Improving Reproducible Deep Learning Workflows with DeepDIVA

The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results. This is especially true in the machine learning community, where it is considered acceptable to have black boxes in your experiments. We present DeepDIVA, a framework designed to facilitate easy experimentation and their reproduction. This framework allows researchers to share their experiments with others, while providing functionality that allows for easy experimentation, such as: boilerplate code, experiment management, hyper-parameter optimization, verification of data integrity and visualization of data and results. Additionally, the code of DeepDIVA is well-documented and supported by several tutorials that allow a new user to quickly familiarize themselves with the framework

DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis

Deep learning methods have shown strong performance in solving tasks for historical document image analysis. However, despite current libraries and frameworks, programming an experiment or a set of experiments and executing them can be time-consuming. This is why we propose an open-source deep learning framework, DIVA-DAF, which is based on PyTorch Lightning and specifically designed for historical document analysis. Pre-implemented tasks such as segmentation and classification can be easily used or customized. It is also easy to create one's own tasks with the benefit of powerful modules for loading data, even large data sets, and different forms of ground truth. The applications conducted have demonstrated time savings for the programming of a document analysis task, as well as for different scenarios such as pre-training or changing the architecture. Thanks to its data module, the framework also allows to reduce the time of model training significantly

On the Challenges of Implementing Machine Learning Systems in Industry

RÉSUMÉ : Dans l’optique de ce mémoire, nous nous concentrons sur les défis de l’implantation de sys-tèmes d’apprentissage automatique dans le contexte de l’industrie. Notre travail est réparti sur deux volets: dans un premier temps, nous explorons des considérations fondamentales sur le processus d’ingénierie de systèmes d’apprentissage automatique et dans un second temps, nous explorons l’aspect pratique de l’ingénierie de tels systèmes dans un cadre industriel. Pour le premier volet, nous explorons un des défis récemment mis en évidence par la com-munauté scientifique: la reproducibilité. Nous expliquons les défis qui s’y rattachent et, à la lueur de cette nouvelle compréhension, nous explorons un des effets rattachés, omniprésent dans l’ingénierie logicielle: la présence de défaut logiciels. À l’aide d’une méthodologie rigoureuse nous cherchons à savoir si la présence de défauts logiciels, parmis un échantillon de taille fixe, dans un cadriciel d’apprentissage automatique impacte le résultat d’un processus d’apprentissage.----------ABSTRACT : Software engineering projects face a number of challenges, ranging from managing their life-cycle to ensuring proper testing methodologies, dealing with defects, building, deploying, among others. As machine learning is becoming more prominent, introducing machine learn-ing in new environments requires skills and considerations from software engineering, machine learning and computer engineering, while also sharing their challenges from these disciplines. As democratization of machine learning has increased by the presence of open-source projects led by both academia and industry, industry practitioners and researchers share one thing in common: the tools they use. In machine learning, tools are represented by libraries and frameworks used as software for the various steps necessary in a machine learning project. In this work, we investigate the challenges in implementing machine learning systems in the industry