From what I can tell,
ray doesn’t make any attempt to persist results in the object store between workflow runs, so if I run a workflow once, then once again later with the code and config unchanged, the whole thing will run from scratch again. Is there any way to get ray to persist job outputs and/or the entire object store, and to check the store for completed tasks? I have some long-running processes I want to run, and re-running all tasks whenever I tweak a function is not viable for me.
It seems that ray already has a caching mechanism, which is what drives the “fault tolerance” stuff, but it just chooses to drop all this data after Python exits instead of providing it for later use.