open-mmlab/mmsegmentation

Too much GPU memory consumption and seems wrong

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#3,167 opened on Jul 6, 2023

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 (18 comments) (0 reactions) (0 assignees)Python (2,386 forks)batch import
good first issue

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Description

When I use resnet101 with deeplabv3+ in this lib 'https://github.com/VainF/DeepLabV3Plus-Pytorch', I can train for 40 batch size on 2 GPUs, 20 batch size for each GPU, and it consumes about 36 GB. However, when I use your lib, for 2 batch size on 1 GPUs, it consumes about 15 GB

Contributor guide

Research direction
Profile GPU memory usage using tools like nvidia smi or torch.cuda.memory summary, compare with DeepLabV3Plus Pytorch to identify differences in tensor allocations or gradient checkpointing.
Tech stack
pythonpytorch
Domain
machine learningbackend
Issue type
Bug
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
PythonPyTorchGPU memory profiling
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
40