Numba is compatible with Python 3.6 or later, and Numpy versions 1.15 or later.
Our supported platforms are:
Automatic parallelization with @jit is only available on 64-bit 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:
$ conda install numba
or:
$ conda update numba
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:
$ conda install cudatoolkit
You do not need to install the CUDA SDK from NVIDIA.
Binary wheels for Windows, Mac, and Linux are also available from PyPI. You can install Numba using pip
:
$ pip install numba
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.
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:
Follow the ROCm installation instructions.
Install roctools
conda package from the numba
channel:
$ conda install -c numba roctools
See the roc-examples repository for sample notebooks.
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:
$ conda install -c numba numba
Berryconda and Numba may work on other Linux-based ARMv7 systems, but this has not been tested.
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:
$ conda config --add channels c4aarch64
$ conda config --add channels conda-forge
Then you can install Numba from the numba
channel:
$ conda install -c numba numba
On CUDA-enabled systems, like the Jetson, the CUDA toolkit should be automatically detected in the environment.
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:
$ git clone git://github.com/numba/numba.git
Source archives of the latest release can also be found on
PyPI. In addition to llvmlite
, you will also need:
x86
: gcc_linux-32
and gxx_linux-32
x86_64
: gcc_linux-64
and gxx_linux-64
POWER
: gcc_linux-ppc64le
and gxx_linux-ppc64le
ARM
: no conda packages, use the system compilerclang_osx-64
and clangxx_osx-64
or the system compiler at
/usr/bin/clang
(Mojave onwards)Then you can build and install Numba from the top level of the source tree:
$ python setup.py install
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).
setuptools
numpy
llvmlite
llvm-openmp
(OSX) - provides headers for compiling OpenMP support into
Numba’s threading backendintel-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 backendsetuptools
numpy
llvmlite
scipy
- provides cython bindings used in Numba’s np.linalg.*
supporttbb
- provides the TBB runtime libraries used by Numba’s TBB threading
backendjinja2
- for “pretty” type annotation output (HTML) via the numba
CLIcffi
- permits use of CFFI bindings in Numba compiled functionsintel-openmp
- (OSX) provides OpenMP library support for Numba’s OpenMP
threading backendipython
- if in use, caching will use IPython’s cache
directories/caching still workspyyaml
- permits the use of a .numba_config.yaml
file for storing per project configuration optionscolorama
- makes error message highlighting workicc_rt
- (numba channel) allows Numba to use Intel SVML for extra
performancepygments
- for “pretty” type annotationgdb
as an executable on the $PATH
- if you would like to use the gdb
supportpycc
for
Ahead-of-Time (AOT) compilationsphinx
pygments
sphinx-bootstrap
numpydoc
make
as an executable on the $PATH
You should be able to import Numba from the Python prompt:
$ python
Python 2.7.15 |Anaconda custom (x86_64)| (default, May 1 2018, 18:37:05)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import numba
>>> numba.__version__
'0.39.0+0.g4e49566.dirty'
You can also try executing the numba --sysinfo
(or numba -s
for short)
command to report information about your system capabilities. See Command line interface for
further information.
$ numba -s
System info:
--------------------------------------------------------------------------------
__Time Stamp__
2018-08-28 15:46:24.631054
__Hardware Information__
Machine : x86_64
CPU Name : haswell
CPU Features :
aes avx avx2 bmi bmi2 cmov cx16 f16c fma fsgsbase lzcnt mmx movbe pclmul popcnt
rdrnd sse sse2 sse3 sse4.1 sse4.2 ssse3 xsave xsaveopt
__OS Information__
Platform : Darwin-17.6.0-x86_64-i386-64bit
Release : 17.6.0
System Name : Darwin
Version : Darwin Kernel Version 17.6.0: Tue May 8 15:22:16 PDT 2018; root:xnu-4570.61.1~1/RELEASE_X86_64
OS specific info : 10.13.5 x86_64
__Python Information__
Python Compiler : GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)
Python Implementation : CPython
Python Version : 2.7.15
Python Locale : en_US UTF-8
__LLVM information__
LLVM version : 6.0.0
__CUDA Information__
Found 1 CUDA devices
id 0 GeForce GT 750M [SUPPORTED]
compute capability: 3.0
pci device id: 0
pci bus id: 1
(output truncated due to length)