Migrating to Positron, a next-generation data science IDE for Python and R

And when you look inside VS Code, it is Chromium.

I was initially interested in Positron, until checking out the license:

"Positron is licensed under the Elastic License 2.0, a source-available license. This license makes Positron available for free to everyone to use, build on, and extend for personal, academic, and commercial use. Its primary restriction is that you can’t host Positron as a service to third parties without Posit’s agreement. This restriction is necessary for us to build a sustainable business around Positron while also offering it free of charge to the community."

"You may not move, change, disable, or circumvent the license key functionality in the software, and you may not remove or obscure any functionality in the software that is protected by the license key."

This is not a recommendation on whether anyone should use Positron. I think it's fair that people know it's just another piece of proprietary software with a license key. Posit is a public benefit corporation, which sounds nice, but you're still subject to the same games any other for-profit private company plays with its customers.

(I use Posit Cloud in my teaching, so my interactions with their products are as a customer, but I use RStudio, which is open source.)

I guess in my mind I was thinking use cases other than AI. Like statistical or hierarchical scientific models, simulations or ETL work. I also don't know if some of the econometricians I know with a less technical background would even know how to get setup with AWS, and I feel more boardly there's enough folks doing data work in a none tech field who know how to use Python or R or Matlab to do their modelling but likely isn't comfortable with cloud infrastructure, but might have an apple laptop with apple silicon that could improve their iteration loop. Folks in AI are probably more comfortable with a cloud solution.

There are aspects of data science which is iterative and you're repeatedly running similar computations with different inputs, I think there's some value in shaving off time between iterations.

In my case I have a temporal geospatial dataset with 20+ million properties for each month over several years each with various attributes, it's in a nonprofessional setting and the main motivator for most of my decisions is "because I can and I think it would be fun and I have a decent enough GPU". While I could probably chuck it on a cluster, I'd like to avoid if I can help it and an optimisation done on my local machine would still pay off if I did end up setting up a cluster. There's quite a bit of ETL preprocessing work before I load it into the database, I think are portions that might be doable on the GPU. But it's more so the computations I'd like to do on the dataset before generating visualisations in which I think I could reduce the iteration wait time for processing for plots, ideally to the point I can make iterations more interactive. There's enough linear operations you could get some wins with a GPU implementation.

I am keen to see how far I'll get, but worst case scenario I learn a lot, and I'm sure those learnings will be transferrable to other GPU experiments.

Was looking forward to trying this, but they haven't got inline chunks for rmarkdown to work yet. This I use religiously. Hopefully they'll be able to ship it eventually, but based on the GitHub issue about it it will be a while.

In beta state.