Vertex AI Pipeline - Container OP `set_cpu_limit` does not work with parameter_values nor at runtime

Hello Kubeflow Team, Hello Google Team,

The container OP .set_cpu_limitonly works when the value is set explicit and not via parameter_values or at runtime https://github.com/kubeflow/pipelines/blob/4906ab2f1142043517249a62b9f22bc122971fdf/sdk/python/kfp/dsl/_container_op.py#L378

Reproduce

1. parameter_values: see steps to reproduce
2. runtime: see https://github.com/kubeflow/pipelines/blob/master/samples/core/resource_spec/runtime_resource_request.py

Environment

• How did you deploy Kubeflow Pipelines (KFP)? Vertex AI Pipelines
• kfp 1.8.4
• kfp-pipeline-spec 0.1.11
• kfp-server-api 1.7.0

Steps to reproduce

Not working

from kfp.v2.dsl import pipeline

@pipeline(name="reproduction",
              pipeline_root="ADD PIPELINE ROOT")
def pipeline(cpu_limit: str):
    train_op = train().set_cpu_limit(cpu_limit)

compiler.Compiler().compile(pipeline_func=pipeline,
        package_path='pipeline.json')

api_client = AIPlatformClient(
                project_id="ADD PROJECT",
                region="us-central1"
                )

response = api_client.create_run_from_job_spec(
    'pipeline.json',
      parameter_values={
          'cpu_limit': "16"
  }
)

Working

from kfp.v2.dsl import pipeline

@pipeline(name="reproduction",
              pipeline_root="ADD PIPELINE ROOT")
def pipeline():
    train_op = train().set_cpu_limit("16")

compiler.Compiler().compile(pipeline_func=pipeline,
        package_path='pipeline.json')

api_client = AIPlatformClient(
                project_id="ADD PROJECT",
                region="us-central1"
                )

response = api_client.create_run_from_job_spec(
    'pipeline.json'
)

Expected result

The CPU limits can be set via parameter_values

Looking forward to your feedback

[SaschaHeyer]

Hi are there any updates? This would be a huge benefit for re-using pipelines without the need to re-compile them.

[saigirishgilly98]

Hi are there any updates? This would be a huge benefit for re-using pipelines without the need to re-compile them.

+++

I agree with @SaschaHeyer, we are building reusable pipeline templates with only data changing and depending on the data size, we would want to be able to configure the CPU and Memory for each of the components through pipeline params or any other way.