🏗️ Serious Development Path

🏗️ Serious Development Path#

Perfect for: “I want to build this myself” • “This is my class assignment” • “I want to understand ML frameworks deeply”

🎯 What You’ll Build#

A complete ML framework from scratch, including:

  • Your own tensor library with operations and autograd

  • Neural network components (layers, activations, optimizers)

  • Training systems that work on real datasets (CIFAR-10)

  • Production features (compression, monitoring, benchmarking)

End result: A working ML framework you built and understand completely.

🚀 Quick Start (5 minutes)#

Step 1: Get the Code#

git clone https://github.com/your-org/tinytorch.git
cd TinyTorch

Step 2: Setup Environment#

# Activate virtual environment  
source bin/activate-tinytorch.sh

# Install dependencies
make install

# Verify everything works
tito system doctor

Step 3: Start Building#

# Open first assignment
cd modules/source/01_setup
jupyter lab setup_dev.py

Step 4: Build → Test → Export → Use#

# After implementing code in the notebook:
tito export               # Export your code to tinytorch package
tito test setup          # Test your implementation

# Now use YOUR own code:
python -c "from tinytorch.core.setup import hello_tinytorch; hello_tinytorch()"
# 🔥 TinyTorch! Built by: [Your Name]

🎓 Learning Path (Progressive Complexity)#

🏗️ Foundation (Weeks 1-2)#

Build the core infrastructure:

Module 01: Setup & CLI

  • Professional development workflow with tito CLI

  • Understanding package architecture and exports

  • Quality assurance with automated testing

Module 01: Tensors

  • Multi-dimensional arrays and operations

  • Memory management and data types

  • Foundation for all ML operations

Module 02: Activations

  • ReLU, Sigmoid, Tanh, Softmax functions

  • Understanding nonlinearity in neural networks

  • Mathematical foundations of deep learning

🧱 Building Blocks (Weeks 3-4)#

Create neural network components:

Module 03: Layers

  • Dense (linear) layers with matrix multiplication

  • Weight initialization strategies

  • Building blocks that stack together

Module 04: Networks

  • Sequential model architecture

  • Composition patterns and forward propagation

  • Creating complete neural networks

Module 05: CNNs

  • Convolutional operations for computer vision

  • Understanding spatial processing

  • Building blocks for image classification

🎯 Training Systems (Weeks 5-6)#

Complete training infrastructure:

Module 06: DataLoader

  • Efficient data loading and preprocessing

  • Real dataset handling (CIFAR-10)

  • Batching, shuffling, and memory management

Module 07: Autograd

  • Automatic differentiation engine

  • Computational graphs and backpropagation

  • The magic that makes training possible

Module 08: Optimizers

  • SGD, Adam, and learning rate scheduling

  • Understanding gradient descent variants

  • Convergence and training dynamics

Module 09: Training

  • Complete training loops and loss functions

  • Model evaluation and metrics

  • Checkpointing and persistence

⚡ Production & Performance (Weeks 7-8)#

Real-world deployment:

Module 10: Compression

  • Model pruning and quantization

  • Reducing model size by 75%+

  • Deployment optimization

Module 11: Kernels

  • High-performance custom operations

  • Hardware-aware optimization

  • Understanding framework internals

Module 12: Benchmarking

  • Systematic performance measurement

  • Statistical validation and reporting

  • MLPerf-style evaluation

Module 13: MLOps

  • Production deployment and monitoring

  • Continuous learning and model updates

  • Complete production pipeline

🛠️ Development Workflow#

The tito CLI System#

TinyTorch includes a complete CLI for professional development:

# System management
tito system doctor          # Check environment health
tito system info           # Show module status

# Module development  
tito export                # Export dev code to package
tito test setup            # Test specific module
tito test --all            # Test everything

# NBGrader integration
tito nbgrader generate setup    # Create assignments
tito nbgrader release setup     # Release to students
tito nbgrader autograde setup   # Auto-grade submissions

Quality Assurance#

Every module includes comprehensive testing:

  • 100+ automated tests ensure correctness

  • Inline tests provide immediate feedback

  • Integration tests verify cross-module functionality

  • Performance benchmarks track optimization

📊 Proven Student Outcomes#

Real Results

After 6-8 weeks, students consistently:

✅ Build multi-layer perceptrons that classify CIFAR-10 images
✅ Implement automatic differentiation from scratch
✅ Create custom optimizers (SGD, Adam) that converge reliably
✅ Optimize models with pruning and quantization
✅ Deploy production ML systems with monitoring
✅ Understand framework internals better than most ML engineers

Test Coverage: 200+ tests across all modules ensure student implementations work

🎯 Why This Approach Works#

Build → Use → Understand#

Every component follows this pattern:

  1. 🔧 Build: Implement ReLU() from scratch

  2. 🚀 Use: from tinytorch.core.activations import ReLU - your code!

  3. 💡 Understand: See how it enables complex pattern learning

Real Data, Real Systems#

  • Work with CIFAR-10 (not toy datasets)

  • Production-style code organization

  • Performance and engineering considerations

  • Professional development practices

Immediate Feedback#

  • Code works immediately after implementation

  • Visual progress indicators and success messages

  • Comprehensive error handling and guidance

  • Professional-quality development experience

🚀 Ready to Start?#

Choose Your Module#

New to ML frameworks? → Start with Setup Have ML experience? → Jump to Tensors Want to see the vision? → Try Activations

Get Help#

  • 💬 Discussions: GitHub Discussions for questions

  • 🐛 Issues: Report bugs or suggest improvements

  • 📧 Support: Direct contact with TinyTorch team

🎉 Ready to build your own ML framework? Your PyTorch-understanding self is 8 weeks away!

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