Build and install TensorFlow from source with MKL DNN support and AVX enabled

In this guide, we will walk you through building and installing TensorFlow from source with support for MKL DNN and with AVX enabled.

Step 1: Clone the source

To get started, you need to clone the source code of TensorFlow:

git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow

Step 2: Chose your version

Currently, you are on the development branch. You can, optionally, switch to a release branch such as 1.11:

git checkout r1.11

Step 3: Install Bazel

Follow the following commands one by one to successfully install TensorFlow:

sudo apt-get install pkg-config zip g++ zlib1g-dev unzip python
wget https://github.com/bazelbuild/bazel/releases/download/0.18.1/bazel-0.18.1-installer-linux-x86_64.sh
chmod +x bazel-0.18.1-installer-linux-x86_64.sh
./bazel-0.18.1-installer-linux-x86_64.sh --user
export PATH="$PATH:$HOME/bin"
sudo add-apt-repository ppa:webupd8team/java
sudo apt-get update && sudo apt-get install oracle-java8-installer
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 -
sudo apt-get update && sudo apt-get install bazel

Step 4: Configure the build

./configure

Follow the instructions carefully

Step 5: Build the pip wheel file

bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma 
--copt=-mfpmath=both --copt=-msse4.1 --copt=-msse4.2 --config=cuda -k 
//tensorflow/tools/pip_package:build_pip_package

Step 6: Build with pip

bazel-bin/tensorflow/tools/pip_package/
build_pip_package /tmp/tensorflow_pkg
sudo pip install /tmp/tensorflow_pkg/
tensorflow-1.11.0-cp27-cp27mu-linux_x86_64.whl

You have installed TensorFlow with MKL DNN support and AVX enabled

You have an efficient version of TensorFlow and you can get started by developing models in TensorFlow