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This repository has been archived by the owner on Dec 2, 2021. It is now read-only.
If I have training job runs once every day, should I create one Run for it and create a Execution every day?
If that's true, I think I only need to create one Workspace and one Run. Consider the SDK init, seems I have to get object and pass as parameter. Could we pass id or name as parameter and it would be easier to use.
If I have a Kubeflow pipeline, is run here equivalent to pipeline concept there?
Since it has more steps and components, what's the best practice?
Have to pass workspace and run object to create instance.
test_run = metadata.Run(
workspace=workspace,
name="run-" + datetime.utcnow().isoformat("T") ,
description="a run in workspace",
)
exec = metadata.Execution(
name = "execution" + datetime.utcnow().isoformat("T") ,
workspace=test_workspace,
run=test_run,
description="execution example",
)
print("An execution is create with id %s" % exec.id)
The text was updated successfully, but these errors were encountered:
/kind feature
I am trying to understand the best practice here.
Run
for it and create a Execution every day?If that's true, I think I only need to create one
Workspace
and oneRun
. Consider the SDK init, seems I have to get object and pass as parameter. Could we pass id or name as parameter and it would be easier to use.run
here equivalent topipeline
concept there?Since it has more steps and components, what's the best practice?
Have to pass workspace and run object to create instance.
The text was updated successfully, but these errors were encountered: