DLHub

Data and Learning Hub for Science

A simple way to find, share, publish, and run machine learning models and discover training data for science

Documentation

Read the Docs Examples Python SDK CLI

DLHub Models

Browse Models

Papers and Presentations

DLHub Paper DLHub Slides

Get Started

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Describe

m = KerasModel()
m.create_model("p1b1-example.h5")

m.set_title("CANDLE Pilot 1 - Benchmark 1")
m.set_name("candle_p1b1") # short name
m.set_domains("genomics","biology","HPC")

Publish

from dlhub_sdk.client import DLHubClient

dl = DLHubClient()
dl.publish_servable(m)

Run

from dlhub_sdk.client import DLHubClient

dl = DLHubClient()

data = np.load("pilot1.npy")
pred = dl.run("test/candle_p1b1", data)

See Full Examples

How to Cite

If you find DLHub useful in your research, please cite the following papers:

Chard, Ryan, Zhuozhao Li, Kyle Chard, Logan Ward, Yadu Babuji, Anna Woodard, Steven Tuecke, Ben Blaiszik, Michael Franklin, and Ian Foster. "DLHub: Model and data serving for science." In 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 283-292. IEEE, 2019. (link)
Blaiszik, Ben, Logan Ward, Marcus Schwarting, Jonathon Gaff, Ryan Chard, Daniel Pike, Kyle Chard, and Ian Foster. “A Data Ecosystem to Support Machine Learning in Materials Science.” MRS Communications (October 10, 2019): 1–9. doi:10.1557/mrc.2019.118. (link)

Project Support

This material is based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.