scikit-image 0.21.0-foss-2022b
There is a newer install of scikit-image
Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world, building upon numpy, scipy, and matplotlib. As a machine-learning module, it provides versatile tools for data mining and analysis in any field of science and engineering. It strives to be simple and efficient, accessible to everybody, and reusable in various contexts.Accessing scikit-image 0.21.0-foss-2022b
To load the module for scikit-image 0.21.0-foss-2022b please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):
📋
module load bear-apps/2022b
module load scikit-image/0.21.0-foss-2022b
BEAR Apps Version
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
- imread 0.7.4
- lazy_loader 0.3
- PyWavelets 1.4.1
- scikit-image-0.21.0
- tifffile 2023.7.18
More Information
For more information visit the scikit-image website.
Dependencies
This version of scikit-image has a direct dependency on: dask/2023.7.1-foss-2022b foss/2022b imageio/2.31.1-foss-2022b matplotlib/3.7.0-gfbf-2022b networkx/3.0-gfbf-2022b Pillow/9.4.0-GCCcore-12.2.0 Python/3.10.8-GCCcore-12.2.0
Required By
This version of scikit-image is a direct dependent of: BEAR-Python-MSc-Bioinformatics/2022b-foss-2022b PyTorch-Geometric/2.5.0-foss-2022b-PyTorch-2.1.2 PyTorch-Geometric/2.5.0-foss-2022b-PyTorch-2.1.2-CUDA-12.0.0
Other Versions
These versions of scikit-image 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 9th August 2023