CIL on the Cloud

CIL-Demos

CIL-Demos is a collection of jupyter notebooks, designed to introduce you to the Core Imaging Library (CIL).

The demos can be found in the demos folder, and the README.md in this folder provides some info about the notebooks, including the additional datasets which are required to run them.

Binder

To open and run the notebooks interactively in an executable environment, please click the Binder link above.

Note: In the Binder interface, there is no GPU available.

Note: In the Google Cloud platform, there is free GPU (16Gb). However, you need to install CIL manually.

Install an environment to run the demos locally

The easiest way to install an environment to run the demos is using our maintained environment file which contains the required packages. Running the command below will create a new environment which has specific and tested versions of all CIL dependencies and additional packages required to run the demos:

conda env create -f https://tomographicimaging.github.io/scripts/env/cil_demos.yml

Or for a CPU-only environment which will work for a limited number of CIL demos

conda env create -f https://tomographicimaging.github.io/scripts/env/cil_demos_cpu.yml

The additional packages include:

cudatoolkit If you have GPU drivers compatible with more recent CUDA versions you can modify this package selector (installing tigre via conda requires 9.2).

ipywidgets will allow you to use interactive widgets in our jupyter notebooks.

Check the main CIL repo for full details on CIL and its dependencies and how to install into a custom environment.

Run the demos locally

• Activate your environment using: conda activate cil-demos.

• Clone the CIL-Demos repository and move into the CIL-Demos folder.

• Run: jupyter-notebook on the command line.

• Navigate into demos/1_Introduction

The best place to start is the 01_intro_walnut_conebeam.ipynb notebook. However, this requires installing the walnut dataset.

To test your notebook installation, instead run 03_preprocessing.ipynb, which uses a dataset shipped with CIL, which will have automatically been installed by conda.

Instead of using the jupyter-notebook command, an alternative is to run the notebooks in VSCode.

Advanced Demos

For more advanced general imaging and tomography demos, please visit the following repositories:

• Core Imaging Library part I: a versatile python framework for tomographic imaging

• Core Imaging Library part II: multichannel reconstruction for dynamic and spectral tomography.