Implementing a Parallel Processing Workflow

 The refactoring work is complete, and the results are promising. By leveraging Python’s multiprocessing module to create a pool of data workers, the pipeline is now able to overlap data I/O and preprocessing with model inference. What previously took hours to run on a large dataset now completes in a matter of minutes.

The GPU utilization has increased significantly, and the performance metrics are finally within the targets I set at the beginning of the project. This architectural change was a major undertaking, but it was essential for building a pipeline that can realistically handle the scale of LSST data.