Hi Team,

I would also like to address this issue but from the perspective of user debugging. I think having an option to set num_workers=0 in the inference dataloader would allow users to better debug error traces that occur.

I see a few issues that would probably benefit from this as well:
#2509
#2514
#2182

Looking forward to hear what you think

@Lars-Kraemer

Sounds like a reasonable proposal.
Currently, time is short on our side so if one of you, @surajpaib or @LennyN95, would be willing to create a PR with the feature in question we can certainly integrate this.

Setting num_workers=0 will probably be more work than just offering a new member function of nnUNetPredictor that covers this. This function would then not be supported via command line but if you are packing nnU-Net in a Docker you might not need that anyways. Would this be a sensible solution to your problem?
Best,
Fabian

I've come along similar issues when trying to deploy nnUNet models in docker containers, and although my multiprocessing issues might come from somewhere else, I also agree that having a sequential predictor might simplify a lot of things, especially when you don't need to do inference in big batches.

In my use-case, a quick fix that didn't require messing with your code is to simply use the predict_single_npy_array function while looping through my input arrays. This isn't a big issue for me as I already do all the dataloading manually, but might be a bit more cumbersome for other applications.

There is now a predict_from_files_sequential function in nnUNetPredictor that should do what you need :-)

@FabianIsensee Thanks Fabian! Did you have a look into the PR we opened and my comment on reproducibility? Is your predict_from_files_sequential used automatically when workers are set to 0?