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From Apache Wiki <wikidi...@apache.org>
Subject [Incubator Wiki] Trivial Update of "HornProposal" by edwardyoon
Date Wed, 26 Aug 2015 12:51:02 GMT
Dear Wiki user,

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The "HornProposal" page has been changed by edwardyoon:
https://wiki.apache.org/incubator/HornProposal?action=diff&rev1=10&rev2=11

  == Abstract ==
  
- (tentatively named "Horn [hɔ:n]", korean meaning of Horn is a "Spirit") is a neuron-centric
programming APIs and execution framework for large-scale deep learning, built on top of Apache
Hama.
+ Horn [hɔ:n] (korean meaning of Horn is a "Spirit") is a neuron-centric programming APIs
and execution framework for large-scale deep learning, built on top of Apache Hama.
  
  == Proposal ==
  
@@ -14, +14 @@

  
  == Rationale ==
  
- While many of deep learning open source softwares such as Caffe, DeepDist, DN4j, and NeuralGiraph
are still data or model parallel only, we aim to support both data and model parallelism and
also fault-tolerant system design. The basic idea of data and model parallelism is use of
the remote parameter server to parallelize model creation and distribute training across machines,
and the BSP framework of Apache Hama for performing asynchronous mini-batches. Within single
BSP job, each task group works asynchronously using region barrier synchronization instead
of global barrier synchronization, and trains large-scale neural network model using assigned
data sets in BSP paradigm. Thus, we achieve data and model parallelism. This architecture
is inspired by Google's !DistBelief (Jeff Dean et al, 2012).
+ While many of deep learning open source softwares such as Caffe, DeepDist, DL4j, and NeuralGiraph
are still data or model parallel only, we aim to support both data and model parallelism and
also fault-tolerant system design. The basic idea of data and model parallelism is use of
the remote parameter server to parallelize model creation and distribute training across machines,
and the BSP framework of Apache Hama for performing asynchronous mini-batches. Within single
BSP job, each task group works asynchronously using region barrier synchronization instead
of global barrier synchronization, and trains large-scale neural network model using assigned
data sets in BSP paradigm. Thus, we achieve data and model parallelism. This architecture
is inspired by Google's !DistBelief (Jeff Dean et al, 2012).
  
  == Initial Goals ==
  
  Some current goals include: 
+ 
   * builds new community
   * provides more intuitive programming APIs
   * needs both data and model parallelism support
@@ -83, +84 @@

  == Required Resources ==
  
  === Mailing Lists ===
+ 
   * horn-private
   * horn-dev 
  
  === Subversion Directory ===
+ 
   * Git is the preferred source control system: git://git.apache.org/horn
  
  === Issue Tracking ===
+ 
   * a JIRA issue tracker, HORN
  
- == Initial Committers and Affiliations ==
+ == Initial Committers ==
+ 
   * Thomas Jungblut (tjungblut AT apache DOT org)
   * Edward J. Yoon (edwardyoon AT apache DOT org)
   * Dongjin Lee (dongjin.lee.kr AT gmail DOT com)
@@ -103, +108 @@

   * Kisuk Lee (ks881115 AT gmail DOT com)
  
  == Affiliations ==
+ 
+ The initial committers are employees/students of Microsoft, Samsung Electronics, Seoul National
University, Technical University of Munich, LINE plus, and Cldi Inc.
+ 
   * Thomas Jungblut (Microsoft)
   * Edward J. Yoon (Samsung Electronics)
   * Donjin Lee (LINE Plus)
@@ -111, +119 @@

   * Behroz Sikander (Technical University of Munich)
   * Hyok S. Choi (Samsung Electronics)
   * Kisuk Lee (Seoul National University)
+ 
+ The nominated mentors are employees of IBM, Samsung Electronics, and Google.
  
  == Sponsors ==
  

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