Ivy is an open-source machine learning framework that enables you to:

• Use your ML models and/or functions in any framework by converting any code from one framework to another using ivy.transpile
• Convert entire ML models and libraries between frameworks by generating identical source code in any frameworkusing ivy.source_to_source (currently in private beta)

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.
  • Explaining how we adhere to existing standards to make this happen.
• 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!