Comments (3)
Stonesjtu
commented on July 17, 2024
Are you printing the memory usage after backward (BP)?
Used memory collects all the PyTorch Tensors in ** Python ** language space.
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lfangyu09
commented on July 17, 2024
I have made a comparison for the same model between the Used Memory by your code and GPUtil. Here are the results.
The Used Memory is 409M and GPU memory by GPUtil is 6486 MiB.
Is the Used Memory by your code not the GPU memory? I am confused by "all the PyTorch Tensors in ** Python ** language space.". Is there any tutorial to explain it in detail? Thanks a lot!
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Stonesjtu
commented on July 17, 2024
You can place the report() before backward(); The GPUtil gets the memory PyTorch requests (or cached), while memory_reporter gets the memory PyTorch actually allocated
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Related Issues (20)
- Variable 'tensor_names' referenced before assignment HOT 1
- Redirect report() to the file HOT 3
- sparse tensors do not have storage HOT 1
- 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
- turn profile decorator on and off HOT 2
- Jupyter Support Issues HOT 1
- Does not work with torch 1.7.1+ HOT 1
- is PyTorch Profiler Used Internally? HOT 3
- weakly-referenced object no longer exists HOT 2
- Naming Tensors within MemReporter HOT 1
- OSError: could not get source code when running LineProfiler HOT 2
- What's the difference between `active_bytes` and `reserved_bytes`? HOT 3
- How to get memory diff between first and second batch? HOT 1
- Nor working HOT 4
- How do I record the maximum memory usage of a script?
- KeyError: "['XXX'] not in index" HOT 1
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