RVC-Project / RVC-Project/Retrieval-based-Voice-Conversion-WebUI
Slower Multi-GPU training with 2x the number of GPUs and 4x the amount of VRAM
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- Dominant language
- Python
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Description
I have two systems training on identical datasets
System A has 4 x NVIDIA RTX A5000 (24GB VRAM per GPU), and a batch size of 12 per GPU.
System B has 7 x NVIDIA RTX A6000 (48GB VRAM per GPU), and a batch size of 18 per GPU.
I would expect System B to train much faster. However...
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System A (96GB total VRAM, batch size 12) takes 11 seconds per epoch.
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System B (336GB total VRAM, batch size 18) takes 13 seconds per epoch.
I'm wondering if this is down to the overhead of multi-GPU training, or if there's something I'm missing here?
Thank you
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Research direction
No files, tests, or training entry points are named. Reproduce the comparison with the two reported GPU and batch-size configurations, then inspect the multi-GPU training path to identify the source of the slower epoch time; done means explaining the discrepancy or identifying a concrete change to investigate.
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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
- 20/100