Comments (5)
Could you provide more info?
- Which config file are used?
- Does workers_per_gpu works for codebases other than mmaction2?
- There is a possibility that the multi-threading lib does not support AMD, but lets rule out other options first.
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Could you provide more info?
- Which config file are used?
- Does workers_per_gpu works for codebases other than mmaction2?
- There is a possibility that the multi-threading lib does not support AMD, but lets rule out other options first.
- Which config file are used?
tsn_r50_1x1x3_80e_ucf101_rgb.py
- Does workers_per_gpu works for codebases other than mmaction2?
I am running the training sample code of tsn on mmaction2.
python tools/train.py configs/recognition/tsn/tsn_r50_1x1x3_80e_ucf101_rgb.py
--work-dir work_dirs/tsn_r50_1x1x3_80e_ucf101_rgb
--validate --seed 0 --deterministic
Thank you.
from mmaction2.
python tools/train.py configs/recognition/tsn/tsn_r50_1x1x3_80e_ucf101_rgb.py --work-dir work_dirs/tsn_r50_1x1x3_80e_ucf101_rgb --validate --seed 0 --deterministic
The environment is: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz, GTX 1080Ti GPU.
The PID 168761-168764
are corresponding to the workers_per_gpu=4
.
There is a possibility that the multi-threading lib does not support AMD. I think you can check whether workers_per_gpu
works for other codebases.
from mmaction2.
python tools/train.py configs/recognition/tsn/tsn_r50_1x1x3_80e_ucf101_rgb.py --work-dir work_dirs/tsn_r50_1x1x3_80e_ucf101_rgb --validate --seed 0 --deterministicThe environment is: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz, GTX 1080Ti GPU.
The PID168761-168764
are corresponding to theworkers_per_gpu=4
.
There is a possibility that the multi-threading lib does not support AMD. I think you can check whetherworkers_per_gpu
works for other codebases.
Thank you, I tested that pytorch's multithreading is invalid for 2990wx.
from mmaction2.
It is grad to see that we find the root of the problem and i will close this issue.
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