Package: delira
delira - A Backend Agnostic High Level Deep Learning LibraryAuthors:Justus Schock, Michael Baumgartner, Oliver Rippel, Christoph HaarburgerCopyright (C) 2020 by RWTH Aachen Universityhttp://www.rwth-aachen.deLicense:This software is dual-licensed under:• Commercial license (please contact:lfb@lfb.rwth-aachen.de)• AGPL (GNU Affero General Public License) open source licenseIntroductiondelirais designed to work as a backend agnostic high level deep learning library. You can choose among several computationbackends.
It allows you to compare different models written for different backends without rewriting them.For this case,deliracouples the entire training and prediction logic in backend-agnostic modules to achieve identical behavior for training in all backends.delirais designed in a very modular way so that almost everything is easily exchangeable or customizable.A (non-comprehensive) list of the features included indelira:Dataset loadingDataset samplingAugmentation (multi-threaded) including 3D images with any number of channels (based onbatchgenerators)A generic trainer class that implements the training process for allbackendsTraining monitoring usingVisdomorTensorboardModel save and load functionsAlready impelemented DatasetsMany operations and utilities for medical imagingWhat about the name?delirastarted as a library to enable deep learning research and fast prototyping in medical imaging (especially in radiology).
That's also where the name comes from:delirawas an acronym forDEepLearningInRAdiology*.
To adapt many other use cases we changed the framework's focus quite a bit, although we are still having many medical-related utilities
and are working on constantly factoring them out.InstallationChoose BackendYou may choose a backend from the list below. If your desired backend is not listed and you want to add it, please open an issue (it should not be hard at all) and we will guide you during the process of doing so.BackendBinary InstallationSource InstallationNotesNonepip install delirapip install git+https://github.com/delira-dev/delira.gitTraining not possible if backend is not installed separatelytorchpip install delira[torch]git clone https://github.com/delira-dev/delira.git && cd delira && pip install .[torch]delirawithtorchbackend supports mixed-precision training viaNVIDIA/apex(must be installed separately).torchscriptpip install delira[torchscript]git clone https://github.com/delira-dev/delira.git && cd delira && pip install .[torchscript]Thetorchscriptbackend currently supports only single-GPU-trainingtensorflow eagerpip install delira[tensorflow]git clone https://github.com/delira-dev/delira.git && cd delira && pip install .[tensorflow]thetensorflowbackend is still very experimental and lacks somefeaturestensorflow graphpip install delira[tensorflow]git clone https://github.com/delira-dev/delira.git && cd delira && pip install .[tensorflow]thetensorflowbackend is still very experimental and lacks somefeaturesscikit-learnpip install delirapip install git+https://github.com/delira-dev/delira.git/chainerpip install delira[chainer]git clone https://github.com/delira-dev/delira.git && cd delira && pip install .[chainer]/Fullpip install delira[full]git clone https://github.com/delira-dev/delira.git && cd delira && pip install .[full]All backends will be installed.DockerThe easiest way to usedelirais via docker (with thenvidia-runtimefor GPU-support) and using theDockerfileor theprebuild-images.ChatWe have acommunity chat on slack. If you need an invitation, just followthis link.Getting StartedThe best way to learn how to use is to have a look at thetutorial notebook.
Example implementations for classification problems, segmentation approaches and GANs are also provided in thenotebooksfolder.DocumentationThe docs are hosted onReadTheDocs/Delira.
The documentation of the latest master branch can always be found at the project'sgithub page.ContributingIf you find a bug or have an idea for an improvement, please have a look at ourcontribution guideline.