Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
As MN-BaB strongly relies on C dependencies, getting it to run, especially on compute clusters where one does not have root access, can be quite a hustle. Therefore, I added Docker and Apptainer (formerly Singularity) Image definitions to give users an easy way to set MN-BaB up, on personal computers as well as compute clusters.
I added documentation in the README file to get both images to run.
The available CUDA images are based on Ubuntu 20.04, so I updated MN-BaB to work with Python 3.8. This was more straightforward than getting Python 3.7 to run in the images. This led to a change in the network loading code (not sure why it was needed to reference the
blocks
property of the network earlier) and in the requirements.txt (I also fixed all dependencies and versions to make the build process bullet-proof).I hope the maintainers see this as a valuable addition to make MN-BaB runnable by a broader audience of researchers. I would be very happy to address any questions you might have and to work on any further suggestions!