June 23, 2022 — The Argonne Leadership Computing Facility will host a virtual hands-on training session on DeepHyper (https://github.com/deephyper/deephyper), on Friday, July 15 from 9 am to 4 pm Central Time. Registration is here, the deadline for registering is Thursday, July 1.
Deep Hyper is a distributed automated machine learning (AutoML) software package for automating the design and development of deep neural networks for scientific and engineering applications. It seeks to bring a scientifically rigorous automated approach to neural network model development by reducing the manually intensive, cumbersome, ad hoc, trial-and-error efforts. It can run on a laptop, medium range clusters, and supercomputers with thousands of compute units (GPUs).
The workshop is organized by:
- Prasanna Balaprakash, R&D Lead and Computer Scientist, MCS Division and ALCF, Argonne National Laboratory
- Romain Égelé, Graduate Student and Research Aide, Université Paris-Saclay and Argonne National Laboratory
- Romit Maulik, Assistant Computational Scientist, MCS Division, Argonne National Laboratory
- Bethany Lusch, Assistant Computer Scientist, ALCF, Argonne National Laboratory
- Krishnan Raghavan, Assistant Computational Mathematician, MCS Division, Argonne National Laboratory
- Anirudh Subramanyam, Postdoctoral Researcher, MCS Division, Argonne National Laboratory
- Sandeep Madireddy, Assistant Computer Scientist, MCS Division, Argonne National Laboratory
- Tanwi Mallick, Assistant Computer Science Specialist, MCS Division, Argonne National Laboratory
- Akhil Pandey Akella, Graduate Research Assistant, Northern Illinois University
- Nesar Soorve Ramachandra, Computational Scientist, CPS Division, Argonne National Laboratory
- Kyle Felker, Assistant Computational Scientist, CPS Division, Argonne National Laboratory
- Sam Foreman, Assistant Computational Scientist, ALCF, Argonne National Laboratory
In this virtual workshop attendees will learn various capabilities of the DeepHyper software to automate the design and development of neural networks.
DeepHyper GitHub repo: https://github.com/deephyper/deephyper
DeepHyper Documentation: https://deephyper.readthedocs.io/en/latest/
8:30 — 9:00: Zoom Check-in and setup
9:00 — 9:10: Welcome & Intro (Prasanna Balaprakash)
9:10 — 9:30: Hyperparameter search (Prasanna Balaprakash)
9:30 — 10:00: Hands-on (Romain Égelé)
10:00 — 10:20: Neural architecture search (Romit Maulik)
10:20 — 10:50: Hands-on (Romit Maulik)
Break for 10 mins
11:00 — 11:20: Ensembles & uncertainty quantification (Bethany Lusch and Krishnan Raghavan)
11:20 — 11:50: Hands-on (Bethany Lusch and Krishnan Raghavan)
Lunch Break and Q & A Session for 40 mins
12:30 — 12:50: Multiobjective hyperparameter search (Anirudh Subramanyam)
12:50 — 1:20: Hands-on (Anirudh Subramanyam)
1:20 to 1:40: Transfer learning in hyperparameter search (Sandeep Madireddy and Tanwi Mallick)
1:40 — 2:10: Hands-on (Sandeep Madireddy and Tanwi Mallick)
Break for 10 mins
2:20 to 2:40: Graph neural architecture search for molecular property prediction (Akhil Pandey Akella)
2:40 — 3:10: Hands-on (Akhil Pandey Akella)
3:10 — 4:00: Running DeepHyper at Scale on Perlmutter/NERSC (Nesar Soorve Ramachandra), ThetaGPU/ALCF (Kyle Felker/Sam Foreman), Summit/OLCF platforms (Kyle Felker) No hands-on; only demo.
All hands-on will be on Google collab.
