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Posted to commits@singa.apache.org by wa...@apache.org on 2017/02/13 05:13:22 UTC
svn commit: r1782721 [1/24] - in /incubator/singa/site/trunk/v1.1.0: ./
_sources/ _sources/community/ _sources/develop/ _sources/docs/
_sources/docs/examples/ _sources/docs/examples/caffe/
_sources/docs/examples/char-rnn/ _sources/docs/examples/cifar10...
Author: wangwei
Date: Mon Feb 13 05:13:19 2017
New Revision: 1782721
URL: http://svn.apache.org/viewvc?rev=1782721&view=rev
Log:
archive the docs for v1.1.0
Added:
incubator/singa/site/trunk/v1.1.0/
incubator/singa/site/trunk/v1.1.0/.buildinfo
incubator/singa/site/trunk/v1.1.0/_sources/
incubator/singa/site/trunk/v1.1.0/_sources/community/
incubator/singa/site/trunk/v1.1.0/_sources/community/issue-tracking.txt
incubator/singa/site/trunk/v1.1.0/_sources/community/mail-lists.txt
incubator/singa/site/trunk/v1.1.0/_sources/community/source-repository.txt
incubator/singa/site/trunk/v1.1.0/_sources/community/team-list.txt
incubator/singa/site/trunk/v1.1.0/_sources/develop/
incubator/singa/site/trunk/v1.1.0/_sources/develop/contribute-code.txt
incubator/singa/site/trunk/v1.1.0/_sources/develop/contribute-docs.txt
incubator/singa/site/trunk/v1.1.0/_sources/develop/how-contribute.txt
incubator/singa/site/trunk/v1.1.0/_sources/develop/schedule.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/
incubator/singa/site/trunk/v1.1.0/_sources/docs.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/cnn.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/converter.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/data.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/dependencies.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/device.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/docker.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/caffe/
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/caffe/README.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/char-rnn/
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/char-rnn/README.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/cifar10/
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/cifar10/README.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/imagenet/
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/imagenet/README.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/index.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/mnist/
incubator/singa/site/trunk/v1.1.0/_sources/docs/examples/mnist/README.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/image_tool.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/index.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/initializer.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/installation.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/layer.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/loss.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/metric.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/
incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/caffe/
incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/caffe/README.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/char-rnn/
incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/char-rnn/README.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/cifar10/
incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/cifar10/README.txt
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incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/examples/char-rnn/
incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/examples/char-rnn/README.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/examples/cifar10/
incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/examples/cifar10/README.txt
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incubator/singa/site/trunk/v1.1.0/_sources/docs/model_zoo/examples/imagenet/alexnet/
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incubator/singa/site/trunk/v1.1.0/_sources/docs/software_stack.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/tensor.txt
incubator/singa/site/trunk/v1.1.0/_sources/docs/utils.txt
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incubator/singa/site/trunk/v1.1.0/searchindex.js
Added: incubator/singa/site/trunk/v1.1.0/.buildinfo
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/.buildinfo?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/.buildinfo (added)
+++ incubator/singa/site/trunk/v1.1.0/.buildinfo Mon Feb 13 05:13:19 2017
@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: e5117cce5cd6d987cdf87be60b3b5c14
+tags: 645f666f9bcd5a90fca523b33c5a78b7
Added: incubator/singa/site/trunk/v1.1.0/_sources/community/issue-tracking.txt
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/_sources/community/issue-tracking.txt?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/_sources/community/issue-tracking.txt (added)
+++ incubator/singa/site/trunk/v1.1.0/_sources/community/issue-tracking.txt Mon Feb 13 05:13:19 2017
@@ -0,0 +1,9 @@
+## Issue Tracking
+
+___
+
+SINGA uses [JIRA](https://www.atlassian.com/software/jira) a J2EE-based, issue tracking and project management application.
+
+Issues, bugs, and feature requests should be submitted to the following issue tracking system for this project.
+
+* https://issues.apache.org/jira/browse/singa
Added: incubator/singa/site/trunk/v1.1.0/_sources/community/mail-lists.txt
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/_sources/community/mail-lists.txt?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/_sources/community/mail-lists.txt (added)
+++ incubator/singa/site/trunk/v1.1.0/_sources/community/mail-lists.txt Mon Feb 13 05:13:19 2017
@@ -0,0 +1,28 @@
+.. 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.
+
+
+Project Mailing Lists
+=====================
+
+These are the mailing lists that have been established for this project. For each list, there is a subscribe, unsubscribe, and an archive link.
+
+.. csv-table:: Mailing Lists
+ :header: "Name", "Post", "Subscribe", "Unsubscribe", "Archive"
+
+ "Development", "dev@singa.incubator.apache.org", "`Subscribe <ma...@singa.incubator.apache.org>`_", "`Unsubscribe <ma...@singa.incubator.apache.org.>`_", "`mail-archives.apache.org <http://mail-archives.apache.org/mod_mbox/singa-dev/>`_"
+ "Commits", "commits@singa.incubator.apache.org", "`Subscribe <ma...@singa.incubator.apache.org>`_", "`Unsubscribe <ma...@singa.incubator.apache.org>`_", "`mail-archives.apache.org <http://mail-archives.apache.org/mod_mbox/singa-commits/>`_"
Added: incubator/singa/site/trunk/v1.1.0/_sources/community/source-repository.txt
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--- incubator/singa/site/trunk/v1.1.0/_sources/community/source-repository.txt (added)
+++ incubator/singa/site/trunk/v1.1.0/_sources/community/source-repository.txt Mon Feb 13 05:13:19 2017
@@ -0,0 +1,22 @@
+# Source Repository
+
+___
+
+This project uses [Git](http://git-scm.com/) to manage its source code. Instructions on Git use can be found at [http://git-scm.com/documentation](http://git-scm.com/documentation).
+
+## Web Access
+
+The following is a link to the online source repository.
+
+* [https://git-wip-us.apache.org/repos/asf?p=incubator-singa.git;a=summary](https://git-wip-us.apache.org/repos/asf?p=incubator-singa.git;a=summary)
+
+
+## Upstream for committers
+
+Committers need to set the upstream endpoint to the Apache git (not github) repo address, e.g.,
+
+ $ git remote add asf https://git-wip-us.apache.org/repos/asf/incubator-singa.git
+
+Then you (committer) can push your code in this way,
+
+ $ git push asf <local-branch>:<remote-branch>
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--- incubator/singa/site/trunk/v1.1.0/_sources/community/team-list.txt (added)
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@@ -0,0 +1,79 @@
+.. 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.
+
+
+The SINGA Team
+==============
+
+A successful project requires many people to play many roles. Some members write code or documentation, while others are valuable as testers, submitting patches and suggestions.
+
+Mentors
+-------
+
+================== ============
+Name Email
+================== ============
+Daniel Dai daijy@apache.org
+Ted Dunning tdunning@apache.org
+Alan Gates gates@apache.org
+Thejas Nair thejas@apache.org
+================== ============
+
+
+Developers
+----------
+
++--------------------+--------------------------------+-----------------------------------------------+
+| Name | Email | Organization |
++====================+================================+===============================================+
+| Gang Chen | cg@zju.edu.cn | Zhejiang University |
++--------------------+--------------------------------+-----------------------------------------------+
+| Haibo Chen | hzchenhaibo@corp.netease.com | NetEase |
++--------------------+--------------------------------+-----------------------------------------------+
+| Anh Dinh | dinhtta@apache.org | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Jinyang Gao | jinyang@apache.org | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Xing Ji | jixin@comp.nus.edu.sg | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Chonho Lee | chonho@gmail.com | Osaka University |
++--------------------+--------------------------------+-----------------------------------------------+
+| Zhaojing Luo | zhaojing@apache.org | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Beng Chin Ooi | ooibc@comp.nus.edu.sg | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Kian-Lee Tan | tankl@apache.org | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Anthony K. H. Tung | atung@comp.nus.edu.sg | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Ji Wang | wangji@comp.nus.edu.sg | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Sheng Wang | wangsh@apache.org | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Wei Wang | wangwei@apache.org | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Yuan Wang | wangyuan@corp.netease.com | NetEase |
++--------------------+--------------------------------+-----------------------------------------------+
+| Wenfeng Wu | wuwf@comp.nus.edu.sg | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Zhongle Xie | zhongle@apache.org | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+| Meihui Zhang | meihui_zhang@sutd.edu.sg | Singapore University of Technology and Design |
++--------------------+--------------------------------+-----------------------------------------------+
+| Kaiping Zheng | kaiping@apache.org | National University of Singapore |
++--------------------+--------------------------------+-----------------------------------------------+
+
Added: incubator/singa/site/trunk/v1.1.0/_sources/develop/contribute-code.txt
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--- incubator/singa/site/trunk/v1.1.0/_sources/develop/contribute-code.txt (added)
+++ incubator/singa/site/trunk/v1.1.0/_sources/develop/contribute-code.txt Mon Feb 13 05:13:19 2017
@@ -0,0 +1,59 @@
+## How to Contribute Code
+
+
+### Coding Style
+
+The SINGA codebase follows the [Google C++ Style Guide](http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml).
+
+To check if your code follows the style, you can use the provided cpplint tool:
+
+ $ ./tool/cpplint.py YOUR_FILE
+
+
+### JIRA format
+
+Like other Apache projects, SINGA uses JIRA to track bugs, improvements and
+other high-level discussions (e.g., system design and features). Github pull requests are
+used for implementation discussions, e.g., code review and code merge.
+
+* Provide a descriptive Title.
+* Write a detailed Description. For bug reports, this should ideally include a
+ short reproduction of the problem. For new features, it may include a design
+ document.
+* Set [required fields](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-JIRA)
+
+### Pull Request
+
+The work flow is
+
+* Fork the [SINGA Github repository](https://github.com/apache/incubator-singa) to
+your own Github account.
+
+* Clone your fork, create a new branch (e.g., feature-foo or fixbug-foo),
+ work on it. After finishing your job,
+ [rebase](https://git-scm.com/book/en/v2/Git-Branching-Rebasing) it to the
+ current latest master and push commits to your own Github account (the new
+ branch).
+
+* Open a pull request against the master branch of apache/incubator-singa.
+The PR title should be of the form SINGA-xxxx Title, where
+SINGA-xxxx is the relevant JIRA number, and Title may be the JIRA's title or a
+more specific title describing the PR itself, for example, "SINGA-6 Implement thread-safe singleton". Detailed description can be copied from the JIRA.
+Consider identifying committers or other contributors who have worked on the
+code being changed. Find the file(s) in Github and click "Blame" to see a
+line-by-line annotation of who changed the code last. You can add @username in
+the PR description to ping them immediately.
+Please state that the contribution is your original work and that you license
+the work to the project under the project's open source license. Further commits (e.g., bug fix)
+to your new branch will be added to this pull request automatically by Github.
+
+* Wait for one committer to review the patch. If no conflicts, the committers will merge it with
+the master branch. The merge should a) not use rebase b) disable fast forward merge c) check the
+commit message format and test the code/feature.
+
+* If there are too many small commit messages, you will be told to squash your commits into fewer meaningful
+commits. If your commit message does not follow the format (i.e., SINGA-xxxx), you will be told to
+reword your commit message. Both changes can be done using interactive git rebase. Once you
+get the commits corrected, push them to you own github again. Your pull request
+will be automatically updated. For details, please refer to
+[Rebase Pull Requests](https://github.com/edx/edx-platform/wiki/How-to-Rebase-a-Pull-Request).
Added: incubator/singa/site/trunk/v1.1.0/_sources/develop/contribute-docs.txt
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+++ incubator/singa/site/trunk/v1.1.0/_sources/develop/contribute-docs.txt Mon Feb 13 05:13:19 2017
@@ -0,0 +1,36 @@
+# How to Contribute to Documentation
+
+
+## Website
+This document gives step-by-step instructions for deploying [Singa website](http://singa.incubator.apache.org).
+
+Singa website is built by [Sphinx](http://www.sphinx-doc.org) >=1.4.4 from a source tree stored in git: https://github.com/apache/incubator-singa/tree/master/doc.
+
+To install Sphinx:
+
+ $ pip install -U Sphinx
+
+To install the markdown support for Sphinx:
+
+ $ pip install recommonmark
+
+To install the rtd theme:
+
+ $ pip install sphinx_rtd_theme
+
+You can build the website by executing the following command from the doc folder:
+
+ $ ./build.sh html
+
+Committers can update the [SINGA website](http://singa.apache.org/en/index.html) by following these steps:
+
+ $ cd _build
+ $ svn co https://svn.apache.org/repos/asf/incubator/singa/site/trunk
+ $ cp -r html/* trunk
+ # svn add <newly added html files>
+ $ svn commit -m "commit messages" --username <committer ID> --password <password>
+
+
+## CPP API
+
+To generate docs, run "doxygen" from the doc folder (Doxygen >= 1.8 recommended)
Added: incubator/singa/site/trunk/v1.1.0/_sources/develop/how-contribute.txt
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@@ -0,0 +1,9 @@
+# How to Contribute to SINGA
+
+As with any open source project, there are several ways you can help:
+
+* Join the [mailing list](http://singa.apache.org/en/community/mail-lists.html) and answer other user's questions.
+* [Build Singa](http://singa.apache.org/en/docs/installation.html) by yourself.
+* Report bugs, feature requests and other issues in the [issue tracking](http://singa.apache.org/en/community/issue-tracking.html) application.
+* Check SINGA's [development schedule](http://singa.apache.org/en/develop/schedule.html) and [contribute code](http://singa.apache.org/en/develop/contribute-code.html) by providing patches.
+* [Help with the documentation](http://singa.apache.org/en/develop/contribute-docs.html) by updating webpages that are lacking or unclear.
Added: incubator/singa/site/trunk/v1.1.0/_sources/develop/schedule.txt
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--- incubator/singa/site/trunk/v1.1.0/_sources/develop/schedule.txt (added)
+++ incubator/singa/site/trunk/v1.1.0/_sources/develop/schedule.txt Mon Feb 13 05:13:19 2017
@@ -0,0 +1,66 @@
+.. 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
+====================
+
+.. csv-table::
+ :header: "Release","Module","Feature"
+
+ "v0.1 Sep 2015 ","Neural Network ","Feed forward neural network, including CNN, MLP "
+ " "," ","RBM-like model, including RBM "
+ " "," ","Recurrent neural network, including standard RNN "
+ " ","Architecture ","One worker group on single node (with data partition) "
+ " "," ","Multi worker groups on single node using `Hogwild <http://www.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf>`_ "
+ " "," ","Distributed Hogwild"
+ " "," ","Multi groups across nodes, like `Downpour <http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks>`_"
+ " "," ","All-Reduce training architecture like `DeepImage <http://arxiv.org/abs/1501.02876>`_ "
+ " "," ","Load-balance among servers "
+ " ","Failure recovery ","Checkpoint and restore "
+ " ","Tools ","Installation with GNU auto Tools "
+ "v0.2 Jan 2016 ","Neural Network ","Feed forward neural network, including AlexNet, cuDNN layers,Tools "
+ " "," ","Recurrent neural network, including GRULayer and BPTT "
+ " "," ","Model partition and hybrid partition "
+ " ","Tools ","Integration with Mesos for resource management "
+ " "," ","Prepare Docker images for deployment"
+ " "," ","Visualization of neural net and debug information "
+ " ","Binding ","Python binding for major components "
+ " ","GPU ","Single node with multiple GPUs "
+ "v0.3 April 2016 ","GPU ","Multiple nodes, each with multiple GPUs"
+ " "," ","Heterogeneous training using both GPU and CPU `CcT <http://arxiv.org/abs/1504.04343>`_"
+ " "," ","Support cuDNN v4 "
+ " ","Installation ","Remove dependency on ZeroMQ, CZMQ, Zookeeper for single node training"
+ " ","Updater ","Add new SGD updaters including Adam, AdamMax and AdaDelta"
+ " ","Binding ","Enhance Python binding for training"
+ "v1.0 Sep 2016 ","Programming abstraction ","Tensor with linear algebra, neural net and random operations "
+ " "," ","Updater for distributed parameter updating "
+ " ","Hardware ","Use Cuda and Cudnn for Nvidia GPU"
+ " "," ","Use OpenCL for AMD GPU or other devices"
+ " ","Cross-platform ","To extend from Linux to MacOS"
+ " "," ","Large image models, e.g., `VGG <https://arxiv.org/pdf/1409.1556.pdf>`_ and `Residual Net <http://arxiv.org/abs/1512.03385>`_"
+ "v1.1 Jan 2017 ","Model Zoo ","GoogleNet; Health-care models"
+ " ","Caffe converter ","Use SINGA to train models configured in caffe proto files"
+ " ","Model components ","Add concat and slice layers; accept multiple inputs to the net"
+ " ","Compilation and installation ","Windows suppport"
+ " "," ","Simplify the installation by compiling protobuf and openblas together with SINGA"
+ " "," ","Build python wheel automatically using Jenkins"
+ " "," ","Install SINGA from Debian packages"
+ "v1.2 April 2017 ","Numpy API ","Implement functions for the tensor module of PySINGA following numpy API"
+ " ","Distributed training ","Migrate distributed training frameworks from V0.3"
+ "v1.3 July 2017 ","Memory optimization ","Replace CNMEM with new memory pool to reduce memory footprint"
+ " ","Execution optimization ","Runtime optimization of execution scheduling"
Added: incubator/singa/site/trunk/v1.1.0/_sources/docs.txt
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@@ -0,0 +1,23 @@
+.. 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.
+
+
+Documentation
+=============
+
+.. toctree::
+ docs/index
Added: incubator/singa/site/trunk/v1.1.0/_sources/docs/cnn.txt
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--- incubator/singa/site/trunk/v1.1.0/_sources/docs/cnn.txt (added)
+++ incubator/singa/site/trunk/v1.1.0/_sources/docs/cnn.txt Mon Feb 13 05:13:19 2017
@@ -0,0 +1,141 @@
+#Quickstart - Cifar10 example
+Convolution neural network (CNN) is a type of feed-forward artificial neural network widely used for image classification. In this example, we will use a deep CNN model to do image classification for the [CIFAR10 dataset](http://www.cs.toronto.edu/~kriz/cifar.html).
+
+## Running instructions for CPP version
+Please refer to [Installation](installation.html) page for how to install SINGA. Currently, we CNN requires CUDNN, hence both CUDA and CUDNN should be installed and SINGA should be compiled with CUDA and CUDNN.
+
+The Cifar10 dataset could be downloaded by running
+
+ # switch to cifar10 directory
+ $ cd ../examples/cifar10
+ # download data for CPP version
+ $ python download_data.py bin
+
+'bin' is for downloading binary version of Cifar10 data.
+
+During downloading, you should see the detailed output like
+
+ Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
+ The tar file does exist. Extracting it now..
+ Finished!
+
+Now you have prepared the data for this Cifar10 example, the final step is to execute the `run.sh` script,
+
+ # in SINGA_ROOT/examples/cifar10/
+ $ ./run.sh
+
+You should see the detailed output as follows: first read the data files in order, show the statistics of training and testing data, then show the details of neural net structure with some parameter information, finally illustrate the performance details during training and validation process. The number of epochs can be specified in `run.sh` file.
+
+ Start training
+ Reading file cifar-10-batches-bin/data_batch_1.bin
+ Reading file cifar-10-batches-bin/data_batch_2.bin
+ Reading file cifar-10-batches-bin/data_batch_3.bin
+ Reading file cifar-10-batches-bin/data_batch_4.bin
+ Reading file cifar-10-batches-bin/data_batch_5.bin
+ Reading file cifar-10-batches-bin/test_batch.bin
+ Training samples = 50000, Test samples = 10000
+ conv1(32, 32, 32, )
+ pool1(32, 16, 16, )
+ relu1(32, 16, 16, )
+ lrn1(32, 16, 16, )
+ conv2(32, 16, 16, )
+ relu2(32, 16, 16, )
+ pool2(32, 8, 8, )
+ lrn2(32, 8, 8, )
+ conv3(64, 8, 8, )
+ relu3(64, 8, 8, )
+ pool3(64, 4, 4, )
+ flat(1024, )
+ ip(10, )
+ conv1_weight : 8.09309e-05
+ conv1_bias : 0
+ conv2_weight : 0.00797731
+ conv2_bias : 0
+ conv3_weight : 0.00795888
+ conv3_bias : 0
+ ip_weight : 0.00798683
+ ip_bias : 0
+ Messages will be appended to an existed file: train_perf
+ Messages will be appended to an existed file: val_perf
+ Epoch 0, training loss = 1.828369, accuracy = 0.329420, lr = 0.001000
+ Epoch 0, val loss = 1.561823, metric = 0.420600
+ Epoch 1, training loss = 1.465898, accuracy = 0.469940, lr = 0.001000
+ Epoch 1, val loss = 1.361778, metric = 0.513300
+ Epoch 2, training loss = 1.320708, accuracy = 0.529000, lr = 0.001000
+ Epoch 2, val loss = 1.242080, metric = 0.549100
+ Epoch 3, training loss = 1.213776, accuracy = 0.571620, lr = 0.001000
+ Epoch 3, val loss = 1.175346, metric = 0.582000
+
+The training details are stored in `train_perf` file in the same directory and the validation details in `val_perf` file.
+
+
+## Running instructions for Python version
+To run CNN example in Python version, we need to compile SINGA with Python binding,
+
+ $ mkdir build && cd build
+ $ cmake -DUSE_PYTHON=ON ..
+ $ make
+
+Now download the Cifar10 dataset,
+
+ # switch to cifar10 directory
+ $ cd ../examples/cifar10
+ # download data for Python version
+ $ python download_data.py py
+
+During downloading, you should see the detailed output like
+
+ Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
+ The tar file does exist. Extracting it now..
+ Finished!
+
+Then execute the `train.py` script to build the model
+
+ $ python train.py
+
+You should see the output as follows including the details of neural net structure with some parameter information, reading data files, and the performance details during training and testing process.
+
+ (32L, 32L, 32L)
+ (32L, 16L, 16L)
+ (32L, 16L, 16L)
+ (32L, 16L, 16L)
+ (32L, 16L, 16L)
+ (32L, 16L, 16L)
+ (32L, 8L, 8L)
+ (32L, 8L, 8L)
+ (64L, 8L, 8L)
+ (64L, 8L, 8L)
+ (64L, 4L, 4L)
+ (1024L,)
+ Start intialization............
+ conv1_weight gaussian 7.938460476e-05
+ conv1_bias constant 0.0
+ conv2_weight gaussian 0.00793507322669
+ conv2_bias constant 0.0
+ conv3_weight gaussian 0.00799657031894
+ conv3_bias constant 0.0
+ dense_weight gaussian 0.00804364029318
+ dense_bias constant 0.0
+ Loading data ..................
+ Loading data file cifar-10-batches-py/data_batch_1
+ Loading data file cifar-10-batches-py/data_batch_2
+ Loading data file cifar-10-batches-py/data_batch_3
+ Loading data file cifar-10-batches-py/data_batch_4
+ Loading data file cifar-10-batches-py/data_batch_5
+ Loading data file cifar-10-batches-py/test_batch
+ Epoch 0
+ training loss = 1.881866, training accuracy = 0.306360 accuracy = 0.420000
+ test loss = 1.602577, test accuracy = 0.412200
+ Epoch 1
+ training loss = 1.536011, training accuracy = 0.441940 accuracy = 0.500000
+ test loss = 1.378170, test accuracy = 0.507600
+ Epoch 2
+ training loss = 1.333137, training accuracy = 0.519960 accuracy = 0.520000
+ test loss = 1.272205, test accuracy = 0.540600
+ Epoch 3
+ training loss = 1.185212, training accuracy = 0.574120 accuracy = 0.540000
+ test loss = 1.211573, test accuracy = 0.567600
+
+This script will call `alexnet.py` file to build the alexnet model. After the training is finished, SINGA will save the model parameters into a checkpoint file `model.bin` in the same directory. Then we can use this `model.bin` file for prediction.
+
+ $ python predict.py
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@@ -0,0 +1,23 @@
+.. 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.
+
+
+Caffe Converter
+================
+
+.. automodule:: singa.converter
+ :members:
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@@ -0,0 +1,25 @@
+.. 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.
+
+
+Data
+========
+
+
+
+.. automodule:: singa.data
+ :members:
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+# Dependent library installation
+
+## Windows
+
+This section is used to compile and install the dependent libraries under
+windows system from source codes. The following instructions ONLY work for Visual Studio 2015 as
+previous VS does not support [C++11 features](https://msdn.microsoft.com/en-us/library/hh567368.aspx) well (including generic lambdas, auto, non-static
+data member initializers). If you intend to generate a 32-bit/64-bit singa solution, please configure all the
+VS projects for the dependent libraries as 32-bit/64-bit. This can be done by
+"Configuration Manager" in VS 2015 or use corresponding generator in cmake. When compiling the following libraries, you
+may get system-specific warnings/errors. Please fix them according to the
+prompts by VS.
+
+### Google Logging
+The glog library is an optional library for singa project. But it is currently necessary for Window compilation.
+Since the latest release version of glog will encounter error C2084 on sprintf function
+under VS2015, we test the compilation and installation using the master branch from [github](https://github.com/google/glog).
+
+Step 1: Download and decompress the source code. Or use `git clone
+https://github.com/google/glog` to get the code.
+
+Step 2: Open "glog.sln" file under project folder. You will get a conversion
+dialog and please finish it by the prompts. Compile all the projects in the solution after
+proper configuration, especially "libglog" and "libglog_static" projects.
+
+Step 3: Copy all the header files and the entire directory named "glog" under
+"src\windows\" folder into the installation include folder (or system folder).
+Copy all the generated library files into the installation library folder (or
+system folder).
+
+Step 4: Done.
+
+
+### Google protobuf
+
+Tested on version 2.6.1:
+
+Step 1: Download and decompress the source code.
+
+Step 2: Open "protobuf.sln" file under "vsprojects" folder. You will get a conversion
+dialog and please finish it by the prompts. Compile all the projects in the solution after proper
+configuration. Especially "libprotobuf", "libprotobuf-lite", "libprotoc" and
+"protoc" projects.
+
+Step 3: Run "extract_includes.bat" script under "vsprojects" folder, you will
+get a new "include" folder with all the headers.
+
+Step 4: Copy the library files, such as "libprotobuf.lib",
+"libprotobuf-lite.lib", "libprotoc.lib", etc., into your installation library folder (or
+system folder). Copy the binary file "protoc" into your installation binary
+folder (or system folder). Copy all the headers and folders in "include" folder into your
+installation include folder (or system folder).
+
+Step 5: Done.
+
+### CBLAS
+
+There are ready-to-use binary packages online
+([link](https://sourceforge.net/projects/openblas/files/)). However, we still install
+OpenBLAS with version 0.2.18 as test:
+
+Step 1: Download and decompress the source code.
+
+Step 2: Start a cmd window under the OpenBLAS folder then run the following
+commands to generate the solution:
+
+ $ md build $$ cd build
+ $ cmake -G "Visual Studio 14" ..
+
+Or run `cmake -G "Visual Studio 14 Win64"` as you wish.
+
+Step 3: Install Perl into your system and put perl.exe on your path. Open "OpenBlas.sln" and build the solution, especially "libopenblas"
+project.
+
+Step 4: Copy the library files under "build\lib" folder and all header files
+under OpenBLAS folder into installation library and include folders (or system
+folders).
+
+Step 5: Done.
+
+
+## FAQ
+
+1. Error C2375 'snprintf': redefinition; different linkage
+
+ Add “HAVE_SNPRINTF” to “C/C++ - Preprocessor - Preprocessor definitions”
+
+2. Error due to hash map
+
+ Add "_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS" to Preprocessor Definitions.
+
+
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+.. 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.
+
+
+Device
+=======
+
+
+The Device abstract represents any hardware device with memory and compuation units.
+All [Tensor operations](tensor.html) are scheduled by the resident device for execution.
+Tensor memory is also managed by the device's memory manager. Therefore, optimization
+of memory and execution are implemented in the Device class.
+
+Specific devices
+----------------
+Currently, SINGA has three Device implmentations,
+
+1. CudaGPU for an Nvidia GPU card which runs Cuda code
+2. CppCPU for a CPU which runs Cpp code
+3. OpenclGPU for a GPU card which runs OpenCL code
+
+
+Python API
+----------
+
+.. automodule:: singa.device
+ :members: create_cuda_gpus, create_cuda_gpus_on, get_default_device
+
+
+The following code provides examples of creating devices::
+
+ from singa import device
+ cuda = device.create_cuda_gpu_on(0) # use GPU card of ID 0
+ host = device.get_default_device() # get the default host device (a CppCPU)
+ ary1 = device.create_cuda_gpus(2) # create 2 devices, starting from ID 0
+ ary2 = device.create_cuda_gpus([0,2]) # create 2 devices on ID 0 and 2
+
+
+CPP API
+---------
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+# Docker Images
+
+
+## Available tags
+
+* `devel`, with SINGA and the development packages installed on Ubuntu16.04 (no GPU)
+* `devel-cuda`, with SINGA, CUDA8.0, CUDNN5, and other development packages installed on Ubuntu16.04
+
+## Use the existing Docker images
+
+Users can pull the Docker images from Dockerhub via
+
+ docker pull apache/singa:devel
+ # or
+ docker pull apache/singa:devel-cuda
+
+Run the docker container using
+
+ docker run -it apache/singa:devel /bin/bash
+ # or
+ docker run -it apache/singa:devel-cuda /bin/bash
+
+The latest SINGA code is under the `incubator-singa` folder.
+
+## Create new Docker images from Dockerfile
+
+New Docker images could be created by executing the following command within the
+Dockerfile folder, e.g., tool/docker/devel/
+
+ docker build -t singa:<TAG> -f Dockerfile
+
+The `<TAG>` is named as
+
+ devel|runtime[-OS][-CUDA|OPENCL][-CUDNN]
+
+* devel: development images with all dependent libs' header files installed and SINGA's source code; runtime: the minimal images which can run SINGA programs.
+* OS: ubuntu, ubuntu14.04, centos, centos6
+* CUDA: cuda, cuda8.0, cuda7.0
+* CUDNN: cudnn, cudnn5, cudnn4
+* OPENCL: opencl, opencl1.2
+
+By default, if the version is not included in the tag, the latest stable version is used.
+The default OS is Ubuntu. The version is the latest stable version (e.g., 16.04 for now).
+For -cuda version, the **cudnn** is included by default. Their versions are also the latest stable version, i.e., cuda8.0 and cudnn5 for now.
+
+Here are some example tags,
+
+`devel`, `devel-cuda`, `runtime`, `runtime-cuda`, `devel-centos7-cuda`, `devel-ubuntu14.04`, `devel-ubuntu14.04-cuda7.5-cudnn4`
+
+Please follow the existing Dockefiles under tool/docker/ to create other Dockefiles.
+The folder structure is like
+
+ level1: devel|runtime
+ level2: Dockerfile, OS
+ level3: Dockerfile, CUDA|OPENCL
+ level4: CUDNN
+
+For example, the path of the Dockerfile for `devel-cuda` is `tool/docker/devel/cuda/Dockerfile`.
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+# Use parameters pre-trained from Caffe in SINGA
+
+In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification.
+
+## Run this example
+You can run this example by simply executing `run.sh vgg16` or `run.sh vgg19`
+The script does the following work.
+
+### Obtain the Caffe model
+* Download caffe model prototxt and parameter binary file.
+* Currently we only support the latest caffe format, if your model is in
+ previous version of caffe, please update it to current format.(This is
+ supported by caffe)
+* After updating, we can obtain two files, i.e., the prototxt and parameter
+ binary file.
+
+### Prepare test images
+A few sample images are downloaded into the `test` folder.
+
+### Predict
+The `predict.py` script creates the VGG model and read the parameters,
+
+ usage: predict.py [-h] model_txt model_bin imgclass
+
+where `imgclass` refers to the synsets of imagenet dataset for vgg models.
+You can start the prediction program by executing the following command:
+
+ python predict.py vgg16.prototxt vgg16.caffemodel synset_words.txt
+
+Then you type in the image path, and the program would output the top-5 labels.
+
+More Caffe models would be tested soon.
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+# Train Char-RNN over plain text
+
+Recurrent neural networks (RNN) are widely used for modelling sequential data,
+e.g., natural language sentences. This example describes how to implement a RNN
+application (or model) using SINGA's RNN layers.
+We will use the [char-rnn](https://github.com/karpathy/char-rnn) model as an
+example, which trains over sentences or
+source code, with each character as an input unit. Particularly, we will train
+a RNN using GRU over Linux kernel source code. After training, we expect to
+generate meaningful code from the model.
+
+
+## Instructions
+
+* Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version >= 5.05.
+
+* Prepare the dataset. Download the [kernel source code](http://cs.stanford.edu/people/karpathy/char-rnn/).
+Other plain text files can also be used.
+
+* Start the training,
+
+ python train.py linux_input.txt
+
+ Some hyper-parameters could be set through command line,
+
+ python train.py -h
+
+* Sample characters from the model by providing the number of characters to sample and the seed string.
+
+ python sample.py 'model.bin' 100 --seed '#include <std'
+
+ Please replace 'model.bin' with the path to one of the checkpoint paths.
+
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+# Train CNN over Cifar-10
+
+
+Convolution neural network (CNN) is a type of feed-forward artificial neural
+network widely used for image and video classification. In this example, we
+will train three deep CNN models to do image classification for the CIFAR-10 dataset,
+
+1. [AlexNet](https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg)
+the best validation accuracy (without data augmentation) we achieved was about 82%.
+
+2. [VGGNet](http://torch.ch/blog/2015/07/30/cifar.html), the best validation accuracy (without data augmentation) we achieved was about 89%.
+3. [ResNet](https://github.com/facebook/fb.resnet.torch), the best validation accuracy (without data augmentation) we achieved was about 83%.
+4. [Alexnet from Caffe](https://github.com/BVLC/caffe/tree/master/examples/cifar10), SINGA is able to convert model from Caffe seamlessly.
+
+
+## Instructions
+
+
+### SINGA installation
+
+Users can compile and install SINGA from source or install the Python version.
+The code can ran on both CPU and GPU. For GPU training, CUDA and CUDNN (V4 or V5)
+are required. Please refer to the installation page for detailed instructions.
+
+### Data preparation
+
+The binary Cifar-10 dataset could be downloaded by
+
+ python download_data.py bin
+
+The Python version could be downloaded by
+
+ python download_data.py py
+
+### Training
+
+There are four training programs
+
+1. train.py. The following command would train the VGG model using the python
+version of the Cifar-10 dataset in 'cifar-10-batches-py' folder.
+
+ python train.py vgg cifar-10-batches-py
+
+ To train other models, please replace 'vgg' to 'alexnet', 'resnet' or 'caffe',
+ where 'caffe' refers to the alexnet model converted from Caffe. By default
+ the training would run on a CudaGPU device, to run it on CppCPU, add an additional
+ argument
+
+ python train.py vgg cifar-10-batches-py --use_cpu
+
+2. alexnet.cc. It trains the AlexNet model using the CPP APIs on a CudaGPU,
+
+ ./run.sh
+
+3. alexnet-parallel.cc. It trains the AlexNet model using the CPP APIs on two CudaGPU devices.
+The two devices run synchronously to compute the gradients of the mode parameters, which are
+averaged on the host CPU device and then be applied to update the parameters.
+
+ ./run-parallel.sh
+
+4. vgg-parallel.cc. It trains the VGG model using the CPP APIs on two CudaGPU devices similar to alexnet-parallel.cc.
+
+### Prediction
+
+predict.py includes the prediction function
+
+ def predict(net, images, dev, topk=5)
+
+The net is created by loading the previously trained model; Images consist of
+a numpy array of images (one row per image); dev is the training device, e.g.,
+a CudaGPU device or the host CppCPU device; It returns the topk labels for each instance.
+
+The predict.py file's main function provides an example of using the pre-trained alexnet model to do prediction for new images.
+The 'model.bin' file generated by the training program should be placed at the cifar10 folder to run
+
+ python predict.py
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+# Train AlexNet over ImageNet
+
+Convolution neural network (CNN) is a type of feed-forward neural
+network widely used for image and video classification. In this example, we will
+use a [deep CNN model](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)
+to do image classification against the ImageNet dataset.
+
+## Instructions
+
+### Compile SINGA
+
+Please compile SINGA with CUDA, CUDNN and OpenCV. You can manually turn on the
+options in CMakeLists.txt or run `ccmake ..` in build/ folder.
+
+We have tested CUDNN V4 and V5 (V5 requires CUDA 7.5)
+
+### Data download
+* Please refer to step1-3 on [Instructions to create ImageNet 2012 data](https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data)
+ to download and decompress the data.
+* You can download the training and validation list by
+ [get_ilsvrc_aux.sh](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh)
+ or from [Imagenet](http://www.image-net.org/download-images).
+
+### Data preprocessing
+* Assuming you have downloaded the data and the list.
+ Now we should transform the data into binary files. You can run:
+
+ sh create_data.sh
+
+ The script will generate a test file(`test.bin`), a mean file(`mean.bin`) and
+ several training files(`trainX.bin`) in the specified output folder.
+* You can also change the parameters in `create_data.sh`.
+ + `-trainlist <file>`: the file of training list;
+ + `-trainfolder <folder>`: the folder of training images;
+ + `-testlist <file>`: the file of test list;
+ + `-testfolder <floder>`: the folder of test images;
+ + `-outdata <folder>`: the folder to save output files, including mean, training and test files.
+ The script will generate these files in the specified folder;
+ + `-filesize <int>`: number of training images that stores in each binary file.
+
+### Training
+* After preparing data, you can run the following command to train the Alexnet model.
+
+ sh run.sh
+
+* You may change the parameters in `run.sh`.
+ + `-epoch <int>`: number of epoch to be trained, default is 90;
+ + `-lr <float>`: base learning rate, the learning rate will decrease each 20 epochs,
+ more specifically, `lr = lr * exp(0.1 * (epoch / 20))`;
+ + `-batchsize <int>`: batchsize, it should be changed regarding to your memory;
+ + `-filesize <int>`: number of training images that stores in each binary file, it is the
+ same as the `filesize` in data preprocessing;
+ + `-ntrain <int>`: number of training images;
+ + `-ntest <int>`: number of test images;
+ + `-data <folder>`: the folder which stores the binary files, it is exactly the output
+ folder in data preprocessing step;
+ + `-pfreq <int>`: the frequency(in batch) of printing current model status(loss and accuracy);
+ + `-nthreads <int>`: the number of threads to load data which feed to the model.
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@@ -0,0 +1,28 @@
+..
+.. 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.
+..
+
+Examples
+========
+
+.. toctree::
+
+ cifar10/README
+ char-rnn/README
+ imagenet/README
+
+
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@@ -0,0 +1,18 @@
+# Train a RBM model against MNIST dataset
+
+This example is to train an RBM model using the
+MNIST dataset. The RBM model and its hyper-parameters are set following
+[Hinton's paper](http://www.cs.toronto.edu/~hinton/science.pdf)
+
+## Running instructions
+
+1. Download the pre-processed [MNIST dataset](https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz)
+
+2. Start the training
+
+ python train.py mnist.pkl.gz
+
+By default the training code would run on CPU. To run it on a GPU card, please start
+the program with an additional argument
+
+ python train.py mnist.pkl.gz --use_gpu
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@@ -0,0 +1,23 @@
+.. 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.
+
+
+Image Tool
+==========
+
+.. automodule:: singa.image_tool
+ :members:
Added: incubator/singa/site/trunk/v1.1.0/_sources/docs/index.txt
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/_sources/docs/index.txt?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/_sources/docs/index.txt (added)
+++ incubator/singa/site/trunk/v1.1.0/_sources/docs/index.txt Mon Feb 13 05:13:19 2017
@@ -0,0 +1,39 @@
+.. 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.
+
+
+Documentation
+=============
+
+.. toctree::
+
+ installation
+ software_stack
+ device
+ tensor
+ layer
+ net
+ initializer
+ loss
+ metric
+ optimizer
+ data
+ image_tool
+ snapshot
+ converter
+ utils
+ model_zoo/index
Added: incubator/singa/site/trunk/v1.1.0/_sources/docs/initializer.txt
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/_sources/docs/initializer.txt?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/_sources/docs/initializer.txt (added)
+++ incubator/singa/site/trunk/v1.1.0/_sources/docs/initializer.txt Mon Feb 13 05:13:19 2017
@@ -0,0 +1,30 @@
+.. 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.
+
+
+Initializer
+===========
+
+Python API
+----------
+
+.. automodule:: singa.initializer
+ :members: uniform, gaussian
+ :member-order: bysource
+
+CPP API
+--------