Extending Flyte

Once you have a hang of the fundamentals of Flyte, you may find that you want to use it in ways that aren’t supported out-of-the-box. Fortunately, Flyte provides multiple extension points that enable you make it more powerful for your specific use cases.

This guide will walk you through two of the many different ways you can extend the Flyte type system and Flyte tasks.

Customizing Flyte types

Flyte has a rich type system that automatically handles the serialization and deserialization of objects so that when you pass data from one task to the next, you don’t have to write a bunch of boilerplate code.

However, the types that ship with Flyte or one of Flyte’s first-party integrations may not fulfill your needs. In this case, you’ll need to create your own.

The easiest way to do this is with the dataclasses module, which lets you compose several Flyte-supported types into a single object. For example, suppose you want to support a coordinates data type with arbitrary metadata:

import typing

from dataclasses import dataclass
from mashumaro.mixins.json import DataClassJSONMixin

class Coordinate(DataClassJSONMixin):
    """A custom type for coordinates with metadata attached."""
    x: float
    y: float
    metadata: typing.Dict[str, float]

You can then use this as a new type in your tasks and workflows:

from flytekit import task

def generate_coordinates(num: int) -> typing.List[Coordinate]:
    """Generate some coordinates."""

def subset_coordinates(
    coordinates: typing.List[Coordinate], x_min: float, x_max: float,
) -> typing.List[Coordinate]:
    """Select coordinates within a certain x-axis range."""


The limitation of using the approach above is that you can only compose types that are already supported by Flyte.

To create entirely new types, you’ll need to use the TypeTransformer interface to explicitly handle the way in which the object is (a) serialized as a task output and (b) deserialized when passed into a task as an input.

See the User Guide for an example of a custom type.

Customizing Flyte tasks

The easiest way to extend Flyte tasks is to use Python decorators. Since Flyte tasks are simply functions, you can wrap the task function in a custom decorator before wrapping the entire function in the @task decorator.

For example, if we want to do something before and after the actual task function is invoked, we can do the following:

from functools import partial, wraps

def decorator(fn):

    def wrapper(*args, **kwargs):
        print("do something before")
        out = fn(*args, **kwargs)
        print("do something after")
        return out

    return wrapper

Then, making sure @task is the outermost decorator, we can modify the behavior of the task:

def add_one(x: int) -> int:
    return x + 1


This approach allows you to call out to some external service or library before and after your task function body is executed. For example, this pattern is used by the MLFlow integration via the mlflow_autolog() decorator to auto-log metrics during a model-training task.


You can stack multiple decorators on top of each other. Learn more in the User Guide.

Flyte also supports a setup-teardown pattern at the workflow level, which allows you to enable/disable services at the beginning/end of your workflows. See the User Guide for more details.

The plugin hierarchy of needs

The decorator approach is great for many surface-level use cases, but there are many more ways to customize Flyte tasks:

Pre-built Container Task Plugins

Task extensions that use pre-built containers, useful for tasks that don’t require user-defined code and simply rely on input parameters.

User Container Task Plugins

Task extensions that require user-built containers when the task also requires user-defined code.

Raw Container Tasks

These tasks can be implemented in other programming languages like R, Julia, etc. Useful for leveraging highly optimized domain-specific libraries in other languages outside of the flytekit SDK language.

Backend Plugins

These tasks plugins require implementing a backend plugin to leverage external services like SageMaker, Snowflake, BigQuery, etc.

What’s next?

Congratulations! 🎉 You’ve just completed the Flyte Fundamentals tour.

The final section in the getting started section of the docs will provide you with some core use cases for implementing your first workflows, whether you’re a data scientist, data analyst, data engineer, or machine learning engineer.