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Git tracks project changes and makes code management simple.
Linux commands help manage files, processes, permissions, and system resources efficiently.
Python, Conda, Docker, Jupyter, and CUDA commands create a complete AI development workflow.
Git and Linux are two of the most important tools in AI and data science. Almost every project depends on them for daily work. Git helps save code, track changes, and manage teamwork. Linux offers a fast and reliable system to handle files, run programs, and control servers. A good knowledge of both tools makes project work easier, faster, and more organized.
This guide explains the most useful Git and Linux commands. It also covers commands that support AI and data science tasks such as Python, Jupyter Notebook, Conda, Docker, and GPU management.
Git is a version control system that records every change in a project. It also allows old versions to return whenever required. The setup process starts with git config, which stores the user name and email address. The command git config --list displays all current settings.
A new repository starts with git init, while git clone copies an existing repository from GitHub or another remote source. After changes appear in project files, git status shows the current state of the repository.
The command git add prepares files for the next commit. A complete folder or the entire project also joins the staging area with git add .. to save time when working in a sub-directory. The command git commit -m saves those changes with a short message that explains the update.
Git keeps a complete record of every commit. The command git log shows the full history, while git log --oneline presents a shorter version. A graphical view appears with git log --graph, which makes branch history easier to understand.
The command git diff compares file changes before or after a commit. Another useful command, git show, displays details about a selected commit.
Branches allow new features or experiments without changing the main project. A new branch comes from git branch or git checkout -b. Modern Git also supports git switch for easier branch management.
After work ends, git merge combines the branch with the main project. Another option, git rebase, creates a cleaner project history by placing one branch on top of another.
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Mistakes happen in every project, and Git offers many recovery options. The command git restore removes unwanted file changes. The command git restore --staged removes files from the staging area. The command git reset returns to an earlier commit, while git revert creates a new commit that cancels a previous one.
Git also connects local work with online repositories. The command git remote -v lists remote connections. New repositories join with git remote add origin. Project updates arrive through git pull, while git push sends local commits to GitHub or another remote server.
Linux provides many commands that help manage files and folders. The command pwd shows the current location, while ls lists files inside a directory. Folder changes happen with cd, and tree presents the directory structure in a clear format.
New files appear with touch, while mkdir creates folders. Files move with mv, copies come from cp, and unwanted files disappear with rm. A symbolic link forms through ln -s.
Linux includes several commands that display file contents. The command cat prints the entire file. Large files become easier to read with less or more. The commands head and tail show the beginning or end of a file. Live updates appear with tail -f, which works well for log files.
The command find searches for files based on name, type, or size. Text searches depend on grep, which locates specific words inside one file or many files. Another helpful command, locate, finds files quickly through a database.
Disk usage becomes easy to check with du, while df reports available storage space. File access depends on permissions, and chmod changes those permissions. Ownership changes through chown.
Large files save space after compression. Linux supports zip, unzip, and several tar commands for archive creation and extraction.
Running programs appear with ps, top, or htop. Unwanted processes stop with kill or pkill. Background tasks start with nohup, which keeps programs active even after the terminal closes.
AI and data science projects depend on Python environments and notebook tools. A virtual environment starts with python -m venv, followed by activation through source venv/bin/activate. Packages install with pip install, while pip freeze creates a requirements file.
Conda offers another environment management system. Commands such as conda create, conda activate, and conda env export help build and share project environments.
Jupyter Notebook and JupyterLab launch through jupyter notebook and jupyter lab. Package details appear with pip list or pip show.
GPU systems depend on nvidia-smi for hardware monitoring, while nvcc --version checks the installed CUDA toolkit.
Docker allows applications to run inside isolated containers. Commands such as docker build, docker run, docker ps, and docker exec help create, manage, and inspect containers. These commands support reliable deployment across different systems.
Remote server access depends on SSH. The command ssh opens a secure connection, while ssh-keygen creates authentication keys. File transfer between systems happens through scp.
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Linux and Git serve as the basis of modern-day data science and AI advancements. Git preserves the project’s history, enables collaboration, and ensures clear version management of code. Linux provides excellent system operation, file management, networking, and automation capabilities.
The combination of these tools along with Python, Conda, Jupyter, Docker, CUDA, and SSH commands can create the complete development of machine learning and data science projects. An understanding of these key technologies enables the seamless development of projects, full organization, and highly efficient working processes.
Git stores project history, tracks changes, supports teamwork, and allows easy recovery of earlier versions of code.
Common commands include ls, cd, pwd, find, grep, cp, mv, rm, chmod, ps, and top.
Branches allow new features or experiments without affecting the main project until the work is ready.
Virtual environments keep project dependencies separate, which prevents conflicts between different Python projects.
Docker runs applications inside isolated containers, which keeps software environments consistent across different computers and servers.
