MLCommons CloudMask基准的改进与评估
Improvements & Evaluations on the MLCommons CloudMask Benchmark
AI总结：
本研究在纽约大学NYU Greene集群上对MLCommons科学云掩膜基准开展深度学习模型性能测试，更新了代码、确定了最优超参数下的最佳模型，给出了最高准确率及训练推理平均耗时，相关代码已开源且可供MLCommons后续使用。
摘要：
本文报告了在纽约大学（NYU）的高性能计算集群NYU Greene上，深度学习模型在MLCommons科学云掩膜基准上的性能基准测试结果。MLCommons是一个开发并维护多项可从AI发展中获益的科学基准的联盟。我们介绍了云掩膜基准任务、更新后的代码，以及采用我们选定的超参数设置时该基准的最优模型。我们的基准测试结果包括在NYU系统上取得的最高准确率，以及在多次运行/不同随机种子下，该基准训练和推理各自的平均耗时。我们的代码可在GitHub上获取。MLCommons团队已获知我们的进展，可能会将开发的代码用于其未来工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
