petuum.github.io

Petuum

Petuum is a distributed machine learning framework. It aims to provide a generic algorithmic and systems interface to large scale machine learning, and takes care of difficult systems "plumbing work" and algorithmic acceleration, while simplifying the distributed implementation of ML programs - allowing you to focus on model perfection and Big Data Analytics. Petuum runs efficiently at scale on research clusters and cloud compute like Amazon EC2 and Google GCE. Petuum v1.1 is now available! Petuum provid...

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Petuum | petuum.github.io Reviews

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Petuum is a distributed machine learning framework. It aims to provide a generic algorithmic and systems interface to large scale machine learning, and takes care of difficult systems plumbing work and algorithmic acceleration, while simplifying the distributed implementation of ML programs - allowing you to focus on model perfection and Big Data Analytics. Petuum runs efficiently at scale on research clusters and cloud compute like Amazon EC2 and Google GCE. Petuum v1.1 is now available! Petuum provid...

<META> KEYWORDS

1 performance

2 research

3 view on github

4 to setup petuum

5 key petuum features

6 bösen

7 strads

8 some speed highlights

9 what's

10 petuum

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performance,research,view on github,to setup petuum,key petuum features,bösen,strads,some speed highlights,what's,petuum,anyway,perpetual motion,latest news,our lightlda paper

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Petuum | petuum.github.io Reviews

https://petuum.github.io

Petuum is a distributed machine learning framework. It aims to provide a generic algorithmic and systems interface to large scale machine learning, and takes care of difficult systems "plumbing work" and algorithmic acceleration, while simplifying the distributed implementation of ML programs - allowing you to focus on model perfection and Big Data Analytics. Petuum runs efficiently at scale on research clusters and cloud compute like Amazon EC2 and Google GCE. Petuum v1.1 is now available! Petuum provid...

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petuum.github.io

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Petuum

http://petuum.github.io/index-zh.html

Petuum可高效地运行于各种硬件环境下，包括实验室集群服务器和云计算平台，如Amazon EC2， Google GCE。 Petuum 是一个分布式机器学习平台，用以处理两类大规模机器学习问题 1 大数据数据样本数目庞大 2 大模型模型参数众多。不同于一般用途的分布式编程平台如数据流系统， Petuum专门为机器学习而设计。 Petuum主要包括两个组件 1 基于受限异步一致性协议的分布式键值存储系统 Bösen. 逻辑回归使用8台机器每台16核，在30GB稀疏数据上学习一个千万维度的模型，用时 20分钟. LDA话题模型使用25台机器每台16核，在8百万文本 1.4万词汇学习1000个话题，用时 17分钟. 矩阵分解协同过滤使用25台机器每台16核，分解一个4.8万乘以2万的矩阵秩设为40 ，用时 2分钟. 之上的分布式卷积神经网络使用8台GPU机器每台一个Tesla K20 GPU ，训练Alexnet 0.6亿参数，用时少于 24小时. MedLDA监督话题模型使用20台机器每台12核，在110万文本 20个类学习1000个话题，用时 85分钟.

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Petuum

http://petuum.github.io/performance.html

Petuum improves the performance of ML applications by making every update or iteration more effective, without compromising on update speed. More effective updates means faster ML completion time - see the examples below. Petuum data-parallel deep learning (Petuum Poseidon v1.0). Settings: 250GB data, 1.2m images, 1000 classes (ILSVRC 2012 dataset), 8 machines (1x K20 GPU each). Petuum model-parallel LDA topic model (Petuum v1.0). Petuum model-parallel sparse logistic regression (Petuum v1.0). Settings: ...

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aurick.net

Aurick Qiao

http://www.aurick.net/home

I am a second year Ph.D student in the. At Carnegie Mellon University. Currently, I am working on the. Project, aimed at scalable machine learning. My advisor is Prof. Eric P. Xing. I completed my undergraduate degree at the University of Waterloo, where I studied computer science. And combinatorics and optimization. There, I briefly did some research into multi-pivot quicksort. With Prof. J. Ian Munro. And Prof. Alejandro López-Ortiz.

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Petuum

Petuum is a distributed machine learning framework. It aims to provide a generic algorithmic and systems interface to large scale machine learning, and takes care of difficult systems "plumbing work" and algorithmic acceleration, while simplifying the distributed implementation of ML programs - allowing you to focus on model perfection and Big Data Analytics. Petuum runs efficiently at scale on research clusters and cloud compute like Amazon EC2 and Google GCE. Petuum v1.1 is now available! Petuum provid...

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