Simplicity at its core — both in development and deployment

Kubeflow entails a significant learning curve because of its proximity to the infrastructure layer. If you’re looking to enhance user experience, simplify data and ML workflow development, and streamline infrastructure navigation, you’ve come to the right place. Thanks to an extensive feature set and infrastructure abstraction, Flyte can help you take your workflows to the next level.

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Tap Kubernetes’ power, not its complexity

Kubeflow is built as a thin layer on top of Kubernetes, and it offers limited management of Kubernetes configuration. Authoring workflows can be a challenge for data scientists and engineers who lack Kubernetes expertise. Flyte provides a clean abstraction on top of Kubernetes that can be removed for complicated use cases; however, it works for 80% of them.

No weird DSL to author workflows

Kubeflow’s DSL deviates from Python and requires users to learn its syntax specifics, such as declaring output type annotations in the input arguments declaration, while Flyte’s SDK aligns with Python.

Mitigate boilerplate code

Kubeflow enforces a type system, but it doesn’t support data types beyond fundamental Python types and artifacts/files. By contrast, Flyte supports varied data types and transformations, and it automates the interaction among different cloud providers and the local file system. The intuitive code constructs in Flyte make it simpler to develop ML workflows, and eliminates the boilerplate you would otherwise have.

“We’ve migrated about 50% of all training pipelines over to Flyte from Kubeflow. In several cases, we saw an 80% reduction in boilerplate between workflows and tasks vs. the Kubeflow pipeline and components. Overall, Flyte is a far simpler system to reason about with respect to how the code actually executes, and it’s more self-serve for our research team to handle.”

— Rahul Mehta, ML Infrastructure/Platform Lead at Theorem LP

Comparing Kubeflow and Flyte

Kubeflow Pipelines v2

Flyte

Not fully supported

Intra-task checkpointing

Why engineers choose Flyte over Kubeflow

Speed development cycles

Lightweight components in Kubeflow are easy to compose, but containerized and custom container components are the preferred choice for production usage and they take you closer to infrastructure code constructs, which isn’t desirable. Flyte offers a Pythonic way of composing workflows locally or during deployment that simplifies development and speeds iteration cycles.

Iterate using a single command

Iterating with Flyte requires you to run a single `pyflyte` command that serializes, registers and triggers the code on the Flyte backend.

Recover when there’s a failure

Flyte lets you recover an individual execution by copying all successful node executions. This is a critical feature for compute-intensive ML workflows to avoid resource overuse. Ideally, skipping successful task node executions means better resource management and quicker iterations.

Join an extremely helpful community

The No. 1 reason people cite for loving Flyte? Our community. We ensure that no question goes unanswered, and we tackle a diverse set of problems with the help of power users spanning several industries.

Be a part of our community

From Kubeflow to Flyte

If you’re a Kubeflow user, transitioning to Flyte is a matter of removing boilerplate code and making your workflows much simpler. After some initial investment in technical integration required, Flyte’s scalability and agility make it well worth the effort. Your team will appreciate the benefits of using Flyte as your workflows, team and needs evolve.

See for yourself how easy it is to migrate code from Kubeflow to Flyte

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from kfp import dsl
from kfp import client


@dsl.component
def addition_component(num1: int, num2: int) -> int:
    return num1 + num2


@dsl.pipeline(name="addition-pipeline")
def my_pipeline(a: int, b: int, c: int = 10):
    add_task_1 = addition_component(num1=a, num2=b)
    add_task_2 = addition_component(num1=add_task_1.output, num2=c)

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from flytekit import task, workflow


@task
def addition_component(num1: int, num2: int) -> int:
    return num1 + num2


@workflow
def my_pipeline(a: int, b: int, c: int = 10):
    add_task_1 = addition_component(num1=a, num2=b)
    add_task_2 = addition_component(num1=add_task_1, num2=c)

Kubeflow and Flyte code samples (we intentionally picked a simple example to showcase the contrast).