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ValueError: could not convert string to float: '[Not Supported]' #26

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artBoffin opened this issue May 7, 2018 · 4 comments
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@artBoffin
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Traceback (most recent call last):
File "/app/entrypoint/entrypoint.py", line 100, in main
styletransfer(contents, styles, savefolder, size, alg, weights, stylescales, tileoverlap, algparams=otherparams)
File "/app/entrypoint/neuralstyle/algorithms.py", line 84, in styletransfer
if fitsingletile(targetshape(content, size), alg):
File "/app/entrypoint/neuralstyle/algorithms.py", line 336, in fitsingletile
mx = maxtile(alg)
File "/app/entrypoint/neuralstyle/algorithms.py", line 366, in maxtile
gname = gpuname()
File "/app/entrypoint/neuralstyle/algorithms.py", line 354, in gpuname
gpus = GPUtil.getGPUs()
File "/opt/miniconda/lib/python3.6/site-packages/GPUtil/init.py", line 82, in getGPUs
gpuUtil[g] = float(vals[i])/100
ValueError: could not convert string to float: '[Not Supported]'

@albarji
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albarji commented May 20, 2018

Could you please give me more details on how were you calling neural-style. Also, does your system have an nVidia GPU? Have you installed all dependencies? It looks like neural-style is failing to find any GPU on your system.

@kuriot
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kuriot commented Sep 27, 2018

Hello.

I have the same issue. All dependencies are installed and other containers show output of nvidia-smi. I try to simply run it with an example run line, of course changing filenames to mine.

nvidia-docker run --rm -v $(pwd):/images albarji/neural-style --content somecontent.png --style somestyle.png

@albarji
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albarji commented Oct 25, 2018

Thanks for reporting this. This seems to be related with a failure to detect the GPU model in your system. I will add a patch that assumes default rendering parameters when this check fails.

@albarji
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albarji commented Oct 27, 2018

I have updated the image for this project in Docker hub. Could you please try again and report what you obtain? I have only been able to reproduce this bug when nvidia-docker was not properly configured. Also make sure you driver version supports CUDA 8.0 or higher.

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3 participants