Machine Learning at NERSC¶

NERSC supports a variety of software for Machine Learning and Deep Learning on our systems.

These docs include details about how to use our system optimized frameworks, multi-node training libraries, and performance guidelines.

Classical Machine Learning¶

Libraries like scikit-learn and other non-deep-learning libraries are supported through our standard installations and environments for Python and R, more under Analytics.

Deep Learning Frameworks¶

We have prioritized support for the following Deep Learning frameworks on Cori:

• TensorFlow
• PyTorch

Deploying with Jupyter¶

Users can deploy distributed deep learning workloads to Cori from Jupyter notebooks using parallel execution libraries such as IPyParallel. Jupyter notebooks can be used to submit workloads to the batch system and also provide powerful interactive capabilities for monitoring and controlling those workloads.

We have some examples for running multi-node training and distributed hyper-parameter optimization jobs from notebooks in this github repository: https://github.com/sparticlesteve/cori-intml-examples

Benchmarks¶

We track general performance of Deep Learning frameworks as well as some specific scientific applications. See the benchmarks for details.

Science use-cases¶

Machine Learning and Deep Learning are increasingly used to analyze scientific data, in diverse fields. We have gathered some examples of work ongoing at NERSC on the science use-cases page including some code and datasets and how to run these at NERSC.