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Machine Learning benchmarking at NERSC

NERSC uses both standard framework-oriented benchmarks as well as scientific benchmarks from research projects in order to characterize our systems for scientific Deep Learning.

Framework benchmarks

TensorFlow

We run a version of the tf_cnn_benchmarks repository as well as a DCGAN model on Cori.

Training results Training results

PyTorch

We have a repository of benchmarks with standard computer vision models, LSTM, and 3D convolutional models here: https://github.com/sparticlesteve/pytorch-benchmarks

Training results Training results

Inference results Inference results

Scientific Deep Learning Benchmarks

HEP-CNN

The HEP-CNN benchmark trains a simple Convolutional Neural Network to classify LHC collision detector images as signal or background.

CosmoFlow

The CosmoFlow benchmark trains a 3D Convolutional Neural Network to predict cosmological parameters from simulated universe volumes.

CosmoGAN

Deep Learning Climate Analytics