Overview#
Three educational ML infrastructure projects, each reimplementing a core part of the modern ML stack from first principles.
The three components#
litetorch — Neural network framework built from scratch inspired by PyTorch
- Implemented forward/backpropagation, autograd, and layer abstractions from scratch
- The goal: understand what PyTorch actually does, not just how to call it
litetune — Hyperparameter search and experiment tracking, inspired by Ray Tune
- Built trial management, search space definition, and result aggregation
- Mirrors Ray Tune’s experiment lifecycle model
sklite — ML preprocessing toolkit inspired by scikit-learn
- Designed preprocessing pipelines and utility functions for educational clarity and extensibility
Why build ML frameworks from scratch?#
In the AI era, knowing how to call an API is table stakes. Understanding what’s happening inside — the gradient flow, the trial scheduling, the data transformation pipeline — is what lets you debug, optimize, and architect ML systems that actually work in production.