CUDA time difference between print function and Profiler TensorBoard
Nobody has claimed this yet.
- Dominant language
- C++
- Stars
- 992
- Forks
- 270
- Avg merge
- 2d 11h
- Merged PRs (30d)
- 12
Description
I try to analyse the time spent on CPU and GPU using the print function and Profiler Tensorboard. But I get the different result about the time on CPU and GPU.
For print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20)), I get the following result:
The above result shows that CPU time is more than GPU time. But when I use the tensorboard to show the result, the GPU time is counted much more:
I am not sure which of these is the correct result, or am I misunderstanding them?
Contributor guide
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.
- Open a pull request that references the issue number.
Research direction
No source file, test, or reproducible example is named. Start by comparing the reported profiler table call with the TensorBoard view and determine which CPU and GPU metrics each presents. Done means the differing measurements are explained and, if applicable, the relevant behavior or documentation is corrected.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- observability-sre, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100