Comments (2)
lizhaoliu-Lec
commented on July 3, 2024
The code supports multi GPU training, controlled by the flag CUDA_VISIBLE_DEVICES.
For now, the default settings for S3DIS and ScanNet V2 are 2 point-clouds per-GPU.
To reduce the GPU memory consumption, just set DATA.batch_size 1. I hope the batch size of 1 can be supported by your GPU.
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mooncakehub
commented on July 3, 2024
thank you for your reply. I have several 2080ti gpus ,but each of them has 12G memory.So I want to use two gpus to train S3DIS.
Because DATA.batch_size 1 may not perform well as your paper said.
I haven't tried many gpus parallels, so I don't know if it's feasible
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Related Issues (12)
- merge_sort: failed to synchronize: cudaErrorIllegalAddress: an illegal memory access was encountered terminate called after throwing an instance of 'c10::Error' HOT 11
- Cannot reproduce the results. HOT 16
- Question about the requirement of GPU HOT 4
- Question about the training script for the 0.02% (S3DIS) HOT 7
- how to prepare weak labels of S3DIS dataset? HOT 1
- Dose your code only can be train on MinkowskiEngine 0.4.3 Env now HOT 7
- What type of data is percentage0.001evenc? HOT 2
- can you provide 0.02% S3DIS experiment scripts？ HOT 1
- How to test? HOT 5
- How to visualise the results after testing? HOT 1
- Cannot reproduce the results
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