Related to 392
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Description
I am running into the same issue as https://github.com/NVIDIA/apex/issues/392.
Using APEX over cross validation leads to OOM.
I just wanted to check the work around solution.
Instantiate model, optimizer.
apex initialize
for k in cross_vals:
train(model, optimizer)
reset( model)
Is that correct?
Problem is that I am using a transformer, so resetting the model indicates losing the transfer learning.
I wasnt clear on how to use the previous version of APEX either. Any suggestions would be appreciated.
Contributor guide
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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.
- Open a pull request that references the issue number.
Research direction
Start by reading issue 392 and compare its reported behavior with the cross-validation sequence described here. Investigate how APEX initialization, model reset, and transfer learning interact in that sequence; done should include a confirmed workaround or a clearly documented reason the proposed sequence cannot avoid the OOM.
Written by the indexing model from the issue text.
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
- 20/100