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📻 Frequently Asked Questions

1. Is this project structure/repository inspired by the cookie-cutter project template?

  • No, this project structure is not directly inspired by the cookie-cutter project template. While there might be some similarities in naming conventions, this Python Machine Learning Template was specifically designed to cater to the unique needs of various machine learning projects, including reinforcement learning, computer vision, and natural language processing. That being said, we do appreciate and recommend the Jupyter Notebook naming convention used in the cookie-cutter project template.

  • To use this template, you don't need to install any additional packages or tools such as cookiecutter. You can directly use it on GitHub by clicking the "Use this template" button. Once you create the project, the cleanup process is automatically handled by a GitHub actions bot.

  • We hold the cookiecutter project in high regard; it's beautifully packaged and serves as an excellent starting point for new projects. However, our template differs from cookiecutter in several ways:

    1. It doesn't require any additional package installations. Technically, only Python is required, and the virtual environment (venv) comes bundled with new Python releases above 3.3.

    2. We didn't want our users to install any additional packages or go through extensive configurations just to start the project. Hence, we created this template, which requires minimal setup steps.

2. What are the main advantages of using this template?
  • The folder structure has been meticulously designed to ensure organization and efficiency.
  • All components within the template have been seamlessly integrated to work together effectively.
  • The only prerequisite for using this template is having Python and a virtual environment (venv) installed. No additional Python packages are required. This is particularly advantageous in organizations with restricted package access, as this template only relies on Python and venv.
  • We have provided comprehensive documentation for this template, addressing all aspects and potential challenges. This includes practical issues encountered during our day-to-day work, ensuring you have a well-rounded understanding of the template's functionality and usage.
3. How is it different from other available templates?
  • Our setup scripts are designed for both Linux/MacOS and Windows operating systems, ensuring a smooth setup process regardless of your platform.
  • Our directory structure is comprehensive, designed to accommodate all types of machine learning projects, providing a solid foundation for your work.
  • The template stands out with its minimal dependencies, eliminating the need for specific Python packages or complex utilities.
  • While there are many excellent templates available, most of them require make or cmake utilities which can be challenging to work with on Windows. To address this issue, we have developed dedicated project setup scripts for both Windows and Linux, eliminating the need for users to worry about platform compatibility.