PyTorch3D 0.4.0-fosscuda-2020b-PyTorch-1.7.1

There is a newer version of PyTorch3D

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data.

Accessing PyTorch3D 0.4.0-fosscuda-2020b-PyTorch-1.7.1

To load the module for PyTorch3D 0.4.0-fosscuda-2020b-PyTorch-1.7.1 please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):

module load PyTorch3D/0.4.0-fosscuda-2020b-PyTorch-1.7.1

BEAR Apps Version

2020b

Architectures

EL8-haswell (GPUs: NVIDIA P100)

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.

Extensions

  • fvcore 0.1.5.post20210617
  • iopath 0.1.8
  • portalocker 2.3.0
  • PyTorch3D 0.4.0
  • termcolor 1.1.0
  • yacs 0.1.8

More Information

For more information visit the PyTorch3D website.

Dependencies

This version of PyTorch3D has a direct dependency on: fosscuda/2020b imageio/2.9.0-fosscuda-2020b IPython/7.18.1-GCCcore-10.2.0 Python/3.8.6-GCCcore-10.2.0 PyTorch/1.7.1-fosscuda-2020b torchvision/0.8.2-fosscuda-2020b-PyTorch-1.7.1 tqdm/4.56.2-GCCcore-10.2.0

Other Versions

These versions of PyTorch3D 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 27th August 2021