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1.8.7.1

Release Notes

  • H2O Driverless AI Release Notes

Overview

  • Why Driverless AI?
  • Key Features
  • Supported Algorithms
  • Driverless AI Workflow

Licensing

  • Driverless AI Licenses

Installation and Upgrade

  • Supported Environments
  • Before You Begin Installing or Upgrading
  • Sizing Requirements
  • Installing and Upgrading Driverless AI
    • Linux X86_64 Installs
      • Linux Docker Images
      • Linux RPMs
      • Linux DEBs
      • Linux TAR SH
      • Linux in the Cloud
    • IBM Power Installs
    • Mac OS X
    • Windows 10

Configuring Driverless AI

  • Using the config.toml File
  • Environment Variables and Configuration Options
  • Enabling Data Connectors
  • Configuring Authentication
  • Enabling Notifications
  • Export Artifacts
  • Changing the Language in the UI

Using Driverless AI

  • Launching Driverless AI
  • The Datasets Page
  • Experiments
  • Diagnosing a Model
  • Project Workspace

Machine Learning Interpretability

  • MLI Overview
  • The Interpreted Models Page
  • MLI for Regular (Non-Time-Series) Experiments
  • MLI for Time-Series Experiments

Scoring on New Datasets

  • Score on Another Dataset

Transforming Datasets

  • Transform Another Dataset

Scoring Pipelines

  • Scoring Pipelines Overview
  • Visualizing the Scoring Pipeline
  • Which Pipeline Should I Use?
  • Driverless AI Standalone Python Scoring Pipeline
  • Driverless AI MLI Standalone Python Scoring Package
  • MOJO Scoring Pipelines

MOJO Pipeline Deployments

  • Deploying the MOJO Pipeline

What's Happening?

  • What’s Happening in Driverless AI?
  • Data Sampling
  • Driverless AI Transformations
  • Internal Validation Technique
  • Missing and Unseen Levels Handling
  • Imputation in Driverless AI
  • Time Series in Driverless AI
  • NLP in Driverless AI

Python and R Clients

  • The Python Client
  • The R Client

Logs

  • Driverless AI Logs
  • Sending Logs to H2O.ai
  • System Logs

Security

  • Driverless AI Security

Frequently Asked Questions

  • FAQ
  • Tips ‘n Tricks

Appendices

  • Appendix A: Custom Recipes
  • Appendix B: Third-Party Integrations

References

  • References
Using Driverless AI
  • »
  • Installing and Upgrading Driverless AI »
  • Linux X86_64 Installs
  • Edit on GitHub

Linux X86_64 Installs¶

This section provides installation steps for Linux 86_64 environments. This includes information for Docker image installs, RPMs, Deb, and Tar installs as well as Cloud installations.

  • Linux Docker Images
    • Install on Ubuntu
    • Install on RHEL
    • Install on NVIDIA GPU Cloud/NGC Registry
  • Linux RPMs
    • Environment
    • Requirements
    • About the Install
    • Installing Driverless AI
    • Starting Driverless AI
    • Starting NVIDIA Persistence Mode
    • Installing CUDA with NVIDIA drivers
    • Installing OpenCL
    • Installing cuDNN
    • Looking at Driverless AI log files
    • Stopping Driverless AI
    • Upgrading Driverless AI
    • Uninstalling Driverless AI
  • Linux DEBs
    • Environment
    • Requirements
    • About the Install
    • Starting NVIDIA Persistence Mode (GPU only)
    • Install OpenCL
    • Installing the Driverless AI Linux DEB
    • Starting Driverless AI
    • Looking at Driverless AI log files
    • Stopping Driverless AI
    • Upgrading Driverless AI
    • Uninstalling Driverless AI
    • Common Problems
  • Linux TAR SH
    • Requirements
    • Installing Driverless AI
    • Starting Driverless AI
    • Starting NVIDIA Persistence Mode
    • Install OpenCL
    • Looking at Driverless AI log files
    • Stopping Driverless AI
    • Uninstalling Driverless AI
    • Upgrading Driverless AI
  • Linux in the Cloud
    • Install on AWS
    • Install on Azure
    • Install on Google Compute
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