JX
JAX
High-performance numerical computing. Composable transformations for autograd, JIT, and vectorization.
About JAX
JAX is a Python library by Google for high-performance numerical computing and machine learning research. It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, and JIT-compile to GPU/TPU.
Key Features
✓ Autograd on native Python/NumPy
✓ XLA JIT compilation
✓ vmap for automatic vectorization
✓ pmap for multi-device parallelism
✓ Functional programming paradigm
✓ TPU & GPU native support
Why choose JAX?
JAX is an open source alternative to PyTorch, TensorFlow. Licensed under Apache-2.0, it gives you full access to the source code and the freedom to modify, self-host, and contribute. It is available as a desktop or web application.