Speaker
Description
High-performance computers (HPC) are essential for bioimage analysis, however the barrier to entry can be high. This project aims to simplify access to bioimage analysis tools and deep learning models on local HPC clusters, enabling frictionless access to software and large computation.
Inspired by the Bioimage ANalysis Desktop (BAND) and ZeroCostDL4Mic, we developed lightweight bash scripts to deploy image analysis tools such as Fiji, QuPath, Ilastik, Cellpose, CellProfiler, and Napari in user-specified directories. Unlike containers, this solution allows users to save software changes, such as installed plugins, across sessions. We also created custom module files for deep learning packages like StarDist, SAM, and micro-SAM for easy environment loading. Finally, we developed Jupyter Notebooks for data preparation, model training, and benchmarking. These have been deployed on Harvard Medical School’s (HMS) HPC cluster, Orchestra 2 (O2), which uses Open OnDemand (OOD) for an interactive interface.
While specific to HMS, this approach can be easily adapted to most HPC clusters. We aim to share our findings with the broader bioimage analysis community and discuss alternative or parallel approaches.
Link: https://hms-iac.github.io/Bioimage-Analysis-on-O2/
| Authors | Ranit Karmakar*, Simon F. Nørrelykke |
|---|---|
| Keywords | HPC, bioimage analysis, deep learning, BIA software |
