Hi team, I am a DS from gojek, currently working on using Flyte for model pipeline. One question I came across is that the resource requested (cpu, memory) is hard coded in the decorator for each task for the current implementation, so it’s the same for different environments. What will be a better way to do overrides for each task in the workflow for different environments? Will it be possible to define it in a config file instead? cc @most-gold-65483 @late-fish-63527

Aah interesting question- you basically want to change it based on domain. May I ask a few clarifying questions So to understand you want to specify different resources for the same task across different domains? Or do you want a common resource for all task la and change it per domain

It is more of the former, specifying different resource requirements for the same task across different domains.

Ya so since registration for each domain happens separately - and there is a variable that is exposed at this time, it is possible to use different values

for domain, do you refer to project domain?

Ya

that will work.

sorry for the late reply @freezing-airport-6809 I had a misunderstanding on the domain. Lemme rephrase my reply, the requirement will be specifying different resource requirements for the same task under the same project domain, the same workflow but different launchplans for the workflow. Example: there are 2 launchplans specified for workflow , one is for staging environment and another is for production. I would like to change the resource request based on the parameter

environment

here.

Why do you need to create 2 launchplans. The same launchplan can be created in staging and production. I am assuming here environment refers to - Flyte domains

It’s essentially one launchplan but with different fixed input i.e.

if os.getenv("WORKFLOW_DOMAIN") == "production":
    # Create scheduled execution in production environment

    # schedule for model training and deployment
    train_and_deploy_wf_schedule = LaunchPlan.get_or_create(
        name=f"{__name__}.train_and_deploy_wf_schedule",
        # name of workflow to be triggered
        workflow=train_and_deploy,
        # fixed_inputs is used to define the workflow input when triggered by scheduler
        # in this case we want to pass "evironment" = "production"
        fixed_inputs={
            "environment": "production"
        },
        schedule=CronSchedule(
            schedule="0 10 * * 1",
            # The execution time is passed to workflow as argument called "kickoff_time"
            kickoff_time_input_arg="kickoff_time",
        ),
    )
elif os.getenv("WORKFLOW_DOMAIN") == "staging":
    # Create scheduled execution in staging environment

    train_and_deploy_wf_schedule = LaunchPlan.get_or_create(
        name=f"{__name__}.train_and_deploy_wf_schedule",
        workflow=train_and_deploy,
        fixed_inputs={
            "environment": "staging"
        },
        schedule=CronSchedule(
            schedule="0 10 * * 1",
            # The execution time is passed to workflow as argument called "kickoff_time"
            kickoff_time_input_arg="kickoff_time",
        ),
    )

Within the workflow, we use

environment

to chose the correct configuration for given domain/environment

Ok cool, something like that should be simple

@average-telephone-1006 I had a chat with @freezing-airport-6809 regarding the resource requests. There are 2 ways to do it. 1. Using env var to select the correct resource requests for the given task, for example:

domain = os.getenv("WORKFLOW_DOMAIN", "staging")
training_resource_requests = {
    "staging" : Resources(cpu="2", mem="4Gi"),
    "production" : Resources(cpu="4", mem="16Gi"),
}
training_resource_limits = {
    "staging" : Resources(cpu="2", mem="4Gi"),
    "production" : Resources(cpu="4", mem="16Gi"),
}
@task(requests=training_resource_requests[domain], limits=training_resource_limits[domain])
def train_model(train_df: pd.DataFrame, config: DictConfig) -> FlyteFile[TypeVar("joblib.dat")]:

We have 

WORKFLOW_DOMAIN

 being set in CI during workflow registration which you can use to configure the workflow. 2. Using dynamics workflow (https://docs.flyte.org/projects/flytekit/en/latest/generated/flytekit.dynamic.html#flytekit.dynamic) This is more powerful but more complex. In this case you can use the 

config

 input variable to store the resource request and the resource request can be adjusted without having to resubmit the workflow. The option 1 above will only configure resource request once during workflow submission.

Cc @thankful-minister-83577 / @high-accountant-32689 / @tall-lock-23197