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PyTorch's success depends on more than users—it needs engineers who understand what's inside. Engineers who can debug framework issues, optimize at the systems level, contribute upstream, and build what comes next. But ML education today produces practitioners who call APIs without understanding them. They train models without knowing why Adam needs 3× the memory of SGD, or what happens when they call loss.backward().
TinyTorch is a 20-module open-source curriculum that closes this gap. Students construct PyTorch's core components—tensors, autograd, optimizers, CNNs, transformers—in pure Python, building a complete framework where every operation is code they wrote. By the final module, they don't just use PyTorch; they understand how to build it.
The curriculum uses progressive disclosure, systems-first profiling from Module 01, and build-to-validate milestones—recreating ML breakthroughs from Perceptron (1958) through Transformers (2017), culminating in MLPerf-style benchmarking.
TinyTorch is how we grow the next generation of PyTorch contributors and the engineers who will build what comes after.
Vijay Janapa Reddi is a Professor at Harvard University, where he leads
research at the intersection of machine learning and computer systems.
He is the author of the open-source Machine Learning Systems textbook
(mlsysbook.ai) and co-founder of MLCommons, the organization behind... Read More →
Research Fellow, University of Genoa & Scuola Superiore Sant'Anna
I am a Research Fellow holding a joint position at the University of Genoa and Scuola Superiore Sant'Anna. My research is centered on Edge AI, where I am currently working to automate the design of applications through Hardware-Aware Neural Architecture Search (NAS). By running these... Read More →
