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As deep learning becomes more expensive, both in terms of time and compute,inefficiencies in machine learning (ML) training prevent practical usage ofstate-of-the-art models for most users. The newest model architectures aresimply too large to be fit onto a single processor. To address the issue, manyML practitioners have turned to model parallelism as a method of distributingthe computational requirements across several devices. Unfortunately, thesequential nature of neural networks causes very low efficiency and deviceutilization in model parallel training jobs. We propose a new form of "shardparallelism" combining task and model parallelism, then package it into aframework we name Hydra. Hydra recasts the problem of model parallelism in themulti-model context to produce a fine-grained parallel workload of independentmodel shards, rather than independent models. This new parallel design promisesdramatic speedups relative to the traditional model parallelism paradigm.

Model-Parallel Model Selection for Deep Learning Systems