Before You Begin Installing or Upgrading

Please review the following information before you begin installing Driverless AI. Be sure to also review the Sizing Requirements in the next section before beginning the installation.

Note about nvidia-docker 1.0

If you have nvidia-docker 1.0 installed, you need to remove it and all existing GPU containers. Refer to https://github.com/NVIDIA/nvidia-docker/blob/master/README.md for more information.

Note about CUDA versions

Your host environment must have CUDA 10.0 or later with NVIDIA drivers >= 410 installed. Driverless AI ships with its own CUDA libraries, but the driver must exist in the host environment. Go to https://www.nvidia.com/Download/index.aspx to get the latest NVIDIA Tesla V/P/K series driver.

Note about Authentication

The default authentication setting in Driverless AI is “unvalidated.” In this case, Driverless AI will accept any login and password combination, it will not validate whether the password is correct for the specified login ID, and it will connect to the system as the user specified in the login ID. This is true for all instances, including Cloud, Docker, and native instances.

We recommend that you configure authentication. Driverless AI provides a number of authentication options, including LDAP, PAM, Local, and None. Refer to Configuring Authentication for information on how to enable a different authentication method.

Note: Driverless AI is also integrated with IBM Spectrum Conductor and supports authentication from Conductor. Contact sales@h2o.ai for more information about using IBM Spectrum Conductor authentication.

Backup Strategy

We recommend that you periodically stop Driverless AI and back up your Driverless AI tmp directory, even if you are not upgrading.

Upgrade Strategy

Keep in mind the following when upgrading Driverless AI:

Experiments, MLIs, and MOJOs reside in the Driverless AI tmp directory and are not automatically upgraded when Driverless AI is upgraded. We recommend performing the following when upgrading.

• Build MLI models before upgrading.
• Build MOJO pipelines before upgrading.
• Stop Driverless AI and then make a backup of your Driverless AI tmp directory before upgrading.

If you did not build MLI on a model before upgrading Driverless AI, then you will not be able to view MLI on that model after upgrading. Before upgrading, be sure to run MLI jobs on models that you want to continue to interpret in future releases. If that MLI job appears in the list of Interpreted Models in your current version, then it will be retained after upgrading.

If you did not build a MOJO pipeline on a model before upgrading Driverless AI, then you will not be able to build a MOJO pipeline on that model after upgrading. Before upgrading, be sure to build MOJO pipelines on all desired models and then back up your Driverless AI tmp directory.

The upgrade process inherits the service user and group from /etc/dai/User.conf and /etc/dai/Group.conf. You do not need to manually specify the DAI_USER or DAI_GROUP environment variables during an upgrade.