Something about OOM
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- Dominant language
- Python
- Stars
- 1k
- Forks
- 174
- PR merge metrics
- No merged PRs in 30d
Description
Hello!
When I run this model (using an A800 graphics card with 80GB of video memory), I found OOM errors in the level layers aggregation and de aggregation (in the encoder and decoder part). So I checked the memory usage and found that the aggregation and de aggregation steps were very memory intensive. Do you have any solutions?
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
The report identifies memory-intensive aggregation and de-aggregation in the encoder and decoder, but names no files or tests. Start by reproducing the model run on an A800 and tracing memory use around those steps; done means identifying a supported way to avoid the OOM or documenting the limitation and evidence.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- 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