Are you leaking h2o? Call plumber!

February 7, 2019

(This article was first published on R – Longhow Lam's Blog, and kindly contributed to R-bloggers)

Create a predictive model with the h2o package.

H2o is a fantastic open source machine learning platform with many different algorithms. There is Graphical user interface, a Python interface and an R interface. Suppose you want to create a predictive model, and you are lazy then just run automl.

Lets say, we have both train and test data sets, and the first column is the target and the columns 2 until 50 are input features. Then we can use the following code in R

out = h2o.automl(
   x = 2:50, 
   y = 1,
   training_frame = TrainData, 
   validation_frame = TestData, 
   max_runtime_secs = 1800

According the help documentation: The current version of automl trains and cross-validates a Random Forest, an Extremely-Randomized Forest, a random grid of Gradient Boosting Machines (GBMs), a random grid of Deep Neural Nets, and then trains a Stacked Ensemble using all of the models.

After a time period that you can set, automl will terminate and has literally tried hundreds of models. You can get the top models, ranked by a certain performance metric. If you go for the champion just use:

championModel = [email protected]

Now the championModel object can be used to score / predict new data with the simple call:

predict(championModel, newdata)

The champion model now lives on my laptop, it’s hard if not impossible to use this model in production. Someone or some process that wants a model score should not depend on you and your laptop!

Instead, the model should be available on a server where model prediction requests can be handled 24/7. This is where plumber comes in handy. First save the champion model to disk so that we can use it later.

h2o.saveModel(championModel, path = "myChampionmodel")

The plumber package, bring your model in production.

In a very simple and concise way, the plumber package allows users to expose existing R code as an API service available to others on the web or intranet. Now suppose you have a saved h2o model with three input features, how can we create an API from it? You decorate your R scoring code with special comments, as shown in the example code below.

# This is a Plumber API. In RStudio 1.2 or newer you can run the API by
# clicking the 'Run API' button above.

mymodel = h2o.loadModel("mySavedModel")

#* @apiTitle My model API engine

#### expose my model #################################
#* Return the prediction given three input features
#* @param input1 description for inp1
#* @param input2 description for inp2
#* @param input3 description for inp3
#* @post /mypredictivemodel
function( input1, input2, input3){
   scoredata = as.h2o(
      data.frame(input1, input2, input3 )
   ), scoredata))

To create and start the API service, you have to put the above code in a file and call the following code in R.

rapi <- plumber::plumb("plumber.R")  # Where 'plumber.R' is the location of the code file shown above 

The output you see looks like:

Starting server to listen on port 8000 
Running the swagger UI at

And if you go to the swagger UI you can test the API in a web interface where you can enter values for the three input parameters.

What about Performance?

At first glance I thought there might be quit some overhead, calling the h2o library, loading the h2o predictive model and then using the h2o predict function to calculate a prediction given the input features.

But I think it is workable. I installed R, h2o and plumber on a small n1-standard-2 Linux server on the Google Cloud Platform. One API call via plumber to calculate a prediction with a h2o random forest model with 100 trees took around 0.3 seconds to finish.

There is much more that plumber has to offer, see the full documentation.

Cheers, Longhow.

To leave a comment for the author, please follow the link and comment on their blog: R – Longhow Lam's Blog. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)