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Ivy

As explained in Deep Learning with JAX

A framework and transpiler that unifies multiple ML backends and can convert code among JAX, TensorFlow, PyTorch, and NumPy.

Need one implementation to work across several machine-learning ecosystems? Ivy provides a unified framework and transpiler for JAX, TensorFlow, PyTorch, and NumPy. It lets code target any supported backend and converts functions, models, or libraries between them. section 12.1.1 section 2.8

PyTorch model to JAX

A practical use case is transpiling a model from PyTorch to JAX, allowing an existing model to be reused in a JAX codebase without rewriting its implementation manually. section 2.8

How Deep Learning with JAX covers it

How deep it goes
Defines backend abstraction and transpilation for functions, models, and libraries, with no implementation internals shown.
Examples
Demonstrates transpiling a PyTorch model to JAX as a concrete interoperability example.
What you can do after
Apply it via Ivy to write backend-portable code or convert models between supported machine-learning frameworks.
How it connects
Connects JAX, TensorFlow, PyTorch, and NumPy through shared implementations and cross-framework conversion.
Cover of Deep Learning with JAX
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with JAX. The section links point to the parts of the book it draws on.