If you want to clone this repository to start your own project, you can choose the license you prefer and feel free to delete anything related to the license you are dropping.
Make sure you have a Python 3 development environment ready. If you don't, you can download and install it from the Python downloads page.
We recommend using a virtual environment to isolate your project's dependencies.
# Create a new Python virtual environment.
python -m venv env
# Activate the virtual environment.
source env/bin/activateWhile activated, your python and pip commands will point to the virtual environment,
so any changes or install dependencies are self-contained.
As a one time setup, let's install the project's dependencies from the requirements.txt file.
# It's always a good idea to update pip before installing dependencies.
pip install -U pip
# Install the project as a local package, this installs all the dependencies as well.
pip install -e .ℹ️ Once you are done, you can run the
deactivatecommand to go back to your global Python installation.
Running your pipeline in Python is as easy as running the script file directly.
# You can run the script file directly.
python main.py
# To run passing command line arguments.
python main.py --input-text="🎉"
# To run the tests.
python -m unittest -vThis project already comes with automated testing via GitHub Actions.
To configure it, look at the .github/workflows/test.yaml file.
To keep this template small, it only includes the Direct Runner.
For a comparison of what each runner currently supports, look at the Beam Capability Matrix.
To add a new runner, visit the runner's page for instructions on how to include it.
Thank you for your interest in contributing! All contributions are welcome! 🎉🎊
Please refer to the CONTRIBUTING.md file for more information.
This software is distributed under the terms of both the MIT license and the Apache License (Version 2.0).
See LICENSE for details.