This article provides insights on best practices for developing projects in Python, particularly focusing on integrating GitHub Actions, creating virtual environments, managing requirements, formatting code, running tests, and creating a Makefile. It emphasizes the importance of code quality and efficient project management. The writer encourages further exploration of these topics to enhance work quality.
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Apply best practices and learn to use GitHub Actions to build robust code
Introduction
Dealing with integrations, deployment, scalability, and other aspects of Machine Learning projects can be complex. In this article, I outline best practices for balancing code quality and implementation time, using Deepnote for collaborative data science projects.
Start Simple — Readme
Keep an up-to-date and visually appealing Readme file to ensure clarity for developers, salespeople, and project managers.
Use virtual environments, your laptop will be happy
Create virtual environments to isolate projects and manage dependencies, enhancing development efficiency.
Create a Requirements file, your colleagues will be happy
Ensure code reproducibility by maintaining a requirements.txt file with all installed libraries, enabling seamless collaboration.
Format your code with Black
Use Black to format code clearly and neatly, enhancing code readability and maintainability.
Analyse your code with PyLint
Utilize PyLint to automatically check for errors, enforce coding standards, and assess code quality, ensuring robustness.
Run Tests, make sure your code is working
Implement unit tests using PyTest to ensure code functionality and reliability, supporting a robust development process.
I am lazy, I’ll use a Makefile
Create a Makefile to simplify and automate routine tasks such as installing requirements, formatting code, and running tests.
Run Everything at every push with GitHub Actions
Automate the entire process using GitHub Actions, ensuring that code quality checks and tests are run automatically on every push to GitHub.
Final Thoughts
Implementing these best practices can significantly improve code quality and streamline the development process. For AI KPI management advice and insights into leveraging AI, connect with us at hello@itinai.com or stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- A Simple CI/CD Setup for ML Projects
- Towards Data Science – Medium
- Twitter – @itinaicom
