[feature] Make `set_env_variable` compatible with the channels [KFP V2]

[alexdashkov]

Feature Area

/area sdk

What feature would you like to see?

I want to be able to use values from the parameters and/or outputs to set environment variables in KFP v2

What is the use case or pain point?

I'm trying to pass a variable in the parameters of the job execution and then set it as a variable. Example:

@dsl.pipeline(name="useful-pipeline")
def training_pipeline(
    env: str = "dev",
):
    great_operator_op = great_operator().set_env_variable("env", env)

However current code raises an exception:

TypeError: Cannot set ml_pipelines.PipelineDeploymentConfig.PipelineContainerSpec.EnvVar.value to {{channel:task=;name=env;type=String;}}: {{channel:task=;name=env;type=String;}} has type <class 'kfp.dsl.pipeline_channel.PipelineParameterChannel'>, but expected one of: (<class 'bytes'>, <class 'str'>) for field EnvVar.value

I've tried to pass trough the intermediate task to cast a type (it helped me with the similar situation to cast parameters types ) but it doesn't work neither.

@dsl.component()
def convert_env_to_string(environment: str) -> str:
    return environment

@dsl.pipeline(name="useful-pipeline")
def training_pipeline(
    env: str = "dev",
):
    convert_env_to_string_op = convert_env_to_string(environment=env)
    great_operator_op = great_operator().set_env_variable("env", convert_env_to_string_op.output)

TypeError: Cannot set ml_pipelines.PipelineDeploymentConfig.PipelineContainerSpec.EnvVar.value to {{channel:task=convert-env-to-string;name=Output;type=String;}}: {{channel:task=convert-env-to-string;name=Output;type=String;}} has type <class 'kfp.dsl.pipeline_channel.PipelineParameterChannel'>, but expected one of: (<class 'bytes'>, <class 'str'>) for field EnvVar.value

Is there a workaround currently?

It's possible to don't use an environment at all, but it's not really a workaround.

---

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[Davidnet]

As an alternative, would you consider to pass as a parameter to great_operator instead?

[alexdashkov]

Hi @Davidnet, yes. I should have been more clear. That's exactly what I meant by don't use an environment at all. This solution works and I'll use it. But still I have to move out from the environment variables. I can imagine even deeper solution by passing this value as a param and then, inject it using for example environ but it looks really hackish.

@dsl.component()
def convert_env_to_string(env: str) -> str:
    import os
    os.environ["env"] = env
    # rest of the code

@dsl.pipeline(name="useful-pipeline")
def training_pipeline(
    env: str = "dev",
):
    great_operator_op = great_operator(env=env)

[ViktorWelbers]

Hi, I am also struggling with this.

I am using the env variables to set the image to a dynamic version inside of the components like so:

@dsl.component(
    base_image=os.environ.get("BASE_IMAGE"),
)
def some_op(foo: str):
...

@dsl.pipeline(name="useful-pipeline")
def training_pipeline(
    base_image: str = "IMAGE_ID"
):
  op = (
       some_op(
            foo="bar",
        )
        .set_env_variable(name="BASE_IMAGE", value=base_image)
    )

Is there any alternative way of doing this? Otherwise I would like to help out on implementing this feature.

[alexdashkov]

@ViktorWelbers , are you sure that you're looking for the same feature? It looks like it won't help you with your problem. If you want it to fill base_image for your component only, you should provide this variable during pipeline compilation, not in runtime.

Something like that:

import os
BASE_IMAGE = =os.environ.get("BASE_IMAGE")

@dsl.component(
    base_image=BASE_IMAGE
)
def some_op(foo: str):
    ...

@dsl.pipeline(name="useful-pipeline")
def training_pipeline(
    base_image: str = "IMAGE_ID"
):
  op = (
       some_op(
            foo="bar",
        )
    )

From my understanding, base_image from the dsl.component won't be accessible at runtime even if you would have a possibility to set env variable from the parameters. So if you really want to change an image from parameters I guess you might want to go trough the container component. Something like that:

from kfp import dsl

@dsl.container_component
def some_op(foo:str, base_image: str):
    return dsl.ContainerSpec(image=base_image, command=['echo'], args=[foo])

@dsl.pipeline(name="useful-pipeline")
def training_pipeline(
    base_image: str = "IMAGE_ID"
):
  op = (
       some_op(
            foo="bar",
            base_image=base_image
        )
    )

[ViktorWelbers]

@alexdashkov

I want to reuse the same component logic with different base images.

You are totally correct. I think the second option you posted is what I was looking for. Thank you.

[fw8]

Same problem with

kubernetes.use_secret_as_env(task, secret_name=secret_name_from_pipeline_args,...)

[github-actions[bot]]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[alexdashkov]

A comment to remove stale label as I still want to have this feature

[alexdashkov]

@beoygnas Where do you run your pipeline? Do you have an on-premise kubeflow instance? I'm using GCP's vertex ai pipelines and it does not pass there. I can compile a pipeline, but then an executor fails.

com.google.cloud.ai.platform.common.errors.AiPlatformException: code=INVALID_ARGUMENT, message=List of found errors: 1.Field: job_spec.worker_pool_specs[0].container_spec.env[0].value; Message: Required field is not set. , cause=null; Failed to create custom job for the task. 

[github-actions[bot]]

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