  We present an end-to-end automated workflow that uses large-scale remote
compute resources and an embedded GPU platform at the edge to enable
AI/ML-accelerated real-time analysis of data collected for x-ray ptychography.
Ptychography is a lensless method that is being used to image samples through a
simultaneous numerical inversion of a large number of diffraction patterns from
adjacent overlapping scan positions. This acquisition method can enable
nanoscale imaging with x-rays and electrons, but this often requires very large
experimental datasets and commensurately high turnaround times, which can limit
experimental capabilities such as real-time experimental steering and
low-latency monitoring. In this work, we introduce a software system that can
automate ptychography data analysis tasks. We accelerate the data analysis
pipeline by using a modified version of PtychoNN -- an ML-based approach to
solve phase retrieval problem that shows two orders of magnitude speedup
compared to traditional iterative methods. Further, our system coordinates and
overlaps different data analysis tasks to minimize synchronization overhead
between different stages of the workflow. We evaluate our workflow system with
real-world experimental workloads from the 26ID beamline at Advanced Photon
Source and ThetaGPU cluster at Argonne Leadership Computing Resources.
