Set Up an AI Development Environment: 12 Free Lessons

Start Phase 00

First lesson Dev Environment

Run this command from the repository root:

python3 phases/00-setup-and-tooling/01-dev-environment/code/verify.py --route beginner

Keep the command, repository-root working directory, exit code, required check results, and the printed Next: command. Optional misses are not failures.

All 12 lessons in Phase 00

1. 01
   Dev Environment

   Your tools shape your thinking. Set them up once, set them up right. Set up Python 3.11+, Node.js 20+, and Rust toolchains from scratch.

   Build · Python · ~45 min

2. 02
   Git & Collaboration

   Version control is not optional. Every experiment, every model, every lesson you build here gets tracked. Configure git identity and use the daily workflow of add, commit, and push.

   Learn · ~30 min

3. 03
   GPU Setup & Cloud

   Training on CPU is fine for learning. Training for real needs a GPU. Verify local GPU availability using nvidia-smi and PyTorch's CUDA API.

   Build · Python · ~45 min

4. 04
   APIs & Keys

   Every AI API works the same way: send a request, get a response. The details change, the pattern doesn't. Store API keys securely using environment variables and .env files.

   Build · Python · ~30 min

5. 05
   Jupyter Notebooks

   Notebooks are the lab bench of AI engineering. You prototype here, then move what works into production. Install and launch JupyterLab, Jupyter Notebook, or VS Code with the Jupyter extension.

   Build · Python · ~30 min

6. 06
   Python Environments

   Dependency hell is real. Virtual environments are the cure. Create isolated virtual environments using uv, venv, or conda.

   Build · Shell · ~30 min

7. 07
   Docker for AI

   Containers make "works on my machine" a thing of the past. Build a GPU-enabled Docker image with CUDA, PyTorch, and AI libraries from a Dockerfile.

   Build · Docker · ~60 min

8. 08
   Editor Setup

   Your editor is your co-pilot. Configure it once so it stays out of your way and starts pulling its weight. Install VS Code with essential extensions for Python, Jupyter, linting, and remote SSH.

   Build · ~20 min

9. 09
   Data Management

   Data is the fuel. How you manage it determines how fast you go. Language: Python Load, stream, and cache datasets using the Hugging Face datasets library.

   Build · Python · ~45 min

10. 10
    Terminal & Shell

    The terminal is where AI engineers live. Get comfortable here. Use piping, redirects, and grep to filter and process training logs from the command line.

    Learn · ~35 min

11. 11
    Linux for AI

    Most AI runs on Linux. You need to know enough to not be stuck. Navigate the Linux file system and perform essential file operations from the command line.

    Learn · ~30 min

12. 12
    Debugging and Profiling

    The worst AI bugs don't crash. They train silently on garbage and report a beautiful loss curve. Language: Python Use conditional breakpoint() and debugprint to inspect tensor shapes, dtypes, and…

    Build · Python · ~60 min

Glossary terms in this phase

• CUDANVIDIA's platform and programming model for general-purpose computation on compatible GPUs.
• VocabularyThe finite mapping between token identifiers and the units a tokenizer can emit, including ordinary, byte-level, and special control tokens.