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Posted to commits@singa.apache.org by wa...@apache.org on 2015/05/18 08:41:55 UTC
svn commit: r1679948 -
/incubator/singa/site/trunk/content/markdown/community/schedule.md
Author: wangwei
Date: Mon May 18 06:41:55 2015
New Revision: 1679948
URL: http://svn.apache.org/r1679948
Log:
add development schedule page
Added:
incubator/singa/site/trunk/content/markdown/community/schedule.md
Added: incubator/singa/site/trunk/content/markdown/community/schedule.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/community/schedule.md?rev=1679948&view=auto
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--- incubator/singa/site/trunk/content/markdown/community/schedule.md (added)
+++ incubator/singa/site/trunk/content/markdown/community/schedule.md Mon May 18 06:41:55 2015
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+Title: Development Schedule
+Notice: Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+ .
+ http://www.apache.org/licenses/LICENSE-2.0
+ .
+ Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+
+## Development Schedule
+
+| Release | Module| Feature | Status |
+|---------|---------|-------------|--------|
+| 0.1 | Neural Network |1.1. Feed forward neural network, including CNN, MLP | done|
+| | |1.2. RBM-like model, including RBM | working|
+| | |1.3. Recurrent neural network, including standard RNN | working|
+| | 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)|working|
+| | |1.6. Multi groups across nodes, like [Downpour](http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks)|working|
+| | Resource Management |1.7. Integration with Mesos | working|
+| | Failure recovery|1.8. Checkpoint and restore |working|
+| | Tools|1.9. Installation with GNU auto tools| done|
+|0.2 | Neural Network |2.1. Feed forward neural network, including auto-encoders, hinge loss layers, HDFS data layers||
+| | |2.2. RBM-like model, including DBM | |
+| | |2.3. Recurrent neural network, including LSTM| |
+| | |2.4. Model partition ||
+| | Communication |2.5. MPI||
+| | GPU |2.6. Single GPU ||
+| | |2.7. Multiple GPUs on single node||
+| | Architecture |2.8. All-Reduce training architecture like [DeepImage](http://arxiv.org/abs/1501.02876)||
+| | Fault Tolerance|2.9. Node failure detection and recovery||
+| | Binding |2.9. Python binding ||
+| | User Interface |2.10. Web front-end for job submission and performance visualization||