How-To Guides¶
About This Section
The How-To Guides section is a repository of in-depth, practical guides for specific tasks within Python projects and broader software development workflows. Each guide delivers concise, step-by-step directions designed to tackle particular challenges, ranging from initial setups to complex processes.
Purpose¶
- Targeted Instruction: The guides are crafted to deliver straightforward, actionable steps for distinct development hurdles.
- Comprehensive Topic Coverage: Encompassing a spectrum of subjects, from fundamental configurations to intricate enhancements.
- Universal Applicability: Suited for developers of all experience levels, offering valuable insights for both beginners and veterans.
Table of Contents¶
- Setting Up DVC for Efficient Data Management
- Project Scaffolding Standards
- File Naming Conventions
- Column Naming Conventions for ML/AI Projects
- Python Docstrings Conventions
- Best Practices for Using Git and Pushing to GitHub
- Code and Comment Length Standards in Python Projects
- Facilitating Team Communication with DVC
- Integrating DVC with VS Code for Enhanced Workflow
- Creating Git Branches Following Best Practices
- Standardizing Commit Messages in ML Projects
- Naming Data Folders in ML Projects for Better Organization
- Adopting Effective Data File Naming Conventions
- Automating the Creation of Metadata for Machine Learning Models
- Implementing GitHub Actions to Enforce Naming Conventions
- Using Cookiecutter for Project Setup Without Internet Access
- ML Experiments Life-Cycle with Weights & Biases
- Templates for Issues/Stories and PRs
- Branching Strategies for ML Projects
- Python Scripting for Data Conversion
- Automating Backups for Weights & Biases
- Configuring Jupyter Notebook Start-up Directory in VS Code
- Project Data Management Practices
- AI/ML Project Lifecycle with Git and GitHub
- Introduction to Doctest
- Code Review Best Practices
- Best Practices for Creating JIRA Stories for ML/AI Projects
- Moving from Jupyter Notebooks to Production Python Code
- Static Type Checking with Mypy
- Using Pre-Commit Hooks to Enforce Coding Standards
- Pytest Introduction Guide
- Using Configuration Files to Avoid Hardcoding Values
- Error Handling and Logging
- Docstrings and Inline Commentaries
- Input Validation with Pydantic
Optimizing Your Workflow
These guides are intentionally designed to function independently, providing the flexibility to directly engage with topics pertinent to your immediate challenges. As a compendium of efficient, practical solutions, they are an excellent resource for enhancing productivity and mastering various development tasks.