Add a single @jit decorator to a Python function that loops over NumPy arrays to make it run much faster
Run number-crunching Python code on a CUDA GPU without rewriting it in a lower-level language
Automatically parallelize Python loops across multiple CPU cores without writing threading code
GPU support requires a CUDA-compatible NVIDIA graphics card and the CUDA toolkit installed.
Numba is a tool that makes Python code run much faster, particularly code that does a lot of number crunching. Python is known for being easy to write but slow to execute compared to lower-level languages. Numba bridges that gap by compiling your Python functions into machine code at the moment they are first called, using a technology called LLVM that many professional compilers rely on. The main audience is scientists, engineers, and data analysts who write Python code that works with large arrays of numbers. Numba is built to work closely with NumPy, a library for numerical computing that is widely used in that community. You add a single decorator (a short annotation above a function) and Numba takes care of the compilation automatically, with no need to rewrite your logic in a faster language. Beyond speeding up regular Python loops and math operations, Numba can also run code on a GPU (the graphics processor, which excels at doing many calculations in parallel). It also supports automatic parallelization, meaning it can split certain loops across multiple processor cores without you having to write any threading code yourself. Numba is sponsored by Anaconda, a company that builds tools for data science, and is free to use under an open source license. The README is brief and points to the project website and documentation for installation steps and full details. Demo notebooks are available via the mybinder.org service if you want to try it without installing anything locally.
← numba on gitmyhub — every repo by this author, as a profile.
Verify against the repo before relying on details.