flyteorg/flyte

★ 7,029GoAudience · dataComplexity · 4/5LicenseSetup · hard

Mindmap

mindmap
  root((flyte))
    What it does
      Orchestrate ML pipelines
      Retry failed steps
      Distribute compute
    Tech Stack
      Python tasks
      Go backend
      FastAPI serving
    Use Cases
      Train and serve models
      Parallel data processing
      Workflow monitoring
    Setup
      Local development
      Cloud production
      Union.ai hosted
    Audience
      ML engineers
      Data scientists

mindmap root((flyte)) What it does Orchestrate ML pipelines Retry failed steps Distribute compute Tech Stack Python tasks Go backend FastAPI serving Use Cases Train and serve models Parallel data processing Workflow monitoring Setup Local development Cloud production Union.ai hosted Audience ML engineers Data scientists

Click or tap to explore — scroll the page freely

Things people build with this

USE CASE 1

Build a multi-step ML pipeline that trains a model and serves predictions with automatic retry on failure.

USE CASE 2

Run data preprocessing and model training in parallel across multiple machines without managing the compute yourself.

USE CASE 3

Monitor and debug ML workflow execution history using the built-in text-based monitoring interface.

Tech stack

PythonGoFastAPI

Getting it running

Difficulty · hard Time to first run · 1h+

Requires cloud infrastructure for production, the open-source backend is listed as coming soon and local development connects to Union.ai hosted version.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

In plain English

Flyte is a Python-based tool for building and running machine learning workflows. If you have a series of steps that need to happen in order, like preparing data, training a model, and then serving predictions, Flyte lets you define those steps as regular Python functions and then coordinates running them, potentially across many machines at the same time. The core idea is that you decorate your Python functions with a marker that tells Flyte to treat them as tasks. Flyte then handles scheduling, retrying failed steps, tracking what ran and when, and distributing work across available compute. This is useful when individual steps take a long time, require a lot of memory or GPU access, or need to run in parallel. Flyte 2, the current version shown in this repository, is designed to run locally for development and connect to cloud infrastructure for production workloads. It includes a command-line tool for running scripts and a text-based interface for monitoring what is happening. Model serving is also supported, with an example showing how to expose a prediction endpoint using FastAPI alongside Flyte. The open-source backend for Flyte 2 is listed as coming soon at the time of this README. An enterprise-ready hosted version is available through Union.ai, the company that maintains the project. The older Flyte 1 is still maintained on a separate branch. Flyte is a graduated project under the Linux Foundation's AI and Data program. It is licensed under Apache 2.0 and has a community Slack workspace and GitHub Discussions for support.

Copy-paste prompts

Prompt 1

Write a Flyte workflow in Python with three tasks: load a CSV, train a scikit-learn classifier, and evaluate accuracy. Use the @task decorator for each step.

Prompt 2

Show me how to configure a Flyte task to request GPU resources and set a memory limit for a PyTorch training job.

Prompt 3

How do I expose a FastAPI prediction endpoint that reads a Flyte-trained model and serves predictions in real time?

Prompt 4

Write a Flyte workflow that fans out to 10 parallel tasks each processing a different data shard then aggregates the results.

Open on GitHub → Explain another repo

← flyteorg on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.