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.
Driverless AI is tested most extensively on Chrome and Firefox. For the best user experience, we recommend using the latest version of Chrome. You may encounter issues if you use other browsers or earlier versions of Chrome and/or Firefox.
sudo or Not to
Many of the installation steps show
sudo prepending different commands. Note that
sudo may not always be required, but the steps that are documented here are the steps that we followed in house.
Note about Docker Configuration (
When running Driverless AI with Docker, it is recommended to configure
ulimit options by using the
--ulimit argument to
docker run. The following is an example of how to configure these options:
--ulimit nproc=65535:65535 \ --ulimit nofile=4096:8192 \
Refer to https://docs.docker.com/engine/reference/commandline/run/#set-ulimits-in-container—ulimit for more information on these options.
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.
nvidia-smi command has been deprecated by NVIDIA. Refer to https://github.com/nvidia/nvidia-docker#upgrading-with-nvidia-docker2-deprecated for more information. The installation steps have been updated for enabling persistence mode for GPUs.
nvidia-container-runtime-hook Requirement for PowerPC Users¶
PowerPC users are now required to install the
nvidia-container-runtime-hook when running in Docker. Refer to https://github.com/nvidia/nvidia-docker#rhel-docker for more information. The IBM Docker installation steps have been updated to reflect this information.
Note About CUDA Versions¶
Your host environment must have CUDA 10.0 or later with NVIDIA drivers >= 440.82 installed (GPU only). 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 firstname.lastname@example.org for more information about using IBM Spectrum Conductor authentication.
Note About the Master Database File¶
If you are running two versions of Driverless AI, keep in mind that newer versions of the master.db file will not work with older versions of Driverless AI.
We recommend that you periodically stop Driverless AI and back up your Driverless AI tmp directory, even if you are not upgrading.
Keep in mind the following when upgrading Driverless AI:
This release deprecates experiments and MLI models from 1.7.0 and earlier.
Experiments, MLIs, and MOJOs reside in the Driverless AI tmp directory and are not automatically upgraded when Driverless AI is upgraded. We recommend you take the following steps before 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.