cupy#
In this session we will focus on image processing using cupy a library that makes processing of images on CUDA-compatible NVidia graphics cards available from Python.
To get started, we need to install cupy, e.g. like this:
mamba create --name cupy39 python=3.9 devbio-napari pyqt cupy cudatoolkit napari-cupy-image-processing -c conda-forge
Afterwards, we can activate the environment and start jupyter lab:
mamba create --name cupy39
jupyter lab
In case your computer does not have an NVidia graphics card, you can follow the exercises on Google colab, where cupy is commonly installed.
You can directly load notebooks there by entering the name of the <notebook> in this URL:
https://colab.research.google.com/github/BiAPoL/PoL-BioImage-Analysis-TS-GPU-Accelerated-Image-Analysis/blob/main/docs/25_cupy/<notebook>.ipynb
When working with Google colab, you may have to install packages in your kernel, such as stackview:
!pip install stackview ipycanvas==0.11
Make sure to select a GPU-runtime from the menu Runtime > Change runtime type (read more)
