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Welcome to the AI OnDemand wiki!
AI OnDemand (AIoD) is a tool that enables running a variety of deep learning segmentation models with ease, simplifying interacting with these models, and running them at scale on HPC.
AIoD is comprised of:
- A Napari plugin to load data, select the model, define any relevant parameters, visualise results, and ultimately submit the Nextflow pipeline...
- A Nextflow pipeline to define how to run the models, and to manage the flow of data and submitting jobs to the appropriate compute environment (HPC, cloud, or otherwise)
- A model registry (this repo!) to define what models are available, where they are located, and any parameters/config needed (which can be edited from the Napari plugin)
- A utilies repo to provide a common set of utilities for the pipeline, Napari plugin, and future interfaces to use for consistency and ease of development (primarily in I/O and preprocessing functions)
AIoD was developed by the Software Engineering & AI team at the Francis Crick Institute to, first and foremost, provide a tailored tool for accelerating research. By working with various groups, primarily the Electron Microscopy STP, we have aimed to create an accessible and useful tool for users at all levels, particularly those working on the microscopes to actually generate the data.
While our approach is Crick-first, the documentation here covers what is needed for those external to the Crick to get AIoD up and running!
See our Getting Started page, or checkout the sidebar if you're looking for something specific!