DeepLabCut 2.3.6-foss-2022a-CUDA-11.7.0

Markerless tracking of user-defined features with deep learning

Accessing DeepLabCut 2.3.6-foss-2022a-CUDA-11.7.0

To load the module for DeepLabCut 2.3.6-foss-2022a-CUDA-11.7.0 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-CUDA-11.7.0

There is a CPU version of this module: DeepLabCut 2.3.6-foss-2022a

BEAR Apps Version

2022a

Architectures

EL8-icelake (GPUs: NVIDIA A100, NVIDIA A30) — 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: CUDA/11.7.0 FFmpeg/4.4.2-GCCcore-11.3.0 foss/2022a imageio/2.22.2-foss-2022a imgaug/0.4.0-foss-2022a-CUDA-11.7.0 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-CUDA-11.7.0 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-CUDA-11.7.0 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