linux下tensorflow安装

如无特殊说明，所有操作都在Linux系统上完成，并假定只有普通用户权限。(仅有CUDA和cudnn安装需要用到管理员权限)

一、 安装TensorFlow

1. 使用pip从二进制文件安装

注意：

• 最新版本的二进制文件链接地址，可以通过TensorFlow官网获取 https://www.tensorflow.org/install/install_linux
• 请安装最新版的pip，或者使用pip install –upgrade pip来进行更新
• 如果安装GPU版本，请确认已经提前安装配置好CUDA

Python 2.7

// CPU版本pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.4.0-cp27-none-linux_x86_64.whl// GPU版本pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.4.0-cp27-none-linux_x86_64.whl

Python 3.4

// CPU版本pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.4.0-cp34-cp34m-linux_x86_64.whl// GPU版本pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.4.0-cp34-cp34m-linux_x86_64.whl

Python 3.5

// CPU版本pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.4.0-cp35-cp35m-linux_x86_64.whl// GPU版本pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.4.0-cp35-cp35m-linux_x86_64.whl

Python 3.6

// CPU版本pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.4.0-cp36-cp36m-linux_x86_64.whl// GPU版本pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.4.0-cp36-cp36m-linux_x86_64.whl

2. 从源码安装

2.1 安装JDK8

从官网http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html下载对应版本。

使用tar命令解压文件

tar zxvf jdk-8u151-linux-x64.tar.gz

使用vi编辑器设置环境变量

# 当前用户（该文件为隐藏文件，可以用ll命令查看）vi /$HOME/.bashrc

在打开的文件末尾添加下列内容并保存，其中JAVA_HOME为解压路径

export JAVA_HOME=/usr/jdk1.8.0_151export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jarexport PATH=$PATH:$JAVA_HOME/bin

使用下述命令让环境变量立即生效：

# 当前用户（该文件为隐藏文件，可以用ll命令查看）source /$HOME/.bashrc

2.2 安装bazel

首先到Release界面https://github.com/bazelbuild/bazel/releases下载bazel。或者使用下面的命令

wget https://github.com/bazelbuild/bazel/releases/download/0.7.0/bazel-0.7.0-dist.zip

下载完成后解压并进入该目录进行编译：

unzip /home/zhaofan/bazel -d bazelcd bazel./compile.sh

安装完成后会在文件夹下生成一个“/output”文件夹，将该路径加入.bashrc中，并使用source立即生效。比如，我的路径为：

export PATH=$PATH:/home/****/3rdparty/bazel/output

2.3 安装CUDA以及cudnn

建议安装CUDA8.0(好像9.0也已经支持了)。CUDA必须以管理员身份安装，否则即使能够成功编译，也会无法正常使用GPU。所以，如果CUDA没有安装，联系你的管理员安装吧。

安装CUDA可以参考http://blog.csdn.net/u013832707/article/details/53157976

建议安装cudnn6或者更高版本，且cudnn文件应和CUDA处于同一目录，即：

cudnn.h应位于cuda/include中， libcudnn.\文件应位于cuda\lib64中

如果需要升级替换较低版本的cudnn，可以参考《ubuntu14.04更改cudnn版本》

2.4 在Anaconda基础上安装tensorflow

为了避免非管理员无法升级安装最新版本python包，建议使用Anaconda。Anaconda的安装方法可参考《 linux下安装Anaconda》

下载tensorflow最新版源码：

git clone --recurse-submodules https://github.com/tensorflow/tensorflow

进入tensorflow根目录中，执行

./configure 

按照提示输入y/n或者路径等信息。需要注意的有：python路径，开启GPU支持，CUDA路径、CUDNN路径等。具体如下：

WARNING: Running Bazel server needs to be killed, because the startup options are different.You have bazel 0.7.0- (@non-git) installed.Please specify the location of python. [Default is /home/zhaofan/anaconda3/bin/python]: Found possible Python library paths:  /home/zhaofan/anaconda3/lib/python3.6/site-packagesPlease input the desired Python library path to use.  Default is [/home/zhaofan/anaconda3/lib/python3.6/site-packages]Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: nNo jemalloc as malloc support will be enabled for TensorFlow.Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: nNo Google Cloud Platform support will be enabled for TensorFlow.Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: nNo Hadoop File System support will be enabled for TensorFlow.Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: nNo Amazon S3 File System support will be enabled for TensorFlow.Do you wish to build TensorFlow with XLA JIT support? [y/N]: nNo XLA JIT support will be enabled for TensorFlow.Do you wish to build TensorFlow with GDR support? [y/N]: nNo GDR support will be enabled for TensorFlow.Do you wish to build TensorFlow with VERBS support? [y/N]: nNo VERBS support will be enabled for TensorFlow.Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: nNo OpenCL SYCL support will be enabled for TensorFlow.Do you wish to build TensorFlow with CUDA support? [y/N]: yCUDA support will be enabled for TensorFlow.Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: 6Please specify the location where cuDNN 6 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:Please specify a list of comma-separated Cuda compute capabilities you want to build with.You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1,6.1,6.1,6.1]Do you want to use clang as CUDA compiler? [y/N]: nnvcc will be used as CUDA compiler.Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: Do you wish to build TensorFlow with MPI support? [y/N]: nNo MPI support will be enabled for TensorFlow.Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: Add "--config=mkl" to your bazel command to build with MKL support.Please note that MKL on MacOS or windows is still not supported.If you would like to use a local MKL instead of downloading, please set the environment variable "TF_MKL_ROOT" every time before build.Configuration finished

配置完成后执行：

bazel build --config=opt --config=cuda --config=monolithic //tensorflow/tools/pip_package:build_pip_package

上面的过程比较耗时，结束后继续执行：

bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

最后安装生成的whl文件：

pip install /tmp/tensorflow_pkg/tensorflow-1.4.0-cp36-cp36m-linux_x86_64.whl

安装完成后，退出tensorflow目录，打开python，执行

import tensorflow as tfhello = tf.constant('Hello, TensorFlow!')sess = tf.Session()print(sess.run(hello))

如果可以成功打印，则表明安装成功。

3. 遇到的一些问题

3.1 关于开启MKL支持

编译时增加mkl标志，即可自动下载所需的mkl：可惜国内网速很可能导致此步失败

bazel build --config=opt --config=cuda --config=monolithic --config=mkl//tensorflow/tools/pip_package:build_pip_package

或者自己下载mkl安装，然后设置TF_MKL_ROOT变量。 可惜这个方法我也没成功

3.2 找不到动态库

比如下面的错误：

ImportError: libcusolver.so.8.0: cannot open shared object file: No such file or directory

解决方法，将库所在路径加入环境变量。

编辑.bashrc文件：

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64