1.3. Installation

1.3.1. Compatibility

Numba is compatible with Python 2.7 and 3.5 or later, and Numpy versions 1.7 to 1.16.

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, and is not supported in Python 2.7 on Windows.

1.3.2. 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:

$ 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.

1.3.3. 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:

$ 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.

1.3.4. 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:

  1. Follow the ROCm installation instructions.

  2. Install roctools conda package from the numba channel:

    $ conda install -c numba roctools

See the roc-examples repository for sample notebooks.

1.3.5. 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:

$ conda install -c numba numba

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

1.3.6. 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:

    $ 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.

1.3.7. 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:

$ 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:

  • 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
  • NumPy

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

$ python setup.py install

1.3.8. 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:
    • setuptools
    • numpy
    • llvmlite
    • funcsigs (Python 2)
    • singledispatch (Python 2)
    • Compiler toolchain mentioned above
  • 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:
    • setuptools
    • numpy
    • llvmlite
    • funcsigs (Python 2)
    • singledispatch (Python 2)
  • 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
  • To build the documentation:
    • sphinx
    • pygments
    • sphinx-bootstrap
    • numpydoc
    • make as an executable on the $PATH

1.3.9. Checking your installation

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

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