numpy-ml collects machine learning algorithms in NumPy, presented as an intentionally readable Python package.
Open source
24 alternativesJax
JAX composes transformations for Python and NumPy programs, including differentiation, vectorization, and compilation.
About Jax
JAX is an open-source Python library for accelerator-oriented array computation and program transformation. It is intended for high-performance numerical computing and large-scale machine learning. The GitHub repository hosts the source code, documentation, examples, and installation guidance.
You can use jax.grad for reverse-mode and forward-mode differentiation of Python and NumPy functions, including code with loops, branches, recursion, and closures. You can apply differentiation to any order. You can use jax.jit to compile pure functions with XLA for GPUs, TPUs, and other hardware accelerators. You can use jax.vmap to map a function along array axes. Compilation, differentiation, and vectorization can be composed.
It suits developers and researchers who work with NumPy-style programs and need program transformations for training and optimization. The project describes itself as a research project and notes users should expect sharp edges.
Key features
- Automatic differentiation with jax.grad to any order
- JIT compilation with jax.jit using XLA
- Auto-vectorization along array axes with jax.vmap
- Runs NumPy programs on GPU, TPU, and accelerators
- Differentiation through loops, branches, recursion, and closures
- Open-source repository with docs and examples
Best for: Researchers and developers doing NumPy-based numerical computing and machine learning who need composable differentiation, vectorization, and compilation.
Good for
- Composable grad, jit, and vmap transformations
- Supports GPU, TPU, and other accelerators via XLA
- Handles Python control flow in differentiated code
- Open source with documentation and examples
Watch out for
- Described as research project with sharp edges
- jax.jit constrains usable Python control flow
Jax at a glance
- Website
- github.com/google/jax
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Jax FAQ
What is JAX used for?
JAX is a Python library for accelerator-oriented array computation and program transformation for numerical computing and large-scale machine learning.
What transformations does JAX provide?
It provides jax.grad for automatic differentiation, jax.jit for compilation, and jax.vmap for vectorization, which can be composed.
Does JAX run on GPUs and TPUs?
Yes, it uses XLA to compile and run NumPy programs on GPUs, TPUs, and other hardware accelerators.
Is JAX open source?
Yes, the source code is available in a public repository with documentation and examples.
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