Distributed/Cloud computing

Trainer: Jean-Marie Burel, Petr Walczysko

Overview: 

This section introduces an emerging cloud-compatible imaging format currently used by the BioImage Archive and the Image Data Resource. It illustrates how it can be used  to access cloud-hosted imaging data and how it facilitates analysis in parallel. The practical included in this section uses Python as the programming language.

Learning outcomes:

After this section you should be able to:

• Identify one of the most used cloud-based imaging file formats
• Retrieve images from Object storage
• Analyse data in parallel using Dask

Materials:

• Jupyter notebook for the practical
• Setup of the practical
• Repository: https://github.com/ome/EMBL-EBI-imaging-course-04-2024/tree/main

Other resources:

• Lightsheet image: http://idr.openmicroscopy.org/webclient/?show=image-4007801
• Reading images notebook: https://github.com/ome/EMBL-EBI-imaging-course-04-2024/blob/main/Day_5/Reading_images.ipynb
• Converted Lightsheet images: https://idr.github.io/ome-ngff-samples/ 
• Load Zarr Image from a public S3 store and analyze it in parallel notebook: https://github.com/ome/omero-guide-python/blob/master/notebooks/idr0044_zarr_segmentation_parallel.ipynb
• Omero forum: https://forum.image.sc/tag/omero
• https://ngff.openmicroscopy.org/