Delira - A Backend Agnostic High Level Deep Learning Library

delira is designed to work as a backend agnostic high level deep learning library. The user can choose among several computation backends. It allows the user to compare various models written for different backends without rewriting them.

For this case, delira couples the entire training and prediction logic in backend-agnostic modules to achieve identical behavior for training in all backends.

delira is designed in a very modular way so that almost everything is easily exchangeable or customizable.

A (non-comprehensive) list of the features included in delira:

• Dataset loading
• Dataset sampling
• Augmentation (multi-threaded) including 3D images with any number of channels (based on batchgenerators)
• A generic trainer class that implements the training process for all backends
• Training monitoring using Visdom or Tensorboard
• Model save and load functions
• Already impelemented Datasets
• Many operations and utilities for medical imaging