Comments (5)
lkeab
commented on May 26, 2024
During training, you can reduce the parameter here and here to save memory.
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pppwzj
commented on May 26, 2024
During training, you can reduce the parameter here and here to save memory.
during test, how to save memory? Thank you.
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pppwzj
commented on May 26, 2024
During training, you can reduce the parameter here and here to save memory.
Could you tell me if I reduce the parameter LIMIT, will it have any effect on the model? Will it reduce the performance? Thank you.
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lkeab
commented on May 26, 2024
limited performance decrease if you are not reducing it extremely; for inference, you can refer to here
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perp
commented on May 26, 2024
I reduce both limit from 30 to 10 , but still cuda out of memory on ti2080 12G
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Related Issues (20)
- Could you wirte a document about how to fine-tune your pre-trianed weight plz? HOT 4
- Strange visualization result. HOT 1
- how to export trained weights to onnx HOT 1
- Asking for code component HOT 3
- Question about segmentation fault HOT 1
- RuntimeError: torch.nn.functional.binary_cross_entropy and torch.nn.BCELoss are unsafe to autocast HOT 2
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- Does the model support exporting onnx?
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