Session on Deep Learning Code Management Held at IUB Premises

Recently, a general trend in the machine learning research community is that most of the research papers include links to the code of their experiments. As such, it becomes very much necessary to be able to write DRY, scalable, and modular code that can easily be used to reproduce experimental results. Additionally, it is equally important to be able to tweak the network and dataset parameters with minimal effort, which is critical when performing ablation studies or simply trying to improve the architecture trainable model.

In an endeavor to enlighten the existing and new RAs in this regard, AGenCy Lab of the Centre for Computation and Data Science (CCDS), IUB arranged a deep learning code management session at the IUB premises on May 24, 2022 (Tuesday) at 7 PM. Our RAs Saif Mahmud, Tanjid Hasan Tonmoay, and Mahieyin Rahmun were the speakers in the session. They talked about how SOLID and DRY principles can help in keeping the codebase modular and manageable while at the same time can make working in teams require minimal effort. Managing configuration files, project structuring, logging and visualization, unit testing, and debugging were discussed. Afterward, a short coding session was arranged where the participants were required to code a basic image classification model and apply the concepts they learned from the session. Interested readers can find the presentation slides here.