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HD 143006 Imaging Tutorial Part 3 #63
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Suggest adding a step demonstrating 'pre-training' to a dirty image (#206) to this tutorial. |
Suggest adding mention of use of a learning rate scheduler (#207) to this tutorial. |
Suggest adding the training diagnostic figure generated by #208 to this tutorial. |
All sounds good. I suggest we descope Ray from the original issue, as well. |
Suggest adding the image comparison figure generated by #211 to this tutorial. |
Cool, I strikethrough'ed the Ray bullet. And that sounds great. Seems like a good time to come to this would be once the total flux prior is implemented and a couple more diagnostic figures I want to add are done. #142 would also be worth addressing (adding figures made by There's a separate issue for this #134, but I'd also like to have a tutorial on the fit 'pipeline' (running a full optimization workflow with |
An overview of the HD 143006 imaging series is provided in #25
Part III is meant to be the "production-ready" version of a MPoL imaging script. The idea is to use computational resources to do hyperparameter sweeps. One example is using Ray Tune, though we might also want to suggest alternatives.
* Explore thorough hyperparameter testing with Ray Tune.This "tutorial" document might not actually be a *.py file -> Jupytext -> ipynb like the other tutorials. Rather, it might just be a regular *.rst file with text describing how one might go about running the each of the steps. If necessary, we can include a final notebook that uses the optimal parameters to visualize the best-fit image.
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