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When using Stochastic Gradient Descent (SGD) for training machine learningmodels, it is often crucial to provide the model with examples sampled atrandom from the dataset. However, for large datasets stored in the cloud,random access to individual examples is often costly and inefficient. A recentwork \cite{corgi}, proposed an online shuffling algorithm called CorgiPile,which greatly improves efficiency of data access, at the cost some performanceloss, which is particularly apparent for large datasets stored in homogeneousshards (e.g., video datasets). In this paper, we introduce a novel two-steppartial data shuffling strategy for SGD which combines an offline iteration ofthe CorgiPile method with a subsequent online iteration. Our approach enjoysthe best of both worlds: it performs similarly to SGD with random access (evenfor homogenous data) without compromising the data access efficiency ofCorgiPile. We provide a comprehensive theoretical analysis of the convergenceproperties of our method and demonstrate its practical advantages throughexperimental results.

Corgi^2: A Hybrid Offline-Online Approach To Storage-Aware Data Shuffling For SGD