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treelite_sysml.html
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<!DOCTYPE html>
<html>
<head>
<meta name="viewport" content="width=device-width" />
<link rel="shortcut icon" href="favicon.ico" />
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<meta name="description" content="One of the obstacles in accelerating sparse
graph applications using GPUs is load imbalance, which in certain cases causes
threads to stall. We investigate a specific application known as hypergraph
coarsening and explore a technique for addressing load imbalance." />
<title>
Treelite: toolbox for decision tree deployment
</title>
</head>
<body>
<div id="container" itemscope
itemtype="http://www.schema.org/ScholarlyArticle">
<p>[<a href=".">← Go back to profile</a>]</p>
<h1 itemprop="name">
Treelite: toolbox for decision tree deployment
</h1>
<p>
with
<a href="https://github.com/mli">Mu Li</a>
</p>
<p>
Poster presented at
<a href="http://www.sysml.cc">SysML 2018</a> (2018)
</p>
<h2>Download</h2>
<ul>
<li>SysML poster:
[<a href="posters/treelite_sysml.pdf">PDF</a>]
</li>
<li>SysML extended abstract:
[<a href="preprints/treelite_sysml.pdf">Extended abstract</a>]</li>
</ul>
<h2>Synopsis</h2>
<p itemprop="description">
This paper introduces a brand new tree library
<a href="https://github.com/dmlc/treelite"><strong>treelite</strong></a>.
The library is a toolbox to facilitate easy
deployment of models and accelerate prediction performance. It has a
Python wrapper that allows users to integrate it as part of their
workflow. Treelite is able to read tree ensemble models that are
trained by any tree libraries, including
<a href="https://github.com/dmlc/xgboost">XGBoost</a>,
<a href="https://github.com/microsoft/lightgbm">LightGBM</a>, and
<a href="https://github.com/scikit-learn/scikit-learn">scikit-learn</a>.
Treelite is also designed to minimize dependencies at the time of
deployment. It used to be the case that one had to ship his tree model
with the original tree library that trained it; with treelite, it is
no longer. Finally, treelite allows for optimizations that improve
prediction performance without changing any detail of the model.
</p>
<h2>Publication Details</h2>
<ul>
<li>
Extended Abstract and Poster:<br>
Hyunsu Cho and Mu Li. “Treelite: toolbox for decision tree
deployment,” <em>SysML 2018</em>, Stanford, CA, February 16, 2018.
</li>
</ul>
<p>[<a href=".">← Go back to profile</a>]</p>
</div>
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