scikit-image 0.18.3-foss-2021a
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 0.18.3-foss-2021a
To load the module for scikit-image 0.18.3-foss-2021a please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):
📋
module load scikit-image/0.18.3-foss-2021a
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
- imageio 2.9.0
- imread 0.7.4
- pooch 1.5.2
- PyWavelets 1.1.1
- scikit-image-0.18.3
- tifffile 2021.10.12
More Information
For more information visit the scikit-image website.
Dependencies
This version of scikit-image has a direct dependency on: dask/2021.9.1-foss-2021a foss/2021a matplotlib/3.4.2-foss-2021a networkx/2.5.1-foss-2021a Pillow/8.2.0-GCCcore-10.3.0 Python/3.9.5-GCCcore-10.3.0
Required By
This version of scikit-image is a direct dependent of: deepBlink/0.1.2-foss-2021a-CUDA-11.3.1 deepBlink/0.1.2-foss-2021a PyImageJ/1.3.1-foss-2021a PyTorch-Geometric/2.0.4-foss-2021a-CUDA-11.3.1
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 7th January 2022