DeepLabCut 2.3.6-foss-2022a

Markerless tracking of user-defined features with deep learning

Accessing DeepLabCut 2.3.6-foss-2022a

To load the module for DeepLabCut 2.3.6-foss-2022a please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load bear-apps/2022a
module load DeepLabCut/2.3.6-foss-2022a

There is a GPU enabled version of this module: DeepLabCut 2.3.6-foss-2022a-CUDA-11.7.0

BEAR Apps Version

2022a

Architectures

EL8-cascadelake — EL8-icelake — EL8-sapphirerapids

The listed architectures consist of two part: OS-CPU. The OS used is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.

Extensions

• DeepLabCut 2.3.6
• dlclibrary 0.0.4
• filterpy 1.4.5
• huggingface-hub-0.17.3
• imageio-ffmpeg-0.4.9
• msgpack-numpy-0.4.8
• tensorpack 0.11
• tf_slim 1.1.0

More Information

For more information visit the DeepLabCut website.

Dependencies

This version of DeepLabCut has a direct dependency on: FFmpeg/4.4.2-GCCcore-11.3.0 foss/2022a imageio/2.22.2-foss-2022a imgaug/0.4.0-foss-2022a matplotlib/3.5.2-foss-2022a numba/0.56.4-foss-2022a PyTables/3.8.0-foss-2022a Python/3.10.4-GCCcore-11.3.0 PyTorch/1.12.1-foss-2022a PyZMQ/24.0.1-GCCcore-11.3.0 ruamel.yaml/0.17.21-GCCcore-11.3.0 scikit-image/0.19.3-foss-2022a scikit-learn/1.1.2-foss-2022a statsmodels/0.13.1-foss-2022a TensorFlow/2.11.0-foss-2022a tqdm/4.64.0-GCCcore-11.3.0

Other Versions

These versions of DeepLabCut are available on the BEAR systems (BlueBEAR and BEAR Cloud VMs). These will be retained in accordance with our Applications Support and Retention Policy.

Last modified on 6th December 2023