Author: buildbot
Date: Mon Jan 18 07:44:36 2016
New Revision: 977960
Log:
Staging update by buildbot for singa
Added:
websites/staging/singa/trunk/content/docs/zh/checkpoint.html
websites/staging/singa/trunk/content/docs/zh/cnn.html
websites/staging/singa/trunk/content/docs/zh/data.html
websites/staging/singa/trunk/content/docs/zh/distributed-training.html
websites/staging/singa/trunk/content/docs/zh/installation_source.html
websites/staging/singa/trunk/content/docs/zh/mlp.html
websites/staging/singa/trunk/content/docs/zh/neural-net.html
websites/staging/singa/trunk/content/docs/zh/rnn.html
websites/staging/singa/trunk/content/docs/zh/train-one-batch.html
websites/staging/singa/trunk/content/docs/zh/updater.html
Modified:
websites/staging/singa/trunk/content/ (props changed)
Propchange: websites/staging/singa/trunk/content/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Mon Jan 18 07:44:36 2016
@@ -1 +1 @@
-1725171
+1725177
Added: websites/staging/singa/trunk/content/docs/zh/checkpoint.html
==============================================================================
--- websites/staging/singa/trunk/content/docs/zh/checkpoint.html (added)
+++ websites/staging/singa/trunk/content/docs/zh/checkpoint.html Mon Jan 18
07:44:36 2016
@@ -0,0 +1,402 @@
+<!DOCTYPE html>
+<!--
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+ <div class="container-fluid">
+ <div id="banner">
+ <div class="pull-left">
+ <a href="../../index.html"
id="bannerLeft" title="Apache SINGA">
+
<img src="../../images/singa-logo.png" alt="Apache SINGA"/>
+ </a>
+ </div>
+ <div class="pull-right"> <div id="bannerRight">
+
<img src="../../images/singa-title.png" alt="Apache SINGA"/>
+ </div>
+ </div>
+ <div class="clear"><hr/></div>
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+
+ <div id="breadcrumbs">
+ <ul class="breadcrumb">
+
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+ <li class="">
+ <a href="../../index.html" title="Apache SINGA">
+ Apache SINGA</a>
+ <span class="divider">/</span>
+ </li>
+ <li class="active ">CheckPoint</li>
+
+
+
+
+ </ul>
+ </div>
+
+
+ <div class="row-fluid">
+ <div id="leftColumn" class="span2">
+ <div class="well sidebar-nav">
+
+
+ <ul class="nav nav-list">
+ <li class="nav-header">Apache SINGA</li>
+
+ <li>
+
+ <a href="../../docs/overview.html"
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+ <span class="none"></span>
+ Introduction</a>
+ </li>
+
+ <li>
+
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+
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title="NUS Site">
+ <span class="none"></span>
+ NUS Site</a>
+ </li>
+ </ul>
+
+
+
+ <hr />
+
+ <div id="poweredBy">
+ <div class="clear"></div>
+ <div class="clear"></div>
+ <div class="clear"></div>
+ <div class="clear"></div>
+
<a href="http://incubator.apache.org";
title="apache-incubator" class="builtBy">
+ <img class="builtBy" alt="Apache Incubator"
src="http://incubator.apache.org/images/egg-logo.png"; />
+ </a>
+ </div>
+ </div>
+ </div>
+
+
+ <div id="bodyColumn" class="span10" >
+
+ <h1>CheckPoint</h1>
+<hr />
+<p>SINGA checkpoints model parameters onto disk periodically according to user
configured frequency. By checkpointing model parameters, we can</p>
+
+<ol style="list-style-type: decimal">
+
+<li>
+<p>resume the training from the last checkpointing. For example, if the
program crashes before finishing all training steps, we can continue the
training using checkpoint files.</p></li>
+
+<li>
+<p>use them to initialize a similar model. For example, the parameters from
training a RBM model can be used to initialize a <a href="rbm.html">deep
auto-encoder</a> model.</p></li>
+</ol>
+<div class="section">
+<h2><a name="Configuration"></a>Configuration</h2>
+<p>Checkpointing is controlled by two configuration fields:</p>
+
+<ul>
+
+<li><tt>checkpoint_after</tt>, start checkpointing after this number of
training steps,</li>
+
+<li><tt>checkpoint_freq</tt>, frequency of doing checkpointing.</li>
+</ul>
+<p>For example,</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint"># job.conf
+checkpoint_after: 100
+checkpoint_frequency: 300
+...
+</pre></div></div>
+<p>Checkpointing files are located at
<i>WORKSPACE/checkpoint/stepSTEP-workerWORKERID</i>. <i>WORKSPACE</i> is
configured in</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">cluster {
+ workspace:
+}
+</pre></div></div>
+<p>For the above configuration, after training for 700 steps, there would be
two checkpointing files,</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">step400-worker0
+step700-worker0
+</pre></div></div></div>
+<div class="section">
+<h2><a name="Application_-_resuming_training"></a>Application - resuming
training</h2>
+<p>We can resume the training from the last checkpoint (i.e., step 700) by,</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf JOB_CONF
-resume
+</pre></div></div>
+<p>There is no change to the job configuration.</p></div>
+<div class="section">
+<h2><a name="Application_-_model_initialization"></a>Application - model
initialization</h2>
+<p>We can also use the checkpointing file from step 400 to initialize a new
model by configuring the new job as,</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint"># job.conf
+checkpoint : "WORKSPACE/checkpoint/step400-worker0"
+...
+</pre></div></div>
+<p>If there are multiple checkpointing files for the same snapshot due to
model partitioning, all the checkpointing files should be added,</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint"># job.conf
+checkpoint : "WORKSPACE/checkpoint/step400-worker0"
+checkpoint : "WORKSPACE/checkpoint/step400-worker1"
+...
+</pre></div></div>
+<p>The training command is the same as starting a new job,</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf JOB_CONF
+</pre></div></div></div>
+ </div>
+ </div>
+ </div>
+
+ <hr/>
+
+ <footer>
+ <div class="container-fluid">
+ <div class="row-fluid">
+
+<p>Copyright © 2015 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.</p>
+ </div>
+
+
+ </div>
+ </footer>
+ </body>
+</html>
Added: websites/staging/singa/trunk/content/docs/zh/cnn.html
==============================================================================
--- websites/staging/singa/trunk/content/docs/zh/cnn.html (added)
+++ websites/staging/singa/trunk/content/docs/zh/cnn.html Mon Jan 18 07:44:36
2016
@@ -0,0 +1,553 @@
+<!DOCTYPE html>
+<!--
+ | Generated by Apache Maven Doxia at 2016-01-18
+ | Rendered using Apache Maven Fluido Skin 1.4
+-->
+<html xmlns="http://www.w3.org/1999/xhtml"; xml:lang="en" lang="en">
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+
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title="SINGA Team">
+ <span class="none"></span>
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+
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+
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class="externalLink" title="Apache Software Foundation">
+ <span class="none"></span>
+ Apache Software Foundation</a>
+ </li>
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+
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href="http://www.comp.nus.edu.sg/~dbsystem/singa/"; class="externalLink"
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+ <hr />
+
+ <div id="poweredBy">
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+
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title="apache-incubator" class="builtBy">
+ <img class="builtBy" alt="Apache Incubator"
src="http://incubator.apache.org/images/egg-logo.png"; />
+ </a>
+ </div>
+ </div>
+ </div>
+
+
+ <div id="bodyColumn" class="span10" >
+
+ <h1>CNN Example</h1>
+<hr />
+<p>Convolutional neural network (CNN) is a type of feed-forward artificial
neural network widely used for image and video classification. In this example,
we will use a deep CNN model to do image classification for the <a
class="externalLink" href="http://www.cs.toronto.edu/~kriz/cifar.html";>CIFAR10
dataset</a>.</p>
+<div class="section">
+<h2><a name="Running_instructions"></a>Running instructions</h2>
+<p>Please refer to the <a href="installation.html">installation</a> page for
instructions on building SINGA, and the <a href="quick-start.html">quick
start</a> for instructions on starting zookeeper.</p>
+<p>We have provided scripts for preparing the training and test dataset in
<i>examples/cifar10/</i>.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint"># in examples/cifar10
+$ cp Makefile.example Makefile
+$ make download
+$ make create
+</pre></div></div>
+<div class="section">
+<h3><a name="Training_on_CPU"></a>Training on CPU</h3>
+<p>We can start the training by</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf
examples/cifar10/job.conf
+</pre></div></div>
+<p>You should see output like</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">Record job information to
/tmp/singa-log/job-info/job-2-20150817-055601
+Executing : ./singa -conf /xxx/incubator-singa/examples/cifar10/job.conf
-singa_conf /xxx/incubator-singa/conf/singa.conf -singa_job 2
+E0817 06:56:18.868259 33849 cluster.cc:51] proc #0 -> 192.168.5.128:49152
(pid = 33849)
+E0817 06:56:18.928452 33871 server.cc:36] Server (group = 0, id = 0) start
+E0817 06:56:18.928469 33872 worker.cc:134] Worker (group = 0, id = 0) start
+E0817 06:57:13.657302 33849 trainer.cc:373] Test step-0, loss : 2.302588,
accuracy : 0.077900
+E0817 06:57:17.626708 33849 trainer.cc:373] Train step-0, loss : 2.302578,
accuracy : 0.062500
+E0817 06:57:24.142645 33849 trainer.cc:373] Train step-30, loss : 2.302404,
accuracy : 0.131250
+E0817 06:57:30.813354 33849 trainer.cc:373] Train step-60, loss : 2.302248,
accuracy : 0.156250
+E0817 06:57:37.556655 33849 trainer.cc:373] Train step-90, loss : 2.301849,
accuracy : 0.175000
+E0817 06:57:44.971276 33849 trainer.cc:373] Train step-120, loss : 2.301077,
accuracy : 0.137500
+E0817 06:57:51.801949 33849 trainer.cc:373] Train step-150, loss : 2.300410,
accuracy : 0.135417
+E0817 06:57:58.682281 33849 trainer.cc:373] Train step-180, loss : 2.300067,
accuracy : 0.127083
+E0817 06:58:05.578366 33849 trainer.cc:373] Train step-210, loss : 2.300143,
accuracy : 0.154167
+E0817 06:58:12.518497 33849 trainer.cc:373] Train step-240, loss : 2.295912,
accuracy : 0.185417
+</pre></div></div>
+<p>After training some steps (depends on the setting) or the job is finished,
SINGA will <a href="checkpoint.html">checkpoint</a> the model
parameters.</p></div>
+<div class="section">
+<h3><a name="Training_on_GPU"></a>Training on GPU</h3>
+<p>Since version 0.2, we can train CNN models on GPU using cuDNN. Please refer
to the <a href="gpu.html">GPU page</a> for details on compiling SINGA with GPU
and cuDNN. The configuration file is similar to that for CPU training, except
that the cuDNN layers are used and the GPU device is configured.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf
examples/cifar10/cudnn.conf
+</pre></div></div></div>
+<div class="section">
+<h3><a name="Training_using_Python_script"></a>Training using Python
script</h3>
+<p>The python helpers coming with SINGA 0.2 make it easy to configure a
training job. For example the <i>job.conf</i> is replaced with a simple python
script <i>mnist_mlp.py</i> which has about 30 lines of code following the <a
class="externalLink" href="http://keras.io/";>Keras API</a>.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint"> # on CPU
+./bin/singa-run.sh -exec tool/python/examples/cifar10_cnn.py
+ # on GPU
+./bin/singa-run.sh -exec tool/python/examples/cifar10_cnn_cudnn.py
+</pre></div></div></div></div>
+<div class="section">
+<h2><a name="Details"></a>Details</h2>
+<p>To train a model in SINGA, you need to prepare the datasets, and a job
configuration which specifies the neural net structure, training algorithm (BP
or CD), SGD update algorithm (e.g. Adagrad), number of training/test steps,
etc.</p>
+<div class="section">
+<h3><a name="Data_preparation"></a>Data preparation</h3>
+<p>Before using SINGA, you need to write a program to convert the dataset into
a format that SINGA can read. Please refer to the <a
href="data.html#example---cifar-dataset">Data Preparation</a> to get details
about preparing this CIFAR10 dataset.</p></div>
+<div class="section">
+<h3><a name="Neural_net"></a>Neural net</h3>
+<p>Figure 1 shows the net structure of the CNN model we used in this example,
which is set following <a class="externalLink"
href="https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg.";>Alex</a>
The dashed circle represents one feature transformation stage, which generally
has four layers as shown in the figure. Sometimes the rectifier layer and
normalization layer are omitted or swapped in one stage. For this example,
there are 3 such stages.</p>
+<p>Next we follow the guide in <a href="neural-net.html">neural net page</a>
and <a href="layer.html">layer page</a> to write the neural net
configuration.</p>
+
+<div style="text-align: center">
+<img src="../images/example-cnn.png" style="width: 200px" alt="" /> <br />
+<b>Figure 1 - Net structure of the CNN example.</b></img>
+</div>
+
+<ul>
+
+<li>
+<p>We configure an input layer to read the training/testing records from a
disk file.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">layer{
+ name: "data"
+ type: kRecordInput
+ store_conf {
+ backend: "kvfile"
+ path: "examples/cifar10/train_data.bin"
+ mean_file: "examples/cifar10/image_mean.bin"
+ batchsize: 64
+ random_skip: 5000
+ shape: 3
+ shape: 32
+ shape: 32
+ }
+ exclude: kTest # exclude this layer for the testing net
+}
+layer{
+ name: "data"
+ type: kRecordInput
+ store_conf {
+ backend: "kvfile"
+ path: "examples/cifar10/test_data.bin"
+ mean_file: "examples/cifar10/image_mean.bin"
+ batchsize: 100
+ shape: 3
+ shape: 32
+ shape: 32
+ }
+ exclude: kTrain # exclude this layer for the training net
+}
+</pre></div></div></li>
+</ul>
+
+<ul>
+
+<li>
+<p>We configure layers for the feature transformation as follows (all layers
are built-in layers in SINGA; hyper-parameters of these layers are set
according to <a class="externalLink"
href="https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg";>Alex’s
setting</a>).</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">layer {
+ name: "conv1"
+ type: kConvolution
+ srclayers: "data"
+ convolution_conf {... }
+ ...
+}
+layer {
+ name: "pool1"
+ type: kPooling
+ srclayers: "conv1"
+ pooling_conf {... }
+}
+layer {
+ name: "relu1"
+ type: kReLU
+ srclayers:"pool1"
+}
+layer {
+ name: "norm1"
+ type: kLRN
+ lrn_conf {... }
+ srclayers:"relu1"
+}
+</pre></div></div></li>
+</ul>
+<p>The configurations for another 2 stages are omitted here.</p>
+
+<ul>
+
+<li>
+<p>There is an <a href="layer.html#innerproductlayer">inner product layer</a>
after the 3 transformation stages, which is configured with 10 output units,
i.e., the number of total labels. The weight matrix Param is configured with a
large weight decay scale to reduce the over-fitting.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">layer {
+ name: "ip1"
+ type: kInnerProduct
+ srclayers:"pool3"
+ innerproduct_conf {
+ num_output: 10
+ }
+ param {
+ name: "w4"
+ wd_scale:250
+ ...
+ }
+ param {
+ name: "b4"
+ ...
+ }
+}
+</pre></div></div></li>
+
+<li>
+<p>The last layer is a <a href="layer.html#softmaxloss">Softmax loss
layer</a></p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">layer{
+ name: "loss"
+ type: kSoftmaxLoss
+ softmaxloss_conf{ topk:1 }
+ srclayers:"ip1"
+ srclayers: "data"
+}
+</pre></div></div></li>
+</ul></div>
+<div class="section">
+<h3><a name="Updater"></a>Updater</h3>
+<p>The <a href="updater.html#updater">normal SGD updater</a> is selected. The
learning rate is changed like going down stairs, and is configured using the <a
href="updater.html#kfixedstep">kFixedStep</a> type.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint"> updater{
+ type: kSGD
+ weight_decay:0.004
+ learning_rate {
+ type: kFixedStep
+ fixedstep_conf:{
+ step:0 # lr for step 0-60000 is 0.001
+ step:60000 # lr for step 60000-65000 is 0.0001
+ step:65000 # lr for step 650000- is 0.00001
+ step_lr:0.001
+ step_lr:0.0001
+ step_lr:0.00001
+ }
+ }
+ }
+</pre></div></div></div>
+<div class="section">
+<h3><a name="TrainOneBatch_algorithm"></a>TrainOneBatch algorithm</h3>
+<p>The CNN model is a feed forward model, thus should be configured to use the
<a href="train-one-batch.html#back-propagation">Back-propagation
algorithm</a>.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">train_one_batch {
+ alg: kBP
+}
+</pre></div></div></div>
+<div class="section">
+<h3><a name="Cluster_setting"></a>Cluster setting</h3>
+<p>The following configuration set a single worker and server for training. <a
href="frameworks.html">Training frameworks</a> page introduces configurations
of a couple of distributed training frameworks.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">cluster {
+ nworker_groups: 1
+ nserver_groups: 1
+}
+</pre></div></div></div></div>
+ </div>
+ </div>
+ </div>
+
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+
+
+ <div id="bodyColumn" class="span10" >
+
+ <h1>Data Preparation</h1>
+<hr />
+<p>SINGA uses input layers to load data. Users can store their data in any
format (e.g., CSV or binary) and at any places (e.g., disk file or HDFS) as
long as there are corresponding input layers that can read the data records and
parse them.</p>
+<p>To make it easy for users, SINGA provides a [StoreInputLayer] to read data
in the format of (string:key, string:value) tuples from a couple of sources.
These sources are abstracted using a <a href="">Store</a> class which is a
simple version of the DB abstraction in Caffe. The base Store class provides
the following operations for reading and writing tuples,</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">Open(string path, Mode mode); //
open the store for kRead or kCreate or kAppend
+Close();
+
+Read(string* key, string* val); // read a tuple; return false if fail
+Write(string key, string val); // write a tuple
+Flush();
+</pre></div></div>
+<p>Currently, two implementations are provided, namely</p>
+
+<ol style="list-style-type: decimal">
+
+<li>
+<p>[KVFileStore] for storing tuples in <a href="">KVFile</a> (a binary file).
The <i>create_data.cc</i> files in <i>examples/cifar10</i> and
<i>examples/mnist</i> provide examples of storing records using
KVFileStore.</p></li>
+
+<li>
+<p>[TextFileStore] for storing tuples in plain text file (one line per
tuple).</p></li>
+</ol>
+<p>The (key, value) tuple are parsed by subclasses of StoreInputLayer
depending on the format of the tuple,</p>
+
+<ul>
+
+<li>
+<p>[ProtoRecordInputLayer] parses the value field from one tuple into a
[SingleLabelImageRecord], which is generated by Google Protobuf according to
[common.proto]. It can be used to store features for images (e.g., using the
pixel field) or other objects (using the data field). The key field is not
used.</p></li>
+
+<li>
+<p>[CSVRecordInputLayer] parses one tuple as a CSV line (separated by
comma).</p></li>
+</ul>
+<div class="section">
+<h2><a name="Using_built-in_record_format"></a>Using built-in record
format</h2>
+<p>SingleLabelImageRecord is a built-in record in SINGA for storing image
features. It is used in the cifar10 and mnist examples.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">message SingleLabelImageRecord {
+ repeated int32 shape = 1; // it obtains 3 (rgb channels), 32
(row), 32 (col)
+ optional int32 label = 2; // label
+ optional bytes pixel = 3; // pixels
+ repeated float data = 4 [packed = true]; // it is used for normalization
+</pre></div></div>
+<p>}</p>
+<p>The data preparation instructions for the <a class="externalLink"
href="http://www.cs.toronto.edu/~kriz/cifar.html";>CIFAR-10 image dataset</a>
will be elaborated here. This dataset consists of 60,000 32x32 color images in
10 classes, with 6,000 images per class. There are 50,000 training images and
10,000 test images. Each image has a single label. This dataset is stored in
binary files with specific format. SINGA comes with the <a class="externalLink"
href="https://github.com/apache/incubator-singa/blob/master/examples/cifar10/create_data.cc";>create_data.cc</a>
to convert images in the binary files into <tt>SingleLabelImageRecord</tt>s
and insert them into training and test stores.</p>
+
+<ol style="list-style-type: decimal">
+
+<li>
+<p>Download raw data. The following command will download the dataset into
<i>cifar-10-batches-bin</i> folder.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint"># in SINGA_ROOT/examples/cifar10
+$ cp Makefile.example Makefile // an example makefile is provided
+$ make download
+</pre></div></div></li>
+
+<li>
+<p>Fill one record for each image, and insert it to store.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">KVFileStore store;
+store.Open(output_file_path, singa::io::kCreate);
+
+singa::SingleLabelImageRecord image;
+for (int image_id = 0; image_id < 50000; image_id ++) {
+ // fill the record with image feature and label from downloaded binay files
+ string str;
+ image.SerializeToString(&str);
+ store.Write(to_string(image_id), str);
+}
+store.Flush();
+store.Close();
+</pre></div></div>
+<p>The data store for testing data is created similarly. In addition, it
computes average values (not shown here) of image pixels and insert the mean
values into a SingleLabelImageRecord, which is then written into a another
store.</p></li>
+
+<li>
+<p>Compile and run the program. SINGA provides an example Makefile that
contains instructions for compiling the source code and linking it with
<i>libsinga.so</i>. Users just execute the following command.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">$ make create
+</pre></div></div></li>
+</ol></div>
+<div class="section">
+<h2><a name="using_user-defined_record_format"></a>using user-defined record
format</h2>
+<p>If users cannot use the SingleLabelImageRecord or CSV record for their
data. They can define their own record format e.g., using Google Protobuf. A
record can be written into a data store as long as it can be converted into
byte string. Correspondingly, subclasses of StoreInputLayer are required to
parse user-defined records.</p></div>
+ </div>
+ </div>
+ </div>
+
+ <hr/>
+
+ <footer>
+ <div class="container-fluid">
+ <div class="row-fluid">
+
+<p>Copyright © 2015 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.</p>
+ </div>
+
+
+ </div>
+ </footer>
+ </body>
+</html>
Added: websites/staging/singa/trunk/content/docs/zh/distributed-training.html
==============================================================================
--- websites/staging/singa/trunk/content/docs/zh/distributed-training.html
(added)
+++ websites/staging/singa/trunk/content/docs/zh/distributed-training.html Mon
Jan 18 07:44:36 2016
@@ -0,0 +1,362 @@
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+
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+
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+ <div class="well sidebar-nav">
+
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+ <span class="none"></span>
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+ </li>
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+ <li>
+
+ <a
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+ <span class="none"></span>
+ NUS Site</a>
+ </li>
+ </ul>
+
+
+
+ <hr />
+
+ <div id="poweredBy">
+ <div class="clear"></div>
+ <div class="clear"></div>
+ <div class="clear"></div>
+ <div class="clear"></div>
+
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+ <img class="builtBy" alt="Apache Incubator"
src="http://incubator.apache.org/images/egg-logo.png"; />
+ </a>
+ </div>
+ </div>
+ </div>
+
+
+ <div id="bodyColumn" class="span10" >
+
+ <h1>Distributed Training</h1>
+<hr />
+<p>SINGA is designed for distributed training of large deep learning models
with huge amount of training data. We also provide high-level descriptions of
design behind SINGA’s distributed architecture. </p>
+
+<ul>
+
+<li>
+<p><a href="architecture.html">System Architecture</a></p></li>
+
+<li>
+<p><a href="frameworks.html">Training Frameworks</a></p></li>
+
+<li>
+<p><a href="communication.html">System Communication</a></p></li>
+</ul>
+<p>SINGA supports different options for training a model in parallel,
includeing data parallelism, model parallelism and hybrid parallelism.</p>
+
+<ul>
+
+<li><a href="hybrid.html">Hybrid Parallelism</a></li>
+</ul>
+<p>SINGA is intergrated with Mesos, so that distributed training can be
started as a Mesos framework. Currently, the Mesos cluster can be set up from
SINGA containers, i.e. we provide Docker images that bundles Mesos and SINGA
together. Refer to the guide below for instructions as how to start and use the
cluster.</p>
+
+<ul>
+
+<li><a href="mesos.html">Distributed training on Mesos</a></li>
+</ul>
+<p>SINGA can run on top of distributed storage system to achieve scalability.
The current version of SINGA supports HDFS.</p>
+
+<ul>
+
+<li><a href="hdfs.html">Running SINGA on HDFS</a></li>
+</ul>
+ </div>
+ </div>
+ </div>
+
+ <hr/>
+
+ <footer>
+ <div class="container-fluid">
+ <div class="row-fluid">
+
+<p>Copyright © 2015 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.</p>
+ </div>
+
+
+ </div>
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+ </body>
+</html>
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