HD 143006 Imaging Tutorial Part 2

[iancze]

An overview of the HD 143006 imaging series is provided in #25

Part II should cover

• Backreference to Part 1
• Set up MPoL optimization loop, including residual imager/dataconnector
• Initialize model to dirty image
• Explore unregularized fit + training loop w/ Tensorboard
  • visualize loss function, changes in image, residuals
• Explore basic cross-validation and hyperparameter testing w/ Tensorboard

[trq5014]

This will need to be an .rst file correct?

[iancze]

I this could be a Jupyter notebook, since the optimization and cross validation tutorials for the ALMA Logo cube are also Jupyter notebooks.

[trq5014]

Ah sounds good, I was going to try and incorporate GPU acceleration here and didn't think git could work with that. So should I not use a GPU for this tutorial then?

[iancze]

Yes, we'll need to avoid GPU acceleration here. This is something that we can include in the Part III "production ready" scripts, because those *.py files won't be executed by the Github Actions build process.

[RCF42]

In retrospect I should have possibly asked this earlier but is this rough outline a good break down for what should be happening in the tutorial?

1. Set up optimization loop (similar to the optimization tutorial one) but don't run it
2. Initialize the starting parameters to be closer to the dirty image (similar to the initialize model w/ dirty image tutorial)
3. Save this model
4. Run optimization loop/explore results in tensorboard
5. Reload model
6. Use cross-validation and the like and explore those results in tensorboard (similar to crossvalidation tutorial)

[iancze]

Yes, this sounds great! I think you can combine the optimization loop setup in (1.) with (4.), so that it's all in one place.

Apparently it is possible to run Tensorboard inside of a Jupyter notebook through the

%load_ext tensorboard

cell command. I haven't tried this, and I'm not sure whether it will work with the way we're using *.py files -> Jupytext to *.ipynb files. But it's worth experimenting with! If it doesn't work, then we'll just have to think of a different way to visualize the results.

[iancze]

Closed by #80