Building a deep learning framework from scratch has been one of the most educational experiences of my engineering journey. forgeNN started as a learning project but evolved into a fully functional framework published on PyPI.
The Challenge of Automatic Differentiation
The heart of any deep learning framework is its automatic differentiation engine. Implementing reverse-mode autodiff (backpropagation) requires careful tracking of the computational graph.
Key Design Decisions
- Dynamic Computation Graph: Like PyTorch, forgeNN builds the graph on-the-fly
- NumPy Backend: Leveraging NumPy’s optimized operations for performance
- Modular Architecture: Clean separation between tensors, operations, and optimizers
Performance Optimization
Achieving competitive performance with pure Python/NumPy required careful optimization:
- Vectorized operations wherever possible
- Memory-efficient gradient accumulation
- Lazy evaluation for certain operations
What I Learned
This project taught me that understanding the fundamentals deeply enables you to build sophisticated systems. The 2.2x speed improvement over PyTorch (for specific benchmarks) came from understanding exactly what operations were necessary.