Developer Accidentally Built a Lightweight AI Training Framework While Learning PyTorch

A developer initially set out to learn PyTorch by building two AI models, aiming to understand core concepts like training, optimizers, and batching. Frustrated by repetitive setup work, they began building Tensorless, a framework designed to automate most training configuration so users could simply provide data and get results. The first version of Tensorless used PyTorch under the hood to handle automatic parameter decisions, and it performed well for its intended purpose. However, concerns about PyTorch-based dependencies ballooning to over 500 MB pushed the developer to rethink the architecture entirely. They ultimately decided to build their own neural network and training system from scratch, transforming Tensorless from a simple PyTorch wrapper into a standalone lightweight framework.