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31 May 2022
@_slack_kubeflow_U01K24XKKK9:matrix.orgJoseph Olaide Yes 15:26:41
@_slack_kubeflow_U0378F85QG4:matrix.orgIrvin Tang hi all. currently, when querying for pipelines using the KFP client, the pipeline object that gets returned has an attribute called parameters which is a list of kfp_server_api.models.api_parameter.ApiParameter objects. These objects only specify the parameter name and value. Is there a way to retrieve the expected type for the pipeline parameter as well? 15:27:31
@_slack_kubeflow_U01C62YLURX:matrix.orgNicholas Kosteski It does mention the visualizations should be available on the Run Outputs though:
The Run output tab shows the visualizations for all pipeline steps in the selected run. To open the tab in the Kubeflow Pipelines UI:
@_slack_kubeflow_U01C62YLURX:matrix.orgNicholas Kosteski I just realized there was a link to the code that is meant to load up the visualizations in that issue. I'll try taking a look to see if something happened to where its not being labeled correctly in the instance I have up 15:31:19
1 Jun 2022
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@_slack_kubeflow_UKM89RPD0:matrix.orgdroctothorpe Is there any particular reason you can’t delete, as opposed to archive, experiments in the web GUI? 14:38:07
@_slack_kubeflow_U01C62YLURX:matrix.orgNicholas Kosteski Looks like I finally found something that’s different between the two versions I have. The actual argo_artifact is being created differently in the version that works the artifact is:
  "name": "mlpipeline-ui-metadata",
  "optional": true,
  "path": "/tmp/outputs/mlpipeline_ui_metadata/data",
  "s3": {
    "accessKeySecret": {
      "key": "accesskey",
      "name": "mlpipeline-minio-artifact"
    "bucket": "mlpipeline",
    "endpoint": "minio-service.kubeflow:9000",
    "insecure": true,
    "key": "artifacts/allocation-tx-5gpvh/allocation-tx-5gpvh-170870440/mlpipeline-ui-metadata.tgz",
    "secretKeySecret": {
      "key": "secretkey",
      "name": "mlpipeline-minio-artifact"
in the one that doesn’t work its created as the argo_artifact:
  "name": "mlpipeline-ui-metadata",
  "optional": true,
  "path": "/tmp/outputs/mlpipeline_ui_metadata/data",
  "s3": {
    "key": "artifacts/markdown-pipeline-ldvxr/2022/06/01/markdown-pipeline-ldvxr-4134324342/mlpipeline-ui-metadata.tgz"
@_slack_kubeflow_U01C62YLURX:matrix.orgNicholas Kosteski It seems like maybe my workflow-controller-configmap might have some kind of issue 16:00:20
@_slack_kubeflow_U01K24XKKK9:matrix.orgJoseph Olaide from typing import NamedTuple import kfp from kfp.components import create_component_from_func @create_component_from_func def produce_markdown() -> NamedTuple('Outputs', [('MLPipeline_UI_metadata', 'UI_metadata')]): import sys, json, subprocess subprocess.run([sys.executable, '-m', 'pip', 'install','pandas']) import pandas as pd matrix = [ ['y', 'y', 10], ['y', 'n', 9], ['n', 'y', 6], ['n', 'y', 7] ] df = pd.DataFrame(matrix,columns=['target','predicted','count']) metadata = { "outputs": [ { "type": "confusion_matrix", "format": "csv", "schema": [ { "name": "target", "type": "CATEGORY" }, { "name": "predicted", "type": "CATEGORY" }, { "name": "count", "type": "NUMBER" } ], "source": df.to_csv(header=False, index=False), "storage": "inline", "labels": [ "yummy", "not yummy" ] } ] } return [json.dumps(metadata)] def my_pipeline(): produce_markdown() kfp.Client().create_run_from_pipeline_func(my_pipeline, arguments={}) 16:30:39
@_slack_kubeflow_U01K24XKKK9:matrix.orgJoseph Olaide Hi Nicholas Kosteski, this sample code above creates a run output artifact and a visualization in the pod. I hope this is helpful 16:33:10
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@_slack_kubeflow_U01AGRLQ8QY:matrix.orgRituraj Kumar Is there a any solution for v1 and v2 compatibility of load_component_from_file ? As I see there is no kfp.v2.components.load_component_from_file method in kfp.v2 versions 17:30:24
@_slack_kubeflow_U02JN8ZRU2E:matrix.orgYingding Wang @Nicholas Kosteski can you open the browser developertool and see if there is any javascript error in the console? For multi-tenant setting in KF1.4, i have also the issue visualization is not shown. I was hoping KF1.5 will solve this issue. 18:12:56
@_slack_kubeflow_U01C62YLURX:matrix.orgNicholas Kosteski Yingding Wang I’m not seeing anything unusual. What is the javascript error you’re seeing? 18:23:45
@_slack_kubeflow_U02JN8ZRU2E:matrix.orgYingding Wang I ran the example code from Joseph Olaide just a moment ago on my on-prem KF 1.4 and got the error in browser console Failed to load resource: the server responded with a status of 503 (Service Unavailable) , i think it is a known issue for kf1.4, but should go away in kf1.5 18:29:41
@_slack_kubeflow_U01C62YLURX:matrix.orgNicholas Kosteski Yeah I definitely didn’t get any specific errors, it seems like the js function that is supposed to grab the artifacts is just kinda coming back with 0 things when I select the Run output tab. So I’m not sure if we’re having the same issue or not but it certainly could be that they’re related? ¯\(ツ)/¯ I don’t know if in a multi-user env this problem persists or not. I’m just using the standalone feature (since its all we need/use) right now. But I might try to look at KF1.5 next since this is taking up much more time than I was expecting… What’s super bizarre is that I can sometimes get it to display in the Run output. It requires that I go to the run before the execution is done, click into the pod/task visualizations until they show up. Then as long as I don’t refresh the page, everything appears exactly how I would expect in both the pod visualizations and the run output tab. Once I refresh the page however, the visualizations disappear in the Run output tab only. It seems something is changing/clearing some state that it shouldn’t be 😖 18:58:46
@_slack_kubeflow_U02LW7FHWLS:matrix.orgRahul Mehta I think the idea is that you don't delete anything (I encountered this too when I found there isn't an out-of-the-box way to enforce a retention policy) -- my understanding is that it's helpful for experiment tracking/archival and audit purposes if you only archives are permitted instead of deletions 20:51:45
@_slack_kubeflow_U02LW7FHWLS:matrix.orgRahul Mehta For example, ml-metadata is designed to be immutable 20:52:21
@_slack_kubeflow_UKM89RPD0:matrix.orgdroctothorpe Interesting! Thanks, Rahul. I guess that helps contextualize the design choice. You got around this with the retention policy you introduced though, right? 21:20:54
@_slack_kubeflow_U02LW7FHWLS:matrix.orgRahul Mehta Yes, basically a k8s CronJob that periodically deletes rows that are older than 30 days from the various tables in the MySQL DB 21:21:33
@_slack_kubeflow_U02LW7FHWLS:matrix.orgRahul Mehta Though, we don't enforce anything like that for experiments (just runs & assoc. metadata) 21:21:48
2 Jun 2022
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3 Jun 2022
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