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Haven-AI: To Run, Manage and Visualize Large Scale Experiments

Authors: Laradji, Issam; Deng, Xinhong; Liu, Zheyu; Chen, Siyu; Fang, Wanqing; Caccia, Massimo; Drouin, Alexandre; +4 Authors

Haven-AI: To Run, Manage and Visualize Large Scale Experiments

Abstract

Haven-AI a library that helps you easily turn your codebase into an effective, large-scale machine learning toolkit. You will be able to launch thousands of experiments in parallel, visualize their results and status, and ensure that they are reliable, reproducible and that the code base is modular to facilitate collaboration and easy integration of new models and datasets. The goal of this library is to help you quickly and efficiently find solutions to machine learning problems, get papers accepted, and win at competitions.

Keywords

python, haven, experiments

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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