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Posted to commits@singa.apache.org by ch...@apache.org on 2020/11/22 06:16:17 UTC
[singa-site] 19/47: delete old website files and add new website
files
This is an automated email from the ASF dual-hosted git repository.
chrishkchris pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/singa-site.git
commit 8df1520dbe1de1be38669a2a4123a03a3e6db40a
Author: wang wei <wa...@gmail.com>
AuthorDate: Thu Apr 9 00:35:54 2020 +0800
delete old website files and add new website files
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diff --git a/content/404.html b/content/404.html
new file mode 100644
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--- /dev/null
+++ b/content/404.html
@@ -0,0 +1,13 @@
+<!DOCTYPE html><html lang=""><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>Page Not Found · Apache SINGA</title><meta name="viewport" content="width=device-width"/><meta name="generator" content="Docusaurus"/><meta name="description" content="Distributed deep learning system"/><meta property="og:title" content="Page Not Found · Apache SINGA"/><meta property="og:type" content="website"/><meta property="og:url" content="https://feynmandna.github. [...]
+ document.addEventListener('DOMContentLoaded', function() {
+ addBackToTop(
+ {"zIndex":100}
+ )
+ });
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>3.0.0.rc1</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-internal"><li class=""><a href="/docs/insta [...]
+ The Apache Software Foundation. All rights reserved.
+ Apache SINGA, Apache, the Apache feather logo, and
+ the Apache SINGA project logos are trademarks of The
+ Apache Software Foundation. All other marks mentioned
+ may be trademarks or registered trademarks of their
+ respective owners.</section></div></footer></div><script>window.twttr=(function(d,s, id){var js,fjs=d.getElementsByTagName(s)[0],t=window.twttr||{};if(d.getElementById(id))return t;js=d.createElement(s);js.id=id;js.src='https://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js, fjs);t._e = [];t.ready = function(f) {t._e.push(f);};return t;}(document, 'script', 'twitter-wjs'));</script></body></html>
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diff --git a/content/404/index.html b/content/404/index.html
new file mode 100644
index 0000000..9fe3496
--- /dev/null
+++ b/content/404/index.html
@@ -0,0 +1,13 @@
+<!DOCTYPE html><html lang=""><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>Page Not Found · Apache SINGA</title><meta name="viewport" content="width=device-width"/><meta name="generator" content="Docusaurus"/><meta name="description" content="Distributed deep learning system"/><meta property="og:title" content="Page Not Found · Apache SINGA"/><meta property="og:type" content="website"/><meta property="og:url" content="https://feynmandna.github. [...]
+ document.addEventListener('DOMContentLoaded', function() {
+ addBackToTop(
+ {"zIndex":100}
+ )
+ });
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>3.0.0.rc1</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-internal"><li class=""><a href="/docs/insta [...]
+ The Apache Software Foundation. All rights reserved.
+ Apache SINGA, Apache, the Apache feather logo, and
+ the Apache SINGA project logos are trademarks of The
+ Apache Software Foundation. All other marks mentioned
+ may be trademarks or registered trademarks of their
+ respective owners.</section></div></footer></div><script>window.twttr=(function(d,s, id){var js,fjs=d.getElementsByTagName(s)[0],t=window.twttr||{};if(d.getElementById(id))return t;js=d.createElement(s);js.id=id;js.src='https://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js, fjs);t._e = [];t.ready = function(f) {t._e.push(f);};return t;}(document, 'script', 'twitter-wjs'));</script></body></html>
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diff --git a/content/_sources/community/issue-tracking.md.txt b/content/_sources/community/issue-tracking.md.txt
deleted file mode 100644
index c6ff200..0000000
--- a/content/_sources/community/issue-tracking.md.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-<!--
- 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
diff --git a/content/_sources/community/mail-lists.rst.txt b/content/_sources/community/mail-lists.rst.txt
deleted file mode 100644
index e4cd2df..0000000
--- a/content/_sources/community/mail-lists.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-.. Licensed to the Apache Software Foundation (ASF) under one
- or more contributor license agreements. See the NOTICE file
- distributed with this work for additional information
- regarding copyright ownership. The ASF licenses this file
- to you under the Apache License, Version 2.0 (the
- "License"); you may not use this file except in compliance
- with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing,
- software distributed under the License is distributed on an
- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, either express or implied. See the License for the
- specific language governing permissions and limitations
- under the License.
-
-
-Project Mailing Lists
-=====================
-
-These are the mailing lists that have been established for this project. For each list, there is a subscribe, unsubscribe, and an archive link.
-
-.. csv-table:: Mailing Lists
- :header: "Name", "Post", "Subscribe", "Unsubscribe", "Archive"
-
- "Development", "dev@singa.incubator.apache.org", "`Subscribe <ma...@singa.incubator.apache.org>`_", "`Unsubscribe <ma...@singa.incubator.apache.org.>`_", "`mail-archives.apache.org <http://mail-archives.apache.org/mod_mbox/singa-dev/>`_"
- "Commits", "commits@singa.incubator.apache.org", "`Subscribe <ma...@singa.incubator.apache.org>`_", "`Unsubscribe <ma...@singa.incubator.apache.org>`_", "`mail-archives.apache.org <http://mail-archives.apache.org/mod_mbox/singa-commits/>`_"
- "Security", "security@singa.apache.org", private, private, private
diff --git a/content/_sources/community/source-repository.rst.txt b/content/_sources/community/source-repository.rst.txt
deleted file mode 100644
index 6571a45..0000000
--- a/content/_sources/community/source-repository.rst.txt
+++ /dev/null
@@ -1,64 +0,0 @@
-.. 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=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/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/singa/pull/xxx, the following instructions should be executed,
-
-.. code-block:: bash
-
- git clone https://github.com/apache/singa.git
- git remote add asf https://gitbox.apache.org/repos/asf/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/singa.git, you can update the master by:
-
-.. code-block:: bash
-
- git remote set-url origin git@github.com/apache/singa.git
-
diff --git a/content/_sources/community/team-list.rst.txt b/content/_sources/community/team-list.rst.txt
deleted file mode 100644
index 7d53c52..0000000
--- a/content/_sources/community/team-list.rst.txt
+++ /dev/null
@@ -1,96 +0,0 @@
-.. 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.
-
-The SINGA community has developers mainly from National University of Singapore, Zhejiang University, NetEase, Osaka University, yzBigData, etc.
-
-PMC
----
-
-+--------------------+--------------------------------+-----------------------------------------------+
-| Name | Email | Organization |
-+====================+================================+===============================================+
-| Anh Dinh | dinhtta@apache.org | Singapore University of Technology and Design |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Alan Gates | gates@apache.org | Apache Software Foundation |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Beng Chin Ooi | ooibc@apache.org | National University of Singapore |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Gang Chen | cg@apache.org | Zhejiang University |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Jinyang Gao | jinyang@apache.org | DAMO Academy, Alibaba Group |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Kaiping Zheng | kaiping@apache.org | National University of Singapore |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Kian-Lee Tan | tankianlee@apache.org | National University of Singapore |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Moaz Reyad | moaz@apache.org | Université Grenoble Alpes |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Ted Dunning | tdunning@apache.org | Apache Software Foundation |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Thejas Nair | thejas@apache.org | Apache Software Foundation |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Wei Wang | wangwei@apache.org | National University of Singapore |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Zhaojing Luo | zhaojing@apache.org | National University of Singapore |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Zhongle Xie | zhongle@apache.org | Hangzhou MZH Technologies |
-+--------------------+--------------------------------+-----------------------------------------------+
-
-Committers
-----------
-
-+--------------------+--------------------------------+-----------------------------------------------+
-| Name | Email | Organization |
-+====================+================================+===============================================+
-| Chonho Lee | chonho@apache.org | Osaka University |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Sheng Wang | wangsh@apache.org | DAMO Academy, Alibaba Group |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Wanqi Xue | xuewanqi@apache.org | National University of Singapore |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Xiangrui Cai | caixr@apache.org | National University of Singapore |
-+--------------------+--------------------------------+-----------------------------------------------+
-
-Contributors
-------------
-
-+--------------------+--------------------------------+-----------------------------------------------+
-| Name | Email | Organization |
-+====================+================================+===============================================+
-| Haibo Chen | hzchenhaibo@corp.netease.com | NetEase |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Xin Ji | jixin@comp.nus.edu.sg | Visenze, Singapore |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Anthony K. H. Tung | atung@comp.nus.edu.sg | National University of Singapore |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Ji Wang | wangji@comp.nus.edu.sg | Hangzhou MZH Technologies |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Yuan Wang | wangyuan@corp.netease.com | NetEase |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Wenfeng Wu | wuwf@comp.nus.edu.sg | Freelancer, China |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Meihui Zhang | meihui_zhang@sutd.edu.sg | Beijing Institute of Technology |
-+--------------------+--------------------------------+-----------------------------------------------+
-| Chang Yao | yaochang2009@gmail.com | Hangzhou MZH Technologies |
-+--------------------+--------------------------------+-----------------------------------------------+
-
diff --git a/content/_sources/develop/build.md.txt b/content/_sources/develop/build.md.txt
deleted file mode 100644
index 8670ddc..0000000
--- a/content/_sources/develop/build.md.txt
+++ /dev/null
@@ -1,408 +0,0 @@
-<!--
- 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.
--->
-
-# Build SINGA from Source
-
-
-The source files could be downloaded either as a
-[tar.gz file](https://dist.apache.org/repos/dist/dev/singa/), or as a git repo
-
- $ git clone https://github.com/apache/singa.git
- $ cd singa/
-
-If you want to contribute code to SINGA, refer to [this page]() for the steps and requirements.
-
-## Use Conda to build SINGA
-
-Conda-build is a building tool that installs the dependent libraries from anaconda cloud and
-executes the building scripts.
-
-To install conda-build (after installing conda)
-
- conda install conda-build
-
-### Build CPU Version
-
-To build the CPU version of SINGA
-
- conda build tool/conda/singa/
-
-The above commands have been tested on Ubuntu (14.04, 16.04 and 18.04) and macOS 10.11.
-Refer to the [Travis-CI page](https://travis-ci.org/apache/singa) for more information.
-
-### Build GPU Version
-
-To build the GPU version of SINGA, the building machine must have Nvida GPU, and the CUDA driver (>= 384.81), CUDA toolkit (>=9) and cuDNN (>=7) must have be installed. The following two Docker images provide the building environment:
-
-1. apache/singa:conda-cuda9.0
-2. apache/singa:conda-cuda10.0
-
-Once the building environment is ready, you need to export the CUDA version first, and then run conda command to build SINGA
-
- export CUDA=x.y (e.g. 9.0)
- conda build tool/conda/singa/
-
-### Post Processing
-
-The location of the generated package file (`.tar.gz`) is shown on the screen.
-The generated package can be installed directly,
-
- conda install -c conda-forge --use-local <path to the package file>
-
-or uploaded to anaconda cloud for others to download and install. You need to register an account on anaconda for [uploading the package](https://docs.anaconda.com/anaconda-cloud/user-guide/getting-started/).
-
- conda install anaconda-client
- anaconda login
- anaconda upload -l main <path to the package file>
-
-After uploading the package to the cloud, you can see it on [Anaconda Cloud](https://anaconda.org/) website or via the following command
-
- conda search -c <anaconda username> singa
-
-Each specific SINGA package is identified by the version and build string. To install a specific SINGA package, you need to provide all the information, e.g.,
-
- conda install -c <anaconda username> -c conda-forge singa=2.1.0.dev=cpu_py36
-
-To make the installation command simple, you can create the following additional packages which depend on the latest CPU and GPU SINGA packages.
-
- # for singa-cpu
- conda build tool/conda/cpu/ --python=3.6
- conda build tool/conda/cpu/ --python=3.7
- # for singa-gpu
- conda build tool/conda/gpu/ --python=3.6
- conda build tool/conda/gpu/ --python=3.7
-
-Therefore, when you run
-
- conda install -c <anaconda username> -c conda-forge singa-xpu
-
-(`xpu` is either 'cpu' or 'gpu'), the corresponding real SINGA package is installed as the dependent library.
-
-## Use native tools to build SINGA on Ubuntu
-
-Refer to SINGA [Dockerfiles](https://github.com/apache/singa/blob/master/tool/docker/devel/ubuntu/cuda9/Dockerfile#L30)
-for the instructions of installing the dependent libraries on Ubuntu 16.04. You can also create a Docker container using the [devel images]() and build SINGA inside the container.
-To build SINGA with GPU, MKLDNN, Python and unit tests, run the following instructions
-
- mkdir build # at the root of singa folder
- cd build
- cmake -DENABLE_TEST=ON -DUSE_CUDA=ON -DUSE_MKLDNN=ON -DUSE_PYTHON3=ON ..
- make
- cd python
- pip install .
-
-The details of the CMake options are explained in the last section of this page.
-The last command install the Python package. You can also run `pip install -e .`, which creates symlinks instead of copying the Python files into the site-package folder.
-
-If SINGA is compiled with ENABLE_TEST=ON, 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.
-
-
-## Use native tools to Build SINGA on Centos7
-
-Building from source will be different for Centos7 as package names differ.Follow the instructions given below.
-
-### Installing dependencies
-
-Basic packages/libraries
-
- sudo yum install freetype-devel libXft-devel ncurses-devel openblas-devel blas-devel lapack devel atlas-devel kernel-headers unzip wget pkgconfig zip zlib-devel libcurl-devel cmake curl unzip dh-autoreconf git python-devel glog-devel protobuf-devel
-For build-essential
-
- sudo yum group install "Development Tools"
-For installing swig
-
- sudo yum install pcre-devel
- wget http://prdownloads.sourceforge.net/swig/swig-3.0.10.tar.gz
- tar xvzf swig-3.0.10.tar.gz
- cd swig-3.0.10.tar.gz
- ./configure --prefix=${RUN}
- make
- make install
- For installing gfortran
-
- sudo yum install centos-release-scl-rh
- sudo yum --enablerepo=centos-sclo-rh-testing install devtoolset-7-gcc-gfortran
-For installing pip and other packages
-
- sudo yum install epel-release
- sudo yum install python-pip
- pip install matplotlib numpy pandas scikit-learn pydot
-
-### Installation
-Follow steps 1-5 of _Use native tools to build SINGA on Ubuntu_
-
-### Testing
-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.
-
-## Compile SINGA on Windows
-
-Instructions for building on Windows with Python support can be found [here](install_win.html).
-
-## More details about the compilation options
-
-### USE_MODULES (deprecated)
-
-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.
-
-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>
-
-### USE_PYTHON
-
-Option for compiling the Python wrapper for SINGA,
-
- $ cmake -DUSE_PYTHON=ON ..
- $ make
- $ cd python
- $ pip install .
-
-
-### USE_CUDA
-
-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 9/10, and cuDNN 7. If cuDNN is
-installed 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 ..
- $ make
-
-### USE_MKLDNN
-
-User can enable MKL-DNN to enhance the performance of CPU computation.
-
-Installation guide of MKL-DNN could be found [here](https://github.com/intel/mkl-dnn#installation).
-
-SINGA has been tested over MKL-DNN v0.17.2.
-
-To build SINGA with MKL-DNN support:
-
- # Dependent libs are installed already
- $ cmake -DUSE_MKLDNN=ON ..
- $ make
-
-
-
-### USE_OPENCL
-
-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 (tested on SINGA 1.1):
-
- $ cmake -DUSE_OPENCL=ON ..
- $ make
-
-
-### PACKAGE
-
-This setting is used to build the Debian package. Set PACKAGE=ON and build the package with make command like this:
-
- $ cmake -DPACKAGE=ON
- $ make package
-
-## FAQ
-
-* Q: Error from 'import singa'
-
- 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, missing of cudnn, numpy version mismatch. Following steps show the solutions for different cases
- 1. Check the cudnn and cuda. 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. You can install protobuf (3.6.1) 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;
-
- 4. If it is not caused by the above reasons, go to the folder of `_singa_wrap.so`,
-
- $ python
- >> import importlib
- >> importlib.import_module('_singa_wrap')
-
- Check the error message. For example, if the numpy version mismatches, the error message would be,
-
- RuntimeError: module compiled against API version 0xb but this version of numpy is 0xa
-
- Then you need to upgrade the numpy.
-
-
-* 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: ‘gflags’ 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, every time 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 SINGA 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 and you installed SINGA via pip (this problem is resolved for installation via conda),
- 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.
\ No newline at end of file
diff --git a/content/_sources/develop/contribute-code.md.txt b/content/_sources/develop/contribute-code.md.txt
deleted file mode 100644
index f4a0c61..0000000
--- a/content/_sources/develop/contribute-code.md.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-<!--
- 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 Style for both [CPP](http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml) and [Python](http://google.github.io/styleguide/pyguide.html) code.
-
-A simple way to enforce the Google coding styles is to use the linting and formating tools in the Visual Studio Code editor:
-
- * [C/C++ extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode.cpptools)
- * [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
-
-Once the extensions are installed, edit the settings.json file.
-
- "editor.formatOnSave": true,
- "python.formatting.provider": "yapf",
- "python.formatting.yapfArgs": [
- "--style",
- "{based_on_style: google}"
- ],
- "python.linting.enabled": true,
- "python.linting.lintOnSave": true,
- "C_Cpp.clang_format_style": "Google"
-
-You need to fix the format errors before submitting the pull requests.
-
-## 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)
-
-## Git Workflow
-
-
-1. Fork the [SINGA Github repository](https://github.com/apache/singa) to your own Github account.
-
-2. Clone the **repo** (short for repository) from your Github
-
- git clone https://github.com/<Github account>/singa.git
- git remote add apache https://github.com/apache/singa.git
-
-3. Create a new branch (e.g., `feature-foo` or `fixbug-foo`), work on it and commit your code.
-
- git checkout -b feature-foo
- # write your code
- git add <created/updated files>
- git commit
-
- The commit message should have a **title which consists of the JIRA ticket No (SINGA-xxx) and title**. A brief description of the commit should be added in the commit message.
-
- If your branch has many small commits, you need to clean those commits via
-
- git rebase -i <commit id>
-
- You can [squash and reword](https://help.github.com/en/articles/about-git-rebase) the commits.
-
-4. When you are working on the code, the `master` of SINGA may have been updated by others; In this case, you need to pull the latest master
-
- git checkout master
- git pull apache master:master
- git checkout feature-foo
-
-
-5. [Rebase](https://git-scm.com/book/en/v2/Git-Branching-Rebasing) `feature-foo` onto the `master` branch and push commits to your own Github account (the new branch).
-
- git rebase master
- git push origin feature-foo:feature-foo
-
-6. Open a pull request (PR) against the master branch of apache/singa on Github website. The PR title should be the JIRA ticket title. If you want to inform other contributors who worked on the same files, you can find the file(s) on Github and click "Blame" to see a line-by-line annotation of who changed the code last. Then, 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 [...]
-
-7. Wait for committers to review the PR. If no conflicts and errors, 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**. During this time, the master of SINGA may have been updated by others, and then you need to [merge the latest master](https://docs.fast.ai/dev/git.html#how-to-keep-your-feature-branch-up-to-date) to resolve conflicts. Some people [rebase t [...]
-
-
-## Developing Environment
-
-Visual Studio Code is recommended as the editor. Extensions like Python, C/C++, Code Spell Checker, autoDocstring, vim, Remote Development could be installed. A reference configuration (i.e., `settings.json`) of these extensions is [here](https://gist.github.com/nudles/3d23cfb6ffb30ca7636c45fe60278c55).
-
-If you update the CPP code, you need to recompile SINGA [from source](./build.md). It is recommended to use the native building tools in the `*-devel` Docker images or `conda build`.
-
-If you only update the Python code, you can install SINGAS once, and then copy the updated Python files to replace those in the Python installation folder,
-
- cp python/singa/xx.py <path to conda>/lib/python3.7/site-packages/singa/
\ No newline at end of file
diff --git a/content/_sources/develop/contribute-docs.md.txt b/content/_sources/develop/contribute-docs.md.txt
deleted file mode 100644
index 59c04d3..0000000
--- a/content/_sources/develop/contribute-docs.md.txt
+++ /dev/null
@@ -1,103 +0,0 @@
-<!--
- 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.apache.org).
-
-SINGA website is built by [Sphinx](http://www.sphinx-doc.org) from a source tree stored in the [git repo](https://github.com/apache/singa/tree/master/doc).
-
-To install Sphinx:
-
- pip install -U Sphinx==1.5.6
-
-To install the markdown support for Sphinx:
-
- pip install recommonmark==0.5.0
-
-To install the rtd theme:
-
- pip install sphinx_rtd_theme==0.4.3
-
-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 copying the updated files to the [website repo](https://github.com/apache/singa-site) (suppose the site repo is ~/singa-sit)
-
- cd _build
- rsync --checksum -rvh html/ ~/singa-site/
- cd ~/singa-site
- git commit -m "update xxxx"
- git push
-
-We fix the versions of the libs in order to generate the same (checksum) html file if the source file is not changed. Otherwise, everytime we build the documentation, the html file of the same source file could be different. As a result, many html files in the site repo need updating.
-
-## Python API
-
-## CPP API
-
-To generate docs, run "doxygen" from the doc folder (Doxygen >= 1.8 recommended)
-
-## Using Visual Studio Code (vscode)
-
-### Preview
-
-The document files (rst and md files) can be previewed in vscode via the [reStructuredText Extension](https://docs.restructuredtext.net/).
-
-1. Install the extension in vscode.
-2. Install the dependent libs. All libs required to build the website should be installed (see the above instructions). In addition, there are two more libs to be installed.
-
- pip install sphinx-autobuild=0.7.1
- pip install doc8=0.8.0
-3. Configure the conf path for `restructuredtext.confPath` to the [conf.py](./conf.py)
-
-### Docstring Snippet
-
-[autoDocstring](https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring) generates the docstring of functions, classes, etc. Choose the DocString Format to `google`.
-
-### Spell Check
-
-[Code Spell Checker](https://marketplace.visualstudio.com/items?itemName=streetsidesoftware.code-spell-checker) can be configured to check the comments of the code, or .md and .rst files.
-
-To do spell check only for comments of Python code, add the following snippet via `File - Preferences - User Snippets - python.json`
-
- "cspell check" : {
- "prefix": "cspell",
- "body": [
- "# Directives for doing spell check only for python and c/cpp comments",
- "# cSpell:includeRegExp #.* ",
- "# cSpell:includeRegExp (\"\"\"|''')[^\1]*\1",
- "# cSpell: CStyleComment",
- ],
- "description": "# spell check only for python comments"
- }
-
-To do spell check only for comments of Cpp code, add the following snippet via `File - Preferences - User Snippets - cpp.json`
-
- "cspell check" : {
- "prefix": "cspell",
- "body": [
- "// Directive for doing spell check only for cpp comments",
- "// cSpell:includeRegExp CStyleComment",
- ],
- "description": "# spell check only for cpp comments"
- }
diff --git a/content/_sources/develop/how-contribute.md.txt b/content/_sources/develop/how-contribute.md.txt
deleted file mode 100644
index c0d5b72..0000000
--- a/content/_sources/develop/how-contribute.md.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-<!--
- 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.
-* [Help with the documentation](http://singa.apache.org/en/develop/contribute-docs.html) by updating webpages that are lacking or unclear.
-* [Contribute code to SINGA](http://singa.apache.org/en/develop/contribute-code.html) by fixing errors or adding new features. [All issues are tracked](http://singa.apache.org/en/community/issue-tracking.html) on the JIRA system.
\ No newline at end of file
diff --git a/content/_sources/develop/how-to-release.rst.txt b/content/_sources/develop/how-to-release.rst.txt
deleted file mode 100644
index 5cf44e7..0000000
--- a/content/_sources/develop/how-to-release.rst.txt
+++ /dev/null
@@ -1,194 +0,0 @@
-.. 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. 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.
-
-Package the release
--------------------
-
-The release should be packaged into : apache-singa-xx.xx.xx.tar.gz
- + src/
- + README
- + LICENSE
- + NOTICE
- + RELEASE_NOTES
- + ...
-
-Upload the release
--------------------
-
-The release is uploaded to the RM’s Apache page: people.apache.org/~ID/...
- + apache-singa-xx.xx.xx.tar.gz
- + KEY
- + XX.acs
- + XX.md5
-
-Roll out artifacts to mirrors
------------------------------
-
-svn add to “dist/release/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: dev@singa.apache.org
-Subject: [VOTE] Release apache-singa-X.Y.Z (release candidate N)
-
-Hi all,
-
-I have created a build for Apache SINGA X.Y.Z, release candidate N.
-
-The artifacts to be voted on are located here:
-https://dist.apache.org/repos/dist/dev/singa/apache-singa-X.Y.Z-rcN/
-
-The hashes of the artifacts are as follows:
-apache-singa-X.Y.Z.tar.gz.md5 XXXX
-apache-singa-X.Y.Z.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.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
-[ ] 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 PMC, committer or contributor can test features for releasing, and feedback. Based on that, PMC will decide whether start a vote.
-
-Call a vote in dev
-------------------
-
-Call a vote in dev@singa.apache.org
-
-Vote Check
-----------
-
-All PMC members and committers should check these before vote +1 :
-
-Vote result mail
-----------------
-
-Template for singa-dev@ voting (results):
-
-
-Subject: [RESULT] [VOTE] Release apache-singa-X.Y.Z (release candidate N)
-To: dev@singa.apache.org
-
-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 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
-
-Publish release
----------------
-
-Template for ANNOUNCING the release
-
-To: announce@apache.org, dev@singa.apache.org
-Subject: [ANNOUNCE] Apache SINGA X.Y.Z released
-
-We are pleased to announce that SINGA 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.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
diff --git a/content/_sources/develop/schedule.rst.txt b/content/_sources/develop/schedule.rst.txt
deleted file mode 100644
index 0dc1572..0000000
--- a/content/_sources/develop/schedule.rst.txt
+++ /dev/null
@@ -1,65 +0,0 @@
-.. 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"
diff --git a/content/_sources/docs.rst.txt b/content/_sources/docs.rst.txt
deleted file mode 100644
index 1b94d02..0000000
--- a/content/_sources/docs.rst.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-.. 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
diff --git a/content/_sources/docs/autograd.md.txt b/content/_sources/docs/autograd.md.txt
deleted file mode 100644
index 30cf28e..0000000
--- a/content/_sources/docs/autograd.md.txt
+++ /dev/null
@@ -1,166 +0,0 @@
-<!--
- 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 e [...]
-
-## 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/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/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)
-```
diff --git a/content/_sources/docs/benchmark.md.txt b/content/_sources/docs/benchmark.md.txt
deleted file mode 100644
index 071eb73..0000000
--- a/content/_sources/docs/benchmark.md.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-<!--
- 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.
--->
-
-
-# Benchmark for Distributed training
-
-
-Workload: we use a deep convolutional neural network, [ResNet-50](https://github.com/apache/singa/blob/master/examples/autograd/resnet.py) as the application. ResNet-50 is has 50 convolution layers for image classification. It requires 3.8 GFLOPs to pass a single image (of size 224x224) through the network. The input image size is 224x224.
-
-
-Hardware: we use p2.8xlarge instances from AWS, each of which has 8 Nvidia Tesla K80 GPUs, 96 GB GPU memory in total, 32 vCPU, 488 GB main memory, 10 Gbps network bandwidth.
-
-Metric: we measure the time per iteration for different number of workers to evaluate the scalability of SINGA. The batch size is fixed to be 32 per GPU. Synchronous training scheme is applied. As a result, the effective batch size is $32N$, where N is the number of GPUs. We compare with a popular open source system which uses the parameter server topology. The first GPU is selected as the server.
-
-<img src="../_static/images/benchmark.png" align="center" width="500px"/>
-<br/>
-<span><strong>Scalability test. Bars are for the throughput; lines are for the communication cost.</strong></span>
\ No newline at end of file
diff --git a/content/_sources/docs/cnn.md.txt b/content/_sources/docs/cnn.md.txt
deleted file mode 100644
index 64aad5a..0000000
--- a/content/_sources/docs/cnn.md.txt
+++ /dev/null
@@ -1,159 +0,0 @@
-<!--
- 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
diff --git a/content/_sources/docs/converter.rst.txt b/content/_sources/docs/converter.rst.txt
deleted file mode 100644
index 16a81b8..0000000
--- a/content/_sources/docs/converter.rst.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-.. 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:
diff --git a/content/_sources/docs/data.rst.txt b/content/_sources/docs/data.rst.txt
deleted file mode 100644
index d495dfd..0000000
--- a/content/_sources/docs/data.rst.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-.. 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:
diff --git a/content/_sources/docs/dependencies.md.txt b/content/_sources/docs/dependencies.md.txt
deleted file mode 100644
index febf6da..0000000
--- a/content/_sources/docs/dependencies.md.txt
+++ /dev/null
@@ -1,110 +0,0 @@
-<!--
- 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.
-
-
diff --git a/content/_sources/docs/device.rst.txt b/content/_sources/docs/device.rst.txt
deleted file mode 100644
index 57993f9..0000000
--- a/content/_sources/docs/device.rst.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-.. 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
----------
diff --git a/content/_sources/docs/docker.md.txt b/content/_sources/docs/docker.md.txt
deleted file mode 100644
index 4cb43e1..0000000
--- a/content/_sources/docs/docker.md.txt
+++ /dev/null
@@ -1,71 +0,0 @@
-<!--
- 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 `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`.
diff --git a/content/_sources/docs/image_tool.rst.txt b/content/_sources/docs/image_tool.rst.txt
deleted file mode 100644
index 764f036..0000000
--- a/content/_sources/docs/image_tool.rst.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-.. 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:
diff --git a/content/_sources/docs/index.rst.txt b/content/_sources/docs/index.rst.txt
deleted file mode 100644
index 015f462..0000000
--- a/content/_sources/docs/index.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-.. 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
- autograd
- onnx
- benchmark
- model_zoo/index
\ No newline at end of file
diff --git a/content/_sources/docs/initializer.rst.txt b/content/_sources/docs/initializer.rst.txt
deleted file mode 100644
index 6790a8e..0000000
--- a/content/_sources/docs/initializer.rst.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-.. 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
---------
diff --git a/content/_sources/docs/install_macos1013.rst.txt b/content/_sources/docs/install_macos1013.rst.txt
deleted file mode 100644
index 22cdb66..0000000
--- a/content/_sources/docs/install_macos1013.rst.txt
+++ /dev/null
@@ -1,153 +0,0 @@
-.. 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.
-
-
-Installing SINGA on macOS 13.10
-===============================
-
-Requirements
-------------
-
-* homebrew is used to install the requirements. Try:
-
-.. code-block:: bash
-
- brew update
-
-If you don't have homebrew in your system or if you upgraded from a previous operating system, you may see an error message. See FAQ below.
-
-* installing required software for building SINGA:
-
-.. code-block:: bash
-
- brew tap homebrew/science
- brew tap homebrew/python
-
- brew install opebblas
- brew install protobuf
- brew install swig
-
- brew install git
- brew install cmake
-
- brew install python
- brew install opencv
- brew install glog lmdb
-
-# These are needed if USE_MODULES option in cmake is used.
-
-.. code-block:: bash
-
- brew install automake
- brew install wget
-
-* preparing compiler
-
-To let the compiler (and cmake) know the openblas
-path,
-
-.. code-block:: bash
-
- 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,
-
-.. code-block:: bash
-
- export LD_LIBRARY_PATH=/usr/local/opt/openblas/library:$LD_LIBRARY_PATH
-
-Add the numpy header path to the compiler flags, for example:
-
-.. code-block:: bash
-
- export CXXFLAGS="-I /usr/local/lib/python2.7/site-packages/numpy/core/include $CXXFLAGS"
-
-* Get the source code and build it:
-
-.. code-block:: bash
-
- git clone https://github.com/apache/singa.git
-
- cd singa
- mkdir build
- cd build
-
- cmake ..
- make
-
-* Optional: create virtual enviromnet:
-
-.. code-block:: bash
-
- virtualenv ~/venv
- source ~/venv/bin/activate
-
-* Install the python module
-
-.. code-block:: bash
-
- cd python
- pip install .
-
-If there is no error message from
-
-.. code-block:: bash
-
- python -c "from singa import tensor"
-
-then SINGA is installed successfully.
-
-* Run Jupyter notebook
-
-.. code-block:: bash
-
- pip install matplotlib
-
- cd ../../doc/en/docs/notebook
- jupyter notebook
-
-Video Tutorial
---------------
-
-See these steps in the following video:
-
-.. |video| image:: https://img.youtube.com/vi/T8xGTH9vCBs/0.jpg
- :scale: 100%
- :align: middle
- :target: https://www.youtube.com/watch?v=T8xGTH9vCBs
-
-+---------+
-| |video| |
-+---------+
-
-FAQ
----
-
-* How to install or update homebrew:
-
-.. code-block:: bash
-
- /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
-
-* There is an error with protobuf.
-
-Try overwriting the links:
-
-.. code-block:: bash
-
- brew link --overwrite protobuf
diff --git a/content/_sources/docs/install_win.rst.txt b/content/_sources/docs/install_win.rst.txt
deleted file mode 100644
index 4cd0142..0000000
--- a/content/_sources/docs/install_win.rst.txt
+++ /dev/null
@@ -1,419 +0,0 @@
-.. 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.
-
-
-Building SINGA on Windows
-=========================
-
-The process of building SINGA from source on Microsoft Windows has four parts: install dependencies, build SINGA source, (optionally) install the python module and (optionally) run the unit tests.
-
-1. Install Dependencies
------------------------
-
-You may create a folder for building the dependencies.
-
-The dependencies are:
-
-* Compiler and IDE
- * Visual Studio. The community edition is free and can be used to build SINGA. https://www.visualstudio.com/
-* CMake
- * Can be downloaded from http://cmake.org/
- * Make sure the path to cmake executable is in the system path, or use full path when calling cmake.
-* SWIG
- * Can be downloaded from http://swig.org/
- * Make sure the path to swig executable is in the system path, or use full path when calling swig. Use a recent version such as 3.0.12.
-
-* Protocol Buffers
- * Download a suitable version such as 2.6.1: https://github.com/google/protobuf/releases/tag/v2.6.1 .
- * Download both protobuf-2.6.1.zip and protoc-2.6.1-win32.zip .
- * Extract both of them in dependencies folder. Add the path to protoc executable to the system path, or use full path when calling it.
- * Open the Visual Studio solution which can be found in vsproject folder.
- * Change the build settings to Release and x64.
- * build libprotobuf project.
-* Openblas
- * Download a suitable source version such as 0.2.20 from http://www.openblas.net
- * Extract the source in the dependencies folder.
- * If you don't have Perl installed, download a perl environment such as Strawberry Perl (http://strawberryperl.com/)
- * Build the Visual Studio solution by running this command in the source folder:
-
- .. code-block:: bash
-
- cmake -G "Visual Studio 15 2017 Win64"
-
- * Open the Visual Studio solution and change the build settings to Release and x64.
- * Build libopenblas project
-
-* Google glog
- * Download a suitable version such as 0.3.5 from https://github.com/google/glog/releases
- * Extract the source in the dependencies folder.
- * Open the Visual Studio solution.
- * Change the build settings to Release and x64.
- * Build libglog project
-
-2. Build SINGA source
----------------------
-
-* Download SINGA source code
-* Compile the protobuf files:
- * Goto src/proto folder
-
-.. code-block:: bash
-
- mkdir python_out
- protoc.exe *.proto --python_out python_out
-
-* Generate swig interfaces for C++ and Python:
- Goto src/api
-
-.. code-block:: bash
-
- swig -python -c++ singa.i
-
-* generate Visual Studio solution for SINGA:
- Goto SINGA source code root folder
-
-.. code-block:: bash
-
- mkdir build
- cd build
-
-* Call cmake and add the paths in your system similar to the following example:
-
-.. code-block:: bash
-
- cmake -G "Visual Studio 15 2017 Win64" ^
- -DGLOG_INCLUDE_DIR="D:/WinSinga/dependencies/glog-0.3.5/src/windows" ^
- -DGLOG_LIBRARIES="D:/WinSinga/dependencies/glog-0.3.5/x64/Release" ^
- -DCBLAS_INCLUDE_DIR="D:/WinSinga/dependencies/openblas-0.2.20/lapack-netlib/CBLAS/include" ^
- -DCBLAS_LIBRARIES="D:/WinSinga/dependencies/openblas-0.2.20/lib/RELEASE" ^
- -DProtobuf_INCLUDE_DIR="D:/WinSinga/dependencies/protobuf-2.6.1/src" ^
- -DProtobuf_LIBRARIES="D:/WinSinga/dependencies/protobuf-2.6.1/vsprojects/x64/Release" ^
- -DProtobuf_PROTOC_EXECUTABLE="D:/WinSinga/dependencies/protoc-2.6.1-win32/protoc.exe" ^
- ..
-
-* Open the generated solution in Visual Studio
-* Change the build settings to Release and x64
-* Add the singa_wrap.cxx file from src/api to the singa_objects project
-* In the singa_objects project, open Additional Include Directories.
-* Add Python include path
-* Add numpy include path
-* Add protobuf include path
-* In the preprocessor definitions of the singa_objects project, add USE_GLOG
-* Build singa_objects project
-
-* In singa project:
- * add singa_wrap.obj to Object Libraries
- * change target name to _singa_wrap
- * change target extension to .pyd
- * change configuration type to Dynamic Library (.dll)
- * goto Additional Library Directories and add the path to python, openblas, protobuf and glog libraries
- * goto Additional Dependencies and add libopenblas.lib, libglog.lib and libprotobuf.lib
-
-* build singa project
-
-
-3. Install Python module
-------------------------
-
-* Change _singa_wrap.so to _singa_wrap.pyd in build/python/setup.py
-* Copy the files in src/proto/python_out to build/python/singa/proto
-
-* Optionally create and activate a virtual environment:
-
-.. code-block:: bash
-
- mkdir SingaEnv
- virtualenv SingaEnv
- SingaEnv\Scripts\activate
-
-* goto build/python folder and run:
-
-.. code-block:: bash
-
- python setup.py install
-
-* Make _singa_wrap.pyd, libglog.dll and libopenblas.dll available by adding them to the path or by copying them to singa package folder in the python site-packages
-
-* Verify that SINGA is installed by running:
-
-.. code-block:: bash
-
- python -c "from singa import tensor"
-
-A video tutorial for the build process can be found here:
-
-
-.. |video| image:: https://img.youtube.com/vi/cteER7WeiGk/0.jpg
- :scale: 100%
- :align: middle
- :target: https://www.youtube.com/watch?v=cteER7WeiGk
-
-+---------+
-| |video| |
-+---------+
-
-
-4. Run Unit Tests
------------------
-
-* In the test folder, generate the Visual Studio solution:
-
-.. code-block:: bash
-
- cmake -G "Visual Studio 15 2017 Win64"
-
-* Open the generated solution in Visual Studio.
-
-* Change the build settings to Release and x64.
-
-* Build glog project.
-
-* In test_singa project:
-
- * Add USE_GLOG to the Preprocessor Definitions.
- * In Additional Include Directories, add path of GLOG_INCLUDE_DIR, CBLAS_INCLUDE_DIR and Protobuf_INCLUDE_DIR which were used in step 2 above. Add also build and build/include folders.
- * Goto Additional Library Directories and add the path to openblas, protobuf and glog libraries. Add also build/src/singa_objects.dir/Release.
- * Goto Additional Dependencies and add libopenblas.lib, libglog.lib and libprotobuf.lib. Fix the names of the two libraries: gtest.lib and singa_objects.lib.
-
-* Build test_singa project.
-
-* Make libglog.dll and libopenblas.dll available by adding them to the path or by copying them to test/release folder
-
-* The unit tests can be executed
-
- * From the command line:
-
- .. code-block:: bash
-
- test_singa.exe
-
- * From Visual Studio:
- * right click on the test_singa project and choose 'Set as StartUp Project'.
- * from the Debug menu, choose 'Start Without Debugging'
-
-A video tutorial for running the unit tests can be found here:
-
-
-.. |video| image:: https://img.youtube.com/vi/393gPtzMN1k/0.jpg
- :scale: 100%
- :align: middle
- :target: https://www.youtube.com/watch?v=393gPtzMN1k
-
-+---------+
-| |video| |
-+---------+
-
-
-5. Build GPU support with CUDA
-------------------------------
-
-In this section, we will extend the previous steps to enable GPU.
-
-5.1 Install Dependencies
-------------------------
-
-In addition to the dependencies in section 1 above, we will need the following:
-
-* CUDA
-
- Download a suitable version such as 9.1 from https://developer.nvidia.com/cuda-downloads . Make sure to install the Visual Studio integration module.
-
-* cuDNN
-
- Download a suitable version such as 7.1 from https://developer.nvidia.com/cudnn
-
-* cnmem:
-
- * Download the latest version from https://github.com/NVIDIA/cnmem
- * Build the Visual Studio solution:
-
- .. code-block:: bash
-
- cmake -G "Visual Studio 15 2017 Win64"
-
- * Open the generated solution in Visual Studio.
- * Change the build settings to Release and x64.
- * Build the cnmem project.
-
-
-5.2 Build SINGA source
-----------------------
-
-* Call cmake and add the paths in your system similar to the following example:
-
- .. code-block:: bash
-
- cmake -G "Visual Studio 15 2017 Win64" ^
- -DGLOG_INCLUDE_DIR="D:/WinSinga/dependencies/glog-0.3.5/src/windows" ^
- -DGLOG_LIBRARIES="D:/WinSinga/dependencies/glog-0.3.5/x64/Release" ^
- -DCBLAS_INCLUDE_DIR="D:/WinSinga/dependencies/openblas-0.2.20/lapack-netlib/CBLAS/include" ^
- -DCBLAS_LIBRARIES="D:/WinSinga/dependencies/openblas-0.2.20/lib/RELEASE" ^
- -DProtobuf_INCLUDE_DIR="D:/WinSinga/dependencies/protobuf-2.6.1/src" ^
- -DProtobuf_LIBRARIES="D:\WinSinga/dependencies/protobuf-2.6.1/vsprojects/x64/Release" ^
- -DProtobuf_PROTOC_EXECUTABLE="D:/WinSinga/dependencies/protoc-2.6.1-win32/protoc.exe" ^
- -DCUDNN_INCLUDE_DIR=D:\WinSinga\dependencies\cudnn-9.1-windows10-x64-v7.1\cuda\include ^
- -DCUDNN_LIBRARIES=D:\WinSinga\dependencies\cudnn-9.1-windows10-x64-v7.1\cuda\lib\x64 ^
- -DSWIG_DIR=D:\WinSinga\dependencies\swigwin-3.0.12 ^
- -DSWIG_EXECUTABLE=D:\WinSinga\dependencies\swigwin-3.0.12\swig.exe ^
- -DUSE_CUDA=YES ^
- -DCUDNN_VERSION=7 ^
- ..
-
-
-* Generate swig interfaces for C++ and Python:
- Goto src/api
-
- .. code-block:: bash
-
- swig -python -c++ singa.i
-
-* Open the generated solution in Visual Studio
-
-* Change the build settings to Release and x64
-
-5.2.1 Building singa_objects
-----------------------------
-
-* Add the singa_wrap.cxx file from src/api to the singa_objects project
-* In the singa_objects project, open Additional Include Directories.
-* Add Python include path
-* Add numpy include path
-* Add protobuf include path
-* Add include path for CUDA, cuDNN and cnmem
-* In the preprocessor definitions of the singa_objects project, add USE_GLOG, USE_CUDA and USE_CUDNN. Remove DISABLE_WARNINGS.
-* Build singa_objects project
-
-5.2.2 Building singa-kernel
----------------------------
-
-* Create a new Visual Studio project of type "CUDA 9.1 Runtime". Give it a name such as singa-kernel.
-* The project comes with an initial file called kernel.cu. Remove this file from the project.
-* Add this file: src/core/tensor/math_kernel.cu
-* In the project settings:
-
- * Set Platform Toolset to "Visual Studio 2015 (v140)"
- * Set Configuration Type to " Static Library (.lib)"
- * In the Include Directories, add build/include.
-
-* Build singa-kernel project
-
-
-5.2.3 Building singa
---------------------
-
-* In singa project:
- * add singa_wrap.obj to Object Libraries
- * change target name to _singa_wrap
- * change target extension to .pyd
- * change configuration type to Dynamic Library (.dll)
- * goto Additional Library Directories and add the path to python, openblas, protobuf and glog libraries
- * Add also the library path to singa-kernel, cnmem, cuda and cudnn.
- * goto Additional Dependencies and add libopenblas.lib, libglog.lib and libprotobuf.lib.
- * Add also: singa-kernel.lib, cnmem.lib, cudnn.lib, cuda.lib , cublas.lib, curand.lib and cudart.lib.
-
-* build singa project
-
-5.3. Install Python module
---------------------------
-
-* Change _singa_wrap.so to _singa_wrap.pyd in build/python/setup.py
-* Copy the files in src/proto/python_out to build/python/singa/proto
-
-* Optionally create and activate a virtual environment:
-
-.. code-block:: bash
-
- mkdir SingaEnv
- virtualenv SingaEnv
- SingaEnv\Scripts\activate
-
-* goto build/python folder and run:
-
-.. code-block:: bash
-
- python setup.py install
-
-* Make _singa_wrap.pyd, libglog.dll, libopenblas.dll, cnmem.dll, CUDA Runtime (e.g. cudart64_91.dll) and cuDNN (e.g. cudnn64_7.dll) available by adding them to the path or by copying them to singa package folder in the python site-packages
-
-* Verify that SINGA is installed by running:
-
-.. code-block:: bash
-
- python -c "from singa import device; dev = device.create_cuda_gpu()"
-
-A video tutorial for this part can be found here:
-
-
-.. |video| image:: https://img.youtube.com/vi/YasKVjRtuDs/0.jpg
- :scale: 100%
- :align: middle
- :target: https://www.youtube.com/watch?v=YasKVjRtuDs
-
-+---------+
-| |video| |
-+---------+
-
-5.4. Run Unit Tests
--------------------
-
-* In the test folder, generate the Visual Studio solution:
-
-.. code-block:: bash
-
- cmake -G "Visual Studio 15 2017 Win64"
-
-* Open the generated solution in Visual Studio, or add the project to the singa solution that was created in step 5.2
-
-* Change the build settings to Release and x64.
-
-* Build glog project.
-
-* In test_singa project:
-
- * Add USE_GLOG; USE_CUDA; USE_CUDNN to the Preprocessor Definitions.
- * In Additional Include Directories, add path of GLOG_INCLUDE_DIR, CBLAS_INCLUDE_DIR and Protobuf_INCLUDE_DIR which were used in step 5.2 above. Add also build, build/include, CUDA and cuDNN include folders.
- * Goto Additional Library Directories and add the path to openblas, protobuf and glog libraries. Add also build/src/singa_objects.dir/Release, singa-kernel, cnmem, CUDA and cuDNN library paths.
- * Goto Additional Dependencies and add libopenblas.lib; libglog.lib; libprotobuf.lib; cnmem.lib; cudnn.lib; cuda.lib; cublas.lib; curand.lib; cudart.lib; singa-kernel.lib. Fix the names of the two libraries: gtest.lib and singa_objects.lib.
-
-
-* Build test_singa project.
-
-* Make libglog.dll, libopenblas.dll, cnmem.dll, cudart64_91.dll and cudnn64_7.dll available by adding them to the path or by copying them to test/release folder
-
-* The unit tests can be executed
-
- * From the command line:
-
- .. code-block:: bash
-
- test_singa.exe
-
- * From Visual Studio:
- * right click on the test_singa project and choose 'Set as StartUp Project'.
- * from the Debug menu, choose 'Start Without Debugging'
-
-A video tutorial for running the unit tests can be found here:
-
-
-.. |video| image:: https://img.youtube.com/vi/YOjwtrvTPn4/0.jpg
- :scale: 100%
- :align: middle
- :target: https://www.youtube.com/watch?v=YOjwtrvTPn4
-
-+---------+
-| |video| |
-+---------+
diff --git a/content/_sources/docs/installation.md.txt b/content/_sources/docs/installation.md.txt
deleted file mode 100644
index 982093b..0000000
--- a/content/_sources/docs/installation.md.txt
+++ /dev/null
@@ -1,120 +0,0 @@
-<!--
- 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.
--->
-# Installation
-
-## From Conda
-
-Conda is a package manager for Python, CPP and other packages.
-
-Currently, SINGA has conda packages for Linux and MacOSX.
-[Miniconda3](https://conda.io/miniconda.html) is recommended to use with SINGA.
-After installing miniconda, execute the one of the following commands to install
-SINGA.
-
-1. CPU only
-
- conda install -c nusdbsystem -c conda-forge singa-cpu
- [](https://colab.research.google.com/drive/1Ntkhi-Z6XTR8WYPXiLwujHd2dOm0772V)
-
-2. GPU with CUDA and cuDNN (CUDA driver >=384.81 is required)
-
- conda install -c nusdbsystem -c conda-forge singa-gpu
-
- [](https://colab.research.google.com/drive/1do_TLJe18IthLOnBOsHCEe-FFPGk1sPJ)
-
-
-3. Install a specific version of SINGA. The following command lists all the available SINGA packages.
-
- conda search -c nusdbsystem singa
-
- Loading channels: done
- # Name Version Build Channel
- singa 2.1.0.dev cpu_py36 nusdbsystem
- singa 2.1.0.dev cpu_py37 nusdbsystem
-
- The following command install a specific version of SINGA,
-
- conda install -c nusdbsystem -c conda-forge singa=2.1.0.dev=cpu_py37
-
-
-If there is no error message from
-
- python -c "from singa import tensor"
-
-then SINGA is installed successfully.
-
-## Using Docker
-
-Install Docker on your local host machine following the [instructions](https://docs.docker.com/install/). Add your user into the [docker group](https://docs.docker.com/install/linux/linux-postinstall/) to run docker commands without `sudo`.
-
-1. CPU-only.
-
- docker run -it apache/singa:2.0.0-cpu /bin/bash
-
-2. With GPU enabled. Install [Nvidia-Docker](https://github.com/NVIDIA/nvidia-docker) after install Docker.
-
- nvidia-docker run -it apache/singa:2.0.0-gpu /bin/bash
-
-3. For the complete list of SINGA Docker images (tags), visit the [docker hub site](https://hub.docker.com/r/apache/singa/). For each docker image, the tag is named as
-
- version-(cpu|gpu)[-devel]
-
- | Tag | Description| Example value|
- | --- | --- | --- |
- | `version`| SINGA version | 'nightly', '2.0.0', '1.2.0'|
- | `cpu` | the image cannot run on GPUs | 'cpu' |
- | `gpu` | the image can run on Nvidia GPUs| 'gpu', or 'cudax.x-cudnnx.x' e.g., 'cuda10.0-cudnn7.3'|
- | `devel`| indicator for development|if absent SINGA Python package is installed for runtime only; if present, the building environment is also created, you can recompile SINGA from source at '/root/singa'
-
-* Please note that using the nightly built images is not recommended excpet for SINGA development and testing. Using an official release is recommended. Official releases have version numbers such as '2.0.0' and '1.2.0'.
-
-## From source
-
-You can [build and install SINGA](../develop/build.md) from the source code using native building tools or conda-build, on local host OS or in a Docker container.
-
-## FAQ
-
-* Q: Error from `from singa import tensor`
-
- A: Check the detailed error from
-
- python -c "from singa import _singa_wrap"
- # go to the folder of _singa_wrap.so
- ldd path to _singa_wrap.so
- python
- >> import importlib
- >> importlib.import_module('_singa_wrap')
-
-
- The folder of `_singa_wrap.so` is like ' ~/miniconda3/lib/python3.7/site-packages/singa'.
- Normally, the error is caused by the mismatch or missing of dependent libraries, e.g. cuDNN or protobuf. The solution is to create a new virtual environment and install SINGA in that environment, e.g.,
-
- conda create -n singa
- conda activate singa
- conda install -c nusdbsystem -c conda-forge singa-cpu
-
-
-* Q: When using virtual environment, every time I install SINGA, numpy would be reinstalled. However, the numpy is not used when I run `import numpy`
-
- A: It could be caused by the `PYTHONPATH` environment variable 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 I run SINGA 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 versions of Python in your system, e.g, the one comes with the OS and the one installed by Homebrew. The Python linked by SINGA must be the same as the Python interpreter. You can check your interpreter by `which python` and check the Python linked by SINGA via `otool -L <path to _singa_wrap.so>`.
- This problem should be resolved if SINGA is installation via conda.
diff --git a/content/_sources/docs/layer.rst.txt b/content/_sources/docs/layer.rst.txt
deleted file mode 100644
index 1a576f1..0000000
--- a/content/_sources/docs/layer.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-.. 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
---------
diff --git a/content/_sources/docs/loss.rst.txt b/content/_sources/docs/loss.rst.txt
deleted file mode 100644
index 18c587a..0000000
--- a/content/_sources/docs/loss.rst.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-.. 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:
diff --git a/content/_sources/docs/metric.rst.txt b/content/_sources/docs/metric.rst.txt
deleted file mode 100644
index 20a7144..0000000
--- a/content/_sources/docs/metric.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-.. 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
diff --git a/content/_sources/docs/model_zoo/caffe/README.md.txt b/content/_sources/docs/model_zoo/caffe/README.md.txt
deleted file mode 100644
index 6ac0fd0..0000000
--- a/content/_sources/docs/model_zoo/caffe/README.md.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-<!--
- 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.
--->
-# 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.
diff --git a/content/_sources/docs/model_zoo/char-rnn/README.md.txt b/content/_sources/docs/model_zoo/char-rnn/README.md.txt
deleted file mode 100644
index 6a3a9bd..0000000
--- a/content/_sources/docs/model_zoo/char-rnn/README.md.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-<!--
- 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.
--->
-# 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.
diff --git a/content/_sources/docs/model_zoo/cifar10/README.md.txt b/content/_sources/docs/model_zoo/cifar10/README.md.txt
deleted file mode 100644
index 7bb63e7..0000000
--- a/content/_sources/docs/model_zoo/cifar10/README.md.txt
+++ /dev/null
@@ -1,94 +0,0 @@
-<!--
- 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.
--->
-# 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
diff --git a/content/_sources/docs/model_zoo/examples/caffe/README.md.txt b/content/_sources/docs/model_zoo/examples/caffe/README.md.txt
deleted file mode 100644
index 6ac0fd0..0000000
--- a/content/_sources/docs/model_zoo/examples/caffe/README.md.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-<!--
- 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.
--->
-# 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.
diff --git a/content/_sources/docs/model_zoo/examples/char-rnn/README.md.txt b/content/_sources/docs/model_zoo/examples/char-rnn/README.md.txt
deleted file mode 100644
index 6a3a9bd..0000000
--- a/content/_sources/docs/model_zoo/examples/char-rnn/README.md.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-<!--
- 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.
--->
-# 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.
diff --git a/content/_sources/docs/model_zoo/examples/cifar10/README.md.txt b/content/_sources/docs/model_zoo/examples/cifar10/README.md.txt
deleted file mode 100644
index 7bb63e7..0000000
--- a/content/_sources/docs/model_zoo/examples/cifar10/README.md.txt
+++ /dev/null
@@ -1,94 +0,0 @@
-<!--
- 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.
--->
-# 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
diff --git a/content/_sources/docs/model_zoo/examples/imagenet/alexnet/README.md.txt b/content/_sources/docs/model_zoo/examples/imagenet/alexnet/README.md.txt
deleted file mode 100644
index c3d261e..0000000
--- a/content/_sources/docs/model_zoo/examples/imagenet/alexnet/README.md.txt
+++ /dev/null
@@ -1,76 +0,0 @@
-<!--
- 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.
--->
-# 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.
diff --git a/content/_sources/docs/model_zoo/examples/imagenet/densenet/README.md.txt b/content/_sources/docs/model_zoo/examples/imagenet/densenet/README.md.txt
deleted file mode 100644
index 5238ce9..0000000
--- a/content/_sources/docs/model_zoo/examples/imagenet/densenet/README.md.txt
+++ /dev/null
@@ -1,63 +0,0 @@
-<!--
- 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 Classification using DenseNet
-
-
-In this example, we convert DenseNet on [PyTorch](https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py)
-to SINGA for image classification.
-
-## Instructions
-
-* Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,
-
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-121.tar.gz
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
- $ tar xvf densenet-121.tar.gz
-
-* Usage
-
- $ python serve.py -h
-
-* Example
-
- # use cpu
- $ python serve.py --use_cpu --parameter_file densenet-121.pickle --depth 121 &
- # use gpu
- $ python serve.py --parameter_file densenet-121.pickle --depth 121 &
-
- The parameter files for the following model and depth configuration pairs are provided:
- [121](https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-121.tar.gz), [169](https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-169.tar.gz), [201](https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-201.tar.gz), [161](https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-161.tar.gz)
-
-* Submit images for classification
-
- $ curl -i -F image=@image1.jpg http://localhost:9999/api
- $ curl -i -F image=@image2.jpg http://localhost:9999/api
- $ curl -i -F image=@image3.jpg http://localhost:9999/api
-
-image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
-
-## Details
-
-The parameter files were converted from the pytorch via the convert.py program.
-
-Usage:
-
- $ python convert.py -h
diff --git a/content/_sources/docs/model_zoo/examples/imagenet/googlenet/README.md.txt b/content/_sources/docs/model_zoo/examples/imagenet/googlenet/README.md.txt
deleted file mode 100644
index a1ea8bd..0000000
--- a/content/_sources/docs/model_zoo/examples/imagenet/googlenet/README.md.txt
+++ /dev/null
@@ -1,77 +0,0 @@
-<!--
- 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 Classification using GoogleNet
-
-
-In this example, we convert GoogleNet trained on Caffe to SINGA for image classification. Tested on [SINGA commit](8c990f7da2de220e8a012c6a8ecc897dc7532744) with [the parameters](https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz).
-
-## Instructions
-
-* Download the parameter checkpoint file into this folder
-
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
- $ tar xvf bvlc_googlenet.tar.gz
-
-* Run the program
-
- # use cpu
- $ python serve.py -C &
- # use gpu
- $ python serve.py &
-
-* Submit images for classification
-
- $ curl -i -F image=@image1.jpg http://localhost:9999/api
- $ curl -i -F image=@image2.jpg http://localhost:9999/api
- $ curl -i -F image=@image3.jpg http://localhost:9999/api
-
-image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
-
-## Details
-
-We first extract the parameter values from [Caffe's checkpoint file](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel) into a pickle version
-After downloading the checkpoint file into `caffe_root/python` folder, run the following script
-
- # to be executed within caffe_root/python folder
- import caffe
- import numpy as np
- import cPickle as pickle
-
- model_def = '../models/bvlc_googlenet/deploy.prototxt'
- weight = 'bvlc_googlenet.caffemodel' # must be downloaded at first
- net = caffe.Net(model_def, weight, caffe.TEST)
-
- params = {}
- for layer_name in net.params.keys():
- weights=np.copy(net.params[layer_name][0].data)
- bias=np.copy(net.params[layer_name][1].data)
- params[layer_name+'_weight']=weights
- params[layer_name+'_bias']=bias
- print layer_name, weights.shape, bias.shape
-
- with open('bvlc_googlenet.pickle', 'wb') as fd:
- pickle.dump(params, fd)
-
-Then we construct the GoogleNet using SINGA's FeedForwardNet structure.
-Note that we added a EndPadding layer to resolve the issue from discrepancy
-of the rounding strategy of the pooling layer between Caffe (ceil) and cuDNN (floor).
-Only the MaxPooling layers outside inception blocks have this problem.
-Refer to [this](http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html) for more detials.
diff --git a/content/_sources/docs/model_zoo/examples/imagenet/inception/README.md.txt b/content/_sources/docs/model_zoo/examples/imagenet/inception/README.md.txt
deleted file mode 100644
index 1c564a4..0000000
--- a/content/_sources/docs/model_zoo/examples/imagenet/inception/README.md.txt
+++ /dev/null
@@ -1,53 +0,0 @@
-<!--
- 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 Classification using Inception V4
-
-In this example, we convert Inception V4 trained on Tensorflow to SINGA for image classification. Tested on SINGA version 1.1.1 with [parameters pretrained by tensorflow](https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz).
-
-## Instructions
-
-* Download the parameter checkpoint file
-
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz
- $ tar xvf inception_v4.tar.gz
-
-* Download [synset_word.txt](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh) file.
-
-* Run the program
-
- # use cpu
- $ python serve.py -C &
- # use gpu
- $ python serve.py &
-
-* Submit images for classification
-
- $ curl -i -F image=@image1.jpg http://localhost:9999/api
- $ curl -i -F image=@image2.jpg http://localhost:9999/api
- $ curl -i -F image=@image3.jpg http://localhost:9999/api
-
-image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
-
-## Details
-
-We first extract the parameter values from [Tensorflow's checkpoint file](http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz) into a pickle version.
-After downloading and decompressing the checkpoint file, run the following script
-
- $ python convert.py --file_name=inception_v4.ckpt
diff --git a/content/_sources/docs/model_zoo/examples/imagenet/resnet/README.md.txt b/content/_sources/docs/model_zoo/examples/imagenet/resnet/README.md.txt
deleted file mode 100644
index eb317b8..0000000
--- a/content/_sources/docs/model_zoo/examples/imagenet/resnet/README.md.txt
+++ /dev/null
@@ -1,66 +0,0 @@
-<!--
- 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 Classification using Residual Networks
-
-
-In this example, we convert Residual Networks trained on [Torch](https://github.com/facebook/fb.resnet.torch) to SINGA for image classification. Tested on [SINGA commit] with the [parameters pretrained by Torch](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz)
-
-## Instructions
-
-* Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,
-
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
- $ tar xvf resnet-18.tar.gz
-
-* Usage
-
- $ python serve.py -h
-
-* Example
-
- # use cpu
- $ python serve.py --use_cpu --parameter_file resnet-18.pickle --model resnet --depth 18 &
- # use gpu
- $ python serve.py --parameter_file resnet-18.pickle --model resnet --depth 18 &
-
- The parameter files for the following model and depth configuration pairs are provided:
- * resnet (original resnet), [18](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz)|[34](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-34.tar.gz)|[101](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-101.tar.gz)|[152](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-152.tar.gz)
- * addbn (resnet with a batch normalization layer after the addition), [50](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-50.tar.gz)
- * wrn (wide resnet), [50](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/wrn-50-2.tar.gz)
- * preact (resnet with pre-activation) [200](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-200.tar.gz)
-
-* Submit images for classification
-
- $ curl -i -F image=@image1.jpg http://localhost:9999/api
- $ curl -i -F image=@image2.jpg http://localhost:9999/api
- $ curl -i -F image=@image3.jpg http://localhost:9999/api
-
-image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
-
-## Details
-
-The parameter files were extracted from the original [torch files](https://github.com/facebook/fb.resnet.torch/tree/master/pretrained) via
-the convert.py program.
-
-Usage:
-
- $ python convert.py -h
diff --git a/content/_sources/docs/model_zoo/examples/imagenet/vgg/README.md.txt b/content/_sources/docs/model_zoo/examples/imagenet/vgg/README.md.txt
deleted file mode 100644
index dbf7ccd..0000000
--- a/content/_sources/docs/model_zoo/examples/imagenet/vgg/README.md.txt
+++ /dev/null
@@ -1,63 +0,0 @@
-<!--
- 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 Classification using VGG
-
-
-In this example, we convert VGG on [PyTorch](https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py)
-to SINGA for image classification.
-
-## Instructions
-
-* Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,
-
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11.tar.gz
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
- $ tar xvf vgg11.tar.gz
-
-* Usage
-
- $ python serve.py -h
-
-* Example
-
- # use cpu
- $ python serve.py --use_cpu --parameter_file vgg11.pickle --depth 11 &
- # use gpu
- $ python serve.py --parameter_file vgg11.pickle --depth 11 &
-
- The parameter files for the following model and depth configuration pairs are provided:
- * Without batch-normalization, [11](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11.tar.gz), [13](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg13.tar.gz), [16](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg16.tar.gz), [19](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg19.tar.gz)
- * With batch-normalization, [11](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11_bn.tar.gz), [13](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg13_bn.tar.gz), [16](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg16_bn.tar.gz), [19](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg19_bn.tar.gz)
-
-* Submit images for classification
-
- $ curl -i -F image=@image1.jpg http://localhost:9999/api
- $ curl -i -F image=@image2.jpg http://localhost:9999/api
- $ curl -i -F image=@image3.jpg http://localhost:9999/api
-
-image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
-
-## Details
-
-The parameter files were converted from the pytorch via the convert.py program.
-
-Usage:
-
- $ python convert.py -h
diff --git a/content/_sources/docs/model_zoo/examples/index.rst.txt b/content/_sources/docs/model_zoo/examples/index.rst.txt
deleted file mode 100644
index fca66f9..0000000
--- a/content/_sources/docs/model_zoo/examples/index.rst.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-..
-.. 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.
-..
-
-Model Zoo
-=========
-
-.. toctree::
-
- cifar10/README
- char-rnn/README
- mnist/README
- imagenet/alexnet/README
- imagenet/densenet/README
- imagenet/googlenet/README
- imagenet/inception/README
- imagenet/resnet/README
- imagenet/vgg/README
-
diff --git a/content/_sources/docs/model_zoo/examples/mnist/README.md.txt b/content/_sources/docs/model_zoo/examples/mnist/README.md.txt
deleted file mode 100644
index 6d91380..0000000
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+++ /dev/null
@@ -1,36 +0,0 @@
-<!--
- 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.
--->
-# Train a RBM model against MNIST dataset
-
-This example is to train an RBM model using the
-MNIST dataset. The RBM model and its hyper-parameters are set following
-[Hinton's paper](http://www.cs.toronto.edu/~hinton/science.pdf)
-
-## Running instructions
-
-1. Download the pre-processed [MNIST dataset](https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz)
-
-2. Start the training
-
- python train.py mnist.pkl.gz
-
-By default the training code would run on CPU. To run it on a GPU card, please start
-the program with an additional argument
-
- python train.py mnist.pkl.gz --use_gpu
diff --git a/content/_sources/docs/model_zoo/imagenet/alexnet/README.md.txt b/content/_sources/docs/model_zoo/imagenet/alexnet/README.md.txt
deleted file mode 100644
index c3d261e..0000000
--- a/content/_sources/docs/model_zoo/imagenet/alexnet/README.md.txt
+++ /dev/null
@@ -1,76 +0,0 @@
-<!--
- 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.
--->
-# 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.
diff --git a/content/_sources/docs/model_zoo/imagenet/densenet/README.md.txt b/content/_sources/docs/model_zoo/imagenet/densenet/README.md.txt
deleted file mode 100644
index 5238ce9..0000000
--- a/content/_sources/docs/model_zoo/imagenet/densenet/README.md.txt
+++ /dev/null
@@ -1,63 +0,0 @@
-<!--
- 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 Classification using DenseNet
-
-
-In this example, we convert DenseNet on [PyTorch](https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py)
-to SINGA for image classification.
-
-## Instructions
-
-* Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,
-
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-121.tar.gz
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
- $ tar xvf densenet-121.tar.gz
-
-* Usage
-
- $ python serve.py -h
-
-* Example
-
- # use cpu
- $ python serve.py --use_cpu --parameter_file densenet-121.pickle --depth 121 &
- # use gpu
- $ python serve.py --parameter_file densenet-121.pickle --depth 121 &
-
- The parameter files for the following model and depth configuration pairs are provided:
- [121](https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-121.tar.gz), [169](https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-169.tar.gz), [201](https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-201.tar.gz), [161](https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-161.tar.gz)
-
-* Submit images for classification
-
- $ curl -i -F image=@image1.jpg http://localhost:9999/api
- $ curl -i -F image=@image2.jpg http://localhost:9999/api
- $ curl -i -F image=@image3.jpg http://localhost:9999/api
-
-image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
-
-## Details
-
-The parameter files were converted from the pytorch via the convert.py program.
-
-Usage:
-
- $ python convert.py -h
diff --git a/content/_sources/docs/model_zoo/imagenet/googlenet/README.md.txt b/content/_sources/docs/model_zoo/imagenet/googlenet/README.md.txt
deleted file mode 100644
index a1ea8bd..0000000
--- a/content/_sources/docs/model_zoo/imagenet/googlenet/README.md.txt
+++ /dev/null
@@ -1,77 +0,0 @@
-<!--
- 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 Classification using GoogleNet
-
-
-In this example, we convert GoogleNet trained on Caffe to SINGA for image classification. Tested on [SINGA commit](8c990f7da2de220e8a012c6a8ecc897dc7532744) with [the parameters](https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz).
-
-## Instructions
-
-* Download the parameter checkpoint file into this folder
-
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
- $ tar xvf bvlc_googlenet.tar.gz
-
-* Run the program
-
- # use cpu
- $ python serve.py -C &
- # use gpu
- $ python serve.py &
-
-* Submit images for classification
-
- $ curl -i -F image=@image1.jpg http://localhost:9999/api
- $ curl -i -F image=@image2.jpg http://localhost:9999/api
- $ curl -i -F image=@image3.jpg http://localhost:9999/api
-
-image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
-
-## Details
-
-We first extract the parameter values from [Caffe's checkpoint file](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel) into a pickle version
-After downloading the checkpoint file into `caffe_root/python` folder, run the following script
-
- # to be executed within caffe_root/python folder
- import caffe
- import numpy as np
- import cPickle as pickle
-
- model_def = '../models/bvlc_googlenet/deploy.prototxt'
- weight = 'bvlc_googlenet.caffemodel' # must be downloaded at first
- net = caffe.Net(model_def, weight, caffe.TEST)
-
- params = {}
- for layer_name in net.params.keys():
- weights=np.copy(net.params[layer_name][0].data)
- bias=np.copy(net.params[layer_name][1].data)
- params[layer_name+'_weight']=weights
- params[layer_name+'_bias']=bias
- print layer_name, weights.shape, bias.shape
-
- with open('bvlc_googlenet.pickle', 'wb') as fd:
- pickle.dump(params, fd)
-
-Then we construct the GoogleNet using SINGA's FeedForwardNet structure.
-Note that we added a EndPadding layer to resolve the issue from discrepancy
-of the rounding strategy of the pooling layer between Caffe (ceil) and cuDNN (floor).
-Only the MaxPooling layers outside inception blocks have this problem.
-Refer to [this](http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html) for more detials.
diff --git a/content/_sources/docs/model_zoo/imagenet/inception/README.md.txt b/content/_sources/docs/model_zoo/imagenet/inception/README.md.txt
deleted file mode 100644
index 1c564a4..0000000
--- a/content/_sources/docs/model_zoo/imagenet/inception/README.md.txt
+++ /dev/null
@@ -1,53 +0,0 @@
-<!--
- 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 Classification using Inception V4
-
-In this example, we convert Inception V4 trained on Tensorflow to SINGA for image classification. Tested on SINGA version 1.1.1 with [parameters pretrained by tensorflow](https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz).
-
-## Instructions
-
-* Download the parameter checkpoint file
-
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz
- $ tar xvf inception_v4.tar.gz
-
-* Download [synset_word.txt](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh) file.
-
-* Run the program
-
- # use cpu
- $ python serve.py -C &
- # use gpu
- $ python serve.py &
-
-* Submit images for classification
-
- $ curl -i -F image=@image1.jpg http://localhost:9999/api
- $ curl -i -F image=@image2.jpg http://localhost:9999/api
- $ curl -i -F image=@image3.jpg http://localhost:9999/api
-
-image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
-
-## Details
-
-We first extract the parameter values from [Tensorflow's checkpoint file](http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz) into a pickle version.
-After downloading and decompressing the checkpoint file, run the following script
-
- $ python convert.py --file_name=inception_v4.ckpt
diff --git a/content/_sources/docs/model_zoo/imagenet/resnet/README.md.txt b/content/_sources/docs/model_zoo/imagenet/resnet/README.md.txt
deleted file mode 100644
index eb317b8..0000000
--- a/content/_sources/docs/model_zoo/imagenet/resnet/README.md.txt
+++ /dev/null
@@ -1,66 +0,0 @@
-<!--
- 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 Classification using Residual Networks
-
-
-In this example, we convert Residual Networks trained on [Torch](https://github.com/facebook/fb.resnet.torch) to SINGA for image classification. Tested on [SINGA commit] with the [parameters pretrained by Torch](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz)
-
-## Instructions
-
-* Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,
-
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
- $ tar xvf resnet-18.tar.gz
-
-* Usage
-
- $ python serve.py -h
-
-* Example
-
- # use cpu
- $ python serve.py --use_cpu --parameter_file resnet-18.pickle --model resnet --depth 18 &
- # use gpu
- $ python serve.py --parameter_file resnet-18.pickle --model resnet --depth 18 &
-
- The parameter files for the following model and depth configuration pairs are provided:
- * resnet (original resnet), [18](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz)|[34](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-34.tar.gz)|[101](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-101.tar.gz)|[152](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-152.tar.gz)
- * addbn (resnet with a batch normalization layer after the addition), [50](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-50.tar.gz)
- * wrn (wide resnet), [50](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/wrn-50-2.tar.gz)
- * preact (resnet with pre-activation) [200](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-200.tar.gz)
-
-* Submit images for classification
-
- $ curl -i -F image=@image1.jpg http://localhost:9999/api
- $ curl -i -F image=@image2.jpg http://localhost:9999/api
- $ curl -i -F image=@image3.jpg http://localhost:9999/api
-
-image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
-
-## Details
-
-The parameter files were extracted from the original [torch files](https://github.com/facebook/fb.resnet.torch/tree/master/pretrained) via
-the convert.py program.
-
-Usage:
-
- $ python convert.py -h
diff --git a/content/_sources/docs/model_zoo/imagenet/vgg/README.md.txt b/content/_sources/docs/model_zoo/imagenet/vgg/README.md.txt
deleted file mode 100644
index dbf7ccd..0000000
--- a/content/_sources/docs/model_zoo/imagenet/vgg/README.md.txt
+++ /dev/null
@@ -1,63 +0,0 @@
-<!--
- 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 Classification using VGG
-
-
-In this example, we convert VGG on [PyTorch](https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py)
-to SINGA for image classification.
-
-## Instructions
-
-* Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,
-
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11.tar.gz
- $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
- $ tar xvf vgg11.tar.gz
-
-* Usage
-
- $ python serve.py -h
-
-* Example
-
- # use cpu
- $ python serve.py --use_cpu --parameter_file vgg11.pickle --depth 11 &
- # use gpu
- $ python serve.py --parameter_file vgg11.pickle --depth 11 &
-
- The parameter files for the following model and depth configuration pairs are provided:
- * Without batch-normalization, [11](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11.tar.gz), [13](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg13.tar.gz), [16](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg16.tar.gz), [19](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg19.tar.gz)
- * With batch-normalization, [11](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11_bn.tar.gz), [13](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg13_bn.tar.gz), [16](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg16_bn.tar.gz), [19](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg19_bn.tar.gz)
-
-* Submit images for classification
-
- $ curl -i -F image=@image1.jpg http://localhost:9999/api
- $ curl -i -F image=@image2.jpg http://localhost:9999/api
- $ curl -i -F image=@image3.jpg http://localhost:9999/api
-
-image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
-
-## Details
-
-The parameter files were converted from the pytorch via the convert.py program.
-
-Usage:
-
- $ python convert.py -h
diff --git a/content/_sources/docs/model_zoo/index.rst.txt b/content/_sources/docs/model_zoo/index.rst.txt
deleted file mode 100644
index fca66f9..0000000
--- a/content/_sources/docs/model_zoo/index.rst.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-..
-.. 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.
-..
-
-Model Zoo
-=========
-
-.. toctree::
-
- cifar10/README
- char-rnn/README
- mnist/README
- imagenet/alexnet/README
- imagenet/densenet/README
- imagenet/googlenet/README
- imagenet/inception/README
- imagenet/resnet/README
- imagenet/vgg/README
-
diff --git a/content/_sources/docs/model_zoo/mnist/README.md.txt b/content/_sources/docs/model_zoo/mnist/README.md.txt
deleted file mode 100644
index 6d91380..0000000
--- a/content/_sources/docs/model_zoo/mnist/README.md.txt
+++ /dev/null
@@ -1,36 +0,0 @@
-<!--
- 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.
--->
-# Train a RBM model against MNIST dataset
-
-This example is to train an RBM model using the
-MNIST dataset. The RBM model and its hyper-parameters are set following
-[Hinton's paper](http://www.cs.toronto.edu/~hinton/science.pdf)
-
-## Running instructions
-
-1. Download the pre-processed [MNIST dataset](https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz)
-
-2. Start the training
-
- python train.py mnist.pkl.gz
-
-By default the training code would run on CPU. To run it on a GPU card, please start
-the program with an additional argument
-
- python train.py mnist.pkl.gz --use_gpu
diff --git a/content/_sources/docs/net.rst.txt b/content/_sources/docs/net.rst.txt
deleted file mode 100644
index 7aff364..0000000
--- a/content/_sources/docs/net.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-.. 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.
-
-
-FeedForward Net
-===============
-
-.. automodule:: singa.net
- :members:
- :member-order: bysource
- :show-inheritance:
- :undoc-members:
diff --git a/content/_sources/docs/neural-net.md.txt b/content/_sources/docs/neural-net.md.txt
deleted file mode 100644
index e59a20c..0000000
--- a/content/_sources/docs/neural-net.md.txt
+++ /dev/null
@@ -1,344 +0,0 @@
-<!--
- 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.
--->
-# Neural Net
-
-
-`NeuralNet` in SINGA represents an instance of user's neural net model. As the
-neural net typically consists of a set of layers, `NeuralNet` comprises
-a set of unidirectionally connected [Layer](layer.html)s.
-This page describes how to convert an user's neural net into
-the configuration of `NeuralNet`.
-
-<img src="../_static/images/model-category.png" align="center" width="200px"/>
-<span><strong>Figure 1 - Categorization of popular deep learning models.</strong></span>
-
-## Net structure configuration
-
-Users configure the `NeuralNet` by listing all layers of the neural net and
-specifying each layer's source layer names. Popular deep learning models can be
-categorized as Figure 1. The subsequent sections give details for each
-category.
-
-### Feed-forward models
-
-<div align = "left">
-<img src="../_static/images/mlp-net.png" align="center" width="200px"/>
-<span><strong>Figure 2 - Net structure of a MLP model.</strong></span>
-</div>
-
-Feed-forward models, e.g., CNN and MLP, can easily get configured as their layer
-connections are undirected without circles. The
-configuration for the MLP model shown in Figure 1 is as follows,
-
- net {
- layer {
- name : 'data"
- type : kData
- }
- layer {
- name : 'image"
- type : kImage
- srclayer: 'data'
- }
- layer {
- name : 'label"
- type : kLabel
- srclayer: 'data'
- }
- layer {
- name : 'hidden"
- type : kHidden
- srclayer: 'image'
- }
- layer {
- name : 'softmax"
- type : kSoftmaxLoss
- srclayer: 'hidden'
- srclayer: 'label'
- }
- }
-
-### Energy models
-
-<img src="../_static/images/rbm-rnn.png" align="center" width="500px"/>
-<span><strong>Figure 3 - Convert connections in RBM and RNN.</strong></span>
-
-
-For energy models including RBM, DBM,
-etc., their connections are undirected (i.e., Category B). To represent these models using
-`NeuralNet`, users can simply replace each connection with two directed
-connections, as shown in Figure 3a. In other words, for each pair of connected layers, their source
-layer field should include each other's name.
-The full [RBM example](rbm.html) has
-detailed neural net configuration for a RBM model, which looks like
-
- net {
- layer {
- name : "vis"
- type : kVisLayer
- param {
- name : "w1"
- }
- srclayer: "hid"
- }
- layer {
- name : "hid"
- type : kHidLayer
- param {
- name : "w2"
- share_from: "w1"
- }
- srclayer: "vis"
- }
- }
-
-### RNN models
-
-For recurrent neural networks (RNN), users can remove the recurrent connections
-by unrolling the recurrent layer. For example, in Figure 3b, the original
-layer is unrolled into a new layer with 4 internal layers. In this way, the
-model is like a normal feed-forward model, thus can be configured similarly.
-The [RNN example](rnn.html) has a full neural net
-configuration for a RNN model.
-
-
-## Configuration for multiple nets
-
-Typically, a training job includes three neural nets for
-training, validation and test phase respectively. The three neural nets share most
-layers except the data layer, loss layer or output layer, etc.. To avoid
-redundant configurations for the shared layers, users can uses the `exclude`
-filed to filter a layer in the neural net, e.g., the following layer will be
-filtered when creating the testing `NeuralNet`.
-
-
- layer {
- ...
- exclude : kTest # filter this layer for creating test net
- }
-
-
-
-## Neural net partitioning
-
-A neural net can be partitioned in different ways to distribute the training
-over multiple workers.
-
-### Batch and feature dimension
-
-<img src="../_static/images/partition_fc.png" align="center" width="400px"/>
-<span><strong>Figure 4 - Partitioning of a fully connected layer.</strong></span>
-
-
-Every layer's feature blob is considered a matrix whose rows are feature
-vectors. Thus, one layer can be split on two dimensions. Partitioning on
-dimension 0 (also called batch dimension) slices the feature matrix by rows.
-For instance, if the mini-batch size is 256 and the layer is partitioned into 2
-sub-layers, each sub-layer would have 128 feature vectors in its feature blob.
-Partitioning on this dimension has no effect on the parameters, as every
-[Param](param.html) object is replicated in the sub-layers. Partitioning on dimension
-1 (also called feature dimension) slices the feature matrix by columns. For
-example, suppose the original feature vector has 50 units, after partitioning
-into 2 sub-layers, each sub-layer would have 25 units. This partitioning may
-result in [Param](param.html) object being split, as shown in
-Figure 4. Both the bias vector and weight matrix are
-partitioned into two sub-layers.
-
-
-### Partitioning configuration
-
-There are 4 partitioning schemes, whose configurations are give below,
-
- 1. Partitioning each singe layer into sub-layers on batch dimension (see
- below). It is enabled by configuring the partition dimension of the layer to
- 0, e.g.,
-
- # with other fields omitted
- layer {
- partition_dim: 0
- }
-
- 2. Partitioning each singe layer into sub-layers on feature dimension (see
- below). It is enabled by configuring the partition dimension of the layer to
- 1, e.g.,
-
- # with other fields omitted
- layer {
- partition_dim: 1
- }
-
- 3. Partitioning all layers into different subsets. It is enabled by
- configuring the location ID of a layer, e.g.,
-
- # with other fields omitted
- layer {
- location: 1
- }
- layer {
- location: 0
- }
-
-
- 4. Hybrid partitioning of strategy 1, 2 and 3. The hybrid partitioning is
- useful for large models. An example application is to implement the
- [idea proposed by Alex](http://arxiv.org/abs/1404.5997).
- Hybrid partitioning is configured like,
-
- # with other fields omitted
- layer {
- location: 1
- }
- layer {
- location: 0
- }
- layer {
- partition_dim: 0
- location: 0
- }
- layer {
- partition_dim: 1
- location: 0
- }
-
-Currently SINGA supports strategy-2 well. Other partitioning strategies are
-are under test and will be released in later version.
-
-## Parameter sharing
-
-Parameters can be shared in two cases,
-
- * sharing parameters among layers via user configuration. For example, the
- visible layer and hidden layer of a RBM shares the weight matrix, which is configured through
- the `share_from` field as shown in the above RBM configuration. The
- configurations must be the same (except name) for shared parameters.
-
- * due to neural net partitioning, some `Param` objects are replicated into
- different workers, e.g., partitioning one layer on batch dimension. These
- workers share parameter values. SINGA controls this kind of parameter
- sharing automatically, users do not need to do any configuration.
-
- * the `NeuralNet` for training and testing (and validation) share most layers
- , thus share `Param` values.
-
-If the shared `Param` instances resident in the same process (may in different
-threads), they use the same chunk of memory space for their values. But they
-would have different memory spaces for their gradients. In fact, their
-gradients will be averaged by the stub or server.
-
-## Advanced user guide
-
-### Creation
-
- static NeuralNet* NeuralNet::Create(const NetProto& np, Phase phase, int num);
-
-The above function creates a `NeuralNet` for a given phase, and returns a
-pointer to the `NeuralNet` instance. The phase is in {kTrain,
-kValidation, kTest}. `num` is used for net partitioning which indicates the
-number of partitions. Typically, a training job includes three neural nets for
-training, validation and test phase respectively. The three neural nets share most
-layers except the data layer, loss layer or output layer, etc.. The `Create`
-function takes in the full net configuration including layers for training,
-validation and test. It removes layers for phases other than the specified
-phase based on the `exclude` field in
-[layer configuration](layer.html):
-
- layer {
- ...
- exclude : kTest # filter this layer for creating test net
- }
-
-The filtered net configuration is passed to the constructor of `NeuralNet`:
-
- NeuralNet::NeuralNet(NetProto netproto, int npartitions);
-
-The constructor creates a graph representing the net structure firstly in
-
- Graph* NeuralNet::CreateGraph(const NetProto& netproto, int npartitions);
-
-Next, it creates a layer for each node and connects layers if their nodes are
-connected.
-
- void NeuralNet::CreateNetFromGraph(Graph* graph, int npartitions);
-
-Since the `NeuralNet` instance may be shared among multiple workers, the
-`Create` function returns a pointer to the `NeuralNet` instance .
-
-### Parameter sharing
-
- `Param` sharing
-is enabled by first sharing the Param configuration (in `NeuralNet::Create`)
-to create two similar (e.g., the same shape) Param objects, and then calling
-(in `NeuralNet::CreateNetFromGraph`),
-
- void Param::ShareFrom(const Param& from);
-
-It is also possible to share `Param`s of two nets, e.g., sharing parameters of
-the training net and the test net,
-
- void NeuralNet:ShareParamsFrom(NeuralNet* other);
-
-It will call `Param::ShareFrom` for each Param object.
-
-### Access functions
-`NeuralNet` provides a couple of access function to get the layers and params
-of the net:
-
- const std::vector<Layer*>& layers() const;
- const std::vector<Param*>& params() const ;
- Layer* name2layer(string name) const;
- Param* paramid2param(int id) const;
-
-
-### Partitioning
-
-
-#### Implementation
-
-SINGA partitions the neural net in `CreateGraph` function, which creates one
-node for each (partitioned) layer. For example, if one layer's partition
-dimension is 0 or 1, then it creates `npartition` nodes for it; if the
-partition dimension is -1, a single node is created, i.e., no partitioning.
-Each node is assigned a partition (or location) ID. If the original layer is
-configured with a location ID, then the ID is assigned to each newly created node.
-These nodes are connected according to the connections of the original layers.
-Some connection layers will be added automatically.
-For instance, if two connected sub-layers are located at two
-different workers, then a pair of bridge layers is inserted to transfer the
-feature (and gradient) blob between them. When two layers are partitioned on
-different dimensions, a concatenation layer which concatenates feature rows (or
-columns) and a slice layer which slices feature rows (or columns) would be
-inserted. These connection layers help making the network communication and
-synchronization transparent to the users.
-
-#### Dispatching partitions to workers
-
-Each (partitioned) layer is assigned a location ID, based on which it is dispatched to one
-worker. Particularly, the pointer to the `NeuralNet` instance is passed
-to every worker within the same group, but each worker only computes over the
-layers that have the same partition (or location) ID as the worker's ID. When
-every worker computes the gradients of the entire model parameters
-(strategy-2), we refer to this process as data parallelism. When different
-workers compute the gradients of different parameters (strategy-3 or
-strategy-1), we call this process model parallelism. The hybrid partitioning
-leads to hybrid parallelism where some workers compute the gradients of the
-same subset of model parameters while other workers compute on different model
-parameters. For example, to implement the hybrid parallelism in for the
-[DCNN model](http://arxiv.org/abs/1404.5997), we set `partition_dim = 0` for
-lower layers and `partition_dim = 1` for higher layers.
-
diff --git a/content/_sources/docs/notebook/README.md.txt b/content/_sources/docs/notebook/README.md.txt
deleted file mode 100644
index c4f9778..0000000
--- a/content/_sources/docs/notebook/README.md.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-<!--
- 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.
--->
-These are some examples in IPython notebooks.
-
-You can open them in [notebook viewer](http://nbviewer.jupyter.org/github/apache/singa/blob/master/doc/en/docs/notebook/index.ipynb).
diff --git a/content/_sources/docs/onnx.rst.txt b/content/_sources/docs/onnx.rst.txt
deleted file mode 100644
index f180f2a..0000000
--- a/content/_sources/docs/onnx.rst.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-.. 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.
-
-
-ONNX
-====
-
-(under construction)
-
-ONNX ....
\ No newline at end of file
diff --git a/content/_sources/docs/optimizer.rst.txt b/content/_sources/docs/optimizer.rst.txt
deleted file mode 100644
index e6f1da9..0000000
--- a/content/_sources/docs/optimizer.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-.. 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.
-
-
-Optimizer
-=========
-
-
-.. automodule:: singa.optimizer
- :members:
- :member-order: bysource
- :show-inheritance:
- :undoc-members:
-
-
diff --git a/content/_sources/docs/security.rst.txt b/content/_sources/docs/security.rst.txt
deleted file mode 100644
index 1b9eaac..0000000
--- a/content/_sources/docs/security.rst.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-.. 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.
-
-Security
-========
-
-Users can report security vulnerabilities to Apache Security Team: http://www.apache.org/security/
-
-SINGA is creating a new team for security. More details will be added soon.
diff --git a/content/_sources/docs/snapshot.rst.txt b/content/_sources/docs/snapshot.rst.txt
deleted file mode 100644
index 7a8be27..0000000
--- a/content/_sources/docs/snapshot.rst.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-.. 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.
-
-
-Snapshot
-========
-
-
-.. automodule:: singa.snapshot
- :members:
diff --git a/content/_sources/docs/software_stack.md.txt b/content/_sources/docs/software_stack.md.txt
deleted file mode 100644
index 141f4ab..0000000
--- a/content/_sources/docs/software_stack.md.txt
+++ /dev/null
@@ -1,117 +0,0 @@
-<!--
- 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.
--->
-# Software Stack
-
-SINGA's software stack includes three major components, namely, core, IO and
-model. Figure 1 illustrates these components together with the hardware.
-The core component provides memory management and tensor operations;
-IO has classes for reading (and writing) data from (to) disk and network; The
-model component provides data structures and algorithms for machine learning models,
-e.g., layers for neural network models, optimizers/initializer/metric/loss for
-general machine learning models.
-
-
-<img src="../_static/images/singav1-sw.png" align="center" width="500px"/>
-<br/>
-<span><strong>Figure 1 - SINGA V1 software stack.</strong></span>
-
-## Core
-
-[Tensor](tensor.html) and [Device](device.html) are two core abstractions in SINGA. Tensor class represents a
-multi-dimensional array, which stores model variables and provides linear algebra
-operations for machine learning
-algorithms, including matrix multiplication and random functions. Each tensor
-instance (i.e. a tensor) is allocated on a Device instance.
-Each Device instance (i.e. a device) is created against one hardware device,
-e.g. a GPU card or a CPU core. Devices manage the memory of tensors and execute
-tensor operations on its execution units, e.g. CPU threads or CUDA streams.
-
-Depending on the hardware and the programming language, SINGA have implemented
-the following specific device classes:
-
-* **CudaGPU** represents an Nvidia GPU card. The execution units are the CUDA streams.
-* **CppCPU** represents a normal CPU. The execution units are the CPU threads.
-* **OpenclGPU** represents normal GPU card from both Nvidia and AMD.
- The execution units are the CommandQueues. Given that OpenCL is compatible with
- many hardware devices, e.g. FPGA and ARM, the OpenclGPU has the potential to be
- extended for other devices.
-
-Different types of devices use different programming languages to write the kernel
-functions for tensor operations,
-
-* CppMath (tensor_math_cpp.h) implements the tensor operations using Cpp for CppCPU
-* CudaMath (tensor_math_cuda.h) implements the tensor operations using CUDA for CudaGPU
-* OpenclMath (tensor_math_opencl.h) implements the tensor operations using OpenCL for OpenclGPU
-
-In addition, different types of data, such as float32 and float16, could be supported by adding
-the corresponding tensor functions.
-
-Typically, users would create a device instance and pass it to create multiple
-tensor instances. When users call the Tensor functions, these function would invoke
-the corresponding implementation (CppMath/CudaMath/OpenclMath) automatically. In
-other words, the implementation of Tensor operations is transparent to users.
-
-Most machine learning algorithms could be expressed using (dense or sparse) tensors.
-Therefore, with the Tensor abstraction, SINGA would be able to run a wide range of models,
-including deep learning models and other traditional machine learning models.
-
-The Tensor and Device abstractions are extensible to support a wide range of hardware device
-using different programming languages. A new hardware device would be supported by
-adding a new Device subclass and the corresponding implementation of the Tensor
-operations (xxxMath).
-
-Optimizations in terms of speed and memory could be implemented by Device, which
-manages both operation execution and memory malloc/free. More optimization details
-would be described in the [Device page](device.html).
-
-
-## Model
-
-On top of the Tensor and Device abstractions, SINGA provides some higher level
-classes for machine learning modules.
-
-* [Layer](layer.html) and its subclasses are specific for neural networks. Every layer provides
- functions for forward propagating features and backward propagating gradients w.r.t the training loss functions.
- They wraps the complex layer operations so that users can easily create neural nets
- by connecting a set of layers.
-
-* [Initializer](initializer.html) and its subclasses provide variant methods of initializing
- model parameters (stored in Tensor instances), following Uniform, Gaussian, etc.
-
-* [Loss](loss.html) and its subclasses defines the training objective loss functions.
- Both functions of computing the loss values and computing the gradient of the prediction w.r.t the
- objective loss are implemented. Example loss functions include squared error and cross entropy.
-
-* [Metric](metric.html) and its subclasses provide the function to measure the
- performance of the model, e.g., the accuracy.
-
-* [Optimizer](optimizer.html) and its subclasses implement the methods for updating
- model parameter values using parameter gradients, including SGD, AdaGrad, RMSProp etc.
-
-
-## IO
-
-The IO module consists of classes for data loading, data preprocessing and message passing.
-
-* Reader and its subclasses load string records from disk files
-* Writer and its subclasses write string records to disk files
-* Encoder and its subclasses encode Tensor instances into string records
-* Decoder and its subclasses decodes string records into Tensor instances
-* Endpoint represents a communication endpoint which provides functions for passing messages to each other.
-* Message represents communication messages between Endpoint instances. It carries both meta data and payload.
diff --git a/content/_sources/docs/tensor.rst.txt b/content/_sources/docs/tensor.rst.txt
deleted file mode 100644
index d9e7f18..0000000
--- a/content/_sources/docs/tensor.rst.txt
+++ /dev/null
@@ -1,48 +0,0 @@
-.. 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.
-
-
-Tensor
-========
-
-Each Tensor instance is a multi-dimensional array allocated on a specific
-Device instance. Tensor instances store variables and provide
-linear algebra operations over different types of hardware devices without user
-awareness. Note that users need to make sure the tensor operands are
-allocated on the same device except copy functions.
-
-
-Tensor implementation
----------------------
-
-SINGA has three different sets of implmentations of Tensor functions, one for each
-type of Device.
-
-* 'tensor_math_cpp.h' implements operations using Cpp (with CBLAS) for CppGPU devices.
-* 'tensor_math_cuda.h' implements operations using Cuda (with cuBLAS) for CudaGPU devices.
-* 'tensor_math_opencl.h' implements operations using OpenCL for OpenclGPU devices.
-
-Python API
-----------
-
-
-.. automodule:: singa.tensor
- :members:
-
-
-CPP API
----------
diff --git a/content/_sources/docs/utils.rst.txt b/content/_sources/docs/utils.rst.txt
deleted file mode 100644
index 5a5aa04..0000000
--- a/content/_sources/docs/utils.rst.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-.. 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.
-
-
-Utils
-=========
-
-
-.. automodule:: singa.utils
- :members:
diff --git a/content/_sources/downloads.md.txt b/content/_sources/downloads.md.txt
deleted file mode 100644
index 07933b5..0000000
--- a/content/_sources/downloads.md.txt
+++ /dev/null
@@ -1,166 +0,0 @@
-<!--
- 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.
--->
-
-# Download SINGA
-
-* To verify the downloaded tar.gz file, download the [KEYS](https://www.apache.org/dist/incubator/singa/KEYS) and ASC files and then execute the following commands
-
- % gpg --import KEYS
- % gpg --verify downloaded_file.asc downloaded_file
-
- You can also check the SHA512 or MD5 values to see if the download is completed.
-
-
-* Incubating v2.0.0 (20 April 2019):
- * [Apache SINGA 2.0.0 (incubating)](http://www.apache.org/dyn/closer.cgi/incubator/singa/2.0.0/apache-singa-incubating-2.0.0.tar.gz)
- [\[SHA512\]](https://www.apache.org/dist/incubator/singa/2.0.0/apache-singa-incubating-2.0.0.tar.gz.sha512)
- [\[ASC\]](https://www.apache.org/dist/incubator/singa/2.0.0/apache-singa-incubating-2.0.0.tar.gz.asc)
- * [Release Notes 2.0.0 (incubating)](releases/RELEASE_NOTES_2.0.0.html)
- * New features and major updates,
- * Enhance autograd (for Convolution networks and recurrent networks)
- * Support ONNX
- * Improve the CPP operations via Intel MKL DNN lib
- * Implement tensor broadcasting
- * Move Docker images under Apache user name
- * Update depdent lib versions in conda-build config
-
-
-* Incubating v1.2.0 (6 June 2018):
- * [Apache SINGA 1.2.0 (incubating)](https://archive.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz)
- [\[SHA512\]](https://archive.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz.sha512)
- [\[ASC\]](https://archive.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz.asc)
- * [Release Notes 1.2.0 (incubating)](releases/RELEASE_NOTES_1.2.0.html)
- * New features and major updates,
- * Implement autograd (currently support MLP model)
- * Upgrade PySinga to support Python 3
- * Improve the Tensor class with the stride field
- * Upgrade cuDNN from V5 to V7
- * Add VGG, Inception V4, ResNet, and DenseNet for ImageNet classification
- * Create alias for conda packages
- * Complete documentation in Chinese
- * Add instructions for running Singa on Windows
- * Update the compilation, CI
- * Fix some bugs
-
-
-
-* Incubating v1.1.0 (12 February 2017):
- * [Apache SINGA 1.1.0 (incubating)](https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz)
- [\[MD5\]](https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.md5)
- [\[ASC\]](https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.asc)
- * [Release Notes 1.1.0 (incubating)](releases/RELEASE_NOTES_1.1.0.html)
- * New features and major updates,
- * Create Docker images (CPU and GPU versions)
- * Create Amazon AMI for SINGA (CPU version)
- * Integrate with Jenkins for automatically generating Wheel and Debian packages (for installation), and updating the website.
- * Enhance the FeedFowardNet, e.g., multiple inputs and verbose mode for debugging
- * Add Concat and Slice layers
- * Extend CrossEntropyLoss to accept instance with multiple labels
- * Add image_tool.py with image augmentation methods
- * Support model loading and saving via the Snapshot API
- * Compile SINGA source on Windows
- * Compile mandatory dependent libraries together with SINGA code
- * Enable Java binding (basic) for SINGA
- * Add version ID in checkpointing files
- * Add Rafiki toolkit for providing RESTFul APIs
- * Add examples pretrained from Caffe, including GoogleNet
-
-
-
-* Incubating v1.0.0 (8 September 2016):
- * [Apache SINGA 1.0.0 (incubating)](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz)
- [\[MD5\]](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz.md5)
- [\[ASC\]](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz.asc)
- * [Release Notes 1.0.0 (incubating)](releases/RELEASE_NOTES_1.0.0.html)
- * New features and major updates,
- * Tensor abstraction for supporting more machine learning models.
- * Device abstraction for running on different hardware devices, including CPU, (Nvidia/AMD) GPU and FPGA (to be tested in later versions).
- * Replace GNU autotool with cmake for compilation.
- * Support Mac OS
- * Improve Python binding, including installation and programming
- * More deep learning models, including VGG and ResNet
- * More IO classes for reading/writing files and encoding/decoding data
- * New network communication components directly based on Socket.
- * Cudnn V5 with Dropout and RNN layers.
- * Replace website building tool from maven to Sphinx
- * Integrate Travis-CI
-
-
-* Incubating v0.3.0 (20 April 2016):
- * [Apache SINGA 0.3.0 (incubating)](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz)
- [\[MD5\]](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz.md5)
- [\[ASC\]](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz.asc)
- * [Release Notes 0.3.0 (incubating)](releases/RELEASE_NOTES_0.3.0.html)
- * New features and major updates,
- * [Training on GPU cluster](v0.3.0/gpu.html) enables training of deep learning models over a GPU cluster.
- * [Python wrapper improvement](v0.3.0/python.html) makes it easy to configure the job, including neural net and SGD algorithm.
- * [New SGD updaters](v0.3.0/updater.html) are added, including Adam, AdaDelta and AdaMax.
- * [Installation](v0.3.0/installation.html) has fewer dependent libraries for single node training.
- * Heterogeneous training with CPU and GPU.
- * Support cuDNN V4.
- * Data prefetching.
- * Fix some bugs.
-
-
-
-* Incubating v0.2.0 (14 January 2016):
- * [Apache SINGA 0.2.0 (incubating)](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz)
- [\[MD5\]](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz.md5)
- [\[ASC\]](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz.asc)
- * [Release Notes 0.2.0 (incubating)](releases/RELEASE_NOTES_0.2.0.html)
- * New features and major updates,
- * [Training on GPU](v0.2.0/gpu.html) enables training of complex models on a single node with multiple GPU cards.
- * [Hybrid neural net partitioning](v0.2.0/hybrid.html) supports data and model parallelism at the same time.
- * [Python wrapper](v0.2.0/python.html) makes it easy to configure the job, including neural net and SGD algorithm.
- * [RNN model and BPTT algorithm](v0.2.0/general-rnn.html) are implemented to support applications based on RNN models, e.g., GRU.
- * [Cloud software integration](v0.2.0/distributed-training.html) includes Mesos, Docker and HDFS.
- * Visualization of neural net structure and layer information, which is helpful for debugging.
- * Linear algebra functions and random functions against Blobs and raw data pointers.
- * New layers, including SoftmaxLayer, ArgSortLayer, DummyLayer, RNN layers and cuDNN layers.
- * Update Layer class to carry multiple data/grad Blobs.
- * Extract features and test performance for new data by loading previously trained model parameters.
- * Add Store class for IO operations.
-
-
-* Incubating v0.1.0 (8 October 2015):
- * [Apache SINGA 0.1.0 (incubating)](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz)
- [\[MD5\]](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz.md5)
- [\[ASC\]](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz.asc)
- * [Amazon EC2 image](https://console.aws.amazon.com/ec2/v2/home?region=ap-southeast-1#LaunchInstanceWizard:ami=ami-b41001e6)
- * [Release Notes 0.1.0 (incubating)](releases/RELEASE_NOTES_0.1.0.html)
- * Major features include,
- * Installation using GNU build utility
- * Scripts for job management with zookeeper
- * Programming model based on NeuralNet and Layer abstractions.
- * System architecture based on Worker, Server and Stub.
- * Training models from three different model categories, namely, feed-forward models, energy models and RNN models.
- * Synchronous and asynchronous distributed training frameworks using CPU
- * Checkpoint and restore
- * Unit test using gtest
-
-**Disclaimer**
-
-Apache SINGA is an effort undergoing incubation at The Apache Software
-Foundation (ASF), sponsored by the name of Apache Incubator PMC. Incubation is
-required of all newly accepted projects until a further review indicates that
-the infrastructure, communications, and decision making process have stabilized
-in a manner consistent with other successful ASF projects. While incubation
-status is not necessarily a reflection of the completeness or stability of the
-code, it does indicate that the project has yet to be fully endorsed by the
-ASF.
diff --git a/content/_sources/index.rst.txt b/content/_sources/index.rst.txt
deleted file mode 100644
index 6a4032e..0000000
--- a/content/_sources/index.rst.txt
+++ /dev/null
@@ -1,160 +0,0 @@
-.. 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.
-
-Welcome to Apache SINGA
-=======================
-
-Recent News
------------
-
-* SINGA graduated as a Top-Level Project (TLP) of the Apache Software Foundation on 16 Oct 2019.
-
-* Food(lg), the mobile app to `help patients with prediabetes <https://ssi.nus.edu.sg/ntfgh/>`_, is powered by SINGA and this app can now be downloaded on the `APP Store <https://apps.apple.com/us/app/food-lg/id1213299378>`_ or `Google Play <https://play.google.com/store/apps/details?id=com.nusidmi.foodlg&hl=en_SG>`_. Watch this `video <https://www.youtube.com/watch?v=MHp-saJiP-0>`_ of Food(lg) for more details.
-
-* A tutorial on SINGA was given at Singapore Infocomm Media Development Authority (IMDA), 16 August, 2019. Snapshots of the tutorial:
-
-.. image:: _static/images/imda2019_1.png
- :width: 45%
-.. image:: _static/images/imda2019_2.png
- :width: 45%
-
-* SINGA participated in mentoring the Google Summer of Code 2019 project `SpamAssassin : Statistical Classifier Plugin <https://summerofcode.withgoogle.com/projects/#5612088771215360>`_ .
-
-* SINGA participated at `EU FOSSA Apache Hackathon <https://eufossa.github.io/apache-hackathon-2019/>`_ in Brussels on 4 and 5 May 2019.
-
-* **Version 2.0.0** is now available, 20 April, 2019. `Download SINGA v2.0.0 <downloads.html>`_
-
-* SINGA was presented at `DISI, University of Trento, Italy <https://www.disi.unitn.it/>`_ on 14 December 2018.
-
-* SINGA was presented at `DIBRIS, University of Genoa, Italy <https://www.dibris.unige.it/>`_ on 16 July 2018.
-
-* **Version 1.2.0** is now available, 6 June, 2018. `Download SINGA v1.2.0 <downloads.html>`_
-
-* **Version 1.1.0** is now available, 12 Feb, 2017. `Download SINGA v1.1.0 <downloads.html>`_
-
-* A tutorial on SINGA V1 will be given at `SGInnovate <https://www.eventbrite.sg/e/ai-eveningssginnovate-apache-singa-tickets-31505061487>`_, on 23 March, 2017
-
-* **Version 1.0.0** is now available, 9 Sep, 2016. `Download SINGA v1.0.0 <downloads.html>`_
-
-* SINGA will be presented at `REWORK <https://www.re-work.co/events/deep-learning-singapore/schedule>`_, 21 Oct, 2016.
-
-* SINGA was presented at `PyDataSG <http://www.meetup.com/PyData-SG/events/229691286/>`_, 16 Aug, 2016.
-
-* **Version 0.3.0** is now available, 20 April, 2016. `Download SINGA v0.3.0 <downloads.html>`_
-
-* **Version 0.2.0** is now available, 14 Jan, 2016. `Download SINGA v0.2.0 <downloads.html>`_.
-
-* SINGA was presented at `Strata+Hadoop <http://strataconf.com/big-data-conference-sg-2015/public/schedule/detail/45123>`_ on 2 Dec, 2015
-
-* SINGA was presented at `ACM Multimedia <http://www.acmmm.org/2015/at-a-glance/>`_ Best Paper session and Open Source Software Competition session, 26-30 Oct, 2015 (`Slides <http://www.comp.nus.edu.sg/~dbsystem/singa//assets/file/mm2015.ppt>`_)
-
-* **Version 0.1.0** is now available, 8 Oct, 2015. `Download SINGA v0.1.0 <downloads.html>`_.
-
-* SINGA was presented at `workshop on deep learning <http://www.comp.nus.edu.sg/~dbsystem/singa/workshop>`_ held on 16 Sep, 2015
-
-* SINGA was presented at `BOSS <http://boss.dima.tu-berlin.de/>`_ of `VLDB 2015 <http://www.vldb.org/2015/>`_ at Hawaii, 4 Sep, 2015. (slides: `overview <http://www.comp.nus.edu.sg/~dbsystem/singa/assets/file/singa-vldb-boss.pptx>`_, `basic <http://www.comp.nus.edu.sg/~dbsystem/singa/assets/file/basic-user-guide.pptx>`_, `advanced <http://www.comp.nus.edu.sg/~dbsystem/singa/assets/file/advanced-user-guide.pptx>`_)
-
-* SINGA was presented at `ADSC/I2R Deep Learning Workshop <http://adsc.illinois.edu/contact-us>`_, 25 Aug, 2015.
-
-* A tutorial on SINGA was given at VLDB summer school at Tsinghua University, 25-31 July, 2015.
-
-* A half day tutorial on SINGA was given at I2R, 29 June, 2015.
-
-* SINGA was presented at `DanaC <http://danac.org/>`_ of `SIGMOD 2015 <http://www.sigmod2015.org/index.shtml>`_ at Melbourne, 31 May - 4 June, 2015.
-
-* SINGA has been accepted by `Apache Incubator <http://incubator.apache.org/>`_, 17 March, 2015.
-
-Getting Started
----------------
-* `Install SINGA <docs/installation.html>`_ via conda, apt-get, or from source.
-
-* Try SINGA on `AWS <https://aws.amazon.com/marketplace/pp/B01NAUAWZW>`_ or via `Docker <https://hub.docker.com/r/apache/singa/>`_.
-
-* Refer to the `Jupyter notebooks <http://nbviewer.jupyter.org/github/apache/singa/blob/master/doc/en/docs/notebook/index.ipynb>`_ for some basic examples and the `model zoo page <./docs/model_zoo/index.html>`_ for more examples.
-
-.. |logo| image:: _static/jupyter.png
- :scale: 25%
- :align: middle
- :target: http://nbviewer.jupyter.org/github/apache/singa/blob/master/doc/en/docs/notebook/index.ipynb
-
-+---------+
-| |logo| |
-+---------+
-
-Documentation
--------------
-
-* Documentation and Python APIs are listed `here <docs.html>`_.
-* `C++ APIs <doxygen/html/index.html>`_ are generated by Doxygen.
-* Research publication list is available `here <http://www.comp.nus.edu.sg/~dbsystem/singa/research/publication/>`_.
-
-How to contribute
-----------------------
-
-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.
-* [Help with the documentation](http://singa.apache.org/en/develop/contribute-docs.html) by updating webpages that are lacking or unclear.
-* [Contribute code to SINGA](http://singa.apache.org/en/develop/contribute-code.html) by fixing errors or adding new features. [All issues are tracked](http://singa.apache.org/en/community/issue-tracking.html) on the JIRA system.
-
-
-History
-------------
-
-SINGA was initiated by the DB System Group at National University of Singapore in 2014, in collaboration with the database group of Zhejiang University.
-Please cite the following two papers if you use SINGA in your research:
-
-* B\. C\. Ooi, K.-L. Tan, S. Wang, W. Wang, Q. Cai, G. Chen, J. Gao, Z. Luo, A. K. H. Tung, Y. Wang, Z. Xie, M. Zhang, and K. Zheng. `SINGA: A distributed deep learning platform <http://www.comp.nus.edu.sg/~ooibc/singaopen-mm15.pdf>`_. ACM Multimedia (Open Source Software Competition) 2015 (`BibTex <http://www.comp.nus.edu.sg/~dbsystem/singa//assets/file/bib-oss.txt>`_).
-
-* W\. Wang, G. Chen, T. T. A. Dinh, B. C. Ooi, K.-L.Tan, J. Gao, and S. Wang. `SINGA: putting deep learning in the hands of multimedia users <http://www.comp.nus.edu.sg/~ooibc/singa-mm15.pdf>`_. ACM Multimedia 2015 (`BibTex <http://www.comp.nus.edu.sg/~dbsystem/singa//assets/file/bib-singa.txt>`_, `Slides <http://www.comp.nus.edu.sg/~dbsystem/singa/assets/file/mm2015.ppt>`_).
-
-Rafiki is a sub module of SINGA. Please cite the following paper if you use Rafiki in your research:
-
-* Wei Wang, Jinyang Gao, Meihui Zhang, Sheng Wang, Gang Chen, Teck Khim Ng, Beng Chin Ooi, Jie Shao, Moaz Reyad. `Rafiki: Machine Learning as an Analytics Service System <http://www.vldb.org/pvldb/vol12/p128-wang.pdf>`_. `VLDB 2019 <http://vldb.org/2019/>`_ (`BibTex <https://dblp.org/rec/bib2/journals/pvldb/WangWGZCNOS18.bib>`_).
-
-Companies like `NetEase <http://tech.163.com/17/0602/17/CLUL016I00098GJ5.html>`_, `yzBigData <http://www.yzbigdata.com/en/index.html>`_, `Shentilium <https://shentilium.com/>`_, `Foodlg <http://www.foodlg.com/>`_ and `Medilot <https://medilot.com/technologies>`_ are using SINGA for their applications.
-
-
-.. toctree::
- :hidden:
-
- docs/index
- downloads
- security
-
-.. toctree::
- :hidden:
- :maxdepth: 2
- :caption: Development
-
- develop/contribute-code
- develop/contribute-docs
- develop/how-to-release
-
-.. toctree::
- :hidden:
- :maxdepth: 2
- :caption: Community
-
- community/source-repository
- community/mail-lists
- community/issue-tracking
- community/team-list
-
-License
-----------
-SINGA is released under `Apache License Version 2.0 <http://www.apache.org/licenses/LICENSE-2.0>`_.
diff --git a/content/_sources/releases/RELEASE_NOTES_0.1.0.md.txt b/content/_sources/releases/RELEASE_NOTES_0.1.0.md.txt
deleted file mode 100644
index b2d8bfb..0000000
--- a/content/_sources/releases/RELEASE_NOTES_0.1.0.md.txt
+++ /dev/null
@@ -1,117 +0,0 @@
-<!--
- 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.
--->
-# singa-incubating-0.1.0 Release Notes
-
----
-
-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.
-
-This release includes following features:
-
- * Job management
- * [SINGA-3](https://issues.apache.org/jira/browse/SINGA-3) Use Zookeeper to check stopping (finish) time of the system
- * [SINGA-16](https://issues.apache.org/jira/browse/SINGA-16) Runtime Process id Management
- * [SINGA-25](https://issues.apache.org/jira/browse/SINGA-25) Setup glog output path
- * [SINGA-26](https://issues.apache.org/jira/browse/SINGA-26) Run distributed training in a single command
- * [SINGA-30](https://issues.apache.org/jira/browse/SINGA-30) Enhance easy-to-use feature and support concurrent jobs
- * [SINGA-33](https://issues.apache.org/jira/browse/SINGA-33) Automatically launch a number of processes in the cluster
- * [SINGA-34](https://issues.apache.org/jira/browse/SINGA-34) Support external zookeeper service
- * [SINGA-38](https://issues.apache.org/jira/browse/SINGA-38) Support concurrent jobs
- * [SINGA-39](https://issues.apache.org/jira/browse/SINGA-39) Avoid ssh in scripts for single node environment
- * [SINGA-43](https://issues.apache.org/jira/browse/SINGA-43) Remove Job-related output from workspace
- * [SINGA-56](https://issues.apache.org/jira/browse/SINGA-56) No automatic launching of zookeeper service
- * [SINGA-73](https://issues.apache.org/jira/browse/SINGA-73) Refine the selection of available hosts from host list
-
-
- * Installation with GNU Auto tool
- * [SINGA-4](https://issues.apache.org/jira/browse/SINGA-4) Refine thirdparty-dependency installation
- * [SINGA-13](https://issues.apache.org/jira/browse/SINGA-13) Separate intermediate files of compilation from source files
- * [SINGA-17](https://issues.apache.org/jira/browse/SINGA-17) Add root permission within thirdparty/install.
- * [SINGA-27](https://issues.apache.org/jira/browse/SINGA-27) Generate python modules for proto objects
- * [SINGA-53](https://issues.apache.org/jira/browse/SINGA-53) Add lmdb compiling options
- * [SINGA-62](https://issues.apache.org/jira/browse/SINGA-62) Remove building scrips and auxiliary files
- * [SINGA-67](https://issues.apache.org/jira/browse/SINGA-67) Add singatest into build targets
-
-
- * Distributed training
- * [SINGA-7](https://issues.apache.org/jira/browse/SINGA-7) Implement shared memory Hogwild algorithm
- * [SINGA-8](https://issues.apache.org/jira/browse/SINGA-8) Implement distributed Hogwild
- * [SINGA-19](https://issues.apache.org/jira/browse/SINGA-19) Slice large Param objects for load-balance
- * [SINGA-29](https://issues.apache.org/jira/browse/SINGA-29) Update NeuralNet class to enable layer partition type customization
- * [SINGA-24](https://issues.apache.org/jira/browse/SINGA-24) Implement Downpour training framework
- * [SINGA-32](https://issues.apache.org/jira/browse/SINGA-32) Implement AllReduce training framework
- * [SINGA-57](https://issues.apache.org/jira/browse/SINGA-57) Improve Distributed Hogwild
-
-
- * Training algorithms for different model categories
- * [SINGA-9](https://issues.apache.org/jira/browse/SINGA-9) Add Support for Restricted Boltzman Machine (RBM) model
- * [SINGA-10](https://issues.apache.org/jira/browse/SINGA-10) Add Support for Recurrent Neural Networks (RNN)
-
-
- * Checkpoint and restore
- * [SINGA-12](https://issues.apache.org/jira/browse/SINGA-12) Support Checkpoint and Restore
-
-
- * Unit test
- * [SINGA-64](https://issues.apache.org/jira/browse/SINGA-64) Add the test module for utils/common
-
-
- * Programming model
- * [SINGA-36](https://issues.apache.org/jira/browse/SINGA-36) Refactor job configuration, driver program and scripts
- * [SINGA-37](https://issues.apache.org/jira/browse/SINGA-37) Enable users to set parameter sharing in model configuration
- * [SINGA-54](https://issues.apache.org/jira/browse/SINGA-54) Refactor job configuration to move fields in ModelProto out
- * [SINGA-55](https://issues.apache.org/jira/browse/SINGA-55) Refactor main.cc and singa.h
- * [SINGA-61](https://issues.apache.org/jira/browse/SINGA-61) Support user defined classes
- * [SINGA-65](https://issues.apache.org/jira/browse/SINGA-65) Add an example of writing user-defined layers
-
-
- * Other features
- * [SINGA-6](https://issues.apache.org/jira/browse/SINGA-6) Implement thread-safe singleton
- * [SINGA-18](https://issues.apache.org/jira/browse/SINGA-18) Update API for displaying performance metric
- * [SINGA-77](https://issues.apache.org/jira/browse/SINGA-77) Integrate with Apache RAT
-
-
-Some bugs are fixed during the development of this release
-
- * [SINGA-2](https://issues.apache.org/jira/browse/SINGA-2) Check failed: zsock_connect
- * [SINGA-5](https://issues.apache.org/jira/browse/SINGA-5) Server early terminate when zookeeper singa folder is not initially empty
- * [SINGA-15](https://issues.apache.org/jira/browse/SINGA-15) Fixg a bug from ConnectStub function which gets stuck for connecting layer_dealer_
- * [SINGA-22](https://issues.apache.org/jira/browse/SINGA-22) Cannot find openblas library when it is installed in default path
- * [SINGA-23](https://issues.apache.org/jira/browse/SINGA-23) Libtool version mismatch error.
- * [SINGA-28](https://issues.apache.org/jira/browse/SINGA-28) Fix a bug from topology sort of Graph
- * [SINGA-42](https://issues.apache.org/jira/browse/SINGA-42) Issue when loading checkpoints
- * [SINGA-44](https://issues.apache.org/jira/browse/SINGA-44) A bug when reseting metric values
- * [SINGA-46](https://issues.apache.org/jira/browse/SINGA-46) Fix a bug in updater.cc to scale the gradients
- * [SINGA-47](https://issues.apache.org/jira/browse/SINGA-47) Fix a bug in data layers that leads to out-of-memory when group size is too large
- * [SINGA-48](https://issues.apache.org/jira/browse/SINGA-48) Fix a bug in trainer.cc that assigns the same NeuralNet instance to workers from diff groups
- * [SINGA-49](https://issues.apache.org/jira/browse/SINGA-49) Fix a bug in HandlePutMsg func that sets param fields to invalid values
- * [SINGA-66](https://issues.apache.org/jira/browse/SINGA-66) Fix bugs in Worker::RunOneBatch function and ClusterProto
- * [SINGA-79](https://issues.apache.org/jira/browse/SINGA-79) Fix bug in singatool that can not parse -conf flag
-
-
-Features planned for the next release
-
- * [SINGA-11](https://issues.apache.org/jira/browse/SINGA-11) Start SINGA using Mesos
- * [SINGA-31](https://issues.apache.org/jira/browse/SINGA-31) Extend Blob to support xpu (cpu or gpu)
- * [SINGA-35](https://issues.apache.org/jira/browse/SINGA-35) Add random number generators
- * [SINGA-40](https://issues.apache.org/jira/browse/SINGA-40) Support sparse Param update
- * [SINGA-41](https://issues.apache.org/jira/browse/SINGA-41) Support single node single GPU training
-
diff --git a/content/_sources/releases/RELEASE_NOTES_0.2.0.md.txt b/content/_sources/releases/RELEASE_NOTES_0.2.0.md.txt
deleted file mode 100644
index d933f54..0000000
--- a/content/_sources/releases/RELEASE_NOTES_0.2.0.md.txt
+++ /dev/null
@@ -1,102 +0,0 @@
-<!--
- 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.
--->
-# singa-incubating-0.2.0 Release Notes
-
----
-
-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.
-
-This release includes the following **major features**:
-
-* [Training on GPU](../docs/gpu.html) enables training of complex models on a single node with multiple GPU cards.
-* [Hybrid neural net partitioning](../docs/hybrid.html) supports data and model parallelism at the same time.
-* [Python wrapper](../docs/python.html) makes it easy to configure the job, including neural net and SGD algorithm.
-* [RNN model and BPTT algorithm](../docs/general-rnn.html) are implemented to support applications based on RNN models, e.g., GRU.
-* [Cloud software integration](../docs/distributed-training.md) includes Mesos, Docker and HDFS.
-
-
-**More details** are listed as follows,
-
- * Programming model
- * [SINGA-80] New Blob Level and Address Level Math Operation Interface
- * [SINGA-82] Refactor input layers using data store abstraction
- * [SINGA-87] Replace exclude field to include field for layer configuration
- * [SINGA-110] Add Layer member datavec_ and gradvec_
- * [SINGA-120] Implemented GRU and BPTT (BPTTWorker)
-
-
- * Neuralnet layers
- * [SINGA-91] Add SoftmaxLayer and ArgSortLayer
- * [SINGA-106] Add dummy layer for test purpose
- * [SINGA-120] Implemented GRU and BPTT (GRULayer and OneHotLayer)
-
-
- * GPU training support
- * [SINGA-100] Implement layers using CUDNN for GPU training
- * [SINGA-104] Add Context Class
- * [SINGA-105] Update GUN make files for compiling cuda related code
- * [SINGA-98] Add Support for AlexNet ImageNet Classification Model
-
-
- * Model/Hybrid partition
- * [SINGA-109] Refine bridge layers
- * [SINGA-111] Add slice, concate and split layers
- * [SINGA-113] Model/Hybrid Partition Support
-
-
- * Python binding
- * [SINGA-108] Add Python wrapper to singa
-
-
- * Predict-only mode
- * [SINGA-85] Add functions for extracting features and test new data
-
-
- * Integrate with third-party tools
- * [SINGA-11] Start SINGA on Apache Mesos
- * [SINGA-78] Use Doxygen to generate documentation
- * [SINGA-89] Add Docker support
-
-
- * Unit test
- * [SINGA-95] Add make test after building
-
-
- * Other improvment
- * [SINGA-84] Header Files Rearrange
- * [SINGA-93] Remove the asterisk in the log tcp://169.254.12.152:*:49152
- * [SINGA-94] Move call to google::InitGoogleLogging() from Driver::Init() to main()
- * [SINGA-96] Add Momentum to Cifar10 Example
- * [SINGA-101] Add ll (ls -l) command in .bashrc file when using docker
- * [SINGA-114] Remove short logs in tmp directory
- * [SINGA-115] Print layer debug information in the neural net graph file
- * [SINGA-118] Make protobuf LayerType field id easy to assign
- * [SIGNA-97] Add HDFS Store
-
-
- * Bugs fixed
- * [SINGA-85] Fix compilation errors in examples
- * [SINGA-90] Miscellaneous trivial bug fixes
- * [SINGA-107] Error from loading pre-trained params for training stacked RBMs
- * [SINGA-116] Fix a bug in InnerProductLayer caused by weight matrix sharing
-
-
diff --git a/content/_sources/releases/RELEASE_NOTES_0.3.0.md.txt b/content/_sources/releases/RELEASE_NOTES_0.3.0.md.txt
deleted file mode 100644
index a5fa1bb..0000000
--- a/content/_sources/releases/RELEASE_NOTES_0.3.0.md.txt
+++ /dev/null
@@ -1,55 +0,0 @@
-<!--
- 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.
--->
-# singa-incubating-0.3.0 Release Notes
-
----
-
-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.
-
-This release includes following features:
-
- * GPU Support
- * [SINGA-131] Implement and optimize hybrid training using both CPU and GPU
- * [SINGA-136] Support cuDNN v4
- * [SINGA-134] Extend SINGA to run over a GPU cluster
- * [SINGA-157] Change the priority of cudnn library and install libsingagpu.so
-
- * Remove Dependences
- * [SINGA-156] Remove the dependency on ZMQ for single process training
- * [SINGA-155] Remove zookeeper for single-process training
-
- * Python Binding
- * [SINGA-126] Python Binding for Interactive Training
-
- * Other Improvements
- * [SINGA-80] New Blob Level and Address Level Math Operation Interface
- * [SINGA-130] Data Prefetching
- * [SINGA-145] New SGD based optimization Updaters: AdaDelta, Adam, AdamMax
-
- * Bugs Fixed
- * [SINGA-148] Race condition between Worker threads and Driver
- * [SINGA-150] Mesos Docker container failed
- * [SIGNA-141] Undesired Hash collision when locating process id to worker…
- * [SINGA-149] Docker build fail
- * [SINGA-143] The compilation cannot detect libsingagpu.so file
-
-
diff --git a/content/_sources/releases/RELEASE_NOTES_1.0.0.md.txt b/content/_sources/releases/RELEASE_NOTES_1.0.0.md.txt
deleted file mode 100644
index b45e600..0000000
--- a/content/_sources/releases/RELEASE_NOTES_1.0.0.md.txt
+++ /dev/null
@@ -1,109 +0,0 @@
-<!--
- 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.
--->
-# singa-incubating-1.0.0 Release Notes
-
----
-
-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.
-
-This release includes following features:
-
- * Core abstractions including Tensor and Device
- * [SINGA-207] Update Tensor functions for matrices
- * [SINGA-205] Enable slice and concatenate operations for Tensor objects
- * [SINGA-197] Add CNMem as a submodule in lib/
- * [SINGA-196] Rename class Blob to Block
- * [SINGA-194] Add a Platform singleton
- * [SINGA-175] Add memory management APIs and implement a subclass using CNMeM
- * [SINGA-173] OpenCL Implementation
- * [SINGA-171] Create CppDevice and CudaDevice
- * [SINGA-168] Implement Cpp Math functions APIs
- * [SINGA-162] Overview of features for V1.x
- * [SINGA-165] Add cross-platform timer API to singa
- * [SINGA-167] Add Tensor Math function APIs
- * [SINGA-166] light built-in logging for making glog optional
- * [SINGA-164] Add the base Tensor class
-
-
- * IO components for file read/write, network and data pre-processing
- * [SINGA-233] New communication interface
- * [SINGA-215] Implement Image Transformation for Image Pre-processing
- * [SINGA-214] Add LMDBReader and LMDBWriter for LMDB
- * [SINGA-213] Implement Encoder and Decoder for CSV
- * [SINGA-211] Add TextFileReader and TextFileWriter for CSV files
- * [SINGA-210] Enable checkpoint and resume for v1.0
- * [SINGA-208] Add DataIter base class and a simple implementation
- * [SINGA-203] Add OpenCV detection for cmake compilation
- * [SINGA-202] Add reader and writer for binary file
- * [SINGA-200] Implement Encoder and Decoder for data pre-processing
-
-
-
- * Module components including layer classes, training algorithms and Python binding
- * [SINGA-235] Unify the engines for cudnn and singa layers
- * [SINGA-230] OpenCL Convolution layer and Pooling layer
- * [SINGA-222] Fixed bugs in IO
- * [SINGA-218] Implementation for RNN CUDNN version
- * [SINGA-204] Support the training of feed-forward neural nets
- * [SINGA-199] Implement Python classes for SGD optimizers
- * [SINGA-198] Change Layer::Setup API to include input Tensor shapes
- * [SINGA-193] Add Python layers
- * [SINGA-192] Implement optimization algorithms for SINGA v1 (nesterove, adagrad, rmsprop)
- * [SINGA-191] Add "autotune" for CudnnConvolution Layer
- * [SINGA-190] Add prelu layer and flatten layer
- * [SINGA-189] Generate python outputs of proto files
- * [SINGA-188] Add Dense layer
- * [SINGA-187] Add popular parameter initialization methods
- * [SINGA-186] Create Python Tensor class
- * [SINGA-184] Add Cross Entropy loss computation
- * [SINGA-183] Add the base classes for optimizer, constraint and regularizer
- * [SINGA-180] Add Activation layer and Softmax layer
- * [SINGA-178] Add Convolution layer and Pooling layer
- * [SINGA-176] Add loss and metric base classes
- * [SINGA-174] Add Batch Normalization layer and Local Response Nomalization layer.
- * [SINGA-170] Add Dropout layer and CudnnDropout layer.
- * [SINGA-169] Add base Layer class for V1.0
-
-
- * Examples
- * [SINGA-232] Alexnet on Imagenet
- * [SINGA-231] Batchnormlized VGG model for cifar-10
- * [SINGA-228] Add Cpp Version of Convolution and Pooling layer
- * [SINGA-227] Add Split and Merge Layer and add ResNet Implementation
-
- * Documentation
- * [SINGA-239] Transfer documentation files of v0.3.0 to github
- * [SINGA-238] RBM on mnist
- * [SINGA-225] Documentation for installation and Cifar10 example
- * [SINGA-223] Use Sphinx to create the website
-
- * Tools for compilation and some utility code
- * [SINGA-229] Complete install targets
- * [SINGA-221] Support for Travis-CI
- * [SINGA-217] build python package with setup.py
- * [SINGA-216] add jenkins for CI support
- * [SINGA-212] Disable the compilation of libcnmem if USE_CUDA is OFF
- * [SINGA-195] Channel for sending training statistics
- * [SINGA-185] Add CBLAS and GLOG detection for singav1
- * [SINGA-181] Add NVCC supporting for .cu files
- * [SINGA-177] Add fully cmake supporting for the compilation of singa_v1
- * [SINGA-172] Add CMake supporting for Cuda and Cudnn libs
diff --git a/content/_sources/releases/RELEASE_NOTES_1.1.0.md.txt b/content/_sources/releases/RELEASE_NOTES_1.1.0.md.txt
deleted file mode 100644
index fffe6b0..0000000
--- a/content/_sources/releases/RELEASE_NOTES_1.1.0.md.txt
+++ /dev/null
@@ -1,67 +0,0 @@
-<!--
- 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.
--->
-# singa-incubating-1.1.0 Release Notes
-
----
-
-SINGA is a general distributed deep learning platform for training big deep
-learning models over large datasets.
-
-This release includes following features:
-
- * Core components
- * [SINGA-296] Add sign and to_host function for pysinga tensor module
-
- * Model components
- * [SINGA-254] Implement Adam for V1
- * [SINGA-264] Extend the FeedForwardNet to accept multiple inputs
- * [SINGA-267] Add spatial mode in batch normalization layer
- * [SINGA-271] Add Concat and Slice layers
- * [SINGA-275] Add Cross Entropy Loss for multiple labels
- * [SINGA-278] Convert trained caffe parameters to singa
- * [SINGA-287] Add memory size check for cudnn convolution
-
- * Utility functions and CI
- * [SINGA-242] Compile all source files into a single library.
- * [SINGA-244] Separating swig interface and python binding files
- * [SINGA-246] Imgtool for image augmentation
- * [SINGA-247] Add windows support for singa
- * [SINGA-251] Implement image loader for pysinga
- * [SINGA-252] Use the snapshot methods to dump and load models for pysinga
- * [SINGA-255] Compile mandatory dependent libaries together with SINGA code
- * [SINGA-259] Add maven pom file for building java classes
- * [SINGA-261] Add version ID into the checkpoint files
- * [SINGA-266] Add Rafiki python toolkits
- * [SINGA-273] Improve license and contributions
- * [SINGA-284] Add python unittest into Jenkins and link static libs into whl file
- * [SINGA-280] Jenkins CI support
- * [SINGA-288] Publish wheel of PySINGA generated by Jenkins to public servers
-
- * Documentation and usability
- * [SINGA-263] Create Amazon Machine Image
- * [SINGA-268] Add IPython notebooks to the documentation
- * [SINGA-276] Create docker images
- * [SINGA-289] Update SINGA website automatically using Jenkins
- * [SINGA-295] Add an example of image classification using GoogleNet
-
- * Bugs fixed
- * [SINGA-245] float as the first operand can not multiply with a tensor object
- * [SINGA-293] Bug from compiling PySINGA on Mac OS X with multiple version of Python
-
-
diff --git a/content/_sources/releases/RELEASE_NOTES_1.2.0.md.txt b/content/_sources/releases/RELEASE_NOTES_1.2.0.md.txt
deleted file mode 100644
index 2c7a134..0000000
--- a/content/_sources/releases/RELEASE_NOTES_1.2.0.md.txt
+++ /dev/null
@@ -1,73 +0,0 @@
-<!--
- 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.
--->
-# singa-incubating-1.2.0 Release Notes
-
----
-
-SINGA is a general distributed deep learning platform for training big deep
-learning models over large datasets.
-
-This release includes following features:
-
- * Core components
- * [SINGA-290] Upgrade to Python 3
- * [SINGA-341] Added stride functionality to tensors for CPP
- * [SINGA-347] Create a function that supports einsum
- * [SINGA-351] Added stride support and cudnn codes to cuda
-
- * Model components
- * [SINGA-300] Add residual networks for imagenet classification
- * [SINGA-312] Rename layer parameters
- * [SINGA-313] Add L2 norm layer
- * [SINGA-315] Reduce memory footprint by Python generator for parameter
- * [SINGA-316] Add SigmoidCrossEntropy
- * [SINGA-324] Extend RNN layer to accept variant seq length across batches
- * [SINGA-326] Add Inception V4 for ImageNet classification
- * [SINGA-328] Add VGG models for ImageNet classification
- * [SINGA-329] Support layer freezing during training (fine-tuning)
- * [SINGA-346] Update cudnn from V5 to V7
- * [SINGA-349] Create layer operations for autograd
- * [SINGA-363] Add DenseNet for Imagenet classification
-
- * Utility functions and CI
- * [SINGA-274] Improve Debian packaging with CPack
- * [SINGA-303] Create conda packages
- * [SINGA-337] Add test cases for code
- * [SINGA-348] Support autograd MLP Example
- * [SINGA-345] Update Jenkins and fix bugs in compliation
- * [SINGA-354] Update travis scripts to use conda-build for all platforms
- * [SINGA-358] Consolidated RUN steps and cleaned caches in Docker containers
- * [SINGA-359] Create alias for conda packages
-
- * Documentation and usability
- * [SINGA-223] Fix side navigation menu in the website
- * [SINGA-294] Add instructions to run CUDA unit tests on Windows
- * [SINGA-305] Add jupyter notebooks for SINGA V1 tutorial
- * [SINGA-319] Fix link errors on the index page
- * [SINGA-352] Complete SINGA documentation in Chinese version
- * [SINGA-361] Add git instructions for contributors and committers
-
- * Bugs fixed
- * [SINGA-330] fix openblas building on i7 7700k
- * [SINGA-331] Fix the bug of tensor division operation
- * [SINGA-350] Error from python3 test
- * [SINGA-356] Error using travis tool to build SINGA on mac os
- * [SINGA-363] Fix some bugs in imagenet examples
- * [SINGA-368] Fix the bug in Cifar10 examples
- * [SINGA-369] the errors of examples in testing
diff --git a/content/_sources/releases/RELEASE_NOTES_2.0.0.md.txt b/content/_sources/releases/RELEASE_NOTES_2.0.0.md.txt
deleted file mode 100644
index b50e288..0000000
--- a/content/_sources/releases/RELEASE_NOTES_2.0.0.md.txt
+++ /dev/null
@@ -1,64 +0,0 @@
-<!--
- 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.
--->
-
-# singa-incubating-2.0.0 Release Notes
-
----
-
-SINGA is a general distributed deep learning platform for training big deep
-learning models over large datasets.
-
-This release includes following features:
-
- * Core components
- * [SINGA-434] Support tensor broadcasting
- * [SINGA-370] Improvement to tensor reshape and various misc. changes related to SINGA-341 and 351
-
- * Model components
- * [SINGA-333] Add support for Open Neural Network Exchange (ONNX) format
- * [SINGA-385] Add new python module for optimizers
- * [SINGA-394] Improve the CPP operations via Intel MKL DNN lib
- * [SINGA-425] Add 3 operators , Abs(), Exp() and leakyrelu(), for Autograd
- * [SINGA-410] Add two function, set_params() and get_params(), for Autograd Layer class
- * [SINGA-383] Add Separable Convolution for autograd
- * [SINGA-388] Develop some RNN layers by calling tiny operations like matmul, addbias.
- * [SINGA-382] Implement concat operation for autograd
- * [SINGA-378] Implement maxpooling operation and its related functions for autograd
- * [SINGA-379] Implement batchnorm operation and its related functions for autograd
-
- * Utility functions and CI
- * [SINGA-432] Update depdent lib versions in conda-build config
- * [SINGA-429] Update docker images for latest cuda and cudnn
- * [SINGA-428] Move Docker images under Apache user name
-
- * Documentation and usability
- * [SINGA-395] Add documentation for autograd APIs
- * [SINGA-344] Add a GAN example
- * [SINGA-390] Update installation.md
- * [SINGA-384] Implement ResNet using autograd API
- * [SINGA-352] Complete SINGA documentation in Chinese version
-
-
- * Bugs fixed
- * [SINGA-431] Unit Test failed - Tensor Transpose
- * [SINGA-422] ModuleNotFoundError: No module named "_singa_wrap"
- * [SINGA-418] Unsupportive type 'long' in python3.
- * [SINGA-409] Basic `singa-cpu` import throws error
- * [SINGA-408] Unsupportive function definition in python3
- * [SINGA-380] Fix bugs from Reshape
diff --git a/content/_sources/security.md.txt b/content/_sources/security.md.txt
deleted file mode 100644
index f9114fa..0000000
--- a/content/_sources/security.md.txt
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@@ -1,22 +0,0 @@
-<!--
- 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.
--->
-
-# Security
-
-Users can report security vulnerabilities to `SINGA Security Team Mail List <ma...@singa.apache.org>`_.
\ No newline at end of file
diff --git a/content/_sources/security.rst.txt b/content/_sources/security.rst.txt
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--- a/content/_sources/security.rst.txt
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@@ -1,23 +0,0 @@
-.. 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.
-
-Security
-========
-
-Users can report security vulnerabilities to `SINGA Security Team Mail List <ma...@singa.apache.org>`_.
-
-
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-.fa:before{-webkit-font-smoothing:antialiased}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-weight:normal;font-style:normal;src:url("../fonts/fontawesome-webfont.eot");src:url("../fonts/fontawesome-webfont.eot?#iefix") format("embedded-opentype"),url("../fonts/fontawesome-webfont.woff") format("woff"),url("../fonts/fontawesome-webfont.ttf") format("truetype"),url("../fonts/fontawesome-webfon [...]
diff --git a/content/_static/css/theme.css b/content/_static/css/theme.css
deleted file mode 100644
index aed8cef..0000000
--- a/content/_static/css/theme.css
+++ /dev/null
@@ -1,6 +0,0 @@
-/* sphinx_rtd_theme version 0.4.3 | MIT license */
-/* Built 20190212 16:02 */
-*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}[hidden]{display:none}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}a:hover,a:active{outline:0}abbr[ [...]
- * Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
- * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
- */@font-face{font-family:'FontAwesome';src:url("../fonts/fontawesome-webfont.eot?v=4.7.0");src:url("../fonts/fontawesome-webfont.eot?#iefix&v=4.7.0") format("embedded-opentype"),url("../fonts/fontawesome-webfont.woff2?v=4.7.0") format("woff2"),url("../fonts/fontawesome-webfont.woff?v=4.7.0") format("woff"),url("../fonts/fontawesome-webfont.ttf?v=4.7.0") format("truetype"),url("../fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular") format("svg");font-weight:normal;font-style:norma [...]
diff --git a/content/_static/docker.png b/content/_static/docker.png
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index 10eaae9..0000000
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diff --git a/content/_static/doctools.js b/content/_static/doctools.js
deleted file mode 100644
index 5654977..0000000
--- a/content/_static/doctools.js
+++ /dev/null
@@ -1,287 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for all documentation.
- *
- * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-/**
- * make the code below compatible with browsers without
- * an installed firebug like debugger
-if (!window.console || !console.firebug) {
- var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
- "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
- "profile", "profileEnd"];
- window.console = {};
- for (var i = 0; i < names.length; ++i)
- window.console[names[i]] = function() {};
-}
- */
-
-/**
- * small helper function to urldecode strings
- */
-jQuery.urldecode = function(x) {
- return decodeURIComponent(x).replace(/\+/g, ' ');
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
- if (typeof s == 'undefined')
- s = document.location.search;
- var parts = s.substr(s.indexOf('?') + 1).split('&');
- var result = {};
- for (var i = 0; i < parts.length; i++) {
- var tmp = parts[i].split('=', 2);
- var key = jQuery.urldecode(tmp[0]);
- var value = jQuery.urldecode(tmp[1]);
- if (key in result)
- result[key].push(value);
- else
- result[key] = [value];
- }
- return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
- function highlight(node) {
- if (node.nodeType == 3) {
- var val = node.nodeValue;
- var pos = val.toLowerCase().indexOf(text);
- if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) {
- var span = document.createElement("span");
- span.className = className;
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- node.parentNode.insertBefore(span, node.parentNode.insertBefore(
- document.createTextNode(val.substr(pos + text.length)),
- node.nextSibling));
- node.nodeValue = val.substr(0, pos);
- }
- }
- else if (!jQuery(node).is("button, select, textarea")) {
- jQuery.each(node.childNodes, function() {
- highlight(this);
- });
- }
- }
- return this.each(function() {
- highlight(this);
- });
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
- jQuery.uaMatch = function(ua) {
- ua = ua.toLowerCase();
-
- var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
- /(webkit)[ \/]([\w.]+)/.exec(ua) ||
- /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
- /(msie) ([\w.]+)/.exec(ua) ||
- ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
- [];
-
- return {
- browser: match[ 1 ] || "",
- version: match[ 2 ] || "0"
- };
- };
- jQuery.browser = {};
- jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
-
-/**
- * Small JavaScript module for the documentation.
- */
-var Documentation = {
-
- init : function() {
- this.fixFirefoxAnchorBug();
- this.highlightSearchWords();
- this.initIndexTable();
-
- },
-
- /**
- * i18n support
- */
- TRANSLATIONS : {},
- PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; },
- LOCALE : 'unknown',
-
- // gettext and ngettext don't access this so that the functions
- // can safely bound to a different name (_ = Documentation.gettext)
- gettext : function(string) {
- var translated = Documentation.TRANSLATIONS[string];
- if (typeof translated == 'undefined')
- return string;
- return (typeof translated == 'string') ? translated : translated[0];
- },
-
- ngettext : function(singular, plural, n) {
- var translated = Documentation.TRANSLATIONS[singular];
- if (typeof translated == 'undefined')
- return (n == 1) ? singular : plural;
- return translated[Documentation.PLURALEXPR(n)];
- },
-
- addTranslations : function(catalog) {
- for (var key in catalog.messages)
- this.TRANSLATIONS[key] = catalog.messages[key];
- this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
- this.LOCALE = catalog.locale;
- },
-
- /**
- * add context elements like header anchor links
- */
- addContextElements : function() {
- $('div[id] > :header:first').each(function() {
- $('<a class="headerlink">\u00B6</a>').
- attr('href', '#' + this.id).
- attr('title', _('Permalink to this headline')).
- appendTo(this);
- });
- $('dt[id]').each(function() {
- $('<a class="headerlink">\u00B6</a>').
- attr('href', '#' + this.id).
- attr('title', _('Permalink to this definition')).
- appendTo(this);
- });
- },
-
- /**
- * workaround a firefox stupidity
- * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
- */
- fixFirefoxAnchorBug : function() {
- if (document.location.hash)
- window.setTimeout(function() {
- document.location.href += '';
- }, 10);
- },
-
- /**
- * highlight the search words provided in the url in the text
- */
- highlightSearchWords : function() {
- var params = $.getQueryParameters();
- var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
- if (terms.length) {
- var body = $('div.body');
- if (!body.length) {
- body = $('body');
- }
- window.setTimeout(function() {
- $.each(terms, function() {
- body.highlightText(this.toLowerCase(), 'highlighted');
- });
- }, 10);
- $('<p class="highlight-link"><a href="javascript:Documentation.' +
- 'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
- .appendTo($('#searchbox'));
- }
- },
-
- /**
- * init the domain index toggle buttons
- */
- initIndexTable : function() {
- var togglers = $('img.toggler').click(function() {
- var src = $(this).attr('src');
- var idnum = $(this).attr('id').substr(7);
- $('tr.cg-' + idnum).toggle();
- if (src.substr(-9) == 'minus.png')
- $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
- else
- $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
- }).css('display', '');
- if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
- togglers.click();
- }
- },
-
- /**
- * helper function to hide the search marks again
- */
- hideSearchWords : function() {
- $('#searchbox .highlight-link').fadeOut(300);
- $('span.highlighted').removeClass('highlighted');
- },
-
- /**
- * make the url absolute
- */
- makeURL : function(relativeURL) {
- return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
- },
-
- /**
- * get the current relative url
- */
- getCurrentURL : function() {
- var path = document.location.pathname;
- var parts = path.split(/\//);
- $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
- if (this == '..')
- parts.pop();
- });
- var url = parts.join('/');
- return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
- },
-
- initOnKeyListeners: function() {
- $(document).keyup(function(event) {
- var activeElementType = document.activeElement.tagName;
- // don't navigate when in search box or textarea
- if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
- switch (event.keyCode) {
- case 37: // left
- var prevHref = $('link[rel="prev"]').prop('href');
- if (prevHref) {
- window.location.href = prevHref;
- return false;
- }
- case 39: // right
- var nextHref = $('link[rel="next"]').prop('href');
- if (nextHref) {
- window.location.href = nextHref;
- return false;
- }
- }
- }
- });
- }
-};
-
-// quick alias for translations
-_ = Documentation.gettext;
-
-$(document).ready(function() {
- Documentation.init();
-});
\ No newline at end of file
diff --git a/content/_static/documentation_options.js b/content/_static/documentation_options.js
deleted file mode 100644
index 3adf28f..0000000
--- a/content/_static/documentation_options.js
+++ /dev/null
@@ -1,10 +0,0 @@
-var DOCUMENTATION_OPTIONS = {
- URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
- VERSION: '2.0.0',
- LANGUAGE: 'None',
- COLLAPSE_INDEX: false,
- FILE_SUFFIX: '.html',
- HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt',
- NAVIGATION_WITH_KEYS: false
-};
\ No newline at end of file
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diff --git a/content/_static/fonts/fontawesome-webfont.eot b/content/_static/fonts/fontawesome-webfont.eot
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diff --git a/content/_static/fonts/fontawesome-webfont.svg b/content/_static/fonts/fontawesome-webfont.svg
deleted file mode 100644
index 855c845..0000000
--- a/content/_static/fonts/fontawesome-webfont.svg
+++ /dev/null
@@ -1,2671 +0,0 @@
-<?xml version="1.0" standalone="no"?>
-<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
-<svg>
-<metadata>
-Created by FontForge 20120731 at Mon Oct 24 17:37:40 2016
- By ,,,
-Copyright Dave Gandy 2016. All rights reserved.
-</metadata>
-<defs>
-<font id="FontAwesome" horiz-adv-x="1536" >
- <font-face
- font-family="FontAwesome"
- font-weight="400"
- font-stretch="normal"
- units-per-em="1792"
- panose-1="0 0 0 0 0 0 0 0 0 0"
- ascent="1536"
- descent="-256"
- bbox="-1.02083 -256.962 2304.6 1537.02"
- underline-thickness="0"
- underline-position="0"
- unicode-range="U+0020-F500"
- />
-<missing-glyph horiz-adv-x="896"
-d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
- <glyph glyph-name=".notdef" horiz-adv-x="896"
-d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
- <glyph glyph-name=".null" horiz-adv-x="0"
- />
- <glyph glyph-name="nonmarkingreturn" horiz-adv-x="597"
- />
- <glyph glyph-name="space" unicode=" " horiz-adv-x="448"
- />
- <glyph glyph-name="dieresis" unicode="¨" horiz-adv-x="1792"
- />
- <glyph glyph-name="copyright" unicode="©" horiz-adv-x="1792"
- />
- <glyph glyph-name="registered" unicode="®" horiz-adv-x="1792"
- />
- <glyph glyph-name="acute" unicode="´" horiz-adv-x="1792"
- />
- <glyph glyph-name="AE" unicode="Æ" horiz-adv-x="1792"
- />
- <glyph glyph-name="Oslash" unicode="Ø" horiz-adv-x="1792"
- />
- <glyph glyph-name="trademark" unicode="™" horiz-adv-x="1792"
- />
- <glyph glyph-name="infinity" unicode="∞" horiz-adv-x="1792"
- />
- <glyph glyph-name="notequal" unicode="≠" horiz-adv-x="1792"
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