Updated January 24, 2024

H2O is an open-source, in-memory, distributed platform designed for machine learning and predictive analytics on big data. Built with speed and scalability in mind, H2O enables you to effortlessly build and deploy high-quality models in an enterprise environment. H2O’s core code, written in Java, leverages a Distributed Key/Value store and a distributed Map/Reduce framework for seamless data access and processing across nodes. With intelligent data parsing and support for multiple formats, H2O simplifies data ingest from various sources.
This image is based on Ubuntu and contains H2O-3, the third incarnation of H2O. It is compatible with NVIDIA® GPUs available in Nebius AI, which allows acceleration of machine learning and other compute-intensive applications.

Deployment instructions
  1. Create an SSH key pair.

  2. Click the button in this card to go to VM creation. The image will be automatically selected under Image/boot disk selection.

  3. Under Network settings, enable a public IP address for the VM (Public IP: Auto for a random address or List if you have a reserved static address).

  4. Under Access, paste the public key from the pair into the SSH key field.

  5. Create the VM.

  6. Connect to the VM via SSH using local forwarding for TCP port 54321. For example:

    ssh -i <path_to_public_SSH_key> -L 54321:localhost:54321 <username>:<VM's_public_IP_address>

    The ufw firewall in this product only allows incoming traffic to port 22 (SSH). This is why you need local port forwarding when connecting.

  7. To use H2O Flow, the H2O-3 user interface, go to http://localhost:54321 in your web browser.

H2O-3 is started as a systemd daemon named h2o.service, listening to incoming connections on port 54321. If you want to set H2O-3 up to start in another way (see Starting H2O in the H2O-3 documentation), you need to stop this service first:

sudo systemctl stop h2o.service
sudo systemctl disable h2o.service

H2O-3 binary files are located in /usr/local/h2o.

Billing type
Virtual Machine
Machine Learning & AI
Use cases
  • Financial services: credit scoring, personalized offers, customer churn, anti-money laundering, trading strategies.
  • Healthcare: clinical workflow, capacity simulation, diagnosis assistance, infectious disease forecasting, pricing, claims assessment.
  • Insurance: fraud detection and mitigation, claims management, personalized rate management and product bundling.
  • Manufacturing: predictive design and maintenance, transportation optimization.
  • Marketing: audience segmentation, content personalization, next best offer and next best action.
  • Retail: assortment and pricing optimization.
  • Telecom: customer support, fleet maintenance.
Technical support

Nebius AI does not provide technical support for the product. If you have any issues, please refer to the developer’s information resources.

Product IDs
Product composition
Ubuntu22.04 LTS
NVIDIA CUDA Toolkit12.1.1
NVIDIA Container Toolkit1.13.3-1
NVIDIA Data Center Driver535.54.03
By using this product you agree to the Nebius AI Marketplace Terms of Service and the terms and conditions of the following software: Apache 2.0NVIDIA DriversDockerNVIDIA EulaH2O.aiUbuntu
Billing type
Virtual Machine
Machine Learning & AI