svn commit: r1724348 [3/6] - in /incubator/singa/site/trunk/content/markdown/docs: ./ jp/ kr/

[wa...@apache.org]

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+# Programming Guide
+
+---
+
+To submit a training job, users must provide the configuration of the
+four components shown in Figure 1:
+
+  * a [NeuralNet](neural-net.html) describing the neural net structure with the detailed layer setting and their connections;
+  * a [TrainOneBatch](train-one-batch.html) algorithm which is tailored for different model categories;
+  * an [Updater](updater.html) defining the protocol for updating parameters at the server side;
+  * a [Cluster Topology](distributed-training.html) specifying the distributed architecture of workers and servers.
+
+The *Basic user guide* section describes how to submit a training job using
+built-in components; while the *Advanced user guide* section presents details
+on writing user's own main function to register components implemented by
+themselves. In addition, the training data must be prepared, which has the same
+[process](data.html) for both advanced users and basic users.
+
+<img src="../images/overview.png" align="center" width="400px"/>
+<span><strong>Figure 1 - SINGA overview.</strong></span>
+
+
+
+## Basic user guide
+
+Users can use the default main function provided SINGA to submit the training
+job. For this case, a job configuration file written as a google protocol
+buffer message for the [JobProto](../api/classsinga_1_1JobProto.html) must be provided in the command line,
+
+    ./bin/singa-run.sh -conf <path to job conf> [-resume]
+
+`-resume` is for continuing the training from last
+[checkpoint](checkpoint.html).
+The [MLP](mlp.html) and [CNN](cnn.html)
+examples use built-in components. Please read the corresponding pages for their
+job configuration files. The subsequent pages will illustrate the details on
+each component of the configuration.
+
+## Advanced user guide
+
+If a user's model contains some user-defined components, e.g.,
+[Updater](updater.html), he has to write a main function to
+register these components. It is similar to Hadoop's main function. Generally,
+the main function should
+
+  * initialize SINGA, e.g., setup logging.
+
+  * register user-defined components.
+
+  * create and pass the job configuration to SINGA driver
+
+
+An example main function is like
+
+    #include "singa.h"
+    #include "user.h"  // header for user code
+
+    int main(int argc, char** argv) {
+      singa::Driver driver;
+      driver.Init(argc, argv);
+      bool resume;
+      // parse resume option from argv.
+
+      // register user defined layers
+      driver.RegisterLayer<FooLayer>(kFooLayer);
+      // register user defined updater
+      driver.RegisterUpdater<FooUpdater>(kFooUpdater);
+      ...
+      auto jobConf = driver.job_conf();
+      //  update jobConf
+
+      driver.Train(resume, jobConf);
+      return 0;
+    }
+
+The Driver class' `Init` method will load a job configuration file provided by
+users as a command line argument (`-conf <job conf>`). It contains at least the
+cluster topology and returns the `jobConf` for users to update or fill in
+configurations of neural net, updater, etc. If users define subclasses of
+Layer, Updater, Worker and Param, they should register them through the driver.
+Finally, the job configuration is submitted to the driver which starts the
+training.
+
+We will provide helper functions to make the configuration easier in the
+future, like [keras](https://github.com/fchollet/keras).
+
+Users need to compile and link their code (e.g., layer implementations and the main
+file) with SINGA library (*.libs/libsinga.so*) to generate an
+executable file, e.g., with name *mysinga*.  To launch the program, users just pass the
+path of the *mysinga* and base job configuration to *./bin/singa-run.sh*.
+
+    ./bin/singa-run.sh -conf <path to job conf> -exec <path to mysinga> [other arguments]
+
+The [RNN application](rnn.html) provides a full example of
+implementing the main function for training a specific RNN model.

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+# クイック スタート
+
+---
+
+## SINGA セットアップ
+
+SINGAのインストールについては[こちら](installation.html)をご覧ください。
+
+### Zookeeper の実行
+
+SINGAのトレーニングは　[zookeeper](https://zookeeper.apache.org/) を利用します。まずは zookeeper サービスが開始されていることを確認してください。
+
+準備された thirdparty のスクリプトを使って zookeeper をインストールした場合、次のスクリプトを実行してください。
+
+    #goto top level folder
+    cd  SINGA_ROOT
+    ./bin/zk-service.sh start
+
+(`./bin/zk-service.sh stop` // zookeeper の停止).
+
+デフォルトのポートを使用せずに zookeeper をスタートさせる時は、`conf/singa.conf`を編集してください。
+
+    zookeeper_host: "localhost:YOUR_PORT"
+
+## スタンドアローンモードでの実行
+
+スタンドアローンモードでSINGAを実行するとは、[Mesos](http://mesos.apache.org/) や [YARN](http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html) のようなクラスターマネージャー利用しない場合のことを言います。
+
+### Single ノードでのトレーニング
+
+１つのプロセスがローンチされます。
+例として、
+[CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar.html) データセットを利用して
+[CNN モデル](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) をトレーニングさせます。
+ハイパーパラメーターは、[cuda-convnet](https://code.google.com/p/cuda-convnet/) に基づいて設定されてあります。
+詳細は、[CNN サンプル](cnn.html) のページをご覧ください。
+
+
+#### データと、ジョブ設定
+
+データセットのダウンロードと、Triaing や Test のためのデータシャードの生成は次のように行います。
+
+    cd examples/cifar10/
+    cp Makefile.example Makefile
+    make download
+    make create
+
+Training と Test データセットは、それぞれ *cifar10-train-shard*
+と *cifar10-test-shard* フォルダーに作られます。　すべての画像の特徴平均を記述した *image_mean.bin* ファイルも作成されます。
+
+CNN モデルのトレーニングに必要なソースコードはすべてSINGAに組み込まれています。コードを追加する必要はありません。
+ジョブ設定ファイル (*job.conf*) を指定して、スクリプト(*../../bin/singa-run.sh*) を実行します。
+SINGAのコードを変更、または追加する時は、[プログラミングガイド](programming-guide.html)をご覧ください。
+
+#### 並列化なしのトレーニング
+
+Cluster Topology のデフォルト値は、１つの worker と　１つの server となっています。
+データとニューラルネットの並列化はされません。
+
+トレーニングを開始するには次のスクリプトを実行します。
+
+    # goto top level folder
+    cd ../../
+    ./bin/singa-run.sh -conf examples/cifar10/job.conf
+
+
+現在、起動中のジョブのリストを表示するには
+
+    ./bin/singa-console.sh list
+
+    JOB ID    |NUM PROCS
+    ----------|-----------
+    24        |1
+
+ジョブの強制終了をするには
+
+    ./bin/singa-console.sh kill JOB_ID
+
+
+ログとジョブの情報は */tmp/singa-log* フォルダーに保存されます。
+*conf/singa.conf* ファイルの `log-dir`で変更可能です。
+
+
+#### 非同期、並列トレーニング
+
+    # job.conf
+    ...
+    cluster {
+      nworker_groups: 2
+      nworkers_per_procs: 2
+      workspace: "examples/cifar10/"
+    }
+
+複数の worker グループをローンチすることによって、
+In SINGA, [非同期トレーニング](architecture.html) を実行することが出来ます。
+例えば、*job.conf* を上記のように変更します。
+デフォルトでは、１つの worker グループが１つの worker を持つよう設定されています。
+上記の設定では、１つのプロセスに２つの worker が設定されているので、２つの worker グループが同じプロセスとして実行されます。
+結果、インメモリ [Downpour](frameworks.html) トレーニングフレームワークとして、実行されます。
+
+ユーザーは、データの分散を気にする必要はありません。
+ランダムオフセットに従い、各 worker グループに、データが振り分けられます。
+各 worker は異なるデータパーティションを担当します。
+
+    # job.conf
+    ...
+    neuralnet {
+      layer {
+        ...
+        sharddata_conf {
+          random_skip: 5000
+        }
+      }
+      ...
+    }
+
+スクリプト実行:
+
+    ./bin/singa-run.sh -conf examples/cifar10/job.conf
+
+#### 同期、並列トレーニング
+
+    # job.conf
+    ...
+    cluster {
+      nworkers_per_group: 2
+      nworkers_per_procs: 2
+      workspace: "examples/cifar10/"
+    }
+
+１つのworkerグループとして複数のworkerをローンチすることで [同期トレーニング](architecture.html)を実行することが出来ます。
+例えば、*job.conf* ファイルを上記のように変更します。
+上記の設定では、１つの worker グループに２つの worker が設定されました。
+worker 達はグループ内で同期します。
+これは、インメモリ [sandblaster](frameworks.html) として実行されます。
+モデルは２つのworkerに分割されます。各レイヤーが２つのworkerに振り分けられます。
+振り分けられたレイヤーはオリジナルのレイヤーと機能は同じですが、特徴インスタンスの数が `B/g` になります。
+ここで、`B`はミニバッチのインスタンスの数で、`g`はグループ内の worker の数です。
+[別のスキーム](neural-net.html) を利用したレイヤー（ニューラルネットワーク）パーティション方法もあります。
+
+他の設定はすべて「並列化なし」の場合と同じです。
+
+    ./bin/singa-run.sh -conf examples/cifar10/job.conf
+
+### クラスタ上でのトレーニング
+
+クラスター設定を変更して、上記トレーニングフレームワークの拡張を行います。
+
+    nworker_per_procs: 1
+
+すべてのプロセスは１つのworkerスレッドを生成します。
+結果、worker 達は異なるプロセス（ノード）内で生成されます。
+クラスター内のノードを特定するには、*SINGA_ROOT/conf/* の *hostfile* の設定が必要です。
+
+e.g.,
+
+    logbase-a01
+    logbase-a02
+
+zookeeper location も設定する必要があります。
+
+e.g.,
+
+    #conf/singa.conf
+    zookeeper_host: "logbase-a01"
+
+スクリプトの実行は「Single ノード トレーニング」と同じです。
+
+    ./bin/singa-run.sh -conf examples/cifar10/job.conf
+
+## Mesos　での実行
+
+*working*...
+
+## 次へ
+
+SINGAのコード変更や追加に関する詳細は、[プログラミングガイド](programming-guide.html) をご覧ください。

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+# RBM Example
+
+---
+
+This example uses SINGA to train 4 RBM models and one auto-encoder model over the
+[MNIST dataset](http://yann.lecun.com/exdb/mnist/). The auto-encoder model is trained
+to reduce the dimensionality of the MNIST image feature. The RBM models are trained
+to initialize parameters of the auto-encoder model. This example application is
+from [Hinton's science paper](http://www.cs.toronto.edu/~hinton/science.pdf).
+
+## Running instructions
+
+Running scripts are provided in *SINGA_ROOT/examples/rbm* folder.
+
+The MNIST dataset has 70,000 handwritten digit images. The
+[data preparation](data.html) page
+has details on converting this dataset into SINGA recognizable format. Users can
+simply run the following commands to download and convert the dataset.
+
+    # at SINGA_ROOT/examples/mnist/
+    $ cp Makefile.example Makefile
+    $ make download
+    $ make create
+
+The training is separated into two phases, namely pre-training and fine-tuning.
+The pre-training phase trains 4 RBMs in sequence,
+
+    # at SINGA_ROOT/
+    $ ./bin/singa-run.sh -conf examples/rbm/rbm1.conf
+    $ ./bin/singa-run.sh -conf examples/rbm/rbm2.conf
+    $ ./bin/singa-run.sh -conf examples/rbm/rbm3.conf
+    $ ./bin/singa-run.sh -conf examples/rbm/rbm4.conf
+
+The fine-tuning phase trains the auto-encoder by,
+
+    $ ./bin/singa-run.sh -conf examples/rbm/autoencoder.conf
+
+
+## Training details
+
+### RBM1
+
+<img src="../images/example-rbm1.png" align="center" width="200px"/>
+<span><strong>Figure 1 - RBM1.</strong></span>
+
+The neural net structure for training RBM1 is shown in Figure 1.
+The data layer and parser layer provides features for training RBM1.
+The visible layer (connected with parser layer) of RBM1 accepts the image feature
+(784 dimension). The hidden layer is set to have 1000 neurons (units).
+These two layers are configured as,
+
+    layer{
+      name: "RBMVis"
+      type: kRBMVis
+      srclayers:"mnist"
+      srclayers:"RBMHid"
+      rbm_conf{
+        hdim: 1000
+      }
+      param{
+        name: "w1"
+        init{
+          type: kGaussian
+          mean: 0.0
+          std: 0.1
+        }
+      }
+      param{
+        name: "b11"
+        init{
+          type: kConstant
+          value: 0.0
+        }
+      }
+    }
+
+    layer{
+      name: "RBMHid"
+      type: kRBMHid
+      srclayers:"RBMVis"
+      rbm_conf{
+        hdim: 1000
+      }
+      param{
+        name: "w1_"
+        share_from: "w1"
+      }
+      param{
+        name: "b12"
+        init{
+          type: kConstant
+          value: 0.0
+        }
+      }
+    }
+
+
+
+For RBM, the weight matrix is shared by the visible and hidden layers. For instance,
+`w1` is shared by `vis` and `hid` layers shown in Figure 1. In SINGA, we can configure
+the `share_from` field to enable [parameter sharing](param.html)
+as shown above for the param `w1` and `w1_`.
+
+[Contrastive Divergence](train-one-batch.html#contrastive-divergence)
+is configured as the algorithm for [TrainOneBatch](train-one-batch.html).
+Following Hinton's paper, we configure the [updating protocol](updater.html)
+as follows,
+
+    # Updater Configuration
+    updater{
+      type: kSGD
+      momentum: 0.2
+      weight_decay: 0.0002
+      learning_rate{
+        base_lr: 0.1
+        type: kFixed
+      }
+    }
+
+Since the parameters of RBM0 will be used to initialize the auto-encoder, we should
+configure the `workspace` field to specify a path for the checkpoint folder.
+For example, if we configure it as,
+
+    cluster {
+      workspace: "examples/rbm/rbm1/"
+    }
+
+Then SINGA will [checkpoint the parameters](checkpoint.html) into *examples/rbm/rbm1/*.
+
+### RBM1
+<img src="../images/example-rbm2.png" align="center" width="200px"/>
+<span><strong>Figure 2 - RBM2.</strong></span>
+
+Figure 2 shows the net structure of training RBM2.
+The visible units of RBM2 accept the output from the Sigmoid1 layer. The Inner1 layer
+is a  `InnerProductLayer` whose parameters are set to the `w1` and `b12` learned
+from RBM1.
+The neural net configuration is (with layers for data layer and parser layer omitted).
+
+    layer{
+      name: "Inner1"
+      type: kInnerProduct
+      srclayers:"mnist"
+      innerproduct_conf{
+        num_output: 1000
+      }
+      param{ name: "w1" }
+      param{ name: "b12"}
+    }
+
+    layer{
+      name: "Sigmoid1"
+      type: kSigmoid
+      srclayers:"Inner1"
+    }
+
+    layer{
+      name: "RBMVis"
+      type: kRBMVis
+      srclayers:"Sigmoid1"
+      srclayers:"RBMHid"
+      rbm_conf{
+        hdim: 500
+      }
+      param{
+        name: "w2"
+        ...
+      }
+      param{
+        name: "b21"
+        ...
+      }
+    }
+
+    layer{
+      name: "RBMHid"
+      type: kRBMHid
+      srclayers:"RBMVis"
+      rbm_conf{
+        hdim: 500
+      }
+      param{
+        name: "w2_"
+        share_from: "w2"
+      }
+      param{
+        name: "b22"
+        ...
+      }
+    }
+
+To load w0 and b02 from RBM0's checkpoint file, we configure the `checkpoint_path` as,
+
+    checkpoint_path: "examples/rbm/rbm1/checkpoint/step6000-worker0"
+    cluster{
+      workspace: "examples/rbm/rbm2"
+    }
+
+The workspace is changed for checkpointing `w2`, `b21` and `b22` into
+*examples/rbm/rbm2/*.
+
+### RBM3
+
+<img src="../images/example-rbm3.png" align="center" width="200px"/>
+<span><strong>Figure 3 - RBM3.</strong></span>
+
+Figure 3 shows the net structure of training RBM3. In this model, a layer with
+250 units is added as the hidden layer of RBM3. The visible units of RBM3
+accepts output from Sigmoid2 layer. Parameters of Inner1 and Innner2 are set to
+`w1,b12,w2,b22` which can be load from the checkpoint file of RBM2,
+i.e., "examples/rbm/rbm2/".
+
+### RBM4
+
+
+<img src="../images/example-rbm4.png" align="center" width="200px"/>
+<span><strong>Figure 4 - RBM4.</strong></span>
+
+Figure 4 shows the net structure of training RBM4. It is similar to Figure 3,
+but according to [Hinton's science paper](http://www.cs.toronto.edu/~hinton/science.pdf), the hidden units of the
+top RBM (RBM4) have stochastic real-valued states drawn from a unit variance
+Gaussian whose mean is determined by the input from the RBM's logistic visible
+units. So we add a `gaussian` field in the RBMHid layer to control the
+sampling distribution (Gaussian or Bernoulli). In addition, this
+RBM has a much smaller learning rate (0.001).  The neural net configuration for
+the RBM4 and the updating protocol is (with layers for data layer and parser
+layer omitted),
+
+    # Updater Configuration
+    updater{
+      type: kSGD
+      momentum: 0.9
+      weight_decay: 0.0002
+      learning_rate{
+        base_lr: 0.001
+        type: kFixed
+      }
+    }
+
+    layer{
+      name: "RBMVis"
+      type: kRBMVis
+      srclayers:"Sigmoid3"
+      srclayers:"RBMHid"
+      rbm_conf{
+        hdim: 30
+      }
+      param{
+        name: "w4"
+        ...
+      }
+      param{
+        name: "b41"
+        ...
+      }
+    }
+
+    layer{
+      name: "RBMHid"
+      type: kRBMHid
+      srclayers:"RBMVis"
+      rbm_conf{
+        hdim: 30
+        gaussian: true
+      }
+      param{
+        name: "w4_"
+        share_from: "w4"
+      }
+      param{
+        name: "b42"
+        ...
+      }
+    }
+
+### Auto-encoder
+In the fine-tuning stage, the 4 RBMs are "unfolded" to form encoder and decoder
+networks that are initialized using the parameters from the previous 4 RBMs.
+
+<img src="../images/example-autoencoder.png" align="center" width="500px"/>
+<span><strong>Figure 5 - Auto-Encoders.</strong></span>
+
+
+Figure 5 shows the neural net structure for training the auto-encoder.
+[Back propagation (kBP)] (train-one-batch.html) is
+configured as the algorithm for `TrainOneBatch`. We use the same cluster
+configuration as RBM models. For updater, we use [AdaGrad](updater.html#adagradupdater) algorithm with
+fixed learning rate.
+
+    ### Updater Configuration
+    updater{
+      type: kAdaGrad
+      learning_rate{
+      base_lr: 0.01
+      type: kFixed
+      }
+    }
+
+
+
+According to [Hinton's science paper](http://www.cs.toronto.edu/~hinton/science.pdf),
+we configure a EuclideanLoss layer to compute the reconstruction error. The neural net
+configuration is (with some of the middle layers omitted),
+
+    layer{ name: "data" }
+    layer{ name:"mnist" }
+    layer{
+      name: "Inner1"
+      param{ name: "w1" }
+      param{ name: "b12" }
+    }
+    layer{ name: "Sigmoid1" }
+    ...
+    layer{
+      name: "Inner8"
+      innerproduct_conf{
+        num_output: 784
+        transpose: true
+      }
+      param{
+        name: "w8"
+        share_from: "w1"
+      }
+      param{ name: "b11" }
+    }
+    layer{ name: "Sigmoid8" }
+
+    # Euclidean Loss Layer Configuration
+    layer{
+      name: "loss"
+      type:kEuclideanLoss
+      srclayers:"Sigmoid8"
+      srclayers:"mnist"
+    }
+
+To load pre-trained parameters from the 4 RBMs' checkpoint file we configure `checkpoint_path` as
+
+    ### Checkpoint Configuration
+    checkpoint_path: "examples/rbm/checkpoint/rbm1/checkpoint/step6000-worker0"
+    checkpoint_path: "examples/rbm/checkpoint/rbm2/checkpoint/step6000-worker0"
+    checkpoint_path: "examples/rbm/checkpoint/rbm3/checkpoint/step6000-worker0"
+    checkpoint_path: "examples/rbm/checkpoint/rbm4/checkpoint/step6000-worker0"
+
+
+## Visualization Results
+
+<div>
+<img src="../images/rbm-weight.PNG" align="center" width="300px"/>
+
+<img src="../images/rbm-feature.PNG" align="center" width="300px"/>
+<br/>
+<span><strong>Figure 6 - Bottom RBM weight matrix.</strong></span>
+&nbsp;
+&nbsp;
+&nbsp;
+&nbsp;
+
+<span><strong>Figure 7 - Top layer features.</strong></span>
+</div>
+
+Figure 6 visualizes sample columns of the weight matrix of RBM1, We can see the
+Gabor-like filters are learned. Figure 7 depicts the features extracted from
+the top-layer of the auto-encoder, wherein one point represents one image.
+Different colors represent different digits. We can see that most images are
+well clustered according to the ground truth.

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+# Recurrent Neural Networks for Language Modelling
+
+---
+
+Recurrent Neural Networks (RNN) are widely used for modelling sequential data,
+such as music and sentences.  In this example, we use SINGA to train a
+[RNN model](http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf)
+proposed by Tomas Mikolov for [language modeling](https://en.wikipedia.org/wiki/Language_model).
+The training objective (loss) is
+to minimize the [perplexity per word](https://en.wikipedia.org/wiki/Perplexity), which
+is equivalent to maximize the probability of predicting the next word given the current word in
+a sentence.
+
+Different to the [CNN](cnn.html), [MLP](mlp.html)
+and [RBM](rbm.html) examples which use built-in
+layers(layer) and records(data),
+none of the layers in this example are built-in. Hence users would learn to
+implement their own layers and data records through this example.
+
+## Running instructions
+
+In *SINGA_ROOT/examples/rnnlm/*, scripts are provided to run the training job.
+First, the data is prepared by
+
+    $ cp Makefile.example Makefile
+    $ make download
+    $ make create
+
+Second, to compile the source code under *examples/rnnlm/*, run
+
+    $ make rnnlm
+
+An executable file *rnnlm.bin* will be generated.
+
+Third, the training is started by passing *rnnlm.bin* and the job configuration
+to *singa-run.sh*,
+
+    # at SINGA_ROOT/
+    # export LD_LIBRARY_PATH=.libs:$LD_LIBRARY_PATH
+    $ ./bin/singa-run.sh -exec examples/rnnlm/rnnlm.bin -conf examples/rnnlm/job.conf
+
+## Implementations
+
+<img src="../images/rnnlm.png" align="center" width="400px"/>
+<span><strong>Figure 1 - Net structure of the RNN model.</strong></span>
+
+The neural net structure is shown Figure 1.  Word records are loaded by
+`DataLayer`. For every iteration, at most `max_window` word records are
+processed. If a sentence ending character is read, the `DataLayer` stops
+loading immediately. `EmbeddingLayer` looks up a word embedding matrix to extract
+feature vectors for words loaded by the `DataLayer`.  These features are transformed by the
+`HiddenLayer` which propagates the features from left to right. The
+output feature for word at position k is influenced by words from position 0 to
+k-1.  Finally, `LossLayer` computes the cross-entropy loss (see below)
+by predicting the next word of each word.
+The cross-entropy loss is computed as
+
+`$$L(w_t)=-log P(w_{t+1}|w_t)$$`
+
+Given `$w_t$` the above equation would compute over all words in the vocabulary,
+which is time consuming.
+[RNNLM Toolkit](https://f25ea9ccb7d3346ce6891573d543960492b92c30.googledrive.com/host/0ByxdPXuxLPS5RFM5dVNvWVhTd0U/rnnlm-0.4b.tgz)
+accelerates the computation as
+
+`$$P(w_{t+1}|w_t) = P(C_{w_{t+1}}|w_t) * P(w_{t+1}|C_{w_{t+1}})$$`
+
+Words from the vocabulary are partitioned into a user-defined number of classes.
+The first term on the left side predicts the class of the next word, and
+then predicts the next word given its class. Both the number of classes and
+the words from one class are much smaller than the vocabulary size. The probabilities
+can be calculated much faster.
+
+The perplexity per word is computed by,
+
+`$$PPL = 10^{- avg_t log_{10} P(w_{t+1}|w_t)}$$`
+
+### Data preparation
+
+We use a small dataset provided by the [RNNLM Toolkit](https://f25ea9ccb7d3346ce6891573d543960492b92c30.googledrive.com/host/0ByxdPXuxLPS5RFM5dVNvWVhTd0U/rnnlm-0.4b.tgz).
+It has 10,000 training sentences, with 71350 words in total and 3720 unique words.
+The subsequent steps follow the instructions in
+[Data Preparation](data.html) to convert the
+raw data into records and insert them into data stores.
+
+#### Download source data
+
+    # in SINGA_ROOT/examples/rnnlm/
+    cp Makefile.example Makefile
+    make download
+
+#### Define record format
+
+We define the word record as follows,
+
+    # in SINGA_ROOT/examples/rnnlm/rnnlm.proto
+    message WordRecord {
+      optional string word = 1;
+      optional int32 word_index = 2;
+      optional int32 class_index = 3;
+      optional int32 class_start = 4;
+      optional int32 class_end = 5;
+    }
+
+It includes the word string and its index in the vocabulary.
+Words in the vocabulary are sorted based on their frequency in the training dataset.
+The sorted list is cut into 100 sublists such that each sublist has 1/100 total
+word frequency. Each sublist is called a class.
+Hence each word has a `class_index` ([0,100)). The `class_start` is the index
+of the first word in the same class as `word`. The `class_end` is the index of
+the first word in the next class.
+
+#### Create data stores
+
+We use code from RNNLM Toolkit to read words, and sort them into classes.
+The main function in *create_store.cc* first creates word classes based on the training
+dataset. Second it calls the following function to create data store for the
+training, validation and test dataset.
+
+    int create_data(const char *input_file, const char *output_file);
+
+`input` is the path to training/validation/testing text file from the RNNLM Toolkit, `output` is output store file.
+This function starts with
+
+    singa::io::KVFile store;
+    store.Open(output, signa::io::kCreate);
+
+Then it reads the words one by one. For each word it creates a `WordRecord` instance,
+and inserts it into the store,
+
+    int wcnt = 0; // word count
+    WordRecord  wordRecord;
+    while(1) {
+      readWord(wordstr, fin);
+      if (feof(fin)) break;
+      ...// fill in the wordRecord;
+      string val;
+      wordRecord.SerializeToString(&val);
+      int length = snprintf(key, BUFFER_LEN, "%05d", wcnt++);
+      store.Write(string(key, length), val);
+    }
+
+Compilation and running commands are provided in the *Makefile.example*.
+After executing
+
+    make create
+
+*train_data.bin*, *test_data.bin* and *valid_data.bin* will be created.
+
+
+### Layer implementation
+
+4 user-defined layers are implemented for this application.
+Following the guide for implementing [new Layer subclasses](layer#implementing-a-new-layer-subclass),
+we extend the [LayerProto](../api/classsinga_1_1LayerProto.html)
+to include the configuration messages of user-defined layers as shown below
+(3 out of the 7 layers have specific configurations),
+
+
+    import "job.proto";     // Layer message for SINGA is defined
+
+    //For implementation of RNNLM application
+    extend singa.LayerProto {
+      optional EmbeddingProto embedding_conf = 101;
+      optional LossProto loss_conf = 102;
+      optional DataProto data_conf = 103;
+    }
+
+In the subsequent sections, we describe the implementation of each layer,
+including its configuration message.
+
+#### RNNLayer
+
+This is the base layer of all other layers for this applications. It is defined
+as follows,
+
+    class RNNLayer : virtual public Layer {
+    public:
+      inline int window() { return window_; }
+    protected:
+      int window_;
+    };
+
+For this application, two iterations may process different number of words.
+Because sentences have different lengths.
+The `DataLayer` decides the effective window size. All other layers call its source layers to get the
+effective window size and resets `window_` in `ComputeFeature` function.
+
+#### DataLayer
+
+DataLayer is for loading Records.
+
+    class DataLayer : public RNNLayer, singa::InputLayer {
+     public:
+      void Setup(const LayerProto& proto, const vector<Layer*>& srclayers) override;
+      void ComputeFeature(int flag, const vector<Layer*>& srclayers) override;
+      int max_window() const {
+        return max_window_;
+      }
+     private:
+      int max_window_;
+      singa::io::Store* store_;
+    };
+
+The Setup function gets the user configured max window size.
+
+    max_window_ = proto.GetExtension(input_conf).max_window();
+
+The `ComputeFeature` function loads at most max_window records. It could also
+stop when the sentence ending character is encountered.
+
+    ...// shift the last record to the first
+    window_ = max_window_;
+    for (int i = 1; i <= max_window_; i++) {
+      // load record; break if it is the ending character
+    }
+
+The configuration of `DataLayer` is like
+
+    name: "data"
+    user_type: "kData"
+    [data_conf] {
+      path: "examples/rnnlm/train_data.bin"
+      max_window: 10
+    }
+
+#### EmbeddingLayer
+
+This layer gets records from `DataLayer`. For each record, the word index is
+parsed and used to get the corresponding word feature vector from the embedding
+matrix.
+
+The class is declared as follows,
+
+    class EmbeddingLayer : public RNNLayer {
+      ...
+      const std::vector<Param*> GetParams() const override {
+        std::vector<Param*> params{embed_};
+        return params;
+      }
+     private:
+      int word_dim_, vocab_size_;
+      Param* embed_;
+    }
+
+The `embed_` field is a matrix whose values are parameter to be learned.
+The matrix size is `vocab_size_` x `word_dim_`.
+
+The Setup function reads configurations for `word_dim_` and `vocab_size_`. Then
+it allocates feature Blob for `max_window` words and setups `embed_`.
+
+    int max_window = srclayers[0]->data(this).shape()[0];
+    word_dim_ = proto.GetExtension(embedding_conf).word_dim();
+    data_.Reshape(vector<int>{max_window, word_dim_});
+    ...
+    embed_->Setup(vector<int>{vocab_size_, word_dim_});
+
+The `ComputeFeature` function simply copies the feature vector from the `embed_`
+matrix into the feature Blob.
+
+    # reset effective window size
+    window_ = datalayer->window();
+    auto records = datalayer->records();
+    ...
+    for (int t = 0; t < window_; t++) {
+      int idx  <- word index
+      Copy(words[t], embed[idx]);
+    }
+
+The `ComputeGradient` function copies back the gradients to the `embed_` matrix.
+
+The configuration for `EmbeddingLayer` is like,
+
+    user_type: "kEmbedding"
+    [embedding_conf] {
+      word_dim: 15
+      vocab_size: 3720
+    }
+    srclayers: "data"
+    param {
+      name: "w1"
+      init {
+        type: kUniform
+        low:-0.3
+        high:0.3
+      }
+    }
+
+#### HiddenLayer
+
+This layer unrolls the recurrent connections for at most max_window times.
+The feature for position k is computed based on the feature from the embedding layer (position k)
+and the feature at position k-1 of this layer. The formula is
+
+`$$f[k]=\sigma (f[t-1]*W+src[t])$$`
+
+where `$W$` is a matrix with `word_dim_` x `word_dim_` parameters.
+
+If you want to implement a recurrent neural network following our
+design, this layer is of vital importance for you to refer to.
+
+    class HiddenLayer : public RNNLayer {
+      ...
+      const std::vector<Param*> GetParams() const override {
+        std::vector<Param*> params{weight_};
+        return params;
+      }
+    private:
+      Param* weight_;
+    };
+
+The `Setup` function setups the weight matrix as
+
+    weight_->Setup(std::vector<int>{word_dim, word_dim});
+
+The `ComputeFeature` function gets the effective window size (`window_`) from its source layer
+i.e., the embedding layer. Then it propagates the feature from position 0 to position
+`window_` -1. The detailed descriptions for this process are illustrated as follows.
+
+    void HiddenLayer::ComputeFeature() {
+      for(int t = 0; t < window_size; t++){
+        if(t == 0)
+          Copy(data[t], src[t]);
+        else
+          data[t]=sigmoid(data[t-1]*W + src[t]);
+      }
+    }
+
+The `ComputeGradient` function computes the gradient of the loss w.r.t. W and the source layer.
+Particularly, for each position k, since data[k] contributes to data[k+1] and the feature
+at position k in its destination layer (the loss layer), grad[k] should contains the gradient
+from two parts. The destination layer has already computed the gradient from the loss layer into
+grad[k]; In the `ComputeGradient` function, we need to add the gradient from position k+1.
+
+    void HiddenLayer::ComputeGradient(){
+      ...
+      for (int k = window_ - 1; k >= 0; k--) {
+        if (k < window_ - 1) {
+          grad[k] += dot(grad[k + 1], weight.T()); // add gradient from position t+1.
+        }
+        grad[k] =... // compute gL/gy[t], y[t]=data[t-1]*W+src[t]
+      }
+      gweight = dot(data.Slice(0, window_-1).T(), grad.Slice(1, window_));
+      Copy(gsrc, grad);
+    }
+
+After the loop, we get the gradient of the loss w.r.t y[k], which is used to
+compute the gradient of W and the src[k].
+
+#### LossLayer
+
+This layer computes the cross-entropy loss and the `$log_{10}P(w_{t+1}|w_t)$` (which
+could be averaged over all words by users to get the PPL value).
+
+There are two configuration fields to be specified by users.
+
+    message LossProto {
+      optional int32 nclass = 1;
+      optional int32 vocab_size = 2;
+    }
+
+There are two weight matrices to be learned
+
+    class LossLayer : public RNNLayer {
+      ...
+     private:
+      Param* word_weight_, *class_weight_;
+    }
+
+The ComputeFeature function computes the two probabilities respectively.
+
+`$$P(C_{w_{t+1}}|w_t) = Softmax(w_t * class\_weight_)$$`
+`$$P(w_{t+1}|C_{w_{t+1}}) = Softmax(w_t * word\_weight[class\_start:class\_end])$$`
+
+`$w_t$` is the feature from the hidden layer for the k-th word, its ground truth
+next word is `$w_{t+1}$`.  The first equation computes the probability distribution over all
+classes for the next word. The second equation computes the
+probability distribution over the words in the ground truth class for the next word.
+
+The ComputeGradient function computes the gradient of the source layer
+(i.e., the hidden layer) and the two weight matrices.
+
+### Updater Configuration
+
+We employ kFixedStep type of the learning rate change method and the
+configuration is as follows. We decay the learning rate once the performance does
+not increase on the validation dataset.
+
+    updater{
+      type: kSGD
+      learning_rate {
+        type: kFixedStep
+        fixedstep_conf:{
+          step:0
+          step:48810
+          step:56945
+          step:65080
+          step:73215
+          step_lr:0.1
+          step_lr:0.05
+          step_lr:0.025
+          step_lr:0.0125
+          step_lr:0.00625
+        }
+      }
+    }
+
+### TrainOneBatch() Function
+
+We use BP (BackPropagation) algorithm to train the RNN model here. The
+corresponding configuration can be seen below.
+
+    # In job.conf file
+    train_one_batch {
+      alg: kBackPropagation
+    }
+
+### Cluster Configuration
+
+The default cluster configuration can be used, i.e., single worker and single server
+in a single process.

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+# Performance Test and Feature Extraction
+
+----
+
+Once SINGA finishes the training of a model, it would checkpoint the model parameters
+into disk files under the [checkpoint folder](checkpoint.html). Model parameters can also be dumped
+into this folder periodically during training if the
+[checkpoint configuration[(checkpoint.html) fields are set. With the checkpoint
+files, we can load the model parameters to conduct performance test, feature extraction and prediction
+against new data.
+
+To load the model parameters from checkpoint files, we need to add the paths of
+checkpoint files in the job configuration file
+
+    checkpoint_path: PATH_TO_CHECKPOINT_FILE1
+    checkpoint_path: PATH_TO_CHECKPOINT_FILE2
+    ...
+
+The new dataset is configured by specifying the ``test_step`` and the data input
+layer, e.g. the following configuration is for a dataset with 100*100 instances.
+
+    test_steps: 100
+    net {
+      layer {
+        name: "input"
+        store_conf {
+          backend: "kvfile"
+          path: PATH_TO_TEST_KVFILE
+          batchsize: 100
+        }
+      }
+      ...
+    }
+
+## Performance Test
+
+This application is to test the performance, e.g., accuracy, of the previously
+trained model. Depending on the application, the test data may have ground truth
+labels or not. For example, if the model is trained for image classification,
+the test images must have ground truth labels to calculate the accuracy; if the
+model is an auto-encoder, the performance could be measured by reconstruction error, which
+does not require extra labels. For both cases, there would be a layer that calculates
+the performance, e.g., the `SoftmaxLossLayer`.
+
+The job configuration file for the cifar10 example can be used directly for testing after
+adding the checkpoint path. The running command is
+
+
+    $ ./bin/singa-run.sh -conf examples/cifar10/job.conf -test
+
+The performance would be output on the screen like,
+
+
+    Load from checkpoint file examples/cifar10/checkpoint/step50000-worker0
+    accuracy = 0.728000, loss = 0.807645
+
+## Feature extraction
+
+Since deep learning models are good at learning features, feature extraction for
+is a major functionality of deep learning models, e.g., we can extract features
+from the fully connected layers of [AlexNet](www.cs.toronto.edu/~fritz/absps/imagenet.pdf) as image features for image retrieval.
+To extract the features from one layer, we simply add an output layer after that layer.
+For instance, to extract the fully connected (with name `ip1`) layer of the cifar10 example model,
+we replace the `SoftmaxLossLayer` with a `CSVOutputLayer` which extracts the features into a CSV file,
+
+    layer {
+      name: "ip1"
+    }
+    layer {
+      name: "output"
+      type: kCSVOutput
+      srclayers: "ip1"
+      store_conf {
+        backend: "textfile"
+        path: OUTPUT_FILE_PATH
+      }
+    }
+
+The input layer and test steps, and the running command are the same as in *Performance Test* section.
+
+## Label Prediction
+
+If the output layer is connected to a layer that predicts labels of images,
+the output layer would then write the prediction results into files.
+SINGA provides two built-in layers for generating prediction results, namely,
+
+* SoftmaxLayer, generates probabilities of each candidate labels.
+* ArgSortLayer, sorts labels according to probabilities in descending order and keep topk labels.
+
+By connecting the two layers with the previous layer and the output layer, we can
+extract the predictions of each instance. For example,
+
+    layer {
+      name: "feature"
+      ...
+    }
+    layer {
+      name: "softmax"
+      type: kSoftmax
+      srclayers: "feature"
+    }
+    layer {
+      name: "prediction"
+      type: kArgSort
+      srclayers: "softmax"
+      argsort_conf {
+        topk: 5
+      }
+    }
+    layer {
+      name: "output"
+      type: kCSVOutput
+      srclayers: "prediction"
+      store_conf {}
+    }
+
+The top-5 labels of each instance will be written as one line of the output CSV file.
+Currently, above layers cannot co-exist with the loss layers used for training.
+Please comment out the loss layers for extracting prediction results.

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+# Train-One-Batch
+
+---
+
+For each SGD iteration, every worker calls the `TrainOneBatch` function to
+compute gradients of parameters associated with local layers (i.e., layers
+dispatched to it).  SINGA has implemented two algorithms for the
+`TrainOneBatch` function. Users select the corresponding algorithm for
+their model in the configuration.
+
+## Basic user guide
+
+### Back-propagation
+
+[BP algorithm](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) is used for
+computing gradients of feed-forward models, e.g., [CNN](cnn.html)
+and [MLP](mlp.html), and [RNN](rnn.html) models in SINGA.
+
+
+    # in job.conf
+    alg: kBP
+
+To use the BP algorithm for the `TrainOneBatch` function, users just simply
+configure the `alg` field with `kBP`. If a neural net contains user-defined
+layers, these layers must be implemented properly be to consistent with the
+implementation of the BP algorithm in SINGA (see below).
+
+
+### Contrastive Divergence
+
+[CD algorithm](http://www.cs.toronto.edu/~fritz/absps/nccd.pdf) is used for
+computing gradients of energy models like RBM.
+
+    # job.conf
+    alg: kCD
+    cd_conf {
+      cd_k: 2
+    }
+
+To use the CD algorithm for the `TrainOneBatch` function, users just configure
+the `alg` field to `kCD`. Uses can also configure the Gibbs sampling steps in
+the CD algorthm through the `cd_k` field. By default, it is set to 1.
+
+
+
+## Advanced user guide
+
+### Implementation of BP
+
+The BP algorithm is implemented in SINGA following the below pseudo code,
+
+    BPTrainOnebatch(step, net) {
+      // forward propagate
+      foreach layer in net.local_layers() {
+        if IsBridgeDstLayer(layer)
+          recv data from the src layer (i.e., BridgeSrcLayer)
+        foreach param in layer.params()
+          Collect(param) // recv response from servers for last update
+
+        layer.ComputeFeature(kForward)
+
+        if IsBridgeSrcLayer(layer)
+          send layer.data_ to dst layer
+      }
+      // backward propagate
+      foreach layer in reverse(net.local_layers) {
+        if IsBridgeSrcLayer(layer)
+          recv gradient from the dst layer (i.e., BridgeDstLayer)
+          recv response from servers for last update
+
+        layer.ComputeGradient()
+        foreach param in layer.params()
+          Update(step, param) // send param.grad_ to servers
+
+        if IsBridgeDstLayer(layer)
+          send layer.grad_ to src layer
+      }
+    }
+
+
+It forwards features through all local layers (can be checked by layer
+partition ID and worker ID) and backwards gradients in the reverse order.
+[BridgeSrcLayer](layer.html#bridgesrclayer--bridgedstlayer)
+(resp. `BridgeDstLayer`) will be blocked until the feature (resp.
+gradient) from the source (resp. destination) layer comes. Parameter gradients
+are sent to servers via `Update` function. Updated parameters are collected via
+`Collect` function, which will be blocked until the parameter is updated.
+[Param](param.html) objects have versions, which can be used to
+check whether the `Param` objects have been updated or not.
+
+Since RNN models are unrolled into feed-forward models, users need to implement
+the forward propagation in the recurrent layer's `ComputeFeature` function,
+and implement the backward propagation in the recurrent layer's `ComputeGradient`
+function. As a result, the whole `TrainOneBatch` runs
+[back-propagation through time (BPTT)](https://en.wikipedia.org/wiki/Backpropagation_through_time)  algorithm.
+
+### Implementation of CD
+
+The CD algorithm is implemented in SINGA following the below pseudo code,
+
+    CDTrainOneBatch(step, net) {
+      # positive phase
+      foreach layer in net.local_layers()
+        if IsBridgeDstLayer(layer)
+          recv positive phase data from the src layer (i.e., BridgeSrcLayer)
+        foreach param in layer.params()
+          Collect(param)  // recv response from servers for last update
+        layer.ComputeFeature(kPositive)
+        if IsBridgeSrcLayer(layer)
+          send positive phase data to dst layer
+
+      # negative phase
+      foreach gibbs in [0...layer_proto_.cd_k]
+        foreach layer in net.local_layers()
+          if IsBridgeDstLayer(layer)
+            recv negative phase data from the src layer (i.e., BridgeSrcLayer)
+          layer.ComputeFeature(kPositive)
+          if IsBridgeSrcLayer(layer)
+            send negative phase data to dst layer
+
+      foreach layer in net.local_layers()
+        layer.ComputeGradient()
+        foreach param in layer.params
+          Update(param)
+    }
+
+Parameter gradients are computed after the positive phase and negative phase.
+
+### Implementing a new algorithm
+
+SINGA implements BP and CD by creating two subclasses of
+the [Worker](../api/classsinga_1_1Worker.html) class:
+[BPWorker](../api/classsinga_1_1BPWorker.html)'s `TrainOneBatch` function implements the BP
+algorithm; [CDWorker](../api/classsinga_1_1CDWorker.html)'s `TrainOneBatch` function implements the CD
+algorithm. To implement a new algorithm for the `TrainOneBatch` function, users
+need to create a new subclass of the `Worker`, e.g.,
+
+    class FooWorker : public Worker {
+      void TrainOneBatch(int step, shared_ptr<NeuralNet> net, Metric* perf) override;
+      void TestOneBatch(int step, Phase phase, shared_ptr<NeuralNet> net, Metric* perf) override;
+    };
+
+The `FooWorker` must implement the above two functions for training one
+mini-batch and testing one mini-batch. The `perf` argument is for collecting
+training or testing performance, e.g., the objective loss or accuracy. It is
+passed to the `ComputeFeature` function of each layer.
+
+Users can define some fields for users to configure
+
+    # in user.proto
+    message FooWorkerProto {
+      optional int32 b = 1;
+    }
+
+    extend JobProto {
+      optional FooWorkerProto foo_conf = 101;
+    }
+
+    # in job.proto
+    JobProto {
+      ...
+      extension 101..max;
+    }
+
+It is similar as [adding configuration fields for a new layer](layer.html#implementing-a-new-layer-subclass).
+
+To use `FooWorker`, users need to register it in the [main.cc](programming-guide.html)
+and configure the `alg` and `foo_conf` fields,
+
+    # in main.cc
+    const int kFoo = 3; // worker ID, must be different to that of CDWorker and BPWorker
+    driver.RegisterWorker<FooWorker>(kFoo);
+
+    # in job.conf
+    ...
+    alg: 3
+    [foo_conf] {
+      b = 4;
+    }

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+# Updater
+
+---
+
+Every server in SINGA has an [Updater](../api/classsinga_1_1Updater.html)
+instance that updates parameters based on gradients.
+In this page, the *Basic user guide* describes the configuration of an updater.
+The *Advanced user guide* present details on how to implement a new updater and a new
+learning rate changing method.
+
+## Basic user guide
+
+There are many different parameter updating protocols (i.e., subclasses of
+`Updater`). They share some configuration fields like
+
+* `type`, an integer for identifying an updater;
+* `learning_rate`, configuration for the
+[LRGenerator](../api/classsinga_1_1LRGenerator.html) which controls the learning rate.
+* `weight_decay`, the co-efficient for [L2 * regularization](http://deeplearning.net/tutorial/gettingstarted.html#regularization).
+* [momentum](http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/).
+
+If you are not familiar with the above terms, you can get their meanings in
+[this page provided by Karpathy](http://cs231n.github.io/neural-networks-3/#update).
+
+### Configuration of built-in updater classes
+
+#### Updater
+The base `Updater` implements the [vanilla SGD algorithm](http://cs231n.github.io/neural-networks-3/#sgd).
+Its configuration type is `kSGD`.
+Users need to configure at least the `learning_rate` field.
+`momentum` and `weight_decay` are optional fields.
+
+    updater{
+      type: kSGD
+      momentum: float
+      weight_decay: float
+      learning_rate {
+        ...
+      }
+    }
+
+#### AdaGradUpdater
+
+It inherits the base `Updater` to implement the
+[AdaGrad](http://www.magicbroom.info/Papers/DuchiHaSi10.pdf) algorithm.
+Its type is `kAdaGrad`.
+`AdaGradUpdater` is configured similar to `Updater` except
+that `momentum` is not used.
+
+#### NesterovUpdater
+
+It inherits the base `Updater` to implements the
+[Nesterov](http://arxiv.org/pdf/1212.0901v2.pdf) (section 3.5) updating protocol.
+Its type is `kNesterov`.
+`learning_rate` and `momentum` must be configured. `weight_decay` is an
+optional configuration field.
+
+#### RMSPropUpdater
+
+It inherits the base `Updater` to implements the
+[RMSProp algorithm](http://cs231n.github.io/neural-networks-3/#sgd) proposed by
+[Hinton](http://www.cs.toronto.edu/%7Etijmen/csc321/slides/lecture_slides_lec6.pdf)(slide 29).
+Its type is `kRMSProp`.
+
+    updater {
+      type: kRMSProp
+      rmsprop_conf {
+       rho: float # [0,1]
+      }
+    }
+
+
+### Configuration of learning rate
+
+The `learning_rate` field is configured as,
+
+    learning_rate {
+      type: ChangeMethod
+      base_lr: float  # base/initial learning rate
+      ... # fields to a specific changing method
+    }
+
+The common fields include `type` and `base_lr`. SINGA provides the following
+`ChangeMethod`s.
+
+#### kFixed
+
+The `base_lr` is used for all steps.
+
+#### kLinear
+
+The updater should be configured like
+
+    learning_rate {
+      base_lr:  float
+      linear_conf {
+        freq: int
+        final_lr: float
+      }
+    }
+
+Linear interpolation is used to change the learning rate,
+
+    lr = (1 - step / freq) * base_lr + (step / freq) * final_lr
+
+#### kExponential
+
+The udapter should be configured like
+
+    learning_rate {
+      base_lr: float
+      exponential_conf {
+        freq: int
+      }
+    }
+
+The learning rate for `step` is
+
+    lr = base_lr / 2^(step / freq)
+
+#### kInverseT
+
+The updater should be configured like
+
+    learning_rate {
+      base_lr: float
+      inverset_conf {
+        final_lr: float
+      }
+    }
+
+The learning rate for `step` is
+
+    lr = base_lr / (1 + step / final_lr)
+
+#### kInverse
+
+The updater should be configured like
+
+    learning_rate {
+      base_lr: float
+      inverse_conf {
+        gamma: float
+        pow: float
+      }
+    }
+
+
+The learning rate for `step` is
+
+    lr = base_lr * (1 + gamma * setp)^(-pow)
+
+
+#### kStep
+
+The updater should be configured like
+
+    learning_rate {
+      base_lr : float
+      step_conf {
+        change_freq: int
+        gamma: float
+      }
+    }
+
+
+The learning rate for `step` is
+
+    lr = base_lr * gamma^ (step / change_freq)
+
+#### kFixedStep
+
+The updater should be configured like
+
+    learning_rate {
+      fixedstep_conf {
+        step: int
+        step_lr: float
+
+        step: int
+        step_lr: float
+
+        ...
+      }
+    }
+
+Denote the i-th tuple as (step[i], step_lr[i]), then the learning rate for
+`step` is,
+
+    step_lr[k]
+
+where step[k] is the smallest number that is larger than `step`.
+
+
+## Advanced user guide
+
+### Implementing a new Updater subclass
+
+The base Updater class has one virtual function,
+
+    class Updater{
+     public:
+      virtual void Update(int step, Param* param, float grad_scale = 1.0f) = 0;
+
+     protected:
+      UpdaterProto proto_;
+      LRGenerator lr_gen_;
+    };
+
+It updates the values of the `param` based on its gradients. The `step`
+argument is for deciding the learning rate which may change through time
+(step). `grad_scale` scales the original gradient values. This function is
+called by servers once it receives all gradients for the same `Param` object.
+
+To implement a new Updater subclass, users must override the `Update` function.
+
+    class FooUpdater : public Updater {
+      void Update(int step, Param* param, float grad_scale = 1.0f) override;
+    };
+
+Configuration of this new updater can be declared similar to that of a new
+layer,
+
+    # in user.proto
+    FooUpdaterProto {
+      optional int32 c = 1;
+    }
+
+    extend UpdaterProto {
+      optional FooUpdaterProto fooupdater_conf= 101;
+    }
+
+The new updater should be registered in the
+[main function](programming-guide.html)
+
+    driver.RegisterUpdater<FooUpdater>("FooUpdater");
+
+Users can then configure the job as
+
+    # in job.conf
+    updater {
+      user_type: "FooUpdater"  # must use user_type with the same string identifier as the one used for registration
+      fooupdater_conf {
+        c : 20;
+      }
+    }
+
+### Implementing a new LRGenerator subclass
+
+The base `LRGenerator` is declared as,
+
+    virtual float Get(int step);
+
+To implement a subclass, e.g., `FooLRGen`, users should declare it like
+
+    class FooLRGen : public LRGenerator {
+     public:
+      float Get(int step) override;
+    };
+
+Configuration of `FooLRGen` can be defined using a protocol message,
+
+    # in user.proto
+    message FooLRProto {
+     ...
+    }
+
+    extend LRGenProto {
+      optional FooLRProto foolr_conf = 101;
+    }
+
+The configuration is then like,
+
+    learning_rate {
+      user_type : "FooLR" # must use user_type with the same string identifier as the one used for registration
+      base_lr: float
+      foolr_conf {
+        ...
+      }
+    }
+
+Users have to register this subclass in the main function,
+
+      driver.RegisterLRGenerator<FooLRGen, std::string>("FooLR")

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+# SINGA 아키텍처
+
+---
+
+## 논리적 아키텍처
+
+<img src = "../ images / logical.png"style = "width : 550px"/>
+<p> <strong> Fig.1 - 시스템 아키텍처 </strong> </p>
+
+SINGA는 다양한 분산 [트레이닝 프레임워크](frameworks.html) (동기 또는 비동기 트레이닝)을 지원하는 유연한 구조를 가지고 있습니다.
+Fig.1. 시스템의 구조를 보여줍니다.
+특징으로는 여러 server 그룹과 worker 그룹을 가지고있다.
+
+* **Server 그룹**
+
+  Server 그룹은 모델 매개 변수의 복제본을 가지고 worker 그룹의 요청에 따라 매개 변수의 업데이트를 담당합니다. 인접한 server 그룹들은 매개 변수를 정기적으로 동기화합니다. 일반적으로 하나의 server 그룹은 여러 server로 구성된 각 server는 모델 매개 변수의 분할 된 부분을 담당합니다.
+
+* **Worker 그룹**
+
+  각 worker 그룹은 하나의 server 그룹과 통신합니다. 하나의 worker 그룹은 매개 변수의 기울기 계산을 담당합니다. 또한 분할 된 데이터의 일부에 대해 "완전한"모델 복제본을 트레이닝합니다. 모든 worker 그룹들은 해당 server 그룹들과 비동기 적으로 통신합니다. 그러나 같은 worker 그룹의 worker들은 동기화합니다.
+
+동일 그룹 내에서 worker들의 분산 트레이닝은 많은 다른 방법이 있습니다.
+
+  * **모델 병렬화** 각 worker 그룹에 배정 된 모든 데이터에 대해 매개 변수의 부분 집합을 계산합니다.
+  * **데이터 병렬화** 각 worker는 배분 된 데이터의 부분 집합에 대해 모든 매개 변수를 계산합니다.
+  * [**하이브리드 병렬화**](hybrid.html) 위의 방법을 조합한 하이브리드 병렬화를 지원합니다.
+
+
+## 임플리멘테이션
+
+SINGA에서 servers와 workers는 다른 스레드에서 움직이는 실행 유닛입니다.
+
+In SINGA, servers and workers are execution units running in separate threads.
+그들은 [messages](communication.html)를 이용하여 통신합니다.
+각 프로세스는 로컬 messages를 수집하고 그것을 지원하는 수신기에 전송하는 stub으로 메인 스레드를 실행합니다.
+
+각 server 그룹과 worker 그룹은 "전체"모델 복제이다 *ParamShard* 개체를 유지합니다.
+만약 workers와 servers 동일한 프로세스에서 달리는한다면,
+그 *ParamShard* (파티션)은 메모리 공간을 공유하도록 설정됩니다.
+이 경우 다른 실행 유닛 사이를 오가는 messages는 통신 비용을 줄이기 위해 데이터의 포인터 만 포함됩니다.
+프로세스 간 통신의 경우와는 달리 messsages는 매개 변수의 값을 포함합니다.

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+# CheckPoint
+
+---
+
+SINGA checkpoints model parameters onto disk periodically according to user
+configured frequency. By checkpointing model parameters, we can
+
+  1. 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.
+
+  2. use them to initialize a similar model. For example, the
+    parameters from training a RBM model can be used to initialize
+    a [deep auto-encoder](rbm.html) model.
+
+## Configuration
+
+Checkpointing is controlled by two configuration fields:
+
+* `checkpoint_after`, start checkpointing after this number of training steps,
+* `checkpoint_freq`, frequency of doing checkpointing.
+
+For example,
+
+    # job.conf
+    checkpoint_after: 100
+    checkpoint_frequency: 300
+    ...
+
+Checkpointing files are located at *WORKSPACE/checkpoint/stepSTEP-workerWORKERID*.
+*WORKSPACE* is configured in
+
+    cluster {
+      workspace:
+    }
+
+For the above configuration, after training for 700 steps, there would be
+two checkpointing files,
+
+    step400-worker0
+    step700-worker0
+
+## Application - resuming training
+
+We can resume the training from the last checkpoint (i.e., step 700) by,
+
+    ./bin/singa-run.sh -conf JOB_CONF -resume
+
+There is no change to the job configuration.
+
+## Application - model initialization
+
+We can also use the checkpointing file from step 400 to initialize
+a new model by configuring the new job as,
+
+    # job.conf
+    checkpoint : "WORKSPACE/checkpoint/step400-worker0"
+    ...
+
+If there are multiple checkpointing files for the same snapshot due to model
+partitioning, all the checkpointing files should be added,
+
+    # job.conf
+    checkpoint : "WORKSPACE/checkpoint/step400-worker0"
+    checkpoint : "WORKSPACE/checkpoint/step400-worker1"
+    ...
+
+The training command is the same as starting a new job,
+
+    ./bin/singa-run.sh -conf JOB_CONF

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+# CNN Example
+
+---
+
+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
+[CIFAR10 dataset](http://www.cs.toronto.edu/~kriz/cifar.html).
+
+
+## Running instructions
+
+Please refer to the [installation](installation.html) page for
+instructions on building SINGA, and the [quick start](quick-start.html)
+for instructions on starting zookeeper.
+
+We have provided scripts for preparing the training and test dataset in *examples/cifar10/*.
+
+    # in examples/cifar10
+    $ cp Makefile.example Makefile
+    $ make download
+    $ make create
+
+
+We can start the training by
+
+    ./bin/singa-run.sh -conf examples/cifar10/job.conf
+
+You should see output like
+
+    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
+
+After training some steps (depends on the setting) or the job is
+finished, SINGA will [checkpoint](checkpoint.html) the model parameters.
+
+## Details
+
+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.
+
+### Data preparation
+
+Before using SINGA, you need to write a program to convert the dataset
+into a format that SINGA can read. Please refer to the
+[Data Preparation](data.html#example---cifar-dataset) to get details about
+preparing this CIFAR10 dataset.
+
+### Neural net
+
+Figure 1 shows the net structure of the CNN model we used in this example, which is
+set following [Alex](https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg.)
+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.
+
+Next we follow the guide in [neural net page](neural-net.html)
+and [layer page](layer.html) to write the neural net configuration.
+
+<div style = "text-align: center">
+<img src = "../images/example-cnn.png" style = "width: 200px"> <br/>
+<strong>Figure 1 - Net structure of the CNN example.</strong></img>
+</div>
+
+* We configure an input layer to read the training/testing records from a disk file.
+
+        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
+        }
+
+
+* 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
+[Alex's setting](https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg)).
+
+        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"
+        }
+
+  The configurations for another 2 stages are omitted here.
+
+* There is an [inner product layer](layer.html#innerproductlayer)
+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.
+
+        layer {
+          name: "ip1"
+          type: kInnerProduct
+          srclayers:"pool3"
+          innerproduct_conf {
+            num_output: 10
+          }
+          param {
+            name: "w4"
+            wd_scale:250
+            ...
+          }
+          param {
+            name: "b4"
+            ...
+          }
+        }
+
+* The last layer is a [Softmax loss layer](layer.html#softmaxloss)
+
+        layer{
+          name: "loss"
+          type: kSoftmaxLoss
+          softmaxloss_conf{ topk:1 }
+          srclayers:"ip1"
+          srclayers: "data"
+        }
+
+### Updater
+
+The [normal SGD updater](updater.html#updater) is selected.
+The learning rate is changed like going down stairs, and is configured using the
+[kFixedStep](updater.html#kfixedstep) type.
+
+        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
+            }
+          }
+        }
+
+### TrainOneBatch algorithm
+
+The CNN model is a feed forward model, thus should be configured to use the
+[Back-propagation algorithm](train-one-batch.html#back-propagation).
+
+    train_one_batch {
+      alg: kBP
+    }
+
+### Cluster setting
+
+The following configuration set a single worker and server for training.
+[Training frameworks](frameworks.html) page introduces configurations of a couple of distributed
+training frameworks.
+
+    cluster {
+      nworker_groups: 1
+      nserver_groups: 1
+    }

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+# Code Structure
+
+---
+
+<!--
+
+### Worker Side
+
+#### Main Classes
+
+<img src="../images/code-structure/main.jpg" style="width: 550px"/>
+
+* **Worker**: start the solver to conduct training or resume from previous training snapshots.
+* **Solver**: construct the neural network and run training algorithms over it. Validation and testing is also done by the solver along the training.
+* **TableDelegate**: delegate for the parameter table physically stored in parameter servers.
+    it runs a thread to communicate with table servers for parameter transferring.
+* **Net**: the neural network consists of multiple layers constructed from input configuration file.
+* **Layer**: the core abstraction, read data (neurons) from connecting layers, and compute the data
+    of itself according to layer specific ComputeFeature functions. Data from the bottom layer is forwarded
+    layer by layer to the top.
+
+#### Data types
+
+<img src="../images/code-structure/layer.jpg" style="width: 700px"/>
+
+* **ComputeFeature**: read data (neurons) from in-coming layers, and compute the data
+    of itself according to layer type. This function can be overrided to implement different
+    types layers.
+* **ComputeGradient**: read gradients (and data) from in-coming layers and compute
+    gradients of parameters and data w.r.t the learning objective (loss).
+
+We adpat the implementation for **PoolingLayer**, **Im2colLayer** and **LRNLayer** from [Caffe](http://caffe.berkeleyvision.org/).
+
+
+<img src="../images/code-structure/darray.jpg" style="width: 400px"/>
+
+* **DArray**: provide the abstraction of distributed array on multiple nodes,
+    supporting array/matrix operations and element-wise operations. Users can use it as a local structure.
+* **LArray**: the local part for the DArray. Each LArray is treated as an
+    independent array, and support all array-related operations.
+* **MemSpace**: manage the memory used by DArray. Distributed memory are allocated
+    and managed by armci. Multiple DArray can share a same MemSpace, the memory
+    will be released when no DArray uses it anymore.
+* **Partition**: maintain both global shape and local partition information.
+    used when two DArray are going to interact.
+* **Shape**: basic class for representing the scope of a DArray/LArray
+* **Range**: basic class for representing the scope of a Partition
+
+### Parameter Server
+
+#### Main classes
+
+<img src="../images/code-structure/uml.jpg" style="width: 750px"/>
+
+* **NetworkService**: provide access to the network (sending and receiving messages). It maintains a queue for received messages, implemented by NetworkQueue.
+* **RequestDispatcher**: pick up next message (request) from the queue, and invoked a method (callback) to process them.
+* **TableServer**: provide access to the data table (parameters). Register callbacks for different types of requests to RequestDispatcher.
+* **GlobalTable**: implement the table. Data is partitioned into multiple Shard objects per table. User-defined consistency model supported by extending TableServerHandler for each table.
+
+#### Data types
+
+<img src="../images/code-structure/type.jpg" style="width: 400px"/>
+
+Table related messages are either of type **RequestBase** which contains different types of request, or of type **TableData** containing a key-value tuple.
+
+#### Control flow and thread model
+
+<img src="../images/code-structure/threads.jpg" alt="uml" style="width: 1000px"/>
+
+The figure above shows how a GET request sent from a worker is processed by the
+table server. The control flow for other types of requests is similar. At
+the server side, there are at least 3 threads running at any time: two by
+NetworkService for sending and receiving message, and at least one by the
+RequestDispatcher for dispatching requests.
+
+-->