#9797th of 168 in ML frameworks and libraries
ivy
Transpiler that converts ML code from PyTorch to TensorFlow, JAX or NumPy
- Stars
- 14.2k
- License
- custom license
- Last commit
- Nov 2026
- Last release
- Jun 2025
- Language
- Python
14,108 on 21 Apr 202614,167 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
ivy provides `ivy.transpile`, which converts functions, classes or whole libraries (e.g. kornia) from one ML framework to another, and `ivy.trace_graph`, which traces a functional graph from code. Per the support table, PyTorch is the only source framework; TensorFlow, JAX and NumPy are targets. Functions and classes are transpiled eagerly, modules lazily on first use.
Who it is for: Engineers porting PyTorch models or libraries to TensorFlow, JAX or NumPy
Strengths
- Transpiles whole libraries lazily, e.g. kornia from PyTorch to TensorFlow
- Single call API: ivy.transpile(fn, source, target)
- Targets TensorFlow, JAX and NumPy from PyTorch code
- Installs with pip; docs include examples and demos
Weaknesses
- Only PyTorch is a supported source today; other sources are marked planned
- PyTorch as a target is only planned
- License is NOASSERTION, so terms need manual checking
- README examples contain errors, e.g. undefined jax_fn and test_fn
What it needs
- Needs PyTorch, TensorFlow, JAX, NumPy
- Models: PyTorch (source), TensorFlow (target), JAX (target), NumPy (target)
Quick answers
Is ivy open source?
Its code is public on GitHub under its own license terms; read them before you use it at work.
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