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Welcome to HEAVY.AI Documentation
Overview
Overview
Release Notes
Installation and Configuration
System Requirements
Installation
Free Version
Installing on CentOS
Installing on Ubuntu
Installing on Docker
Getting Started on AWS
Getting Started on GCP
Getting Started on Azure
Upgrading
Uninstalling
Ports
Services and Utilities
Configuration Parameters
Security
Distributed Configuration
Loading and Exporting Data
Supported Data Sources
Command Line
SQL
Data Definition (DDL)
Data Manipulation (DML)
Heavy Immerse
Introduction to Heavy Immerse
Admin Portal
Working with Dashboards
Measures and Dimensions
Using Parameters
Using Filters
Multilayer Charts
SQL Editor
Customization
Chart Types
Data Science
Data Science Foundation
JupyterLab Installation and Configuration
Using HEAVY.AI with JupyterLab
Introduction to Ibis
Interactive Data Exploration with Altair
Additional Examples
UDF and UDTF
Remote Backend Compiler (RBC)
APIs and Interfaces
Overview
heavysql
Thrift
JDBC
ODBC
Vega
RJDBC
Python: heavyai
SQuirreL SQL
mapd-connector
Tutorials and Demos
Loading Data
Using Heavy Immerse
Hello World
Creating a Kafka Streaming Application
Getting Started with Open Source
Try Vega
Troubleshooting and Special Topics
Vulkan Renderer
FAQ
Optimizing
Known Issues and Limitations
Logs and Monitoring
HeavyConnect
HeavyConnect Release Overview
Getting Started
Best Practices
Examples
Command Reference
Parquet Data Wrapper Reference
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Getting Started on Azure
Getting Started with HEAVY.AI on Microsoft Azure
Follow these instructions to get started with HEAVY.AI on Microsoft Azure.

Prerequisites

You must have a Microsoft Azure account. If you do not have an account, go to the Micrsoft Azure home page to sign up for one.

Configure Your HEAVY.AI Instance

To launch HEAVY.AI on Microsoft Azure, you configure a GPU-enabled instance.
1) Log in to you Microsoft Azure portal.
2) On the left side menu, create a Resource group, or use one that your organization has created.
azure_resource_group.png
3) On the left side menu, click Virtual machines, and then click Add.
azure_vm
4) Create your virtual machine:
azure_configure_vm
  • On the Basics tab:
    • In Project Details, specify the Resource group.
    • Specify the Instance Details:
      • Virtual machine name
      • Region
      • Image (Ubuntu 16.04 or higher, or CentOS/RHEL 7.0 or higher)
      • Size. Click Change size and use the Family filter to filter on GPU, based on your use case and requirements. Not all GPU VM variants are available in all regions.
    • For Username, add any user name other than admin.
    • In Inbound Port Rules, click Allow selected ports and select one or more of the following:
      • HTTP (80)
      • HTTPS (443)
      • SSH (22)
  • On the Disks tab, select Premium or Standard SSD, depending on your needs.
  • For the rest of the tabs and sections, use the default values.
5) Click Review + create. Azure reviews your entries, creates the required services, deploys them, and starts the VM.
6) Once the VM is running, select the VM you just created and click the Networking tab.
azure_networking
7) Click the Add inbound button and configure security rules to allow any source, any destination, and destination port 6273 so you can access Heavy Immerse from a browser on that port. Consider renaming the rule to 6273-Immerse or something similar so that the default name makes sense.
8) Click Add and verify that your new rule appears.
Azure-specific configuration is complete. Now, follow standard HEAVY.AI installation instructions for your Linux distribution and installation method.
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Contents
Prerequisites
Configure Your HEAVY.AI Instance