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amd64-gpu image is out of date #97
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@brainwater Just thought I'd say I saw your post. I'd been having the same problem. Seeing your solution made me want to try again. This time starting with a fresh pull it worked immediately, something I'd never seen before. Running under WSL2. Just a bit of fiddling with WSL2 to get the port working beyond the local machine. No other changes necessary. So I can't say the issue is closed as I don't see any updates in the repository, but it's definitely working for me now. |
I did actually just rebuild the image and pushed it. It may have picked up some new stuff from the base image. I meant to post here but I forgot. |
Actually it seems I was mistaken when I said it was working. I neglected to add --gpus all to the docker run command initially. So it was only operating in CPU mode. When I added it the YOLOv5 startup lists the GPU instead of the CPU but exits without an error. YOLOv5 🚀 2024-1-1 Python-3.8.10 torch-2.1.2+cu121 CUDA:0 (NVIDIA GeForce GTX 970, 4096MiB) So mine is probably a different issue at this point but I'd love to see it working with the current CUDA. Not sure how to proceed with troubleshooting. |
Apologies if this is a newbie question, but what is the minimum required compute capability for running this? I didn't see anything listed. My test case is currently 5.2. If it needs significantly higher I may need to rethink my ideas. I'm using it in the context of Home Assistant and I'm not sure what would satisfy the requirements. |
@keyboarderror honestly, I don't know what it requires... I don't do much with the Nvidia GPU side of things. I wouldn't think it requires very high as the model it uses is old but it's good.I really can't tell you to be sure. |
This is the version the container has currently:
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Okay, I tried building with updated tensorflow quite a few times but something was wrong with Docker hub. I just finally tried again and it took. Maybe try now. I believe this will have updated Cuda. |
OK. It fails with or without enabling the GPU, but at least there's an error. It's the same trying to run on CPU. Doesn't appear to be a Cuda problem.
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I think the fix is to update pydantic within requirements.txt I'm getting the same issue,
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Okay, I just updated everything to tensorflow 2.14 which should have updated the CUDA version. Try it now. |
It's back to exiting without any errors. CPU mode works. |
But GPU does not? |
No. It just returns to the command prompt a couple moments after the message Fusing layers... No error message. |
I'm getting the same using the gpu
Tonight I'll see if I can debug it to get more details on exactly where it had an error. |
The problem is due to out-of-date apt packages. I can work around the issue by running an
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Confirmed that fixes it here too. Excellent. And thanks @brainwater for the --entrypoint=bash switch. I'm still pretty new to docker and couldn't figure out how to get a persistent shell if the container didn't want to run. Now I can poke around. |
Awesome! Thanks for tracking that down. I updated the Docker builds and pushed everything out. I even dug out my GTX970 and verified it runs now. Closing this issue., LMK if still problems. |
It's working for me now. Thanks for your work @snowzach ! |
Yes, I pulled the update and it works immediately. Thank you very much @snowzach! |
The image snowzach/doods2:amd64-gpu is out of date and I believe isn't compatible with cuda 12.2
When running
docker run -it -p 8080:8080 --gpu all snowzach/doods2:amd64-gpu
I got the following error:The image
snowzach/doods2:amd64
worked fine on the same machine.This is a new installation of ubuntu server 22.04, with docker-engine installed via the instructions on the Docker website (i.e. not the ubuntu docker snap, since the snap is not compatible with gpu acceleration of containers).
I ran the following from within a container using the base image
snowzach/doods2:amd64
:At this point, I tested it and it ran much better and faster, presumably indicating it successfully used the GPU.
I assume the image would work if the image build process were run again, but I was unable to find any instructions on the build process. I'd also appreciate instructions on building doods2 images locally.
nvidia-smi output:
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