Project has taken place from June 2020 to September 2020 Description: Storage is one of the main limiting factors to the recording of information from proton-proton collision events at the Large Hadron Collider at CERN. This project aims to implement an autoencoder based deep-compression algorithm on an ATLAS trigger machine to reduce this storage requirement and improve accessibility time of the collision data captured by a trigger system. We also plan to design an autoencoder model that has a better compression factor and adequate execution time and memory requirements in order to be deployed on a trigger system. Link to the Project Link to the Report Index of files uploaded Initial proposal. Our project diverged a bit as we wanted to test event-level compression first. Work log Meeting log Timeline and deliverables HTCondor instruction + GPU Full presentation Presentation for OpenLab Processed data and description All the plots with a README file containing the description of what the folders contain