H2O makes Hadoop do math! H2O scales statistics, machine learning and math over BigData. H2O is extensible and users can build blocks using simple math legos in the core. H2O keeps familiar interfaces like R, Excel & JSON so that BigData enthusiasts & experts can explore, munge, model and score datasets using a range of simple to advanced algorithms. Data collection is easy. Decision making is hard. H2O makes it fast and easy to derive insights from your data through faster and better predictive modeling. H2O has a vision of online scoring and modeling in a single platform.
H2O product, the Analytics Engine will scale Classification and Regression.
- RandomForest, Gradient Boosted Methods (GBM), Generalized Linear Modeling (GLM), Deep Learning and K-Means available over R / REST / JSON-API
- Basic Linear Algebra as building blocks for custom algorithms
- High predictive power of the models
- High speed and scale for modeling and scoring over BigData
Data Sources
- We read and write from/to HDFS, S3, NoSQL, SQL
- We ingest data in CSV format from local and distributed filesystems (nfs)
- A JDBC driver for SQL and DataAdapters for NoSQL datasources is in the roadmap. (v2)
Console provides Adhoc Data Analytics at scale via R-like Parser on BigData
- Able to pass and evaluate R-like expressions, slicing and filters make this the most powerful web calculator on BigData
Primary users are Data Analysts looking to wield a powerful tool for Data Modeling in the Real-Time. Microsoft Excel, R, SAS wielding Data Analysts and Statisticians. Hadoop users with data in HDFS will have a first class citizen for doing Math in Hadoop ecosystem. Java and Math engineers can extend core functionality by using and extending legos in a simple java that reads like math. See package hex. Extensibility can also come from writing R expressions that capture your domain.
We use the best execution framework for the algorithm at hand. For first cut parallel algorithms: Map Reduce over distributed fork/join framework brings fine grain parallelism to distributed algorithms. Our algorithms are cache oblivious and fit into the heterogeneous datacenter and laptops to bring best performance. Distributed Arraylets & Data Partitioning to preserve locality. Move code, not data, not people.
One of our first powerful extension will be a small tool belt of stats and math legos for Fraud Detection. Dealing with Unbalanced Datasets is a key focus for this. Users will use JSON/REST-api via H2O.R through connects the Analytics Engine into R-IDE/RStudio.
Every nightly build publishes R, python, java and scala artifacts to a per-build repository. In particular, you can find java artifacts in the maven/repo directory.
Here is an example snippet of a gradle build file using h2o-dev as a dependency. Replace x, y, z, and nnnn with valid numbers.
// h2o-dev dependency information
def h2oBranch = 'master'
def h2oBuildNumber = 'nnnn'
def h2oProjectVersion = "x.y.z.${h2oBuildNumber}"
repositories {
// h2o-dev dependencies
maven {
url "https://s3.amazonaws.com/h2o-release/h2o-dev/${h2oBranch}/${h2oBuildNumber}/maven/repo/"
}
}
dependencies {
compile "ai.h2o:h2o-core:${h2oProjectVersion}"
compile "ai.h2o:h2o-algos:${h2oProjectVersion}"
compile "ai.h2o:h2o-web:${h2oProjectVersion}"
compile "ai.h2o:h2o-app:${h2oProjectVersion}"
}
See the latest H2O Dev nightly build page for information about installing nightly build artifacts.
See the h2o-droplets github repository for a working example of how to use java artifacts with gradle.
Note: Stable H2O Dev artifacts are periodically published to Maven Central (click here to search) but may lag substantially behind H2O Dev Bleeding Edge nightly builds.
Getting started with H2O development requires JDK 1.7, Node.js, and Gradle. We use the Gradle wrapper (called gradlew) to ensure an up-to-date local version of Gradle and other dependencies are installed in your development directory.
##Making a clean build
Making a clean build is strongly recommended if:
- the gradle build fails
- there have been many changes in the last pull
- source files have been renamed or deleted
If you try to create a gradle build using ./gradlew build and any of the conditions listed above apply, the build is likely to fail.
Creating a build using ./gradlew clean, then ./gradlew build takes more time to complete but is the best way to ensure all files are current. Run ./gradlew clean first, then run ./gradlew build as two separate commands. We strongly recommend using ./gradlew clean after every git pull.
While using ./gradlew clean is not required, it helps to prevent errors due to missing or outdated files during the build process. However, due to the increased time required for this method, we recommend using ./gradlew clean only for integrating recent changes or troubleshooting failed builds.
`pip install grip`
`pip install tabulate`
`pip install wheel`
Step 1: Download and install Python for Windows.
From the command line, validate python is using the newly-installed package. Update the Environment variable with the Python path.
C:\Python27\;C:\Python27\Scripts\
`pip install grip`
`pip install tabulate`
`pip install wheel`
Install Java 1.7 and add the appropriate directory C:\Program Files\Java\jdk1.7.0_65\bin with java.exe to PATH in Environment Variables. To make sure the command prompt is detecting the correct Java version, run:
javac -version
The CLASSPATH variable also needs to be set to the lib subfolder of the JDK:
CLASSPATH=/<path>/<to>/<jdk>/lib
Install Node.js and add the installed directory C:\Program Files\nodejs, which must include node.exe and npm.cmd to PATH if not already prepended.
To install these packages from within an R session, enter:
R> install.packages("RCurl")
R> install.packages("rjson")
R> install.packages("statmod")
R> install.packages(c("devtools", "roxygen2", "testthat"))
Install R and add the preferred bin\i386 or bin\x64 directory to your PATH.
Note: Acceptable versions of R are >= 2.13 && <= 3.0.0 && >= 3.1.1.
To manually install packages, download the releases of the following R packages: bitops, devtools, digest, Rcpp, RCurl, rjson, roxygen2, statmod, stringr, and testthat.
cd Downloads
R CMD INSTALL bitops_x.x-x.zip
R CMD INSTALL RCurl_x.xx-x.x.zip
R CMD INSTALL rjson_x.x.xx.zip
R CMD INSTALL statmod_x.x.xx.zip
R CMD INSTALL Rcpp_x.xx.x.zip
R CMD INSTALL digest_x.x.x.zip
R CMD INSTALL testthat_x.x.x.zip
R CMD INSTALL stringr_x.x.x.zip
R CMD INSTALL roxygen2_x.x.x.zip
R CMD INSTALL devtools_x.x.x.zip
Finally, install Rtools, which is a collection of command line tools to facilitate R development on Windows. NOTE: During Rtools installation, do not install Cygwin.dll.
Step 6. Install Cygwin
NOTE: During installation of Cygwin, deselect the Python packages to avoid a conflict with the Python.org package.
If Cygwin is already installed, remove the Python packages or ensure that Native Python is before Cygwin in the PATH variable.
Step 8. Git Clone h2o-dev
If you don't already have a Git client, please install one. The default one can be found here http://git-scm.com/downloads . Make sure that during the install command prompt support is turned on.
Download and update h2o-dev source codes:
git clone https://github.com/h2oai/h2o-dev
cd h2o-dev
gradlew build
If you encounter errors run again with
--stacktracefor more instructions on missing dependencies.
If you don't have Homebrew, we recommend installing it. It makes package management for OS X easy.
Install Java 1.7. To make sure the command prompt is detecting the correct Java version, run:
javac -version
Using Homebrew:
brew install node
Otherwise, install from the NodeJS website.
Install R and add the bin directory to your PATH if not already included.
Install the following R packages: RCurl, rjson, statmod, devtools, roxygen2 and testthat.
cd Downloads
R CMD INSTALL bitops_x.x-x.tgz
R CMD INSTALL RCurl_x.xx-x.x.tgz
R CMD INSTALL rjson_x.x.xx.tgz
R CMD INSTALL statmod_x.x.xx.tgz
R CMD INSTALL Rcpp_x.xx.x.tgz
R CMD INSTALL digest_x.x.x.tgz
R CMD INSTALL testthat_x.x.x.tgz
R CMD INSTALL stringr_x.x.x.tgz
R CMD INSTALL roxygen2_x.x.x.tgz
R CMD INSTALL devtools_x.x.x.tgz
To install these packages from within an R session:
R> install.packages("RCurl")
R> install.packages("rjson")
R> install.packages("statmod")
R> install.packages(c("devtools", "roxygen2", "testthat"))
Step 4. Git Clone h2o-dev
OS X should have with Git installed. To download and update h2o-dev source codes:
git clone https://github.com/h2oai/h2o-dev
cd h2o-dev
./gradlew build
If you encounter errors run again with
--stacktracefor more instructions on missing dependencies.
sudo apt-get install npm
sudo ln -s /usr/bin/nodejs /usr/bin/node
npm install -g bower
Install Java 1.7. Installation instructions can be found here JDK installation. To make sure the command prompt is detecting the correct Java version, run:
javac -version
Step 3. Git Clone h2o-dev
If you don't already have a Git client:
sudo apt-get install git
Download and update h2o-dev source codes:
git clone https://github.com/h2oai/h2o-dev
cd h2o-dev
./gradlew build
If you encounter errors run again with
--stacktracefor more instructions on missing dependencies.
Make sure that you are not running as root, since
bowerwill reject such a run.
On Ubuntu 13.10, the default Node.js (v0.10.15) is sufficient, but the default npm (v1.2.18) is too old, so use a fresh install from the npm website:
sudo apt-get install node
sudo ln -s /usr/bin/nodejs /usr/bin/node
wget http://npmjs.org/install.sh
sudo apt-get install curl
sudo sh install.sh
For users of Intellij's IDEA, generate project files with:
./gradlew idea
For users of Eclipse, generate project files with:
./gradlew eclipse
To build H2O with Hadoop support, do the following from the top-level h2o-dev directory:
(export BUILD_HADOOP=1; ./gradlew build -x test)
./gradlew dist
This will create a directory called 'target' and generate zip files there.
In the h2o-hadoop directory each hadoop version has a build directory for the driver and an assembly directory for the fatjar.
You need to:
- Add a new driver directory and assembly directorie (each with a build.gradle file) in h2o-hadoop
- Add these new projects to h2o-dev/settings.gradle
- Add the new hadoop version to HADOOP_VERSIONS in make-dist.sh
- Add the new hadoop version to wget list in h2o-dist/index.html
Sparkling Water combines two open source technologies: Apache Spark and H2O, our machine learning engine. It makes H2O’s library of Advanced Algorithms, including Deep Learning, GLM, GBM, KMeans, PCA, and Random Forest, accessible from Spark workflows. Spark users are provided with options to select the best features from either platforms to meet their Machine Learning needs. Users can combine Sparks’ RDD API and Spark MLLib with H2O’s machine learning algorithms, or use H2O independent of Spark in the model building process and post-process the results in Spark.
Spark 1.2 for Sparkling Water v0.2.4-65+ Spark 1.1 for older versions
Compatiable Hadoop Distributions: CDH4, CDH5, and HDP2.1.
- To install on your Hadoop Cluster clone the git repository and make a build:
git clone https://github.com/0xdata/sparkling-water.git
cd sparkling-water
./gradlew build
- Then set MASTER to the IP address of where your Spark Master Node is launched and set SPARK_HOME to the location of your Spark installation. In the example below the path for SPARK_HOME is the default location of Spark preinstalled on a CDH5 cluster. Please change MASTER below:
export MASTER="spark://mr-0xd9-precise1.0xdata.loc:7077"
export SPARK_HOME="/opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.11/lib/spark"
- Launch Sparkling Shell:
./bin/sparkling-shell
The initialization of H2O remains the same with the exception of importing data from a HDFS path. Please change path variable below to one suitable for your data.
import org.apache.spark.h2o._
import org.apache.spark.examples.h2o._
// Create H2O context
val h2oContext = new H2OContext(sc).start()
// Export H2O context to the
import h2oContext._
// URI to access HDFS file
val path = "hdfs://mr-0xd6-precise1.0xdata.loc:8020/datasets/airlines_all.05p.csv"
val d = new java.net.URI(path)
val f = new DataFrame(d)We will breathe & sustain a vibrant community with the focus of taking software engineering approach to data science and empower everyone interested in data to be able to hack data using math and algorithms. Join us on google groups h2ostream.
Team & Committers
SriSatish Ambati
Cliff Click
Tom Kraljevic
Tomas Nykodym
Michal Malohlava
Kevin Normoyle
Spencer Aiello
Anqi Fu
Nidhi Mehta
Arno Candel
Josephine Wang
Amy Wang
Max Schloemer
Ray Peck
Prithvi Prabhu
Patrick Aboyoun
Brandon Hill
Jeff Gambera
Ariel Rao
Viraj Parmar
Kendall Harris
Anand Avati
Jessica Lanford
Yan Zou
Alex Tellez
Allison Washburn
Amy Wang
Erik Eckstrand
James Dean
Neeraja Madabhusi
Sebastian Vidrio
Scientific Advisory Council
Stephen Boyd
Rob Tibshirani
Trevor Hastie
Systems, Data, FileSystems and Hadoop
Doug Lea
Chris Pouliot
Dhruba Borthakur
Jishnu Bhattacharjee, Nexus Venture Partners
Anand Babu Periasamy
Anand Rajaraman
Ash Bhardwaj
Rakesh Mathur
Michael Marks
Egbert Bierman