[backend] Retry setting for components not functioning as expected in custom Minio setup

[kunihito]

Hello,
I am using Kubeflow in an EKS environment and am encountering an issue where the retry settings are not functioning as expected.

Environment

• How did you deploy Kubeflow Pipelines (KFP)?
  Using a custom deployment on AWS EKS.

• KFP version:
  Kubeflow version: 1.9.0

• KFP SDK version:

$ pip list | grep kfp
kfp                         2.9.0

Steps to reproduce

1. I am using a custom Minio setup instead of the default one.

2. ConfigMap for Minio setup:

   # kubectl describe configmap workflow-controller-configmap -n kubeflow
   
   Name:         workflow-controller-configmap
   Namespace:    kubeflow
   
   Data
   ====
   artifactRepository:
   ----
   archiveLogs: true
   s3:
     endpoint: "minio.kf-storage.svc.cluster.local:80"
     bucket: "mlpipeline"

3. Defined the following pipeline with retry settings:

   from kfp import dsl, compiler
   
   @dsl.component
   def random_failure_op(exit_codes: str):
       """A component that fails randomly."""
       import random
       import sys
   
       exit_code = int(random.choice(exit_codes.split(",")))
       print(exit_code)
       sys.exit(exit_code)
   
   @dsl.pipeline(
       name='retry-random-failures',
       description='The pipeline includes two steps which fail randomly. It shows how to use ContainerOp(...).set_retry(...).'
   )
   def retry_sample_pipeline():
       op1 = random_failure_op(exit_codes='0,1,2,3').set_retry(10)
       op2 = random_failure_op(exit_codes='0,1').set_retry(5)
   
   if __name__ == '__main__':
       compiler.Compiler().compile(retry_sample_pipeline, __file__ + '.yaml')

4. When the task fails, retries are not executed as expected.

5. Logs from the dag-driver POD where Minio is referenced incorrectly:

   // log of retry-random-failures-tw2sp-system-dag-driver-3164055661
   I1016 10:45:56.065592      17 main.go:121] input ContainerSpec:{}
   I1016 10:45:56.065684      17 main.go:128] input RuntimeConfig:{}
   I1016 10:45:56.065742      17 main.go:136] input kubernetesConfig:{}
   I1016 10:45:56.066311      17 cache.go:139] Cannot detect ml-pipeline in the same namespace, default to ml-pipeline.kubeflow:8887 as KFP endpoint.
   I1016 10:45:56.066337      17 cache.go:116] Connecting to cache endpoint ml-pipeline.kubeflow:8887
   I1016 10:45:56.084156      17 env.go:65] cannot find launcher configmap: name="kfp-launcher" namespace="kubeflow-user-example-com", will use default config
   I1016 10:45:56.084194      17 driver.go:153] PipelineRoot="minio://mlpipeline/v2/artifacts" from default config
   I1016 10:45:56.084219      17 config.go:166] Cannot detect minio-service in the same namespace, default to minio-service.kubeflow:9000 as MinIO endpoint. 
   I1016 10:45:56.226159      17 client.go:302] Pipeline Context: id:806  name:"retry-random-failures"  type_id:20 type:"system.Pipeline"  create_
   time_since_epoch:1729047175840  last_update_time_since_epoch:1729047175840
   

Expected result

Retry logic should be triggered when tasks fail, and retries should be reflected in the flow diagram on the dashboard. The custom Minio endpoint should be referenced correctly.

Materials and Reference

• Logs indicating the incorrect reference to the Minio service.
• Custom pipeline code with retry logic.
• ConfigMap for the custom Minio setup.

In addition, I would like to know the specific steps or configurations required to make Kubeflow Pipelines (KFP) refer to the custom Minio we have prepared.

Let me know if any further information is needed. Thank you for your help!