Author: wangwei
Date: Sat Feb 25 15:30:33 2017
New Revision: 1784386
URL: http://svn.apache.org/viewvc?rev=1784386&view=rev
Log:
update docs for conda install
Added:
incubator/singa/site/trunk/en/_sources/community/issue-tracking.md.txt
incubator/singa/site/trunk/en/_sources/community/mail-lists.rst.txt
incubator/singa/site/trunk/en/_sources/community/source-repository.md.txt
incubator/singa/site/trunk/en/_sources/community/team-list.rst.txt
incubator/singa/site/trunk/en/_sources/develop/contribute-code.md.txt
incubator/singa/site/trunk/en/_sources/develop/contribute-docs.md.txt
incubator/singa/site/trunk/en/_sources/develop/how-contribute.md.txt
incubator/singa/site/trunk/en/_sources/develop/schedule.rst.txt
incubator/singa/site/trunk/en/_sources/docs.rst.txt
incubator/singa/site/trunk/en/_sources/docs/cnn.md.txt
incubator/singa/site/trunk/en/_sources/docs/converter.rst.txt
incubator/singa/site/trunk/en/_sources/docs/data.rst.txt
incubator/singa/site/trunk/en/_sources/docs/dependencies.md.txt
incubator/singa/site/trunk/en/_sources/docs/device.rst.txt
incubator/singa/site/trunk/en/_sources/docs/docker.md.txt
incubator/singa/site/trunk/en/_sources/docs/image_tool.rst.txt
incubator/singa/site/trunk/en/_sources/docs/index.rst.txt
incubator/singa/site/trunk/en/_sources/docs/initializer.rst.txt
incubator/singa/site/trunk/en/_sources/docs/installation.md.txt
incubator/singa/site/trunk/en/_sources/docs/layer.rst.txt
incubator/singa/site/trunk/en/_sources/docs/loss.rst.txt
incubator/singa/site/trunk/en/_sources/docs/metric.rst.txt
incubator/singa/site/trunk/en/_sources/docs/model_zoo/caffe/README.md.txt
incubator/singa/site/trunk/en/_sources/docs/model_zoo/char-rnn/README.md.txt
incubator/singa/site/trunk/en/_sources/docs/model_zoo/cifar10/README.md.txt
incubator/singa/site/trunk/en/_sources/docs/model_zoo/imagenet/alexnet/README.md.txt
incubator/singa/site/trunk/en/_sources/docs/model_zoo/imagenet/googlenet/README.md.txt
incubator/singa/site/trunk/en/_sources/docs/model_zoo/imagenet/resnet/
incubator/singa/site/trunk/en/_sources/docs/model_zoo/imagenet/resnet/README.md.txt
incubator/singa/site/trunk/en/_sources/docs/model_zoo/index.rst.txt
incubator/singa/site/trunk/en/_sources/docs/model_zoo/mnist/README.md.txt
incubator/singa/site/trunk/en/_sources/docs/net.rst.txt
incubator/singa/site/trunk/en/_sources/docs/neural-net.md.txt
incubator/singa/site/trunk/en/_sources/docs/notebook/README.md.txt
incubator/singa/site/trunk/en/_sources/docs/optimizer.rst.txt
incubator/singa/site/trunk/en/_sources/docs/snapshot.rst.txt
incubator/singa/site/trunk/en/_sources/docs/software_stack.md.txt
incubator/singa/site/trunk/en/_sources/docs/tensor.rst.txt
incubator/singa/site/trunk/en/_sources/docs/utils.rst.txt
incubator/singa/site/trunk/en/_sources/downloads.md.txt
incubator/singa/site/trunk/en/_sources/index.rst.txt
incubator/singa/site/trunk/en/_sources/releases/RELEASE_NOTES_0.1.0.md.txt
incubator/singa/site/trunk/en/_sources/releases/RELEASE_NOTES_0.2.0.md.txt
incubator/singa/site/trunk/en/_sources/releases/RELEASE_NOTES_0.3.0.md.txt
incubator/singa/site/trunk/en/_sources/releases/RELEASE_NOTES_1.0.0.md.txt
incubator/singa/site/trunk/en/_sources/releases/RELEASE_NOTES_1.1.0.md.txt
incubator/singa/site/trunk/en/_static/jquery-3.1.0.js
incubator/singa/site/trunk/en/docs/model_zoo/imagenet/resnet/
incubator/singa/site/trunk/en/docs/model_zoo/imagenet/resnet/README.html
incubator/singa/site/trunk/zh/_sources/index.rst.txt
incubator/singa/site/trunk/zh/_static/jquery-3.1.0.js
Modified:
incubator/singa/site/trunk/en/.buildinfo
incubator/singa/site/trunk/en/_static/basic.css
incubator/singa/site/trunk/en/_static/comment-bright.png
incubator/singa/site/trunk/en/_static/comment-close.png
incubator/singa/site/trunk/en/_static/comment.png
incubator/singa/site/trunk/en/_static/doctools.js
incubator/singa/site/trunk/en/_static/down-pressed.png
incubator/singa/site/trunk/en/_static/down.png
incubator/singa/site/trunk/en/_static/file.png
incubator/singa/site/trunk/en/_static/fonts/Lato-Bold.ttf
incubator/singa/site/trunk/en/_static/fonts/Lato-Regular.ttf
incubator/singa/site/trunk/en/_static/fonts/fontawesome-webfont.eot
incubator/singa/site/trunk/en/_static/fonts/fontawesome-webfont.svg
incubator/singa/site/trunk/en/_static/fonts/fontawesome-webfont.ttf
incubator/singa/site/trunk/en/_static/fonts/fontawesome-webfont.woff
incubator/singa/site/trunk/en/_static/jquery.js
incubator/singa/site/trunk/en/_static/js/modernizr.min.js
incubator/singa/site/trunk/en/_static/minus.png
incubator/singa/site/trunk/en/_static/plus.png
incubator/singa/site/trunk/en/_static/pygments.css
incubator/singa/site/trunk/en/_static/searchtools.js
incubator/singa/site/trunk/en/_static/underscore.js
incubator/singa/site/trunk/en/_static/up-pressed.png
incubator/singa/site/trunk/en/_static/up.png
incubator/singa/site/trunk/en/_static/websupport.js
incubator/singa/site/trunk/en/community/issue-tracking.html
incubator/singa/site/trunk/en/community/mail-lists.html
incubator/singa/site/trunk/en/community/source-repository.html
incubator/singa/site/trunk/en/community/team-list.html
incubator/singa/site/trunk/en/develop/contribute-code.html
incubator/singa/site/trunk/en/develop/contribute-docs.html
incubator/singa/site/trunk/en/develop/how-contribute.html
incubator/singa/site/trunk/en/develop/schedule.html
incubator/singa/site/trunk/en/docs.html
incubator/singa/site/trunk/en/docs/cnn.html
incubator/singa/site/trunk/en/docs/converter.html
incubator/singa/site/trunk/en/docs/data.html
incubator/singa/site/trunk/en/docs/dependencies.html
incubator/singa/site/trunk/en/docs/device.html
incubator/singa/site/trunk/en/docs/docker.html
incubator/singa/site/trunk/en/docs/image_tool.html
incubator/singa/site/trunk/en/docs/index.html
incubator/singa/site/trunk/en/docs/initializer.html
incubator/singa/site/trunk/en/docs/installation.html
incubator/singa/site/trunk/en/docs/layer.html
incubator/singa/site/trunk/en/docs/loss.html
incubator/singa/site/trunk/en/docs/metric.html
incubator/singa/site/trunk/en/docs/model_zoo/caffe/README.html
incubator/singa/site/trunk/en/docs/model_zoo/char-rnn/README.html
incubator/singa/site/trunk/en/docs/model_zoo/cifar10/README.html
incubator/singa/site/trunk/en/docs/model_zoo/imagenet/alexnet/README.html
incubator/singa/site/trunk/en/docs/model_zoo/imagenet/googlenet/README.html
incubator/singa/site/trunk/en/docs/model_zoo/index.html
incubator/singa/site/trunk/en/docs/model_zoo/mnist/README.html
incubator/singa/site/trunk/en/docs/net.html
incubator/singa/site/trunk/en/docs/neural-net.html
incubator/singa/site/trunk/en/docs/notebook/README.html
incubator/singa/site/trunk/en/docs/optimizer.html
incubator/singa/site/trunk/en/docs/snapshot.html
incubator/singa/site/trunk/en/docs/software_stack.html
incubator/singa/site/trunk/en/docs/tensor.html
incubator/singa/site/trunk/en/docs/utils.html
incubator/singa/site/trunk/en/downloads.html
incubator/singa/site/trunk/en/genindex.html
incubator/singa/site/trunk/en/index.html
incubator/singa/site/trunk/en/objects.inv
incubator/singa/site/trunk/en/py-modindex.html
incubator/singa/site/trunk/en/releases/RELEASE_NOTES_0.1.0.html
incubator/singa/site/trunk/en/releases/RELEASE_NOTES_0.2.0.html
incubator/singa/site/trunk/en/releases/RELEASE_NOTES_0.3.0.html
incubator/singa/site/trunk/en/releases/RELEASE_NOTES_1.0.0.html
incubator/singa/site/trunk/en/releases/RELEASE_NOTES_1.1.0.html
incubator/singa/site/trunk/en/search.html
incubator/singa/site/trunk/en/searchindex.js
incubator/singa/site/trunk/zh/.buildinfo
incubator/singa/site/trunk/zh/_static/basic.css
incubator/singa/site/trunk/zh/_static/comment-bright.png
incubator/singa/site/trunk/zh/_static/comment-close.png
incubator/singa/site/trunk/zh/_static/comment.png
incubator/singa/site/trunk/zh/_static/doctools.js
incubator/singa/site/trunk/zh/_static/down-pressed.png
incubator/singa/site/trunk/zh/_static/down.png
incubator/singa/site/trunk/zh/_static/file.png
incubator/singa/site/trunk/zh/_static/fonts/Lato-Bold.ttf
incubator/singa/site/trunk/zh/_static/fonts/Lato-Regular.ttf
incubator/singa/site/trunk/zh/_static/fonts/fontawesome-webfont.eot
incubator/singa/site/trunk/zh/_static/fonts/fontawesome-webfont.svg
incubator/singa/site/trunk/zh/_static/fonts/fontawesome-webfont.ttf
incubator/singa/site/trunk/zh/_static/fonts/fontawesome-webfont.woff
incubator/singa/site/trunk/zh/_static/jquery.js
incubator/singa/site/trunk/zh/_static/js/modernizr.min.js
incubator/singa/site/trunk/zh/_static/minus.png
incubator/singa/site/trunk/zh/_static/plus.png
incubator/singa/site/trunk/zh/_static/pygments.css
incubator/singa/site/trunk/zh/_static/searchtools.js
incubator/singa/site/trunk/zh/_static/underscore.js
incubator/singa/site/trunk/zh/_static/up-pressed.png
incubator/singa/site/trunk/zh/_static/up.png
incubator/singa/site/trunk/zh/_static/websupport.js
incubator/singa/site/trunk/zh/genindex.html
incubator/singa/site/trunk/zh/index.html
incubator/singa/site/trunk/zh/objects.inv
incubator/singa/site/trunk/zh/search.html
incubator/singa/site/trunk/zh/searchindex.js
Modified: incubator/singa/site/trunk/en/.buildinfo
URL:
http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/.buildinfo?rev=1784386&r1=1784385&r2=1784386&view=diff
==============================================================================
--- incubator/singa/site/trunk/en/.buildinfo (original)
+++ incubator/singa/site/trunk/en/.buildinfo Sat Feb 25 15:30:33 2017
@@ -1,4 +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
+config: ef8bb4943a01394658c5425db4358da9
tags: 645f666f9bcd5a90fca523b33c5a78b7
Added: incubator/singa/site/trunk/en/_sources/community/issue-tracking.md.txt
URL:
http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/community/issue-tracking.md.txt?rev=1784386&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/community/issue-tracking.md.txt
(added)
+++ incubator/singa/site/trunk/en/_sources/community/issue-tracking.md.txt Sat
Feb 25 15:30:33 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/en/_sources/community/mail-lists.rst.txt
URL:
http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/community/mail-lists.rst.txt?rev=1784386&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/community/mail-lists.rst.txt (added)
+++ incubator/singa/site/trunk/en/_sources/community/mail-lists.rst.txt Sat Feb
25 15:30:33 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", "[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/>`_"
Added: incubator/singa/site/trunk/en/_sources/community/source-repository.md.txt
URL:
http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/community/source-repository.md.txt?rev=1784386&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/community/source-repository.md.txt
(added)
+++ incubator/singa/site/trunk/en/_sources/community/source-repository.md.txt
Sat Feb 25 15:30:33 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>
Added: incubator/singa/site/trunk/en/_sources/community/team-list.rst.txt
URL:
http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/community/team-list.rst.txt?rev=1784386&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/community/team-list.rst.txt (added)
+++ incubator/singa/site/trunk/en/_sources/community/team-list.rst.txt Sat Feb
25 15:30:33 2017
@@ -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 [email protected]
+Ted Dunning [email protected]
+Alan Gates [email protected]
+Thejas Nair [email protected]
+================== ============
+
+
+Developers
+----------
+
++--------------------+--------------------------------+-----------------------------------------------+
+| 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 |
++--------------------+--------------------------------+-----------------------------------------------+
+| Xing 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 |
++--------------------+--------------------------------+-----------------------------------------------+
+
Added: incubator/singa/site/trunk/en/_sources/develop/contribute-code.md.txt
URL:
http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/develop/contribute-code.md.txt?rev=1784386&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/develop/contribute-code.md.txt
(added)
+++ incubator/singa/site/trunk/en/_sources/develop/contribute-code.md.txt Sat
Feb 25 15:30:33 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/en/_sources/develop/contribute-docs.md.txt
URL:
http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/develop/contribute-docs.md.txt?rev=1784386&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/develop/contribute-docs.md.txt
(added)
+++ incubator/singa/site/trunk/en/_sources/develop/contribute-docs.md.txt Sat
Feb 25 15:30:33 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)
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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.
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15:30:33 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"
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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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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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15:30:33 2017
@@ -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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15:30:33 2017
@@ -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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15:30:33 2017
@@ -0,0 +1,92 @@
+# 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 âHAVE_SNPRINTFâ to âC/C++ - Preprocessor - Preprocessor
definitionsâ
+
+2. Error due to hash map
+
+ Add "_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS" to Preprocessor
Definitions.
+
+
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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.
+
+
+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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@@ -0,0 +1,58 @@
+# 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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15:30:33 2017
@@ -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:
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--- incubator/singa/site/trunk/en/_sources/docs/index.rst.txt (added)
+++ incubator/singa/site/trunk/en/_sources/docs/index.rst.txt Sat Feb 25
15:30:33 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
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--- incubator/singa/site/trunk/en/_sources/docs/initializer.rst.txt (added)
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15:30:33 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
+--------
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15:30:33 2017
@@ -0,0 +1,428 @@
+# Installation
+
+## From Conda
+
+Conda is a package manager provided by
[Anaconda](https://www.continuum.io/downloads) or
[Miniconda](https://conda.io/miniconda.html).
+Currently, SINGA has conda packages (Python2.7) for Linux and MacOSX.
+
+### Linux
+
+1. CPU only
+
+ conda install -c nusdbsystem singa
+
+2. GPU via CUDA+cuDNN
+
+ conda install -c nusdbsystem singa-cudax.y-cudnnz
+
+ where <x,y,z> is one of <8.0, 5>, <7.5, 5> and <7.5, 4>.
+ Users need to install CUDA and cuDNN before installing SINGA.
+ If cuDNN is not in system folders (e.g., /usr/local), export the folder of
libcudnn.so to LD_LIBRARY_PATH
+
+
+### Mac OSX
+
+Only the CPU version is available,
+
+ conda config --add channels conda-forge
+ conda install -c nusdbsystem singa
+
+
+If there is no error message from
+
+ python -c "from singa import tensor"
+
+then SINGA is installed successfully.
+
+## From Debian Package
+
+The following Debian packages (on architecture: amd64) are available
+
+<table border="1">
+ <tr>
+ <th>OS</th>
+ <th>Device</th>
+ <th>CUDA/cuDNN</th>
+ <th>Link</th>
+ </tr>
+ <tr>
+ <td>Ubuntu14.04</td>
+ <td>CPU</td>
+ <td>-</td>
+ <td><a
href="http://comp.nus.edu.sg/~dbsystem/singa/assets/file/debian/latest/ubuntu14.04-cpp/python-singa.deb";>latest</a>,
<a
href="http://www.comp.nus.edu.sg/~dbsystem/singa/assets/file/debian";>history</a></td>
+ </tr>
+ <tr>
+ <td>Ubuntu14.04</td>
+ <td>GPU</td>
+ <td>CUDA7.5+cuDNN4</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>Ubuntu14.04</td>
+ <td>GPU</td>
+ <td>CUDA7.5+cuDNN5</td>
+ <td>coming soon</td>
+ </tr>
+ <tr>
+ <td>Ubuntu14.04</td>
+ <td>GPU</td>
+ <td>CUDA8.0+cuDNN5</td>
+ <td></td>
+ </tr>
+</table>
+
+Download the deb file and install it via
+
+ apt-get install <path to the deb file, e.g., ./python-singa.deb>
+
+Note that the path must include `./` if the file in inside the current folder.
+
+## From source
+
+The source files could be downloaded either as a [tar.gz
file](https://dist.apache.org/repos/dist/dev/incubator/singa/), or as a git repo
+
+ $ git clone https://github.com/apache/incubator-singa.git
+ $ cd incubator-singa/
+
+### Pre-requisite
+
+The following libraries are required
+* cmake (>=2.8)
+* gcc (>=4.8.1) or Clang
+* google protobuf (>=2.5)
+* blas (tested with openblas >=0.2.10)
+* swig(>=3.0.10) for compiling PySINGA
+* numpy(>=1.11.0) for compiling PySINGA
+
+The following libraries are optional
+* opencv (tested with 2.4.8)
+* lmdb (tested with 0.9)
+* glog
+
+### Instructions
+
+1. create a `build` folder inside incubator-singa and go into that folder
+2. run `cmake [options] ..`
+ by default all options are OFF except `USE_PYTHON`
+
+ * `USE_MODUELS=ON`, used if protobuf and blas are not installed a prior
+ * `USE_CUDA=ON`, used if CUDA and cuDNN is available
+ * `USE_PYTHON=ON`, used for compiling PySINGA
+ * `USE_OPENCL=ON`, used for compiling with OpenCL support
+3. compile the code, e.g., `make`
+4. goto python folder
+5. run `pip install .`
+6. [optional] run `python setup.py bdist_wheel` to generate the wheel file
+
+Step 4 and 5 are to install PySINGA.
+Details on the installation of dependent libraries and the instructions for
each OS are given in the following sections.
+
+### Linux and Mac OS
+
+Most of the dependent libraries could be installed from source or via package
mangers like
+apt-get, yum, and homebrew. Please refer to FAQ for problems caused by the
path setting of the dependent libraries.
+
+The following instructions are tested on Ubuntu 14.04 and 16.04for installing
dependent libraries.
+
+ # required libraries
+ $ sudo apt-get install libprotobuf-dev libopenblas-dev protobuf-compiler
+
+ # optional libraries
+ $ sudo apt-get install python2.7-dev python-pip python-numpy
+ $ sudo apt-get install libopencv-dev libgoogle-glog-dev liblmdb-dev
+
+The following instructions are tested on Mac OS X Yosemite (10.11 and 10.12)
for installing dependent libraries.
+
+ # required libraries
+ $ brew tap homebrew/science
+ $ brew install openblas
+ $ brew install protobuf260
+
+ # optional libraries
+ $ brew tap homebrew/python
+ $ brew install python
+ $ brew install opencv
+ $ brew install -vd glog lmdb
+
+By default, openblas is installed into /usr/local/opt/openblas. To let the
compiler (and cmake) know the openblas
+path,
+
+ $ export
CMAKE_INCLUDE_PATH=/usr/local/opt/openblas/include:$CMAKE_INCLUDE_PATH
+ $ export CMAKE_LIBRARY_PATH=/usr/local/opt/openblas/lib:$CMAKE_LIBRARY_PATH
+
+To let the runtime know the openblas path,
+
+ $ export LD_LIBRARY_PATH=/usr/local/opt/openblas/library:$LD_LIBRARY_PATH
+
+
+#### Compile with USE_MODULES=ON
+
+If protobuf and openblas are not installed, you can compile SINGA together
with them
+
+ $ In SINGA ROOT folder
+ $ mkdir build
+ $ cd build
+ $ cmake -DUSE_MODULES=ON ..
+ $ make
+
+cmake would download OpenBlas and Protobuf (2.6.1) and compile them together
+with SINGA.
+
+After compiling SINGA, you can run the unit tests by
+
+ $ ./bin/test_singa
+
+You can see all the testing cases with testing results. If SINGA passes all
+tests, then you have successfully installed SINGA.
+
+You can use `ccmake ..` to configure the compilation options.
+If some dependent libraries are not in the system default paths, you need to
export
+the following environment variables
+
+ export CMAKE_INCLUDE_PATH=<path to the header file folder>
+ export CMAKE_LIBRARY_PATH=<path to the lib file folder>
+
+#### Compile with USE_PYTHON=ON
+swig and numpy can be install by
+
+ $ Ubuntu 14.04 and 16.04
+ $ sudo apt-get install python-numpy
+ # Ubuntu 16.04
+ $ sudo apt-get install swig
+
+Note that swig has to be installed from source on Ubuntu 14.04.
+After installing numpy, export the header path of numpy.i as
+
+ $ export CPLUS_INCLUDE_PATH=`python -c "import numpy; print
numpy.get_include()"`:$CPLUS_INCLUDE_PATH
+
+Similar to compile CPP code, PySINGA is compiled by
+
+ $ cmake -DUSE_PYTHON=ON ..
+ $ make
+ $ cd python
+ $ pip install .
+
+Developers can build the wheel file via
+
+ # under the build directory
+ $ cd python
+
+The generated wheel file is under "dist" directory.
+
+
+#### Compile SINGA with USE_CUDA=ON
+
+Users are encouraged to install the CUDA and
+[cuDNN](https://developer.nvidia.com/cudnn) for running SINGA on GPUs to
+get better performance.
+
+SINGA has been tested over CUDA (7, 7.5, 8), and cuDNN (4 and 5). If cuDNN is
+decompressed into non-system folder, e.g. /home/bob/local/cudnn/, the following
+commands should be executed for cmake and the runtime to find it
+
+ $ export
CMAKE_INCLUDE_PATH=/home/bob/local/cudnn/include:$CMAKE_INCLUDE_PATH
+ $ export CMAKE_LIBRARY_PATH=/home/bob/local/cudnn/lib64:$CMAKE_LIBRARY_PATH
+ $ export LD_LIBRARY_PATH=/home/bob/local/cudnn/lib64:$LD_LIBRARY_PATH
+
+The cmake options for CUDA and cuDNN should be switched on
+
+ # Dependent libs are install already
+ $ cmake -DUSE_CUDA=ON ..
+
+#### Compile SINGA with USE_OPENCL=ON
+
+SINGA uses opencl-headers and viennacl (version 1.7.1 or newer) for OpenCL
support, which
+can be installed using via
+
+ # On Ubuntu 16.04
+ $ sudo apt-get install opencl-headers, libviennacl-dev
+ # On Fedora
+ $ sudo yum install opencl-headers, viennacl
+
+Additionally, you will need the OpenCL Installable Client Driver (ICD) for the
platforms that you want to run OpenCL on.
+
+* For AMD and nVidia GPUs, the driver package should also install the correct
OpenCL ICD.
+* For Intel CPUs and/or GPUs, get the driver from the [Intel
website.](https://software.intel.com/en-us/articles/opencl-drivers) Note that
the drivers provided on that website only supports recent CPUs and Iris GPUs.
+* For older Intel CPUs, you can use the `beignet-opencl-icd` package.
+
+Note that running OpenCL on CPUs is not currently recommended because it is
slow. Memory transfer is on the order of whole seconds (1000's of ms on CPUs as
compared to 1's of ms on GPUs).
+
+More information on setting up a working OpenCL environment may be found
[here](https://wiki.tiker.net/OpenCLHowTo).
+
+If the package version of ViennaCL is not at least 1.7.1, you will need to
build it from source:
+
+Clone [the repository from here](https://github.com/viennacl/viennacl-dev),
checkout the `release-1.7.1` tag and build it.
+Remember to add its directory to `PATH` and the built libraries to
`LD_LIBRARY_PATH`.
+
+To build SINGA with OpenCL support, you need to pass the flag during cmake:
+
+ cmake -DUSE_OPENCL=ON ..
+
+### Compile SINGA on Windows
+
+For the dependent library installation, please refer to
[Dependencies](dependencies.md).
+After all the dependencies are successfully installed, just run the following
commands to
+generate the VS solution in cmd under singa folder:
+
+ $ md build && cd build
+ $ cmake -G "Visual Studio 14" -DUSE_CUDA=OFF -DUSE_PYTHON=OFF ..
+
+The default project generated by the command is 32-bit version. You can also
+specify a 64-bit version project by:
+
+ $ md build && cd build
+ $ cmake -G "Visual Studio 14 Win64" -DUSE_CUDA=OFF -DUSE_PYTHON=OFF ..
+
+If you get error outputs like "Could NOT find xxxxx" indicating a dependent
+library missing, configure your library file and include path for cmake or the
system.
+For example, you get an error "Could NOT find CBLAS" and suppose you installed
+openblas header files at "d:\include" and openblas library at "d:\lib". You
should run the
+following command to specify your cblas parameters in cmake:
+
+ $ cmake -G "Visual Studio 14" -DUSE_CUDA=OFF -DUSE_PYTHON=OFF
-DCBLAS_INCLUDE_DIR="d:\include" -DCBLAS_LIBRARIES="d:\lib\libopenblas.lib"
-DProtobuf_INCLUDE_DIR=<include dir of protobuf> -DProtobuf_LIBRARIES=<path to
libprotobuf.lib> -DProtobuf_PROTOC_EXECUTABLE=<path to protoc.exe>
-DGLOG_INCLUDE_DIR=<include dir of glog> -DGLOG_LIBRARIES=<path to libglog.lib>
..
+
+To find out the parameters you need to specify for some special libraries, you
+can run the following command:
+
+ $ cmake -LAH
+
+If you use cmake GUI tool in windows, make sure you configure the right
+parameters for the singa solution by select "Advanced" box. After generating
the VS project,
+open the "singa.sln" project file under
+the "build" folder and compile it as a normal VS solution. You will find the
+unit tests file named "test_singa" in the project binary folder.
+If you get errors when running test_singa.exe due to
libglog.dll/libopenblas.dll missing,
+just copy the dll files into the same folder as test_singa.exe
+
+## FAQ
+
+* Q: Error from 'import singa' using PySINGA installed from wheel.
+
+ A: Please check the detailed error from `python -c "from singa import
_singa_wrap"`. Sometimes it is
+ caused by the dependent libraries, e.g. there are multiple versions of
protobuf or missing of cudnn. Following
+ steps show the solutions for different cases
+ 1. Check the cudnn and cuda and gcc versions, cudnn5 and cuda7.5 and
gcc4.8/4.9 are preferred. if gcc is 5.0, then downgrade it.
+ If cudnn is missing or not match with the wheel version, you can
download the correct version of cudnn into ~/local/cudnn/ and
+
+ $ echo "export
LD_LIBRARY_PATH=/home/<yourname>/local/cudnn/lib64:$LD_LIBRARY_PATH" >>
~/.bashrc
+
+ 2. If it is the problem related to protobuf, then download the newest whl
files which have [compiled protobuf and openblas into the
whl](https://issues.apache.org/jira/browse/SINGA-255) file of PySINGA.
+ Or you can install protobuf from source into a local folder, say
~/local/;
+ Decompress the tar file, and then
+
+ $ ./configure --prefix=/home/<yourname>local
+ $ make && make install
+ $ echo "export
LD_LIBRARY_PATH=/home/<yourname>/local/lib:$LD_LIBRARY_PATH" >> ~/.bashrc
+ $ source ~/.bashrc
+
+ 3. If it cannot find other libs including python, then create virtual env
using pip or conda;
+ and then install SINGA via
+
+ $ pip install --upgrade <url of singa wheel>
+
+
+* Q: Error from running `cmake ..`, which cannot find the dependent libraries.
+
+ A: If you haven't installed the libraries, install them. If you installed
+ the libraries in a folder that is outside of the system folder, e.g.
/usr/local,
+ you need to export the following variables
+
+ $ export CMAKE_INCLUDE_PATH=<path to your header file folder>
+ $ export CMAKE_LIBRARY_PATH=<path to your lib file folder>
+
+
+* Q: Error from `make`, e.g. the linking phase
+
+ A: If your libraries are in other folders than system default paths, you
need
+ to export the following varaibles
+
+ $ export LIBRARY_PATH=<path to your lib file folder>
+ $ export LD_LIBRARY_PATH=<path to your lib file folder>
+
+
+* Q: Error from header files, e.g. 'cblas.h no such file or directory exists'
+
+ A: You need to include the folder of the cblas.h into CPLUS_INCLUDE_PATH,
+ e.g.,
+
+ $ export CPLUS_INCLUDE_PATH=/opt/OpenBLAS/include:$CPLUS_INCLUDE_PATH
+
+* Q:While compiling SINGA, I get error `SSE2 instruction set not enabled`
+
+ A:You can try following command:
+
+ $ make CFLAGS='-msse2' CXXFLAGS='-msse2'
+
+* Q:I get `ImportError: cannot import name enum_type_wrapper` from
google.protobuf.internal when I try to import .py files.
+
+ A: You need to install the python binding of protobuf, which could be
installed via
+
+ $ sudo apt-get install protobuf
+
+ or from source
+
+ $ cd /PROTOBUF/SOURCE/FOLDER
+ $ cd python
+ $ python setup.py build
+ $ python setup.py install
+
+* Q: When I build OpenBLAS from source, I am told that I need a Fortran
compiler.
+
+ A: You can compile OpenBLAS by
+
+ $ make ONLY_CBLAS=1
+
+ or install it using
+
+ $ sudo apt-get install libopenblas-dev
+
+* Q: When I build protocol buffer, it reports that GLIBC++_3.4.20 not found in
/usr/lib64/libstdc++.so.6.
+
+ A: This means the linker found libstdc++.so.6 but that library
+ belongs to an older version of GCC than was used to compile and link the
+ program. The program depends on code defined in
+ the newer libstdc++ that belongs to the newer version of GCC, so the linker
+ must be told how to find the newer libstdc++ shared library.
+ The simplest way to fix this is to find the correct libstdc++ and export
it to
+ LD_LIBRARY_PATH. For example, if GLIBC++_3.4.20 is listed in the output of
the
+ following command,
+
+ $ strings /usr/local/lib64/libstdc++.so.6|grep GLIBC++
+
+ then you just set your environment variable as
+
+ $ export LD_LIBRARY_PATH=/usr/local/lib64:$LD_LIBRARY_PATH
+
+* Q: When I build glog, it reports that "src/logging_unittest.cc:83:20: error:
âgflagsâ is not a namespace-name"
+
+ A: It maybe that you have installed gflags with a different namespace such
as "google". so glog can't find 'gflags' namespace.
+ Because it is not necessary to have gflags to build glog. So you can
change the configure.ac file to ignore gflags.
+
+ 1. cd to glog src directory
+ 2. change line 125 of configure.ac to "AC_CHECK_LIB(gflags, main,
ac_cv_have_libgflags=0, ac_cv_have_libgflags=0)"
+ 3. autoreconf
+
+ After this, you can build glog again.
+
+* Q: When using virtual environment, everytime I run pip install, it would
reinstall numpy. However, the numpy would not be used when I `import numpy`
+
+ A: It could be caused by the `PYTHONPATH` which should be set to empty
when you are using virtual environment to avoid the conflicts with the path of
+ the virtual environment.
+
+* Q: When compiling PySINGA from source, there is a compilation error due to
the missing of <numpy/objectarray.h>
+
+ A: Please install numpy and export the path of numpy header files as
+
+ $ export CPLUS_INCLUDE_PATH=`python -c "import numpy; print
numpy.get_include()"`:$CPLUS_INCLUDE_PATH
+
+* Q: When I run PySINGA in Mac OS X, I got the error "Fatal Python error:
PyThreadState_Get: no current thread Abort trap: 6"
+
+ A: This error happens typically when you have multiple version of Python
on your system,
+ e.g, the one comes with the OS and the one installed by Homebrew. The
Python linked by PySINGA must be the same as the Python interpreter.
+ You can check your interpreter by `which python` and check the Python
linked by PySINGA via `otool -L <path to _singa_wrap.so>`.
+ To fix this error, compile SINGA with the correct version of Python.
+ In particular, if you build PySINGA from source, you need to specify the
paths when invoking
[cmake](http://stackoverflow.com/questions/15291500/i-have-2-versions-of-python-installed-but-cmake-is-using-older-version-how-do)
+
+ $ cmake -DPYTHON_LIBRARY=`python-config
--prefix`/lib/libpython2.7.dylib -DPYTHON_INCLUDE_DIR=`python-config
--prefix`/include/python2.7/ ..
+
+ If installed PySINGA from binary packages, e.g. debian or wheel, then you
need to change the python interpreter, e.g., reset the $PATH to put the correct
path of Python at the front position.
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--- incubator/singa/site/trunk/en/_sources/docs/layer.rst.txt (added)
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15:30:33 2017
@@ -0,0 +1,32 @@
+.. 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.
+
+
+Layer
+======
+
+Python API
+-----------
+.. automodule:: singa.layer
+ :members:
+ :member-order: bysource
+ :show-inheritance:
+ :undoc-members:
+
+
+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.
+
+
+Loss
+=========
+
+
+.. automodule:: singa.loss
+ :members:
+ :show-inheritance:
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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.
+
+
+Metric
+=========
+
+
+.. automodule:: singa.metric
+ :members:
+ :show-inheritance:
+ :member-order: bysource
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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.
