In this setup, from a julia project directory, you can:

• drop into a julia REPL that is running on your k8s cluster
• edit source files locally, use Revise and get back results saved to disk, via a 2-way sync between the local julia project directory and the corresponding directory in the k8s container
• sync REPL history across local and remote julia sessions
• easily spin up and use Distributed workers from within the julia session
• automatically build and use images containing julia, with chosen dependencies baked in a PkgCompiler sysimage, precompiled julia project, and (optionally) CUDA
• minimize time-to-first-command-completion with cached image builds; first use in a project directory takes a long time to build, but subsequently spinning up is fast
• set RAM/cpu/disk resources for the main julia session and any Distributed workers
• set julia (and CUDA) versions independently for each session
• run your work as a non-interactive job once it is ready

This tries to make minimal assumptions about the k8s setup; requirements are access to the cluster via kubectl and to a container registry that the k8s cluster can pull from.

Tools needing to be installed locally are:

• kubectl
• docker buildkit
• devspace sync

The julia-specific tools developped to make this possible are K8sClusterManagers.jl and julia_pod.

This workflow is developped and used day-to-day at Beacon Biosignals.