A question about scaled_loss.backward. It cost me about 1s when i use a single GPU. However
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 25/100
- Issue type
- Bug
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python, pytorch
- Domain
- distributed-systems, machine-learning
Research direction
Start with scaled_loss.backward in NVIDIA/apex and compare the reported single-GPU and two-GPU timings. Done means explaining the roughly 1-second versus 10-second cost, but the issue provides no model, environment, reproduction, or test to run.
Written by the indexing model from the issue text.
Description
I try to train my model with 2 gpus, this function cost me about 10s. How to explain this phenomenon?
- Dominant language
- Python
- Stars
- 9k
- Forks
- 1.5k
- Avg merge
- 2d 4h
- Merged PRs (30d)
- 3
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