Maastricht Optimization Toolbox
Project description
A library for parallel optimization and sampling using Python and OpenCL.
Free software: LGPL v3 license
Full documentation: https://mot.readthedocs.org
Project home: https://github.com/cbclab/MOT
Uses the GitLab workflow
Tags: optimization, parallel, opencl, python
For the diffusion MRI package MDT which builds on top of this, please see https://github.com/cbclab/MDT.
Quick installation guide
The basic requirements for MOT are:
Python 3.x (recommended) or Python 2.7
OpenCL 1.2 (or higher) supper in GPU driver or CPU runtime
Linux
For Ubuntu >= 16 you can use:
sudo add-apt-repository ppa:robbert-harms/cbclab
sudo apt-get update
sudo apt-get install python3-mot
For Debian users and Ubuntu < 16 users, install MOT with:
sudo apt-get install python3 python3-pip python3-pyopencl
sudo pip3 install MOT
Windows
Install Python Anaconda 3.* 64bit from https://www.continuum.io/downloads
- Compile PyOpenCL with pip install pyopencl, this requires:
Visual Studio 2015 (Community edition or higher) with the Python and Common Tools for Visual C++ options enabled
OpenCL development kit (NVidia CUDA or Intel OpenCL SDK or the AMD APP SDK)
Open a Anaconda shell and type: pip install MOT
For more information and for more elaborate installation instructions, please see: https://mot.readthedocs.org
Caveats
There are a few caveats and known issues, primarily related to OpenCL:
Windows support is experimental due to the difficulty of installing PyOpenCL, hopefully installing PyOpenCL will get easier on Windows soon.
For AMD users with Ubuntu >= 16, the new AMD GPU-Pro driver is still in beta and may not work with all the kernels in MOT. Our recommendation at the moment (October 2016) is to use Ubuntu version 14.
GPU acceleration is not possible in most virtual machines due to lack of GPU or PCI-E pass-through, this will change whenever virtual machines vendors program this feature. Our recommendation is to install neurodebian on your machine and run it on that.
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