3.26.8-2.3
About Sparkling Water
Typical Use Case
Sparkling Water Requirements
Installing and Starting
Design
Configuration
Deployment
How to…
Extending H2O Jar Manually
Calling H2O Algorithms
Running Sparkling Water
Changing the Default Port
Hive Support in Sparkling Water
Enabling SSL
Enabling LDAP
Enabling Kerberos Authentication
Running Sparkling Water on Kerberized Hadoop Cluster
Using SSL to secure H2O Flow
Spark Frame <–> H2O Frame Conversions
H2O Frame as Spark’s Data Source
Creating H2OFrame from an Existing Key
Import & Export H2O Frames from/to S3
Train XGBoost Model in Sparkling Water
Train AutoML Model in Sparkling Water
Train KMeans Model in Sparkling Water
Target Encoding in Sparkling Water
Obtain SHAP values from MOJO model
Using Grid Search GBM in Spark Pipelines
Change Sparkling Shell Logging Level
Change Sparkling Shell Logs Location
Obtain Sparkling Water Logs
Use Sparkling Water via Spark Packages
Development
PySparkling
RSparkling
Frequently Asked Questions
Change Log
H2O Sparkling Water
Docs
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General
Extending H2O Jar Manually
Calling H2O Algorithms
Running Sparkling Water
Changing the Default Port
Hive Support in Sparkling Water
Security
Enabling SSL
Enabling LDAP
Configuring LDAP in Scala
Configuring LDAP in Python (PySparkling)
Enabling Kerberos Authentication
Configuring Kerberos Auth in Scala
Configuring Kerberos Auth in Python (PySparkling)
Running Sparkling Water on Kerberized Hadoop Cluster
Internal Backend
External Backend
Using SSL to secure H2O Flow
Using existing Java keystore
Generate the files automatically
Frames Conversions and Creation
Spark Frame <–> H2O Frame Conversions
Converting an H2OFrame into an RDD[T]
Converting an H2OFrame into a DataFrame
Converting an RDD[T] into an H2OFrame
Converting a DataFrame into an H2OFrame
H2O Frame as Spark’s Data Source
Usage in Python - PySparkling
Usage in Scala
Specifying Saving Mode
Creating H2OFrame from an Existing Key
Import & Export H2O Frames from/to S3
Specify the AWS Dependencies
Configuring S3A
Configuring S3N
Sparkling Water Example Code
PySparkling Example Code
Modelling
Train XGBoost Model in Sparkling Water
XGBoost Memory Configuration
Train AutoML Model in Sparkling Water
Enabling XGBoost Models when Running Sparkling Water in a Distributed Environment (YARN)
Train KMeans Model in Sparkling Water
H2O KMeans Parameters
Target Encoding in Sparkling Water
Parameters
Using Target Encoder
Obtain SHAP values from MOJO model
Train model pipeline & get contributions
Get Contributions from Raw MOJO
Spark Pipelines
Using Grid Search GBM in Spark Pipelines
Prepare the environment
Define the Pipeline Stages
Create and Train the Pipeline
Run Predictions
Logging
Change Sparkling Shell Logging Level
Change Sparkling Shell Logs Location
Client
Worker Nodes
Obtain Sparkling Water Logs
Logs for Sparkling Water on YARN
Logs for Standalone Sparkling Water
As Spark Package
Use Sparkling Water via Spark Packages