the evaluation subgraphs, while sharing the trainable parameters.
    def model(data, train=False):
        """The Model definition."""
        # 2D convolution, with 'SAME' padding (i.e. the output feature map has
        # the same size as the input). Note that {strides} is a 4D array whose
        # shape matches the data layout: [image index, y, x, depth].
        conv = tf.nn.conv2d(data,
                            conv1_weights,
                            strides=[1, 1, 1, 1],
                            padding='SAME')
        # Bias and rectified linear non-linearity.
        relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
        # Max pooling. The kernel size spec {ksize} also follows the layout of
        # the data. Here we have a pooling window of 2, and a stride of 2.
        pool = tf.nn.max_pool(relu,
                              ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1],
                              padding='SAME')
        conv = tf.nn.conv2d(pool,
                            conv2_weights,
                            strides=[1, 1, 1, 1],
                            padding='SAME')
        relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))
        pool = tf.nn.max_pool(relu,
                              ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1],
                              padding='SAME')
        # Reshape the feature map cuboid into a 2D matrix to feed it to the
        # fully connected layers.
        pool_shape = pool.get_shape().as_list()
        reshape = tf.reshape(
            pool,
            [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])
        # Fully connected layer. Note that the '+' operation automatically
        # broadcasts the biases.
        hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)
        # Add a 50% dropout during training only. Dropout also scales
        # activations such that no rescaling is needed at evaluation time.
        if train:
            hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
        return tf.matmul(hidden, fc2_weights) + fc2_biases

    # Training computation: logits + cross-entropy loss.
    logits = model(train_data_node, True)
    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
        labels=train_labels_node, logits=logits))

    # L2 regularization for the fully connected parameters.
    regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +
                    tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))
    # Add the regularization term to the loss.
    loss += 5e-4 * regularizers

    # Optimizer: set up a variable that's incremented once per batch and
    # controls the learning rate decay.
    batch = tf.Variable(0, dtype=data_type())
    # Decay once per epoch, using an exponential schedule starting at 0.01.
    learning_rate = tf.train.exponential_decay(
        0.01,  # Base learning rate.
        batch * BATCH_SIZE,  # Current index into the dataset.
        train_size,  # Decay step.
        0.95,  # Decay rate.
        staircase=True)
    # Use simple momentum for the optimization.
    optimizer = tf.train.MomentumOptimizer(learning_rate,
                                           0.9).minimize(loss,
                                                         global_step=batch)

    # Predictions for the current training minibatch.
    train_prediction = tf.nn.softmax(logits)

    # Predictions for the test and validation, which we'll compute less often.
    eval_prediction = tf.nn.softmax(model(eval_data))

    # Small utility function to evaluate a dataset by feeding batches of data to
    # {eval_data} and pulling the results from {eval_predictions}.
    # Saves memory and enables this to run on smaller GPUs.
    def eval_in_batches(data, sess):
        """Get all predictions for a dataset by running it in small batches."""
        size = data.shape[0]
        if size < EVAL_BATCH_SIZE:
            logging.error("batch size for evals larger than dataset: %d" % size)
            raise ValueError("batch size for evals larger than dataset: %d" % size)
        predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32)
        for begin in xrange(0, size, EVAL_BATCH_SIZE):
            end = begin + EVAL_BATCH_SIZE
            if end <= size:
                predictions[begin:end, :] = sess.run(
                    eval_prediction,
                    feed_dict={eval_data: data[begin:end, ...]})
            else:
                batch_predictions = sess.run(
                    eval_prediction,
                    feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})
                predictions[begin:, :] = batch_predictions[begin - size:, :]
        return predictions

    # Create a local session to run the training.
    start_time = time.time()
    with tf.Session() as sess:
        # Run all the initializers to prepare the trainable parameters.
        tf.global_variables_initializer().run()
        logging.info('Initialized!')
        print('Initialized!')
        # Loop through training steps.
        for step in xrange(int(num_epochs * train_size) // BATCH_SIZE):
            # Compute the offset of the current minibatch in the data.
            # Note that we could use better randomization across epochs.
            offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)
            batch_data = train_data[offset:(offset + BATCH_SIZE), ...]
            batch_labels = train_labels[offset:(offset + BATCH_SIZE)]
            # This dictionary maps the batch data (as a numpy array) to the
            # node in the graph it should be fed to.
            feed_dict = {train_data_node: batch_data,
                         train_labels_node: batch_labels}
            # Run the optimizer to update weights.
            sess.run(optimizer, feed_dict=feed_dict)
            # print some extra information once reach the evaluation frequency
            if step % EVAL_FREQUENCY == 0:
                # fetch some extra nodes' data
                l, lr, predictions = sess.run([loss, learning_rate, train_prediction],
                                              feed_dict=feed_dict)
                elapsed_time = time.time() - start_time
                start_time = time.time()
                logging.info('Step %d (epoch %.2f), %.1f ms' %(step, float(step) * BATCH_SIZE / train_size, 1000 * elapsed_time / EVAL_FREQUENCY))
                print('Step %d (epoch %.2f), %.1f ms' %
                      (step, float(step) * BATCH_SIZE / train_size,
                       1000 * elapsed_time / EVAL_FREQUENCY))
                logging.info('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))
                print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))
                logging.info('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))
                print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))
                logging.info('Validation error: %.1f%%' % error_rate(eval_in_batches(validation_data, sess), validation_labels))
                print('Validation error: %.1f%%' % error_rate(
                    eval_in_batches(validation_data, sess), validation_labels))
                sys.stdout.flush()
        # Finally print the result!
        test_error = error_rate(eval_in_batches(test_data, sess), test_labels)
        logging.info('Test error: %.1f%%' % test_error)
        print('Test error: %.1f%%' % test_error)
        if FLAGS.self_test:
            logging.info('test_error' + test_error)
            print('test_error', test_error)
            assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (
                test_error,)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--use_fp16',
        default=False,
        help='Use half floats instead of full floats if True.',
        action='store_true')
    parser.add_argument(
        '--self_test',
        default=False,
        action='store_true',
        help='True if running a self test.')

    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

这里我在原来的程序基础上面稍微改了下,因为我已经提前将数据下载好了,所以我让程序直接读取本机指定目录下的训练数据,同时增加了日志文件输出.这是为了在公司的容器云平台上测试获取容器输出文件

编写Dockerfile

我们可以在我们的用户目录下,创建一个空的文件夹,将mnist数据集以及程序文件都拷贝进这个文件夹下.其实数据集应该是放在数据卷中,但是这里为了方便,我直接将训练数据打进了镜像中.然后创建Dockerfile,文件内容如下

FROM tensorflow/tensorflow:1.9.0-devel-py3

COPY . /home/ll
WORKDIR /home/ll
CMD ['python', 'convolutional.py']

即Dockerfile文件中最后一行表示容器启动的运行的命令

build镜像

docker build -t tf:1.9 .

-t参数指定镜像跟tag,最后的.指定了镜像中的上下文.构建完之后使用docker images可以查看多了tf:1.9镜像

运行镜像

运行下面的命令,运行上一步构建好的镜像

docker run -it --name test tf:1.9

然后就能够看到训练的输出.

同时可以在看一个连接,进入容器,即运行下面命令

docker exec -it test /bin/bash

可以看到如下内容

即看到了cnn_mnist.log的日志输出文件