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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 just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran, without having to switch languages or Python interpreters.

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3 Best Numba Videos


Accelerating Scientific Workloads with Numba - Siu Kwan Lam
Fast Feature Evaluation with MCPT and Numba
GPU-accelerating UDFs in PySpark with Numba and PyGDF

Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack.

The Numba project is supported by Anaconda, Inc. (formerly known as Continuum Analytics) and The Gordon and Betty Moore Foundation (Grant GBMF5423).

Editors Note:

I would like to work with open source projects to create a branch of the tree with all of the best videos for your open source project. Please send me an email if you are interested.