If you are interested in compiling the latest version of TensorFlow for x86_64 computers then this project will show you how. You should be experienced with Linux, TensorFlow and Python (or Java or C++) to make the most of this project. Latest Ubuntu 18.04 and probably other similar distributions will work. This is for a basic CPU based build. If you want to use GPU and/or CPU optimizations then you can tackle that by searching online. I needed a build for VMs and my old desktop CPU that doesn't have all the latest features.
I created an Ubuntu 18.04.3 LTS desktop with VirtualBox 6, so I could install Eclipse and other tools to experiment with my fresh TensorFlow build. You could go headless if you choose and adjust memory as needed. I used 4 vCPUs and 8G of vRAM. Build time was around 9 hours. I checked every few hours because of various connection failures during the build process. Just restart using the same build line from terminal history.
- Install dependencies
sudo apt install git pkg-config zip g++ zlib1g-dev unzip python3 python3-dev python3-pip
- Make Python 3 and pip 3 default
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 1sudo update-alternatives --install /usr/bin/pip pip /usr/bin/pip3 1
- Download Bazel (I had to use this version instead of the latest. Build will notify you if Bazel version is incorrect)
wget https://github.com/bazelbuild/bazel/releases/download/0.26.1/bazel-0.26.1-installer-linux-x86_64.sh
- Run install script
chmod a+x bazel-0.26.1-installer-linux-x86_64.sh./bazel-0.26.1-installer-linux-x86_64.sh --usersudo reboot
- Run Bazel from terminal.
bazel
- Install dependencies
pip install -U --user pip==9.0.1 six pyyaml h5py numpy==1.16.4 wheel setuptools mock future>=0.17.1pip install -U --user keras_applications==1.0.6 --no-depspip install -U --user keras_preprocessing==1.0.5 --no-deps
- Download source
git clone https://github.com/tensorflow/tensorflow.git
- Build Tensorflow pip package (I used defaults during configure)
cd tensorflow./configurebazel build --config=v2 //tensorflow/tools/pip_package:build_pip_package
- Build the package
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
- Install the package
pip install -U --user /tmp/tensorflow_pkg/tensorflow*.whl
- Downgrade numpy (see Tons of warnings just by importing tf (2.0.0-beta1) #31364)
pip uninstall numpypip install --user numpy==1.16.4
sudo apt install gitcd ~/git clone --depth 1 https://github.com/sgjava/install-tensorflow.git
I provide examples of training the MNIST dataset with two different models and then predict against sample digits.
- Train model 1 using MNIST dataset
cd install-tensorflowpython mnist1train.py
- Predict model 1 using MNIST dataset
python mnist1predict.py
- Train model 2 using MNIST dataset
python mnist2train.py
- Predict model 2 using MNIST dataset
python mnist2predict.py