标签：tar bsp 3.5 ubuntu14 评价 root images down ++
这是《使用亚马逊的云服务器EC2做深度学习》系列的第三篇文章。
（一）申请竞价实例 （二）配置Jupyter Notebook服务器 （三）配置TensorFlow
TensorFlow是Google发布的深度学习框架，支持Python和C++的接口。TensorFlow既可以用于学术研究，也可以用于生产环境。许多Google的内部服务，就使用了TensorFlow，比如Gmail、语音识别等。
网络上TensorFlow的教程也很丰富，官方文档在第一时间就被翻译成来中文。
如果让我来评价一下的话，我会说Google出品必属精品。
配置TensorFlow的环境，需要安装很多GPU的驱动，非常繁琐。下面的配置脚本是我根据其它教程提供的脚本修改而来。
TensorFlow的版本是目前的最新版本0.11，Python使用的是Anaconda3发行版，Python的版本是Python3.5。
一个注意事项是，选择AWS EC2的区的时候，尽量选择美国或者欧洲地区，不然下载驱动的输入比较慢，需要耗费很长时间。
（1）更新系统，安装必要文件
# install the required packages sudo apt-get update && sudo apt-get -y upgrade sudo apt-get -y install linux-headers-$(uname -r) linux-image-extra-`uname -r`
（2）安装Cuda 7.5
# install cuda 7.5 CUDA_FILE=cuda-repo-ubuntu1404_7.5-18_amd64.deb wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/${CUDA_FILE} sudo dpkg -i ${CUDA_FILE} rm ${CUDA_FILE} sudo apt-get update sudo apt-get install -y cuda-7-5
（3）安装cudnn 5.1
# get cudnn 5.1 CUDNN_FILE=cudnn-7.5-linux-x64-v5.1.tgz wget http://developer.download.nvidia.com/compute/redist/cudnn/v5.1/${CUDNN_FILE} tar xvzf ${CUDNN_FILE} rm ${CUDNN_FILE} sudo cp cuda/include/cudnn.h /usr/local/cuda/include # move library files to /usr/local/cuda sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* rm -rf cuda
（4）添加环境变量
# set the appropriate library path echo ‘export CUDA_HOME=/usr/local/cuda export CUDA_ROOT=/usr/local/cuda export PATH=$PATH:$CUDA_ROOT/bin:$HOME/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64 ‘ >> ~/.bashrc
（5）安装Anaconda
# install anaconda ANACONDA_FILE=Anaconda3-4.2.0-Linux-x86_64.sh wget https://repo.continuum.io/archive/${ANACONDA_FILE} bash ${ANACONDA_FILE} -b -p /mnt/bin/anaconda3 rm ${ANACONDA_FILE} echo ‘export PATH="/mnt/bin/anaconda3/bin:$PATH"‘ >> ~/.bashrc
（6）安装TensorFlow
# install tensorflow TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp35-cp35m-linux_x86_64.whl /mnt/bin/anaconda3/bin/pip install $TF_BINARY_URL
exec bash
下面是完整的配置脚本：
#!/bin/bash # stop on error set -e ############################################ # install into /mnt/bin sudo mkdir -p /mnt/bin sudo chown ubuntu:ubuntu /mnt/bin # install the required packages sudo apt-get update && sudo apt-get -y upgrade sudo apt-get -y install linux-headers-$(uname -r) linux-image-extra-`uname -r` # install cuda 7.5 CUDA_FILE=cuda-repo-ubuntu1404_7.5-18_amd64.deb wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/${CUDA_FILE} sudo dpkg -i ${CUDA_FILE} rm ${CUDA_FILE} sudo apt-get update sudo apt-get install -y cuda-7-5 # get cudnn 5.1 CUDNN_FILE=cudnn-7.5-linux-x64-v5.1.tgz wget http://developer.download.nvidia.com/compute/redist/cudnn/v5.1/${CUDNN_FILE} tar xvzf ${CUDNN_FILE} rm ${CUDNN_FILE} sudo cp cuda/include/cudnn.h /usr/local/cuda/include # move library files to /usr/local/cuda sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* rm -rf cuda # set the appropriate library path echo ‘export CUDA_HOME=/usr/local/cuda export CUDA_ROOT=/usr/local/cuda export PATH=$PATH:$CUDA_ROOT/bin:$HOME/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64 ‘ >> ~/.bashrc # install anaconda ANACONDA_FILE=Anaconda3-4.2.0-Linux-x86_64.sh wget https://repo.continuum.io/archive/${ANACONDA_FILE} bash ${ANACONDA_FILE} -b -p /mnt/bin/anaconda3 rm ${ANACONDA_FILE} echo ‘export PATH="/mnt/bin/anaconda3/bin:$PATH"‘ >> ~/.bashrc # install tensorflow TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp35-cp35m-linux_x86_64.whl /mnt/bin/anaconda3/bin/pip install $TF_BINARY_URL # install monitoring programs #sudo wget https://git.io/gpustat.py -O /usr/local/bin/gpustat #sudo chmod +x /usr/local/bin/gpustat #sudo nvidia-smi daemon #sudo apt-get -y install htop # reload .bashrc exec bash
使用亚马逊的云服务器EC2做深度学习（三）配置TensorFlow
标签：tar bsp 3.5 ubuntu14 评价 root images down ++
原文地址：http://www.cnblogs.com/meelo/p/5994814.html
