ubuntu16.04.3安装Bazel+源码编译安装tensorflow(CUDA9.0)

一、安装Bazel

Using Bazel custom APT repository (recommended)

1. Install JDK 8

Install JDK 8 by using:

sudo apt-get install openjdk-8-jdk

On Ubuntu 14.04 LTS you'll have to use a PPA:

sudo add-apt-repository ppa:webupd8team/java

sudo apt-get update&&sudo apt-get install oracle-java8-installer

2. Add Bazel distribution URI as a package source (one time setup)

echo"deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8"|sudo tee /etc/apt/sources.list.d/bazel.list

curl https://bazel.build/bazel-release.pub.gpg|sudo apt-key add -

If you want to install the testing version of Bazel, replace stable with testing.

3. Install and update Bazel

sudo apt-get update&&sudo apt-get install bazel

Once installed, you can upgrade to a newer version of Bazel with:

sudo apt-get upgrade bazel

官网的安装步骤，亲身实践有效。

二、源码编译安装Tensorflow

1.克隆tensorflow源码

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

2.配置tensorflow

进入到tensorflow目录下，有一个configure文件

输入：

./configure

3.配置选项

按如下规则配置tensorflow

You have bazel 0.6.1 installed.

Please specify the location of python. [Default is /usr/bin/python]:/usr/bin/python3

Found possible Python library paths:

/home/shengchun/tensorflow/models/

/bin

/usr/local/cuda/bin

/sbin

/home/shengchun/tensorflow/models/slim

/usr/bin

/usr/local/sbin

/usr/local/games

/usr/lib/python3/dist-packages

/usr/games

/home/shengchun/mxnet-ssd/mxnet/python

/usr/sbin

/usr/local/bin

/usr/local/lib/python3.5/dist-packages

Please input the desired Python library path to use.  Default is [/home/shengchun/tensorflow/models/]/usr/local/lib/python3.5/dist-packages

Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]:[Enter]

jemalloc as malloc support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]:[Enter]

Google Cloud Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Hadoop File System support? [Y/n]:[Enter]

Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]:[Enter]

No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]:[Enter]

No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]:[Enter]

No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL support? [y/N]:[Enter]

No OpenCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]:y

CUDA 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]:9.0

Please specify the location where CUDA 9.0 toolkit is

installed. Refer to README.md for more details. [Default is

/usr/local/cuda]:[Enter]

Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]:7.0.3

Please specify the location where cuDNN 7.0.3 library is

installed. Refer to README.md for more details. [Default is /usr/local/cuda]:[Enter]

**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: 5.0]**[Enter]

Do you want to use clang as CUDA compiler? [y/N]:[Enter]

nvcc 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]:[Enter]

No 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]:[Enter]

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

4.编译tensorflow, 开启 GPU 支持：

bazelbuild--config=opt--config=cuda//tensorflow/tools/pip_package:build_pip_package

时间很长。

5.生成whl文件

bazel编译命令建立了一个名为build_pip_package的脚本。运行如下的命令将会在 /tmp/tensorflow_pkg路径下生成一个.whl文件：

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

6.pip安装tensorflow

sudo pip3 install /tmp/tensorflow_pkg/tensorflow-1.4.0-cp35-cp35m-linux_x86_64.whl

7.验证安装

打开任意一个新的终端，注意不要在tensorflow的安装路径下，运行

python3

输入以下代码

import tensorflow as tf

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

sess = tf.Session()

print(sess.run(hello))

tensorflow源码编译安装完成。

参考：https://www.jianshu.com/p/65d3fe64df53