Productionizing H2O¶
(Note: This section is a work in progress.)
About POJOs and MOJOs¶
H2O allows you to convert the models you have built to either a Plain Old Java Object (POJO) or a Model ObJect, Optimized (MOJO).
H2O-generated MOJO and POJO models are intended to be easily embeddable in any Java environment. The only compilation and runtime dependency for a generated model is the h2o-genmodel.jar
file produced as the build output of these packages.
Users can refer to the following Quick Start files for more information about generating POJOs and MOJOs:
Note: MOJOs are supported for GBM, DRF, GLM, K-Means, and XGBoost models only.
Developers can refer to the the POJO and MOJO Model Javadoc.
Example Design Patterns¶
Here is a collection of example design patterns for how to productionize H2O.
Consumer loan application¶
Characteristic | Value |
---|---|
Pattern name | Jetty servlet |
Example training language | R |
Example training data source | CSV file |
Example scoring data source | User input to Javascript application running in browser |
Scoring environment | REST API service provided by Jetty servlet |
Scoring engine | H2O POJO |
Scoring latency SLA | Real-time |
Craigslist application¶
Characteristic | Value |
---|---|
Pattern name | Sparkling water streaming |
Example training language | Scala |
Example training data source | CSV file |
Example scoring data source | User input to Javascript application running in browser |
Scoring engine | H2O cluster |
Scoring latency SLA | Real-time |
Resource | Location |
---|---|
Git repos | https://github.com/h2oai/app-ask-craig |
Blogs | |
Slides | http://www.slideshare.net/0xdata/sparkling-water-ask-craig http://www.slideshare.net/0xdata/sparkling-water-applications-meetup-072115 |
Malicious domain application¶
Characteristic | Value |
---|---|
Pattern name | AWS Lambda |
Example training language | Python |
Example training data source | CSV file |
Example scoring data source | User input to Javascript application running in browser |
Scoring environment | AWS Lambda REST API endpoint |
Scoring engine | H2O POJO |
Scoring latency SLA | Real-time |
Storm bolt¶
Characteristic | Value |
---|---|
Pattern name | Storm bolt |
Example training language | R |
Example training data source | CSV file |
Example scoring data source | Storm spout |
Scoring environment | POJO embedded in a Storm bolt |
Scoring engine | H2O POJO |
Scoring latency SLA | Real-time |
Resource | Location |
---|---|
Git repos | https://github.com/h2oai/h2o-tutorials/tree/master/tutorials/streaming/storm |
Tutorials | http://docs.h2o.ai/h2o-tutorials/latest-stable/tutorials/streaming/storm/index.html |
Invoking POJO directly in R¶
Characteristic | Value |
---|---|
Pattern name | POJO in R |
Example training language | R |
Example training data source | (Need example) |
Example scoring data source | (Need example) |
Scoring environment | R |
Scoring engine | H2O POJO |
Scoring latency SLA | Batch |
Hive UDF¶
Characteristic | Value |
---|---|
Pattern name | Hive UDF |
Example training language | R |
Example training data source | HDFS directory with hive part files output by a SELECT |
Example scoring data source | Hive |
Scoring environment | Hive SELECT query (parallel MapReduce) running UDF |
Scoring engine | H2O POJO |
Scoring latency SLA | Batch |
MOJO as a JAR Resource¶
Characteristic | Value |
---|---|
Pattern name | MOJO JAR |
Example training language | R |
Example training data source | Iris |
Example scoring data source | Single Row |
Scoring environment | Portable |
Scoring engine | H2O MOJO |
Scoring latency SLA | Real-time example, but can be adapted (use in Hive UDF etc.) |
Resource | Location |
---|---|
Git repos | https://github.com/h2oai/h2o-tutorials/tree/master/tutorials/mojo-resource |
Steam Scoring Server from H2O.ai¶
Characteristic | Value |
---|---|
Pattern name | Steam |
Scoring data source | REST API client |
Scoring environment | Steam scoring server |
Scoring engine | H2O POJO |
Scoring latency SLA | Real-time |
Resource | Location |
---|---|
Web sites | http://www.h2o.ai/steam/ |