CTR prediction using Apache Beam and Deep Java Library(DJL)¶

In this demo, we will show you how to run click-through rate (CTR) prediction on Ads Data at scale using Apache Beam and Deep Java Library

Utilizing Apache Beam, you can train you model once and deploy using different big data solutions(Flink, Spark, and more) that suits your needs.

Train your model¶

To Learn more background about Deep Learning and training, we highly recommend you read the Dive into Deep Learning book.

The model we used is based on DeepFM, you can read more about it in chapter: Feature-Rich Recommender Systems and Deep Factorization Machines

We have prepared a trained model here

Deploy using Apache Beam and DJL¶

1. Prepare model¶

Download the above trained model and unzip it, or train your own model. The model folder will contain the following files:

deepfm
├── deepfm-0001.params
├── deepfm-symbol.json
└── feature_map.json

2. Download test data¶

You can download the sample dataset provided by the book from here. Unzip it and you will have the following files:

ctr
├── test.csv
└── train.csv

3. Run inference using Apache Beam¶

You can run inference using different runners supported by Apache Beam. Refer to Apache Beam Documentation for more runners.

Here we will show you local runner, Flink runner, and Spark runner:

Remember to specify: * the main class CtrPrediction * model location using -Dai.djl.repository.zoo.location=./deepfm * input file location --inputFile=./ctr/test.csv

Using local runner¶

mvn compile exec:java -Dexec.mainClass=org.example.ctrprediction.CtrPrediction -Dai.djl.repository.zoo.location=./deepfm -Dexec.args="--inputFile=./ctr/test.csv" -Pdirect-runner

Using Flink runner¶

mvn compile exec:java -Dexec.mainClass=org.example.ctrprediction.CtrPrediction -Dai.djl.repository.zoo.location=./deepfm -Dexec.args="--runner=FlinkRunner--inputFile=./ctr/test.csv" -Pflink-runner

Using Spark runner¶

mvn compile  exec:java -Dexec.mainClass=org.example.ctrprediction.CtrPrediction -Dai.djl.repository.zoo.location=./deepfm -Dexec.args="--runner=SparkRunner --inputFile=./ctr/test.csv" -Pspark-runner

You can find the results in "ctr-*" files. First column is record ids, and second column is predicted ctr.

d3bb711c-fe45-4cf9-a73e-70ff0bb2bee3    1.0
bc48f39c-e779-47dc-9924-5100ebca8b5e    0.99999917
87b5fb12-c990-499b-b67a-986b46122d1e    1.0