Train XGBoost Model in Sparkling Water¶
Sparkling Water provides API for H2O XGBoost in Scala and Python. The following sections describe how to train the XGBoost model in Sparkling Water in both languages. See also Parameters of H2OXGBoost.
- Scala
- Python
First, let’s start Sparkling Shell as
./bin/sparkling-shell
Start H2O cluster inside the Spark environment
import ai.h2o.sparkling._
import java.net.URI
val hc = H2OContext.getOrCreate()
Parse the data using H2O and convert them to Spark Frame
import org.apache.spark.SparkFiles
spark.sparkContext.addFile("https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate/prostate.csv")
val rawSparkDF = spark.read.option("header", "true").option("inferSchema", "true").csv(SparkFiles.get("prostate.csv"))
val sparkDF = rawSparkDF.withColumn("CAPSULE", $"CAPSULE" cast "string")
val Array(trainingDF, testingDF) = sparkDF.randomSplit(Array(0.8, 0.2))
Train the model. You can configure all the available XGBoost arguments using provided setters, such as the label column.
import ai.h2o.sparkling.ml.algos.H2OXGBoost
val estimator = new H2OXGBoost().setLabelCol("CAPSULE")
val model = estimator.fit(trainingDF)
By default, the H2OXGBoost
algorithm distinguishes between a classification and regression problem based on the type of
the label column of the training dataset. If the label column is a string column, a classification model will be trained.
If the label column is a numeric column, a regression model will be trained. If you don’t want be worried about
column data types, you can explicitly identify the problem by using ai.h2o.sparkling.ml.algos.classification.H2OXGBoostClassifier
or ai.h2o.sparkling.ml.algos.regression.H2OXGBoostRegressor
instead.
You can also get raw model details by calling the getModelDetails() method available on the model as:
model.getModelDetails()
Run Predictions
model.transform(testingDF).show(false)
XGBoost Memory Configuration¶
H2O XGBoost uses additionally to Java memory, off-heap memory. This means that it requires some additional memory available on the system.
When running on YARN or Kubernetes, please make sure to set the spark.yarn.executor.memoryOverhead
so XGBoost has enough
native memory on executors. It’s recommended to set the property to 12O% of the value set in spark.executor.memory
.
Note: spark.yarn.executor.memoryOverhead
must be set in MiB.
Example¶
If you set spark.executor.memory
to 10g
, spark.yarn.executor.memoryOverhead
should be set to 12288
.
The size of the corresponding YARN or Kubernetes container will be at least 22 GiB.
Note: In case of Pysparkling, the YARN container will be bigger about the memory required by the Python process.
Memory Overhead on Spark driver¶
If you enabled a H2O client (a special H2O node representing an entry point for the communication with the H2O cluster)
to run on the Spark driver, you should also set the following properties in the same way as spark.yarn.executor.memoryOverhead
.
spark.yarn.am.memoryOverhead
- in case of deploying to YARN in the client modespark.yarn.driver.memoryOverhead
- in case of deploying to YARN in the cluster mode and other deployments (Kubernetes, Mesos)
Note: A H2O client can run on the Spark driver only with Sparkling Water in Scala/Java API and the property
spark.ext.h2o.rest.api.based.client
set to false
. The default value of the property is true
.