Best and Official Numba alternatives will be discussed in this article. Modern Python compiler Numba transforms a portion of NumPy and Python code into speedy and effective machine code. The industry-standard LLVM compiler library is used to create the enhanced and high-level machine code. The various algorithms built in Python have the potential to reach FORTRAN or C’s performance levels. There is no requirement to remove the Python interpreter or run a specific compiler step.
Simply adding a single Numba decorator to the function will take care of the rest, eliminating the need for manual involvement. The solution is designed to work with NumPy’s arrays and functions. To improve efficiency, it generates optimised code for various array designs and data formats. To provide a user-friendly computing environment, the tool pairs ideally with Jupyter notebooks and distributed execution frameworks like Spark and Dask. The code may be easily parallelized for GPUs and CPUs with a variety of choices available to you.
Top 9 Best Numba Alternatives in 2022
Top 9 Best Numba Alternatives are explained here.
1. Cx Freeze
Cx Freeze, an open-source programme that converts Python scripts into standalone executables with unmatched speed and precision. Because it is cross-platform, it may be used on any platform that Python supports. The PSF license’s terms govern the release of the solution. It continues to receive upgrades as some of the best features are added.
The library has seen several enhancements in the most recent version, including support for pathlib and a refactored freezer. This is another numba alternative.
Path, several bug fixes, code modernization, multiprocessing advancements, and the inclusion of ModuleFinder engine that utilises importlib.machinery. Significant improvements have also been made to library distribution and detection. The most recent or enhanced hooks, with a focus on PySide2, matplotlib, PyQt5, and NumPy, are another newly incorporated highlight. The support is also expanded to include the new DistributionCache and the package metadata enhancing module.
2. Nuitka
An open-source Python compiler built entirely in Python is called Nuitka. It works wonderfully with a variety of Python versions, including 2.6, 2.7, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, and 3.9. It is distributed under the Apache 2.0 licence. It is simple to use since you only need to run it within the Python application; after that, it will carry out the required operations before producing the extension module or executable. Also check NDSR software
This is another numba alternative. All of the Python extension modules and library modules can be used by anyone without any problems. The library converts Python modules into a C-level application, which is subsequently run just like CPython using libpython and static C files. Every single optimization function entails avoiding overhead when it is not necessary.
3. Py2exe
Without the requirement for a Python installation, Py2exe is a potent Python Disutilis addon that transforms Python scripts into MS Windows executable applications. The repository is hosted by GitHub, where you may access the source code and all related information. Everyone may access the email list, svn, and Python 2 downloads from there.
The original development of py2exe was carried out by Thomas Heller, who continues to contribute to its upkeep in a number of ways. Alberto Sottile, Jimmy Retzlaff, and Mark Hammond are three other authors that contributed to the development. In addition, community members have the option of making contributions. Some of the top businesses on the Internet, including SpamBytes, BitTorrent, and many more, use the library.
4. Shed Skin
A Python to C++ compiler that is open-source and distributed under the GPL-3.0 licence is called Shed Skin. It functions as an experimental compiler that can transform unmodified Python programmes into improved C++ equivalents. You may import and utilise the stand-alone executables or extension modules that the solution can create in large Python projects. The Python standard library is incompatible with applications other than the type constraint.
Additionally, a lot of Python features, such as variable numbers and nested functions, are not supported. The ideal answer to these issues is provided by this library, which is intended to make life easier for both programmers and developers. It is kept up by a community made up of people from many nations.
5. Bbfreeze
Everyone may create stand-alone executables or extensions from Python programmes using the cross-platform Bbfreeze Python module. It functions wonderfully on all of the aforementioned platforms and is compatible with Linux, Mac, and Windows. The method, which uses Python scripts to produce stand-alone executables, is entirely open-source. The tool shares features with well-known programmes like PyInstaller, cx Freeze, Py2app for OS X, and Py2exe for Windows. This is another numba alternative.
You may install it using the easy install command, which is one of its key advantages. If a module is used from an eggfile, the zop/egg file import tracking keeps track of all zip file imports, including entire egg files. The shared libraries and DLLs needed by a frozen application are included in the library, which has the ability to monitor binary dependencies. Distutils command “bdist bbfreeze,” multiple script freezing, automated pathname rewriting, and inclusion of a Python interpreter are some further highlights of the approach.
6. PyPy
Python can be quickly and effectively replaced with PyPy. It offers a variety of benefits and distinctive qualities, with its speed being the most notable. The Just-in-Time compiler is used to maintain the quick speed. Python scripts run more quickly on PyPy. Less memory usage is another fantastic feature that makes it possible for memory-intensive Python programmes to operate on less disc space than CPython.
The solution can execute well-known python libraries like Django and Twisted and is compatible with existing Python code, cppyy, and cffi applications. It can also use the C-extension Compatibility Layer to run Scikit-learn, NumPy, and other programmes. The stackless mode is fully supported and unmatched by PyPy, enabling extremely high concurrency through the use of micro-threads.
7. Cython
The Python and Cython programming languages are compatible with Cython, an improved static compiler. It makes it simpler to create C extensions for Python, requires less work, and is fairly simple to understand. The approach makes it simple for you to write Python code that can produce calls from and to C or C++ by giving you access to the strength of C and Python.
This is another numba alternative. By introducing static Type declarations, which are also included in the Python syntax, it transforms legible Python code into fundamental C performance. Through the unified source code level debugging, errors in the C, Python, and Cython code may be found rapidly. You may work with big data sets, such as multidimensional NumPy arrays, using the compiler. Within the vast, open, and well-known CPython environment, apps may be made. The compiler may be integrated with data and code from extremely sophisticated programmes and libraries. Also check ungerboek software
8. PyInstaller
A Python application and all of its dependencies are packaged together by PyInstaller. By doing so, the user may run the packed application without having to deal with Python interpreters or any of the relevant modules. With Python versions 3.7 and later, the package operates without any issues. Many Python packages, including wxPython, NumPy, PyQt, matpotlib, and others, are accurately wrapped by it.
The package is not a cross-compiler, which is crucial to keep in mind, but you may create a Windows app by executing the solution on Windows. In a similar vein, Linux users must run it on Linux in order to develop a Linux programme. On well-known operating systems like OpenBSD, AIX, FreeBSD, and Solaris, PyInstallers functions without error; nevertheless, the tests are not repeated.
9. PyOxidizer
The MPL-2.0 licence applies to PyOxidizer, an open-source, highly efficient method for packaging and distributing Python apps. It is the best method for creating binaries with Python embedded. The goal is to simplify complex distribution and packaging issues so that app maintainers may focus on creating apps without distractions. This is another numba alternative.
The utility is designed to produce a single executable file that contains a copy of Python and any associated dependencies. One exe file may be copied to a separate computer, where you can run the embedded Python programme. It also displays lower-level capabilities for embedding self-ontained Python interpreters as a software library and a tool, which is a fantastic feature. Also check CXT software
