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From wang...@apache.org
Subject [8/8] incubator-singa git commit: add release note
Date Tue, 05 Jan 2016 18:19:20 GMT
add release note


Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/ae1b33a7
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/ae1b33a7
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/ae1b33a7

Branch: refs/heads/master
Commit: ae1b33a7b0ec18bb6c0db43d37f4e293fd86881b
Parents: 27a48c2
Author: WANG Sheng <wangsheng1001@gmail.com>
Authored: Thu Dec 31 15:43:45 2015 +0800
Committer: Wei Wang <wangwei@comp.nus.edu.sg>
Committed: Wed Jan 6 02:18:29 2016 +0800

----------------------------------------------------------------------
 RELEASE_NOTES | 57 ++++++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 55 insertions(+), 2 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/ae1b33a7/RELEASE_NOTES
----------------------------------------------------------------------
diff --git a/RELEASE_NOTES b/RELEASE_NOTES
index bd89d57..a73157a 100644
--- a/RELEASE_NOTES
+++ b/RELEASE_NOTES
@@ -1,4 +1,4 @@
-Release Notes - SINGA - Version singa-incubating-0.1.0
+Release Notes - SINGA - Version singa-incubating-0.2.0
 -----------------------------------------
 
 SINGA is a general distributed deep learning platform for training big deep learning models
over large datasets. It is
@@ -7,6 +7,60 @@ deep learning models.
 
 This release includes following features:
 
+  * Programming model
+    * [SINGA-80] New Blob Level and Address Level Math Operation Interface 
+    * [SINGA-82] Refactor input layers using data store abstraction
+    * [SINGA-87] Replace exclude field to include field for layer configuration
+    * [SINGA-110] Add Layer member datavec_ and gradvec_
+
+  * Neuralnet layers
+    * [SINGA-91] Add SoftmaxLayer and ArgSortLayer
+    * [SINGA-106] Add dummy layer for test purpose 
+
+  * GPU training support
+    * [SINGA-100] Implement layers using CUDNN for GPU training
+    * [SINGA-104] Add Context Class
+    * [SINGA-105] Update GUN make files for compiling cuda related code
+
+  * Model/Hybrid partition
+    * [SINGA-109] Refine bridge layers
+    * [SINGA-111] Add slice, concate and split layers
+    * [SINGA-113] Model/Hybrid Partition Support 
+
+  * Python binding
+    * [SINGA-108] Add Python wrapper to singa
+
+  * Predict-only mode
+    * [SINGA-85] Add functions for extracting features and test new data
+
+  * Integrate with third-party tools
+    * [SINGA-11] Start SINGA on Apache Mesos
+    * [SINGA-78] Use Doxygen to generate documentation
+    * [SINGA-89] Add Docker support
+  
+  * Unit test
+    * [SINGA-95] Add make test after building
+  
+  * Other improvment
+    * [SINGA-84] Header Files Rearrange
+    * [SINGA-93] Remove the asterisk in the log tcp://169.254.12.152:*:49152
+    * [SINGA-94] Move call to google::InitGoogleLogging() from Driver::Init() to main()
+    * [SINGA-96] Add Momentum to Cifar10 Example
+    * [SINGA-101] Add ll (ls -l) command in .bashrc file when using docker
+    * [SINGA-114] Remove short logs in tmp directory
+    * [SINGA-115] Print layer debug information in the neural net graph file 
+    * [SINGA-118] Make protobuf LayerType field id easy to assign 
+
+  * Bugs fixed
+    * [SINGA-85] Fix compilation errors in examples
+    * [SINGA-90] Miscellaneous trivial bug fixes
+    * [SINGA-107] Error from loading pre-trained params for training stacked RBMs
+    * [SINGA-116] Fix a bug in InnerProductLayer caused by weight matrix sharing 
+
+-----------------------------------------
+
+Features included in singa-incubating-0.1.0:
+
   * Job management
     * [SINGA-3] Use Zookeeper to check stopping (finish) time of the system
     * [SINGA-16] Runtime Process id Management
@@ -84,4 +138,3 @@ Features planned for the next release
   * [SINGA-35] Add random number generators
   * [SINGA-40] Support sparse Param update
   * [SINGA-41] Support single node single GPU training
-


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