Comments (1)
I'm afraid not because PyTorch does not name any tensor.
Probably we can manage a tensor -> name
mapping in this tool, but it requires considerable amount of code changes.
I would recommend you to report the memory usage at multiple critical locations so you can have a deeper understand the memory system underneath.
a = torch.Tensor(5,5,5)
reporter.report()
b = a + 5
reporter.report()
c = b * 5
reporter.report()
from pytorch_memlab.
Related Issues (20)
- Variable 'tensor_names' referenced before assignment HOT 1
- Redirect report() to the file HOT 3
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- Documentation for pl.LightningModule that includes many nn.Modules HOT 6
- Fail to install using pip HOT 4
- Request: solving the lack of incremental reporting in loops / functions HOT 2
- Error when running on Colab CPU instance HOT 8
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- What's the difference between `active_bytes` and `reserved_bytes`? HOT 3
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- Nor working HOT 4
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from pytorch_memlab.