# How to Run a Specific Lesson's Code from the Command Line in AI Engineering From Scratch > Learn to run specific AI Engineering From Scratch lesson code from the command line. Discover the simple commands to execute Python or Rust code directly. - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: how-to-guide - Published: 2026-06-17 --- **Navigate to the lesson directory under `phases///` and execute the run command documented in [`code/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/code/README.md), such as `python3 code/main.py` for Python implementations or `cargo run` for Rust.** The *AI Engineering From Scratch* repository by rohitg00 organizes hands-on curriculum content into structured phase directories. Each lesson includes a self-contained implementation in the `code/` subdirectory that you can execute directly from the terminal to validate concepts and experiment with modifications. ## Locate the Lesson Directory Every lesson resides at `phases///` within the repository root. For example, the "Gradient Checkpointing" lesson lives under `phases/10-llms-from-scratch/34-gradient-checkpointing/`. Navigate to the lesson root before executing any commands: ```bash cd phases/10-llms-from-scratch/34-gradient-checkpointing ``` Inside this directory, the `code/` folder contains the implementation files. The [`code/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/code/README.md) file documents the exact command required to run that specific lesson. ## Install Required Dependencies Most lessons are self-contained, but some require third-party packages. The [`code/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/code/README.md) specifies the exact installation command for the lesson's programming language. **Python** lessons typically use: ```bash pip install -r requirements.txt ``` **TypeScript** lessons require: ```bash npm install ``` **Rust** lessons handle dependencies automatically through [`Cargo.toml`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/Cargo.toml) when you run `cargo build`, but ensure you have the Rust toolchain installed. ## Execute the Lesson Code The [`code/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/code/README.md) in each lesson specifies the precise command to launch the implementation. The convention varies by language: - **Python**: `python3 code/main.py` - **TypeScript**: `npm start` or `npx ts-node code/main.ts` - **Rust**: `cargo run --manifest-path code/Cargo.toml` - **Julia**: `julia code/main.jl` For example, to run the Gradient Checkpointing lesson according to the source code: ```bash cd phases/10-llms-from-scratch/34-gradient-checkpointing python3 code/main.py ``` This prints the equivalence check and cost table, then exits with status `0` on success. For the Terminal-Native Coding Agent TypeScript lesson: ```bash cd phases/19-capstone-projects/01-terminal-native-coding-agent npm install npm start ``` To launch the interactive REPL mode instead: ```bash npm start -- --repl ``` For Rust implementations like the Pre-training Mini-GPT lesson: ```bash cd phases/10-llms-from-scratch/04-pre-training-mini-gpt cargo run --manifest-path code/Cargo.toml ``` ## Run the Test Suite Each lesson includes a `code/tests/` directory containing unit tests. Execute the language-specific test runner to verify the implementation's correctness. **Python**: ```bash python3 -m unittest discover code/tests -v ``` **TypeScript**: ```bash npm test ``` **Rust**: ```bash cargo test --manifest-path code/Cargo.toml ``` **Julia**: ```bash julia --project=code -e 'using Pkg; Pkg.test()' ``` All lessons exit with status `0` when tests pass. A non-zero exit status indicates a failure in the lesson's self-test suite. ## Summary - Each lesson in *AI Engineering From Scratch* follows the path `phases///` with implementation files in the `code/` subdirectory. - The [`code/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/code/README.md) file contains the exact terminal command required to run that lesson's code. - **Python** lessons execute via `python3 code/main.py`, **TypeScript** via `npm start`, **Rust** via `cargo run --manifest-path code/Cargo.toml`, and **Julia** via `julia code/main.jl`. - Install dependencies using `pip install -r requirements.txt` or `npm install` as documented in the lesson's README before running. - Run the test suite using language-specific commands like `python3 -m unittest discover code/tests` or `cargo test` to verify correctness. ## Frequently Asked Questions ### How do I find the correct command to run a specific lesson? Check the [`code/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/code/README.md) file inside the lesson directory. This file explicitly documents the run command, such as `python3 code/main.py` for Python lessons or `npm start` for Node.js projects, along with any required dependencies and flags. ### What should I do if a lesson requires external packages? Install the dependencies listed in the lesson's [`code/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/code/README.md). Python lessons typically provide a [`requirements.txt`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/requirements.txt) file for `pip install`, while TypeScript lessons require running `npm install` to fetch packages like `hono` or `zod` before executing `npm start`. ### How do I verify that a lesson executed correctly? All lessons exit with status `0` on successful completion. The output will display the lesson's results, such as benchmark tables or equivalence checks. For additional verification, run the unit tests in `code/tests/` using the appropriate language test runner. ### Can I run lessons interactively or modify the code? Yes. After navigating to the lesson directory, you can edit `code/main.` directly. For interactive sessions, some lessons support specific flags; for example, the Terminal-Native Coding Agent supports `npm start -- --repl` to launch an interactive REPL instead of the scripted demo.