Visualizing pytorch Emited NVTX with Tensorboard Profiler
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- C++
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Description
There are important Pytorch Specific markers generated during the training loop either using NVTX or other means from pytorch lightning.
It would be useful to show the time line view with execution time for different pytorch training sections of the code as shown in the attached image. The current trace view is quite detailed and goes down one-step further with detailed cuda kernels etc. It would be useful to have Pytorch Execution specific trace view that is easy to understand and intuit to find any synchronization or communication bottlenecks in the training loop and also compare different execution time for different executing region of the training/validation code (like forward, loss, data-loading etc).
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
Start by reviewing the current trace view and the attached timeline example, then investigate how PyTorch-specific NVTX or other training markers enter the profiler output. Define the scope and completion criteria for a PyTorch execution-focused view covering regions such as forward, loss, data loading, synchronization, and communication.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- pytorch
- Domain
- performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100