Performance with python build?
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- Dominant language
- Python
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Description
Is there some sense on how the python build performs, compared with the C/NCCL compile? I'm not getting an error, but I get the warning :
Warning: multi_tensor_applier fused unscale kernel is unavailable, possibly because apex was installed without --cuda_ext --cpp_ext. Using Python fallback. Original ImportError was: ModuleNotFoundError("No module named 'amp_C'")
So, it's still running. I guess I wanted to know much more performance I would be getting if I chased this down (our institution makes it difficult to troubleshoot sometimes). I have a fairly large (500GB) run across 6x V100's on our DGX1 on opt_level 01.
Ubuntu 16.04.6
Pytorch 1.1.0
Torchvision 0.3.0
CUDA 10.0
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
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Research direction
Start by reviewing the reported Apex installation warning and the --cuda_ext --cpp_ext build options in the issue context. Reproduce the Python fallback and compare it with a C/NCCL extension build on the stated PyTorch, CUDA, and V100 setup; done means providing a measured performance comparison or a clear explanation of the difference.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100