HEAVY.AI Docs
v8.1.0
v8.1.0
  • Welcome to HEAVY.AI Documentation
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        • HEAVY.AI Installation using Docker on Ubuntu
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        • HEAVY.AI Installation on Ubuntu
        • Install NVIDIA Drivers and Vulkan on Ubuntu
      • Installing on Rocky Linux / RHEL
        • HEAVY.AI Installation on RHEL
        • Install NVIDIA Drivers and Vulkan on Rocky Linux and RHEL
      • Getting Started on AWS
      • Getting Started on GCP
      • Getting Started on Azure
      • Getting Started on Kubernetes (BETA)
      • Upgrading
        • Upgrading HEAVY.AI
        • Upgrading from Omnisci to HEAVY.AI 6.0
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        • Uber H3 Hexagonal Modeling
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        • generate_random_strings
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        • tf_raster_graph_shortest_slope_weighted_path
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  • HeavyImmerse
    • Introduction to HeavyImmerse
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      • Bar
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    • HeavyIQ SQL Notebook
  • HEAVYIQ Conversational Analytics
    • HeavyIQ Overview
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  • HeavyRF
    • Introduction to HeavyRF
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  • HeavyConnect
    • HeavyConnect Release Overview
    • Getting Started
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    • Parquet Data Wrapper Reference
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  • HeavyML (BETA)
    • HeavyML Overview
    • Clustering Algorithms
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      • Linear Regression
      • Random Forest Regression
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      • Gradient Boosting Tree Regression
    • Principal Components Analysis
  • Python / Data Science
    • Data Science Foundation
    • JupyterLab Installation and Configuration
    • Using HEAVY.AI with JupyterLab
    • Python User-Defined Functions (UDFs) with the Remote Backend Compiler (RBC)
      • Installation
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      • User-Defined Table Functions
      • RBC UDF/UDTF Example Notebooks
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    • Ibis
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  • APIs and Interfaces
    • Overview
    • heavysql
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    • Vega
      • Vega Tutorials
        • Vega at a Glance
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        • Getting More from Your Data
        • Creating More Advanced Charts
        • Using Polys Marks Type
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        • Using Transform Aggregation
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      • Vega Reference Overview
        • data Property
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      • Try Vega
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  • Tutorials and Demos
    • Loading Data
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    • Hello World
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    • Try Vega
  • Troubleshooting and Special Topics
    • FAQs
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    • Vulkan Renderer
    • Optimizing
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    • Archived Release Notes
      • Release 6.x
      • Release 5.x
      • Release 4.x
      • Release 3.x
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On this page
  • Upgrading from Omnisci
  • Upgrading Using Docker
  • Upgrading HEAVY.AI Using Package Managers and Tarball
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  1. Installation and Configuration
  2. Installation
  3. Upgrading

Upgrading HEAVY.AI

This section is giving a recipe to upgrade between fully compatible products version.

PreviousUpgradingNextUpgrading from Omnisci to HEAVY.AI 6.0

Last updated 11 months ago

This section is giving a recipe to upgrade between fully compatible products version.

As with any software upgrade, it is important that you back up your data before upgrading. Each release introduces efficiencies that are not necessarily compatible with earlier releases of the platform. HeavyAI is never expected to be backward compatible.

Back up the contents of your $HEAVYAI_STORAGE directory.

Upgrading from Omnisci

If you need to upgrade from Omnisci to HEAVY.AI 6.0 or later, please refer to the specific recipe.

Direct upgrades from Omnisci to HEAVY.AI version later than 6.0 aren't allowed nor supported.

Upgrading Using Docker

To upgrade HEAVY.AI in place in Docker

In a terminal window, get the Docker container ID.

sudo docker container ps --format "{{.Id}} {{.Image}}" \
-f status=running | grep omnisci\/

You should see output similar to the following. The first entry is the container ID. In this example, it is 9e01e520c30c:

9e01e520c30c omnisci/omnisci-ee-gpu

Stop the HEAVY.AI Docker container. For example:

docker container stop 9e01e520c30c

Optionally, remove the HEAVY.AI Docker container. This removes unused Docker containers on your system and saves disk space.

docker container rm 9e01e520c30c

Backup the Omnisci data directory (typically /var/lib/omnisci)

tar zcvf /backup_dir/omnisci_storage_backup.tar.gz /var/lib/omnisci

Download the latest version of the HEAVY.AI Docker image according to the Edition and device you are actually coming from Select the tab depending on the Edition (Enterprise, Free, or Open Source) and execution Device (GPU or CPU) you are upgrading.

sudo docker run -d --gpus=all \
  -v /var/lib/heavyai:/var/lib/heavyai \
  -p 6273-6278:6273-6278 \
  heavyai/heavyai-ee-cuda:latest
sudo docker run -d -v \
/var/lib/heavyai:/var/lib/heavyai \
-p 6273-6278:6273-6278 \
heavyai/heavyai-ee-cpu:latest
sudo docker run -d --gpu=all \
  -v /var/lib/heavyai:/var/lib/heavyai \
  -p 6273-6278:6273-6278 \
  heavyai/core-os-cuda:latest
sudo docker run -d -v \
/var/lib/heavyai:/var/lib/heavyai \
-p 6273-6278:6273-6278 \
heavyai/core-os-cpu:latest

If you don't want to upgrade to the latest version but want to upgrade to a specific version, change thelatesttag with the version needed.

If the version needed is the 6.0 use v6.0.0 as the version tag in the image name

heavyai/heavyai-ee-cuda:v6.0.0

Check that the docker is up and running a docker ps commnd:

sudo docker container ps --format "{{.Image}} {{.Status}}" \
-f status=running | grep heavyai\/

You should see an output similar to the following.

heavyai/heavyai-ee-cuda Up 48 seconds ago 

This runs both the HEAVY.AI database and Immerse in the same container.

You can optionally add --rm to the Docker run command so that the container is removed when it is stopped.

Upgrading HEAVY.AI Using Package Managers and Tarball

To upgrade an existing system installed with package managers or tarball. The commands upgrade HEAVY.AI in place without disturbing your configuration or stored data

Stop the HEAVY.AI services.

sudo systemctl stop heavydb heavy_web_server

Back up your $HEAVYAI_STORAGE directory (the default location is /var/lib/heavyai).

Run the appropriate set of commands depending on the method used to install the previous version of the software.

sudo yum update heavyai.x86_64
sudo apt update
sudo apt upgrade heavyai

Make a backup of your actual installation

sudo mv /opt/heavyai /opt/heavyai_backup

When the upgrade is complete, start the HEAVY.AI services.

sudo systemctl start heavydb heavy_web_server

See also the note regarding the in Optimizing Performance.

Download and Install the latest version following the install documentation for your Operative System and

Upgrading from Omnisci to HEAVY.AI 6.0
CUDA JIT Cache
CentOS/RHEL
Ubuntu