Installing TensorFlow from Sources
This guide explains how to build TensorFlow sources into a TensorFlowbinary and how to install that TensorFlow binary. Note that we providewell-tested, pre-built TensorFlow binaries for Linux, Mac, and Windowssystems. In addition, there are pre-built TensorFlowdocker images.So, don‘t build a TensorFlow binary yourself unless you are verycomfortable building complex packages from source and dealing withthe inevitable aftermath should things not go exactly as documented.
If the last paragraph didn‘t scare you off, welcome. This guide explainshow to build TensorFlow on the following operating systems:
- Ubuntu
- Mac OS X
We don‘t officially support building TensorFlow on Windows; however, you may tryto build TensorFlow on Windows if you don‘t mind using the highly experimentalBazel on WindowsorTensorFlow CMake build.
Determine which TensorFlow to install
You must choose one of the following types of TensorFlow to build andinstall:
- 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:
- Installing TensorFlow on Mac OS
Clone the TensorFlow repository
Start the process of building TensorFlow by cloning a TensorFlowrepository.
To clone the latest TensorFlow repository, issue the following command:
$ git clone https://github.com/tensorflow/tensorflow
The preceding git clone command creates a subdirectorynamed tensorflow. After cloning, you may optionally build aspecific branch (such as a release branch) by invoking thefollowing commands:
$ cd tensorflow$ git checkout Branch # where Branch is the desired branch
For example, to work with the r1.0 release instead of the master release,issue the following command:
$ git checkout r1.0
Next, you must prepare your environment forLinuxorMac OS
Prepare environment for Linux
Before building TensorFlow on Linux, install the following buildtools on your system:
- bazel
- TensorFlow Python dependencies
- optionally, NVIDIA packages to support TensorFlow for GPU.
Install Bazel
If bazel is not installed on your system, install it now by followingthese directions.
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 NVIDIA hardware must be installed on your system:
- GPU card with CUDA Compute Capability 3.0 or higher. See NVIDIA documentation for a list of supported GPU cards.
The following NVIDIA software must be installed on your system:
- NVIDIA‘s Cuda Toolkit (>= 7.0). We recommend version 8.0. For details, see NVIDIA‘s documentation. Ensure that you append the relevant Cuda pathnames to the
LD_LIBRARY_PATHenvironment variable as described in the NVIDIA documentation. - The NVIDIA drivers associated with NVIDIA‘s Cuda Toolkit.
- cuDNN (>= v3). We recommend version 6.0. For details, see NVIDIA‘s documentation, particularly the description of appending the appropriate pathname to your
LD_LIBRARY_PATHenvironment variable.
Finally, you must also install libcupti-dev by invoking the followingcommand:
$ sudo apt-get install libcupti-dev
Next
After preparing the environment, you must nowconfigure the installation.
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
If bazel is not installed on your system, install it now by followingthese directions.
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.
You may install the python dependencies using pip. If you don‘t have pipon your machine, we recommend using homebrew to install Python and pip asdocumented here.If you follow these instructions, you will not need to disable SIP.
After installing pip, invoke the following commands:
$ sudo pip install six numpy wheel
Optional: install TensorFlow for GPU prerequisites
If you do not have brew installed, install it by followingthese instructions.
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 thisproblem, 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 script prior tocreating the pip package and installing TensorFlow.
If you wish to build TensorFlow with GPU, configure will 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 default.
One of the questions that configure will ask is as follows:
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]
This question refers to a later phase in which you‘ll use bazel to build the pip package. We recommend accepting the default (-march=native), which willoptimize the generated code for your local machine‘s CPU type. However,if you are building TensorFlow on one CPU type but will run TensorFlow ona different CPU type, then consider specifying a more specific optimizationflag as described in the gccdocumentation.
Here is an example execution of the configure script. Note that yourown input will likely differ from our sample input:
$ cd tensorflow # cd to the top-level directory created$ ./configurePlease specify the location of python. [Default is /usr/bin/python]: /usr/bin/python2.7Found possible Python library paths: /usr/local/lib/python2.7/dist-packages /usr/lib/python2.7/dist-packagesPlease 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-packagesDo you wish to build TensorFlow with MKL support? [y/N]No MKL support will be enabled for TensorFlowPlease 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 enabledDo you wish to build TensorFlow with Google Cloud Platform support? [y/N]No Google Cloud Platform support will be enabled for TensorFlowDo you wish to build TensorFlow with Hadoop File System support? [y/N]No Hadoop File System support will be enabled for TensorFlowDo you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N]No XLA support will be enabled for TensorFlowDo you wish to build TensorFlow with VERBS support? [y/N]No VERBS support will be enabled for TensorFlowDo you wish to build TensorFlow with OpenCL support? [y/N]No OpenCL support will be enabled for TensorFlowDo you wish to build TensorFlow with CUDA support? [y/N] YCUDA support will be enabled for TensorFlowDo you want to use clang as CUDA compiler? [y/N]nvcc will be used as CUDA compilerPlease specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: 8.0Please 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]: 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: "3.5,5.2"]: 3.0Do you wish to build TensorFlow with MPI support? [y/N] MPI support will not be enabled for TensorFlowConfiguration finished
If you told configure to build for GPU support, then configurewill 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 the configure script before re-invokingthe bazel build command.
Note the following:
- Although it is possible to build both Cuda and non-Cuda configs under the same source tree, we recommend running
bazel cleanwhen switching between these two configurations in the same source tree. - If you don‘t run the
configurescript before running thebazel buildcommand, thebazel buildcommand will fail.
Build the pip package
To build a pip package for TensorFlow with CPU-only support,you would typically 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 theTensorFlow website are built with gcc 4, which uses the older ABI. Tomake your build compatible with the older ABI, you need to add--cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" to your bazel build command.ABI compatibility allows custom ops built against the TensorFlow pip packageto 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,.5,1.0 whileinvoking bazel.
The bazel build command builds a script namedbuild_pip_package. Running this script as follows will builda .whl file within the /tmp/tensorflow_pkg directory:
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
Install the pip package
Invoke pip install to install that pip package.The filename of the .whl file depends on your platform.For example, the following command will install the pip package
for TensorFlow 1.4.1 on Linux:
$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.4.1-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 thetensorflow subdirectory from which you invoked the configure command.
Invoke python:
$ python
Enter the following short program inside the python interactive shell:
# Python
import tensorflow as tf
hello = tf.constant(‘Hello, TensorFlow!‘)
sess = tf.Session()
print(sess.run(hello))
If the system outputs the following, then you are ready to begin writingTensorFlow programs:
Hello, TensorFlow!
If you are new to TensorFlow, see Getting Started withTensorFlow.
If the system outputs an error message instead of a greeting, see Commoninstallation problems.
Common installation problems
The installation problems you encounter typically depend on theoperating system. See the "Common installation problems" sectionof 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 the tensorflow tag.
| Stack Overflow Link | Error Message |
|---|---|
| 41293077 | W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn‘t compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. |
| 42013316 | ImportError: libcudart.so.8.0: cannot open shared object file: No such file or directory |
| 42013316 | ImportError: libcudnn.5: cannot open shared object file: No such file or directory |
| 35953210 | Invoking `python` or `ipython` generates the following error: ImportError: cannot import name pywrap_tensorflow |
Tested source configurations
Linux
| Version: | CPU/GPU: | Python Version: | Compiler: | Build Tools: | cuDNN: | CUDA: |
|---|---|---|---|---|---|---|
| tensorflow-1.4.1 | CPU | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.5.4 | N/A | N/A |
| tensorflow_gpu-1.4.1 | GPU | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.5.4 | 6 | 8 |
| tensorflow-1.3.0 | CPU | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.5 | N/A | N/A |
| tensorflow_gpu-1.3.0 | GPU | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.5 | 6 | 8 |
| tensorflow-1.2.0 | CPU | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.5 | N/A | N/A |
| tensorflow_gpu-1.2.0 | GPU | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.5 | 5.1 | 8 |
| tensorflow-1.1.0 | CPU | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.2 | N/A | N/A |
| tensorflow_gpu-1.1.0 | GPU | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.2 | 5.1 | 8 |
| tensorflow-1.0.0 | CPU | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.2 | N/A | N/A |
| tensorflow_gpu-1.0.0 | GPU | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.2 | 5.1 | 8 |
Mac
| Version: | CPU/GPU: | Python Version: | Compiler: | Build Tools: | cuDNN: | CUDA: |
|---|---|---|---|---|---|---|
| tensorflow-1.4.1 | CPU | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.5.4 | N/A | N/A |
| tensorflow-1.3.0 | CPU | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.5 | N/A | N/A |
| tensorflow-1.2.0 | CPU | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.5 | N/A | N/A |
| tensorflow-1.1.0 | CPU | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.2 | N/A | N/A |
| tensorflow_gpu-1.1.0 | GPU | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.2 | 5.1 | 8 |
| tensorflow-1.0.0 | CPU | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.2 | N/A | N/A |
| tensorflow_gpu-1.0.0 | GPU | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.2 | 5.1 | 8 |
Windows
| Version: | CPU/GPU: | Python Version: | Compiler: | Build Tools: | cuDNN: | CUDA: |
|---|---|---|---|---|---|---|
| tensorflow-1.4.1 | CPU | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | N/A | N/A |
| tensorflow_gpu-1.4.1 | GPU | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 6 | 8 |
| tensorflow-1.3.0 | CPU | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | N/A | N/A |
| tensorflow_gpu-1.3.0 | GPU | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 6 | 8 |
| tensorflow-1.2.0 | CPU | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | N/A | N/A |
| tensorflow_gpu-1.2.0 | GPU | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 5.1 | 8 |
| tensorflow-1.1.0 | CPU | 3.5 | MSVC 2015 update 3 | Cmake v3.6.3 | N/A | N/A |
| tensorflow_gpu-1.1.0 | GPU | 3.5 | MSVC 2015 update 3 | Cmake v3.6.3 | 5.1 | 8 |
| tensorflow-1.0.0 | CPU | 3.5 | MSVC 2015 update 3 | Cmake v3.6.3 | N/A | N/A |
| tensorflow_gpu-1.0.0 | GPU | 3.5 | MSVC 2015 update 3 | Cmake v3.6.3 | 5.1 | 8 |