GSoC 2025

 The pipeline works for a single image, but how does it perform at scale? This week, I pointed RIPPLe at a full tract of mock LSST data, which contains thousands of potential targets. The results were illuminating. While the pipeline completed the task without crashing, the processing time was unacceptably long. The performance metrics clearly indicate that the current, sequential approach to data fetching and preprocessing is a major bottleneck. Each cutout is processed one by one, leaving the GPU idle for long periods. It's clear that to handle the volume of data LSST will produce, a more sophisticated, parallelized workflow is necessary. The next phase of this project will be dedicated to optimization.

 I've reached a significant milestone: the first successful end-to-end run of the RIPPLe pipeline. I provided a set of sky coordinates, and the system automatically fetched the data from the Butler, preprocessed the images, fed them into a DeepLense classification model, and returned a prediction. Seeing the entire chain of operations execute without a single crash was incredibly satisfying. The performance is, as expected, not yet optimized. A single prediction takes several minutes, which is far from our goal. However, this successful run serves as a critical proof-of-concept. It validates the overall architecture and demonstrates that all the individual components can work together. Now, the focus shifts from functionality to performance.

 This week, my focus has been on software architecture, specifically how the pipeline will interact with the various DeepLense models. The DeepLense project includes a diverse set of models—classifiers, regressors, and generative models—each with slightly different input and output requirements. To avoid writing custom code for each one, I've designed a ModelInterface using an abstract base class. The goal is to create a consistent API that allows the pipeline to load and run any compatible model with a single, unified command. This upfront investment in a flexible architecture should make the system much easier to maintain and extend in the future as new models are developed. It’s a classic software engineering problem, and solving it correctly now will prevent significant headaches down the road.

 I'm back at the keyboard after a productive week away at the Dunlap Summer School. While the focus there was on radio astronomy, stepping away from the RIPPLe project has provided some much-needed perspective. It’s easy to get lost in the details of a complex software project, and I’m finding that returning with a clear mind is helping me identify issues and solutions that I previously overlooked. The core challenge remains: bridging the gap between the LSST Science Pipelines and the DeepLense models. With the first half of the project focused on building the foundational data access and preprocessing layers, the next step is to integrate the machine learning models and create a true end-to-end workflow. It’s time to get started.