BEAR Applications

ptwt 0.1.4.post2-foss-2022a-CUDA-11.7.0

Differentiable and gpu enabled fast wavelet transforms in PyTorch

Accessing ptwt 0.1.4.post2-foss-2022a-CUDA-11.7.0

To load the module for ptwt 0.1.4.post2-foss-2022a-CUDA-11.7.0 please use this command on the BEAR systems (BlueBEAR, BEAR Cloud VMs, and CaStLeS VMs):

module load bear-apps/2022a
module load ptwt/0.1.4.post2-foss-2022a-CUDA-11.7.0

There is a CPU version of this module: ptwt 0.1.4.post2-foss-2022a

BEAR Apps Version

2022a

Architectures

EL8-haswell (GPUs: NVIDIA P100) — EL8-icelake (GPUs: NVIDIA A100, NVIDIA A30)

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 Cloud and CaStLeS 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.

Extensions

• ptwt 0.1.4.post2

More Information

For more information visit the ptwt website.

Dependencies

This version of ptwt has a direct dependency on: CUDA/11.7.0 foss/2022a matplotlib/3.5.2-foss-2022a Python/3.10.4-GCCcore-11.3.0 PyTorch/1.12.1-foss-2022a-CUDA-11.7.0 scikit-image/0.19.3-foss-2022a SciPy-bundle/2022.05-foss-2022a

Other Versions

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

Last modified on 18th May 2023