Lingqing Gan

icon indicating an email [email protected]

icon company @Google icon location Seattle, WA

Results 35 comments of Lingqing Gan

[feature] How to pass secret pipeline parameters?

Maybe try using a container op and wrap the secret parameter inside? https://kubernetes.io/docs/concepts/configuration/secret/#using-secrets-as-files-from-a-pod Here is an example of passing gcp secret: https://github.com/kubeflow/examples/blob/871895c54402f68685c8e227c954d86a81c0575f/pipelines/mnist-pipelines/mnist_pipeline.py#L97 Maybe AWS has some similar function.

feat(backend) add maximum_cache_staleness and default_cache_staleness

Thank you @juliusvonkohout! The PR looks good to me, just requesting some small changes.

feat(backend) add maximum_cache_staleness and default_cache_staleness

@juliusvonkohout Thank you for the work! Looks good. /lgtm /cc @chensun @gkcalat

feat(backend): Implement RuntimeConfig in backend

/retest

[bug] Considerable delay in showing steps in Kubeflow Pipelines UI after navigating to Runs

Could you check the resource usage in your cluster, and see if any limit was reached? You may also consider upgrading to 1.8.

[bug] Minio S3 Gateway results in 'pipeline spec is invalid'

Could you please share the pipeline template that you used?

[backend] KFP v2 currently doesn't support "Retry run" function

Retry run is supported now, see #8804

This step is in Error state with this message: Error (exit code 1): failed to put file: You did not provide the number of bytes specified by the Content-Length HTTP header.

Hi @grapefruitL and @vietanhdev, could you provide a minimum code that will reproduce the error?

[backend] Panic while connection to default cache endpoint ml-pipeline.kubeflow:8887

Hi @andre-lx, thank you for bringing up this issue. I tried the same pipeline on a newly deployed 2.0.0 cluster, and the run finished without issue. looking at the log...

[backend] Panic while connection to default cache endpoint ml-pipeline.kubeflow:8887

Also, could you let me know which way you used to deploy KFP, standalone or via kubeflow?