Overview¶
Welcome to the H2O Sparkling Water documentation site! This document describes how to install and run Sparkling Water.
Depending on your area of interest, select a learning path from the sidebar, or look at the full content outline below. To view Sparkling Water examples, please visit the Sparkling Water GitHub repository at https://github.com/h2oai/sparkling-water/tree/master/examples.
Have questions about Sparkling Water? Post them on Stack Overflow using the sparkling-water tag, or join the chat on Gitter.
Contents
- About Sparkling Water
- Typical Use Case
- Sparkling Water Requirements
- Installing and Starting
- Design
- Configuration
- Deployment
- Sparkling Water Backends
- Importing H2O MOJOs from H2O-3
- Importing MOJO Pipelines from Driverless AI
- Deploying PySparkling Pipeline Models
- Use Sparkling Water with Amazon EMR
- Use Sparkling Water with Amazon EMR from the Edge Node
- Start Sparkling Water on Amazon EMR using our Terraform Template
- Running Sparkling Water on Databricks Azure Cluster
- Running RSparkling on Databricks Azure Cluster
- Running PySparkling on Databricks Azure Cluster
- Download H2O Logs from Databricks Cloud
- Running Sparkling Water on Google Cloud Dataproc
- Using Sparkling Water with Microsoft Azure HDInsight - Beta
- Use Sparkling Water in Windows Environments
- Use RSparkling in Windows Environments
- Sparkling Water and Zeppelin
- Machine Learning
- Train AutoML Model in Sparkling Water
- Train GAM Model in Sparkling Water
- Train GLM Model in Sparkling Water
- Train DRF Model in Sparkling Water
- Train Sparkling Water Algorithms with Grid Search
- Train KMeans Model in Sparkling Water
- Train XGBoost Model in Sparkling Water
- Train Isolation Forest Model in Sparkling Water
- Train CoxPH Model in Sparkling Water
- Train Deep Learning Model in Sparkling Water
- Train RuleFit Model in Sparkling Water
- Train Stacked Ensemble Model in Sparkling Water
- Autoencoder in Sparkling Water
- Target Encoding in Sparkling Water
- Train Word2Vec Model in Sparkling Water
- Principal Component Analysis (PCA) in Sparkling Water
- Generalized Low Rank Models (GLRM) in Sparkling Water
- Obtain SHAP values from MOJO model
- Using H2O Binary Model in Sparkling Water
- Metric Classes
- H2OAnomalyMetrics Class
- H2OClusteringMetrics Class
- H2OGLRMMetrics Class
- H2OCommonMetrics Class
- H2OOrdinalGLMMetrics Class
- H2ORegressionMetrics Class
- H2OBinomialMetrics Class
- H2OMultinomialGLMMetrics Class
- H2OMultinomialMetrics Class
- H2OBinomialGLMMetrics Class
- H2OAutoEncoderMetrics Class
- H2ORegressionCoxPHMetrics Class
- H2OPCAMetrics Class
- H2OOrdinalMetrics Class
- H2ORegressionGLMMetrics Class
- Model Details
- Details of H2OGAMMOJOModel
- Details of H2ODeepLearningMOJOModel
- Details of H2OGBMMOJOModel
- Details of H2ORuleFitMOJOModel
- Details of H2OKMeansMOJOModel
- Details of H2OCoxPHMOJOModel
- Details of H2OStackedEnsembleMOJOModel
- Details of H2OIsolationForestMOJOModel
- Details of H2OGLMMOJOModel
- Details of H2ODRFMOJOModel
- Details of H2OXGBoostMOJOModel
- Details of H2OTargetEncoderMOJOModel
- Details of H2OWord2VecMOJOModel
- Details of H2OGLRMMOJOModel
- Details of H2OPCAMOJOModel
- Details of H2OAutoEncoderMOJOModel
- Algorithm Parameters
- Parameters of H2OGridSearch
- Parameters of H2OGAM
- Parameters of H2ODeepLearning
- Parameters of H2OGBM
- Parameters of H2ORuleFit
- Parameters of H2OKMeans
- Parameters of H2OCoxPH
- Parameters of H2OStackedEnsemble
- Parameters of H2OIsolationForest
- Parameters of H2OAutoML
- Parameters of H2OGLM
- Parameters of H2ODRF
- Parameters of H2OXGBoost
- Parameters of H2OTargetEncoder
- Parameters of H2OWord2Vec
- Parameters of H2OGLRM
- Parameters of ColumnPruner
- Parameters of H2OPCA
- Parameters of H2OAutoEncoder
- How to…
- Running Sparkling Water
- Changing the Default Port
- Hive Support in Sparkling Water
- Sparkling Water Logging
- Enabling SSL
- Enabling LDAP
- Enabling Kerberos Authentication
- Running Sparkling Water on Kerberized Hadoop Cluster
- Using SSL to secure H2O Flow UI
- Spark Frame <–> H2O Frame Conversions
- H2O Frame as Spark’s Data Source
- Import & Export H2O Frames from/to S3
- Use Sparkling Water via Spark Packages
- Development
- PySparkling
- RSparkling
- Migration Guide
- Frequently Asked Questions
- Change Log
- v3.36.1.3-1 (2022-07-11)
- v3.36.1.2-1 (2022-05-30)
- v3.36.1.1-1 (2022-04-20)
- v3.36.0.4-1 (2022-04-01)
- v3.36.0.3-1 (2022-02-18)
- v3.36.0.2-1 (2022-01-27)
- v3.36.0.1-1 (2022-01-06)
- v3.34.0.8-1 (2022-01-14)
- v3.34.0.7-1 (2021-12-22)
- v3.34.0.6-1 (2021-12-17)
- v3.34.0.4-1 (2021-11-19)
- v3.34.0.3-1 (2021-10-08)
- v3.34.0.1-1 (2021-09-16)
- v3.32.1.7-1 (2021-09-08)
- v3.32.1.6-1 (2021-08-20)
- v3.32.1.5-1 (2021-08-06)
- v3.32.1.4-1 (2021-07-15)
- v3.32.1.3-1 (2021-05-27)
- v3.32.1.2-1 (2021-05-04)
- v3.32.1.1-1 (2021-03-30)
- v3.32.0.5-1 (2021-03-18)
- v3.32.0.4-1 (2021-02-02)
- v3.32.0.3-1 (2020-12-30)
- v3.32.0.2-1 (2020-11-19)
- v3.32.0.1-2 (2020-10-15)
- v3.30.1.3-1 (2020-10-05)
- v3.30.1.2-1 (2020-09-08)
- v3.30.1.1-1 (2020-08-12)
- v3.30.0.7-1 (2020-07-24)
- v3.30.0.6-1 (2020-07-03)
- v3.30.0.5-1 (2020-06-22)
- v3.30.0.4-1 (2020-06-04)
- v3.30.0.3-1 (2020-05-14)
- v3.30.0.2-1 (2020-05-04)
- v3.30.0.1-1 (2020-04-06)
- v3.28.1.3-1 (2020-04-06)
- v3.28.1.2-1 (2020-03-19)
- v3.28.1.1-1 (2020-03-06)
- v3.28.0.4-1 (2020-02-25)
- v3.28.0.3-1 (2020-02-06)
- v3.28.0.2-1 (2020-01-23)
- v3.28.0.1-1 (2019-12-19)
- v3.26.11 (2019-12-06)
- v3.26.10 (2019-11-07)
- v3.26.9 (2019-10-31)
- v3.26.8 (2019-10-18)
- v3.26.7 (2019-10-11)
- v3.26.6 (2019-10-02)
- v3.26.5 (2019-09-16)
- v3.26.3 (2019-08-28)
- v3.26.2 (2019-07-30)
- v2.1.56, v2.2.42, v2.3.31, v2.4.13 (2019-06-24)
- v2.1.55, v2.2.41, v2.3.30, v2.4.12 (2019-06-03)
- v2.1.54, v2.2.40, v2.3.29, v2.4.11 (2019-05-17)
- v2.1.53, v2.2.39, v2.3.28, v2.4.10 (2019-04-26)
- v2.1.52, v2.2.38, v2.3.27, v2.4.9 (2019-04-03)
- v2.1.51, v2.2.37, v2.3.26, v2.4.8 (2019-03-15)
- v2.1.50, v2.2.36, v2.3.25, v2.4.7 (2019-03-07)
- v2.1.49, v2.2.35, v2.3.24, v2.4.6 (2019-02-18)
- v2.1.48, v2.2.34, v2.3.23, v2.4.5 (2019-01-29)
- v2.1.47, v2.2.33, v2.3.22, v2.4.4 (2019-01-21)
- v2.1.46, v2.2.32, v2.3.21, v2.4.3 (2019-01-17)
- v2.1.45, v2.2.31, v2.3.20, v2.4.2 (2019-01-08)
- v2.1.44, v2.2.30, v2.3.19, v2.4.1 (2018-12-27)
- v2.1.43, v2.2.29, v2.3.18 (2018-11-27)
- v2.1.42, v2.2.28, v2.3.17 (2018-10-27)
- v2.1.41, v2.2.27, v2.3.16 (2018-10-17)
- v2.1.40, v2.2.26, v2.3.15 (2018-10-02)
- v2.1.39, v2.2.25, v2.3.14 (2018-09-24)
- v2.1.38, v2.2.24, v2.3.13 (2018-09-14)
- v2.1.37, v2.2.23, v2.3.12 (2018-08-28)
- v2.1.36, v2.2.22, v2.3.11 (2018-08-09)
- v2.1.35, v2.2.21, v2.3.10 (2018-08-01)
- v2.1.34, v2.2.20, v2.3.9 (2018-07-16)
- v2.1.33, v2.2.19, v2.3.8 (2018-06-18)
- v2.1.32, v2.2.18, v2.3.7 (2018-06-18)
- v2.1.31, v2.2.17, v2.3.6 (2018-06-13)
- v2.1.30, v2.2.16, v2.3.5 (2018-05-23)
- v2.1.29, v2.2.15, v2.3.4 (2018-05-18)
- v2.1.28, v2.2.14, v2.3.3 (2018-05-15)
- v2.1.27, v2.2.13, v2.3.2 (2018-05-02)
- v2.1.26, v2.2.12, v2.3.1 (2018-04-19)
- v2.1.25, v2.2.11, v2.3.0 (2018-03-29)
- v2.1.24, v2.2.10 (2018-03-08)
- v2.1.23, v2.2.9 (2018-02-26)
- v2.1.22, v2.2.8 (2018-02-14)
- v2.1.21, v2.2.7 (2018-01-18)
- v2.1.20, v2.2.6 (2018-01-03)
- v2.1.19, v2.2.5 (2017-12-11)
- v2.1.18, v2.2.4 (2017-12-01)
- v2.1.17, v2.2.3 (2017-11-25)
- v2.1.16, v2.2.2 (2017-10-23)
- v2.1.15, v2.2.1 (2017-10-10)
- v2.1.14, v2.2.0 (2017-08-23)
- v2.1.13 (2017-08-02)
- v2.1.12 (2017-07-17)
- v2.1.11 (2017-07-12)
- v2.1.10 (2017-06-29)
- v2.1.9 (2017-06-15)
- v2.1.8 (2017-05-25)
- v2.1.7 (2017-05-10)
- v2.1.6 (2017-05-09)
- v2.1.5 (2017-04-27)
- v2.1.4 (2017-04-20)
- v2.1.3 (2017-04-7)
- v2.1.2 (2017-03-20)
- v2.1.1 (2017-03-18)
- v2.1.0 (2017-03-02)