Accelerating Machine Learning Research with MI-Prometheus

Tomasz Kornuta, Vincent Marois, Ryan L. McAvoy, Younes Bouhadjar, Alexis Asseman, Vincent Albouy, T.S. Jayram, Ahmet S. Ozcan

Sep 29, 2018 NIPS 2018 Workshop MLOSS Submission readers: everyone
  • Abstract: The paper introduces MI-Prometheus (Machine Intelligence - Prometheus), an open-source framework aiming at accelerating Machine Learning Research, by fostering the rapid development of diverse neural network-based models and facilitating their comparison. In its core, to accelerate the computations on their own, MI-Prometheus relies on PyTorch and extensively uses its mechanisms for the distribution of computations on CPUs/GPUs. The paper discusses the motivation of our work, the formulation of the requirements and presents the core concepts. We also briefly describe some of the problems and models currently available in the framework.
  • TL;DR: The paper presents a PyTorch-based, open-source framework for machine learning.
  • Keywords: machine learning, pytorch, model, problem, grid worker
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