Installation of PredictionIO on MacOS

I'm kicking the tires of PredictionIO, an open-source machine learning server. I'm hoping that this might be a great platform for my BigData/Scala class. It looks really promising.

However, kicking the tires is a lot easier at the car dealership. In order to get something like this running, there are many hurdles to jump. It would no doubt be easier if I was running on Windows, or if I wanted to install to virtual box or via Docker or something like that. The manual installation process for Mac took me several hours to crack as the error messages do not make it clear what the problem is. It's not unusual for an early-stage (incubating) project to have documentation/installation issues. On the whole, they've done an outstanding job. But, in case anyone else out there is having similar problems, here are my fixes to the installation docs. For the Mac (or linux), the installation procedure I followed is at Installing Apache PredictionIO (incubating) on Linux / Mac OS X.

PredictionIO is based on Spark, as well as some combination of storage solutions. You can use Postgres, MySQL or a combination of HBase and ElasticSearch. I chose the latter as it appears to be the recommended solution.

After following the instructions, including editing the conf/pio-env.sh
file to specify my own existing Spark installation, by editing SPARK_HOME as follows:

  SPARK_HOME=Applications/spark-1.6.1

I started the PIO suite using $PIO_HOME/bin/pio-start-all, which apparently went OK.

However, the first problem I ran into resulted in the following cryptic error message (when checking the status via pio status):

/Applications/spark-1.6.1 is probably an Apache Spark development tree. Please make sure you are using at least 1.3.0.

It turns out that you need a version of Spark pre-built with Hadoop (I had built this one from source). Before realizing that was the problem, I simply downloaded their recommended version into the vendors directory (rather than trying to put it in my own area) and edited the pio-env.sh file:

  SPARK_HOME=$PIO_HOME/vendors/spark-1.5.1-bin-hadoop2.6

Now, I tried it again. Again, getting the status showed a problem:

[ERROR] [Console$] Unable to connect to all storage backends successfully. The following shows the error message from the storage backend.

[ERROR] [Console$] Connection to localhost:5432 refused. Check that the hostname and port are correct and that the postmaster is accepting TCP/IP connections. (org.postgresql.util.PSQLException)

It isn't enough to set up the HBase and ElasticSearch configuration as instructed by the installation guide. You also have comment out the Postgres and MySQL variables and set up the PIO_STORAGE_REPOSITORIES_xxx variables as follows:

PIO_STORAGE_REPOSITORIES_METADATA_NAME=predictionio_metadata

PIO_STORAGE_REPOSITORIES_METADATA_SOURCE=ELASTICSEARCH

PIO_STORAGE_REPOSITORIES_EVENTDATA_NAME=predictionio_eventdata

PIO_STORAGE_REPOSITORIES_EVENTDATA_SOURCE=HBASE

PIO_STORAGE_REPOSITORIES_MODELDATA_NAME=pio_

PIO_STORAGE_REPOSITORIES_MODELDATA_SOURCE=LOCALFS

Then don't forget to configure local storage...

PIO_STORAGE_SOURCES_LOCALFS_TYPE=localfs

PIO_STORAGE_SOURCES_LOCALFS_PATH=$PIO_FS_BASEDIR/models

You also need to configure the PIO_STORAGE_SOURCES_xxx variables but the installation instructions already tell you to do that (I think).

Restarting and checking the status generated this error:

[ERROR] [RecoverableZooKeeper] ZooKeeper exists failed after 1 attempts

This took me a while to figure out. I actually installed ZooKeeper, although it turns out that I didn't need to -- and in fact it's incorrect to have ZooKeeper running if you're simply running HBase locally. The problem turned out to be that I hadn't properly configured HBase in the vendors/hbase-1.0.0/conf/hbase-site.xml configuration file. Look carefully at the values that you will find here. They do not match your setup!

Once I had fixed that problem, and rechecked the status, I saw the following wonderful message:

[INFO] [Console$] Your system is all ready to go.

You can also check that all is well by running jps. You should see something like the following:

41109 Console

41014 Elasticsearch

41079 HMaster

41127 Jps

29769 

If you don't see HMaster included, then HBase is not running correctly.

Now, it's time to clone a template. I'm interested in recommender systems so I navigated to the following set of instructions: which state in section 2. Create a new Engine from an Engine Template that you should run the following command:

pio template get PredictionIO/template-scala-parallel-recommendation MyRecommendation

However, that is (currently at least) incorrect. Instead, use the following:

pio template get PredictionIO/template-scala-parallel-universal-recommendation MyUniversalRecommendation

Once I figured out the correct mumbo-jumbo to clone the template, I was able to create my own recommendation engine. Now, I shall (hopefully) enjoy actually doing some implementation!

Stay tuned in case I have more tips.

Update 9/30/2016: there's one more thing you have to do, at least for the universal recommendation engine, and which is not spelled out in the installation instructions. Before you can import the sample data, you must install the Python predictionio package. Do this by entering the following:

pip install prediction

If that doesn't work for you, then consult this github project: https://github.com/apache/incubator-predictionio-sdk-python

One more problem which arose during the training phase:

[INFO] [Engine$] Data sanity check is on.

Exception in thread "main" java.lang.NumberFormatException: For input string: "558:feed::1"

at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)

The solution this time is something that I never would have imagined in a million years, let alone solved! Here's the link to the issue. But the bottom line is, if you're running on a Mac, change the TCP/IP wifi network settings so that Configure IPv6 is manual, not automatic.

And another problem after deploying and trying to make a query:

[ERROR] [OneForOneStrategy] Cannot support TLS_RSA_WITH_AES_256_CBC_SHA with currently installed providers

Here's a discussion of the issue.The solution is to upgrade your Java encryption policy to unlimited. Follow the instructions in the discussion. Ensure that you put the jars into the directory lib/security which lives under the JRE home, typically that would be at $JAVA_HOME/jre.

It turned out, however, that this particular solution was necessary but not sufficient. In the same discussion there's a mention of the SSL problem that I ran into next:

[ERROR] [HttpServerConnection] Aborting encrypted connection to /0:0:0:0:0:0:0:1:52194 due to [SSLException:Unrecognized SSL message, plaintext connection?]

You need to edit the shell script  examples/single-query-handmade.sh which sends queries to the PredictionIO server as follows (add "k" to the curl options and "s" to the http protocol value):

curl -kH "Content-Type: application/json" -d '

{

    "user": "u1"

}' https://localhost:8000/queries.json

Finally, after all this work, I was able to see the blessedly wonderful response from my recommendation engine:

Recommendations for user: u1

{"itemScores":[{"item":"Galaxy","score":0.8880454897880554},{"item":"Nexus","score":0.24007925391197205},{"item":"Surface","score":0.043848853558301926}]}