Package: cockpit-for-pytorch
A Practical Debugging Tool for Training Deep Neural NetworksA better status screen for deep learning.Installation•Docs•Experiments•License•Citationpipinstallcockpit-for-pytorchCockpit is a visual and statistical debugger specifically designed for deep learning.Training a deep neural network is often a pain! Successfully training such a network usually requires either years of intuition or expensive parameter searches involving lots of trial and error. Traditional debuggers provide only limited help: They can findsyntactical errorsbut nottraining bugssuch as ill-chosen learning rates.Cockpitoffers a closer, more meaningful look into the training process with multiple well-choseninstruments.InstallationTo installCockpitsimply runpipinstallcockpit-for-pytorchConda environmentFor convenience, we also provide a conda environment, which can be installed via the conda yml file. It includes all the necessary requirements to build the docs, execute the tests and run the examples.DocumentationThedocumentationprovides a full tutorial on how to get started usingCockpitas well as a detailed documentation of its API.ExperimentsTo showcase the capabilities ofCockpitwe performed several experiments illustrating the usefulness of our debugging tool. The code for the experiments can be found in aseparate repository. For a discussion of those experiments please refer to ourpaper.LicenseDistributed under the MIT License. SeeLICENSEfor more information.CitationIf you useCockpit, please consider citing:Frank Schneider, Felix Dangel, Philipp HennigCockpit: A Practical Debugging Tool for Training Deep Neural NetworksarXiv 2102.06604@misc{schneider2021cockpit,title={{Cockpit: A Practical Debugging Tool for Training Deep Neural Networks}},author={Frank Schneider and Felix Dangel and Philipp Hennig},year={2021},eprint={2102.06604},archivePrefix={arXiv},primaryClass={cs.LG}}ChangelogAll notable changes to this project will be documented in this file.The format is based onKeep a Changelog.Unreleased1.0.2- 2021-10-26AddedAdded references to a separateexperiment repositorythat publishes the code for all experiments shown in the paper.FixedProtects thebatch_gradfield in the case where non-SGD is used together with other quantities that freebatch_gradfor memory performance. [#5,PR]1.0.1- 2021-10-13From this version on,cockpitwill be available ascockpit-for-pytorchon
PyPI.AddedMake librarypip-installable ascockpit-for-pytorch[PR]Require BackPACK main release
[PR]Added asavenameargument to theCockpitPlotter.plot()function, which lets you define the name, and now thesavedirshould really only describe thedirectory. [PR, Fixes #8]Added optionalsavefig_kwargsargument to theCockpitPlotter.plot()function that gets passed to thematplotlibfunctionfig.savefig()to, e.g., specify DPI value or use a different file format (e.g. PDF). [PR, Fixes #10]InternalFix#6: Don't execute
extension hook on modules with children
[PR]1.0.0- 2021-04-30AddedFirst public release version ofCockpit.MIT LicenseCopyright (c) 2019 Frank Schneider, Felix Dangel & Philipp HennigPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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