tensorflow1.5源码安装

最新推荐文章于 2020-12-14 21:50:21 发布

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最新推荐文章于 2020-12-14 21:50:21 发布

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本文介绍了如何在特定环境中源码安装TensorFlow 1.5。首先，需要安装Bazel构建工具，接着是Python依赖包的安装。在确认源码信息后，可能会遇到因pip版本导致的安装错误，解决方案是升级pip到最新版。

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tensorflow1.5源码安装

昨天听同学说 TensorFlow1.5.0-rc0 发布了，该版本将动态图集成到了tensorflow模块里面，不用像1.4的版本需要另外安装插件，很是激动，打算赶紧安装上手一波。但是由于才发布不久，不支持终端的pip直接安装，所以要想使用最新版本，还得自己编译源码安装。话不多说，直接动手！（tensorflow CPU版本安装，渣渣笔记本

）

源码下载

环境：

ubuntu14.04、python2.7

tensorflow安装环境准备

1、Bazel安装

Bazel是一个构建工具，即一个可以运行编译和测试来组装软件的工具，跟Make、Ant、Gradle、Buck、Pants和Maven一样。Bazel官网安装教程。选择官网推荐的第一种安装方式 Use our custom APT repository (recommended)。

第一步 install JDK 8。 ubuntu14.04应该先添加PPA，终端输入命令：

sudo add-apt-repository ppa:webupd8team/java
sudo apt-get update && sudo apt-get install oracle-java8-installer

第二句我执行时报了错，但是分开执行就通过了，即先执行

sudo apt-get update

然后执行

sudo apt-get install oracle-java8-installer

sudo apt-get install oracle-java8-installer。

第二步 Add Bazel distribution URI as a package source (one time setup)，执行命令：

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 -

第二句我执行的时候报了错，提示没有***gpg，第二天写的这个记录，具体的错忘了

。于是先把密钥下载下来吧，直接在浏览器地址栏输入https://bazel.build/bazel-release.pub.gpg，然后就可以下载该密钥文件了，然后将密钥文件拷贝到某路径path下，再执行命令

sudo apt-key add path/密钥文件名

第三步安装Bazel，执行命令

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

实际上执行完这几步就可以了，在终端输入baz ，双击Tab键可以看到提示的命令中会出现bazel命令。

2、python依赖包安装

输入命令

sudo apt-get install python-numpy python-dev python-pip python-wheel （适用于python2.7）

3、源码信息确认

首先解压上面步骤中下载的源码包，解压包并进入到根目录

tar -zxvf tensorflow包名
cd tensorflow解压目录
./configure

出现如下配置信息：

$ cd tensorflow  # cd to the top-level directory created
$ ./configure
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python2.7
Found possible Python library paths:
  /usr/local/lib/python2.7/dist-packages
  /usr/lib/python2.7/dist-packages
Please input the desired Python library path to use.  Default is [/usr/lib/python2.7/dist-packages]

Using python library path: /usr/local/lib/python2.7/dist-packages
Do you wish to build TensorFlow with MKL support? [y/N]
No MKL 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]:
Do you wish to use jemalloc as the malloc implementation? [Y/n]
jemalloc enabled
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N]
No Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with Hadoop File System support? [y/N]
No Hadoop File System support will be enabled for TensorFlow
Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N]
No XLA support will be enabled for TensorFlow
Do you wish to build TensorFlow with VERBS support? [y/N]
No VERBS support will be enabled for TensorFlow
Do you wish to build TensorFlow with OpenCL support? [y/N]
No OpenCL support will be enabled for TensorFlow
Do you wish to build TensorFlow with CUDA support? [y/N] N
Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: 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 which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: 6
Please 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: "3.5,5.2"]: 3.0
Do you wish to build TensorFlow with MPI support? [y/N]
MPI support will not be enabled for TensorFlow
Configuration finished

根据自己的需要进行选择,我没有选择cuda选项。
然后编译pip包，输入命令(会编译很长时间)

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

在tensorflow的根目录下执行

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

会在/tmp/tensorflow_pkg/下生成tensorflow-1.5.0rc0-cp27-cp27mu-linux_x86_64.whl

安装tensorflow

执行命令

sudo pip install --ignore-installed --upgrade /tmp/tensorflow_pkg/tensorflow-1.5.0rc0-cp27-cp27mu-linux_x86_64.whl

安装可能会遇到如下错误（参考链接）

tensorflow-1.5.0rc0-cp27-cp27mu-linux_x86_64.whl is not a supported wheel on this platform.
Storing debug log for failure in /home/xxx/.pip/pip.log

这是因为pip版本的缘故，解决办法是下载get-pip.py 文件，进入下载目录执行

$sudo python2.7 get-pip.py

再次安装即可

另启动一个终端，启动python环境，导入tensorflow包看是否成功