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Kubernetes Operator for AI and Bigdata Elastic Training
Hi!Is it stable now? Can it be applied to a production environment? thanks!
Now launcher use ssh when attach to workers, there are some problem:
A better way is using kubectl exec
to replace ssh.
The total available resources in cluster may change over time, it would be nice to support auto-scale if user want their training jobs to use as most resources as possible.
Common case will be:
--np
, --min-np
and --max-np
, --np
tend to be small since launcher won't start if not at least np workers ready--max-np
If this is not the default behavior, it can be controlled by something like a scalePolicy:auto
.
When testing horovod elastic worker preemption, the whole job may fail in some cases with uncertain reason.
It'd be better if operator support job level restart(restartpolicy + backofflimit).
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