Closed Amrit-Mann closed 2 months ago
/assign
@zijianjoy should we aim for making all arguments available for the user or keep a selected few(I see that we currently only support some options)?
After looking in to this I believe this is actually already possible @Amrit-Mann, if you look at the options: for model
and version
the take a json following:
where in you have the options to specify the region that you like to deploy to.
@zijianjoy should we aim for making all arguments available for the user or keep a selected few(I see that we currently only support some options)?
All available options should already be possible to set. Let me know if you still has some issues @Amrit-Mann
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Thanks @NikeNano for the research! Since you can use ML-engine REST API, looks like you should be able to call this endpoint to specify regions.
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Hi, I am using the ml-engine component to deploy a ML model to AI Platform Prediction (from a Kubeflow deployment in AI Platform Pipelines), however there is no available parameter to deploy the model into a particular region. This results in the model deployment defaulting to the us-central1 region. Is there any way to deploy a model using this component to a specific region? Any help on this will be much appreciated!. Thanks.
Link to component: https://github.com/kubeflow/pipelines/tree/master/components/gcp/ml_engine/deploy
What steps did you take: 1 - Trained a model using AI Platform Pipelines 2 - Saved this model to Google Cloud Storage as a .pkl file 3 - Tried to use the deploy component to deploy this model to AI Platform Prediction, however What happened: There is no region parameter for this deploy component hence the deployment happens in the default region (us-central1) What did you expect to happen: Be able to specify the region parameter in the input for this component. (Since we can do this when deploying the model from either gcloud commands/GCP Console Anything else you would like to add: /kind feature /kind component