datascience-labs/GreenAccounter: GreenAccounter Demo
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| Format: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866902223615688704 |
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| author | Jeonghyeon Park JaekyeongKim jhparkinglot |
| author_facet | Jeonghyeon Park JaekyeongKim jhparkinglot |
| contents | <p>We are excited to announce the initial release of GreenAccounter, an open-source toolkit designed for carbon-aware orchestration of deep learning workloads in geo-distributed multi-cloud environments. This software implements the methodology discussed in our paper, focusing on reducing the environmental impact of long-running AI training through dynamic workload migration.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18067314 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | datascience-labs/GreenAccounter: GreenAccounter Demo Jeonghyeon Park JaekyeongKim jhparkinglot <p>We are excited to announce the initial release of GreenAccounter, an open-source toolkit designed for carbon-aware orchestration of deep learning workloads in geo-distributed multi-cloud environments. This software implements the methodology discussed in our paper, focusing on reducing the environmental impact of long-running AI training through dynamic workload migration.</p> |
| title | datascience-labs/GreenAccounter: GreenAccounter Demo |
| url | https://doi.org/10.5281/zenodo.18067314 |