Beamline Deployment
Cross-source consensus on Beamline Deployment from 1 sources and 4 claims.
1 sources · 4 claims
Uses
How it works
Benefits
Highlighted claims
- The method has been deployed in production at an ALS beamline inside CDTools. — Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction
- For a ptycho-tomography dataset of 74 projections, ML-augmented reconstruction reduced per-projection runtime from about 153 seconds to about 41 seconds under standard operating conditions. — Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction
- Deployment uses Prefect-based GPU-aware orchestration to manage GPU availability, memory, data ingestion, conversion, and reconstruction pipelines. — Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction
- Containerized reconstruction allows different algorithm versions and dependencies to coexist. — Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction