Improvements & Evaluations on the MLCommons CloudMask Benchmark
This work offers incremental improvements for researchers and practitioners using the MLCommons cloud-masking benchmark by optimizing performance on a specific high-performance computing cluster.
The paper reports benchmarking results of deep learning models on the MLCommons Science cloud-masking benchmark using NYU Greene, providing updated code and the best model with specific hyperparameter settings, achieving the highest accuracy and average training/inference times on that system.
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.