Git & GitHub Questions

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Git & GitHub Questions

Version control allows AI teams to track code and data changes over time, collaborate efficiently, and ensure reproducibility of model training and results.

Git LFS (Large File Storage) allows you to manage large datasets or model weights efficiently by storing them outside your main Git repository.

GitHub Actions automate repetitive tasks like training, testing, and deploying AI models by triggering workflows on code pushes or pull requests.

Branching allows data scientists to safely experiment with different model versions, preprocessing methods, or hyperparameters without disrupting the main codebase.

GitHub provides a central platform for code collaboration, version tracking, reviewing model contributions, and managing tasks through issues and pull requests.

Git can version not only the code but also experiment configurations, model architecture changes, and logs, making it easier to track which version produced what result.

GitHub Pages allows AI developers to create and host model documentation, visual reports, and dashboards directly from their GitHub repository for public or team access.

Reproducibility is key in AI for scientific credibility. Git ensures that code, data, and model checkpoints can be rolled back and shared to replicate results precisely.

GitHub Issues help organize AI development by tracking bugs, feature requests, data cleanup tasks, and assigning specific items to team members.

GitHub Actions can run unit tests, model validation scripts, and threshold checks before merging or deploying a model, ensuring model performance and stability.

Git tags can be used to label specific commits representing model milestones like "v1.0 trained" or "v2.0 optimized", ensuring clear checkpoints.

Yes, AI tools can suggest meaningful and standardized commit messages based on code changes, making history easier to read and track.

GitHub Copilot uses AI to auto-suggest code, saving time during model development, data preprocessing, or building AI applications.

The `.gitignore` file prevents large datasets, logs, or sensitive files (like API keys) from being tracked, keeping the repo clean and secure.

Developers can create separate branches for each AI experiment (e.g., new dataset, model tweak), enabling parallel testing and cleaner merging.

GitHub can track small datasets directly or use Git LFS for large files. Best practices involve versioning data separately or linking to it.

DVC (Data Version Control) extends Git for large files, allowing tracking of datasets, experiments, and models without bloating the repo.

GitHub Wikis are used to document model architectures, training setups, papers, benchmark results, and collaboration guides.

Yes, you can configure workflows to deploy trained AI models to cloud services like AWS, Azure, or Hugging Face automatically upon merging.

Pull requests allow review of code or model changes before merging. They encourage collaboration, code quality, and discussion in AI teams.

Git history allows tracking of past changes, helping pinpoint when bugs or performance drops were introduced in the AI pipeline.

GitHub can trigger CI/CD pipelines that test AI models, validate outputs, check metrics, and deploy them in an automated fashion.

Yes, AI tools can read your code and generate README content, API docs, or usage examples to improve project clarity and onboarding.

AI bots like DeepCode, Codacy, and CodeQL can review pull requests for bugs, vulnerabilities, and coding standard violations before merging.

You can log model metrics to GitHub Actions or external tools and set alerts when accuracy drops across versions, helping catch model drift early.

Yes, Jupyter notebooks can be versioned in Git. Tools like `nbdime` help make diffs and merges readable for data scientists.

GitHub Actions or external dashboards linked to the repo can track logs, inference counts, accuracy, and other live AI model stats.

Semantic versioning (e.g., 1.0.1) helps track AI model changes. Git tags or releases reflect major model changes, tweaks, and bug fixes.

Yes, performance metrics like accuracy, loss, and latency can be logged in markdown or files, and linked to releases and pull requests.

Use git checkout -b branch-name to create and switch to a new branch.

Run git branch -d branch-name to delete a branch locally.

It applies a commit from another branch onto your current branch by copying the changes.

Use git clone https://github.com/username/repo.git to copy the remote repository to your machine.

Use git branch -m old-name new-name to rename a branch.

Run git log filename to view the commit history for a specific file.

HEAD is a pointer that refers to the current branch reference, typically the last commit.

git reset moves HEAD and optionally modifies the working directory. git revert creates a new commit that undoes a previous commit.

Run git push origin branch-name to push a branch to the remote repository.

Git LFS (Large File Storage) helps track and store large files outside the main Git repository.

Go to the repository's settings on GitHub and change the visibility to "Private" under "Danger Zone".
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Run git branch -d branch-name to delete a branch locally. Use -D to force delete.

git cherry-pick applies the changes introduced by a specific commit from another branch onto your current branch.

Use git clone https://github.com/username/repo.git to create a local copy of a remote GitHub repository.

Use git branch -m old-name new-name to rename the branch you're on. For a different branch, use git branch -m branch-name new-name.

Use git log filename to display all commits related to a particular file.

HEAD is a pointer that refers to the latest commit in the current checked-out branch. It's the snapshot you're currently working on.

git reset moves the HEAD and can change history. git revert adds a new commit that undoes a previous commit without modifying history.

Use git push origin branch-name to upload your branch to the GitHub repository.

A detached HEAD means you're not on a branch, but rather on a specific commit. Changes made won't belong to any branch unless explicitly committed to one.

Use git diff commit1 commit2 to view changes made between two commits.

Use git checkout -- filename or git restore filename to discard uncommitted changes in a file.

Use git pull to fetch and merge changes from the remote repository into your current branch.

Edit the conflicting files to resolve the differences, then run git add and git commit to complete the merge.

Use git fetch followed by git rebase origin/main while on your branch to apply your commits on top of the latest main branch.

Use git diff to show changes in the working directory not yet staged.

Use git rm --cached filename to remove a file from the index but keep it on disk.

It tells Git which files or directories to ignore in a project—commonly used for logs, datasets, or AI model outputs.

Use Git LFS (Large File Storage) to manage large files such as datasets and model weights.

Use GitHub Actions to write workflows that test AI models automatically during CI/CD pipelines.

Use git init to initialize a new local Git repository.

Fork the repo, clone it, make changes in a branch, push the branch, and create a pull request.

Use git add filename to start tracking a new file.

Use git revert commit_id to safely create a new commit that undoes the changes.

Forking allows you to create a copy of a repository in your account to modify and propose changes via pull requests.

AI tools like GitHub Copilot or CodeQL can automatically detect bugs, suggest improvements, and explain code during reviews.

GitHub Pages allows you to host static websites directly from a GitHub repository.

Use git status to view which files are staged for commit.

Tags mark specific points in history, usually for releases. Use git tag v1.0 to create one.

Version control tracks model changes, datasets, hyperparameters, and enables collaboration and reproducibility.

Fork is on GitHub (web-level), clone is local. You clone a repo to your machine; you fork to your GitHub account.

Use git checkout -- filename to discard local changes in your working directory.

HEAD is your current local branch's latest commit, origin is the remote branch reference.

AI can summarize code diffs and generate clear commit messages using tools like Copilot or GPT integrations.

Use git branch -d branchname for local and git push origin --delete branchname for remote.

Git stash temporarily saves uncommitted changes, allowing you to work on something else and come back later.

Use git stash apply to reapply the latest stash or git stash pop to apply and remove it.

A merge conflict occurs when Git can’t automatically resolve differences between two branches.

AI can analyze code history, suggest resolution strategies, and merge logic intelligently using tools like Copilot Labs.

Use git branch for local branches and git branch -r for remote branches.

CI is the practice of automating code integration, testing, and building whenever developers commit code.

Go to the repository's “Issues” tab and click “New Issue” to describe a bug or propose a feature.

Rebase rewrites history to make a linear commit tree, merge preserves history and creates a merge commit.

Use git clone -b branchname --single-branch url.

Git hooks are scripts triggered by Git events like commits, merges, etc., used for automation and checks.

Not by default. Use Git LFS (Large File Storage) to efficiently track large files.

AI models can analyze code changes, detect logic errors, and suggest corrections before merge using tools like CodeQL or DeepCode.

Add the original repo as upstream, then run git fetch upstream followed by git merge upstream/main.

Use git log --graph --oneline --all or Git GUI tools like GitKraken or SourceTree.

A bare repository has no working directory; it’s used as a central remote repository for collaboration.

GitHub Copilot uses OpenAI’s Codex to suggest code in real-time based on comments and existing code context.