I was intrigued because the development environment problem is similar to the data scientist one - data gravity, GPU sharing, etc - but I'm confused on the solution? Oddly, I left with a funny alternate takeaway: One by one, their clever inhouse tweaks & scheduling preferences were recognized by the community and turned into standard k8s knobs So I'm back to the original question... What is fundamentally left? It sou…
Still, some of the core challenges remain: - the flexibility Kubernetes affords makes it hard to build and distribute a product with such specific requirements across the broad swath of differently set up Kubernetes installations. Managed Kubernetes services help, but come with their own restrictions (e.g. Kernel versions on GKE). - state handling and storage remains unsolved. PVCs are not reliable enough, subject to a lot of variance (see point above), and depending on the backing storage have vastly different behaviour. Local disks (which we use to this day), make workspace startup and backup expensive from a resource perspective and hard to predict timing wise. - user namespaces have come a long way in Kubernetes, but by themselves are not enough. /proc is still masked, FUSE is still not usable. - startup times, specifically container pulls and backup restoration, are hard to optimize because they depend on a lot of factors outside of our control (image homogeneity, cluster configuration)
Fundamentally, Kubernetes simply isn't the right choice here. It's possible to make it work, but at some point the ROI of running on Kubernetes simply isn't there.