Overview

MOFA training can be massively speed up by using GPUs. We have implemented all computations usin CuPy, an open-source array library for GPU-accelerated computing with Python. We only provide this when traning models from Python.

We currently do no suport GPU training from R.

Installation

To use GPU need to make sure that you have a functional NVIDIA GPU with the right version of CUDA and CuPy installed. Please check the CuPy installation instructions

Docker file

Frederik Ziebell kindly prepared a Dockerfile, available here. If you try it please leave your feedback in the github issue.

Conda environment

First, create a file called mofa_conda_env_gpu.yml with the following content

name: mofa_env_gpu
channels:
  - conda-forge
dependencies:
  - cupy
  - cudatoolkit=11.0 # note that you might have to change this depending on your desired cuda version
  - dtw-python
  - pip
  - pip:
    - mofapy2==0.6.7

and then run:

conda env create --file mofa_conda_env_gpu.yml

Basic example

We provide some simple scripts to test whether your GPU set up is working:

• Script to test basic linear alebra operations using CuPy
• Script to train a MOFA model with a toy data set