ml-engineering VS OutRun

There is a lot of work to make the actual infrastructure and lower level management of lots and lots of GPUs/TPUs open as well - my team focuses on making the infrastructure bit at least a bit more approachable on GKE and Kubernetes.

https://github.com/GoogleCloudPlatform/ai-on-gke/tree/main

and

https://github.com/google/xpk (a bit more focused on HPC, but includes AI)

and

https://github.com/stas00/ml-engineering (not associated with GKE, but describes training with SLURM)

The actual training is still a bit of a small pool of very experienced people, but it's getting better. And every day serving models gets that much faster - you can often simply draft on Triton and TensorRT-LLM or vLLM and see significant wins month to month.

OK, the pdf is ready now: https://github.com/stas00/ml-engineering#pdf-version

this might be interesting: https://github.com/stas00/ml-engineering/blob/master/transformers/make-tiny-models.md

Ive had a similar problem to you, but I have mostly solved it by using the Outrun app (which is free and open source), and going into the settings and turning up the GPS smoothing. I mostly run in central london, and it seems to work with the tall buildings here.

https://github.com/timfraedrich/OutRun

https://github.com/timfraedrich/OutRun/issues/91

> I wouldn't necessarily say abandoned. I still work on it from time to time, but progress is very very slow and I cannot prioritise it over other things atm

Shout out to the app "Out-Run" that I use to track my rides. Privacy focused way to track your rides without all of the bullshit social media that comes with Strava. No account, all data stored locally, can easily export your rides, no data collected. Have exchanged a few emails with the dev and he's a super cool dude, so I shout out his app at every opportunity. All open source as well.

This is the GitHub page.