"A lot of people mistakenly believe that convolutional neural networks (CNNs) can only work with one fixed image size, and that that must be rectangular. However, most libraries support “adaptive” or “global” pooling layers, which entirely avoid this limitation. It doesn’t help that some libraries (such as Pytorch) distribute models that do not use this feature – it means that unless users of these libraries replace those layers, they are stuck with just one image size and shape (generally 224x224 pixels). The fastai library automatically converts fixed-size models to *** dynamically sized models ***."<br>
(Meanwhile, it is a standard architectural feature in most state of the art network implementations and is of course not confined to fast.ai library)
# How to implement multiple nodes computing using horovod (19.3.2021)
* @jitsev1: Have a look on our workshop's tutorials, especially Day 2 Tutorial 2 that gives intro into "Horovodifying" single node code for multi node execution: https://gitlab.version.fz-juelich.de/MLDL_FZJ/juhaicu/jsc_public/sharedspace/teaching/intro[…]course-material/-/tree/master/tutorials/day2/tutorial2
* In general, our "Intro into Scalable Deep Learning Course" is a good way to start (Horovod things come from Day 2 on) https://gitlab.version.fz-juelich.de/MLDL_FZJ/juhaicu/jsc_public/sharedspace/teaching/intro_scalable_dl_2021/course-material/<br>
There are also code examples there how to run training on our HPC machines with Horovod on multiple nodes
