Quick Start¶
Installation¶
The MinkowskiEngine can be installed via pip or using conda. Currently, the installation requirements are:
Ubuntu 14.04 or higher
CUDA 10.1 or higher if you want CUDA acceleration
pytorch 1.3 or higher
python 3.6 or higher
GCC 6 or higher
System requirements¶
MinkowskiEngine requires openblas, python3-dev and torch, numpy python packages. Using anaconda is highly recommended and the following instructions will install all the requirements.
Installation¶
The MinkowskiEngine is distributed via PyPI MinkowskiEngine which can be installed simply with pip.
pip3 install -U MinkowskiEngine
To install the latest version, use pip3 install -U git+https://github.com/NVIDIA/MinkowskiEngine.
Running a segmentation network¶
Download the MinkowskiEngine and run the example code.
git clone https://github.com/NVIDIA/MinkowskiEngine.git
cd MinkowskiEngine
python -m examples.indoor
When you run the above example, it will download pretrained weights of a Minkowski network and will visualize the semantic segmentation results of a 3D scene.
CPU only compilation¶
git clone https://github.com/NVIDIA/MinkowskiEngine.git
cd MinkowskiEngine
python setup.py install --cpu_only
Other BLAS and MKL support¶
On intel CPU devices, conda installs numpy with Intel Math Kernel Library or MKL. The Minkowski Engine will automatically detect the MKL using numpy and use MKL instead of openblas or atlas.
In many cases, this will be done automatically. However, if the numpy is not using MKL, but you have an Intel CPU, use conda to install MKL.
conda install -c intel mkl mkl-include
python setup.py install --blas=mkl
If you want to use a specific BLAS among MKL, ATLAS, OpenBLAS, and the system BLAS, provide the blas name as follows:
cd MinkowskiEngine
python setup.py install --blas=openblas
