---

layout: docs title: Engines

Engines

An engine represents a type of prediction. Some examples of engines are Item Recommendation, Item Ranking, Churn Analysis.

Getting Started with Engine Instance

Let say you want to deploy an instance of Item Recommendation Engine for product recommendation. First of all, you will need to create an engine instance project based on the default Item Recommendation Engine. The new project will contain configuration for your engine instance.

$ $PIO_HOME/bin/pio instance io.prediction.engines.itemrec
$ cd io.prediction.engines.itemrec
$ $PIO_HOME/bin/pio register

where $PIO_HOME is your installation path of PredictionIO.

Specific the Target App

Inside the engine instance project, edit params/datasource.json and change the value of appId to fit your app.

{
  "appId": 1,
  "actions": [
    "view",
    "like",
    "conversion",
    ...

Deploying an Engine Instance

$ $PIO_HOME/bin/pio train
...
2014-09-11 16:25:44,591 INFO  spark.SparkContext - Job finished: collect at Workflow.scala:674, took 0.078664 s
2014-09-11 16:25:44,737 INFO  workflow.CoreWorkflow$ - Saved engine instance with ID: KxOsC2FRSdGGe1lv0oaHiw

where $PIO_HOME is your installation path of PredictionIO.

If your training was successful, you should see the lines shown above. Now you are ready to deploy the instance:

$ $PIO_HOME/bin/pio deploy
...
[INFO] [09/11/2014 16:26:16.525] [pio-server-akka.actor.default-dispatcher-2] [akka://pio-server/user/IO-HTTP/listener-0] Bound to localhost/127.0.0.1:8000
[INFO] [09/11/2014 16:26:16.526] [pio-server-akka.actor.default-dispatcher-5] [akka://pio-server/user/master] Bind successful. Ready to serve.

The deploy command runs the engine instance in the foreground. To run more than one engine instance, either launch a new console, or put the process into the background, then repeat the same command on a different port (by adding a --port argument).

If it is your first time using PredictionIO, these tutorials and samples should be helpful.

Schedule Model Re-training

You may set up a crontab in Linux to update the predictive model with new data regularly. For example, to run the re-training every 6 hours:

$ crontab -e

0 */6 * * *     cd <engine instance project directory>; $PIO_HOME/bin/pio train; $PIO_HOME/bin/pio deploy

where $PIO_HOME is your installation path of PredictionIO.

The deploy command will automatically undeploy any running engine instance on the same IP address and port. It is not necessary to use the undeploy command.

Built-in Engines

PredictionIO comes with the following engines.

• Item Ranking
• Item Recommendation
• Item Similarity

You may start with these tutorials and samples.

Building your own Engine

Please read the Engine Builders' Guide for details.