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Ivy enables you to:

• Convert ML models, tools and libraries between frameworks while maintaining complete functionality usingivy.transpile
• Create optimized graph-based models and functions in any native framework (PyTorch, TensorFlow, etc..) withivy.trace_graph

You can find Ivy's documentation on theDocs page, which includes:

• Motivation: This contextualizes the problem Ivy is trying to solve by going over
  • The currentML Explosion.
  • Explaining why it is importantto 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. Whetherit's writing code, fixing bugs, or simply sharing feedback,your contributions are definitely welcome and appreciated 🙌

Check out all of ourOpen Tasks,and find out more info in ourContributing guidein the docs! Or to immediately dive into a useful task, look for any failing tests on ourTest 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 onourDiscord 👾 server, which is theperfect place to ask questions, share ideas, and get help from bothfellow developers and the Ivy Team directly.

See you there!

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