# How to Set Up the rohitg00/ai-engineering-from-scratch Project: Complete Installation Guide > Set up the rohitg00/ai-engineering-from-scratch project easily. Follow our complete installation guide to clone the repo, install dependencies, and verify your environment is ready for AI engineering. - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: getting-started - Published: 2026-06-06 --- **Clone the repository, create a Python virtual environment, install dependencies from [`requirements.txt`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/requirements.txt), and run [`phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py) to verify the curriculum environment is ready.** The `rohitg00/ai-engineering-from-scratch` repository is a comprehensive 503-lesson curriculum organized into 20 phases that teaches AI fundamentals by building every algorithm from raw mathematics before introducing frameworks. To set up the rohitg00/ai-engineering-from-scratch project correctly, you must configure an isolated Python environment and validate the installation against the phase-based lesson structure that implements the **Build-It / Use-It** pattern. ## Step 1: Clone the Repository and Explore the Structure Start by cloning the codebase from GitHub. The repository contains no large binary assets; all lesson content and artifacts are generated on-the-fly. ```bash git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch ``` The root directory contains the [`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md) entry point, [`requirements.txt`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/requirements.txt) for Python dependencies, and the `phases/` directory where all curriculum phases live. Each lesson follows the path `phases/-/-/` and contains three sub-folders: **`code/`** for runnable implementations, **[`docs/en.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/docs/en.md)** for narrative documentation, and **`outputs/`** for generated artifacts. ## Step 2: Configure the Python Environment The curriculum is designed to be **stdlib-first**, but later phases require optional heavy libraries like PyTorch. Create a virtual environment to isolate these dependencies and prevent conflicts with your global Python installation. ```bash python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt ``` The [`requirements.txt`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/requirements.txt) file contains a minimal, curated list of allowed packages including `numpy`, `torch`, and `h5py`. This ensures all 503 lessons have access to necessary libraries while maintaining a clean development environment. ## Step 3: Verify Your Installation Confirm your environment works by running a foundational lesson from Phase 01. Execute the vector implementation to validate the math foundations layer: ```bash python phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py ``` Alternatively, run the lesson's unit tests to ensure full compatibility with the CI pipeline defined in [`.github/workflows/curriculum.yml`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/.github/workflows/curriculum.yml): ```bash python -m unittest discover -s phases/01-math-foundations/01-linear-algebra-intuition/code/tests -v ``` Successful execution (silent exit with status 0, or passing tests) confirms your setup is complete. ## Step 4: Install Generated Artifacts (Optional) Each lesson writes reusable artifacts—skills, prompts, agents, or MCP servers—to its local `outputs/` folder. To consolidate these into a single directory for use with Claude, Cursor, or Codex, run the installation script: ```bash python scripts/install_skills.py ./my-llm-skills ``` This creates [`./my-llm-skills/manifest.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/./my-llm-skills/manifest.json) and organizes each artifact with a [`SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/SKILL.md) front-matter file compatible with major LLM systems. ### Advanced Installation Options The [`scripts/install_skills.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/install_skills.py) utility supports several filtering and layout options: - **`--type {skill,prompt,agent,all}`**: Choose which artifact class to copy (e.g., `--type agent`) - **`--phase N`**: Restrict to specific phase number (e.g., `--phase 14`) - **`--tag TAG`**: Filter by custom tags in artifact front-matter (e.g., `--tag vision`) - **`--layout {flat,by-phase,skills}`**: Choose directory structure (e.g., `--layout by-phase`) - **`--dry-run`**: Preview actions without writing files - **`--force`**: Overwrite existing files Example command for advanced filtering: ```bash python scripts/install_skills.py ./my-llm-skills \ --type all \ --phase 14 \ --layout by-phase \ --dry-run ``` When not in dry-run mode, the script writes a [`manifest.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/manifest.json) consumable by downstream tooling and agent runtimes. ## Understanding the Build-It/Use-It Pattern Every lesson in the repository implements a dual-structure: first you build the algorithm from scratch using only standard library or raw math, then you run the same logic through a production library to compare implementations. For example, in `phases/07-transformers-deep-dive/02-self-attention-from-scratch/`, you would run: ```bash python code/main.py # Build-it implementation python code/main.py --use # Use-it implementation with production library ``` This pattern ensures you understand both low-level mathematics and high-level framework behavior. Contributors should consult [`AGENTS.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENTS.md) for the strict "one-commit-per-lesson" workflow rules before modifying any lesson code. ## Summary To successfully set up the rohitg00/ai-engineering-from-scratch project: - Clone the repository and navigate to the project root - Create and activate a Python virtual environment (`.venv`) - Install dependencies from [`requirements.txt`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/requirements.txt) - Validate by running [`phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py) or the lesson's unit tests - Optionally install generated artifacts using [`scripts/install_skills.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/install_skills.py) with filters for type, phase, or tags ## Frequently Asked Questions ### Do I need to install all Python dependencies at once? No. While [`requirements.txt`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/requirements.txt) contains all dependencies, the curriculum is designed to be **stdlib-first**. You can install packages incrementally as you progress through phases, though installing everything upfront ensures you won't encounter missing module errors in later transformer or agent-engineering lessons that rely on `torch` or `h5py`. ### What Python version is required for this project? The repository requires **Python 3**. The [`requirements.txt`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/requirements.txt) specifies compatible versions of `numpy`, `torch`, and `h5py` that work with modern Python 3.x releases. Always use the provided virtual environment to avoid version conflicts with system packages and to maintain the isolated environment expected by the curriculum scripts. ### How do I know if the installation succeeded? Run the unit test discovery command: `python -m unittest discover -s phases/01-math-foundations/01-linear-algebra-intuition/code/tests -v`. Passing tests indicate your environment matches the CI pipeline configuration. Additionally, running any lesson's [`code/main.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/code/main.py) should execute without import errors or crashes, and the [`scripts/audit_lessons.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/audit_lessons.py) utility can lint lesson structure to verify installation completeness. ### Where are the generated skills stored after running the install script? By default, [`scripts/install_skills.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/install_skills.py) writes to your specified output directory (e.g., `./my-llm-skills`), creating a [`manifest.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/manifest.json) index and subdirectories containing [`SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/SKILL.md) files. These artifacts are compatible with Claude, Cursor, Codex, and other LLM systems that consume markdown-based skill definitions, and can be organized using the `--layout` option to create flat, by-phase, or skills-based directory structures.