MLCommons Algorithmic Efficiency is a benchmark and competition measuring neural network training speedups due to algorithmic improvements in both training algorithms and models.
Adds a subsection about ported submissions (e.g. submitting both a JAX and PyTorch version of an algorithm). Submitters are required to designate one of those as eligible, with the remaining ones being considered baselines.
Adds a subsection about ported submissions (e.g. submitting both a JAX and PyTorch version of an algorithm). Submitters are required to designate one of those as eligible, with the remaining ones being considered baselines.