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DeepPoseKit

DeepPoseKit is a Python toolkit with a high-level API for 2D pose estimation of user-defined keypoints using deep learning.

While DeepPoseKit can be self-installed e.g. using conda as per the project's installation instructions, the legacy codebase requires a specific set of outdated dependencies with a supported TensorFlow/CUDA combination. We have bundled a working environment in an Apptainer container which can be launched from the OOD JupyterLab app or used on the commandline.

The project website links to a number of notebooks detailing usage and the general workflow.

Jupyter

The DeepPoseKit variant can be selected in the OOD JupyterLab app. For CUDA support, make sure to select a GPU/CUDA partition.

Note that the annotation GUI unfortunately cannot be run from the web-based Jupyterlab and needs to be run from an interactive graphical environment; This can be as a Jupyter notebook on your local workstation, or a jupyter server or python session run from an OOD desktop session (see commandline use below).

Commandline

DeepPoseKit has an interactive graphical annotation window (cf. 'step 2') that cannot be run from the web-based OOD JupyterLab. For this purpose, you could instead start a notebook server or run the python code from a terminal in a graphical OOD desktop session:

Terminal

module load apptainer/deepposekit
start-notebook.py

and copy-paste the full 127.0.0.1 URL displayed into the browser (Firefox) within the desktop session to access the notebook. (e.g. http://127.0.0.1:8888/lab?token=2edd3d93b0411e75da1a14029d3c5b23a6214b5048d11e76 )

DeepPoseKit is made available on the cluster as a shared Apptainer container image.

You can use the apptainer/deepposekit module to add a convenient alias to python in the container:

Terminal

#SBATCH <slurm job script options>

module load apptainer/deepposekit
# The following is required to use aliases in a non-interactive/SLURM batch script:
shopt -s expand_aliases
python -c 'import deepposekit; print(deepposekit.__version__)'

Alternatively run with apptainer directly; i.e. to run binaries within the container, prefix any command with apptainer -s run --nv <$APPTAINER_IMG/apptainer_image.sif>.

For CUDA support, make sure to specify --nv. e.g.

Terminal

apptainer -s run --nv $APPTAINER_IMG/jupyter_deepposekit_<version>.sif python