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<h1 class="header center light-blue-text text-accent-2">Deep Java Library</h1>
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<h5 class="header col s12 light">Open source library to build and deploy deep learning in Java</h5>
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<h5 class="center">Engine Agnostic</h5>
<p class="light">Write once and run anywhere. Develop your model using DJL and run it on an
engine of your choice</p>
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<h5>“The Netflix observability team's future plans with DJL include trying out its training
API, scaling usage of transfer learning inference, and exploring its bindings for
PyTorch and MXNet to harness the power and availability of transfer learning.”</h5>
<h5 class="light grey-text text-lighten-3">Stanislav Kirdey, Engineer at Netflix
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<h5>“Using DJL allowed us to run large batch inference on Spark for Pytorch models. DJL
helped reduce inference time from over six hours to under two hours.”</h5>
<h5 class="light grey-text text-lighten-3">-- Xiaoyan Zhang, Data Scientist at TalkingData
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<h5>“DJL enables us to run models built with different ML frameworks side by side in the
same JVM without infrastructure changes. ”</h5>
<h5 class="light grey-text text-lighten-3">-- Hermann Burgmeier, Engineer at Amazon
Advertising team </h5>
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<h5>“Our science team prefers using Python. Our engineering team prefers using Java/Scala.
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Tensorflow, Pytorch, and MXNet, and engineering team can run inference on these models
using DJL. We found that our batch inference time was reduced by 85% from using DJL.”
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<h5 class="light grey-text text-lighten-3">-- Vaibhav Goel, Engineer at Amazon Behavior
Analytics team</h5>
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