[wonton53]

I think it is quite common. I develop financial systems, and I compile apps regularly for running tests locally and large parts of the system is running locally using docker compose. This has the benefit of fast iteration for unit and system tests. Of course the tests and deployment is also run by a CI server but that is just for QA purposes so people dont have to remember to run their tests locally and to keep a clean main branch. I cannot imagine how long our feedback loop would be if we had to wait for the CI server for every change, or if we had a common dev server that everyone interacted with through a «dirty» development branch. Basically I think it is just different use cases. My experience with jupyter is that you use it for data analysis which is really more IO bound than cpu bound, also big data is often not possible to keep locally (because of the size and also in europe GDPR)

[aulin]

Note that I didn't necessarily refer to CI servers when talking about remote machines. Sometimes it's just a better specced workstation in the office, sometimes it's my own headless machine learning rig at home. Sure different engineers and different fields have different workflows but I believe it's still easier to build a good workstation from components than finding a laptop that excels in both portability and specs.

[wonton53]

Interesting approach. I have some coleagues that actually remote desktops to their powerful desktop computer. I never did that because I fear all the tiny issues I have to deal with, but they tell me it works quite well. Also just offloading the heavy lifting through ssh or other types of build servers seems super interesting (I think some build systems have built in support for a common server, not sure if it is true, but it could be very helpful if it works well)

[robertlagrant]

Yes, I specced my machines for local docker-compose of testing. It's pretty valuable, although different engineers use it different amounts.