  We introduce a stochastic framework into the open--source Core Imaging
Library (CIL) which enables easy development of stochastic algorithms. Five
such algorithms from the literature are developed, Stochastic Gradient Descent,
Stochastic Average Gradient (-Am\'elior\'e), (Loopless) Stochastic Variance
Reduced Gradient. We showcase the functionality of the framework with a
comparative study against a deterministic algorithm on a simulated 2D PET
dataset, with the use of the open-source Synergistic Image Reconstruction
Framework. We observe that stochastic optimisation methods can converge in
fewer passes of the data than a standard deterministic algorithm.
