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🤖 A new training is being planned.
At the end of the training, the models will be released and this PR will be notified again. |
🚀 Models for pp-2016-MU-Sim10b-gha_multi_gpu were releasedYou can review the models developed in this PR in Release pp-2016-MU-Sim10b-gha_multi_gpu-2025-08-29T12h05m47 |
🤖 A new training is being planned.
At the end of the training, the models will be released and this PR will be notified again. |
🚀 Models for pp-2016-MU-Sim10b-gha_multi_gpu were releasedYou can review the models developed in this PR in Release pp-2016-MU-Sim10b-gha_multi_gpu-2025-08-29T13h33m54 |
🤖 A new training is being planned.
At the end of the training, the models will be released and this PR will be notified again. |
🤖 A new training is being planned.
At the end of the training, the models will be released and this PR will be notified again. |
🚀 Models for pp-2016-MU-Sim10b-gha_multi_gpu were releasedYou can review the models developed in this PR in Release pp-2016-MU-Sim10b-gha_multi_gpu-2025-09-01T11h28m17 |
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The workflow can be easily parallelized on 4 parallel worker by Snakemake, but natively there is no logic to assign different gpus to different workers.
I have created a small gpu_picker module that keep tracks of which GPUs are allocated and never assign the same gpu to two jobs.
I'll try first to run on a single worker, but enabling GPU allocation via gpu_picker, if that works, I'll plug the 4-gpu runner.