What is Numba?
It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.
Numba is a tool in the Machine Learning Tools category of a tech stack.
Who uses Numba?
Companies
Developers
16 developers on StackShare have stated that they use Numba.
Numba Integrations
Python, C++, TensorFlow, Ludwig, and GraphPipe are some of the popular tools that integrate with Numba. Here's a list of all 5 tools that integrate with Numba.
Numba's Features
- On-the-fly code generation
- Native code generation for the CPU (default) and GPU hardware
- Integration with the Python scientific software stack
Numba Alternatives & Comparisons
What are some alternatives to Numba?
Julia
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
NumPy
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
PyPy
It is a very compliant implementation of the Python language, featuring a JIT compiler. It runs code about 7 times faster than CPython.
Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
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