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=== fosscuda/2018b ===
 
=== fosscuda/2018b ===
 
This build includes a CUDA-enabled OpenMPI for using multiple GPU nodes to speed up processing.
 
This build includes a CUDA-enabled OpenMPI for using multiple GPU nodes to speed up processing.
 +
 +
<pre>
 +
CMake/3.12.1-GCCcore-7.3.0
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Python-3.6.6-fosscuda-2018b
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</pre>

Revision as of 16:46, 12 August 2020

"Region Based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision and specifically object detection." -- Wikipedia

The page above mentions a number of packages available for using R-CNNs. For now, this page will concentrate on Detectron2.

Detectron2

"Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark." --Detectron2 site

It also includes support for Fast R-CNN, Faster R-CNN and other R-CNNs.

See the Directron2 site for using and training. For now, this page will only cover installation.

Installing Detectron2 in a Python virtual environment on HPRC clusters

foss/2019b

This is a basic/starter build. Note that this build does not include a CUDA-enabled OpenMPI so is limited to the GPUs on a single node.

Modules used include:

Make/3.15.3-GCCcore-8.3.0
Python/3.7.4-GCCcore-8.3.0
cuDNN/7.0.5-CUDA-9.0.176
(optional?) Graphviz/2.42.2-foss-2019b

Start with a clean module environment and install directory.

ml purge
rm -rf $SCRATCH/Detectron2-foss-2019b
<pre>
Create and activate a Python VE to install into.
<pre>
ml Python/3.7.4-GCCcore-8.3.0
<pre>

=== fosscuda/2018b ===
This build includes a CUDA-enabled OpenMPI for using multiple GPU nodes to speed up processing.

<pre>
CMake/3.12.1-GCCcore-7.3.0
Python-3.6.6-fosscuda-2018b