PyTorch 1.2.0-foss-2019a-Python-3.7.2

There is a newer install of PyTorch

PyTorch provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system.

Accessing PyTorch 1.2.0-foss-2019a-Python-3.7.2

To load the module for PyTorch 1.2.0-foss-2019a-Python-3.7.2 please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load PyTorch/1.2.0-foss-2019a-Python-3.7.2

BEAR Apps Version




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.

Known Issues

On EL7-power9, the BEAR AI nodes, the following PyTorch tests were disabled to allow the build to complete:

  • In test_embedding_bag_cuda
  • In test_bilinear
  • In test_clamp
  • In test_einsum

If your use of PyTorch functionality that form part of these tests you may experience problems. We will be continuing to work to debug and fix these issues.

More Information

For more information visit the PyTorch website.


This version of PyTorch has a direct dependency on: FFmpeg/4.1.3-GCCcore-8.2.0 foss/2019a GMP/6.1.2-GCCcore-8.2.0 MPFR/4.0.2-GCCcore-8.2.0 numactl/2.0.12-GCCcore-8.2.0 Pillow/6.0.0-GCCcore-8.2.0 Python/3.7.2-GCCcore-8.2.0 PyYAML/5.1-GCCcore-8.2.0 SciPy-bundle/2019.03-foss-2019a

Required By

This version of PyTorch is a direct dependent of: ELMoForManyLangs/0.0.2-foss-2019a-Python-3.7.2 torchvision/0.3.0-foss-2019a-Python-3.7.2

Other Versions

These versions of PyTorch 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 3rd September 2019