Website |
Docs |
Demos |
Design |
FAQ |
Ivy enables you to:
- Convert ML models, tools and libraries between frameworks while maintaining complete functionality using
ivy.transpile - Create optimized graph-based models and functions in any native framework (PyTorch, TensorFlow, etc..) with
ivy.trace_graph
You can find Ivy's documentation on the Docs page, which includes:
- Motivation: This contextualizes the problem Ivy is trying to solve by going over
- The current ML Explosion.
- Explaining why it is important to solve this problem.
- Related Work: Which paints a picture of the role Ivy plays in the ML stack, comparing it to other existing solutions in terms of functionalities and abstraction level.
- Design: A user-focused guide about the design decision behind the architecture and the main building blocks of Ivy.
- Deep Dive: Which delves deeper into the implementation details of Ivy and is oriented towards potential contributors to the code base.
We believe that everyone can contribute and make a difference. Whether it's writing code, fixing bugs, or simply sharing feedback, your contributions are definitely welcome and appreciated 🙌
Check out all of our Open Tasks, and find out more info in our Contributing guide in the docs! Or to immediately dive into a useful task, look for any failing tests on our Test Dashboard!
Join our growing community on a mission to make conversions between frameworks simple and accessible to all! Whether you are a seasoned developer or just starting out, you'll find a place here! Join the Ivy community on our Discord 👾 server, which is the perfect place to ask questions, share ideas, and get help from both fellow developers and the Ivy Team directly.
See you there!
