Do we still need this repository?

[bioimage-io]

Do we still need this repository?

[FynnBe]

yes, tiktorch's model adapters, or even the PredictionPipeline with pre- and postprocessing.
But each such adapter/pipeline/runner potentially goes together with it's own set of transformation implementations...
We could merge it all, but then we should have kept everything in python-bioimage-io.
I think some splitting makes sense and the spec just happens to live in python, but I see that more like a coincidence. we could scratch the whole validator and go json schema, who knows?^^

Example use cases could live there though, but not full implementations for several frameworks (and languages!?). Maybe merging pytorch-bioimage-io into python-bioimage-io (but keeping the separate modules bioimageio.core and bioimageio.torch as it is now) would make sense.

• bioimageio.spec: python-bioimage-io -> spec-bioimage-io
• bioimageio.core: python-bioimage-io
• bioimageio.torch: pytorch-bioimage-io -> python-bioimage-io?
• bioimageio.tf: -> python-bioimage-io?

[constantinpape]

(Also, if we keep this repository and move pytorch-bioimage-io and stuff from tiktorch here, we should remove the outdated stuff in https://github.com/bioimage-io/python-bioimage-io/tree/master/bioimageio).

[FynnBe]

There is not much left here, but

• the runner should live here (still in tiktorch atm)
• the transformations should be updated and live here (for numpy only as there is clear overlap with [bioimageio.torch](https://github.com/bioimage-io/pytorch-bioimage-io and potentially with a 'bioimageio.tf')

[constantinpape]

• the runner should live here (still in tiktorch atm)

What exactly do you mean by runner? This: https://github.com/ilastik/tiktorch/tree/master/tiktorch/server/prediction_pipeline/_model_adapters?

• the transformations should be updated and live here (for numpy only as there is clear overlap with [bioimageio.torch](https://github.com/bioimage-io/pytorch-bioimage-io and potentially with a 'bioimageio.tf')

For these, I thought that the spec repo would also be the better place, because they could serve as reference implementations.

[constantinpape]

yes, tiktorch's model adapters, or even the PredictionPipeline with pre- and postprocessing.
But each such adapter/pipeline/runner potentially goes together with it's own set of transformation implementations...
We could merge it all, but then we should have kept everything in python-bioimage-io.
I think some splitting makes sense and the spec just happens to live in python, but I see that more like a coincidence. we could scratch the whole validator and go json schema, who knows?^^

I agree that some separation makes sense, but I don't think having separate repositories for python-bioimage-io, pytorch-bioimage-io and tf-bioimage-io make sense, especially since the prediction pipelines depend on having the frameworks available, so I very much like this idea:

• bioimageio.spec: -> spec-bioimage-io
• bioimageio.core: python-bioimage-io
• bioimageio.torch: pytorch-bioimage-io -> python-bioimage-io?
• bioimageio.tf: -> python-bioimage-io?

I still think that the the pure numpy implementations of pre-and-postprocessings should go into the spec repository and serve as the reference implementation. Then, the spec repo is self-contained and python-bioimage-io depends on it anyways, so can use these transformations.