SimpleChains is an open source pure-Julia machine learning library developed by PumasAI and JuliaComputing in collaboration with Roche and the University of Maryland, Baltimore. It is specialized for relatively small-sized models and NeuralODEs, attaining best in class performance for these problems. The performance advantage remains significant when scaling to tens of thousands of parameters, where it's still >5x faster than Flux or Pytorch while all use a CPU, even outperforming GPUs.
SimpleChains is a pure-Julia library that is simple in two ways:
It additionally manages memory manually, and currently relies on hand written pull back definitions. In combination, these allow it to be 50x faster than Flux training an MNIST example on a 10980XE.
This talk will focus on introducing the library, showing off a few examples, and explaining some of they "why" behind it's performance.