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PyTorch is a high-productivity Deep Learning framework based on dynamic computation graphs and automatic differentiation. It is designed to be as close to native Python as possible for maximum flexibility and expressivity.

Availability on Cori

PyTorch can be picked up from the Anaconda python installations (e.g. via module load python) or from dedicated modules with MPI enabled. You can see which versions are available with module avail pytorch.

The currently recommended version of PyTorch to use on Cori is the Intel-optimized version v1.0.0, which can be loaded with

module load pytorch/v1.0.0-intel

The Intel optimizations in this installation can give you considerable speedups on Cori relative to the standard v1.0.0. See the benchmarks page for comparisons.

Multi-node training

PyTorch makes it fairly easy to get up and running with multi-node training via its included distributed package. Refer to the distributed tutorial for details:

Note the above tutorial doesn't actually document our currently recommended approach of using DistributedDataParallelCPU. See examples below.


We're putting together a coherent set of example problems, datasets, models, and training code in this repository:

This repository can serve as a template for your research projects with a flexibly organized design for layout and code structure. The template branch contains the core layout without all of the examples so you can build your code on top of that minimal, fully functional setup. The code provided should minimize your own boiler plate and let you get up and running in a distributed fashion on Cori as quickly and seamlessly as possible.

The examples include:

  • A simple hello-world example
  • HEP-CNN classifier
  • ResNet50 CIFAR10 image classification
  • HEP-GAN for generation of RPV SUSY images.

The repository will also be used to benchmark our system for single and multi-node training.