Improvements & Evaluations on the MLCommons CloudMask Benchmark
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914707395313664 |
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| author | Chennamsetti, Varshitha Mehnaz, Laiba Zhao, Dan Ghosh, Banani Samsonau, Sergey V. |
| author_facet | Chennamsetti, Varshitha Mehnaz, Laiba Zhao, Dan Ghosh, Banani Samsonau, Sergey V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04553 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improvements & Evaluations on the MLCommons CloudMask Benchmark Chennamsetti, Varshitha Mehnaz, Laiba Zhao, Dan Ghosh, Banani Samsonau, Sergey V. Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | Improvements & Evaluations on the MLCommons CloudMask Benchmark |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2403.04553 |