Framework for distributed machine learning. Its purpose is to improve transparency, reproducibility, robustness, and to provide fair performance measures as well as reference implementations, helping adoption of distributed machine learning methods both in industry and in the academic community. Besides algorithm comparison, a main use case is to help the selection of hardware (CPU, GPU) used to run AI applications, as well as how to connect it into a cluster to get a good cost/performance tradeoff.