Installation¶

Compatibility¶

Numba is compatible with Python 3.6 or later, and Numpy versions 1.15 or later.

Our supported platforms are:

Linux x86 (32-bit and 64-bit)

Linux ppcle64 (POWER8)

Windows 7 and later (32-bit and 64-bit)

OS X 10.9 and later (64-bit)

NVIDIA GPUs of compute capability 2.0 and later

AMD ROC dGPUs (linux only and not for AMD Carrizo or Kaveri APU)

ARMv7 (32-bit little-endian, such as Raspberry Pi 2 and 3)

ARMv8 (64-bit little-endian, such as the NVIDIA Jetson)

Automatic parallelization with @jit is only available on 64-bit platforms.

Installing using conda on x86/x86_64/POWER Platforms¶

The easiest way to install Numba and get updates is by using conda , a cross-platform package manager and software distribution maintained by Anaconda, Inc. You can either use Anaconda to get the full stack in one download, or Miniconda which will install the minimum packages required for a conda environment.

Once you have conda installed, just type:

Note that Numba, like Anaconda, only supports PPC in 64-bit little-endian mode.

To enable CUDA GPU support for Numba, install the latest graphics drivers from NVIDIA for your platform. (Note that the open source Nouveau drivers shipped by default with many Linux distributions do not support CUDA.) Then install the cudatoolkit package:

You do not need to install the CUDA SDK from NVIDIA.

Installing using pip on x86/x86_64 Platforms¶

Binary wheels for Windows, Mac, and Linux are also available from PyPI. You can install Numba using pip :

This will download all of the needed dependencies as well. You do not need to have LLVM installed to use Numba (in fact, Numba will ignore all LLVM versions installed on the system) as the required components are bundled into the llvmlite wheel.

To use CUDA with Numba installed by pip , you need to install the CUDA SDK from NVIDIA. Please refer to Setting CUDA Installation Path for details. Numba can also detect CUDA libraries installed system-wide on Linux.

Enabling AMD ROCm GPU Support¶

The ROCm Platform allows GPU computing with AMD GPUs on Linux. To enable ROCm support in Numba, conda is required, so begin with an Anaconda or Miniconda installation with Numba 0.40 or later installed. Then:

Install roctools conda package from the numba channel:

See the roc-examples repository for sample notebooks.

Installing on Linux ARMv7 Platforms¶

Berryconda is a conda-based Python distribution for the Raspberry Pi. We are now uploading packages to the numba channel on Anaconda Cloud for 32-bit little-endian, ARMv7-based boards, which currently includes the Raspberry Pi 2 and 3, but not the Pi 1 or Zero. These can be installed using conda from the numba channel:

Berryconda and Numba may work on other Linux-based ARMv7 systems, but this has not been tested.

Installing on Linux ARMv8 (AArch64) Platforms¶

We build and test conda packages on the NVIDIA Jetson TX2, but they are likely to work for other AArch64 platforms. (Note that while the Raspberry Pi CPU is 64-bit, Raspbian runs it in 32-bit mode, so look at Installing on Linux ARMv7 Platforms instead.)

Conda-forge support for AArch64 is still quite experimental and packages are limited, but it does work enough for Numba to build and pass tests. To set up the environment:

Install conda4aarch64. This will create a minimal conda environment.

Add the c4aarch64 and conda-forge channels to your conda configuration:

Then you can install Numba from the numba channel:

On CUDA-enabled systems, like the Jetson, the CUDA toolkit should be automatically detected in the environment.

Installing from source¶

Installing Numba from source is fairly straightforward (similar to other Python packages), but installing llvmlite can be quite challenging due to the need for a special LLVM build. If you are building from source for the purposes of Numba development, see Build environment for details on how to create a Numba development environment with conda.

If you are building Numba from source for other reasons, first follow the llvmlite installation guide. Once that is completed, you can download the latest Numba source code from Github:

Source archives of the latest release can also be found on PyPI. In addition to llvmlite , you will also need:

A C compiler compatible with your Python installation. If you are using Anaconda, you can use the following conda packages:

Linux x86 : gcc_linux-32 and gxx_linux-32

Linux x86_64 : gcc_linux-64 and gxx_linux-64

Linux POWER : gcc_linux-ppc64le and gxx_linux-ppc64le

Linux ARM : no conda packages, use the system compiler

Mac OSX: clang_osx-64 and clangxx_osx-64 or the system compiler at /usr/bin/clang (Mojave onwards)

Windows: a version of Visual Studio appropriate for the Python version in use

Then you can build and install Numba from the top level of the source tree:

Dependency List¶

Numba has numerous required and optional dependencies which additionally may vary with target operating system and hardware. The following lists them all (as of September 2019).

Required build time:

Compiler toolchain mentioned above

OpenMP C headers and runtime libraries compatible with the compiler toolchain mentioned above and accessible to the compiler via standard flags (Linux, Windows).

Optional build time:

llvm-openmp (OSX) — provides headers for compiling OpenMP support into Numba’s threading backend

intel-openmp (OSX) — provides OpenMP library support for Numba’s threading backend.

tbb-devel — provides TBB headers/libraries for compiling TBB support into Numba’s threading backend

Required run time:

Optional runtime are:

scipy — provides cython bindings used in Numba’s np.linalg.* support

tbb — provides the TBB runtime libraries used by Numba’s TBB threading backend

jinja2 — for “pretty” type annotation output (HTML) via the numba CLI

cffi — permits use of CFFI bindings in Numba compiled functions

intel-openmp — (OSX) provides OpenMP library support for Numba’s OpenMP threading backend

ipython — if in use, caching will use IPython’s cache directories/caching still works

pyyaml — permits the use of a .numba_config.yaml file for storing per project configuration options

colorama — makes error message highlighting work

icc_rt — (numba channel) allows Numba to use Intel SVML for extra performance

pygments — for “pretty” type annotation

gdb as an executable on the $PATH — if you would like to use the gdb support

Compiler toolchain mentioned above, if you would like to use pycc for Ahead-of-Time (AOT) compilation

r2pipe — required for assembly CFG inspection.

radare2 as an executable on the $PATH — required for assembly CFG inspection. See here for information on obtaining and installing.

graphviz — for some CFG inspection functionality.

Увеличиваем скорость работы Python до уровня C++ с Numba

Увеличиваем скорость работы Python до уровня C++ с Numba

В этой статье автор разобрался, как увеличить скорость работы Python, и продемонстрировал реализацию на реальном примере.

Прим. ред. Это перевод. Мнение редакции может не совпадать с мнением автора оригинала.

Тест базовой скорости

Для сравнения базовой скорости Python и C++ я буду использовать алгоритм генерации случайных простых чисел.

Блок-схема алгоритма генерации простых чисел

Реализация на Python

Реализация на C++

Результат

• Python: скорость выполнения 80,137 секунд;
• C++: скорость выполнения 3,174 секунды.

Комментарий

Как и ожидалось, программа на C++ выполняется в 25 раз быстрее, чем на Python. Ожидания подтвердились, потому что:

• Python — это динамически типизированный язык;
• GIL(Global Interpreter Lock) — не поддерживает параллельное программирование.

Благодаря тому, что Python это гибкий универсальный язык, наш результат можно улучшить. Один из лучших способов увеличить скорость Python — Numba.

Numba

Numba — это Open Source JIT-компилятор, который переводит код на Python и NumPy в быстрый машинный код.

Чтобы начать использовать Numba, просто установите её через консоль:

Реализация на Python с использованием Numba

Как вы могли заметить, в коде добавились декораторы njit:

• parallel=True — включает параллельное выполнение программы на процессоре;
• fastmath=True — разрешает использование небезопасных преобразований с плавающей точкой;
• cache=True — позволяет сократить время компиляции функции, если она уже была скомпилирована.

Итоговая скорость Python

• Python: скорость выполнения 1,401 секунды;
• C++: скорость выполнения 3,174 секунды.

Теперь вы знаете что Python способен обогнать C++. О других способах увеличения скорости работы Python читайте в статье про пять проектов, которые помогают ускорить код на Python.

pip install numba in Python3.8 amd64&Win10 with Error #529

Comments

homepeople commented Nov 14, 2019

C:\Users\Administrator>pip install numba
Collecting numba
Using cached https://files.pythonhosted.org/packages/33/6f/47d87ded23f3d1aa1e221d75278caf427e8da80649c14312172857bad64f/numba-0.46.0.tar.gz
ERROR: Command errored out with exit status 1:
command: ‘c:\users\administrator\appdata\local\programs\python\python38\python.exe’ -c ‘import sys, setuptools, tokenize; sys.argv[0] = ‘»‘»‘C:\Users\Administrator\AppData\Local\Temp\pip-install-9oo7ymxj\numba\setup.py'»‘»‘; file='»‘»‘C:\Users\Administrator\AppData\Local\Temp\pip-install-9oo7ymxj\numba\setup.py'»‘»‘;f=getattr(tokenize, ‘»‘»‘open'»‘»‘, open)(file);code=f.read().replace(‘»‘»‘\r\n'»‘»‘, ‘»‘»‘\n'»‘»‘);f.close();exec(compile(code, file, ‘»‘»‘exec'»‘»‘))’ egg_info —egg-base ‘C:\Users\Administrator\AppData\Local\Temp\pip-install-9oo7ymxj\numba\pip-egg-info’
cwd: C:\Users\Administrator\AppData\Local\Temp\pip-install-9oo7ymxj\numba
Complete output (207 lines):
Could not locate executable g77
Could not locate executable f77
Could not locate executable ifort
Could not locate executable ifl
Could not locate executable f90
Could not locate executable DF
Could not locate executable efl
Could not locate executable gfortran
Could not locate executable f95
Could not locate executable g95
Could not locate executable efort
Could not locate executable efc
Could not locate executable flang
don’t know how to compile Fortran code on platform ‘nt’
‘svnversion’ 不是内部或外部命令，也不是可运行的程序
或批处理文件。
non-existing path in ‘numpy\distutils’: ‘site.cfg’
Running from numpy source directory.
C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\setup.py:419: UserWarning: Unrecognized setuptools command, proceeding with generating Cython sources and expanding templates
run_build = parse_setuppy_commands()
C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\numpy\distutils\system_info.py:690: UserWarning:
Optimized (vendor) Blas libraries are not found.
Falls back to netlib Blas library which has worse performance.
A better performance should be easily gained by switching
Blas library.
self.calc_info()
C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\numpy\distutils\system_info.py:690: UserWarning:
Blas (http://www.netlib.org/blas/) libraries not found.
Directories to search for the libraries can be specified in the
numpy/distutils/site.cfg file (section [blas]) or by setting
the BLAS environment variable.
self.calc_info()
C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\numpy\distutils\system_info.py:690: UserWarning:
Blas (http://www.netlib.org/blas/) sources not found.
Directories to search for the sources can be specified in the
numpy/distutils/site.cfg file (section [blas_src]) or by setting
the BLAS_SRC environment variable.
self.calc_info()
C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\numpy\distutils\system_info.py:1712: UserWarning:
Lapack (http://www.netlib.org/lapack/) libraries not found.
Directories to search for the libraries can be specified in the
numpy/distutils/site.cfg file (section [lapack]) or by setting
the LAPACK environment variable.
if getattr(self, ‘calc_info<>‘.format(lapack))():
C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\numpy\distutils\system_info.py:1712: UserWarning:
Lapack (http://www.netlib.org/lapack/) sources not found.
Directories to search for the sources can be specified in the
numpy/distutils/site.cfg file (section [lapack_src]) or by setting
the LAPACK_SRC environment variable.
if getattr(self, ‘calc_info<>‘.format(lapack))():
c:\users\administrator\appdata\local\programs\python\python38\lib\distutils\dist.py:274: UserWarning: Unknown distribution option: ‘define_macros’
warnings.warn(msg)
Traceback (most recent call last):
File «c:\users\administrator\appdata\local\programs\python\python38\lib\distutils\core.py», line 148, in setup
dist.run_commands()
File «c:\users\administrator\appdata\local\programs\python\python38\lib\distutils\dist.py», line 966, in run_commands
self.run_command(cmd)
File «c:\users\administrator\appdata\local\programs\python\python38\lib\distutils\dist.py», line 985, in run_command
cmd_obj.run()
File «c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\setuptools\command\bdist_egg.py», line 163, in run
self.run_command(«egg_info»)
File «c:\users\administrator\appdata\local\programs\python\python38\lib\distutils\cmd.py», line 313, in run_command
self.distribution.run_command(command)
File «c:\users\administrator\appdata\local\programs\python\python38\lib\distutils\dist.py», line 985, in run_command
cmd_obj.run()
File «C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\numpy\distutils\command\egg_info.py», line 26, in run
File «c:\users\administrator\appdata\local\programs\python\python38\lib\distutils\cmd.py», line 313, in run_command
self.distribution.run_command(command)
File «c:\users\administrator\appdata\local\programs\python\python38\lib\distutils\dist.py», line 985, in run_command
cmd_obj.run()
File «C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\numpy\distutils\command\build_src.py», line 142, in run
File «C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\numpy\distutils\command\build_src.py», line 150, in build_sources
File «C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\numpy\distutils\command\build_src.py», line 267, in build_py_modules_sources
File «C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\numpy\distutils\misc_util.py», line 2270, in generate_config_py
File «c:\users\administrator\appdata\local\programs\python\python38\lib\distutils\dir_util.py», line 70, in mkpath
os.mkdir(head, mode)
File «c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\setuptools\sandbox.py», line 310, in wrap
path = self._remap_input(name, path, *args, **kw)
File «c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\setuptools\sandbox.py», line 452, in _remap_input
self._violation(operation, os.path.realpath(path), *args, **kw)
File «c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\setuptools\sandbox.py», line 407, in _violation
raise SandboxViolation(operation, args, kw)
setuptools.sandbox.SandboxViolation: SandboxViolation: mkdir(‘C:\Users\ADMINI 1\AppData\Local\Temp\easy_install-g2a7a883\numpy-1.17.4\build’, 511) <>

ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.

The text was updated successfully, but these errors were encountered:

homepeople commented Nov 14, 2019 •

I had installed numba with Python3.6.5 without any problem at some PC.So I am pretty sure numba and Python3.8 have some Compatibility problem.Upstair is all the ErrorInfo.
Just found out Numba doesn’t support Python3.8 yet,sorry

jjpepper commented Oct 21, 2020

I’m having the same problem. Is there any resolution for this. I’m using python3.9

seibert commented Oct 21, 2020

Numba does not support Python 3.9 yet, so we haven’t done a new llvmlite release for 3.9 yet.

esc commented Oct 21, 2020

@homepeople have you tried this again recently? Does the problem persist. I am asking, because it was reported over a year ago and perhaps a more recent version of Numba has fixed your issues?

esc commented Dec 1, 2020

Closing this issue as no new information has been provided. It is assumed that the above resolved the reported issue, if this is not the case please reopen the issue with new information. Many thanks.

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