大數據下基於Tensorflow框架的深度學習示例教程

近幾年，信息時代的快速發展產生了海量數據，誕生了無數前沿的大數據技術與應用。在當今大數據時代的產業界，商業決策日益基於數據的分析做出。當數據膨脹到必定規模時，基於機器學習對海量複雜數據的分析更能產生較好的價值，而深度學習在大數據場景下更能揭示數據內部的邏輯關係。本文就以大數據做爲場景，經過自底向上的教程詳述在大數據架構體系中如何應用深度學習這一技術。大數據架構中採用的是hadoop系統以及Kerberos安全認證，深度學習採用的是分佈式的Tensorflow架構，hadoop解決了大數據的存儲問題，而分佈式Tensorflow解決了大數據訓練的問題。本教程是咱們團隊在開發基於深度學習的實時欺詐預警服務時，部署深度學習這一模塊時總結出的經驗，感興趣的歡迎深刻交流。

安裝Tensorflow

咱們安裝Tensorflow選擇的是Centos7，由於Tensorflow須要使用GNU發佈的1.5版本的libc庫，Centos6系統並不適用該版本庫而被拋棄。對於如何聯網在線安裝Tensorflow，官網有比較詳盡的教程。本教程着重講一下網上資料較少的離線安裝方式，系統的安裝更須要在乎的是各軟件版本的一致性，下面教程也是解決了不少版本不一致的問題後給出的一個方案。首先咱們先將整個系統搭建起來吧。

1.安裝編程語言Python3.5：在官網下載軟件並解壓後執行以下安裝命令：

./configure
 make make test sudo make install

2.安裝基於Python的科學計算包python-numpy：在官網下載軟件並解壓後執行以下安裝命令：

python setup.py install

3.安裝Python模塊管理的工具wheel：在官網下載軟件後執行以下安裝命令：

pip install wheel-0.30.0a0-py2.py3-none-any.whl

4.安裝自動下載、構建、安裝和管理 python 模塊的工具setuptools：在官網下載軟件並解壓後執行以下安裝命令：

python setup.py install

5.安裝Python開發包python-devel：在官網下載軟件後執行以下安裝命令：

sudo rpm -i --nodeps python3-devel-3.5.2-4.fc25.x86_64.rpm

6.安裝Python包安裝管理工具six：在官網下載軟件後執行以下安裝命令：

sudo pip install six-1.10.0-py2.py3-none-any.whl

7.安裝Java 開發環境JDK8：在官網下載軟件並解壓後執行以下移動命令：

mv java1.8 /usr/local/software/jdk

設置JDK的環境變量，編輯文件 .bashrc，加入下面內容

export JAVA_HOME=/usr/local/software/jdk export JRE_HOME=${JAVA_HOME}/jre export CLASSPATH=$CLASSPATH:${JAVA_HOME}/lib:${JRE_HOME}/lib export PATH=$PATH:${JAVA_HOME}/bin

進行Java版本的切換，選擇對應的版本

sudo update-alternatives --config java sudo update-alternatives --config javac

8.安裝Bazel：Bazel是一個相似於Make的工具，是Google爲其內部軟件開發的特色量身定製的工具，構建Tensorflow項目。在官網下載後執行以下安裝命令：

chmod +x bazel-0.4.3-installer-linux-x86_64.sh ./bazel-0.4.3-installer-linux-x86_64.sh –user

9.安裝Tensorflow：在官網下載軟件後執行以下安裝命令：

pip install --upgrade tensorflow-0.12.1-cp35-cp35m-linux_x86_64.whl

Tensorflow訪問HDFS的部署

1.首先安裝Hadoop客戶端，在官網下載後執行下面解壓移動命令：

tar zxvf hadoop-2.6.0.tar.gz mv hadoop-2.6.0.tar.gz /usr/local/software/Hadoop

進行環境變量的配置/etc/profile，加入以下內容

export PATH=$PATH:/usr/local/software/hadoop/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$JAVA_HOME/jre/lib/amd64/server export HADOOP_HOME=/usr/local/software/hadoop export HADOOP_HDFS_HOME=/usr/local/software/hadoop

配置完後進行配置更新source /etc/profile

2.其次，安裝完客戶端後，配置本身的hadoop集羣環境文件。

Tensorflow與Kerberos驗證的部署

在Tesorflow0.12版本中已經支持了Kerberos驗證，本機只要配置好Kerberos文件便可使用。該文中不詳述Kerberos的配置內容，羅列一下相關的配置流程。

• 首先在/etc/krb5.conf文件中進行服務器跟驗證策略的配置；
• 而後在Kerberos服務端生成一個用戶文件傳至本機；
• 最後進行Kerberos客戶端的權限認證並設置定時任務。

大數據場景下基於分佈式Tensorflow的深度學習示例

1、進行數據格式的轉換

本文的示例是作的MNIST數據的識別模型，爲了更好的讀取數據更好的利用內存，咱們將本地GZ文件轉換成Tensorflow的內定標準格式TFRecord，而後再將轉換後的文件上傳到HDFS存儲。在實際應用中，咱們實際利用Spark作了大規模格式轉換的處理程序。咱們對本地數據處理的相應的轉換代碼爲：

from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import tensorflow as tf from tensorflow.contrib.learn.python.learn.datasets import mnist SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' TRAIN_IMAGES = 'train-images-idx3-ubyte.gz' # MNIST filenames TRAIN_LABELS = 'train-labels-idx1-ubyte.gz' TEST_IMAGES = 't10k-images-idx3-ubyte.gz' TEST_LABELS = 't10k-labels-idx1-ubyte.gz' FLAGS = None def _int64_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) def _bytes_feature(value): return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) def convert_to(data_set, name): images = data_set.images labels = data_set.labels num_examples = data_set.num_examples if images.shape[0] != num_examples: raise ValueError('Images size %d does not match label size %d.' % (images.shape[0], num_examples)) rows = images.shape[1] cols = images.shape[2] depth = images.shape[3] filename = os.path.join(FLAGS.directory, name + '.tfrecords') print('Writing', filename) writer = tf.python_io.TFRecordWriter(filename) for index in range(num_examples): image_raw = images[index].tostring() example = tf.train.Example(features=tf.train.Features(feature={ 'height': _int64_feature(rows), 'width': _int64_feature(cols), 'depth': _int64_feature(depth), 'label': _int64_feature(int(labels[index])), 'image_raw': _bytes_feature(image_raw)})) writer.write(example.SerializeToString()) writer.close() def main(argv): # Get the data. data_sets = mnist.read_data_sets(FLAGS.directory, dtype=tf.uint8, reshape=False, validation_size=FLAGS.validation_size) # Convert to Examples and write the result to TFRecords. convert_to(data_sets.train, 'train') convert_to(data_sets.validation, 'validation') convert_to(data_sets.test, 'test') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--directory', type=str, default='/tmp/data', help='Directory to download data files and write the converted result' ) parser.add_argument( '--validation_size', type=int, default=5000, help="""\ Number of examples to separate from the training data for the validation set.\ """ ) FLAGS = parser.parse_args() tf.app.run()

2、Tensorflow讀取HDFS數據的設置

文中前面內容介紹了HDFS的配置以及將數據轉換後存儲到HDFS，Tensorflow讀取HDFS時只須要簡單的兩步，首先執行項目時須要加入環境前綴：

CLASSPATH=$($HADOOP_HDFS_HOME/bin/hadoop classpath --glob) python example.py

其次讀取數據時，須要在數據的路徑前面加入HDFS前綴，好比：

hdfs://default/user/data/example.txt

3、分佈式模型的示例代碼

該示例代碼是讀取HDFS上的MNIST數據，創建相應的server與work集羣構建出一個三層的深度網絡，包含兩層卷積層以及一層SoftMax層。代碼以下：

from __future__ import print_function import math import os import tensorflow as tf flags = tf.app.flags # Flags for configuring the task flags.DEFINE_string("job_name", None, "job name: worker or ps") flags.DEFINE_integer("task_index", 0, "Worker task index, should be >= 0. task_index=0 is " "the chief worker task the performs the variable " "initialization") flags.DEFINE_string("ps_hosts", "", "Comma-separated list of hostname:port pairs") flags.DEFINE_string("worker_hosts", "", "Comma-separated list of hostname:port pairs") # Training related flags flags.DEFINE_string("data_dir", None, "Directory where the mnist data is stored") flags.DEFINE_string("train_dir", None, "Directory for storing the checkpoints") flags.DEFINE_integer("hidden1", 128, "Number of units in the 1st hidden layer of the NN") flags.DEFINE_integer("hidden2", 128, "Number of units in the 2nd hidden layer of the NN") flags.DEFINE_integer("batch_size", 100, "Training batch size") flags.DEFINE_float("learning_rate", 0.01, "Learning rate") FLAGS = flags.FLAGS TRAIN_FILE = "train.tfrecords" NUM_CLASSES = 10 IMAGE_SIZE = 28 IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE def inference(images, hidden1_units, hidden2_units): with tf.name_scope('hidden1'): weights = tf.Variable( tf.truncated_normal([IMAGE_PIXELS, hidden1_units], stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),name='weights') biases = tf.Variable(tf.zeros([hidden1_units]),name='biases') hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases) with tf.name_scope('hidden2'): weights = tf.Variable( tf.truncated_normal([hidden1_units, hidden2_units], stddev=1.0 / math.sqrt(float(hidden1_units))), name='weights') biases = tf.Variable(tf.zeros([hidden2_units]), name='biases') hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases) with tf.name_scope('softmax_linear'): weights = tf.Variable( tf.truncated_normal([hidden2_units, NUM_CLASSES], stddev=1.0 / math.sqrt(float(hidden2_units))),name='weights') biases = tf.Variable(tf.zeros([NUM_CLASSES]),name='biases') logits = tf.matmul(hidden2, weights) + biases return logits def lossFunction(logits, labels): labels = tf.to_int64(labels) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( logits, labels, name='xentropy') loss = tf.reduce_mean(cross_entropy, name='xentropy_mean') return loss def training(loss, learning_rate): tf.summary.scalar(loss.op.name, loss) optimizer = tf.train.GradientDescentOptimizer(learning_rate) global_step = tf.Variable(0, name='global_step', trainable=False) train_op = optimizer.minimize(loss, global_step=global_step) return train_op 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), }) # Convert from a scalar string tensor (whose single string has # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape # [mnist.IMAGE_PIXELS]. image = tf.decode_raw(features['image_raw'], tf.uint8) image.set_shape([IMAGE_PIXELS]) image = tf.cast(image, tf.float32) * (1. / 255) - 0.5 # Convert label from a scalar uint8 tensor to an int32 scalar. label = tf.cast(features['label'], tf.int32) return image, label def inputs(batch_size): """Reads input data. Args: batch_size: Number of examples per returned batch. Returns: A tuple (images, labels), where: * images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS] in the range [-0.5, 0.5]. * labels is an int32 tensor with shape [batch_size] with the true label, a number in the range [0, mnist.NUM_CLASSES). """ filename = os.path.join(FLAGS.data_dir, TRAIN_FILE) with tf.name_scope('input'): filename_queue = tf.train.string_input_producer([filename]) # Even when reading in multiple threads, share the filename # queue. image, label = read_and_decode(filename_queue) # Shuffle the examples and collect them into batch_size batches. # (Internally uses a RandomShuffleQueue.) # We run this in two threads to avoid being a bottleneck. images, sparse_labels = tf.train.shuffle_batch( [image, label], batch_size=batch_size, num_threads=2, capacity=1000 + 3 * batch_size, # Ensures a minimum amount of shuffling of examples. min_after_dequeue=1000) return images, sparse_labels def device_and_target(): # If FLAGS.job_name is not set, we're running single-machine TensorFlow. # Don't set a device. if FLAGS.job_name is None: raise ValueError("Must specify an explicit `job_name`") # Otherwise we're running distributed TensorFlow. print("Running distributed training") if FLAGS.task_index is None or FLAGS.task_index == "": raise ValueError("Must specify an explicit `task_index`") if FLAGS.ps_hosts is None or FLAGS.ps_hosts == "": raise ValueError("Must specify an explicit `ps_hosts`") if FLAGS.worker_hosts is None or FLAGS.worker_hosts == "": raise ValueError("Must specify an explicit `worker_hosts`") cluster_spec = tf.train.ClusterSpec({ "ps": FLAGS.ps_hosts.split(","), "worker": FLAGS.worker_hosts.split(","), }) server = tf.train.Server( cluster_spec, job_name=FLAGS.job_name, task_index=FLAGS.task_index) return ( cluster_spec, server, ) def main(unused_argv): if FLAGS.data_dir is None or FLAGS.data_dir == "": raise ValueError("Must specify an explicit `data_dir`") if FLAGS.train_dir is None or FLAGS.train_dir == "": raise ValueError("Must specify an explicit `train_dir`") cluster_spec, server = device_and_target() if FLAGS.job_name == "ps": server.join() elif FLAGS.job_name == "worker": with tf.device(tf.train.replica_device_setter(worker_device = "/job:worker/task:{}".format(FLAGS.task_index), cluster=cluster_spec)): images, labels = inputs(FLAGS.batch_size) logits = inference(images, FLAGS.hidden1, FLAGS.hidden2) loss = lossFunction(logits, labels) train_op = training(loss, FLAGS.learning_rate) with tf.train.MonitoredTrainingSession( master=server.target, is_chief=(FLAGS.task_index == 0), checkpoint_dir=FLAGS.train_dir) as sess: while not sess.should_stop(): sess.run(train_op) if __name__ == "__main__": tf.app.run()

4、分佈式模型的啓動

首先關閉防火牆

sudo iptable –F

而後在不一樣的機器上面啓動服務

#在246.1機器上面運行參數服務器，命令： CLASSPATH=$($HADOOP_HDFS_HOME/bin/hadoop classpath --glob) python /home/bdusr01/tine/Distributed_Tensorflow_MNIST_Model_Used_NN_Read_TFRecords_On_HDFS_Support_Kerberos.py --ps_hosts=10.142.246.1:1120 --worker_hosts=10.142.78.41:1121,10.142.78.45:1122 --data_dir=hdfs://default/user/bdusr01/asy/MNIST_data --train_dir=/home/bdusr01/checkpoint/ --job_name=ps --task_index=0 #在78.41機器上面運行worker0，命令： CLASSPATH=$($HADOOP_HDFS_HOME/bin/hadoop classpath --glob) python /home/bdusr01/tine/Distributed_Tensorflow_MNIST_Model_Used_NN_Read_TFRecords_On_HDFS_Support_Kerberos.py --ps_hosts=10.142.246.1:1120 --worker_hosts=10.142.78.41:1121,10.142.78.45:1122 --data_dir=hdfs://default/user/bdusr01/asy/MNIST_data --train_dir=/home/bdusr01/checkpoint/ --job_name=worker --task_index=0 #在78.45機器上面運行worker1，命令： CLASSPATH=$($HADOOP_HDFS_HOME/bin/hadoop classpath --glob) python /home/bdusr01/tine/Distributed_Tensorflow_MNIST_Model_Used_NN_Read_TFRecords_On_HDFS_Support_Kerberos.py--ps_hosts=10.142.246.1:1120 --worker_hosts=10.142.78.41:1121,10.142.78.45:1122 --data_dir=hdfs://default/user/bdusr01/asy/MNIST_data --train_dir=/home/bdusr01/checkpoint/ --job_name=worker --task_index=1 #在78.41機器上面運行監控，命令： tensorboard --logdir=/home/bdusr01/checkpoint/

5、模型監控

咱們在剛剛的41機器上面啓動了TensorBoard，能夠經過地址http://10.142.78.41:6006/進行模型的監控。模型訓練過程當中參數能夠動態的進行觀測，示例以下：

 

 

模型的網絡結構能夠詳細的參看每一個細節，示例以下：

 

 

當咱們利用分佈式的Tensorflow對大數據進行訓練完成後，能夠利用Bazel構建一個靈活高可用的服務–TensorFlow Serving，可以很方便的將深度學習生產化，解決了模型沒法提供服務的弊端。到此爲止，本文就將本身項目中的一個基礎模塊的示例介紹完了，本項目更有含金量的是模型創建、工程開發、業務邏輯部分，若有機會再進行更詳細的交流。

出處：http://geek.csdn.net/news/detail/136629