Comments (2)
I think it works either way. I've seen docker images including pytorch/tensorflow natively, because they are big packages to install.
Within the image, we run some cache-cleanup
to reduce the image size, but those steps will be neglected if we let users to install packages in each container. Downside is that we might need to provide cpu-image and gpu-image separately.
At this point, we can just specify in readme that python/pytorch are natively provided, and indicate their version. We let users to re-install if they need other version.
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If we need to provide both the cpu-image and gpu-image, it might be better to remove the pytorch and let users to install there own package... It doesn't take too much time to install I believe. (At least for me today when I install the FastRodReconstruction repository)
Also, the python version is needed to be specified as well, I think.
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Related Issues (8)
- Use action to build Docker image and publish to Docker Hub HOT 2
- Action CI on Pull Request Commit and Pull Request Merge to main branch HOT 1
- python package required HOT 1
- Mock Vicon publisher node name HOT 4
- Vicon system assistance HOT 1
- Description and usage of ros2bag HOT 2
- Update README for ros2-bag usage
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