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Posted to commits@singa.apache.org by wa...@apache.org on 2016/01/11 17:05:12 UTC
svn commit: r1724070 -
/incubator/singa/site/trunk/content/markdown/develop/schedule.md
Author: wangwei
Date: Mon Jan 11 16:05:11 2016
New Revision: 1724070
URL: http://svn.apache.org/viewvc?rev=1724070&view=rev
Log:
Update schedule page for v0.2
Modified:
incubator/singa/site/trunk/content/markdown/develop/schedule.md
Modified: incubator/singa/site/trunk/content/markdown/develop/schedule.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/develop/schedule.md?rev=1724070&r1=1724069&r2=1724070&view=diff
==============================================================================
--- incubator/singa/site/trunk/content/markdown/develop/schedule.md (original)
+++ incubator/singa/site/trunk/content/markdown/develop/schedule.md Mon Jan 11 16:05:11 2016
@@ -3,7 +3,7 @@
| Release | Module| Feature | Status |
|---------|---------|-------------|--------|
-| 0.1 Sep | Neural Network |1.1. Feed forward neural network, including CNN, MLP | done|
+| 0.1 Sep 2015 | Neural Network |1.1. Feed forward neural network, including CNN, MLP | done|
| | |1.2. RBM-like model, including RBM | done|
| | |1.3. Recurrent neural network, including standard RNN | done|
| | Architecture |1.4. One worker group on single node (with data partition)| done|
@@ -14,18 +14,17 @@
| | |1.9. Load-balance among servers | done|
| | Failure recovery|1.10. Checkpoint and restore |done|
| | Tools|1.11. Installation with GNU auto tools| done|
-|0.2 Dec | Neural Network |2.1. Feed forward neural network, including VGG model, CSV input layer, HDFS output layer, etc.||
-| | |2.2. Recurrent neural network, including GRU and LSTM| |
-| | |2.3. Model partition and hybrid partition||
-| | Configuration |2.4. Configuration helpers for popular models, e.g., CNN, MLP, Auto-encoders||
-| | Tools |2.5. Integration with Mesos for resource management||
-| | |2.6. Prepare Docker images for deployment||
-| | Binding |2.7. Python binding for major components ||
-| | GPU |2.8. Single node with multiple GPUs ||
-|0.3 Mar | GPU | 3.1 Multiple nodes, each with multiple GPUs||
+|0.2 Jan 2016 | Neural Network |2.1. Feed forward neural network, including AlexNet, cuDNN layers, etc.| done |
+| | |2.2. Recurrent neural network, including GRULayer and BPTT|done |
+| | |2.3. Model partition and hybrid partition|done|
+| | Tools |2.4. Integration with Mesos for resource management|done|
+| | |2.5. Prepare Docker images for deployment|done|
+| | |2.6. Visualization of neural net and debug information |done|
+| | Binding |2.7. Python binding for major components |done|
+| | GPU |2.8. Single node with multiple GPUs |done|
+|0.3 Mar 2016 | GPU | 3.1 Multiple nodes, each with multiple GPUs||
| | | 3.2 Heterogeneous training using both GPU and CPU [CcT](http://arxiv.org/abs/1504.04343)||
-| | Phi | 3.3 Integration with Intel Phi co-processors (depending on the availability of the hardware)||
-| | Tools| 3.4 Deep learning as a service ||
-| | Applications | 3.5 Image classification, product search, etc.||
-| | Optimization | 3.6 ... ||
+| | Tools| 3.3 Deep learning as a service ||
+| | Applications | 3.4 Image classification, product search, etc.||
+| | Optimization | 3.5 ... ||