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 1.15.

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)

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. Then you may need to set the following environment variables so Numba can locate the required libraries:

  • NUMBAPRO_CUDA_DRIVER - Path to the CUDA driver shared library file
  • NUMBAPRO_NVVM - Path to the CUDA libNVVM shared library file
  • NUMBAPRO_LIBDEVICE - Path to the CUDA libNVVM libdevice directory which contains .bc files

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 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 install the Linux compiler conda packages gcc_linux-64 and gxx_linux-64, or macOS packages clang_osx-64 and clangxx_osx-64.
  • NumPy

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

$ python setup.py install

1.3.6. 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 -s command to report information about your system capabilities:

$ 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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