H2O’s core code is written in Java. Inside H2O, a Distributed Key/Value store is used to access and reference data, models, objects, etc., across all nodes and machines. The algorithms are implemented on top of H2O’s distributed Map/Reduce framework and utilize the Java Fork/Join framework for multi-threading. The data is read in parallel and is distributed across the cluster and stored in memory in a columnar format in a compressed way. H2O’s data parser has built-in intelligence to guess the schema of the incoming dataset and supports data ingest from multiple sources in various formats.
H2O’s REST API allows access to all the capabilities of H2O from an external program or script via JSON over HTTP. The Rest API is used by H2O’s web interface (Flow UI), R binding (H2O-R), and Python binding (H2O-Python).
The speed, quality, ease-of-use, and model-deployment for the various cutting edge Supervised and Unsupervised algorithms like Deep Learning, Tree Ensembles, and GLRM make H2O a highly sought after API for big data data science.
Requirements
At a minimum, we recommend the following for compatibility with H2O:
Operating Systems:
Windows 7 or later
OS X 10.9 or later
Ubuntu 12.04
RHEL/CentOS 6 or later
Languages: Scala, R, and Python are not required to use H2O unless you want to use H2O in those environments, but Java is always required. Supported versions include:
Java 7 or later. Note: Java 9 is not yet released and is not currently supported.
To build H2O or run H2O tests, the 64-bit JDK is required.
To run the H2O binary using either the command line, R, or Python packages, only 64-bit JRE is required.
Both of these are available on the Java download page.
Scala 2.10 or later
R version 3 or later
Python 2.7.x or 3.5.x
Browser: An internet browser is required to use H2O’s web UI, Flow.
