| # Lab 00: Getting Started (The Simplest Starting Point) | |
| ## Overview | |
| Welcome! Before spinning up massive cloud GPUs or diving into complex AI loops, this is the absolute best place to start. This lab provides a completely local, interactive introduction to the foundational concepts of AI training and the Modular ecosystem. | |
| ## Setup | |
| No cloud GPU is required. This lab runs entirely on your local CPU. | |
| If you don't have Jupyter installed, you can launch it using `uv`: | |
| ```bash | |
| uv tool run --from jupyterlab jupyter-lab | |
| ``` | |
| ## Usage | |
| Open `00_getting_started.ipynb` in the Jupyter interface. The notebook is an interactive journey that walks you through: | |
| 1. **The Data:** Inspecting the raw `TinyStories` dataset. | |
| 2. **The Tokenizer:** Seeing how English sentences are translated into numerical tokens using our custom BPE tokenizer. | |
| 3. **The Mojo Compiler:** Compiling and executing our first Mojo code locally using the `pixi` package manager. | |
| ## Goals | |
| - Understand the raw ingredients of an AI model (Data + Tokens). | |
| - Verify your local Python environment can read parquet data shards. | |
| - Verify your local `pixi` environment can successfully compile Mojo (`.mojo`) files. | |
| - Establish the baseline knowledge needed to start writing Neural Network architectures in Lab 03. |