HumairAK
commented
1 day ago I'm thinking we do something like this:
@dsl.component(base_image="docker.io/python:3.9.17")
def empty_component():
pass
@dsl.pipeline(name='pipeline-accel')
def pipeline_accel(img: str):
task = empty_component()
task.set_container_image(img) # overwrite base_image="docker.io/python:3.9.17"Which would require us to treat the container spec image as a pipeline channel
It looks like we can just follow the same pattern we have followed for setting accelerators https://github.com/kubeflow/pipelines/blob/64e390069d6c60c97ea03e833529a0930398620f/sdk/python/kfp/compiler/pipeline_spec_builder.py#L618
seems pretty straight forward
Feature Area
/area sdk
What feature would you like to see?
As a user of KFP sdk I want to be able to provide container images as pipeline parameters.
What is the use case or pain point?
There are a number of use cases for this, one example might be requiring a specific image for a specific accelerator type. Having to create multiple pipelines just for different container images is unnecessarily redundant.
Is there a workaround currently?
No
Also note this is a regression from v1.
Previous discussion on this topic: https://github.com/kubeflow/pipelines/issues/5834 It was decided not to add this for v2 due to technical hurdles and concerns around component interface breaking. Let's re-evaluate.
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