Dose data_prefetcher() really speed up training?
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Description
I used your Python code https://github.com/NVIDIA/apex/blob/master/examples/imagenet/main_amp.py#L256
My code is
- https://github.com/zhangpzh/maskrcnn-benchmark/blob/Falcon/tools/train_net.py
- replace this for-each-loop by your
while True - pytorch: torch.nn.parallel.DistributedDataParallel
- 8 GPUs
I think this data_prefetcher could speed up training, because there is another stream sending data to GPU memory when model is running in the GPU. So there is a very small gap between two iteration.
However, this trick does not work for me. So please help me
data_prefetcheris really for speed up?- why it dosen't work for my case
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Research direction
Start with examples/imagenet/main_amp.py around the referenced data_prefetcher implementation, then compare it with maskrcnn_benchmark/engine/trainer.py and tools/train_net.py using DistributedDataParallel across 8 GPUs. Measure iteration timing with and without prefetching; done would be a documented explanation of whether it improves this setup and why the observed behavior differs.
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Assessment
- Tech stack
- python, pytorch
- Domain
- distributed-systems, machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100