系統環境：centos7

1. 安裝 Python 2.7

# yum -y install zlib-devel bzip2-devel openssl-devel ncurses-devel sqlite-devel readline-devel tk-devel gcc gcc-c++ make

# download and extract Python 2.7

su hdfs

cd ~/Downloads

curl -O https://www.python.org/ftp/python/2.7.12/Python-2.7.12.tgz

tar -xvf Python-2.7.12.tgz

# compile into local PYTHON_ROOT

# export PYTHON_ROOT=~/Python

export PYTHON_ROOT=~/TensorFlowOnSpark-Work/Python_gpu

pushd Python-2.7.12

./configure --prefix="${PYTHON_ROOT}" --enable-unicode=ucs4 make make install popd

# install pip

pushd "${PYTHON_ROOT}"

curl -O https://bootstrap.pypa.io/get-pip.py

bin/python get-pip.py

rm -rf get-pip.py

# 安裝依賴庫

export PYTHON_ROOT=~/TensorFlowOnSpark-Work/Python_gpu

${PYTHON_ROOT}/bin/pip install yarn-api-client

${PYTHON_ROOT}/bin/pip install uniout

${PYTHON_ROOT}/bin/pip install numpy

${PYTHON_ROOT}/bin/pip install pydoop

# 打包python_gpu

export PYTHON_ROOT=/var/lib/hadoop-hdfs/TensorFlowOnSpark-Work/Python_gpu

pushd "${PYTHON_ROOT}" zip -r Python_gpu.zip *

2. 在 Python 2.7 中安裝tensorflow GPU

# 安裝顯卡驅動和cuda和cudda

參考文檔

http://www.linuxidc.com/Linux/2016-11/137561.htm

# 安裝nvidia-modprobe

yum -y install nvidia-modprobe

#查看gpu信息

export PATH="$PATH:/usr/local/cuda-8.0/bin"

export LD_LIBRARY_PATH="/usr/local/cuda-8.0/lib64"

nvidia-smi

能看到這個信息說明gpu已經安裝配置成功

# 安裝tensorflow

su hdfs

export PYTHON_ROOT=~/TensorFlowOnSpark-Work/Python_gpu

${PYTHON_ROOT}/bin/pip install tensorflow-gpu

# 安裝keras

export PYTHON_ROOT=~/TensorFlowOnSpark-Work/Python_gpu

${PYTHON_ROOT}/bin/pip install keras

# 驗證一 tensorflow

su hdfs

export PYTHON_ROOT=~/TensorFlowOnSpark-Work/Python_gpu

export PATH="$PATH:/usr/local/cuda-8.0/bin"

export LD_LIBRARY_PATH="/usr/local/cuda-8.0/lib64"

${PYTHON_ROOT}/bin/python

import tensorflow as tf

hello = tf.constant(‘Hello, TensorFlow!‘)

sess = tf.Session()

print (sess.run(hello))

a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name=‘a‘)

b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name=‘b‘)

c = tf.matmul(a, b)

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

print(sess.run(c))

# 驗證二 tensorflow

su hdfs

cd /var/lib/hadoop-hdfs/Downloads/tensorflow/tensorflow/examples/tutorials/mnist

export PYTHON_ROOT=~/TensorFlowOnSpark-Work/Python_gpu

export PATH="$PATH:/usr/local/cuda-8.0/bin"

export LD_LIBRARY_PATH="/usr/local/cuda-8.0/lib64"

${PYTHON_ROOT}/bin/python mnist_deep.py

已經可以看到 gpu的進程和負載了

CUDA之nvidia-smi命令詳解

http://blog.csdn.net/bruce_0712/article/details/63683787

centos7下安裝部署tensorflow GPU 版本