| Title |
A Workflow for Training and Deploying Machine Learning Models to EPICS |
| Authors |
- M.F. Leputa, K.R.L. Baker, M. Romanovschi
STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
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| Abstract |
The transition to EPICS as the control system for the ISIS Neutron and Muon Source accelerators is an opportunity to more easily integrate machine learning into operations. But developing high quality machine learning (ML) models is insufficient. Integration into critical operations requires good development practices to ensure stability and reliability during deployment and to allow robust and easy maintenance. For these reasons we implemented a workflow for training and deploying models that utilize off-the-shelf, industry-standard tools such as MLflow. Our experience of how adoption of these tools can make developer’s lives easier during the training phase of a project is discussed. We describe how these tools may be used in an automated deployment pipeline to allow the ML model to interact with our EPICS ecosystem through Python-based IOCs within a containerized environment. This reduces the developer effort required to produce GUIs to interact with the models within the ISIS Main Control Room as tools familiar to operators, such as Phoebus, may be used.
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| Paper |
download TU1BCO01.PDF [0.968 MB / 5 pages] |
| Slides |
download TU1BCO01_TALK.PDF [3.365 MB] |
| Cite |
download ※ BibTeX
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※ EndNote |
| Conference |
ICALEPCS2023 |
| Series |
International Conference on Accelerator and Large Experimental Physics Control Systems (19th) |
| Location |
Cape Town, South Africa |
| Date |
09-13 October 2023 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Volker RW Schaa (GSI, Darmstadt, Germany); Andy Götz (ESRF, Grenoble, France); Johan Venter (SARAO, Cape Town, South Africa); Karen White (SNS, Oak Ridge, TN, USA); Marie Robichon (ESRF, Grenoble, France); Vivienne Rowland (SARAO, Cape Town, South Africa) |
| Online ISBN |
978-3-95450-238-7 |
| Online ISSN |
2226-0358 |
| Received |
05 October 2023 |
| Accepted |
12 October 2023 |
| Issued/td>
| 19 October 2023 |
| DOI |
doi:10.18429/JACoW-ICALEPCS2023-TU1BCO01 |
| Pages |
244-248 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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