-
Notifications
You must be signed in to change notification settings - Fork 3.2k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Memory Configurations for Argo Operator #1139
Comments
I'm looking for documentation how to configure and/or calculate the memory (RAM) used by argo operator. |
could anyone help me? |
+1 ... would be interesting to understand what to specify for both controller as well. Is there any more insight into those limits of 0.1 CPU + 64Mi memory for executor? Is this fixed, in which case, can it be smaller? Or should this in some way be correlated to a particular step of a workflow? |
any update? |
The memory configuration required by the controller are actually dependent on how many running workflows are in your system, so there's no one size fits all. The controller uses a Kubernetes informer cache to operate on workflows and the more running workflows, then more memory is required. As for executor resources, this also sometimes depends on the size of artifacts being downloaded. For example, it's been reported for very large artifacts (tens of GB), the memory usage is large. See: |
In
docs/workflow-controller-configmap.yaml
:Do those resource settings work for almost scenes, right?
Also, above setting is for executor, not operator.
Is there any practice about resource setting for operator?
Or for ~100 jobs, how much resources need i set for operator?
The text was updated successfully, but these errors were encountered: