[gautamcgoel]

I'm a PhD student at Caltech, working on the theoretical foundations of ML. I personally don't do a lot of coding, but basically everyone in my department uses Python (especially Pytorch) for deep learning/ML. This all runs on Nvidia GPUs (never seen an AMD GPU in the office). Occasionally people code in Matlab, especially if they work in optimization or control. Tmux and git are the only command line tools I see commonly used. Occasionally people ssh into an Amazon box if they need more compute.

[chronic379019]

You didn't really describe a stack. Which is fine, because academic research usually doesn't really reuse code ;)

A proper ML stack is something like:

- Data format in X schema

- Model trained on Y library/platform

- Evaluated and tested using Z

- Serialized in A format

- Stored on cloud B

- Deployed using C

- Versioned using D

- Real-time monitoring using E