There is a newer version of scikit-imageScikit-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, BEARCloud VMs, and CaStLeS VMs):
module load scikit-image/0.18.3-foss-2021a
BEAR Apps Version
EL8-cascadelake — EL8-haswell — EL8-icelake
The listed architectures consist of two part: OS-CPU.
- BlueBEAR: The OS used on BlueBEAR 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.
- BEAR and CaStLeS Cloud VMs: These VMs can have one of two OSes. Those with access to a BEAR Cloud or CaStLeS VM should check that the listed architectures for an application include the OS of VM being used. The VMs, irrespective of OS, will use the haswell CPU type.
- imageio 2.9.0
- imread 0.7.4
- pooch 1.5.2
- PyWavelets 1.1.1
- tifffile 2021.10.12
For more information visit the scikit-image website.
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
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
These versions of scikit-image are available on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs). These will be retained in accordance with our Applications Support and Retention Policy.
Last modified on 7th January 2022