Gladstone-Bioinformatics-Workshop/Using-git-and-github/workflows.md

git workflows

Higher-level tips and strategies for more effective usage of git.

Workflow Models

The appropriate workflow model depends on the size of your team and the type of your project. You have many options, e.g., Atlassian, Driessen, GitHub and GitLab, but they tend to be designed for projects like websites that are continuously running and supported by large teams, making them more complex than is needed for a one-off bioinformatics project created by a single individual or a small team. For these types of projects, the GitLab workflow is a good starting point, but you should feel free to customize it to suit your specific needs.

tldr

• Use feature branches, e.g., add-cleaning-step for changes
• When code in feature branch is solid, merge it into main branch
• Always keep your main branch in "last known good" state (working, production-ready, deployable)

What to Commit

Don't commit everything: be deliberate. Never commit files with passwords or API keys. Avoid committing build artifacts, temporary and meta files like *.swp, *.DS_Store, *.jar and *.pyc files. Be judicious when deciding whether to commit source data. If you can reference the source and have your scripts download the data if it's not already present, you can reduce the size of your git repo.

To tell git to ignore certain types of files, you can add a .gitignore_global file to your home directory and a .gitignore file to your project directory. Reference these templates for examples.

tldr Be sure your home directory has a .gitignore_global file like one of these: Linux, macOS or Windows

Tags

Whenever you deploy code or run a publishable job, tag your code using semantic versioning.

Rebase

To update a feature branch so the changes it contains are applied on top of the latest from origin/main:

git fetch
git checkout my-new-feat
git rebase origin/main

See this post.