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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（项目中用到的是/tensorflow:1.9.0）:
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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
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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.
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:
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# 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
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
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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（注：在实际运行过程中，出现了一些python依赖包的缺失，可以将最后一个指令改为CMD ["sh","-c","apt-get update && apt-get install python3-tk && python ssd_notebook_result.py"]，启动容器后先下载依赖，再执行程序）
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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
运行下面的命令,运行上一步构建好的镜像
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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
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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
实际操作中并非一帆风顺，当我在实际操作过程中运行一个类似上述的容器过程中，多次报错，最后排查是缺失python的一些依赖导致，可能是由于版本原因，有些依赖在此版本中并未安装，此处我想到的解决方案是：拉取初始镜像，后台运行对应容器，并进入容器，在容器中下载缺失的依赖，下载完成后停止容器，再将此时的容器封装成镜像，在重复上面的镜像构建步骤，具体如下
###通过容器创建镜像
-通过基础镜像启动容器
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docker run -it --name tf-1.9 tensorflow/tensorflow:1.9.0-devel-py3 /bin/bash
-在容器中下载依赖
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apt-get update
apt-get install python3-tk
-将容器打包成新的镜像
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docker commit -a "chengh97" -m "tensorflow:1.9.0-devel-py3 patch version"
c982c62b168d tensorflow/tensorflow:1.9.0-devel-py3-patch
-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
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FROM tensorflow/tensorflow:1.9.0-devel-py3-patch
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（注：在实际运行过程中，出现了一些python依赖包的缺失，可以将最后一个指令改为CMD ["sh","-c","apt-get update && apt-get install python3-tk && python ssd_notebook_result.py"]，启动容器后先下载依赖，再执行程序）
-build镜像
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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
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docker run -it --name test tf:1.9
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