安装tensorflow
下载tensorflow源文件
安装Bazel，Bazel是Google开源构建工具，类似于Make的工具，用来编译构建tensorflow
Bazel下载地址：
sudo apt-get install pkg-config zip g++zlib1g-dev unzip
chmod +x bazel-version-installer-os.sh
./bazel-version-installer-os.sh --user
export PATH="$PATH:$HOME/bin"
或者安装bazel with apt
If you want to use the JDK 7, please replacejdk1.8withjdk1.7and if youwant to install the testing version of Bazel, replacestablewithtesting.
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
安装tensorflow其他的依赖
配置tensorflow，需要你指定相应文件的安装目录。cd进tensorflow源文件。
sudo ./configure
配置好，会出现以下内容：
Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Setting up CUPTI include
Setting up CUPTI lib64
Configuration finished
使用Bazel编译构建
bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer
bazel build-c opt /tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/cc/tutorials_example_trainer--use_gpu
# Lots of output. This tutorial iteratively calculates the major eigenvalue of# a 2x2 matrix, on GPU. The last few lines look like this.000009/000005 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]000006/000001 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]000009/000009 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
OK!
Test:
cd /home/lei/tensorflow/tensorflow/models/image/mnist
python convolutional.py
Installing TensorFlow from Sources
If the last paragraph didn't scare you off, welcome. This guide explains
how to build TensorFlow on the following operating systems:
Ubuntu
Mac OS X
Determine which TensorFlow to install
You must choose one of the following types of TensorFlow to build and
install:
TensorFlow with CPU support only. If your system does not have a NVIDIA® GPU, build and install this version. Note that this version of TensorFlow is typically easier to build and install, so even if you have an NVIDIA GPU, we recommend building and installing this version first.
TensorFlow with GPU support. TensorFlow programs typically run significantly faster on a GPU than on a CPU. Therefore, if your system has a NVIDIA GPU and you need to run performance-critical applications, you should ultimately build and install this version. Beyond the NVIDIA GPU itself, your system must also fulfill the NVIDIA software requirements described in one of the following documents:
Clone the TensorFlow repository
Start the process of building TensorFlow by cloning a TensorFlow
repository.
To clonethe latestTensorFlow repository, issue the following command:
The precedinggit clonecommand creates a subdirectorynamedtensorflow. After cloning, you may optionally build aspecific branch(such as a release branch) by invoking thefollowing commands:
$cd tensorflow$git checkoutBranch# whereBranchis the desired branch
For example, to work with ther1.0release instead of the master release,issue the following command:
$git checkout r1.0
Prepare environment for Linux
Before building TensorFlow on Linux, install the following build
tools on your system:
bazel
TensorFlow Python dependencies
optionally, NVIDIA packages to support TensorFlow for GPU.
Install Bazel
Install TensorFlow Python dependencies
To install TensorFlow, you must install the following packages:
numpy, which is a numerical processing package that TensorFlow requires.
dev, which enables adding extensions to Python.
pip, which enables you to install and manage certain Python packages.
wheel, which enables you to manage Python compressed packages in the wheel (.whl) format.
To install these packages for Python 2.7, issue the following command:
$sudo apt-get install python-numpy python-dev python-pip python-wheel
To install these packages for Python 3.n, issue the following command:
$sudo apt-get install python3-numpy python3-dev python3-pip python3-wheel
Optional: install TensorFlow for GPU prerequisites
If you are building TensorFlow without GPU support, skip this section.
The following NVIDIAhardwaremust be installed on your system:
The following NVIDIAsoftwaremust be installed on your system:
The NVIDIA drivers associated with NVIDIA's Cuda Toolkit.
Finally, you must also installlibcupti-devby invoking the followingcommand:
$sudo apt-get install libcupti-dev
Next
Prepare environment for Mac OS
Before building TensorFlow, you must install the following on your system:
bazel
TensorFlow Python dependencies.
optionally, NVIDIA packages to support TensorFlow for GPU.
Install bazel
Install python dependencies
To install TensorFlow, you must install the following packages:
six
numpy, which is a numerical processing package that TensorFlow requires.
wheel, which enables you to manage Python compressed packages
in the wheel (.whl) format.
After installing pip, invoke the following commands:
$sudo pip install six numpy wheel
Optional: install TensorFlow for GPU prerequisites
After installing brew, install GNU coreutils by issuing the following command:
$brew install coreutils
If you want to compile tensorflow and have XCode 7.3 and CUDA 7.5 installed,
note that Xcode 7.3 is not yet compatible with CUDA 7.5. To remedy this
problem, do either of the following:
Upgrade to CUDA 8.0.
Download Xcode 7.2 and select it as your default by issuing the following
command:
$sudo xcode-select -s /Application/Xcode-7.2/Xcode.app
NOTE:Your system must fulfill the NVIDIA software requirements describedin one of the following documents:
Configure the installation
The root of the source tree contains a bash script namedconfigure. This script asks you to identify the pathname of allrelevant TensorFlow dependencies and specify other build configuration optionssuch as compiler flags. You must run this scriptpriortocreating the pip package and installing TensorFlow.
If you wish to build TensorFlow with GPU,configurewill askyou to specify the version numbers of Cuda and cuDNN. If severalversions of Cuda or cuDNN are installed on your system, explicitly selectthe desired version instead of relying on the system default.
Here is an example execution of theconfigurescript. Note that yourown input will likely differ from our sample input:
If you toldconfigureto build for GPU support, thenconfigurewill create a canonical set of symbolic links to the Cuda librarieson your system. Therefore, every time you change the Cuda library paths,you must rerun theconfigurescript before re-invokingthebazel buildcommand.
Note the following:
Although it is possible to build both Cuda and non-Cuda configs under the same source tree, we recommend runningbazel cleanwhen switching between these two configurations in the same source tree.
If you don't run theconfigurescriptbeforerunning thebazel buildcommand, thebazel buildcommand will fail.
Build the pip package
To build a pip package for TensorFlow with CPU-only support,
invoke the following command:
$bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
To build a pip package for TensorFlow with GPU support,
invoke the following command:
$bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
NOTE on gcc 5 or later:the binary pip packages available on the TensorFlow website are built with gcc 4, which uses the older ABI. To make your build compatible with the older ABI, you need to add-cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0"to yourbazel buildcommand. ABI compatibility allows custom ops built against the TensorFlow pip package to continue to work against your built package.
Tip:By default, building TensorFlow from sources consumesa lot of RAM. If RAM is an issue on your system, you may limit RAM usageby specifying--local_resources 2048,.5,1.0whileinvokingbazel.
Thebazel buildcommand builds a script namedbuild_pip_package. Running this script as follows will builda.whlfile within the/tmp/tensorflow_pkgdirectory:
$bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
Install the pip package
Invokepip installto install that pip package.The filename of the.whlfile depends on your platform.For example, the following command will install the pip packagefor TensorFlow 1.1.0 on Linux:
$sudo pip install /tmp/tensorflow_pkg/tensorflow-1.1.0-py2-none-any.whl
Validate your installation
Validate your TensorFlow installation by doing the following:
Start a terminal.
Change directory (cd) to any directory on your system other than thetensorflowsubdirectory from which you invoked theconfigurecommand.
Invoke python:
$python
Enter the following short program inside the python interactive shell:
>>>importtensorflowastf
>>>hello=tf.constant('Hello, TensorFlow!')
>>>sess=tf.Session()
>>>print(sess.run(hello))
If the system outputs the following, then you are ready to begin writing
TensorFlow programs:
Hello, TensorFlow!
TensorFlow.
installation problems.
Common installation problems
The installation problems you encounter typically depend on the
operating system. See the "Common installation problems" section
of one of the following guides:
Beyond the errors documented in those two guides, the following tablenotes additional errors specific to building TensorFlow. Note that weare relying on Stack Overflow as the repository for build and installationproblems. If you encounter an error message not listed in the precedingtwo guides or in the following table, search for it on Stack Overflow. IfStack Overflow doesn't show the error message, ask a new question onStack Overflow and specify thetensorflowtag.