Comments (9)
It is OK to run two eval_net.py with 4 workers each. There might be some other error.
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Hi @GBJim
I used to meet this problem too. Try adding CUDA_VISIBLE_DEVICES=0,1,2,3 before the python command, where the numbers indicate the available GPUs , It works fine for me.
from temporal-segment-networks.
This error means you might be requesting more workers than the number of available GPU.
Also, you are running 4 workers using eval_net.py
. I don't quite understand the title of this issue.
from temporal-segment-networks.
@yjxiong
Oh, I get it.
I have 4 GPU cores and have an eval_net.py
process running with 4 workers already.
When I run the second process of eval_net.py
with 4 workers, the total 8 workers exceeds the amount of my GPU cores and promtps the error messages.
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@yjxiong
Thanks a lot! I will try it again.
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@xlliuplus7
Thanks for the tip!
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It is strange. I did not add CUDA_VISIBLE_DEVICES=0,1,2,3
or anything else. I can still run two eval_net
with 4 workers perfectly.
I have 4 GPU.
from temporal-segment-networks.
@xlliu7 http://stackoverflow.com/questions/34775522/tensorflow-mutiple-sessions-with-mutiple-gpus
you mean this ->$ CUDA_VISIBLE_DEVICES=0 python my_script.py # Uses GPU 0.
Right?
But I tried, and get error: error = cudaSuccess: (10 vs 0) invalid device ordinal
.
Do you have any suggestions?
from temporal-segment-networks.
@KnightOfTheMoonlight That's exactly what I mean.
I use a workstation with 4 GPUs + CUDA 7.5 + cuDNN v5. The toolbox was compiled with MPI support.
I found it neccessary to add the "CUDA_VISIBLE_DEVICES" environment to use multiple GPUs. Otherwise I will get the "invalid device ordinal" error. I guess the caffe toolbox fails to detect available GPUs on my workstation. Your problem seems to be opposite to mine.
In fact, you can also mannually specify the dev_id in "eval_net.py". See https://github.com/yjxiong/temporal-segment-networks/blob/master/tools/eval_net.py#L32
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