Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems

Abstract

On High-Performance Computing (HPC) systems, several hyperparameter configurations can be evaluated in parallel to speed up the Hyperparameter Optimization (HPO) process. State-of-the-art HPO methods follow a bandit-based approach and build on top of successive halving, where the final performance of a combination is estimated based on a lower than fully trained fidelity performance metric and more promising combinations are assigned more resources over time. Frequently, the number of epochs is treated as a resource, letting more promising combinations train longer. Another option is to use the number of workers as a resource and directly allocate more workers to more promising configurations via data-parallel training. This article proposes a novel Resource-Adaptive Successive Doubling Algorithm (RASDA), which combines a resource-adaptive successive doubling scheme with the plain Asynchronous Successive Halving Algorithm (ASHA). Scalability of this approach is shown on up to 1,024 Graphics Processing Units (GPUs) on modern HPC systems. It is applied to different types of Neural Networks (NNs) and trained on large datasets from the Computer Vision (CV), Computational Fluid Dynamics (CFD), and Additive Manufacturing (AM) domains, where performing more than one full training run is usually infeasible. Empirical results show that RASDA outperforms ASHA by a factor of up to 1.9 with respect to the runtime. At the same time, the solution quality of final ASHA models is maintained or even surpassed by the implicit batch size scheduling of RASDA. With RASDA, systematic HPO is applied to a terabyte-scale scientific dataset for the first time in the literature, enabling efficient optimization of complex models on massive scientific data. The implementation of RASDA is available on https://github.com/olympiquemarcel/rasda

References in corpus (11)

• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
• One weird trick for parallelizing convolutional neural networks
• Tune: A Research Platform for Distributed Model Selection and Training
• BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
• Population Based Training of Neural Networks
• An Empirical Model of Large-Batch Training
• PyTorch Distributed: Experiences on Accelerating Data Parallel Training
• Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
• HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
• Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors
• Reference Dataset and Benchmark for Reconstructing Laser Parameters from On-axis Video in Powder Bed Fusion of Bulk Stainless Steel