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Posted to commits@singa.apache.org by wa...@apache.org on 2016/04/12 08:22:21 UTC
svn commit: r1738695 [1/10] - in /incubator/singa/site/trunk/content: ./
markdown/docs/ markdown/docs/zh/ markdown/releases/ markdown/v0.2.0/
markdown/v0.2.0/jp/ markdown/v0.2.0/kr/ markdown/v0.2.0/zh/
Author: wangwei
Date: Tue Apr 12 06:22:20 2016
New Revision: 1738695
URL: http://svn.apache.org/viewvc?rev=1738695&view=rev
Log:
update docs (quickstart, install, gpu) for v0.3
Added:
incubator/singa/site/trunk/content/markdown/releases/RELEASE_NOTES_0.3.0.md
incubator/singa/site/trunk/content/markdown/v0.2.0/
incubator/singa/site/trunk/content/markdown/v0.2.0/architecture.md
incubator/singa/site/trunk/content/markdown/v0.2.0/checkpoint.md
incubator/singa/site/trunk/content/markdown/v0.2.0/cnn.md
incubator/singa/site/trunk/content/markdown/v0.2.0/code-structure.md
incubator/singa/site/trunk/content/markdown/v0.2.0/communication.md
incubator/singa/site/trunk/content/markdown/v0.2.0/data.md
incubator/singa/site/trunk/content/markdown/v0.2.0/debug.md
incubator/singa/site/trunk/content/markdown/v0.2.0/distributed-training.md
incubator/singa/site/trunk/content/markdown/v0.2.0/docker.md
incubator/singa/site/trunk/content/markdown/v0.2.0/examples.md
incubator/singa/site/trunk/content/markdown/v0.2.0/frameworks.md
incubator/singa/site/trunk/content/markdown/v0.2.0/general-rnn.md
incubator/singa/site/trunk/content/markdown/v0.2.0/gpu.md
incubator/singa/site/trunk/content/markdown/v0.2.0/hdfs.md
incubator/singa/site/trunk/content/markdown/v0.2.0/hybrid.md
incubator/singa/site/trunk/content/markdown/v0.2.0/index.md
incubator/singa/site/trunk/content/markdown/v0.2.0/installation.md
incubator/singa/site/trunk/content/markdown/v0.2.0/installation_source.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/architecture.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/checkpoint.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/cnn.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/code-structure.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/communication.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/data.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/debug.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/distributed-training.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/docker.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/examples.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/frameworks.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/index.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/installation.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/installation_source.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/layer.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/mesos.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/mlp.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/model-config.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/neural-net.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/neuralnet-partition.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/overview.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/param.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/programmer-guide.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/programming-guide.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/quick-start.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/rbm.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/rnn.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/test.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/train-one-batch.md
incubator/singa/site/trunk/content/markdown/v0.2.0/jp/updater.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/architecture.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/checkpoint.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/cnn.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/code-structure.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/communication.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/data.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/debug.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/distributed-training.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/docker.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/examples.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/frameworks.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/index.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/installation.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/installation_source.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/layer.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/mesos.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/mlp.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/model-config.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/neural-net.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/neuralnet-partition.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/overview.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/param.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/programmer-guide.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/programming-guide.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/quick-start.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/rbm.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/rnn.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/test.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/train-one-batch.md
incubator/singa/site/trunk/content/markdown/v0.2.0/kr/updater.md
incubator/singa/site/trunk/content/markdown/v0.2.0/layer.md
incubator/singa/site/trunk/content/markdown/v0.2.0/mesos.md
incubator/singa/site/trunk/content/markdown/v0.2.0/mlp.md
incubator/singa/site/trunk/content/markdown/v0.2.0/model-config.md
incubator/singa/site/trunk/content/markdown/v0.2.0/neural-net.md
incubator/singa/site/trunk/content/markdown/v0.2.0/neuralnet-partition.md
incubator/singa/site/trunk/content/markdown/v0.2.0/overview.md
incubator/singa/site/trunk/content/markdown/v0.2.0/param.md
incubator/singa/site/trunk/content/markdown/v0.2.0/programming-guide.md
incubator/singa/site/trunk/content/markdown/v0.2.0/python.md
incubator/singa/site/trunk/content/markdown/v0.2.0/quick-start.md
incubator/singa/site/trunk/content/markdown/v0.2.0/rbm.md
incubator/singa/site/trunk/content/markdown/v0.2.0/rnn.md
incubator/singa/site/trunk/content/markdown/v0.2.0/test.md
incubator/singa/site/trunk/content/markdown/v0.2.0/train-one-batch.md
incubator/singa/site/trunk/content/markdown/v0.2.0/updater.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/checkpoint.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/cnn.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/data.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/distributed-training.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/index.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/installation_source.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/mlp.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/neural-net.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/overview.md (with props)
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/programming-guide.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/rnn.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/train-one-batch.md
incubator/singa/site/trunk/content/markdown/v0.2.0/zh/updater.md
Modified:
incubator/singa/site/trunk/content/markdown/docs/gpu.md
incubator/singa/site/trunk/content/markdown/docs/installation_source.md
incubator/singa/site/trunk/content/markdown/docs/layer.md
incubator/singa/site/trunk/content/markdown/docs/quick-start.md
incubator/singa/site/trunk/content/markdown/docs/zh/index.md
incubator/singa/site/trunk/content/site.xml
Modified: incubator/singa/site/trunk/content/markdown/docs/gpu.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/gpu.md?rev=1738695&r1=1738694&r2=1738695&view=diff
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/gpu.md (original)
+++ incubator/singa/site/trunk/content/markdown/docs/gpu.md Tue Apr 12 06:22:20 2016
@@ -36,6 +36,8 @@ the GPU you want to use. The simplest co
gpu: 0
...
+
+#### Single node with multiple GPUs
This configuration will run the worker on GPU 0. If you want to launch multiple
workers, each on a separate GPU, you can configure it as
@@ -75,6 +77,10 @@ implemented using CUDNN library. To trai
The [cifar10 example](cnn.html) and [Alexnet example](alexnet.html) have complete
configurations for ConvNet.
+#### GPU cluster
+For distributed training over a (GPU) cluster, you just need to configure SINGA with
+`--enable-dist`, which would then compile SINGA with zookeeper and ZeroMQ.
+
## Implementation details
SINGA implements the GPU training by assigning each worker a GPU device at the beginning
Modified: incubator/singa/site/trunk/content/markdown/docs/installation_source.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/installation_source.md?rev=1738695&r1=1738694&r2=1738695&view=diff
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/installation_source.md (original)
+++ incubator/singa/site/trunk/content/markdown/docs/installation_source.md Tue Apr 12 06:22:20 2016
@@ -10,10 +10,15 @@ The following dependent libraries are re
* glog version 0.3.3
- * google-protobuf version 2.6.0
+ * google-protobuf version 2.5 and 2.6
* openblas version >= 0.2.10
+
+Optional dependencies include:
+
+ * lmdb version 0.9.10
+
* zeromq version >= 3.2
* czmq version >= 3
@@ -21,12 +26,8 @@ The following dependent libraries are re
* zookeeper version 3.4.6
-Optional dependencies include:
-
- * lmdb version 0.9.10
-
-You can install all dependencies into $PREFIX folder by
+You can install all dependencies (including optional dependent libraries) into $PREFIX folder by
# make sure you are in the thirdparty folder
cd thirdparty
@@ -57,10 +58,6 @@ There are two ways to build SINGA,
$ ./configure
$ make
- Note: It is an oversight that we forgot to delete the singa repo under [nusinga](https://github.com/orgs/nusinga)
- account after we became Apache Incubator project -- the source
- in that repo was not up to date, and we apologize for any inconvenience.
-
* If you download a release package, please follow the instructions below,
$ tar xvf singa-xxx
@@ -74,16 +71,9 @@ There are two ways to build SINGA,
$ ./configure --enable-lmdb
-<!---
-Zhongle: please update the code to use the follow command
-
- $ make test
-
-After compilation, you will find the binary file singatest. Just run it!
-More details about configure script can be found by running:
+ More options can be found by
- $ ./configure -h
--->
+ $ ./configure --help
After compiling SINGA successfully, the *libsinga.so* and the executable file
*singa* will be generated into *.libs/* folder.
@@ -91,12 +81,6 @@ After compiling SINGA successfully, the
If some dependent libraries are missing (or not detected), you can use the
following script to download and install them:
-<!---
-to be updated after zhongle changes the code to use
-
- ./install.sh libname \-\-prefix=
-
--->
# must goto thirdparty folder
$ cd thirdparty
$ ./install.sh LIB_NAME PREFIX
@@ -130,12 +114,6 @@ Here is a table showing the first argume
indicate `zeromq` location.
The installation commands of `czmq` is:
-<!---
-to be updated to
-
- $./install.sh czmq \-\-prefix=/usr/local \-\-zeromq=/usr/local/zeromq
--->
-
$./install.sh czmq /usr/local -f=/usr/local/zeromq
After the execution, `czmq` will be installed in */usr/local*. The last path
@@ -198,7 +176,7 @@ google.protobuf.internal when I try to i
* Q6: While compiling SINGA and installing `glog` on mac OS X, I get fatal error
`'ext/slist' file not found`
- A6:Please install `glog` individually and try :
+ A6:We haven't tested SINGA thorough on Mac OS. This error may be fixed by :
$ make CFLAGS='-stdlib=libstdc++' CXXFLAGS='stdlib=libstdc++'
@@ -250,11 +228,11 @@ google.protobuf.internal when I try to i
* Q10: When I build glog, it reports that "src/logging_unittest.cc:83:20: error: âgflagsâ is not a namespace-name"
A10: It maybe that you have installed gflags with a different namespace such as "google". so glog can't find 'gflags' namespace.
-
+
Because it doesn't require gflags to build glog. So you can change the configure.ac file to ignore gflags.
1. cd to glog src directory
2. change line 125 of configure.ac to "AC_CHECK_LIB(gflags, main, ac_cv_have_libgflags=0, ac_cv_have_libgflags=0)"
- 3. autoreconf
-
+ 3. autoreconf
+
After this, you can build glog again.
Modified: incubator/singa/site/trunk/content/markdown/docs/layer.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/layer.md?rev=1738695&r1=1738694&r2=1738695&view=diff
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/layer.md (original)
+++ incubator/singa/site/trunk/content/markdown/docs/layer.md Tue Apr 12 06:22:20 2016
@@ -73,7 +73,8 @@ The configuration for this layer is in `
store_conf {
backend: # "kvfile" or "textfile"
path: # path to the data store
- batchsize :
+ batchsize : 32
+ prefetching: true #default value is false
...
}
@@ -290,6 +291,13 @@ Store, e.g., text file. The configuratio
Neuron layers conduct feature transformations.
+#### ActivationLayer
+
+ type: kActivation
+ activation_conf {
+ type: {RELU, SIGMOID, TANH, STANH}
+ }
+
##### ConvolutionLayer
[ConvolutionLayer](../api/classsinga_1_1ConvolutionLayer.html) conducts convolution transformation.
@@ -385,6 +393,22 @@ This scheme helps deep learning model aw
For `WITHIN_CHANNEL`, it means the side length of the space region which will be summed up.
+
+### CuDNN layers
+
+CuDNN v3 and v4 are supported in SINGA, which include the following layers,
+
+* CudnnActivationLayer (activation functions are SIGMOID, TANH, RELU)
+* CudnnConvLayer
+* CudnnLRNLayer
+* CudnnPoolLayer
+* CudnnSoftmaxLayer
+
+These layers have the same configuration as the corresponding CPU layers.
+For CuDNN v4, the batch normalization layer is added, which is named as
+`CudnnBMLayer`.
+
+
#### Loss Layers
Loss layers measures the objective training loss.
@@ -463,22 +487,15 @@ implement a new Layer subclass.
#### Members
LayerProto layer_conf_;
- Blob<float> data_, grad_;
+ vector<Blob<float>> datavec_, gradvec_;
vector<AuxType> aux_data_;
The base layer class keeps the user configuration in `layer_conf_`.
-Almost all layers has $b$ (mini-batch size) feature vectors, which are stored
-in the `data_` [Blob](../api/classsinga_1_1Blob.html) (A Blob is a chunk of memory space, proposed in
-[Caffe](http://caffe.berkeleyvision.org/)).
+`datavec_` stores the features associated with this layer.
There are layers without feature vectors; instead, they share the data from
source layers.
-The `grad_` Blob is for storing the gradients of the
-objective loss w.r.t. the `data_` Blob. It is necessary in [BP algorithm](../api/classsinga_1_1BPWorker.html),
-hence we put it as a member of the base class. For [CD algorithm](../api/classsinga_1_1CDWorker.html), the `grad_`
-field is not used; instead, the layers for the RBM model may have a Blob for the positive
-phase feature and a Blob for the negative phase feature. For a recurrent layer
-in RNN, one row of the feature blob corresponds to the feature of one internal layer.
-The `aux_data_` stores the auxiliary data, e.g., image label (set `AuxType` to int).
+The `gradvec_` is for storing the gradients of the
+objective loss w.r.t. the `datavec_`. The `aux_data_` stores the auxiliary data, e.g., image label (set `AuxType` to int).
If images have variant number of labels, the AuxType can be defined to `vector<int>`.
Currently, we hard code `AuxType` to int. It will be added as a template argument of Layer class later.
@@ -496,17 +513,6 @@ The `Setup` function reads user configur
from source layers, e.g., mini-batch size, to set the
shape of the `data_` (and `grad_`) field as well
as some other layer specific fields.
-<!---
-If `npartitions` is larger than 1, then
-users need to reduce the sizes of `data_`, `grad_` Blobs or Param objects. For
-example, if the `partition_dim=0` and there is no source layer, e.g., this
-layer is a (bottom) data layer, then its `data_` and `grad_` Blob should have
-`b/npartitions` feature vectors; If the source layer is also partitioned on
-dimension 0, then this layer should have the same number of feature vectors as
-the source layer. More complex partition cases are discussed in
-[Neural net partitioning](neural-net.html#neural-net-partitioning). Typically, the
-Setup function just set the shapes of `data_` Blobs and Param objects.
--->
Memory will not be allocated until computation over the data structure happens.
The `ComputeFeature` function evaluates the feature blob by transforming (e.g.
Modified: incubator/singa/site/trunk/content/markdown/docs/quick-start.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/quick-start.md?rev=1738695&r1=1738694&r2=1738695&view=diff
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/quick-start.md (original)
+++ incubator/singa/site/trunk/content/markdown/docs/quick-start.md Tue Apr 12 06:22:20 2016
@@ -4,34 +4,8 @@
## SINGA setup
-Please refer to the
-[installation](installation.html) page
-for guidance on installing SINGA.
+Please refer to the [installation](installation.html) page for guidance on installing SINGA.
-### Starting Zookeeper
-
-SINGA uses [zookeeper](https://zookeeper.apache.org/) to coordinate the
-training. Please make sure the zookeeper service is started before running
-SINGA.
-
-If you installed the zookeeper using our thirdparty script, you can
-simply start it by:
-
- #goto top level folder
- cd SINGA_ROOT
- ./bin/zk-service.sh start
-
-(`./bin/zk-service.sh stop` stops the zookeeper).
-
-Otherwise, if you launched a zookeeper by yourself but not used the
-default port, please edit the `conf/singa.conf`:
-
- zookeeper_host: "localhost:YOUR_PORT"
-
-## Running in standalone mode
-
-Running SINGA in standalone mode is on the contrary of running it using cluster
-managers like [Mesos](http://mesos.apache.org/) or [YARN](http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html).
### Training on a single node
@@ -52,13 +26,12 @@ Download the dataset and create the data
make download
make create
-A training dataset and a test dataset are created under *cifar10-train-shard*
-and *cifar10-test-shard* folder respectively. An *image_mean.bin* file is also
+A training dataset and a test dataset are created respectively. An *image_mean.bin* file is also
generated, which contains the feature mean of all images.
Since all code used for training this CNN model is provided by SINGA as
built-in implementation, there is no need to write any code. Instead, users just
-execute the running script (*../../bin/singa-run.sh*) by providing the job
+execute the running script by providing the job
configuration file (*job.conf*). To code in SINGA, please refer to the
[programming guide](programming-guide.html).
@@ -71,25 +44,7 @@ The training is started by running:
# goto top level folder
cd ../../
- ./bin/singa-run.sh -conf examples/cifar10/job.conf
-
-
-You can list the current running jobs by,
-
- ./bin/singa-console.sh list
-
- JOB ID |NUM PROCS
- ----------|-----------
- 24 |1
-
-Jobs can be killed by,
-
- ./bin/singa-console.sh kill JOB_ID
-
-
-Logs and job information are available in */tmp/singa-log* folder, which can be
-changed to other folders by setting `log-dir` in *conf/singa.conf*.
-
+ ./singa -conf examples/cifar10/job.conf
#### Asynchronous parallel training
@@ -116,7 +71,7 @@ run as on different data partitions.
neuralnet {
layer {
...
- sharddata_conf {
+ store_conf {
random_skip: 5000
}
}
@@ -125,7 +80,7 @@ run as on different data partitions.
The running command is:
- ./bin/singa-run.sh -conf examples/cifar10/job.conf
+ ./singa -conf examples/cifar10/job.conf
#### Synchronous parallel training
@@ -151,10 +106,33 @@ workers in a group. It is also possible
using [other schemes](neural-net.html).
All other settings are the same as running without partitioning
- ./bin/singa-run.sh -conf examples/cifar10/job.conf
+ ./singa -conf examples/cifar10/job.conf
+
### Training in a cluster
+#### Starting Zookeeper
+
+SINGA uses [zookeeper](https://zookeeper.apache.org/) to coordinate the
+training, and uses ZeroMQ for transferring messages. After installing zookeeper
+and ZeroMQ, you need to configure SINGA with `--enable-dist` before compiling.
+Please make sure the zookeeper service is started before running SINGA.
+
+If you installed the zookeeper using our thirdparty script, you can
+simply start it by:
+
+ #goto top level folder
+ cd SINGA_ROOT
+ ./bin/zk-service.sh start
+
+(`./bin/zk-service.sh stop` stops the zookeeper).
+
+Otherwise, if you launched a zookeeper by yourself but not used the
+default port, please edit the `conf/singa.conf`:
+
+ zookeeper_host: "localhost:YOUR_PORT"
+
+
We can extend the above two training frameworks to a cluster by updating the
cluster configuration with:
@@ -165,21 +143,36 @@ would be created in different processes
must be provided under *SINGA_ROOT/conf/* specifying the nodes in the cluster,
e.g.,
- logbase-a01
- logbase-a02
+ 192.168.0.1
+ 192.168.0.2
And the zookeeper location must be configured correctly, e.g.,
#conf/singa.conf
zookeeper_host: "logbase-a01"
-The running command is the same as for single node training:
+The running command is :
./bin/singa-run.sh -conf examples/cifar10/job.conf
-## Running with Mesos
+You can list the current running jobs by,
+
+ ./bin/singa-console.sh list
+
+ JOB ID |NUM PROCS
+ ----------|-----------
+ 24 |2
+
+Jobs can be killed by,
+
+ ./bin/singa-console.sh kill JOB_ID
+
+
+Logs and job information are available in */tmp/singa-log* folder, which can be
+changed to other folders by setting `log-dir` in *conf/singa.conf*.
-*working*...
+### Training with GPUs
+Please refer to the [GPU page][gpu.html] for details on training using GPUs.
## Where to go next
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+++ incubator/singa/site/trunk/content/markdown/docs/zh/index.md Tue Apr 12 06:22:20 2016
@@ -3,6 +3,6 @@ SINGA ä¸æææ¡£
---
* [ç®ä»](overview.html)
-* [å®è£
说æ](installation_source.html)
+* [å®è£
](installation_source.html)
* [使ç¨æå](programming-guide.html)
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@@ -0,0 +1,37 @@
+#singa-incubating-0.3.0 Release Notes
+
+---
+
+SINGA is a general distributed deep learning platform for training big deep
+learning models over large datasets. It is designed with an intuitive
+programming model based on the layer abstraction. SINGA supports a wide variety
+of popular deep learning models.
+
+This release includes following features:
+
+ * GPU Support
+ * [SINGA-131] Implement and optimize hybrid training using both CPU and GPU
+ * [SINGA-136] Support cuDNN v4
+ * [SINGA-134] Extend SINGA to run over a GPU cluster
+ * [Singa-157] Change the priority of cudnn library and install libsingagpu.so
+
+ * Remove Dependences
+ * [SINGA-156] Remove the dependency on ZMQ for single process training
+ * [SINGA-155] Remove zookeeper for single-process training
+
+ * Python Binding
+ * [SINGA-126] Python Binding for Interactive Training
+
+ * Other Improvements
+ * [SINGA-80] New Blob Level and Address Level Math Operation Interface
+ * [SINGA-130] Data Prefetching
+ * [SINGA-145] New SGD based optimization Updaters: AdaDelta, Adam, AdamMax
+
+ * Bugs Fixed
+ * [SINGA-148] Race condition between Worker threads and Driver
+ * [SINGA-150] Mesos Docker container failed
+ * [SIGNA-141] Undesired Hash collision when locating process id to workerâ¦
+ * [SINGA-149] Docker build fail
+ * [Singa-143] The compilation cannot detect libsingagpu.so file
+
+
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+# SINGA Architecture
+
+---
+
+## Logical Architecture
+
+<img src="../images/logical.png" style="width: 550px"/>
+<p><strong> Fig.1 - Logical system architecture</strong></p>
+
+SINGA has flexible architecture to support different distributed
+[training frameworks](frameworks.html) (both synchronous and asynchronous).
+The logical system architecture is shown in Fig.1.
+The architecture consists of multiple server groups and worker groups:
+
+* **Server group**
+ A server group maintains a complete replica of the model parameters,
+ and is responsible for handling get/update requests from worker groups.
+ Neighboring server groups synchronize their parameters periodically.
+ Typically, a server group contains a number of servers,
+ and each server manages a partition of model parameters.
+* **Worker group**
+ Each worker group communicates with only one server group.
+ A worker group trains a complete model replica
+ against a partition of the training dataset,
+ and is responsible for computing parameter gradients.
+ All worker groups run and communicate with the corresponding
+ server groups asynchronously.
+ However, inside each worker group,
+ the workers synchronously compute parameter updates for the model replica.
+
+There are different strategies to distribute the training workload among workers
+within a group:
+
+ * **Model parallelism**. Each worker computes a subset of parameters
+ against all data partitioned to the group.
+ * **Data parallelism**. Each worker computes all parameters
+ against a subset of data.
+ * [**Hybrid parallelism**](hybrid.html). SINGA also supports hybrid parallelism.
+
+
+## Implementation
+In SINGA, servers and workers are execution units running in separate threads.
+They communicate through [messages](communication.html).
+Every process runs the main thread as a stub that aggregates local messages
+and forwards them to corresponding (remote) receivers.
+
+Each server group and worker group have a *ParamShard*
+object representing a complete model replica. If workers and servers
+resident in the same process, their *ParamShard* (partitions) can
+be configured to share the same memory space. In this case, the
+messages transferred between different execution units just contain
+pointers to the data, which reduces the communication cost.
+Unlike in inter-process cases,
+the messages have to include the parameter values.
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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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@@ -0,0 +1,239 @@
+# 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
+
+
+### Training on CPU
+
+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.
+
+### Training on GPU
+
+Since version 0.2, we can train CNN models on GPU using cuDNN. Please refer to
+the [GPU page](gpu.html) 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.
+
+ ./bin/singa-run.sh -conf examples/cifar10/cudnn.conf
+
+### Training using Python script
+
+The python helpers coming with SINGA 0.2 make it easy to configure a training
+job. For example the *job.conf* is replaced with a simple python script
+*mnist_mlp.py* which has about 30 lines of code following the [Keras API](http://keras.io/).
+
+ # 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
+
+## 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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@@ -0,0 +1,76 @@
+# 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.
+
+-->
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@@ -0,0 +1,453 @@
+# Communication
+
+---
+
+Different messaging libraries has different benefits and drawbacks. For instance,
+MPI provides fast message passing between GPUs (using GPUDirect), but does not
+support fault-tolerance well. On the contrary, systems using ZeroMQ can be
+fault-tolerant, but does not support GPUDirect. The AllReduce function
+of MPI is also missing in ZeroMQ which is efficient for data aggregation for
+distributed training. In Singa, we provide general messaging APIs for
+communication between threads within a process and across processes, and let
+users choose the underlying implementation (MPI or ZeroMQ) that meets their requirements.
+
+Singa's messaging library consists of two components, namely the message, and
+the socket to send and receive messages. **Socket** refers to a
+Singa defined data structure instead of the Linux Socket.
+We will introduce the two components in detail with the following figure as an
+example architecture.
+
+<img src="../images/arch/arch2.png" style="width: 550px"/>
+<img src="../images/arch/comm.png" style="width: 550px"/>
+<p><strong> Fig.1 - Example physical architecture and network connection</strong></p>
+
+Fig.1 shows an example physical architecture and its network connection.
+[Section-partition server side ParamShard](architecture.html}) has a detailed description of the
+architecture. Each process consists of one main thread running the stub and multiple
+background threads running the worker and server tasks. The stub of the main
+thread forwards messages among threads . The worker and
+server tasks are performed by the background threads.
+
+## Message
+
+<object type="image/svg+xml" style="width: 100px" data="../images/msg.svg" > Not
+supported </object>
+<p><strong> Fig.2 - Logical message format</strong></p>
+
+Fig.2 shows the logical message format which has two parts, the header and the
+content. The message header includes the sender's and receiver's IDs, each consisting of
+the group ID and the worker/server ID within the group. The stub forwards
+messages by looking up an address table based on the receiver's ID.
+There are two sets of messages according to the message type defined below.
+
+ * kGet/kPut/kRequest/kSync for messages about parameters
+
+ * kFeaBlob/kGradBlob for messages about transferring feature and gradient
+ blobs of one layer to its neighboring layer
+
+There is a target ID in the header. If the message body is parameters,
+the target ID is then the parameter ID. Otherwise the message is related to
+layer feature or gradient, and the target ID consists of the layer ID and the
+blob ID of that layer. The message content has multiple frames to store the
+parameter or feature data.
+
+The API for the base Msg is:
+
+ /**
+ * Msg used to transfer Param info (gradient or value), feature blob, etc
+ * between workers, stubs and servers.
+ *
+ * Each msg has a source addr and dest addr identified by a unique integer.
+ * It is also associated with a target field (value and version) for ease of
+ * getting some meta info (e.g., parameter id) from the msg.
+ *
+ * Other data is added into the message as frames.
+ */
+ class Msg {
+ public:
+ ~Msg();
+ Msg();
+ /**
+ * Construct the msg providing source and destination addr.
+ */
+ Msg(int src, int dst);
+ /**
+ * Copy constructor.
+ */
+ Msg(const Msg& msg);
+ /**
+ * Swap the src/dst addr
+ */
+ void SwapAddr();
+ /**
+ * Add a frame (a chunk of bytes) into the message
+ */
+ void AddFrame(const void* addr, int nBytes);
+ /**
+ * @return num of bytes of the current frame.
+ */
+ int FrameSize();
+ /**
+ * @return the pointer to the current frame data.
+ */
+ void* FrameData();
+ /**
+ * @return the data of the current frame as c string
+ */
+ char* FrameStr();
+ /**
+ * Move the cursor to the first frame.
+ */
+ void FirstFrame();
+ /**
+ * Move the cursor to the last frame.
+ */
+ void LastFrame();
+ /**
+ * Move the cursor to the next frame
+ * @return true if the next frame is not NULL; otherwise false
+ */
+ bool NextFrame();
+ /**
+ * Add a 'format' frame to the msg (like CZMQ's zsock_send).
+ *
+ * The format is a string that defines the type of each field.
+ * The format can contain any of these characters, each corresponding to
+ * one or two arguments:
+ * i = int (signed)
+ * 1 = uint8_t
+ * 2 = uint16_t
+ * 4 = uint32_t
+ * 8 = uint64_t
+ * p = void * (sends the pointer value, only meaningful over inproc)
+ * s = char**
+ *
+ * Returns size of the added content.
+ */
+ int AddFormatFrame(const char *format, ...);
+ /**
+ * Parse the current frame added using AddFormatFrame(const char*, ...).
+ *
+ * The format is a string that defines the type of each field.
+ * The format can contain any of these characters, each corresponding to
+ * one or two arguments:
+ * i = int (signed)
+ * 1 = uint8_t
+ * 2 = uint16_t
+ * 4 = uint32_t
+ * 8 = uint64_t
+ * p = void * (sends the pointer value, only meaningful over inproc)
+ * s = char**
+ *
+ * Returns size of the parsed content.
+ */
+ int ParseFormatFrame(const char* format, ...);
+
+ #ifdef USE_ZMQ
+ void ParseFromZmsg(zmsg_t* msg);
+ zmsg_t* DumpToZmsg();
+ #endif
+
+ /**
+ * @return msg size in terms of bytes, ignore meta info.
+ */
+ int size() const;
+ /**
+ * Set source addr.
+ * @param addr unique identify one worker/server/stub in the current job
+ */
+ void set_src(int addr) { src_ = addr; }
+ /**
+ * @return source addr.
+ */
+ int src() const { return src_; }
+ /**
+ * Set destination addr.
+ * @param addr unique identify one worker/server/stub in the current job
+ */
+ void set_dst(int addr) { dst_ = addr; }
+ /**
+ * @return dst addr.
+ */
+ int dst() const { return dst_; }
+ /**
+ * Set msg type, e.g., kPut, kGet, kUpdate, kRequest
+ */
+ void set_type(int type) { type_ = type; }
+ /**
+ * @return msg type.
+ */
+ int type() const { return type_; }
+ /**
+ * Set msg target.
+ *
+ * One msg has a target to identify some entity in worker/server/stub.
+ * The target is associated with a version, e.g., Param version.
+ */
+ void set_trgt(int val, int version) {
+ trgt_val_ = val;
+ trgt_version_ = version;
+ }
+ int trgt_val() const {
+ return trgt_val_;
+ }
+ int trgt_version() const {
+ return trgt_version_;
+ }
+
+ };
+
+In order for a Msg object to be routed, the source and dest address should be attached.
+This is achieved by calling the set_src and set_dst methods of the Msg object.
+The address parameter passed to these two methods can be manipulated via a set of
+helper functions, shown as below.
+
+ /**
+ * Wrapper to generate message address
+ * @param grp worker/server group id
+ * @param id_or_proc worker/server id or procs id
+ * @param type msg type
+ */
+ inline int Addr(int grp, int id_or_proc, int type) {
+ return (grp << 16) | (id_or_proc << 8) | type;
+ }
+
+ /**
+ * Parse group id from addr.
+ *
+ * @return group id
+ */
+ inline int AddrGrp(int addr) {
+ return addr >> 16;
+ }
+ /**
+ * Parse worker/server id from addr.
+ *
+ * @return id
+ */
+ inline int AddrID(int addr) {
+ static const int mask = (1 << 8) - 1;
+ return (addr >> 8) & mask;
+ }
+
+ /**
+ * Parse worker/server procs from addr.
+ *
+ * @return procs id
+ */
+ inline int AddrProc(int addr) {
+ return AddrID(addr);
+ }
+ /**
+ * Parse msg type from addr
+ * @return msg type
+ */
+ inline int AddrType(int addr) {
+ static const int mask = (1 << 8) -1;
+ return addr & mask;
+ }
+
+
+## Socket
+
+In SINGA, there are two types of sockets, the Dealer Socket and the Router
+Socket, whose names are adapted from ZeroMQ. All connections are of the same type, i.e.,
+Dealer<-->Router. The communication between dealers and routers are
+asynchronous. In other words, one Dealer
+socket can talk with multiple Router sockets, and one Router socket can talk
+with multiple Dealer sockets.
+
+### Base Socket
+
+The basic functions of a Singa Socket is to send and receive messages. The APIs
+are:
+
+ class SocketInterface {
+ public:
+ virtual ~SocketInterface() {}
+ /**
+ * Send a message to connected socket(s), non-blocking. The message
+ * will be deallocated after sending, thus should not be used after
+ * calling Send();
+ *
+ * @param msg The message to be sent
+ * @return 1 for success queuing the message for sending, 0 for failure
+ */
+ virtual int Send(Msg** msg) = 0;
+ /**
+ * Receive a message from any connected socket.
+ *
+ * @return a message pointer if success; nullptr if failure
+ */
+ virtual Msg* Receive() = 0;
+ /**
+ * @return Identifier of the implementation dependent socket. E.g., zsock_t*
+ * for ZeroMQ implementation and rank for MPI implementation.
+ */
+ virtual void* InternalID() const = 0;
+ };
+
+A poller class is provided to enable asynchronous communication between routers and dealers.
+One can register a set of SocketInterface objects with a poller instance via calling its Add method, and
+then call the Wait method of this poll object to wait for the registered SocketInterface objects to be ready
+for sending and receiving messages. The APIs of the poller class is shown below.
+
+ class Poller {
+ public:
+ Poller();
+ Poller(SocketInterface* socket);
+ /**
+ * Add a socket for polling; Multiple sockets can be polled together by
+ * adding them into the same poller.
+ */
+ void Add(SocketInterface* socket);
+ /**
+ * Poll for all sockets added into this poller.
+ * @param timeout Stop after this number of mseconds
+ * @return pointer To the socket if it has one message in the receiving
+ * queue; nullptr if no message in any sockets,
+ */
+ SocketInterface* Wait(int duration);
+
+ /**
+ * @return true if the poller is terminated due to process interupt
+ */
+ virtual bool Terminated();
+ };
+
+
+### Dealer Socket
+
+The Dealer socket inherits from the base Socket. In Singa, every Dealer socket
+only connects to one Router socket as shown in Fig.1. The connection is set up
+by connecting the Dealer socket to the endpoint of a Router socket.
+
+ class Dealer : public SocketInterface {
+ public:
+ /*
+ * @param id Local dealer ID within a procs if the dealer is from worker or
+ * server thread, starts from 1 (0 is used by the router); or the connected
+ * remote procs ID for inter-process dealers from the stub thread.
+ */
+ Dealer();
+ explicit Dealer(int id);
+ ~Dealer() override;
+ /**
+ * Setup the connection with the router.
+ *
+ * @param endpoint Identifier of the router. For intra-process
+ * connection, the endpoint follows the format of ZeroMQ, i.e.,
+ * starting with "inproc://"; in Singa, since each process has one
+ * router, hence we can fix the endpoint to be "inproc://router" for
+ * intra-process. For inter-process, the endpoint follows ZeroMQ's
+ * format, i.e., IP:port, where IP is the connected process.
+ * @return 1 connection sets up successfully; 0 otherwise
+ */
+ int Connect(const std::string& endpoint);
+ int Send(Msg** msg) override;
+ Msg* Receive() override;
+ void* InternalID() const override;
+ };
+
+### Router Socket
+
+The Router socket inherits from the base Socket. One Router socket connects to
+at least one Dealer socket. Upon receiving a message, the router forwards it to
+the appropriate dealer according to the receiver's ID of this message.
+
+ class Router : public SocketInterface {
+ public:
+ Router();
+ /**
+ * There is only one router per procs, hence its local id is 0 and is not set
+ * explicitly.
+ *
+ * @param bufsize Buffer at most this number of messages
+ */
+ explicit Router(int bufsize);
+ ~Router() override;
+ /**
+ * Setup the connection with dealers.
+ *
+ * It automatically binds to the endpoint for intra-process communication,
+ * i.e., "inproc://router".
+ *
+ * @param endpoint The identifier for the Dealer socket in other process
+ * to connect. It has the format IP:Port, where IP is the host machine.
+ * If endpoint is empty, it means that all connections are
+ * intra-process connection.
+ * @return number of connected dealers.
+ */
+ int Bind(const std::string& endpoint);
+ /**
+ * If the destination socket has not connected yet, buffer this the message.
+ */
+ int Send(Msg** msg) override;
+ Msg* Receive() override;
+ void* InternalID() const override;
+
+ };
+
+## Implementation
+
+### ZeroMQ
+
+**Why [ZeroMQ](http://zeromq.org/)?** Our previous design used MPI for
+communication between Singa processes. But MPI is a poor choice when it comes
+to fault-tolerance, because failure at one node brings down the entire MPI
+cluster. ZeroMQ, on the other hand, is fault tolerant in the sense that one
+node failure does not affect the other nodes. ZeroMQ consists of several basic
+communication patterns that can be easily combined to create more complex
+network topologies.
+
+<img src="../images/msg-flow.png" style="width: 550px"/>
+<p><strong> Fig.3 - Messages flow for ZeroMQ</strong></p>
+
+The communication APIs of Singa are similar to the DEALER-ROUTER pattern of
+ZeroMQ. Hence we can easily implement the Dealer socket using ZeroMQ's DEALER
+socket, and Router socket using ZeroMQ's ROUTER socket.
+The intra-process can be implemented using ZeroMQ's inproc transport, and the
+inter-process can be implemented using the tcp transport (To exploit the
+Infiniband, we can use the sdp transport). Fig.3 shows the message flow using
+ZeroMQ as the underlying implementation. The messages sent from dealers has two
+frames for the message header, and one or more frames for the message content.
+The messages sent from routers have another frame for the identifier of the
+destination dealer.
+
+Besides the DEALER-ROUTER pattern, we may also implement the Dealer socket and
+Router socket using other ZeroMQ patterns. To be continued.
+
+### MPI
+
+Since MPI does not provide intra-process communication, we have to implement
+it inside the Router and Dealer socket. A simple solution is to allocate one
+message queue for each socket. Messages sent to one socket is inserted into the
+queue of that socket. We create a SafeQueue class to ensure the consistency of
+the queue. All queues are created by the main thread and
+passed to all sockets' constructor via *args*.
+
+ /**
+ * A thread safe queue class.
+ * There would be multiple threads pushing messages into
+ * the queue and only one thread reading and popping the queue.
+ */
+ class SafeQueue{
+ public:
+ void Push(Msg* msg);
+ Msg* Front();
+ void Pop();
+ bool empty();
+ };
+
+For inter-process communication, we serialize the message and call MPI's
+send/receive functions to transfer them. All inter-process connections are
+setup by MPI at the beginning. Consequently, the Connect and Bind functions do
+nothing for both inter-process and intra-process communication.
+
+MPI's AllReduce function is efficient for data aggregation in distributed
+training. For example, [DeepImage of Baidu](http://arxiv.org/abs/1501.02876)
+uses AllReduce to aggregate the updates of parameter from all workers. It has
+similar architecture as [Fig.2](architecture.html),
+where every process has a server group and is connected with all other processes.
+Hence, we can implement DeepImage in Singa by simply using MPI's AllReduce function for
+inter-process communication.
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+# Data Preparation
+
+---
+
+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.
+
+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 [Store]() 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,
+
+ 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();
+
+Currently, two implementations are provided, namely
+
+1. [KVFileStore] for storing tuples in [KVFile]() (a binary file).
+The *create_data.cc* files in *examples/cifar10* and *examples/mnist* provide
+examples of storing records using KVFileStore.
+
+2. [TextFileStore] for storing tuples in plain text file (one line per tuple).
+
+The (key, value) tuple are parsed by subclasses of StoreInputLayer depending on the
+format of the tuple,
+
+* [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.
+
+* [CSVRecordInputLayer] parses one tuple as a CSV line (separated by comma).
+
+
+## Using built-in record format
+
+SingleLabelImageRecord is a built-in record in SINGA for storing image features.
+It is used in the cifar10 and mnist examples.
+
+ 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
+ }
+
+The data preparation instructions for the [CIFAR-10 image dataset](http://www.cs.toronto.edu/~kriz/cifar.html)
+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 [create_data.cc](https://github.com/apache/incubator-singa/blob/master/examples/cifar10/create_data.cc)
+to convert images in the binary files into `SingleLabelImageRecord`s and insert them into training and test stores.
+
+1. Download raw data. The following command will download the dataset into *cifar-10-batches-bin* folder.
+
+ # in SINGA_ROOT/examples/cifar10
+ $ cp Makefile.example Makefile // an example makefile is provided
+ $ make download
+
+2. Fill one record for each image, and insert it to store.
+
+ 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();
+
+ 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.
+
+3. Compile and run the program. SINGA provides an example Makefile that contains instructions
+ for compiling the source code and linking it with *libsinga.so*. Users just execute the following command.
+
+ $ make create
+
+## using user-defined record format
+
+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.
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+# How to Debug
+
+---
+
+Since SINGA is developed on Linux using C++, GDB is the preferred debugging
+tool. To use GDB, the code must be compiled with `-g` flag. This is enabled by
+
+ ./configure --enable-debug
+ make
+
+## Debugging for single process job
+
+If your job launches only one process, then use the default *conf/singa.conf*
+for debugging. The process will be launched locally.
+
+To debug, first start zookeeper if it is not started yet, and launch GDB
+
+ # do this for only once
+ ./bin/zk-service.sh start
+ # do this every time
+ gdb .libs/singa
+
+Then set the command line arguments
+
+ set args -conf JOBCONF
+
+Now you can set your breakpoints and start running.
+
+## Debugging for jobs with multiple processes
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+# Distributed Training
+
+---
+
+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.
+
+* [System Architecture](architecture.html)
+
+* [Training Frameworks](frameworks.html)
+
+* [System Communication](communication.html)
+
+SINGA supports different options for training a model in parallel, includeing data parallelism, model parallelism and hybrid parallelism.
+
+* [Hybrid Parallelism](hybrid.html)
+
+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.
+
+* [Distributed training on Mesos](mesos.html)
+
+SINGA can run on top of distributed storage system to achieve scalability. The current version of SINGA supports HDFS.
+
+* [Running SINGA on HDFS](hdfs.html)
+
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+# Building SINGA Docker container
+
+This guide explains how to set up a development environment for SINGA using Docker. It requires only Docker to be installed. The resulting image contains the complete working environment for SINGA. The image can then be used to set up cluster environment over one or multiple physical nodes.
+
+1. [Build SINGA base](#build_base)
+2. [Build SINGA with Mesos and Hadoop](#build_mesos)
+3. [Pre-built images](#pre_built)
+4. [Launch and stop SINGA (stand alone mode)](#launch_stand_alone)
+5. [Launch pseudo-distributed SINGA on one node](#launch_pseudo)
+6. [Launch fully distributed SINGA on multiple nodes](#launch_distributed)
+
+---
+
+<a name="build_base"></a>
+#### Build SINGA base image
+
+````
+$ cd tool/docker/singa
+$ sudo docker build -t singa/base .
+$ sudo docker images
+REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE
+singa/base latest XXXX XXX 2.01 GB
+````
+
+The result is the image containing a built version of SINGA.
+
+ 
+
+ *Figure 1. singa/base Docker image, containing library dependencies and SINGA built from source.*
+
+---
+
+<a name="build_mesos"></a>
+#### Build SINGA with Mesos and Hadoop
+````
+$ cd tool/docker/mesos
+$ sudo docker build -t singa/mesos .
+$ sudo docker images
+REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE
+singa/mesos latest XXXX XXX 4.935 GB
+````
+ 
+
+ *Figure 2. singa/mesos Docker image, containing Hadoop and Mesos built on
+top of SINGA. The default namenode address for Hadoop is `node0:9000`*
+
+**Notes** A common failure observed during the build process is caused by network failure occuring when downloading dependencies. Simply re-run the build command.
+
+---
+
+<a name="pre_built"></a>
+#### Pre-built images on epiC cluster
+For users with access to the `epiC` cluster, there are pre-built and loaded Docker images at the following nodes:
+
+ ciidaa-c18
+ ciidaa-c19
+
+The available images at those nodes are:
+
+````
+REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE
+singa/base latest XXXX XXX 2.01 GB
+singa/mesos latest XXXX XXX 4.935 GB
+weaveworks/weaveexec 1.1.1 XXXX 11 days ago 57.8 MB
+weaveworks/weave 1.1.1 XXXX 11 days ago 17.56 MB
+````
+
+---
+
+<a name="launch_stand_alone"></a>
+#### Launch and stop SINGA in stand-alone mode
+To launch a test environment for a single-node SINGA training, simply start a container from `singa/base` image. The following starts a container called
+`XYZ`, then launches a shell in the container:
+
+````
+$ sudo docker run -dt --name XYZ singa/base /usr/bin/supervisord
+$ sudo docker exec -it XYZ /bin/bash
+````
+
+
+
+ *Figure 3. Launch SINGA in stand-alone mode: single node training*
+
+Inside the launched container, the SINGA source directory can be found at `/root/incubator-singa`.
+
+**Stopping the container**
+
+````
+$ sudo docker stop XYZ
+$ sudo docker rm ZYZ
+````
+
+---
+
+<a name="launch_pseudo"></a>
+#### Launch SINGA on pseudo-distributed mode (single node)
+To simulate a distributed environment on a single node, one can repeat the
+previous step multiple times, each time giving a different name to the
+container. Network connections between these containers are already supported,
+thus SINGA instances/nodes in these container can readily communicate with each
+other.
+
+The previous approach requires the user to start SINGA instances individually
+at each container. Although there's a bash script for that, we provide a better
+way. In particular, multiple containers can be started from `singa/mesos` image
+which already bundles Mesos and Hadoop with SINGA. Using Mesos makes it easy to
+launch, stop and monitor the distributed execution from a single container.
+Figure 4 shows `N+1` containers running concurrently at the local host.
+
+````
+$ sudo docker run -dt --name node0 singa/mesos /usr/bin/supervisord
+$ sudo docker run -dt --name node1 singa/mesos /usr/bin/supervisord
+...
+````
+
+
+
+*Figure 4. Launch SINGA in pseudo-distributed mode : multiple SINGA nodes over one single machine*
+
+**Starting SINGA distributed training**
+
+Refer to the [Mesos
+guide](mesos.html)
+for details of how to start training with multiple SINGA instances.
+
+**Important:** the container that assumes the role of Hadoop's namenode (and often Mesos's and Zookeeper's mater node as well) **must** be named `node0`. Otherwise, the user must log in to individual containers and change the Hadoop configuration separately.
+
+---
+
+<a name="launch_distributed"></a>
+#### Launch SINGA on fully distributed mode (multiple nodes)
+The previous section has explained how to start a distributed environment on a
+single node. But running many containers on one node does not scale. When there
+are multiple physical hosts available, it is better to distribute the
+containers over them.
+
+The only extra requirement for the fully distributed mode, as compared with the
+pseudo distributed mode, is that the containers from different hosts are able
+to transparently communicate with each other. In the pseudo distributed mode,
+the local docker engine takes care of such communication. Here, we rely on
+[Weave](http://weave.works/guides/weave-docker-ubuntu-simple.html) to make the
+communication transparent. The resulting architecture is shown below.
+
+
+
+*Figure 5. Launch SINGA in fully distributed mode: multiple SINGA nodes over multiple machines*
+
+**Install Weave at all hosts**
+
+```
+$ curl -L git.io/weave -o /usr/local/bin/weave
+$ chmod a+x /usr/local/bin/weave
+```
+
+**Starting Weave**
+
+Suppose `node0` will be launched at host with IP `111.222.111.222`.
+
++ At host `111.222.111.222`:
+
+ $ weave launch
+ $ eval "$(weave env)" //if there's error, do `sudo -s` and try again
+
++ At other hosts:
+
+ $ weave launch 111.222.111.222
+ $ eval "$(weave env)" //if there's error, do `sudo -s` and try again
+
+**Starting containers**
+
+The user logs in to each host and starts the container (same as in [pseudo-distributed](#launch_pseudo) mode). Note that container acting as the head node of the cluster must be named `node0` (and be running at the host with IP `111.222.111.222`, for example).
+
+**_Important_:** when there are other containers sharing the same host as `node0`, say `node1` and `node2` for example,
+there're additional changes to be made to `node1` and `node2`. Particularly, log in to each container and edit
+`/etc/hosts` file:
+
+````
+# modified by weave
+...
+X.Y.Z node0 node0.bridge //<- REMOVE this line
+..
+````
+This is to ensure that name resolutions (of `node0`'s address) from `node1` and `node2` are correct. By default,
+containers of the same host resolves each other's addresses via the Docker bridge. Instead, we want they to use
+addressed given by Weave.
+
+
+**Starting SINGA distributed training**
+
+Refer to the [Mesos guide](mesos.html)
+for details of how to start training with multiple SINGA instances.
+
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+# Example Models
+
+---
+
+Different models are provided as examples to help users get familiar with SINGA.
+[Neural Network](neural-net.html) gives details on the models that are
+supported by SINGA.
+
+
+### Feed-forward neural networks
+
+ * [MultiLayer Perceptron](mlp.html) trained on MNIST dataset for handwritten
+ digits recognition.
+
+ * [Convolutional Neural Network](cnn.html) trained on MNIST and CIFAR10 for
+ image classification.
+
+ * [Deep Auto-Encoders](rbm.html) trained on MNIST for dimensionality
+
+
+### Recurrent neural networks (RNN)
+
+ * [RNN language model](rnn.html) trained on plain text for language modelling.
+
+### Energy models
+
+ * [RBM](rbm.html) used to pre-train deep auto-encoders for dimensionality
+ reduction.
+
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+# Distributed Training Framework
+
+---
+
+## Cluster Topology Configuration
+
+Here we describe how to configure SINGA's cluster topology to support
+different distributed training frameworks.
+The cluster topology is configured in the `cluster` field in `JobProto`.
+The `cluster` is of type `ClusterProto`:
+
+ message ClusterProto {
+ optional int32 nworker_groups = 1;
+ optional int32 nserver_groups = 2;
+ optional int32 nworkers_per_group = 3 [default = 1];
+ optional int32 nservers_per_group = 4 [default = 1];
+ optional int32 nworkers_per_procs = 5 [default = 1];
+ optional int32 nservers_per_procs = 6 [default = 1];
+
+ // servers and workers in different processes?
+ optional bool server_worker_separate = 20 [default = false];
+
+ ......
+ }
+
+
+The mostly used fields are as follows:
+
+ * `nworkers_per_group` and `nworkers_per_procs`:
+ decide the partitioning of worker side ParamShard.
+ * `nservers_per_group` and `nservers_per_procs`:
+ decide the partitioning of server side ParamShard.
+ * `server_worker_separate`:
+ separate servers and workers in different processes.
+
+## Different Training Frameworks
+
+In SINGA, worker groups run asynchronously and
+workers within one group run synchronously.
+Users can leverage this general design to run
+both **synchronous** and **asynchronous** training frameworks.
+Here we illustrate how to configure
+popular distributed training frameworks in SINGA.
+
+<img src="../images/frameworks.png" style="width: 800px"/>
+<p><strong> Fig.1 - Training frameworks in SINGA</strong></p>
+
+###Sandblaster
+
+This is a **synchronous** framework used by Google Brain.
+Fig.2(a) shows the Sandblaster framework implemented in SINGA.
+Its configuration is as follows:
+
+ cluster {
+ nworker_groups: 1
+ nserver_groups: 1
+ nworkers_per_group: 3
+ nservers_per_group: 2
+ server_worker_separate: true
+ }
+
+A single server group is launched to handle all requests from workers.
+A worker computes on its partition of the model,
+and only communicates with servers handling related parameters.
+
+
+###AllReduce
+
+This is a **synchronous** framework used by Baidu's DeepImage.
+Fig.2(b) shows the AllReduce framework implemented in SINGA.
+Its configuration is as follows:
+
+ cluster {
+ nworker_groups: 1
+ nserver_groups: 1
+ nworkers_per_group: 3
+ nservers_per_group: 3
+ server_worker_separate: false
+ }
+
+We bind each worker with a server on the same node, so that each
+node is responsible for maintaining a partition of parameters and
+collecting updates from all other nodes.
+
+###Downpour
+
+This is a **asynchronous** framework used by Google Brain.
+Fig.2(c) shows the Downpour framework implemented in SINGA.
+Its configuration is as follows:
+
+ cluster {
+ nworker_groups: 2
+ nserver_groups: 1
+ nworkers_per_group: 2
+ nservers_per_group: 2
+ server_worker_separate: true
+ }
+
+Similar to the synchronous Sandblaster, all workers send
+requests to a global server group. We divide workers into several
+worker groups, each running independently and working on parameters
+from the last *update* response.
+
+###Distributed Hogwild
+
+This is a **asynchronous** framework used by Caffe.
+Fig.2(d) shows the Distributed Hogwild framework implemented in SINGA.
+Its configuration is as follows:
+
+ cluster {
+ nworker_groups: 3
+ nserver_groups: 3
+ nworkers_per_group: 1
+ nservers_per_group: 1
+ server_worker_separate: false
+ }
+
+Each node contains a complete server group and a complete worker group.
+Parameter updates are done locally, so that communication cost
+during each training step is minimized.
+However, the server group must periodically synchronize with
+neighboring groups to improve the training convergence.