Katib is used for hyperparameter tuning, and Pipeline is used for end-to-end ML workflows, and the Pipeline do need some parameters provided by Katib to improve the efficiency, such as enabling Pipeline get the best parameters provided by Katib etc, any document or best practise for how Pipeline can get the parameters generated by Katib?
FYI @hougangliu @jinchihe
gyliu513
All 6 comments
Maybe we can add an example of pipeline embedding katib studyjob. I think put the example in pipeline project is better.
@YujiOshima @richardsliu @jlewi any suggestion?
hougangliu
on 21 Jan 2019
/assign
hougangliu
on 21 Jan 2019
An example of using Pipelines to orchestrate hyperparameter tuning would be great.
You can take a look at the TFJob launcher to get some sense of what a component for Katib might look like
https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/launcher
There is an open issue to figure out best practices with respect to integrating pipelines with K8s resources; see kubeflow/pipelines#677
jlewi
on 22 Jan 2019
hougangliu
on 8 Mar 2019
@hougangliu: Closing this issue.
In response to this:
Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes/test-infra repository.
k8s-ci-robot
on 8 Mar 2019
/cc @animeshsingh
gyliu513
on 22 Mar 2019
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Most helpful comment
An example of using Pipelines to orchestrate hyperparameter tuning would be great.
You can take a look at the TFJob launcher to get some sense of what a component for Katib might look like
https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/launcher
There is an open issue to figure out best practices with respect to integrating pipelines with K8s resources; see kubeflow/pipelines#677