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TensorWeaver

TensorWeaver Logo

🧠 A transparent, debuggable deep learning framework
PyTorch-compatible implementation with full visibility into internals

PyPI version License GitHub stars Documentation

🤔 Ever feel like PyTorch is a black box?

# What's actually happening here? 🤷‍♂️
loss.backward()  # Magic? 
optimizer.step()  # More magic?

You're not alone. Most ML students and engineers use deep learning frameworks without understanding the internals. That's where TensorWeaver comes in.

🎯 What is TensorWeaver?

TensorWeaver is a transparent deep learning framework that reveals exactly how PyTorch works under the hood. Built from scratch in pure Python, it provides complete visibility into automatic differentiation, neural networks, and optimization algorithms.

Think of it as "PyTorch with full transparency" 🔧

🎓 Perfect for:

  • ML Engineers debugging complex gradient issues and understanding framework internals
  • Researchers who need full control over their implementations
  • Software Engineers building custom deep learning solutions
  • Technical Teams who need to understand and modify framework behavior
  • Developers who refuse to accept "black box" solutions

💡 Pro Tip: Use import tensorweaver as torch for seamless PyTorch compatibility!

⚡ Quick Start - See the Magic Yourself

pip install tensorweaver
import tensorweaver as torch  # PyTorch-compatible API!

# 1. Prepare Data (y = 2x)
x = torch.tensor([[1.0], [2.0], [3.0], [4.0]])
y = torch.tensor([[2.0], [4.0], [6.0], [8.0]])

# 2. Define Model
model = torch.nn.Linear(1, 1)

# 3. Train
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = torch.nn.MSELoss()

print("Training...")
for epoch in range(100):
    # Forward
    y_pred = model(x)
    loss = criterion(y_pred, y)
    
    # Backward
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    if epoch % 20 == 0:
        print(f"Epoch {epoch}: Loss = {loss.item():.4f}")

# 4. Result
# The difference? You can see EXACTLY what happens inside! 👀
print(f"\nPrediction for x=5.0: {model(torch.tensor([[5.0]])).item():.4f} (Expected: 10.0)")

🚀 Try it live in your browser →

🧠 What You'll Learn

🔬 Deep Learning Internals

  • How automatic differentiation works
  • Backpropagation step-by-step
  • Computational graph construction
  • Gradient computation and flow

🛠️ Framework Design

  • Tensor operations implementation
  • Neural network architecture
  • Optimizer algorithms
  • Model export (ONNX) mechanisms

💎 Why TensorWeaver?

🏭 Production Frameworks 🔬 TensorWeaver
❌ Complex C++ codebase ✅ Pure Python - fully debuggable
❌ Optimized for speed only ✅ Optimized for understanding and modification
❌ "Trust us, it works" ✅ "Here's exactly how it works"
❌ Black box internals ✅ Complete transparency and control

🚀 Key Features

  • 🔍 Transparent Implementation: Every operation is visible, debuggable, and modifiable
  • 🐍 Pure Python: No hidden C++ complexity - full control over the codebase
  • 🎯 PyTorch-Compatible API: Drop-in replacement with complete visibility
  • 🛠️ Engineering Excellence: Clean architecture designed for understanding and extension
  • 🧪 Complete Functionality: Autodiff, neural networks, optimizers, ONNX export
  • 📊 Production Ready: Export trained models to ONNX for deployment

🗺️ Technical Roadmap

🔧 Core Components

  1. Tensor Operations - Fundamental tensor mechanics and operations
  2. Linear Models - Basic neural network implementation
  3. Automatic Differentiation - Gradient computation engine (coming soon)

🏗️ Advanced Architecture

  1. Deep Networks - Multi-layer perceptron and complex architectures
  2. Optimization Algorithms - Advanced training techniques (coming soon)
  3. Model Deployment - ONNX export for production systems

⚡ Performance & Extensions

  1. Custom Operators - Framework extension capabilities (coming soon)
  2. Performance Engineering - Optimization techniques (coming soon)
  3. Hardware Acceleration - GPU computation support (in development)

📝 Note: Some documentation links are still in development. Check our milestones for working examples!

🚀 Get Started Now

📦 Installation

# Option 1: Install from PyPI (recommended)
pip install tensorweaver

# Option 2: Install from source (for contributors)
git clone https://github.com/howl-anderson/tensorweaver.git
cd tensorweaver
uv sync --group dev

🎯 Quick Start Guide

  1. 📂 Browse Examples - Working implementations and demos
  2. 🚀 Try Online - Browser-based environment
  3. 💬 Community Forum - Technical discussions and support
  4. 📖 Documentation - Complete API reference (expanding)

🤝 Contributing

TensorWeaver thrives on community contributions! Whether you're:

  • 🐛 Reporting bugs
  • 💡 Suggesting features
  • 📖 Improving documentation
  • 🧪 Adding examples
  • 🔧 Writing code

We welcome you! Please open an issue or submit a pull request - contribution guidelines coming soon!

📚 Resources

⭐ Why Stars Matter

If TensorWeaver helped you debug, understand, or build better models, please consider starring the repository! It helps other engineers discover this transparent framework.

GitHub stars

📄 License

TensorWeaver is MIT licensed. See LICENSE for details.

🙏 Acknowledgments

  • Inspired by transparent implementations: Micrograd, TinyFlow, and DeZero
  • Thanks to the PyTorch team for the elegant API design
  • Grateful to all contributors and the open-source community

Ready for complete transparency in deep learning?
🚀 Explore TensorWeaver at tensorweaver.ai