Taking an H2O Model to Production

H2O
MLOps
Published

August 22, 2016

Introduction

One of the best feelings as a Data Scientist is when the model you have poured your heart and soul into, moves into production. Your model is now grown-up and you get to watch it mature.

This post shows how to take a H2O model and move it into a production environment. In this post, we will develop a simple H2O based predictive model, convert it into a Plain Old Java Object (POJO), compile it along with other Java packages, and package the compiled class files into a deployable JAR file so that it can readily be deployed onto any Java based application servers. This model will accept the input data set in the form of CSV file and return the predicted output in CSV format.

H2O is one of my favorite tools for building models because it is well designed from an algorithm perspective, easy to use, and can scale to larger datasets. However, H2O’s documentation, though voluminous, doesn’t have clear instructions for moving a POJO model into production. This post will discuss this approach in greater detail besides providing code for how to do this. (H2O does have a post on doing real time predictions with storm). Special thanks to Socrates Krishnamurthy who co-wrote this post with me.

Building H2O Model

As a starting point, lets use our favorite ice cream dataset to create a toy model in H2O:

  library(h2o)  
  library(Ecdat)  
  data(Icecream)  
  h2o.init()  
  train.h2o <- as.h2o(Icecream)  
  rf <- h2o.randomForest(x=2:4, y=1, ntrees=2, training_frame=train.h2o)

Once you have developed your model in H2O, then the next step is downloading the POJO:

h2o.download_pojo(rf, getjar=TRUE, path="~/Code/h2o-3.9.1.3459/test/")
# you must give a path to download a file

This will save two files, a H2O jar file about the model and an actual model file (that begins with DRF and ends with .java). Go ahead and open the model file in a text editor if you want to have a look at it.

Compiling the H2O Model

The next step is to compile and run the model (say, the downloaded model name is DRF_model_R_1470416938086_15.java), then type:

> javac -cp h2o-genmodel.jar -J-Xmx2g DRF_model_R_1470416938086_15.java

This creates a bunch of java class files.

Scoring the Input Data

The final step is scoring some input data. Prior to running the model, it is necessary to have files created for the input and output. For the input, the default setting is to read the first row as a header. The assumption is that the csv is well formed (this approach is not using the H2O parser). Once that is done, run:

> java -cp .:h2o-genmodel.jar hex.genmodel.tools.PredictCsv --header --model DRF_model_R_1470416938086_15 --input input.csv --output output.csv

If you open the output.csv file, it can be noticed that the predicted values are in Hexadecimal and not in Numeric format. For example, the output will be something like this:

0x1.a24dd2p-2

Fixing the Hexadecimal Issue

The model is now predicting, but the predictions are in the wrong format. Yikes! To fix this issue requires some hacking of the java code. The rest of this post will show you how to hack the java code in PredictCsv, which can fix this issue and other unexpected issues with PredictCsv (for example, if your input comes tab separated).

If we take a deeper look at the PredictCsv java file located in the h2o github, the myDoubleToString method returns Hexadecimal string. But the challenge is this method being static in nature, cannot be overridden in a subclass or cannot be updated directly since it was provided by H2O jar file, to return regular numeric value in String format.

This can be fixed by creating a new java file (say, NewPredictCsv.java) by copying the entire content of PredictCsv.java from the above location and saving it locally. You then need to:

  • comment out the first line, so it should be //package hex.genmodel.tools;
  • change the name of the class name (~line 20) to read: public class NewPredictCsv {
  • correct the hexadecimal issue by changing the return statement of myDoubleToString method to .toString() in lieu of .toHexString() (~line 131).

After creating NewPredictCsv.java, compile it using the following command:

> javac -cp h2o-genmodel.jar -J-Xmx2g NewPredictCsv.java DRF_model_R_1470416938086_15.java

Run the compiled file by providing input and output CSV files using the following command (Ensure that the input.csv file is in the current folder where you will run this):

> java -cp .:h2o-genmodel.jar NewPredictCsv --header --model DRF_model_R_1470416938086_15 --input input.csv --output output.csv

If you open the output.csv file now, it will be in the proper numeric format as follows:

0.40849998593330383

Deploying the Solution into Production:

At this point, we have a workable flow for using our model to score new data. But we can clean up the code to make it a little friendlier for our data engineers. First, create a jar file out of the class files created in previous steps. To do that, issue the following command:

> jar cf my-RF-model.jar *.class

This will place all the class files and our NewPredictCsv inside the jar. This is helpful when we have a model with say 500 trees. Now all we need is three files to run our scorer. So copy the above two jar files along with input.csv file in any folder/directory from where the program has to be executed. After copying, the folder should contain following files:

> my-RF-model.jar  
> h2o-genmodel.jar  
> input.csv

The above input.csv file contains the dataset for which the dependent variable has to be predicted. To compute/ predict the values, run the java command as below:

> java -cp .:my-RF-model.jar:h2o-genmodel.jar NewPredictCsv --header --model DRF_model_R_1470416938086_15 --input input.csv --output output.csv

Note:

Replace : with ; in above commands if you are working in Windows (yuck).