File size: 1,968 Bytes
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## 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](https://pypi.org/project/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
```
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