Author: wangwei
Date: Wed Apr 24 14:57:35 2019
New Revision: 1858059

URL: http://svn.apache.org/viewvc?rev=1858059&view=rev
Log:
update the docs by jenkins for commit 6eb7942

Added:
    incubator/singa/site/trunk/en/.buildinfo
    incubator/singa/site/trunk/en/_sources/
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+# 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: 1499d409d023c2775f8afadba5faaacb
+tags: 645f666f9bcd5a90fca523b33c5a78b7

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@@ -0,0 +1,27 @@
+<!--
+    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.
+-->
+## 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

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24 14:57:35 2019
@@ -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", "[email protected]", "`Subscribe 
<mailto:[email protected]>`_", "`Unsubscribe 
<mailto:[email protected].>`_", 
"`mail-archives.apache.org 
<http://mail-archives.apache.org/mod_mbox/singa-dev/>`_"
+        "Commits", "[email protected]", "`Subscribe 
<mailto:[email protected]>`_", "`Unsubscribe 
<mailto:[email protected]>`_", 
"`mail-archives.apache.org  
<http://mail-archives.apache.org/mod_mbox/singa-commits/>`_"

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(added)
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Wed Apr 24 14:57:35 2019
@@ -0,0 +1,64 @@
+.. 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.
+
+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 .
+
+Web Access
+----------
+
+The following is a link to the online source repository.
+
+* https://gitbox.apache.org/repos/asf?p=incubator-singa.git
+
+Contributors
+------------
+
+Contributors are encouraged to rebase their commits onto the latest master 
before sending the pull requests to make the git history clean. The following 
git instructors should be executed after committing the current work:
+
+.. code-block:: bash
+
+    git checkout master
+    git pull <apache/incubator-singa upstream> master:master
+    git checkout <new feature branch>
+    git rebase master
+
+Committers
+----------
+
+* To connect your Apache account with your Github account, Please follow the 
instructions on: https://gitbox.apache.org/setup/. After that you can directly 
merge PRs using GitHub’s UI.
+
+To merge pull request https://github.com/apache/incubator-singa/pull/xxx, the 
following instructions should be executed,
+
+.. code-block:: bash
+
+    git clone https://github.com/apache/incubator-singa.git
+    git remote add asf https://gitbox.apache.org/repos/asf/incubator-singa.git
+    # optional
+    git pull asf master:master
+    git fetch origin pull/xxx/head:prxxx
+    git merge prxxx
+    git push asf master:master
+
+* To migrate from git-wip-us.apache.org to Gitbox: If you already cloned the 
SINGA repository from the old repo 
https://git-wip-us.apache.org/repos/asf/incubator-singa.git, you can update the 
master by:
+
+.. code-block:: bash
+
+    git remote set-url origin [email protected]/apache/singa.git
+

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@@ -0,0 +1,84 @@
+.. 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           [email protected]
+Ted Dunning          [email protected]
+Alan Gates           [email protected]
+Thejas Nair          [email protected]
+==================   ============
+
+
+Developers
+----------
+
+The SINGA community has developers mainly from National University of 
Singapore, Zhejiang University, NetEase, Osaka University, yzBigData, etc.
+
++--------------------+--------------------------------+-----------------------------------------------+
+| Name               | Email                          | Organization           
                       |
++====================+================================+===============================================+
+| Gang Chen          | [email protected]                  | Zhejiang University  
                         |
++--------------------+--------------------------------+-----------------------------------------------+
+| Haibo Chen         | [email protected]   | NetEase                
                       |
++--------------------+--------------------------------+-----------------------------------------------+
+| Anh Dinh           | [email protected]             | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Jinyang Gao        | [email protected]             | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Xin Ji             | [email protected]          | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Chonho Lee         | [email protected]               | Osaka University       
                       |
++--------------------+--------------------------------+-----------------------------------------------+
+| Zhaojing Luo       | [email protected]            | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Beng Chin Ooi      | [email protected]          | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Kian-Lee Tan       | [email protected]               | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Anthony K. H. Tung | [email protected]          | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Ji Wang            | [email protected]         | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Sheng Wang         | [email protected]              | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Wei Wang           | [email protected]             | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Yuan Wang          | [email protected]      | NetEase                
                       |
++--------------------+--------------------------------+-----------------------------------------------+
+| Wenfeng Wu         | [email protected]           | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Zhongle Xie        | [email protected]             | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Meihui Zhang       | [email protected]       | Singapore University 
of Technology and Design |
++--------------------+--------------------------------+-----------------------------------------------+
+| Kaiping Zheng      | [email protected]             | National University of 
Singapore              |
++--------------------+--------------------------------+-----------------------------------------------+
+| Moaz Reyad         | [email protected]                | University of Genoa    
                       |
++--------------------+--------------------------------+-----------------------------------------------+
+| Chang Yao          | [email protected]         | yzBigdata              
                       |
++--------------------+--------------------------------+-----------------------------------------------+

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@@ -0,0 +1,77 @@
+<!--
+    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.
+-->
+## 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).

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@@ -0,0 +1,54 @@
+<!--
+    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.
+-->
+# 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)

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@@ -0,0 +1,36 @@
+<!--
+    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.
+-->
+# 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.
+
+
+# How to become a SINGA committer
+
+SINGA committers are members that have write access to SINGA code and 
documentation repositories. They can contribute by themselves and accept other 
contributions.
+
+Becoming a SINGA committer requires understanding the Apache Way and being an 
active member in the SINGA project. An active member participates on mailing 
lists, answers questions, contributes code, tests and documentation, presents 
SINGA in talks and seminars, verifies releases and report issues or feature 
requests.
+
+New committers can be suggested by current committers or PPMC members, and 
voted upon by the PPMC according to Apache guidelines. You can start by talking 
to other committers and ask for their advice and guidance.

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Apr 24 14:57:35 2019
@@ -0,0 +1,228 @@
+.. 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.
+
+How to prepare a release
+========================
+
+This is a guide for the release preparing process in SINGA.
+
+Select a release manager
+------------------------
+
+The release manager (RM) is the coordinator for the release process. It is the 
RM's signature (.asc) that is uploaded together with the release. The RM 
generates KEY (RSA 4096-bit) and uploads it to a public key server. The RM 
needs to get his key endorsed (signed) by other Apache user, to be connected to 
the web of trust. He should first ask the mentor to help signing his key. 
http://www.apache.org/dev/release-signing.html
+ 
+Check:
+        + The codebase does not include third-party code which is not 
compatible to APL
+        + The dependencies are compatible with APL. GNU-like licenses are NOT 
compatible
+        + All source files written by us MUST include the Apache license 
header: http://www.apache.org/legal/src-headers.html. There's a script in there 
which helps propagating the header to all files.
+        + The build process is error-free. 
+        + Unit tests are included (as much as possible)
+        + The Jupyter notebooks are working with the new release
+        + The online documentation on the Apache website is up to date. 
+        
+Prepare LICENSE file
+--------------------
+
+copy and paste this http://apache.org/licenses/LICENSE-2.0.txt
+
+Prepare NOTICE file
+-------------------
+
+        + Use this template: http://apache.org/legal/src-headers.html#notice
+        + If we include any third party code  in the release package which is 
not APL, must state it at the end of the NOTICE file.
+        + Example: http://apache.org/licenses/example-NOTICE.txt
+
+Prepare RELEASE_NOTES file
+--------------------------
+
+        + Introduction, Features, Bugs (link to JIRA), Changes (N/A for first 
erlease), Dependency list, Incompatibility issues.
+        + Follow this example:  
http://commons.apache.org/proper/commons-digester/commons-digester-3.0/RELEASE-NOTES.txt
+
+Prepare README file
+-------------------
+        + How to build, run test, run examples
+        + List of dependencies.
+        + Mail list, website, etc. Any information useful for user to start.
+
+Prepare DISCLAIMER file
+-----------------------
+        + modify from 
http://incubator.apache.org/guides/branding.html#disclaimers
+
+Package the release
+-------------------
+
+The release should be packaged into : apache-singa-incubating-xx.xx.xx.tar.gz
+         + src/
+         + README
+         + LICENSE
+         + NOTICE
+         + RELEASE_NOTES
+         + DISCLAIMER
+         + ...
+
+Upload the release
+-------------------
+
+The release is uploaded to the RM’s Apache page: people.apache.org/~ID/...
+        + apache-singa-incubating-xx.xx.xx.tar.gz
+        + KEY
+        + XX.acs
+        + XX.md5
+
+Roll out artifacts to mirrors 
+-----------------------------
+
+svn add to “dist/release/incubator/singa”
+
+Delete old artifacts (automatically archived)
+
+Update the Download page
+------------------------
+
+The tar.gz file MUST be downloaded from mirror, using closer.cgi script
+other artifacts MUST be downloaded from main Apache site
+Good idea to update EC2 image and make it available for download as well
+
+Make the internal announcements
+-------------------------------
+
+Template for singa-dev@ voting:
+
+To: [email protected]
+Subject: [VOTE] Release apache-singa-X.Y.Z-incubating (release candidate N)
+
+Hi all,
+
+I have created a build for Apache SINGA X.Y.Z-incubating, release candidate N.
+
+The artifacts to be voted on are located here:
+https://dist.apache.org/repos/dist/dev/incubator/singa/apache-singa-X.Y.Z-incubating-rcN/
+
+The hashes of the artifacts are as follows:
+apache-singa-X.Y.Z-incubating.tar.gz.md5 XXXX
+apache-singa-X.Y.Z-incubating.tar.gz.sha256 XXXX
+
+Release artifacts are signed with the following key:
+https://people.apache.org/keys/committer/{Apache ID of the Release Manager}.asc
+
+and the signature file is:
+apache-singa-X.Y.Z-incubating.tar.gz.asc
+
+Please vote on releasing this package. The vote is open for at least 72 hours 
and passes if a majority of at least three +1 votes are cast.
+
+[ ] +1 Release this package as Apache SINGA X.Y.Z-incubating
+[ ]  0 I don't feel strongly about it, but I'm okay with the release
+[ ] -1 Do not release this package because...
+
+Here is my vote:
+
++1 
+
+{SINGA Team Member Name} 
+
+Wait at least 48 hours for test responses
+
+Any PPMC, committer or contributor can test features for releasing, and 
feedback. Based on that, PPMC will decide whether start a vote.
+
+Call a vote in dev
+------------------
+
+Call a vote in [email protected]
+
+
+
+Vote Check
+----------
+
+All PPMC members and committers should check these before vote +1 :
+
+
+Call for a vote in Apache IPMC
+------------------------------
+
+Call a vote in [email protected]
+
+Vote result mail
+----------------
+
+Template for singa-dev@ voting (results):
+
+
+Subject: [RESULT] [VOTE] Release apache-singa-X.Y.Z-incubating (release 
candidate N)
+To: [email protected]
+
+Thanks to everyone who has voted and given their comments. The tally is as 
follows.
+
+N binding +1s:
+<names>
+
+N non-binding +1s:
+<names>
+
+No 0s or -1s.
+
+I am delighted to announce that the proposal to release
+Apache SINGA X.Y.Z-incubating has passed.
+
+I'll now start a vote on the general list. Those of you in the IPMC, please 
recast your vote on the new thread.
+
+{SINGA Team Member Name} 
+
+Template for general@ voting - results
+
+
+To: [email protected]
+Subject: [RESULT] [VOTE] Release Apache SINGA X.Y.Z (incubating)
+
+Thanks everyone for their votes and comments.
+
+I'm delighted to announce that this vote passes with the following results:
+
+N binding +1s:
+<names>
+
+N non-binding +1s:
+<names>
+
+No 0s or -1s.
+
+We’ll now roll the release out to the mirrors.
+
+{SINGA Team Member Name} 
+
+
+Publish release
+---------------
+
+Template for ANNOUNCING the release
+
+To: [email protected], [email protected]
+Subject: [ANNOUNCE] Apache SINGA (incubating) X.Y.Z released
+
+We are pleased to announce that SINGA (incubating) X.Y.Z is released. 
+
+SINGA is a general distributed deep learning platform for training big deep 
learning models over large datasets. It is designed with an intuitive 
programming model based on the layer abstraction. SINGA supports a wide variety 
of popular deep learning models.
+
+The release is available at:
+http://singa.incubator.apache.org/downloads.html
+
+The main features of this release include XXX
+
+We look forward to hearing your feedbacks, suggestions, and contributions to 
the project. 
+
+On behalf of the SINGA team, 
+{SINGA Team Member Name} 
\ No newline at end of file

Added: incubator/singa/site/trunk/en/_sources/develop/schedule.rst.txt
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--- incubator/singa/site/trunk/en/_sources/develop/schedule.rst.txt (added)
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14:57:35 2019
@@ -0,0 +1,65 @@
+.. 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 June 2018     ","AutoGrad                     ","AutoGrad for BP"
+  "                   ","Python 3                     ","Support Python 3 for 
PySinga"
+  "                   ","Models                       ","Add popular models, 
including VGG, ResNet, DenseNet, InceptionNet"

Added: incubator/singa/site/trunk/en/_sources/docs.rst.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

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--- incubator/singa/site/trunk/en/_sources/docs/autograd.md.txt (added)
+++ incubator/singa/site/trunk/en/_sources/docs/autograd.md.txt Wed Apr 24 
14:57:35 2019
@@ -0,0 +1,166 @@
+<!--
+    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.
+-->
+
+
+# Autograd in Singa
+
+There are two typical ways to implement autograd, via symbolic differentiation 
like [Theano](http://deeplearning.net/software/theano/index.html) or reverse 
differentiation like 
[Pytorch](https://pytorch.org/docs/stable/notes/autograd.html). Singa follows 
Pytorch way, which records the computation graph and apply the backward 
propagation automatically after forward propagation. The autograd algorithm is 
explained in details 
[here](https://pytorch.org/docs/stable/notes/autograd.html). We explain the 
relevant modules in Singa and give an example to illustrate the usage. 
+
+## Relevant Modules
+
+There are three classes involved in autograd, namely  `singa.tensor.Tensor` , 
`singa.autograd.Operation`, and `singa.autograd.Layer`. In the rest of this 
article, we use tensor, operation and layer to refer to an instance of the 
respective class.
+
+### Tensor
+
+Three attributes of Tensor are used by autograd, 
+-  `.creator` is an `Operation` instance. It records the operation that 
generates the Tensor instance.
+-  `.requires_grad` is a boolean variable. It is used to indicate that the 
autograd algorithm needs to compute the gradient of the tensor (i.e., the 
owner). For example, during backpropagation, the gradients of the tensors for 
the weight matrix of a linear layer and the feature maps of a convolution layer 
(not the bottom layer) should be computed.
+-  `.stores_grad` is a boolean variable. It is used to indicate that the 
gradient of the owner tensor should be stored and output by the backward 
function. For example, the gradient of the feature maps is computed during 
backpropagation, but is not included in the output of the backward function. 
+
+Programmers can change `requires_grad` and `stores_grad` of a Tensor instance. 
For example, if later is set to True, the corresponding gradient is included in 
the output of the backward function. It should be noted that if `stores_grad` 
is True, then `requires_grad` must be true, not vice versa.
+
+
+### Operation
+
+It takes one or more `Tensor` instances as input, and then outputs one or more 
`Tensor` instances. For example, ReLU can be implemented as a specific 
Operation subclass. When an `Operation` instance is called (after 
instantiation), the following two steps are executed:
+
+1. record the source operations, i.e., the `creator`s of the input tensors.    
2. do calculation by calling member function `.forward()`
+
+There are two member functions for forwarding and backwarding, i.e., 
`.forward()` and `.backward()`. They take `Tensor.data` as inputs (the type is 
`CTensor`), and output `Ctensor`s. To add a specific operation, subclass 
`operation` should implement their own `.forward()` and `.backward()`. The 
`backward()` function is called by the `backward()` function of autograd 
automatically during backward propogation to compute the gradients of inputs 
(according to the `require_grad` field). 
+
+### Layer
+
+For those operations that require parameters, we package them into a new 
class, `Layer`. For example, convolution operation is wrapped into a 
convolution layer. `Layer` manages (stores) the parameters and calls the 
corresponding `Operation`s to implement the transformation.
+
+
+
+## Examples
+
+Multiple examples are provided in the [example 
folder](https://github.com/apache/incubator-singa/tree/master/examples/autograd).
 We explain two representative examples here.
+
+### Operation only
+
+The following codes implement a MLP model using only Operation instances (no 
Layer instances).
+
+#### Import packages
+
+```
+from singa.tensor import Tensor
+from singa import autograd
+from singa import opt
+```
+
+#### Create weight matrix and bias vector
+
+The parameter tensors are created with both `requires_grad` and `stores_grad` 
set to True.
+
+```
+w0 = Tensor(shape=(2, 3), requires_grad=True, stores_grad=True)
+w0.gaussian(0.0, 0.1)
+b0 = Tensor(shape=(1, 3), requires_grad=True, stores_grad=True)
+b0.set_value(0.0)
+
+w1 = Tensor(shape=(3, 2), requires_grad=True, stores_grad=True)
+w1.gaussian(0.0, 0.1)
+b1 = Tensor(shape=(1, 2), requires_grad=True, stores_grad=True)
+b1.set_value(0.0)
+```
+
+#### Training
+```
+inputs = Tensor(data=data)  # data matrix
+target = Tensor(data=label) # label vector
+autograd.training = True    # for training
+sgd = opt.SGD(0.05)   # optimizer
+
+for i in range(10):
+    x = autograd.matmul(inputs, w0) # matrix multiplication
+    x = autograd.add_bias(x, b0)    # add the bias vector
+    x = autograd.relu(x)            # ReLU activation operation
+
+    x = autograd.matmul(x, w1)
+    x = autograd.add_bias(x, b1)
+    
+    loss = autograd.softmax_cross_entropy(x, target)
+    
+    for p, g in autograd.backward(loss):        
+        sgd.update(p, g)
+```
+
+
+### Operation + Layer
+
+The following 
[example](https://github.com/apache/incubator-singa/blob/master/examples/autograd/mnist_cnn.py)
 implements a CNN model using layers provided by the autograd module.
+
+#### Create the layers
+
+```
+conv1 = autograd.Conv2d(1, 32, 3, padding=1, bias=False)
+bn1 = autograd.BatchNorm2d(32)
+pooling1 = autograd.MaxPool2d(3, 1, padding=1)
+conv21 = autograd.Conv2d(32, 16, 3, padding=1)
+conv22 = autograd.Conv2d(32, 16, 3, padding=1)
+bn2 = autograd.BatchNorm2d(32)
+linear = autograd.Linear(32 * 28 * 28, 10)    
+pooling2 = autograd.AvgPool2d(3, 1, padding=1)
+```
+
+#### Define the forward function
+
+The operations in the forward pass will be recorded automatically for backward 
propagation.
+
+```
+def forward(x, t):
+    # x is the input data (a batch of images)
+    # t the the label vector (a batch of integers)
+    y = conv1(x)           # Conv layer  
+    y = autograd.relu(y)   # ReLU operation
+    y = bn1(y)             # BN layer
+    y = pooling1(y)        # Pooling Layer
+    
+    # two parallel convolution layers
+    y1 = conv21(y)
+    y2 = conv22(y)
+    y = autograd.cat((y1, y2), 1)  # cat operation
+    y = autograd.relu(y)           # ReLU operation
+    y = bn2(y)
+    y = pooling2(y)
+
+    y = autograd.flatten(y)        # flatten operation
+    y = linear(y)                  # Linear layer
+    loss = autograd.softmax_cross_entropy(y, t)  # operation 
+    return loss, y
+```
+
+#### Training
+
+```
+autograd.training = True
+for epoch in range(epochs):
+    for i in range(batch_number):
+        inputs = tensor.Tensor(device=dev, data=x_train[
+                               i * batch_sz:(1 + i) * batch_sz], 
stores_grad=False)
+        targets = tensor.Tensor(device=dev, data=y_train[
+                                i * batch_sz:(1 + i) * batch_sz], 
requires_grad=False, stores_grad=False)
+
+        loss, y = forward(inputs, targets) # forward the net
+    
+        for p, gp in autograd.backward(loss):  # auto backward
+            sgd.update(p, gp)
+```

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@@ -0,0 +1,159 @@
+<!--
+    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.
+-->
+# 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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+.. 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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+.. 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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+<!--
+    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.
+-->
+# 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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+<!--
+    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.
+-->
+# 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[-CUDA|CPU][-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.
+* CUDA: cuda10.0, cuda9.0
+* CUDNN: cudnn7
+
+Here are some example tags:
+
+`devel-cuda9-cudnn7`, `devel-cuda9-cudnn7`, `devel-cuda10-cudnn7`, 
`devel-cpu`, `runtime-gpu` and `runtime-cpu`
+
+
+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|MKLDNN
+
+
+For example, the path of the Dockerfile for `devel-cuda9-cudnn7` is 
`tool/docker/devel/ubuntu/cuda9/Dockerfile`.


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