Flyte 2: Reliable ML Orchestration at Scale
Reliably orchestrate ML pipelines, models, and agents at scale — in pure Python.
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What does this agent do, and when should you use it?
Flyte 2 is an open-source, Kubernetes-native AI orchestration platform that coordinates data, models, and compute to build AI workflows at scale. It provides a Python SDK and CLI for defining and running tasks, workflows, and serving models with FastAPI. Flyte 2 handles asynchronous task execution and concurrency, and is a graduated project of the LF AI & Data Foundation. This repository currently contains the SDK and local development experience (including a TUI), while the Kubernetes backend is announced as coming soon or available for enterprise via Union.ai.
Flyte 2 allows users to define tasks using Python decorators (e.g., @env.task) and orchestrate workflows using async concurrency. It provides TaskEnvironment to specify container images and dependencies. Users can run workflows either from Python or CLI, with CLI commands like flyte run hello.py main --numbers '[1,2,3]'. Models can be served via FastAPI applications using FastAPIAppEnvironment and flyte serve. The platform simplifies running multi-step ML pipelines by managing orchestration and concurrency of async executions.
- Data scientists who want to define an ML pipeline using pure Python without deep infrastructure knowledge.
- ML engineers needing to orchestrate asynchronous tasks reliably and at scale on Kubernetes.
- Teams that want to serve models with a REST API using FastAPI.
- Developers looking for a rich local development experience with a TUI for rapid iteration.
- Organizations that need a production-grade orchestration platform for AI workloads at scale.
What are this agent's strengths and limitations?
- Pure Python SDK for ease of adoption.
- Async task support for efficient concurrency.
- Local development TUI for enhanced developer experience.
- Kubernetes-native for scalable deployments.
- Graduated project under LF AI & Data Foundation.
- Backend is not yet available; must wait or use enterprise solution.
- Requires Python 3.12 and uv, which may necessitate environment adjustments.
- Potential migration complexity from Flyte 1.
- Documentation points to external sites, not in-repo.
How do you install or deploy this agent?
Install Flyte using uv pip install flyte. For the TUI local development experience, use uv pip install flyte[tui].
How do you use this agent?
- Initialize a Flyte environment:
env = flyte.TaskEnvironment(name="hello_world", image=flyte.Image.from_debian_base(python_version=(3, 12))) - Define tasks using
@env.taskand call them with async functions or concurrently. - Initialize Flyte with
flyte.init()and run the workflow withflyte.run(main, numbers=list(range(10))). - Alternatively, use the CLI:
flyte run hello.py main --numbers '[1,2,3]'. - For serving models, create a FastAPI app, wrap it with
FastAPIAppEnvironment, and serve withflyte.serve(env).