JAXinbuilt

JAXinbuilt is a conceptual term that refers to the functionalities and features that are intrinsically part of the JAX numerical computation library. JAX is a high-performance numerical computation library developed by Google, designed for machine learning research. It combines automatic differentiation with XLA (Accelerated Linear Algebra) to compile NumPy-like code into highly optimized kernels that run efficiently on GPUs and TPUs.

The "inbuilt" aspects of JAX encompass its core transformations and primitive operations. These include `grad` for automatic differentiation, `jit` for just-in-time compilation using XLA, `vmap` for automatic vectorization, and `pmap` for data parallelism. These transformations allow users to write numerical code in a familiar NumPy-like style and then apply these powerful transformations to achieve significant performance gains and enable complex operations like gradient-based optimization and distributed training.

Beyond these core transformations, JAXinbuilt also refers to the rich set of numerical primitives that mirror NumPy's API. This includes array manipulation, mathematical functions, linear algebra routines, and random number generation. The seamless integration of these NumPy-like operations with JAX's transformations is a key design principle, enabling users to leverage existing knowledge and codebases while benefiting from JAX's advanced features. The library's focus on functional programming paradigms, such as immutability and pure functions, is also considered an inherent characteristic of JAXinbuilt, contributing to its predictability and composability.