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Push: Concurrent Probabilistic Programming for Bayesian Deep Learning

arXiv 2306.06528 ↗First posted 10 Jun 2023cs.LG cs.AI cs.PLcited by 0

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Abstract (arXiv)

We introduce a library called Push that takes a probabilistic programming approach to Bayesian deep learning (BDL). This library enables concurrent execution of BDL inference algorithms on multi-GPU hardware for neural network (NN) models. To accomplish this, Push introduces an abstraction that represents an input NN as a particle. Push enables easy creation of particles so that an input NN can be replicated and particles can communicate asynchronously so that a variety of parameter updates can be expressed, including common BDL algorithms. Our hope is that Push lowers the barrier to experimenting with BDL by streamlining the scaling of particles across GPUs. We evaluate the scaling behavior of particles on single-node multi-GPU devices on vision and scientific machine learning (SciML) tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 45 of its 69 references that are in the map. First posted on arXiv 10 Jun 2023.

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References · 45

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