Installation ============ 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) * ARMv7 (32-bit little-endian, such as Raspberry Pi 2 and 3) :ref:`numba-parallel` is only available on 64-bit platforms, and is not supported in Python 2.7 on Windows. 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. 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 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. 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 incudes the 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. .. Hide this until we have the conda packages available Installing on Linux ARMv8 (AArch64) Platforms --------------------------------------------- Although many ARMv8 platforms are likely to require building Numba from source, we do build and test conda packages on the `NVIDIA Jetson TX2 `_. These packages rely on an infrequently updated port of Berryconda to ARMv8 called `Jetconda `_. Jetconda is built on Ubuntu 16.04 running on the Jetson TX2, although the packages may work on other ARMv8 systems running Ubuntu 16.04. Once Jetconda is installed, Numba can be installed from the ``numba`` channel:: $ conda install -c numba numba To enable CUDA support on the TX2, set the ``NUMBAPRO`` environment variables as described above. .. _numba-source-install-instructions: 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 :ref:`buildenv` 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 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 (output truncated due to length)