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#Uber has open-sourced #Fiber, a new library which aims to empower users in implementing large-scale machine learning computation on computer clusters. The main objectives of the library are to leverage heterogeneous computing hardware, dynamically scale algorithms, and reduce the burden on engineers implementing complex algorithms on clusters.

It’s a challenge for machine learning frameworks to remain flexible enough to support reinforcement learning- (RL) and population-based algorithms together with other heuristics like deep learning because the requirements can vary greatly. While established frameworks like TensorFlow and PyTorch cover the setup of distributed training for most common machine learning methods, these frameworks are less fit for RL-based and population-based methods, which often require frequent interaction with simulators and a complex and dynamic scaling strategy. Fiber provides a unified Python user interface to its distributed computing framework to support these new requirements.


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