From d654b76d179025ebe0a6f19f9ed694ca4eae4e84 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Sun, 12 Oct 2025 22:21:24 -0400 Subject: [PATCH] Update README.md --- README.md | 49 ++++++++++--------------------------------------- 1 file changed, 10 insertions(+), 39 deletions(-) diff --git a/README.md b/README.md index 66ab6d5b..b2a71514 100644 --- a/README.md +++ b/README.md @@ -7,11 +7,18 @@ [![Documentation](https://img.shields.io/badge/docs-jupyter_book-orange.svg)](https://mlsysbook.github.io/TinyTorch/) ![Status](https://img.shields.io/badge/status-active-success.svg) -> 🚧 **Work in Progress** - We're actively developing TinyTorch for Spring 2025! All core modules are complete and tested. Join us in building the future of ML systems education. +--- +> 🚧 **This Project is Actively Under Development** +> +> TinyTorch is not yet complete. Modules, docs, and examples are being added and refined weekly. +> A stable release is planned for **end of this year**. +> Expect rapid updates, occasional breaks, and lots of new content. +> You are welcome to skim this web +--- ## 📖 Table of Contents - [Why TinyTorch?](#why-tinytorch) -- [What You'll Build](#what-youll-build) - Including the **CIFAR-10 North Star Goal** +- [What You'll Build](#what-youll-build) - Including several north star goals - [Quick Start](#quick-start) - Get running in 5 minutes - [Learning Journey](#learning-journey) - 20 progressive modules - [Learning Progression & Checkpoints](#learning-progression--checkpoints) - 21 capability checkpoints @@ -188,48 +195,12 @@ model.fit(X, y) # Magic happens - **Debugging Skills** - Fix problems at any level of the stack - **Production Ready** - Learn patterns used in real ML systems -## Learning Progression & Checkpoints - -### 16-Checkpoint Capability System - -Track your progress through **capability-based checkpoints** that validate your ML systems knowledge: - -```bash -# Check your current progress -tito checkpoint status - -# See your capability development timeline -tito checkpoint timeline -``` - -**Checkpoint Progression:** -- **00-02**: Foundation (Environment, Tensors, Activations) -- **03-07**: Core Networks (Layers, Losses, Autograd, Optimizers, Training) -- **08-10**: Computer Vision (Spatial ops, DataLoaders, Real datasets) -- **11-14**: Language Models (Tokenization, Embeddings, Attention, Transformers) -- **15**: Capstone (Complete end-to-end ML systems) - -Each checkpoint asks: **"Can I build this capability from scratch?"** with hands-on validation. - -### Module Completion Workflow - -```bash -# Complete a module (automatic export + testing) -tito module complete 01_tensor - -# This automatically: -# 1. Exports your implementation to the tinytorch package -# 2. Runs the corresponding capability checkpoint test -# 3. Shows your achievement and suggests next steps -``` - ## Key Features ### Essential-Only Design - **Focus on What Matters**: ReLU + Softmax (not 20 activation functions) - **Production Relevance**: Adam + SGD (the optimizers you actually use) - **Core ML Systems**: Memory profiling, performance analysis, scaling insights -- **Real Applications**: CIFAR-10 CNNs, not toy examples ### For Students - **Interactive Demos**: Rich CLI visualizations for every concept @@ -424,4 +395,4 @@ cd modules/01_tensor && jupyter lab tensor_dev.py --- -**Start Small. Go Deep. Build ML Systems.** \ No newline at end of file +**Start Small. Go Deep. Build ML Systems.**