H2O

H2O scales statistics, machine learning, and math over Big Data.

H2O uses familiar interfaces like R, Python, Scala, the Flow notebook graphical interface, Excel, & JSON so that Big Data enthusiasts & experts can explore, munge, model, and score datasets using a range of algorithms including advanced ones like Deep Learning. H2O is extensible so that developers can add data transformations and model algorithms of their choice and access them through all of those clients.

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 allows online scoring and modeling in a single platform.

• Downloading H2O-3
• Open Source Resources
  • Issue tracking
• Using H2O-3 Code Artifacts (libraries)
• Building H2O-3
• Launching H2O after Building
• Building H2O on Hadoop
• Sparkling Water
• Documentation
• Community / Advisors / Investors

1. Downloading H2O-3

While most of this README is written for developers who do their own builds, most H2O users just download and use a pre-built version. If that's you, just follow these steps:

1. Point to http://h2o.ai
2. Click on Download
3. Scroll down to find the section for H2O-3
4. Click the version you want (generally the latest numbered release)

2. Open Source Resources

Most people interact with three primary open source resources: GitHub (which you've already found), JIRA (for issue tracking), and h2ostream (a community discussion forum).

2.1 Issue tracking

You can browse and create new issues in our open source JIRA: http://jira.h2o.ai

• You can browse and search for issues without logging in to JIRA:
  1. Click the Issues menu
  2. Click Search for issues
• To create an issue (either a bug or a feature request), please create yourself an account first:
  1. Click the Log In button on the top right of the screen
  2. Click Create an acccount near the bottom of the login box
  3. Once you have created an account and logged in, use the Create button on the menu to create an issue
  4. Create H2O-3 issues in the PUBDEV project

(Note: There is only one issue tracking system for the project. GitHub issues are not enabled, you must use JIRA.)

2.2 List of open source resources

• GitHub
  • https://github.com/h2oai/h2o-3
• JIRA - file issues here (PUBDEV contains issues for the current H2O-3 project)
  • http://jira.h2o.ai
• h2ostream community forum - ask your questions here
  • Web: https://groups.google.com/d/forum/h2ostream
  • Mail to: h2ostream@googlegroups.com
• Documentation
  • Bleeding edge nightly build page: http://s3.amazonaws.com/h2o-release/h2o-3/master/latest.html
  • FAQ: http://h2o.ai/product/faq/
• Download (pre-built packages)
  • http://h2o.ai/download
• Jenkins
  • http://test.h2o.ai
• Website
  • http://h2o.ai
• Follow us on Twitter, @h2oai

3. Using H2O-3 Artifacts

Every nightly build publishes R, Python, Java, and Scala artifacts to a build-specific 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-3 as a dependency. Replace x, y, z, and nnnn with valid numbers.

// h2o-3 dependency information
def h2oBranch = 'master'
def h2oBuildNumber = 'nnnn'
def h2oProjectVersion = "x.y.z.${h2oBuildNumber}"

repositories {
  // h2o-3 dependencies
  maven {
    url "https://s3.amazonaws.com/h2o-release/h2o-3/${h2oBranch}/${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}"
}

Refer to the latest H2O-3 bleeding edge nightly build page for information about installing nightly build artifacts.

Refer to the h2o-droplets GitHub repository for a working example of how to use Java artifacts with gradle.

Note: Stable H2O-3 artifacts are periodically published to Maven Central (click here to search) but may substantially lag behind H2O-3 Bleeding Edge nightly builds.

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4. Building H2O-3

Getting started with H2O development requires JDK 1.7, Node.js, and Gradle. We use the Gradle wrapper (called gradlew) to ensure up-to-date local versions of Gradle and other dependencies are installed in your development directory.

4.1. Building from the command line (Quick Start)

To build H2O from the repository, perform the following steps.

Recipe 1: Clone fresh, build, skip tests, and run H2O

# Build H2O
git clone https://github.com/h2oai/h2o-3.git
cd h2o-3
./gradlew build -x test

# Start H2O
java -jar build/h2o.jar

# Point browser to http://localhost:54321

Recipe 2: Clone fresh, build, and run tests

git clone https://github.com/h2oai/h2o-3.git
cd h2o-3
./gradlew syncSmalldata
./gradlew build

Note: Running tests starts five test JVMs that form an H2O cluster and requires at least 8GB of RAM (preferably 16GB of RAM).

Recipe 3: Pull, clean, build, and run tests

git pull
./gradlew syncSmalldata
./gradlew clean
./gradlew build

Notes

A ./gradlew clean is recommended after each git pull.

Skip tests by adding -x test at the end the gradle build command line. Tests typically run for 7-10 minutes on a Macbook Pro laptop with 4 CPUs (8 hyperthreads) and 16 GB of RAM.

Syncing smalldata is not required after each pull, but if tests fail due to missing data files, then try ./gradlew syncSmalldata as the first troubleshooting step. Syncing smalldata grabs data files from AWS S3 to the smalldata directory in your workspace. The sync is incremental. Do not check in these files. The smalldata directory is in .gitignore. If you do not run any tests, you do not need the smalldata directory.

4.2. Setup on all Platforms

Install required Python packages (prepending with sudo if unsuccessful)

pip install grip
pip install tabulate
pip install wheel
pip install scikit-learn

Python tests require:

pip install scikit-learn
pip install numpy
pip install scipy
pip install pandas
pip install statsmodels
pip install patsy

4.3. Setup on Windows

Step 1: Download and install WinPython.

From the command line, validate python is using the newly installed package by using which python (or sudo which python). Update the Environment variable with the WinPython path.

Step 2: Install required Python packages:

pip install grip
pip install tabulate
pip install wheel

Step 3: Install JDK

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

Step 4. Install Node.js

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.

Step 5. Install R, the required packages, and Rtools:

To install these packages from within an R session, enter:

R> install.packages("RCurl")
R> install.packages("jsonlite")
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
• jsonlite
• roxygen2
• statmod
• stringr
• testthat.

    cd Downloads
    R CMD INSTALL bitops_x.x-x.zip
    R CMD INSTALL RCurl_x.xx-x.x.zip
    R CMD INSTALL jsonlite_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.

Step 6b. Validate Cygwin

If Cygwin is already installed, remove the Python packages or ensure that Native Python is before Cygwin in the PATH variable.

Step 7. Update or validate the Windows PATH variable to include R, Java JDK, Cygwin.

Step 8. Git Clone h2o-3

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 command prompt support is enabled before the installation.

Download and update h2o-3 source codes:

git clone https://github.com/h2oai/h2o-3

Step 9. Run the top-level gradle build:

cd h2o-3
./gradlew.bat build

If you encounter errors run again with --stacktrace for more instructions on missing dependencies.

4.4. Setup on OS X

If you don't have Homebrew, we recommend installing it. It makes package management for OS X easy.

Step 1. Install JDK

Install Java 1.7. To make sure the command prompt is detecting the correct Java version, run:

javac -version

Step 2. Install Node.js:

Using Homebrew:

brew install node

Otherwise, install from the NodeJS website.

Step 3. Install R and the required packages:

Install R and add the bin directory to your PATH if not already included.

Install the following R packages:

• RCurl
• jsonlite
• statmod
• devtools
• roxygen2
• testthat.

    cd Downloads
    R CMD INSTALL bitops_x.x-x.tgz
    R CMD INSTALL RCurl_x.xx-x.x.tgz
    R CMD INSTALL jsonlite_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("jsonlite")
R> install.packages("statmod")
R> install.packages(c("devtools", "roxygen2", "testthat"))

Step 4. Git Clone h2o-3

OS X should already have Git installed. To download and update h2o-3 source codes:

git clone https://github.com/h2oai/h2o-3

Step 5. Run the top-level gradle build:

cd h2o-3
./gradlew build

If you encounter errors run again with --stacktrace for more instructions on missing dependencies.

4.5. Setup on Ubuntu 14.04

Step 1. Install Node.js, npm, and bower:

sudo apt-get install npm
sudo ln -s /usr/bin/nodejs /usr/bin/node
npm install -g bower

Step 2. Install JDK:

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. Install R and the required packages:

Installation instructions can be found here R installation. Click “Download R for Linux”. Click “ubuntu”. Follow the given instructions.

To install the required packages, follow the same instructions as for OS X above.

Step 4. Git Clone h2o-3

If you don't already have a Git client:

sudo apt-get install git

Download and update h2o-3 source codes:

git clone https://github.com/h2oai/h2o-3

Step 5. Run the top-level gradle build:

cd h2o-3
./gradlew build

If you encounter errors, run again using --stacktrace for more instructions on missing dependencies.

Make sure that you are not running as root, since bower will reject such a run.

4.6. Setup on Ubuntu 13.10

Step 1. Install Node.js, npm, and bower

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

Steps 2-4. Follow steps 2-4 for Ubuntu 14.04

4.7. Setting up your preferred IDE environment

For users of Intellij's IDEA, generate project files with:

./gradlew idea

For users of Eclipse, generate project files with:

./gradlew eclipse

5. Launching H2O after Building

java -jar build/h2o.jar

6. Building H2O on Hadoop

Pre-built H2O-on-Hadoop zip files are available on the download page. Each Hadoop distribution version has a separate zip file in h2o-3.

To build H2O with Hadoop support yourself, first install sphinx for python: pip install sphinx Then start the build by entering the following from the top-level h2o-3 directory:

(export BUILD_HADOOP=1; ./gradlew build -x test)
./gradlew dist

This will create a directory called 'target' and generate zip files there. Note that BUILD_HADOOP is the default behavior when the username is jenkins (refer to settings.gradle); otherwise you have to request it, as shown above.

Adding support for a new version of Hadoop

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:

1. Add a new driver directory and assembly directory (each with a build.gradle file) in h2o-hadoop
2. Add these new projects to h2o-3/settings.gradle
3. Add the new Hadoop version to HADOOP_VERSIONS in make-dist.sh
4. Add the new Hadoop version to the list in h2o-dist/buildinfo.json

Debugging HDFS

These are the required steps to debug HDFS in IDEA as a standalone H2O process.

Debugging H2O on Hadoop as a hadoop jar hadoop mapreduce job is a difficult thing to do. However, what you can do relatively easily is tweak the gradle settings for the project so that H2OApp has HDFS as a dependency. Here are the steps:

1. Make the following changes to gradle build files below
   • Change the hadoop-client version in h2o-persist-hdfs to the desired version
   • Add h2o-persist-hdfs as a dependency to h2o-app
2. Close IDEA
3. ./gradlew cleanIdea
4. ./gradlew idea
5. Re-open IDEA
6. Run or debug H2OApp, and you will now be able to read from HDFS inside the IDE debugger

h2o-persist-hdfs is normally only a dependency of the assembly modules, since those are not used by any downstream modules. We want the final module to define its own version of HDFS if any is desired.

Note this example is for MapR 4, which requires the additional org.json dependency to work properly.

$ git diff
diff --git a/h2o-app/build.gradle b/h2o-app/build.gradle
index af3b929..097af85 100644
--- a/h2o-app/build.gradle
+++ b/h2o-app/build.gradle
@@ -8,5 +8,6 @@ dependencies {
   compile project(":h2o-algos")
   compile project(":h2o-core")
   compile project(":h2o-genmodel")
+  compile project(":h2o-persist-hdfs")
 }
 
diff --git a/h2o-persist-hdfs/build.gradle b/h2o-persist-hdfs/build.gradle
index 41b96b2..6368ea9 100644
--- a/h2o-persist-hdfs/build.gradle
+++ b/h2o-persist-hdfs/build.gradle
@@ -2,5 +2,6 @@ description = "H2O Persist HDFS"
 
 dependencies {
   compile project(":h2o-core")
-  compile("org.apache.hadoop:hadoop-client:2.0.0-cdh4.3.0")
+  compile("org.apache.hadoop:hadoop-client:2.4.1-mapr-1408")
+  compile("org.json:org.json:chargebee-1.0")
 }

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7. Sparkling Water

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, K-Means, and Distributed Random Forest, accessible from Spark workflows. Spark users can select the best features from either platform to meet their Machine Learning needs. Users can combine Spark's RDD API and Spark MLLib with H2O’s machine learning algorithms, or use H2O independently of Spark for the model building process and post-process the results in Spark.

Sparkling Water Resources:

• Download page for pre-built packages (Scroll down for Sparkling Water)
• Sparkling Water GitHub repository
• README
• Developer documentation

8. Documentation

Generate REST API documentation

To generate the REST API documentation, use the following commands:

cd ~/h2o-3
cd py
python ./generate_rest_api_docs.py  # to generate Markdown only
python ./generate_rest_api_docs.py --generate_html  --github_user GITHUB_USER --github_password GITHUB_PASSWORD # to generate Markdown and HTML

The default location for the generated documentation is build/docs/REST.

If the build fails, try gradlew clean, then git clean -f.

Bleeding edge build documentation

Documentation for each bleeding edge nightly build is available on the nightly build page.

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9. Community

We will breathe & sustain a vibrant community with the focus of taking a software engineering approach to data science and empowering everyone interested in data to be able to hack data using math and algorithms. Join us on google groups at h2ostream and feel free to file issues directly on our JIRA.

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
Brandon Hill
Jeff Gambera
Ariel Rao
Viraj Parmar
Kendall Harris
Anand Avati
Jessica Lanford
Alex Tellez
Allison Washburn
Amy Wang
Erik Eckstrand
Neeraja Madabhushi
Sebastian Vidrio
Ben Sabrin
Matt Dowle
Mark Landry
Erin LeDell
Oleg Rogynskyy
Nick Martin
Nancy Jordan 
Nishant Kalonia
Nadine Hussami
Jeff Cramer
Stacie Spreitzer
Vinod Iyengar
Charlene Windom
Parag Sanghavi

Advisors

Scientific Advisory Council

Stephen Boyd
Rob Tibshirani
Trevor Hastie

Systems, Data, FileSystems and Hadoop

Doug Lea
Chris Pouliot
Dhruba Borthakur

Investors

Jishnu Bhattacharjee, Nexus Venture Partners
Anand Babu Periasamy
Anand Rajaraman
Ash Bhardwaj
Rakesh Mathur
Michael Marks
Egbert Bierman
Rajesh Ambati