Want faster number-crunching in Python? You can speed up your existing Python code with the Numba JIT, often with only one instruction.

Intel highlights how to achieve high levels of parallelism in large scale Python applications using the Intel Distribution for Python with Numba.

A number of scientific packages for Python, such as Scikit-learn, draw on Cython features like this to keep operations lean and fast. Numba Numba combines two of the previous approaches.

Numba gives you the power to speed up your applications with high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be ...

The first part of the simplification is to utilise the excellent NUMBA python JIT compiler to allow easy-to-understand code to be deployed as GPU machine code.

This process is running in Python using the NumPy module, as you would expect, but accelerated using the Numba JIT compiler.