Joe Schmid09/26/2019, 5:58 PM
josh09/27/2019, 1:11 PM
Joe Schmid09/27/2019, 2:50 PM
worker type • Adaptive mode would only use the
worker type • cluster.scale(n) would use the
worker type • Provide the ability to add any number of worker types, stored as dict entries of str -> pod spec • Provide the ability to call cluster.scale(n, worker_type='gpu') to manually scale specific worker types I have this prototyped and sorta working in a fork of dask-kubernetes. I need to clean it up, make sure it scales down workers types correctly, and do much more testing, but the basics seem to work so far. In theory adaptive mode (using the
worker type) may be able to co-exist with manual scaling of specific worker types, but I haven't looked at that yet. I debate whether this is something appropriate to be merged into dask-kubernetes or whether it's a bit of a one-off that happens to meet our needs. I'd love any thoughts & feedback on the way I'm thinking about it.