Python Productivity Stack
January 1, 2021
Python Machine Learning Productivity Development Tools
A comprehensive collection of tools, libraries, and practices to enhance productivity when working with Python for machine learning projects.
Core Components
Environment Management
- Pyenv: Manage multiple Python versions
- Pipenv: Dependency management and virtual environments
- Poetry: Modern dependency management alternative
Development Tools
- Black: Automatic code formatting
- Flake8: Linting and style checking
- Mypy: Static type checking
- Pre-commit: Git hooks for code quality
ML Development
- Jupyter Lab: Interactive development environment
- Hydra: Configuration management
- MLflow: Experiment tracking
- DVC: Data version control
- Weights & Biases: Experiment tracking and visualization
Testing
- Pytest: Testing framework
- Hypothesis: Property-based testing
- Great Expectations: Data validation
Getting Started
The repository includes setup scripts and configuration files to quickly bootstrap a productive Python environment for machine learning projects. Follow the documentation to set up a consistent environment across your team or personal projects.
Benefits
- Consistent development environments
- Automated code quality checks
- Reproducible machine learning experiments
- Streamlined workflow from data preparation to model deployment
- Best practices baked into your development process