We introduce DeepDIVA: an infrastructure designed to enable quick and\nintuitive setup of reproducible experiments with a large range of useful\nanalysis functionality. Reproducing scientific results can be a frustrating\nexperience, not only in document image analysis but in machine learning in\ngeneral. Using DeepDIVA a researcher can either reproduce a given experiment\nwith a very limited amount of information or share their own experiments with\nothers. Moreover, the framework offers a large range of functions, such as\nboilerplate code, keeping track of experiments, hyper-parameter optimization,\nand visualization of data and results. To demonstrate the effectiveness of this\nframework, this paper presents case studies in the area of handwritten document\nanalysis where researchers benefit from the integrated functionality. DeepDIVA\nis implemented in Python and uses the deep learning framework PyTorch. It is\ncompletely open source, and accessible as Web Service through DIVAServices.\n