Author: wangwei
Date: Wed May 4 09:59:15 2016
New Revision: 1742242
URL: http://svn.apache.org/viewvc?rev=1742242&view=rev
Log:
update schedule for v1.0
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=1742242&r1=1742241&r2=1742242&view=diff
==============================================================================
--- incubator/singa/site/trunk/content/markdown/develop/schedule.md (original)
+++ incubator/singa/site/trunk/content/markdown/develop/schedule.md Wed May 4
09:59:15 2016
@@ -3,31 +3,38 @@
| Release | Module| Feature | Status |
|---------|---------|-------------|--------|
-| 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|
-| | |1.5. Multi worker groups on single node using
[Hogwild](http://www.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf)|done|
-| | |1.6. Distributed Hogwild|done|
-| | |1.7. Multi groups across nodes, like
[Downpour](http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks)|done|
-| | |1.8. All-Reduce training architecture like
[DeepImage](http://arxiv.org/abs/1501.02876)|done|
-| | |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 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 April 2016 | GPU | 3.1 Multiple nodes, each with multiple GPUs|done|
-| | | 3.2 Heterogeneous training using both GPU and CPU
[CcT](http://arxiv.org/abs/1504.04343)|done|
-| | | 3.3 Support cuDNN v4 | done|
-| | Installation| 3.4 Remove dependency on ZeroMQ, CZMQ,
Zookeeper for single node training|done|
-| | Updater| 3.5 Add new SGD updaters including Adam, AdamMax
and AdaDelta|done|
-| | Binding| 3.6 Enhance Python binding for training|done|
-|0.4 July 2016 | Rafiki | 4.1 Deep learning as a service| |
-| | | 4.2 Product search using Rafiki| |
-
+| 0.1 Sep 2015 | Neural Network | Feed forward neural network, including
CNN, MLP | done|
+| | | RBM-like model, including RBM | done|
+| | | Recurrent neural network, including standard RNN
| done|
+| | Architecture | One worker group on single node (with data
partition)| done|
+| | | Multi worker groups on single node using
[Hogwild](http://www.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf)|done|
+| | | Distributed Hogwild|done|
+| | | Multi groups across nodes, like
[Downpour](http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks)|done|
+| | | All-Reduce training architecture like
[DeepImage](http://arxiv.org/abs/1501.02876)|done|
+| | | Load-balance among servers | done|
+| | Failure recovery| Checkpoint and restore |done|
+| | Tools| Installation with GNU auto tools| done|
+|0.2 Jan 2016 | Neural Network | Feed forward neural network, including
AlexNet, cuDNN layers, etc.| done |
+| | | Recurrent neural network, including GRULayer and
BPTT|done |
+| | | Model partition and hybrid partition|done|
+| | Tools | Integration with Mesos for resource management|done|
+| | | Prepare Docker images for deployment|done|
+| | | Visualization of neural net and debug information
|done|
+| | Binding | Python binding for major components |done|
+| | GPU | Single node with multiple GPUs |done|
+|0.3 April 2016 | GPU | Multiple nodes, each with multiple GPUs|done|
+| | | Heterogeneous training using both GPU and CPU
[CcT](http://arxiv.org/abs/1504.04343)|done|
+| | | Support cuDNN v4 | done|
+| | Installation| Remove dependency on ZeroMQ, CZMQ, Zookeeper
for single node training|done|
+| | Updater| Add new SGD updaters including Adam, AdamMax and
AdaDelta|done|
+| | Binding| Enhance Python binding for training|done|
+|0.4 June 2016 | Rafiki | Deep learning as a service| |
+| | | Product search using Rafiki| |
+|1.0 July 2016 | Programming abstraction|Tensor with linear algebra, neural
net and random operations| |
+| | |Updater for distributed parameter
updating||
+| | Optimization | Execution and memory optimization||
+| | Hardware | Use Cuda and Cudnn for Nvidia GPU||
+| | | Use OpenCL for AMD GPU or other
devices||
+| | Cross-platform | To extend from Linux to MacOS and Windows||
+| | Examples | Speech recognition example||
+| | |Large image models, e.g.,
[GoogLeNet](http://arxiv.org/abs/1409.4842),
[VGG](https://arxiv.org/pdf/1409.1556.pdf) and [Residual
Net](http://arxiv.org/abs/1512.03385)||
