There is a newer version of PyTorchPyTorch provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system.
Accessing PyTorch 1.3.1-foss-2019b-Python-3.7.4
To load the module for PyTorch 1.3.1-foss-2019b-Python-3.7.4 please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):
module load PyTorch/1.3.1-foss-2019b-Python-3.7.4
There is a GPU enabled version of this module: PyTorch 1.3.1-fosscuda-2019b-Python-3.7.4
BEAR Apps Version
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.
For more information visit the PyTorch website.
This version of PyTorch has a direct dependency on: FFmpeg/4.2.1-GCCcore-8.3.0 foss/2019b gflags/2.2.2-GCCcore-8.3.0 glog/0.4.0-GCCcore-8.3.0 GMP/6.1.2-GCCcore-8.3.0 MPFR/4.0.2-GCCcore-8.3.0 numactl/2.0.12-GCCcore-8.3.0 Pillow/6.2.1-GCCcore-8.3.0 Python/3.7.4-GCCcore-8.3.0 PyYAML/5.1.2-GCCcore-8.3.0 SciPy-bundle/2019.10-foss-2019b-Python-3.7.4
This version of PyTorch is a direct dependent of: torchvision/0.4.2-foss-2019b-Python-3.7.4
These versions of PyTorch 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 19th November 2019