Open iancze opened 3 years ago
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. Depending on how the training and crossval stuff ends up, I think we should also revise Tutorial 2, or possibly combine 2&3.
Suggest adding the image comparison figure generated by #211 to this tutorial.
All sounds good. I suggest we descope Ray from the original issue, as well. Depending on how the training and crossval stuff ends up, I think we should also revise Tutorial 2, or possibly combine 2&3.
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 vis_histogram_fig
and split_diagnostics_fig
figures to the tutorials) when we address this.
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 python -m mpol.pipeline
) when that's done.
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.