We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination, and reusability of neural networks designed for genetic polymorphism data. dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna input/output and other utility functions, training procedure and test environment, which not only saves time but also decreases the probability of bugs. Second, implemented networks can be re-optimized based on user-specified training sets and/or tasks. Finally, users can apply pretrained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without the need of extensive knowledge in deep learning. Thanks to dnadna, newly implemented architectures and pretrained networks are easily shareable with the community for further benchmarking or applications. dnadna comes with a peer-reviewed exchangeable neural network allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to contributions from the community.