DeepLabCut is an open source toolbox that builds on a state-of-the-art animal pose estimation algorithm. It allows training of a deep neural network by using limited training data to precisely track user-defined features, so that the human labeling accuracy will be matched.

References:

The DeepLabCut application installed on Biowulf is supposed to be run using GPU. As the first step, please allocate an interactive session:

Deeplabcut/2.3.4 (available on biowulf) command lines should be run on gpu with python-DLC instead of python:

[user@biowulf]$ sinteractive --gres=gpu:p100:1,lscratch:10 --mem=20g -c14
[user@cn4469 ~]$ module load DeepLabCut/2.3.4 
[+] Loading singularity  3.10.5  on cn4469
[+] Loading DeepLabCut  2.3.4

It is essential to create a module directory under /data/$USER/DLC and put the model there so that the program can have write right to the directory:

[user@cn4469 user]$ mkdir -p /data/$USER/DLC && cd /data/$USER/DLC
[user@cn4469 user]$ cp -r $DLC_MODEL/pretrained .

Clone the DeepLabCut GitHub repository, which contains some examples (optional):

[user@cn4469]$ git clone https://github.com/AlexEMG/DeepLabCut
[user@cn4469]$ cd DeepLabCut/examples

Test run in a terminal:

[user@cn4469]$ python-DLC testscript.py # only work for DeepLabCut/2.3.4
[user@cn4469]$ python-DLC testscript_multianimal.py# work for both DeepLabCut/2.3.4 and DeepLabCut/2.3.9

deeplabcut GUI could to be opened through NoMachine:

       python-DLC -m "deeplabcut"

deeplabcut GUI could also be opened through visual partition svis desktop session:

       vglrun python-DLC -m "deeplabcut"