2. Why use Nemesyst

Nemesyst is a highly configurable hybrid parallelization deep learning framework, for distributed deep learning, that uses other backend framework(s) of your choice (Pytorch, TensorFlow, etc.) for training.

This image is a use case example of Nemesyst applied to a distributed refrigeration fleet over multiple sites, and both online and offline learning capabilities occuring simultaneously.

Nemesyst uses MongoDB as its core message passing interface (MPI). This means MongoDB is used to store, distribute, retrieve, and transform the data; store, distribute, and retrieve the trained models. In future we also hope to use it to transfer more specific processing instructions to individual learners. This way we use the already advanced functionality of MongoDB to handle complex and non-trivial problems such as tracing models back to the specific data trained with, the results and arguments present at the point of training, and being able to reload pre-trained models for further use, and, or training. This also means the same data can be transformed differently for different learners from the same source dynamically at the point of need.