Flyte is an open-source workflow orchestration platform for building data, ML and analytics workflows with ease.

Flyte is a free and open source platform that provides a full suite of powerful features for orchestrating AI workflows. Flyte empowers AI development teams to rapidly ship high-quality …

Flyte supports the development and debugging of tasks and workflows in a local setting, which increases the iteration speed of building out your data- or machine-learning-driven applications.

Flyte will take this into account to ensure that the task pod is scheduled to run on a Kubernetes node that meets the specified resource profile. Resources are specified in the @fl.task decorator.

To register and run workflows in Flyte, your client (the CLI in your machine or an external system) needs to connect to the Flyte control plane through an endpoint.

This document aims to demystify how Flyte’s major components Flyteidl, Flytekit, Flytectl, FlyteConsole, FlyteAdmin, FlytePropeller, and FlytePlugins fit together at a high level.

Print out information about the current Flyte Python CLI environment - like the version of Flytekit, the version of Flyte Backend Version, backend endpoint currently configured, etc.

In this guide, you learned about the Flyte demo cluster, Flyte configuration, and the different registration patterns you can leverage during the workflow development lifecycle.

Flyte lets you serialize your models and store them on cloud so you can associate them with different versions of your runs. Track the lineage of all your ML experiments and those of your …

Flyte lets you write code in any language using raw containers, or choose Python, Java, Scala or JavaScript SDKs to develop your Flyte workflows. You can use the languages you are most …