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From wang...@apache.org
Subject svn commit: r1692346 - /incubator/singa/site/trunk/content/markdown/quick-start.md
Date Thu, 23 Jul 2015 05:15:47 GMT
Author: wangwei
Date: Thu Jul 23 05:15:47 2015
New Revision: 1692346

URL: http://svn.apache.org/r1692346
Log:
update some typos of quickstart

Modified:
    incubator/singa/site/trunk/content/markdown/quick-start.md

Modified: incubator/singa/site/trunk/content/markdown/quick-start.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/quick-start.md?rev=1692346&r1=1692345&r2=1692346&view=diff
==============================================================================
--- incubator/singa/site/trunk/content/markdown/quick-start.md (original)
+++ incubator/singa/site/trunk/content/markdown/quick-start.md Thu Jul 23 05:15:47 2015
@@ -2,13 +2,6 @@
 
 ___
 
-### Notice!
-
-The newest code has dependency on zookeeper. Please install the zookeeper by
-
-    cd thirdparty
-    ./install.sh zookeeper
-
 ### Installation
 
 Clone the SINGA code from [Github](https://github.com/apache/incubator-singa)
@@ -40,8 +33,8 @@ CPU and memory resources as it needs.
 
 ### Training on a single node
 
-For single node training, one process will be launched to run the SINGA code on
-the node where SINGA is started. We train the [CNN model](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)
over the
+For single node training, one process will be launched to run the SINGA.
+We train the [CNN model](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)
over the
 [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar.html) dataset as an example.
 The hyper-parameters are set following
 [cuda-convnet](https://code.google.com/p/cuda-convnet/).
@@ -59,15 +52,17 @@ Download the dataset and create the data
 A training dataset and a test dataset are created under *train-shard* and
 *test-shard* folder respectively. A image_mean.bin file is also generated, which
 contains the feature mean of all images.
+
 <!--After creating the data shards, you  to update the paths in the
 model configuration file (*model.conf*) for the
-training data shard, test data shard and the mean file.-->
+training data shard, test data shard and the mean file.
+-->
 
 Since all modules used for training this CNN model are provided by SINGA as
 built-in modules, there is no need to write any code. You just execute the
 script (*../../bin/singa-run.sh*) by providing the workspace which includes the
 job configuration file (*job.conf*).  If you want to implement your own
-modules, e.g., layer, then you have to register your modules in the [driver
+modules, then you have to register your modules in the [driver
 program](user-guide.html).
 
 Start the training by running:
@@ -130,9 +125,9 @@ described in the [System Architecture](d
 
 #### Distributed Training
 
-To train the model in distributed environment, we first change the job
-configuration to use 2 worker groups (one worker per group) and 2 servers (from
-the same server group).
+To train the model in a distributed environment, we first change the job
+configuration, e.g., using 2 worker groups (one worker per group) and 2 servers
+(from the same server group).
 
     // job.conf
     cluster {
@@ -140,8 +135,10 @@ the same server group).
       nserver_groups: 1
       nservers_per_group: 2
     }
+    ...
 
-This configuration would run SINGA using Downpour training framework.
+This configuration would run SINGA using [Downpour training
+framework](architecture.html).
 In specific, the 2 worker groups run asynchronously to compute the parameter
 gradients. Each server maintains a subset of parameters, i.e., updating the
 parameters based on gradients passed by workers.
@@ -168,9 +165,9 @@ To start the training, run
     ./bin/singa-run.sh -workspace=examples/cifar10
 
 The `singa-run.sh` will calculate the number of nodes (i.e., processes) to
-launch and will generate a job.hosts file under workspace by looping all nodes
-in conf/hostfile. Hence if there are few nodes in the hostfile, then multiple
-processes would be launched in one node.
+launch and will generate a job.hosts file under workspace by looping through
+all nodes in conf/hostfile. Hence if there are few nodes in the hostfile, then
+multiple processes would be launched in one node.
 
 You can get some job information like job ID and running processes using the
 singa-console.sh script:
@@ -208,8 +205,8 @@ Sample training output is
     Train step-300, loss : 2.129271, accuracy : 0.208056
 
 
-We can see that the accuracy (resp. loss) distributed training increases (resp.
-decreases) faster than single node training.
+We can see that the accuracy (resp. loss) from distributed training increases (resp.
+decreases) faster than that for the single node training.
 
 You can stop the training by singa-stop.sh
 



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