Oracle Corporation
MEMORY USAGE PREDICTION FOR MACHINE LEARNING AND DEEP LEARNING MODELS

Abstract:

Herein, a computer receives a new training dataset for a target ML model. Proven or unproven respective values of hyperparameters of the target ML model are selected. An already-trained ML metamodel predicts an amount of memory that the target ML model will need, when configured with the respective values of the hyperparameters, to train with the new training dataset. In an embodiment, supervised training of the ML metamodel is as follows. The ML metamodel receives feature vectors that each contains distinct details of a respective past training of the target ML model of many and varied trainings of the target ML model. Those distinct details of each past training includes: respective values of the hyperparameters, and respective values of metafeatures of a respective training dataset of many training datasets. Each feature vector is labeled with a respective amount of memory that the target ML model needed during the respective past training.