EvoStore: Towards Scalable Storage of Evolving Learning Models

Deep Learning (DL) has seen rapid adoption in all domains. Since training DL models is expensive, both in terms of time and resources, application workflows that make use of DL increasingly need to operate with a large number of derived learning models, which are obtained through transfer learning and fine-tuning. At scale, thousands of such derived DL models are accessed concurrently by a large number of processes. In this context, an important question is how to design and develop specialized DL model repositories that remain scalable under concurrent access, while addressing key challenges: how to query the DL model architectures for specific patterns? How to load/store a subset of layers/tensors from a DL model? How to efficiently share unmodified layers/tensors between DL models derived from each other through transfer learning? How to maintain provenance and answer ancestry queries? State of art leaves a gap regarding these challenges. To fill this gap, we introduce EvoStore, a distributed DL model repository with scalable data and metadata support to store and access derived DL models efficiently. Large-scale experiments on hundreds of GPUs show significant benefits over state-of-art with respect to I/O and metadata performance, as well as storage space utilization.