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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="page-title"><h1>Modeling Workflow and 
DASE</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#training-the-model">Training The Model</a> </
 li> </ul> </aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/machinelearning/modelingworkflow.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>Modeling 
Workflow and DASE</h1></div></div><div class="content"> <p>In addition to the 
DASE components, we also introduce the Data Model and Training Model 
abstractions. The Data Model abstraction refers to the set of Scala classes 
dealing with the implementation of modeling choices relating to feature 
<strong>extraction</strong>, <strong>preparation</strong>, and/or 
<strong>selection</strong>. For this illustration, this only includes the 
vectorization of text and t.f.-i.d.f. processing which is entirely implemented 
in the PreparedData class. The Training Model abstraction refers to any set of 
classes that individually take in a set of feature observations and outp
 ut a predictive model. This predictive model is leveraged by the Algorithm 
component to produce prediction results to queries in real-time. In the engine 
template, this abstraction is implemented in the NBModel class. <strong>Please 
note that these are conceptual abstractions that are designed to make engine 
development easier by decoupling class functionality.</strong> Keeping these 
abstractions in mind will help you in the future with debugging your code, and 
also make it easier to incorporate different modeling ideas into your 
engine.</p><p>The figure below shows a graphical representation of the engine 
architecture just described, as well as its interactions with your web/app and 
a provided Event Server:</p><p><img alt="Engine Overview" 
src="/images/demo/text_classification_template/engine_overview-27e09a89.png"/></p><h2
 id='training-the-model' class='header-anchors'>Training The Model</h2><p>This 
section will guide you through the two Training Model implementations that come 
wi
 th this engine template. Recall that the Training Model abstraction refers to 
an arbitrary set Scala Class that outputs a predictive model (i.e. implements 
some method that can be used for prediction). The general problem this engine 
template is tackling is text classification, so that our Training Model 
abstraction domain is restricted to implementations producing classifiers. In 
particular, the classification model that is implemented in this engine 
template is based on Multinomial Naive Bayes using t.f.-i.d.f. vectorized 
text.</p></div></div></div></div><footer><div class="container"><div 
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class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>Deploying 
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with Rate Event</span></a></li><li class="level-3"><a class="final" 
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 sification</span></a><ul><li class="level-3"><a class="final" 
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href="#">Getting Help</a><span class="spacer">&gt;</span></li><li><span 
class="last">FAQs</span></li></ul></div><div id="page-title"><h1>Frequently 
Asked Questions</h1></div></div><div id="tab
 le-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> 
<li> <a href="#using-predictionio">Using PredictionIO</a> </li> <li> <a 
href="#problem-with-event-server">Problem with Event Server</a> </li> <li> <a 
href="#engine-training">Engine Training</a> </li> <li> <a 
href="#deploy-engine">Deploy Engine</a> </li> <li> <a 
href="#building-predictionio">Building PredictionIO</a> </li> <li> <a 
href="#engine-development">Engine Development</a> </li> <li> <a 
href="#running-hbase">Running HBase</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/resources/faq.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">Getting Help</a><span 
class="spacer">&gt;</span></li><li><span 
class="last">FAQs</span></li></ul></div><div id="page-title"><h1>Fre
 quently Asked Questions</h1></div></div><div class="content"> <p>If you have 
questions that are not resolved below, you can subscribe and post to the user 
mailing list. You can follow the instructions <a 
href="/support">here</a>.</p><h2 id='using-predictionio' 
class='header-anchors'>Using PredictionIO</h2><h3 
id='q:-how-do-i-check-to-see-if-various-dependencies,-such-as-elasticsearch-and-hbase,-are-running?'
 class='header-anchors'>Q: How do I check to see if various dependencies, such 
as Elasticsearch and HBase, are running?</h3><p>You can run <code>$ pio 
status</code> from the terminal and it will return the status of various 
components that PredictionIO depends on.</p> <ul> <li> You should see the 
following message if everything is OK:</li> </ul> <div class="highlight 
shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19</pre></td><td class="code"><pre><span class="gp">$ </span>pio status
+PredictionIO
+  Installed at: /home/vagrant/PredictionIO
+  Version: 0.8.6
+
+Apache Spark
+  Installed at: /home/vagrant/PredictionIO/vendors/spark-1.2.0
+  Version: 1.2.0 <span class="o">(</span>meets minimum requirement of 
1.2.0<span class="o">)</span>
+
+Storage Backend Connections
+  Verifying Meta Data Backend
+  Verifying Model Data Backend
+  Verifying Event Data Backend
+  Test write Event Store <span class="o">(</span>App Id 0<span 
class="o">)</span>
+2015-02-03 18:52:38,904 INFO  hbase.HBLEvents - The table 
predictionio_eventdata:events_0 doesn<span class="s1">'t exist yet. Creating 
now...
+2015-02-03 18:52:39,868 INFO  hbase.HBLEvents - Removing table 
predictionio_eventdata:events_0...
+
+(sleeping 5 seconds for all messages to show up...)
+Your system is all ready to go.
+</span></pre></td></tr></tbody></table> </div> <ul> <li>If you see the 
following error message, it usually means Elasticsearch is not running 
properly:</li> </ul> <div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1
+2
+3
+4
+5
+6
+7
+8
+9</pre></td><td class="code"><pre>  ...
+Storage Backend Connections
+  Verifying Meta Data Backend
+  ...
+Caused by: org.elasticsearch.client.transport.NoNodeAvailableException: None 
of the configured nodes are available: <span class="o">[]</span>
+    at 
org.elasticsearch.client.transport.TransportClientNodesService.ensureNodesAreAvailable<span
 class="o">(</span>TransportClientNodesService.java:298<span class="o">)</span>
+  ...
+
+Unable to connect to all storage backend<span class="o">(</span>s<span 
class="o">)</span> successfully. Please refer to error message<span 
class="o">(</span>s<span class="o">)</span> above. Aborting.
+</pre></td></tr></tbody></table> </div> <p>You can check if there is any 
Elasticsearch process by running &#39;jps&#39;.</p><p>Please see <strong>How to 
start Elasticsearch</strong> below.</p> <ul> <li>If you see the following error 
message, it usually means HBase is not running properly:</li> </ul> <div 
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14
+15</pre></td><td class="code"><pre>Storage Backend Connections
+  Verifying Meta Data Backend
+  Verifying Model Data Backend
+  Verifying Event Data Backend
+2015-02-03 18:40:04,810 ERROR zookeeper.RecoverableZooKeeper - ZooKeeper 
exists failed after 1 attempts
+2015-02-03 18:40:04,812 ERROR zookeeper.ZooKeeperWatcher - 
hconnection-0x1e4075ce, <span class="nv">quorum</span><span 
class="o">=</span>localhost:2181, <span class="nv">baseZNode</span><span 
class="o">=</span>/hbase Received unexpected KeeperException, re-throwing 
exception
+org.apache.zookeeper.KeeperException<span 
class="nv">$ConnectionLossException</span>: KeeperErrorCode <span 
class="o">=</span> ConnectionLoss <span class="k">for</span> /hbase/hbaseid
+...
+2015-02-03 18:40:07,021 ERROR hbase.StorageClient - Failed to connect to 
HBase. Plase check <span class="k">if </span>HBase is running properly.
+2015-02-03 18:40:07,026 ERROR storage.Storage<span class="nv">$ </span>- Error 
initializing storage client <span class="k">for </span><span class="nb">source 
</span>HBASE
+2015-02-03 18:40:07,027 ERROR storage.Storage<span class="nv">$ </span>- 
Can<span class="s1">'t connect to ZooKeeper
+java.util.NoSuchElementException: None.get
+...
+
+Unable to connect to all storage backend(s) successfully. Please refer to 
error message(s) above. Aborting.
+</span></pre></td></tr></tbody></table> </div> <p>You can check if there is 
any HBase-related process by running &#39;jps&#39;.</p><p>Please see 
<strong>How to start HBase</strong> below.</p><h3 
id='q:-how-to-start-elasticsearch?' class='header-anchors'>Q: How to start 
Elasticsearch?</h3><p>If you followed the <a 
href="/install/install-sourcecode/">instructions</a> to install PredictionIO, 
Elasticsearch would have been installed at 
<code>PredictionIO/vendors/elasticsearch-x.y.z/</code> where x.y.z is the 
version number. To start it, run:</p><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ 
</span>~/PredictionIO/vendors/elasticsearch-x.y.z/bin/elasticsearch
+</pre></td></tr></tbody></table> </div> <p>If you didn&#39;t use install 
script, please go to where Elasticsearch is installed to start it.</p><div 
class="alert-message info"><p>It may take some time (15 seconds or so) for 
Elasticsearch to become ready after you start it (wait a bit before you run 
<code>pio status</code> again).</p></div><h3 id='q:-how-to-start-hbase-?' 
class='header-anchors'>Q: How to start HBase ?</h3><p>If you followed the <a 
href="/install/install-sourcecode/">instructions</a> to install PredictionIO, 
the HBase is installed at <code>~/PredictionIO/vendors/hbase-x.y.z/</code> 
where x.y.z is the version number. To start it, run:</p><div class="highlight 
shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1</pre></td><td 
class="code"><pre><span class="gp">$ 
</span>~/PredictionIO/vendors/hbase-x.y.z/bin/start-hbase.sh
+</pre></td></tr></tbody></table> </div> <p>If you didn&#39;t use install 
script, please go to where HBase is installed to start it.</p><div 
class="alert-message info"><p>It may take some time (15 seconds or so) for 
HBase to become ready after you start it (wait a bit before you run <code>pio 
status</code> again).</p></div><h2 id='problem-with-event-server' 
class='header-anchors'>Problem with Event Server</h2><h3 
id='q:-how-do-i-increase-the-jvm-heap-size-of-the-event-server?' 
class='header-anchors'>Q: How do I increase the JVM heap size of the Event 
Server?</h3><p>Add the <code>JAVA_OPTS</code> environmental variable to supply 
JVM options, e.g.</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ 
</span><span class="nv">JAVA_OPTS</span><span class="o">=</span>-Xmx16g bin/pio 
eventserver ...
+</pre></td></tr></tbody></table> </div> <h2 id='engine-training' 
class='header-anchors'>Engine Training</h2><h3 
id='q:-how-to-increase-spark-driver-program-and-worker-executor-memory-size?' 
class='header-anchors'>Q: How to increase Spark driver program and worker 
executor memory size?</h3><p>In general, the PredictionIO <code>bin/pio</code> 
scripts wraps around Spark&#39;s <code>spark-submit</code> script. You can 
specify a lot of Spark configurations (i.e. executor memory, cores, master url, 
etc.) with it. You can supply these as pass-through arguments at the end of 
<code>bin/pio</code> command.</p><p>If the engine training seems stuck, 
it&#39;s possible that the the executor doesn&#39;t have enough 
memory.</p><p>First, follow <a 
href="http://spark.apache.org/docs/latest/spark-standalone.html";>instruction 
here</a> to start standalone Spark cluster and get the master URL. If you use 
the provided quick install script to install PredictionIO, the Spark is 
installed at <code>Prediction
 IO/vendors/spark-1.2.0/</code> where you could run the Spark commands in 
<code>sbin/</code> as described in the Spark documentation. Then use following 
train commmand to specify executor memory (default is only 512 MB) and driver 
memory.</p><p>For example, the follow command set the Spark master to 
<code>spark://localhost:7077</code> (the default url of standalone cluster), 
set the driver memory to 16G and set the executor memory to 24G for <code>pio 
train</code>.</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>pio 
train -- --master spark://localhost:7077 --driver-memory 16G --executor-memory 
24G
+</pre></td></tr></tbody></table> </div> <h3 
id='q:-how-to-resolve-&quot;exception-in-thread-&quot;main&quot;-org.apache.spark.sparkexception:-job-aborted-due-to-stage-failure:-serialized-task-165:35-was-110539813-bytes,-which-exceeds-max-allowed:-spark.akka.framesize-(10485760-bytes)---reserved-(204800-bytes).-consider-increasing-spark.akka.framesize-or-using-broadcast-variables-for-large-values.&quot;?'
 class='header-anchors'>Q: How to resolve &quot;Exception in thread 
&quot;main&quot; org.apache.spark.SparkException: Job aborted due to stage 
failure: Serialized task 165:35 was 110539813 bytes, which exceeds max allowed: 
spark.akka.frameSize (10485760 bytes) - reserved (204800 bytes). Consider 
increasing spark.akka.frameSize or using broadcast variables for large 
values.&quot;?</h3><p>A likely reason is the local algorithm model is larger 
than the default frame size. You can specify a larger value as a pass-thru 
argument to spark-submit when you <code>pio train</code>. The followin
 g command increase the frameSize to 1024MB.</p><div class="highlight 
shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1</pre></td><td 
class="code"><pre><span class="gp">$ </span>pio train -- --conf 
spark.akka.frameSize<span class="o">=</span>1024
+</pre></td></tr></tbody></table> </div> <h2 id='deploy-engine' 
class='header-anchors'>Deploy Engine</h2><h3 
id='q:-how-to-increase-heap-space-memory-for-&quot;pio-deploy&quot;?' 
class='header-anchors'>Q: How to increase heap space memory for &quot;pio 
deploy&quot;?</h3><p>If you see the following error during <code>pio 
deploy</code>, it means there is not enough heap space memory.</p><div 
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+2
+3
+4</pre></td><td class="code"><pre>...
+<span class="o">[</span>ERROR] <span class="o">[</span>LocalFSModels] Java 
heap space
+<span class="o">[</span>ERROR] <span class="o">[</span>OneForOneStrategy] 
None.get
+...
+</pre></td></tr></tbody></table> </div> <p>To increase the heap space, specify 
the &quot;-- --driver-memory &quot; parameter in the command. For example, set 
the driver memory to 8G when deploy the engine:</p><div class="highlight 
shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1</pre></td><td 
class="code"><pre><span class="gp">$ </span>pio deploy -- --driver-memory 8G
+</pre></td></tr></tbody></table> </div> <h2 id='building-predictionio' 
class='header-anchors'>Building PredictionIO</h2><h3 
id='q:-how-to-resolve-&quot;error:-could-not-find-or-load-main-class-org.apache.predictionio.tools.console&quot;-after-./make_distribution.sh?'
 class='header-anchors'>Q: How to resolve &quot;Error: Could not find or load 
main class org.apache.predictionio.tools.Console&quot; after 
./make_distribution.sh?</h3><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1
+2</pre></td><td class="code"><pre><span class="gp">$ </span>bin/pio app
+Error: Could not find or load main class org.apache.predictionio.tools.Console
+</pre></td></tr></tbody></table> </div> <p>When PredictionIO bumps a version, 
it creates another JAR file with the new version number.</p><p>Delete 
everything but the latest <code>pio-assembly-&lt;VERSION&gt;.jar</code> in 
<code>$PIO_HOME/assembly</code> directory. For example:</p><div 
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+2
+3
+4
+5
+6
+7
+8
+9</pre></td><td class="code"><pre><span class="gp">PredictionIO$ </span><span 
class="nb">cd </span>assembly/
+<span class="gp">PredictionIO/assembly$ </span>ls -al
+total 197776
+drwxr-xr-x  2 yipjustin yipjustin      4096 Nov 12 00:08 .
+drwxr-xr-x 17 yipjustin yipjustin      4096 Nov 12 00:09 ..
+-rw-r--r--  1 yipjustin yipjustin 101184982 Nov  5 06:05 
pio-assembly-0.8.1-SNAPSHOT.jar
+-rw-r--r--  1 yipjustin yipjustin 101324859 Nov 12 00:09 pio-assembly-0.8.2.jar
+
+<span class="gp">PredictionIO/assembly$ </span>rm 
pio-assembly-0.8.1-SNAPSHOT.jar
+</pre></td></tr></tbody></table> </div> <h3 
id='q:-how-to-resolve-&quot;.......[error]-(data/compile:compile)-java.lang.assertionerror:-assertion-failed:-java.lang.autocloseable&quot;-when-./make_distribution.sh?'
 class='header-anchors'>Q: How to resolve &quot;.......[error] 
(data/compile:compile) java.lang.AssertionError: assertion failed: 
java.lang.AutoCloseable&quot; when ./make_distribution.sh?</h3><p>PredictionIO 
only support Java 8 or later. Please make sure you have the correct Java 
version with the command:</p><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ 
</span>javac -version
+</pre></td></tr></tbody></table> </div> <h2 id='engine-development' 
class='header-anchors'>Engine Development</h2><h3 
id='q:-what&#39;s-the-difference-between-p--and-l--prefixed-classes-and-functions?'
 class='header-anchors'>Q: What&#39;s the difference between P- and L- prefixed 
classes and functions?</h3><p>PredictionIO v0.8 is built on the top of Spark, a 
massively scalable programming framework. A spark algorithm is different from 
conventional single machine algorithm in a way that spark algorithms use the <a 
href="http://spark.apache.org/docs/1.0.1/programming-guide.html#resilient-distributed-datasets-rdds";>RDD</a>
 abstraction as its primary data type.</p><p>PredictionIO framework natively 
support both RDD-based algorithms and traditional single-machine algorithms. 
For controllers prefixed by &quot;P&quot; (i.e. PJavaDataSource, 
PJavaAlgorithm), their data include RDD abstraction; For &quot;L&quot; 
controllers, they are traditional single machine algorithms.</p><h2 id='running-
 hbase' class='header-anchors'>Running HBase</h2><h3 
id='q:-how-to-resolve-&#39;exception-in-thread-&quot;main&quot;-java.lang.nullpointerexception-at-org.apache.hadoop.net.dns.reversedns(dns.java:92)&#39;?'
 class='header-anchors'>Q: How to resolve &#39;Exception in thread 
&quot;main&quot; java.lang.NullPointerException at 
org.apache.hadoop.net.DNS.reverseDns(DNS.java:92)&#39;?</h3><p>HBase relies on 
reverse DNS be set up properly to function. If your network configuration 
changes (such as working on a laptop with public WiFi hotspots), there could be 
a chance that reverse DNS does not function properly. You can install a DNS 
server on your own computer. Some users have reported that using <a 
href="https://developers.google.com/speed/public-dns/";>Google Public DNS</a> 
would also solve the problem.</p><h3 
id='q:-how-to-fix-hbase-issues-after-cleaning-up-a-disk-that-was-full?' 
class='header-anchors'>Q: How to fix HBase issues after cleaning up a disk that 
was full?</h3><p>You may recei
 ve error messages like <code>write error: No space left on device</code> when 
disk is full, and also receive error from <code>pio status</code> even after 
restarting PredictionIO services (due to <a 
href="https://issues.apache.org/jira/browse/ZOOKEEPER-1621";>an issue</a> in 
ZooKeeper).</p><p>One quick fix is to delete the newest 
<code>snapshot.xxxxx</code> and <code>log.xxxoo</code> in the ZooKeeper data 
directory (e.g. <code>$HBASE_HOME/zookeeper/zookeeper_0/version-2</code>). 
Restart all services with <code>pio-start-all</code>, and use <code>pio 
status</code> to check whether your setup is good to go again.</p><p>If you 
still have problems connecting to the event server, take a look at the HBase 
dashboard to see if there are <code>regions under transition</code>. If so, try 
the following:</p> <ol> <li><p> Try <code>hbase hbck -repair</code> and 
<code>hbase hbck -repairHoles</code>. If it solves the problem, you are all 
set. Otherwise, continue on.</p></li> <li><p> Find out failin
 g regions by <code>hbase hbck</code>.</p><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1
+2
+3
+4
+5
+6
+7</pre></td><td class="code"><pre>...
+Summary:
+Table pio_event:events_1 is inconsistent.
+  Number of regions: 2
+  Deployed on:  prediction.io,54829,1489213832255
+...
+2 inconsistencies detected.
+</pre></td></tr></tbody></table> </div></li> <li><p> Shutdown HBase process 
and delete <code>recovered.edits</code> folders in the HBase data directory 
(e.g. <code>$HBASE_HOME/hbase/data/pio_event/events_1</code> in this example) 
for failing regions.</p></li> <li><p> Run <code>hbase hbck -repairHoles</code> 
and restart all PredictionIO services.</p></li> </ol> 
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