Skip to content

Repository for the NESAP Extreme Spatio-Temporal Learning project

Notifications You must be signed in to change notification settings

sparticlesteve/nesap-stl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

NESAP Extreme Spatio-Temporal Learning

Deep learning on large spatio-temporal data, including fMRI and climate data.

Datasets

  • Moving MNIST
  • Brain fMRI
  • Climate

Models

  • PredRNN++

Package layout

The directory layout of this repo is designed to be flexible:

  • Configuration files (in YAML format) go in configs/
  • Dataset specifications using PyTorch's Dataset API go into datasets/
  • Model implementations go into models/
  • Trainer implementations go into trainers/. Trainers inherit from BaseTrainer and are responsible for constructing models as well as training and evaluating them.

All examples are run with the generic training script, train.py.

How to run

To run the examples on the Cori supercomputer, you may use the provided example SLURM batch script. Here's how to run the Hello World example on 4 Haswell nodes:

sbatch -N 4 scripts/train_cori.sh configs/hello.yaml

About

Repository for the NESAP Extreme Spatio-Temporal Learning project

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published