Comments (26)
I'm downloading/uploading those other VC runtimes.
There might be some truth in this thread:
https://discuss.pytorch.org/t/ubuntu-what-version-of-cuda-pytorch-etc-can-run-on-a-nvidia-gtx-680-compute-capability-3-0/118469/4
The files mention 3.7, however there might be some issues with 3.7. The Tesla-driver I downloaded is supporting Cuda 11.4, so that might be introducing other issues.
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Even on that near empty K80 8GB is just too short
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It's already running more than an hour on quadrupalizing a 300 dpi A4, so the 6 core K80 is no luxury.
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After one and a half hour the memory footprint has more than doubled:
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Next morning still a bit more memory... Glad I disabled Windows update...
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Used memory still growing a bit:
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Wow! Thank you for the comprehensive tests that you did!
I suppose that Pytorch-directml is not well optimized with CPU, in my tests i found that even when i set more cpus, it just use 1. The only thing i can say is that this library is still in alpha state, so maibe in future will be better optimized.
:D
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The K80 contains a GPU, with CUDA codes sm_30, sm_35 and sm_37. It might be ignored by the used software.
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Yes, sadly the library just ignore this GPU
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How long do you estimate a 300 dpi A4 will take with only 6 cores?
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Do you know an Azure VM which supports DirectML?
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I estimate from 15 to 30 minutes
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Sorry, i m not pratical with Azure stuff
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DirectML should support Kepler according to the readme:
https://github.com/microsoft/DirectML
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Yes, but i suppose maibe they refers to commercial GPUs like GTX
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Just to be sure, can you try first a x0.5 upscale or x1 to check if everything works fine, this must be quick
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Just broke off the long run and started x0.5
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oook thanks, i was thinking did you installed nvidia drivers on the vm?
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Should the tool nvidia-smi be on Windows? It can't find it.
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There was a popup stating K80 driver installed.
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I'm now installing the Tesla-driver from the Nvidia-site.
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yes, maibe that will do the catch because when you screened the task manager i saw that there was no GPU panel. That usually happen when the driver is not installed
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yes, maibe that will do the catch because when you screened the task manager i saw that there was no GPU panel. That usually happen when the driver is not installed
That feature only works with WDDM 2.0 I understand from this video.
https://www.youtube.com/watch?v=gOo73cyeMUU
The K80 has 1.3.
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I've restarted the x1 upscale after a reboot. nvidia-smi responds.
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 473.47 Driver Version: 473.47 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla K80 TCC | 00000001:00:00.0 Off | 0 |
| N/A 32C P8 35W / 149W | 9MiB / 11448MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
CPU is high, nvidia-smi states "No running processes found"
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Really strange, so if you upscale on gpu it does nothing?
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Ah, also did you try install all Visual Studio runtimes?
https://www.techpowerup.com/download/visual-c-redistributable-runtime-package-all-in-one/
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