  Deep Learning (DL) has greatly highlighted the potential impact of optimized
machine learning in both the scientific and clinical communities. The advent of
open-source DL libraries from major industrial entities, such as TensorFlow
(Google), PyTorch (Facebook), and MXNet (Apache), further contributes to DL
promises on the democratization of computational analytics. However, increased
technical and specialized background is required to develop DL algorithms, and
the variability of implementation details hinders their reproducibility.
Towards lowering the barrier and making the mechanism of DL development,
training, and inference more stable, reproducible, and scalable, without
requiring an extensive technical background, this manuscript proposes the
\textbf{G}ener\textbf{a}lly \textbf{N}uanced \textbf{D}eep \textbf{L}earning
\textbf{F}ramework (GaNDLF). With built-in support for $k$-fold
cross-validation, data augmentation, multiple modalities and output classes,
and multi-GPU training, as well as the ability to work with both radiographic
and histologic imaging, GaNDLF aims to provide an end-to-end solution for all
DL-related tasks, to tackle problems in medical imaging and provide a robust
application framework for deployment in clinical workflows.
