Comments (4)
anibali
commented on August 22, 2024
1
I'm pretty sure that it is mostly redundant, but I can't see a way around it since PyTorch requires cudatoolkit. I have dodged doubling up on CUDNN by not using the CUDNN base image, but I'm pretty sure that having a CUDA base image is required for proper handling of the GPU (i.e. I don't think that a stock Ubuntu base image would work). If you find a way of avoiding the redundancy please let me know.
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jbohnslav
commented on August 22, 2024
It seems like the only way to avoid this is to build from source?
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anibali
commented on August 22, 2024
@jbohnslav Right, that would work but would also require some fairly major changes to this project. I'll probably look into it at some point.
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anibali
commented on August 22, 2024
The NVIDIA CUDA base images are actually very small (e.g. https://hub.docker.com/layers/nvidia/cuda/11.3.1-base-ubuntu20.04/images/sha256-5a8e4366bd66c2734183c8b2e58da108c2e74f9edb6005be7c828786132fef5a), so I'm going to close this issue until someone has a good, concrete suggestion. Building PyTorch from source would complicate things considerably and I'm concerned that there would be no real gain and potentially compatibility issues.
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Related Issues (20)
- this is the issue which it's showing while running the docker file HOT 2
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