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Machine Learning Library Reference

The machine learning library contains an extensible collection of machine learning routines which are easy and efficient to use and are designed to execute in parallel across a cluster. The list of modules supported will continue to grow over time.

Configure a Thor Cluster on AWS

Running an HPCC Systems Thor cluster inside an Amazon Web Services (AWS) Elastic Cloud (EC2) allows you to instantiate and run HPCC Systems Thor clusters of different sizes on the fly.

One-Click Thor^TM

Manually set-up and configure a Thor cluster

Running HPCC in a Virtual Machine

This outlines the system requirements and steps for installing and running an HPCC system on a single node inside a virtual machine.

Installing & Running the HPCC Platform

These instructions will guide you through installing and running the HPCC Community Edition on a single node to start and then optionally, expand it to a larger cluster of nodes.

System Requirements

The following system requirements cover what is needed to support the HPCC Platform and ECL IDE included in the Community Edition.

HPCC Data Tutorial: with an Introduction to Thor

This tutorial provides a walk-through of the development process from beginning to end and is designed to be an introduction to working with data on HPCC, as well as give an introduction to Thor. The Sample Data Files are required references for the tutorial.

OriginalPerson.tar.gz | OriginalPerson.zip

Six Degrees of Kevin Bacon ECL Example

This step by step ECL Programming Example details how to solve the challenge of finding the Six degrees of Separation between any actor and Kevin Bacon.

ECL Programmers Guide

The ECL Programmers Guide gives an introduction to the ECL language along with example data and use cases.

ECL Language Reference

ECL is the Enterprise Control Language designed specifically for huge data projects using the HPCC platform. Its extreme scalability comes from a design that allows you to leverage every query you create for re-use in subsequent queries as needed. To do this, ECL takes a dictionary approach to building queries wherein each ECL definition defines an Attribute. Each previously defined Attribute can then be used in succeeding ECL Attribute definitions as the language extends itself as you use it.

Visualizing ECL Results

The ECL compiler now supports adding external resources to a Workunit using a manifest file. The manifest, in XML format, adds external resources such as XSLT transformations, or other objects to the archive before it is compiled and deployed to the server. This document demonstrates how to use this feature to visualize ECL job results using Google Chart Tools.