<commit_before>Setup and Installation
======================

This module depends on *requests* and *tabulate* modules. Both of which are available on pypi.

    $ pip install requests
    $ pip install tabulate


The H2O Python Module
=====================

This module provides access to the H2O JVM (and extensions thereof), its objects, its
machine-learning algorithms, and modeling support (basic munging and feature generation)
capabilities.

The H2O JVM sports a web server such that all communication occurs on a socket (specified
by an IP address and a port) via a series of REST calls (see connection.py for the REST
layer implementation and details). There is a single active connection to the H2O JVM at
any one time, and this handle is stashed in the \_\_H2OCONN\_\_ global object. The \_\_H2OCONN\_\_
is not currently a singleton object (but it may become one in the future).

What is H2O?
============

H2O is a piece of java software for data modeling and general computing. There are many
different views of the H2O software, but the primary view of H2O is that of a distributed
(many machines), parallel (many CPUs), in memory (several hundred GBs Xmx) processing
"engine". How H2O achieves within node parallelism and efficient horizontal scaling is
discussed in detail elsewhere, but it suffices to state that Doug Lea's Fork Join
framework (which can be thought of as a classical recursive descent divide and conquer
approach to doing "work") enables parallelism per JVM, and a distributed version of Cliff
Click's non-blocking hash map enables coherency across nodes in a cluster allowing for<commit_msg>missing new line<commit_after>Setup and Installation
======================

This module depends on *requests* and *tabulate* modules. Both of which are available on pypi.

    $ pip install requests

    $ pip install tabulate


The H2O Python Module
=====================

This module provides access to the H2O JVM (and extensions thereof), its objects, its
machine-learning algorithms, and modeling support (basic munging and feature generation)
capabilities.

The H2O JVM sports a web server such that all communication occurs on a socket (specified
by an IP address and a port) via a series of REST calls (see connection.py for the REST
layer implementation and details). There is a single active connection to the H2O JVM at
any one time, and this handle is stashed in the \_\_H2OCONN\_\_ global object. The \_\_H2OCONN\_\_
is not currently a singleton object (but it may become one in the future).

What is H2O?
============

H2O is a piece of java software for data modeling and general computing. There are many
different views of the H2O software, but the primary view of H2O is that of a distributed
(many machines), parallel (many CPUs), in memory (several hundred GBs Xmx) processing
"engine". How H2O achieves within node parallelism and efficient horizontal scaling is
discussed in detail elsewhere, but it suffices to state that Doug Lea's Fork Join
framework (which can be thought of as a classical recursive descent divide and conquer
approach to doing "work") enables parallelism per JVM, and a distributed version of Cliff
Click's non-blocking hash map enables coherency across nodes in a cluster allowing for
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