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Posted to commits@singa.apache.org by bu...@apache.org on 2016/01/11 17:06:15 UTC

svn commit: r977485 - in /websites/staging/singa/trunk/content: ./ develop/schedule.html

Author: buildbot
Date: Mon Jan 11 16:06:15 2016
New Revision: 977485

Log:
Staging update by buildbot for singa

Modified:
    websites/staging/singa/trunk/content/   (props changed)
    websites/staging/singa/trunk/content/develop/schedule.html

Propchange: websites/staging/singa/trunk/content/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Mon Jan 11 16:06:15 2016
@@ -1 +1 @@
-1724011
+1724070

Modified: websites/staging/singa/trunk/content/develop/schedule.html
==============================================================================
--- websites/staging/singa/trunk/content/develop/schedule.html (original)
+++ websites/staging/singa/trunk/content/develop/schedule.html Mon Jan 11 16:06:15 2016
@@ -335,7 +335,7 @@
     
 <tr class="b">
       
-<td>0.1 Sep </td>
+<td>0.1 Sep 2015 </td>
       
 <td>Neural Network </td>
       
@@ -456,11 +456,13 @@
     
 <tr class="a">
       
-<td>0.2 Dec </td>
+<td>0.2 Jan 2016 </td>
       
 <td>Neural Network </td>
       
-<td colspan="2">2.1. Feed forward neural network, including VGG model, CSV input layer, HDFS output layer, etc.</td>
+<td>2.1. Feed forward neural network, including AlexNet, cuDNN layers, etc.</td>
+      
+<td>done </td>
     </tr>
     
 <tr class="b">
@@ -469,9 +471,9 @@
       
 <td> </td>
       
-<td>2.2. Recurrent neural network, including GRU and LSTM</td>
+<td>2.2. Recurrent neural network, including GRULayer and BPTT</td>
       
-<td> </td>
+<td>done </td>
     </tr>
     
 <tr class="a">
@@ -480,25 +482,31 @@
       
 <td> </td>
       
-<td colspan="2">2.3. Model partition and hybrid partition</td>
+<td>2.3. Model partition and hybrid partition</td>
+      
+<td>done</td>
     </tr>
     
 <tr class="b">
       
 <td> </td>
       
-<td>Configuration </td>
+<td>Tools </td>
+      
+<td>2.4. Integration with Mesos for resource management</td>
       
-<td colspan="2">2.4. Configuration helpers for popular models, e.g., CNN, MLP, Auto-encoders</td>
+<td>done</td>
     </tr>
     
 <tr class="a">
       
 <td> </td>
       
-<td>Tools </td>
+<td> </td>
       
-<td colspan="2">2.5. Integration with Mesos for resource management</td>
+<td>2.5. Prepare Docker images for deployment</td>
+      
+<td>done</td>
     </tr>
     
 <tr class="b">
@@ -507,7 +515,9 @@
       
 <td> </td>
       
-<td colspan="2">2.6. Prepare Docker images for deployment</td>
+<td>2.6. Visualization of neural net and debug information </td>
+      
+<td>done</td>
     </tr>
     
 <tr class="a">
@@ -516,7 +526,9 @@
       
 <td>Binding </td>
       
-<td colspan="2">2.7. Python binding for major components </td>
+<td>2.7. Python binding for major components </td>
+      
+<td>done</td>
     </tr>
     
 <tr class="b">
@@ -525,12 +537,14 @@
       
 <td>GPU </td>
       
-<td colspan="2">2.8. Single node with multiple GPUs </td>
+<td>2.8. Single node with multiple GPUs </td>
+      
+<td>done</td>
     </tr>
     
 <tr class="a">
       
-<td>0.3 Mar </td>
+<td>0.3 Mar 2016 </td>
       
 <td>GPU </td>
       
@@ -550,36 +564,27 @@
       
 <td> </td>
       
-<td>Phi </td>
-      
-<td colspan="2">3.3 Integration with Intel Phi co-processors (depending on the availability of the hardware)</td>
-    </tr>
-    
-<tr class="b">
-      
-<td> </td>
-      
 <td>Tools</td>
       
-<td colspan="2">3.4 Deep learning as a service </td>
+<td colspan="2">3.3 Deep learning as a service </td>
     </tr>
     
-<tr class="a">
+<tr class="b">
       
 <td> </td>
       
 <td>Applications </td>
       
-<td colspan="2">3.5 Image classification, product search, etc.</td>
+<td colspan="2">3.4 Image classification, product search, etc.</td>
     </tr>
     
-<tr class="b">
+<tr class="a">
       
 <td> </td>
       
 <td>Optimization </td>
       
-<td colspan="2">3.6 &#x2026; </td>
+<td colspan="2">3.5 &#x2026; </td>
     </tr>
   </tbody>
 </table>