diff --git a/tinytorch/site/getting-started.md b/tinytorch/site/getting-started.md index 46c675c33..792f1f14b 100644 --- a/tinytorch/site/getting-started.md +++ b/tinytorch/site/getting-started.md @@ -6,46 +6,41 @@ You're ahead of the curve. TinyTorch is functional but still being refined. Expe **Best approach right now:** Browse the code and concepts. For hands-on building, check back when we announce classroom readiness (Summer/Fall 2026). -Questions or feedback? [Join the discussion โ†’](https://github.com/harvard-edge/cs249r_book/discussions/1076) +Questions or feedback? [Join the discussion](https://github.com/harvard-edge/cs249r_book/discussions/1076) ``` ```{note} Prerequisites Check This guide requires **Python programming** (classes, functions, NumPy basics) and **basic linear algebra** (matrix multiplication). ``` -Welcome to TinyTorch! This comprehensive guide will get you started whether you're a student building ML systems, an instructor setting up a course, or a TA supporting learners. +## The Journey -
-

Choose Your Path

-

Jump directly to your role-specific guide

+TinyTorch follows a simple pattern: **build modules, unlock milestones, recreate ML history**. -
+```{mermaid} +:align: center +graph LR + A[Install] --> B[Setup] + B --> C[Start Module] + C --> D[Complete Module] + D --> E[Run Milestone] + E --> C - -
-
Students
-
Setup + Build Workflow
- + style A fill:#e3f2fd + style B fill:#e3f2fd + style C fill:#fff3e0 + style D fill:#f0fdf4 + style E fill:#fce4ec +``` - -
-
Instructors & TAs
-
Coming Soon
- +As you complete modules, you unlock milestones that recreate landmark moments in ML historyโ€”using YOUR code. -
-
+--- - - -## For Students: Build Your ML Framework - -### Quick Setup (2 Minutes) - -Get your development environment ready to build ML systems from scratch: +## Step 1: Install & Setup (2 Minutes) ```bash -# One-line install (run from a project folder like ~/projects) +# Install TinyTorch (run from a project folder like ~/projects) curl -sSL tinytorch.ai/install | bash # Activate and verify @@ -61,116 +56,145 @@ tito setup - Installs all dependencies - Verifies installation -**Keeping up to date:** -```bash -tito update # Check for and install updates (your work is preserved) -``` +--- -### Join the Community (Optional) +## Step 2: Your First Module (15 Minutes) -After setup, join the global TinyTorch community and validate your installation: +Let's build Module 01 (Tensor)โ€”the foundation of all neural networks. + +### Start the module ```bash -# Log in to join the community -tito community login - -# Run baseline benchmark to validate setup -tito benchmark baseline +tito module start 01 ``` -All community data is stored locally in `.tinytorch/` directory. See **[Community Guide](community.md)** for complete features. +This opens the module notebook and tracks your progress. -### The TinyTorch Build Cycle +### Work in the notebook -TinyTorch follows a simple three-step workflow that you'll repeat for each module: - -```{mermaid} -:align: center -:caption: "**TinyTorch Build Cycle.** The three-step workflow you repeat for each module: edit in Jupyter, export to the package, and validate with milestone scripts." -graph LR - A[1. Edit Module
modules/NN_name.ipynb] --> B[2. Export to Package
tito module complete N] - B --> C[3. Validate with Milestones
Run milestone scripts] - C --> A - - style A fill:#fffbeb - style B fill:#f0fdf4 - style C fill:#fef3c7 -``` - -#### Step 1: Edit Modules - -Work on module notebooks interactively: +Edit `modules/01_tensor/01_tensor.ipynb` in Jupyter: ```bash -# Example: Working on Module 01 (Tensor) -cd modules/01_tensor -jupyter lab 01_tensor.ipynb +jupyter lab modules/01_tensor/01_tensor.ipynb ``` -Each module is a Jupyter notebook where you'll: -- Implement the required functionality from scratch -- Add docstrings and comments -- Run and test your code inline -- See immediate feedback - -#### Step 2: Export to Package - -Once your implementation is complete, export it to the main TinyTorch package: - -```bash -tito module complete MODULE_NUMBER - -# Example: -tito module complete 01 # Export Module 01 (Tensor) -``` - -After export, your code becomes importable: -```python -from tinytorch.core.tensor import Tensor # YOUR implementation! -``` - -#### Step 3: Validate with Milestones - -Run milestone scripts to prove your implementation works: - -```bash -tito milestone run perceptron # Uses YOUR Tensor, Activations, Layers -``` - -Each milestone validates that your modules work together correctly. Use `tito milestone list` to see all available milestones and their required modules. - -**What if validation fails?** If a milestone script produces errors: -1. Read the error message carefullyโ€”it usually points to the problem -2. Run module tests: `tito module test 01` to check your implementation -3. Return to your Jupyter notebook to debug and fix -4. Re-export with `tito module complete 01` and try again - -**See [Milestone System](tito/milestones)** for the complete progression through ML history. - -### Your First Module (15 Minutes) - -Start with Module 01 to build tensor operations - the foundation of all neural networks: - -```bash -# Step 1: Edit the module -cd modules/01_tensor -jupyter lab 01_tensor.ipynb - -# Step 2: Export when ready -tito module complete 01 - -# Step 3: Validate -from tinytorch.core.tensor import Tensor -x = Tensor([1, 2, 3]) # YOUR implementation! -``` - -**What you'll implement:** +You'll implement: - N-dimensional array creation - Mathematical operations (add, multiply, matmul) - Shape manipulation (reshape, transpose) -- Memory layout understanding -### Module Progression +### Complete the module + +When your implementation is ready, export it to the TinyTorch package: + +```bash +tito module complete 01 +``` + +Your code is now importable: + +```python +from tinytorch.core.tensor import Tensor # YOUR implementation! +x = Tensor([1, 2, 3]) +``` + +--- + +## Step 3: Your First Milestone + +Now for the payoff! After completing the required modules (01-03), run a milestone: + +```bash +tito milestone run perceptron +``` + +The milestone uses YOUR implementations to recreate Rosenblatt's 1957 Perceptron: + +``` +๐Ÿ” Checking prerequisites for Milestone 01... +โœ… All required modules completed! + +๐Ÿงช Testing YOUR implementations... + โœ“ Tensor import successful + โœ“ Activations import successful + โœ“ Layers import successful +โœ… YOUR Tiny๐Ÿ”ฅTorch is ready! + +โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ ๐Ÿ† Milestone 01 (1957) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ +โ”‚ ๐Ÿง  Milestone 01: Perceptron (1957) โ”‚ +โ”‚ Frank Rosenblatt's First Neural Network โ”‚ +โ”‚ โ”‚ +โ”‚ ๐Ÿ“‚ Running: milestones/01_1957_perceptron/01_rosenblatt_forward.py โ”‚ +โ”‚ All code uses YOUR Tiny๐Ÿ”ฅTorch implementations! โ”‚ +โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ + +๐Ÿš€ Starting Milestone 01... + +๐Ÿง  Assembling perceptron with YOUR Tiny๐Ÿ”ฅTorch modules... + โœ“ Linear layer: 2 โ†’ 1 (YOUR Module 03!) + โœ“ Activation: Sigmoid (YOUR Module 02!) + +โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ โœจ Achievement Unlocked โœจ โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ +โ”‚ ๐Ÿ† MILESTONE ACHIEVED! โ”‚ +โ”‚ โ”‚ +โ”‚ You completed Milestone 01: Perceptron (1957) โ”‚ +โ”‚ Frank Rosenblatt's First Neural Network โ”‚ +โ”‚ โ”‚ +โ”‚ What makes this special: โ”‚ +โ”‚ โ€ข Every tensor operation: YOUR Tensor class โ”‚ +โ”‚ โ€ข Every layer: YOUR Linear implementation โ”‚ +โ”‚ โ€ข Every activation: YOUR Sigmoid function โ”‚ +โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ +``` + +You're recreating ML history with your own code. + +--- + +## The Pattern Continues + +As you complete more modules, you unlock more milestones: + +| Modules Completed | Milestone Unlocked | What You Recreate | +|-------------------|-------------------|-------------------| +| 01-03 | `perceptron` | The 1957 Perceptron | +| 01-05 | `backprop` | 1986 Backpropagation | +| 01-07 | `lenet` | 1989 LeNet CNN | +| 01-09 | `alexnet` | 2012 AlexNet | +| 01-13 | `transformer` | 2017 Transformer | +| 01-19 | `mlperf` | MLPerf Benchmarks | + +See all milestones and their requirements: + +```bash +tito milestone list +``` + +--- + +## Quick Reference + +Here are the commands you'll use throughout your journey: + +```bash +# Module workflow +tito module start # Start working on module N +tito module complete # Export module to package +tito module status # See your progress across all modules + +# Milestones +tito milestone list # See all milestones & requirements +tito milestone run # Run a milestone with your code + +# Utilities +tito setup # Verify installation +tito update # Update TinyTorch (your work is preserved) +tito --help # Full command reference +``` + +--- + +## Module Progression TinyTorch has 20 modules organized in progressive tiers: @@ -185,72 +209,34 @@ TinyTorch has 20 modules organized in progressive tiers: **See [Foundation Tier Overview](tiers/foundation)** for detailed module descriptions. -### Essential Commands Reference +--- -The most important commands you'll use daily: +## Join the Community (Optional) + +After setup, join the global TinyTorch community: ```bash -# Export module to package -tito module complete MODULE_NUMBER - -# Check module status -tito module status - -# System information -tito system info - -# Community features -tito community login -tito benchmark baseline +tito community login # Join the community ``` -**See [TITO CLI Reference](tito/overview.md)** for complete command documentation. +See **[Community Guide](community.md)** for complete features. -### Notebook Platform Options +--- -**For Viewing & Exploration (Online):** -- Jupyter/MyBinder: Click "Launch Binder" on any notebook page -- Google Colab: Click "Launch Colab" for GPU access -- Marimo: Click "~ Open in Marimo" for reactive notebooks - -**For Full Development (Local - Required):** - -To actually build the framework, you need local installation: -- Full `tinytorch.*` package available -- Run milestone validation scripts -- Use `tito` CLI commands -- Execute complete experiments -- Export modules to package - -**Note for NBGrader assignments**: Submit `.ipynb` files to preserve grading metadata. - -### What's Next? - -1. **Continue Building**: Follow the module progression (01 โ†’ 02 โ†’ 03...) -2. **Run Milestones**: Prove your implementations work with real ML history -3. **Build Intuition**: Understand ML systems from first principles - -The goal isn't just to write code - it's to **understand** how modern ML frameworks work by building one yourself. - - -## For Instructors & TAs: Classroom Support Coming Soon +## For Instructors & TAs ```{note} -We're building comprehensive classroom support with NBGrader integration. For hands-on building today, TinyTorch is fully functional for self-paced learning. +Classroom support with NBGrader integration is coming (target: Summer/Fall 2026). TinyTorch works for self-paced learning today. ``` **What's Planned:** - Automated assignment generation with solutions removed - Auto-grading against test suites -- Manual review interface for ML Systems Thinking questions - Progress tracking across all 20 modules - Grade export to CSV for LMS integration -**Current Status:** TinyTorch works for self-paced learning today. For classroom deployment, we recommend waiting for the official NBGrader integration (target: Summer/Fall 2026). - **Interested in early adoption?** [Join the discussion](https://github.com/harvard-edge/cs249r_book/discussions/1076) to share your use case. -Check back for detailed setup instructions and grading rubrics when classroom support is available. +--- - -**Ready to start building?** Head to the [Foundation Tier](tiers/foundation) and begin with Module 01! +**Ready to start?** Run `tito module start 01` and begin building! diff --git a/tinytorch/site/intro.md b/tinytorch/site/intro.md index c2a04b437..fd45c6bf0 100644 --- a/tinytorch/site/intro.md +++ b/tinytorch/site/intro.md @@ -22,7 +22,7 @@ Don't import it. Build it.

-Build a complete ML framework from tensors to transformersโ€”understand how PyTorch, TensorFlow, and JAX really work. +From tensors to systems. An educational framework for building and optimizing MLโ€”understand how PyTorch, TensorFlow, and JAX really work.