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Add documentation standards and development setup

Browse Source 
- Create .claude directory with team structure and guidelines
- Add MODULE_DEVELOPMENT_GUIDELINES.md for educational patterns
- Add EDUCATIONAL_PATTERN_TEMPLATE.md for consistent module structure
- Add GIT_WORKFLOW_STANDARDS.md for branch management
- Create setup-dev.sh for automated environment setup
- Add notebook workflow documentation
- Add CI/CD workflow for notebook testing

This commit establishes consistent development standards and documentation
for the TinyTorch educational ML framework development.
This commit is contained in:
Vijay Janapa Reddi
2025-09-15 13:30:10 -04:00
parent ce77693723
commit ebc37265ee
5 changed files with 933 additions and 0 deletions
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.claude/GIT_WORKFLOW_STANDARDS.md Normal file
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# TinyTorch Git Workflow Standards
## 🎯 Core Principle: Always Work on Feature Branches
**NEVER work directly on main or dev branches.** Every piece of work, no matter how small, starts with creating a new branch.
## 📋 Branch Creation Standards
### When to Create a New Branch
- **Before ANY module enhancement**
- **Before ANY bug fix**
- **Before ANY documentation update**
- **Before ANY infrastructure change**
- **Before ANY agent-driven development**
### Branch Naming Convention
```bash
# Format: <type>/<description>
# Examples:
feature/enhance-module-04-layers
fix/tensor-import-error
docs/update-educational-patterns
infra/notebook-conversion-workflow
agent/quality-assurance-validation
```
### Branch Types
- `feature/` - New features or module enhancements
- `fix/` - Bug fixes
- `docs/` - Documentation updates
- `infra/` - Infrastructure, CI/CD, build system changes
- `agent/` - Work initiated by specific agents
- `test/` - Testing improvements
- `refactor/` - Code refactoring
## 🔄 Standard Workflow
### 1. Start Work: Create Branch
```bash
# Always start from dev (or main if no dev)
git checkout dev
git pull origin dev
# Create and switch to new branch
git checkout -b feature/enhance-module-04-layers
```
### 2. During Work: Regular Commits
```bash
# Stage changes
git add .
# Commit with meaningful message
git commit -m "Add comprehensive educational scaffolding to layers module
- Enhanced conceptual foundation with 150+ lines
- Added visual diagrams and mathematical explanations
- Implemented TODO/APPROACH/EXAMPLE/HINTS pattern
- Created integration tests with tensor and activations"
```
### 3. Complete Work: Push and PR
```bash
# Push branch to remote
git push -u origin feature/enhance-module-04-layers
# Create Pull Request via GitHub CLI
gh pr create --title "Enhance Module 04 (Layers) with educational patterns" \
--body "Description of changes..."
```
## 📝 Commit Message Standards
### Format
```
<type>: <subject>
<body>
<footer>
```
### Example
```
feature: Enhance Module 04 (Layers) with comprehensive educational scaffolding
- Added 150+ lines of conceptual foundation
- Implemented visual diagrams for matrix multiplication
- Enhanced Dense layer with production connections
- Created integration tests with previous modules
- Added checkpoint sections for student validation
This completes the foundation layer sequence (modules 01-05) with
consistent educational patterns.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
```
## 🚫 Never Do This
### ❌ Bad Practices
```bash
# NEVER commit directly to main
git checkout main
git add .
git commit -m "quick fix" # NO!
# NEVER work without a branch
vim module.py
git add .
git commit -m "changes" # NO!
# NEVER use vague commit messages
git commit -m "updates" # NO!
git commit -m "fixes" # NO!
```
### ✅ Good Practices
```bash
# ALWAYS create a branch first
git checkout -b fix/tensor-shape-error
# ALWAYS use descriptive commits
git commit -m "Fix tensor shape preservation in arithmetic operations"
# ALWAYS push to feature branch
git push -u origin fix/tensor-shape-error
```
## 🔀 Merging Standards
### Before Merging
- [ ] All tests pass
- [ ] Code reviewed (if team environment)
- [ ] Documentation updated
- [ ] Validation workflow passes
- [ ] No conflicts with target branch
### Merge Process
```bash
# Update your branch with latest dev
git checkout dev
git pull origin dev
git checkout feature/your-branch
git merge dev
# Resolve any conflicts
# Test everything works
# Then merge via PR
```
## 🏷️ Protected Branches
These branches should have protection rules:
- `main` - Production ready code only
- `dev` - Development integration branch
- No direct pushes allowed
- Require PR reviews
- Require status checks to pass
## 📊 Branch Lifecycle
```mermaid
graph LR
A[Create Branch] --> B[Develop]
B --> C[Commit Changes]
C --> D[Push to Remote]
D --> E[Create PR]
E --> F[Review/Test]
F --> G[Merge to Dev]
G --> H[Delete Branch]
```
## 🎯 Agent-Specific Standards
When agents create branches:
- **Education Architect**: `agent/education-design-module-XX`
- **Module Developer**: `feature/enhance-module-XX-name`
- **Quality Assurance**: `test/validation-module-XX`
- **Documentation Publisher**: `docs/update-module-XX-docs`
- **DevOps Engineer**: `infra/component-description`
## 🔧 Git Configuration
### Recommended Setup
```bash
# Set up useful aliases
git config --global alias.co checkout
git config --global alias.br branch
git config --global alias.ci commit
git config --global alias.st status
# Set default branch name
git config --global init.defaultBranch main
# Set pull strategy
git config --global pull.rebase false
```
## 📝 Codified Rules
1. **ALWAYS create a new branch before starting ANY work**
2. **NEVER commit directly to main or dev branches**
3. **ALWAYS use descriptive branch names following conventions**
4. **ALWAYS write meaningful commit messages**
5. **ALWAYS test before pushing**
6. **ALWAYS create a PR for code review**
7. **ALWAYS delete branches after merging**
8. **ALWAYS pull latest changes before creating new branches**
This workflow ensures code quality, enables collaboration, maintains history, and prevents conflicts in the TinyTorch project.

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name: Test Notebook Conversion
on:
push:
branches: [ main, dev ]
paths:
- 'modules/source/**/*.py'
- 'tito/**/*.py'
- 'requirements.txt'
- '.github/workflows/test-notebooks.yml'
pull_request:
branches: [ main, dev ]
paths:
- 'modules/source/**/*.py'
- 'tito/**/*.py'
- 'requirements.txt'
- '.github/workflows/test-notebooks.yml'
jobs:
test-notebook-conversion:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Cache pip dependencies
uses: actions/cache@v3
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
restore-keys: |
${{ runner.os }}-pip-
- name: Create virtual environment
run: |
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
- name: Install essential dependencies
run: |
source .venv/bin/activate
pip install setuptools wheel
pip install rich>=13.0.0
pip install jupytext>=1.14.0
pip install pytest>=7.0.0
pip install numpy>=1.21.0
- name: Install remaining dependencies (allow failures)
run: |
source .venv/bin/activate
pip install -r requirements.txt || echo "Some dependencies failed - continuing with essential packages"
- name: Install TinyTorch in development mode
run: |
source .venv/bin/activate
pip install -e . || echo "Development install failed - continuing"
- name: Run environment diagnosis
run: |
source .venv/bin/activate
python -m tito.main system doctor || echo "Doctor check completed with issues"
- name: Test notebook conversion (dry run)
run: |
source .venv/bin/activate
python -m tito.main module notebooks --dry-run
- name: Test conversion of specific modules
run: |
source .venv/bin/activate
# Test tensor module
if [ -f "modules/source/02_tensor/tensor_dev.py" ]; then
python -m tito.main module notebooks --module 02_tensor
[ -f "modules/source/02_tensor/tensor_dev.ipynb" ] && echo "✓ Tensor notebook created"
fi
# Test activations module
if [ -f "modules/source/03_activations/activations_dev.py" ]; then
python -m tito.main module notebooks --module 03_activations
[ -f "modules/source/03_activations/activations_dev.ipynb" ] && echo "✓ Activations notebook created"
fi
- name: Validate notebook structure
run: |
source .venv/bin/activate
# Install notebook validation tools
pip install nbformat || echo "Could not install nbformat"
# Check generated notebooks have valid structure
for notebook in modules/source/*/*.ipynb; do
if [ -f "$notebook" ]; then
echo "Validating $notebook"
python -c "
import json
try:
with open('$notebook') as f:
nb = json.load(f)
assert 'cells' in nb, 'No cells found'
assert len(nb['cells']) > 0, 'Empty notebook'
print('✓ $notebook is valid')
except Exception as e:
print('✗ $notebook validation failed:', e)
exit(1)
"
fi
done
- name: Test batch conversion
run: |
source .venv/bin/activate
# Clean up previous notebooks
find modules/source -name "*.ipynb" -delete
# Test converting all modules at once
python -m tito.main module notebooks
# Check that notebooks were created
notebook_count=$(find modules/source -name "*.ipynb" | wc -l)
echo "Created $notebook_count notebooks"
[ "$notebook_count" -gt 0 ] && echo "✓ Batch conversion successful"
- name: Archive generated notebooks
uses: actions/upload-artifact@v3
if: always()
with:
name: generated-notebooks-python-${{ matrix.python-version }}
path: modules/source/**/*.ipynb
retention-days: 7
test-student-workflow:
runs-on: ubuntu-latest
needs: test-notebook-conversion
steps:
- uses: actions/checkout@v4
- name: Set up Python 3.11
uses: actions/setup-python@v4
with:
python-version: '3.11'
- name: Test automated setup script
run: |
chmod +x setup-dev.sh
./setup-dev.sh
- name: Test student workflow
run: |
source .venv/bin/activate
# Simulate student workflow
echo "Testing complete student workflow..."
# Convert a module to notebook
python -m tito.main module notebooks --module 03_activations
# Verify notebook exists and is valid
[ -f "modules/source/03_activations/activations_dev.ipynb" ] && echo "✓ Student can create notebooks"
# Test TITO commands work
python -m tito.main --help > /dev/null && echo "✓ TITO CLI accessible"
python -m tito.main system doctor > /dev/null && echo "✓ Environment diagnosis works"

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# TinyTorch Notebook Conversion Workflow
This document provides comprehensive instructions for converting TinyTorch Python modules to Jupyter notebooks using the TITO CLI.
## Overview
The TinyTorch project uses a sophisticated workflow where educational content is developed in Python files with special cell markers (`%%`) and then converted to interactive Jupyter notebooks using Jupytext. This approach provides:
- **Version control friendly**: Python files are easier to track in git
- **Cell-based development**: Use `%%` markers to define notebook cells
- **Automatic conversion**: TITO CLI handles the conversion seamlessly
- **Student-friendly**: Students get interactive notebooks for learning
## Quick Start
### 1. Environment Setup
**Automated Setup (Recommended):**
```bash
# Clone and navigate to repository
git clone https://github.com/your-org/TinyTorch.git
cd TinyTorch
# Run automated setup script
chmod +x setup-dev.sh
./setup-dev.sh
```
**Manual Setup:**
```bash
# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate
# Install essential dependencies
pip install rich>=13.0.0 jupytext>=1.14.0 pytest>=7.0.0 numpy>=1.21.0
# Install full requirements (optional)
pip install -r requirements.txt
# Install TinyTorch in development mode
pip install -e .
```
### 2. Environment Verification
```bash
# Activate virtual environment
source .venv/bin/activate
# Run environment diagnosis
python -m tito.main system doctor
```
This should show:
- ✅ Python 3.8+ detected
- ✅ Virtual environment active
- ✅ Essential dependencies installed
### 3. Convert Modules to Notebooks
**Convert Single Module:**
```bash
python -m tito.main module notebooks --module 03_activations
```
**Convert All Modules:**
```bash
python -m tito.main module notebooks
```
**Dry Run (Preview):**
```bash
python -m tito.main module notebooks --dry-run
```
## Detailed Workflow
### Python Module Structure
Python modules are located in `modules/source/` with the following structure:
```
modules/source/
├── 01_setup/
│ ├── setup_dev.py # Python module with %% cell markers
│ ├── module.yaml # Module metadata
│ └── README.md # Module documentation
├── 02_tensor/
│ ├── tensor_dev.py # Enhanced with educational content
│ └── ...
├── 03_activations/
│ ├── activations_dev.py # Enhanced with educational content
│ └── ...
```
### Cell Markers in Python Files
Use `%%` to define notebook cells in Python files:
```python
# %% [markdown]
"""
# Activations - Nonlinearity in Neural Networks
Welcome to the Activations module!
"""
# %%
import numpy as np
import matplotlib.pyplot as plt
# %% [markdown]
"""
## Understanding ReLU Activation
"""
# %%
def relu(x):
"""ReLU activation function."""
return np.maximum(0, x)
```
### TITO CLI Commands
The TITO CLI provides comprehensive module management:
```bash
# Get help
python -m tito.main --help
python -m tito.main module --help
python -m tito.main module notebooks --help
# System commands
python -m tito.main system info # System information
python -m tito.main system doctor # Environment diagnosis
# Module management
python -m tito.main module status # Module status
python -m tito.main module notebooks # Convert to notebooks
python -m tito.main module test # Run tests
python -m tito.main module export # Export to package
# Notebook conversion
python -m tito.main module notebooks --module 02_tensor # Single module
python -m tito.main module notebooks --dry-run # Preview only
python -m tito.main module notebooks --force # Force rebuild
```
## Working with Generated Notebooks
### Opening Notebooks
```bash
# Start Jupyter Lab
jupyter lab
# Navigate to modules/source/03_activations/
# Open activations_dev.ipynb
```
### Student Workflow
1. **Get the repository:**
```bash
git clone https://github.com/your-org/TinyTorch.git
cd TinyTorch
```
2. **Setup environment:**
```bash
./setup-dev.sh
source .venv/bin/activate
```
3. **Convert modules to notebooks:**
```bash
python -m tito.main module notebooks
```
4. **Work with notebooks:**
```bash
jupyter lab
```
### Developer Workflow
1. **Edit Python modules** in `modules/source/*/` directories
2. **Test conversion:** `python -m tito.main module notebooks --dry-run`
3. **Convert to notebooks:** `python -m tito.main module notebooks`
4. **Export to package:** `python -m tito.main module export`
5. **Run tests:** `python -m tito.main module test`
## Troubleshooting
### Common Issues
**Issue: `ModuleNotFoundError: No module named 'rich'`**
```bash
# Solution: Install essential dependencies
pip install rich>=13.0.0 jupytext>=1.14.0 pytest>=7.0.0
```
**Issue: `jupytext not found`**
```bash
# Solution: Install jupytext
pip install jupytext>=1.14.0
```
**Issue: `Virtual environment not activated`**
```bash
# Solution: Activate virtual environment
source .venv/bin/activate
```
**Issue: `No *_dev.py files found`**
```bash
# Check you're in the right directory
pwd # Should be /path/to/TinyTorch
# Check modules exist
ls modules/source/
```
**Issue: Architecture mismatch with NumPy**
```bash
# Create fresh virtual environment
rm -rf .venv
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install numpy>=1.21.0,<2.0.0
```
### Environment Diagnosis
Always start troubleshooting with the doctor command:
```bash
python -m tito.main system doctor
```
This will show:
- Python version and virtual environment status
- Installed dependencies and versions
- Module structure validation
- Actionable recommendations
### Getting Help
```bash
# CLI help
python -m tito.main --help
# Specific command help
python -m tito.main module notebooks --help
# Environment diagnosis
python -m tito.main system doctor
# Module status
python -m tito.main module status
```
## CI/CD Integration
The repository includes automated testing of the notebook conversion workflow:
- **On every push**: Tests notebook conversion across Python 3.8-3.12
- **Validates**: Generated notebooks have correct structure
- **Archives**: Generated notebooks as artifacts
- **Student workflow**: Tests the complete student setup process
See `.github/workflows/test-notebooks.yml` for implementation details.
## Advanced Usage
### Custom Module Conversion
```bash
# Convert specific modules only
python -m tito.main module notebooks --module 02_tensor --module 03_activations
# Force rebuild existing notebooks
python -m tito.main module notebooks --force
# Preview what would be converted
python -m tito.main module notebooks --dry-run
```
### Batch Operations
```bash
# Convert all modules and export to package
python -m tito.main module notebooks && python -m tito.main module export --all
# Test after conversion
python -m tito.main module notebooks && python -m tito.main module test --all
```
## File Locations
After successful conversion, you'll find:
```
modules/source/03_activations/
├── activations_dev.py # Original Python module
├── activations_dev.ipynb # Generated notebook ← NEW!
├── module.yaml # Module metadata
└── README.md # Module documentation
```
The generated `.ipynb` files are fully functional Jupyter notebooks that can be opened in Jupyter Lab, VS Code, or any notebook environment.
## Summary
The TinyTorch notebook conversion workflow provides a robust, automated way to transform educational Python modules into interactive notebooks. The TITO CLI handles all the complexity, providing students with a simple, reliable workflow to get started with interactive learning.
**Key Benefits:**
- **One-command setup**: `./setup-dev.sh`
- **Reliable conversion**: TITO CLI with comprehensive error handling
- **Student-friendly**: Clear error messages and helpful guidance
- **Developer-friendly**: Automated CI/CD testing across Python versions
- **Production-ready**: Used in real educational environments
For questions or issues, run `python -m tito.main system doctor` for comprehensive environment diagnosis.

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# TinyTorch: Quick Start for Interactive Notebooks
Get up and running with TinyTorch interactive notebooks in under 5 minutes!
## 🚀 One-Command Setup
```bash
# Clone repository
git clone https://github.com/your-org/TinyTorch.git
cd TinyTorch
# Run automated setup
chmod +x setup-dev.sh
./setup-dev.sh
# Activate environment
source .venv/bin/activate
```
## 📓 Convert Modules to Notebooks
```bash
# Convert all modules to interactive notebooks
python -m tito.main module notebooks
# Or convert specific modules
python -m tito.main module notebooks --module 03_activations
```
## 🎯 Start Learning
```bash
# Open Jupyter Lab
jupyter lab
# Navigate to modules/source/ and open any .ipynb file
# Try: 03_activations/activations_dev.ipynb
```
## ✅ Verify Everything Works
```bash
# Run environment check
python -m tito.main system doctor
# Should show:
# ✅ Python 3.8+
# ✅ Virtual Environment Active
# ✅ Essential Dependencies Installed
```
## 🆘 Need Help?
- **Full Documentation**: [NOTEBOOK_WORKFLOW.md](NOTEBOOK_WORKFLOW.md)
- **Environment Issues**: `python -m tito.main system doctor`
- **Command Help**: `python -m tito.main --help`
## 📋 Available Modules
After conversion, you'll have interactive notebooks for:
- **01_setup**: Environment setup and course introduction
- **02_tensor**: Tensor operations and broadcasting
- **03_activations**: Neural network activation functions
- **04_layers**: Building neural network layers
- **05_dense**: Fully connected networks
- **06_spatial**: Convolutional neural networks
- **07_attention**: Attention mechanisms and transformers
- **08_dataloader**: Data loading and preprocessing
- **09_autograd**: Automatic differentiation
- **10_optimizers**: Gradient descent and optimization
- **11_training**: Training loops and validation
- **12_compression**: Model compression techniques
- **13_kernels**: Custom CUDA kernels
- **14_benchmarking**: Performance measurement
- **15_mlops**: Production deployment
Each module includes:
- 📚 Educational content with explanations
- 💻 Interactive code cells
- 🧪 Comprehensive tests via NBGrader
- 🎯 Hands-on exercises and experiments
Happy learning! 🔥

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#!/bin/bash
# TinyTorch Development Environment Setup Script
# This script sets up a complete development environment for TinyTorch
set -e # Exit on any error
echo "🔧 Setting up TinyTorch development environment..."
# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color
print_status() {
echo -e "${BLUE}==>${NC} $1"
}
print_success() {
echo -e "${GREEN}✓${NC} $1"
}
print_warning() {
echo -e "${YELLOW}⚠${NC} $1"
}
print_error() {
echo -e "${RED}✗${NC} $1"
}
# Function to check if command exists
command_exists() {
command -v "$1" >/dev/null 2>&1
}
# Check Python version
print_status "Checking Python installation..."
if ! command_exists python3; then
print_error "Python 3 is not installed. Please install Python 3.8+ first."
exit 1
fi
python_version=$(python3 -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')")
required_version="3.8"
if ! python3 -c "import sys; exit(0 if sys.version_info >= (3, 8) else 1)"; then
print_error "Python 3.8+ required, found Python $python_version"
exit 1
fi
print_success "Python $python_version found"
# Create virtual environment if it doesn't exist
print_status "Setting up virtual environment..."
if [ ! -d ".venv" ]; then
python3 -m venv .venv
print_success "Virtual environment created"
else
print_warning "Virtual environment already exists"
fi
# Activate virtual environment
print_status "Activating virtual environment..."
source .venv/bin/activate
print_success "Virtual environment activated"
# Upgrade pip
print_status "Upgrading pip..."
pip install --upgrade pip
# Install requirements with error handling
print_status "Installing dependencies..."
# Install core dependencies first (these are most likely to have issues)
print_status "Installing core dependencies..."
pip install setuptools>=70.0.0 wheel>=0.42.0
# Install numerical dependencies
print_status "Installing numerical computing dependencies..."
pip install "numpy>=1.21.0,<2.0.0"
# Install TITO CLI dependencies
print_status "Installing TITO CLI dependencies..."
pip install rich>=13.0.0 jupytext>=1.14.0
# Install testing dependencies
print_status "Installing testing dependencies..."
pip install pytest>=7.0.0
# Install remaining dependencies
print_status "Installing remaining dependencies..."
pip install -r requirements.txt || {
print_warning "Some dependencies failed to install. Continuing with essential packages..."
}
# Install the package in development mode
print_status "Installing TinyTorch in development mode..."
pip install -e . || {
print_warning "Failed to install in development mode. You may need to run this manually."
}
# Verify TITO CLI works
print_status "Verifying TITO CLI installation..."
if python -m tito.main --help > /dev/null 2>&1; then
print_success "TITO CLI is working correctly"
else
print_error "TITO CLI verification failed"
exit 1
fi
# Run doctor command to check environment
print_status "Running environment diagnostics..."
python -m tito.main system doctor || {
print_warning "Some environment issues detected. See output above."
}
# Create helpful aliases
print_status "Setting up helpful aliases..."
echo "# TinyTorch Development Aliases" > .env
echo "alias tito='python -m tito.main'" >> .env
echo "alias nb='python -m tito.main module notebooks'" >> .env
echo "alias export-all='python -m tito.main export --all'" >> .env
# Success message
echo ""
echo "🎉 Setup complete!"
echo ""
echo "Next steps:"
echo " 1. Activate the environment: source .venv/bin/activate"
echo " 2. Test TITO CLI: python -m tito.main"
echo " 3. Convert modules to notebooks: python -m tito.main module notebooks"
echo " 4. Open notebooks: jupyter lab"
echo ""
echo "Quick commands:"
echo " • Convert single module: python -m tito.main module notebooks --module 03_activations"
echo " • Convert all modules: python -m tito.main module notebooks"
echo " • Export modules: python -m tito.main export --all"
echo " • Run tests: python -m tito.main module test"
echo ""
print_success "TinyTorch development environment is ready!"