Just install Anaconda and install TensorFlow by running
conda install tensorflow
conda install tensorflow-gpu
and you’re good to go since Continuum Analytics have applied our patch.
Do NOT install TensorFlow package from conda-forge as that is for newer version of Linux distros.
The official-released binary packages of TensorFlow are built for newer version of Linux distros. Here is how to build TensorFlow binary package for CentOS 6.
Admin privilege is required here.
OpenJDK (for Bazel)
yum install java-1.8.0-openjdk-devel
Developer Toolset (for Bazel and TensorFlow)
C++11 compatible compiler is required during the building process. Follow the instructions in this article to install Developer Toolset. It is worth noting that, in this case, installing just
devtoolset-6-toolchain would be sufficient.
Python 2.7 (for TensorFlow)
This step also requires Software Collections installed. Therefore, if you’ve installed Developer Toolset by following the instructions above, you should have no problem installing these dependencies.
yum install python27 python27-numpy python27-python-devel python27-python-wheel
Build and Install Bazel
Enter Build Environment
Enter Software Collection environment with Developer Toolset.
scl enable devtoolset-6 bash
Prepare Source Code
Method 1 (Recommended)
The standard way of compiling a release version of Bazel from source is to use a distribution archive. From version 0.4.1, bootstrapping Bazel from GitHub repository is no longer supported as only the distribution archives contain generated artifacts required for direct compilation. Download
bazel-<VERSION>-dist.zip from the release page.
# download distribution archive wget https://github.com/bazelbuild/bazel/releases/download/0.9.0/bazel-0.9.0-dist.zip unzip bazel-0.9.0-dist.zip -d bazel-0.9.0-dist cd bazel-0.9.0-dist
Method 2 (Only Works for Version <= 0.4.0)
# clone source code repository git clone https://github.com/bazelbuild/bazel.git cd bazel # select version (optional) git checkout 0.4.0
Build and Install
# compile ./compile.sh # install mkdir -p ~/bin cp output/bazel ~/bin/ # exit from Software Collection environment exit
Build TensorFlow with Bazel
Enter Build Environment
Enter Software Collection environment with Developer Toolset and Python 2.7.
scl enable devtoolset-6 python27 bash
Prepare Source Code
# clone source code repository git clone https://github.com/tensorflow/tensorflow.git cd tensorflow # select version (optional) git checkout v1.5.0
Since GNU C library version in CentOS 6 is less than 2.17, a slight modification needs to be applied before compilation.
tf_extension_linkopts function in
def tf_extension_linkopts(): return  # No extension link opts
def tf_extension_linkopts(): return ["-lrt"]
After above modification, we can now start building!
Here we disable jemalloc when configuring — otherwise the build will fail.
# configure workspace export \ PYTHON_BIN_PATH=/opt/rh/python27/root/usr/bin/python \ PYTHON_LIB_PATH=/opt/rh/python27/root/usr/lib/python2.7/site-packages \ TF_NEED_JEMALLOC=0 \ TF_NEED_GCP=0 \ TF_NEED_HDFS=0 \ TF_NEED_S3=0 \ TF_ENABLE_XLA=0 \ TF_NEED_GDR=0 \ TF_NEED_VERBS=0 \ TF_NEED_OPENCL=0 \ TF_NEED_CUDA=0 \ TF_NEED_MPI=0 \ CC_OPT_FLAGS="-march=native" ./configure # build ~/bin/bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg # exit from Software Collection environment exit
Finally, you will found a
tensorflow-1.5.0-cp27-none-linux_x86_64.whl (filename may vary upon versions) in
/tmp/tensorflow_pkg/. With this self-built binary package, we can now deploy TensorFlow to CentOS 6 by
Enter Python Environment on Deployment Target
Enter Python 2.7 environment either by Software Collection or by tools like pyenv.
# by Software Collection scl enable python27 bash # or by pyenv pyenv shell anaconda2-5.0.1
Depending on your environment setup, admin privilege might be needed here.
pip install tensorflow-1.5.0-cp27-none-linux_x86_64.whl
sudo clears environment variables before running commands for security reasons, causing Software Collection environment, which relies on environment variables, to dysfunction. If you are installing TensorFlow package in Software Collection environment through
sudo, you might find this command helpful.
sudo scl enable python27 -- pip install tensorflow-1.5.0-cp27-none-linux_x86_64.whl
It sets up Software Collection environment variables AFTER
sudo cleans them up.
- Installing TensorFlow from Sources – TensorFlow
- Compiling from source – Installing Bazel – Bazel
- Distribution Artifact for Bazel – Bazel
- Dockerfile.devel-cpu-mkl – tensorflow/tensorflow – GitHub
- ImportError: undefined symbol: clock_gettime #121 – tensorflow/tensorflow – GitHub
- Anaconda package lists – Anaconda Documentation