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GitHub Developer Guide

Environment Setup

We recommend setting up a local environment with the needed dev tools.

  1. Bazel is the tool to build and test TFGNN. See the installation guide for how to install and config bazel for your local environment.
  2. git for code repository management.
  3. python to build and code in TFGNN.

The following commands check the above tools are successfully installed. Note that tensorflow_gnn requires at least the version specified in setup.py.

bazel --version
git --version
python --version

A Python virtual environment (venv) is a powerful tool to create a self-contained environment that isolates any changes from system level configuration. It is highly recommended to avoid any unexpected dependency or version issue.

With the following commands, you create a new venv, named venv_dir.

mkdir venv_dir
python3 -m venv venv_dir

You can activate the venv with the following command. You should always run the tests with the venv activated. You need to activate the venv every time you open a new shell.

source venv_dir/bin/activate  # for linux or MacOS

Clone your forked repo to your local machine. Go to the cloned directory to install the dependencies into the venv.

git clone https://github.com/YOUR_GITHUB_USERNAME/gnn.git
cd gnn
pip install -r requirements-dev.txt
pip install tensorflow

For TF2.16+, you will additionally need to follow the instructions from the Keras version guide.

Building the package and running tests

We use Bazel to build and run tests. Since we use Protocol Buffers for serialization and schemas, there is an intermediate build step before running tests. To avoid needing to build all of TensorFlow in this build step, we build only the protobuf components and run tests with the pip package in a separate directory.

Build the wheel

First, we need to build the Python package and install it in the venv.

python3 setup.py bdist_wheel
pip install dist/tensorflow_gnn-*.whl

Create a clean test directory

Next, we need to set up a test directory with all the build and test targets. We do this by sym-linking to the current tensorflow_gnn directory and using specific Bazel flags as in the next section.

mkdir -p bazel_pip
ln -s "$(pwd)"/tensorflow_gnn "$(pwd)"/bazel_pip/tensorflow_gnn

Run all tests

After setting up the test directory, you can run all tests locally by running the following command in the repo root directory.

bazel test --build_tag_filters=-no_oss,-oss_excluded --test_tag_filters=-no_oss,-oss_excluded --test_output=errors --verbose_failures=true --build_tests_only --define=no_tfgnn_py_deps=true --keep_going --experimental_repo_remote_exec --test_env="TF_USE_LEGACY_KERAS=1" //bazel_pip/tensorflow_gnn/...

The --define=no_tfgnn_py_deps=true flag directs bazel to assume that all dependencies are provided in the environment (where we just installed the .whl)

The flags --build_tag_filters=-no_oss,-oss_excluded and --test_tag_filters=-no_oss,-oss_excluded disable tests that pass in the internal production environment but fail on GitHub.

The flag --test_env="TF_USE_LEGACY_KERAS=1" comes from the Keras version guide and is required for TF2.16+.

Run a single test file

It is also possible to run a single test file by specifying its path (for example)

bazel test --define=no_tfgnn_py_deps=true --experimental_repo_remote_exec --test_env="TF_USE_LEGACY_KERAS=1" //bazel_pip/tensorflow_gnn/models/gcn:gcn_conv_test

Run a single test case

To run a single test case, use the --test_filter flag.

bazel test --define=no_tfgnn_py_deps=true --experimental_repo_remote_exec --test_env="TF_USE_LEGACY_KERAS=1" //bazel_pip/tensorflow_gnn/models/gcn:gcn_conv_test --test_filter=*test_gcnconv_activation*

Cleanup

To clean all the artifacts produced, run the following commands.

bazel clean
rm -rf bazel-*
rm -rf build
rm -rf dist
rm -rf *.egg-info
rm -rf bazel_pip
pip uninstall -y tensorflow_gnn