Is there any guide or best practice to set custom resource specs (CPUs, RAM, GCE node spec, …) for KubeflowV2DagRunner? I checked the related issue and protobuf specs.
- Issue: Request cpu / memory for containers in Kubeflow · Issue #3194 · tensorflow/tfx · GitHub
- Proto spec that KubeflowV2DagRunner.run returns: pipelines/pipeline_spec.proto at master · kubeflow/pipelines · GitHub
It works, but I think that it is not a clean or good way to set the specs compared to the V1 Runner way, since it relies on undocumented JSON data. (In V1 runner, It supports
pipeline_operator_funcs arg in