Named outputs¶

Tags: Basic

By default, Flyte employs a standardized convention to assign names to the outputs of tasks or workflows. Each output is sequentially labeled as o1, o2, o3, … on, where o serves as the standard prefix, and 1, 2, … n indicates the positional index within the returned values.

However, Flyte allows the customization of output names for tasks or workflows. This customization becomes beneficial when you’re returning multiple outputs and you wish to assign a distinct name to each of them.

The following example illustrates the process of assigning names to outputs for both a task and a workflow.

Note

To clone and run the example code on this page, see the Flytesnacks repo.

To begin, import the required dependencies:

basics/named_outputs.py¶

from typing import NamedTuple

from flytekit import task, workflow

Define a NamedTuple and assign it as an output to a task:

basics/named_outputs.py¶

slope_value = NamedTuple("slope_value", [("slope", float)])


@task
def slope(x: list[int], y: list[int]) -> slope_value:
    sum_xy = sum([x[i] * y[i] for i in range(len(x))])
    sum_x_squared = sum([x[i] ** 2 for i in range(len(x))])
    n = len(x)
    return (n * sum_xy - sum(x) * sum(y)) / (n * sum_x_squared - sum(x) ** 2)

Likewise, assign a NamedTuple to the output of intercept task:

basics/named_outputs.py¶

intercept_value = NamedTuple("intercept_value", [("intercept", float)])


@task
def intercept(x: list[int], y: list[int], slope: float) -> intercept_value:
    mean_x = sum(x) / len(x)
    mean_y = sum(y) / len(y)
    intercept = mean_y - slope * mean_x
    return intercept

Note

While it’s possible to create NamedTuples directly within the code, it’s often better to declare them explicitly. This helps prevent potential linting errors in tools like mypy.

def slope() -> NamedTuple("slope_value", slope=float):
    pass

You can easily unpack the NamedTuple outputs directly within a workflow. Additionally, you can also have the workflow return a NamedTuple as an output.

Note

Remember that we are extracting individual task execution outputs by dereferencing them. This is necessary because NamedTuples function as tuples and require this dereferencing:

basics/named_outputs.py¶

slope_and_intercept_values = NamedTuple("slope_and_intercept_values", [("slope", float), ("intercept", float)])


@workflow
def simple_wf_with_named_outputs(x: list[int] = [-3, 0, 3], y: list[int] = [7, 4, -2]) -> slope_and_intercept_values:
    slope_value = slope(x=x, y=y)
    intercept_value = intercept(x=x, y=y, slope=slope_value.slope)
    return slope_and_intercept_values(slope=slope_value.slope, intercept=intercept_value.intercept)

You can run the workflow locally as follows:

basics/named_outputs.py¶

if __name__ == "__main__":
    print(f"Running simple_wf_with_named_outputs() {simple_wf_with_named_outputs()}")