Improvements & Evaluations on the MLCommons CloudMask Benchmark

Autor:	Chennamsetti, Varshitha, Mehnaz, Laiba, Zhao, Dan, Ghosh, Banani, Samsonau, Sergey V.
Rok vydání:	2024
Předmět:	Computer Science - Distributed Parallel and Cluster Computing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In this paper, we report the performance benchmarking results of deep learning models on MLCommons' Science cloud-masking benchmark using a high-performance computing cluster at New York University (NYU): NYU Greene. MLCommons is a consortium that develops and maintains several scientific benchmarks that can benefit from developments in AI. We provide a description of the cloud-masking benchmark task, updated code, and the best model for this benchmark when using our selected hyperparameter settings. Our benchmarking results include the highest accuracy achieved on the NYU system as well as the average time taken for both training and inference on the benchmark across several runs/seeds. Our code can be found on GitHub. MLCommons team has been kept informed about our progress and may use the developed code for their future work. Comment: arXiv admin note: text overlap with arXiv:2401.08636
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.04553 Zobrazit plný text záznamu View this record from Arxiv