Flyte

// Overview

Flyte is a workflow engine for orchestrating machine learning pipelines, models, and agents in pure Python. You define tasks as plain Python functions, and Flyte runs them as a versioned, reproducible workflow that can scale from your laptop to a distributed cluster.

It is built for ML and data engineers who want to move from local scripts to production without rewriting their code. Flyte 2 lets you write ordinary async Python, run it locally with a single command, and execute the same code on a Kubernetes-native backend.

Within the compute orchestration space, Flyte focuses on reproducibility and versioning. Each task runs in a defined container image, and the same workflow can be invoked from Python or the Flyte CLI.

// What it does

• Define workflow tasks as plain Python functions using the @env.task decorator
• Write async pipelines and fan out work with calls like calculate.aio(num) plus asyncio.gather
• Pin each task to a container image with flyte.Image.from_debian_base and with_pip_packages
• Run the same code locally with python or remotely with the flyte run CLI
• Serve models as endpoints, including FastAPI apps via FastAPIAppEnvironment
• Local TUI for a richer development experience via the flyte[tui] extra

// Getting started

Install Flyte, write a task in Python, and run it locally or through the CLI.

uv pip install flyte

import asyncio
import flyte

env = flyte.TaskEnvironment(
    name="hello_world",
    image=flyte.Image.from_debian_base(python_version=(3, 12)),
)

@env.task
def calculate(x: int) -> int:
    return x * 2 + 5

@env.task
async def main(numbers: list[int]) -> float:
    results = await asyncio.gather(*[
        calculate.aio(num) for num in numbers
    ])
    return sum(results) / len(results)

if __name__ == "__main__":
    flyte.init()
    run = flyte.run(main, numbers=list(range(10)))
    print(f"Result: {run.result}")

flyte run hello.py main --numbers '[1,2,3]'

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Turn local ML training scripts into versioned, reproducible pipelines that run on a cluster
• Fan out parallel data or model tasks using async Python and asyncio.gather
• Serve a trained model as an HTTP endpoint with a FastAPI app
• Run the same workflow locally during development and remotely in production without code changes

// How Flyte compares

Flyte alongside other open-source gpu & compute clouds tools AI/TLDR tracks, ranked by GitHub stars.