Performance Models for Distributed Deep Learning Training Jobs on Ray

Deep Learning applications are pervasive today, and efficient strategies are designed to reduce the computational time and resource demand of the training process. The Distributed Deep Learning (DDL) paradigm yields a significant speed-up by partitioning the training into multiple, parallel tasks. The Ray framework supports DDL applications exploiting data parallelism by enhancing the scalability with minimal user effort. This work aims at evaluating the performance of DDL training applications, by profiling their execution on a Ray cluster and developing Machine Learning-based models to predict the training time when changing the dataset size, the number of parallel workers and the amount of computational resources. Such performance-prediction models are crucial to forecast computational resources usage and costs in Cloud environments. Experimental results prove that our models achieve average prediction errors between 3 and 15% for both interpolation and extrapolation, thus demonstrating their applicability to unforeseen scenarios.