Linux TAR SH¶
The Driverless AI software is available for use in pure user-mode environments as a self-extracting TAR SH archive. This form of installation does not require a privileged user to install or to run.
This artifact has the same compatibility matrix as the RPM and DEB packages (combined), it just comes packaged slightly differently. See those sections for a full list of supported environments.
The installation steps assume that you have a license key for Driverless AI. For information on how to obtain a license key for Driverless AI, visit https://www.h2o.ai/products/h2o-driverless-ai/. Once obtained, you will be prompted to paste the license key into the Driverless AI UI when you first log in.
- RedHat 7
- Cuda 9 or 9.2 with NVIDIA drivers >= 396
- cuDNN >=7.2.1 (Required only if using TensorFlow.)
- OpenCL (Required for LightGBM support on GPUs.)
- Driverless AI TAR SH, available from https://www.h2o.ai/download/.
Installing Driverless AI¶
Run the following commands to install the Driverless AI RPM. Replace <VERSION> with your specific version.
# Install Driverless AI. chmod 755 dai-VERSION.sh ./dai-VERSION.sh
Optionally install the cairo and pango graphical libraries on your system (using a system package management tool like yum, apt-get or zypper) to enable PDF generation.
You may now cd to the unpacked directory and optionally make changes to config.toml.
Starting Driverless AI¶
# Start Driverless AI. ./run-dai.sh
Starting NVIDIA Persistence Mode¶
If you have NVIDIA GPUs, you must run the following two NVIDIA commands. These commands need to be run every reboot. For more information: http://docs.nvidia.com/deploy/driver-persistence/index.html.
sudo nvidia-persistenced --user dai sudo nvidia-smi -pm 1
OpenCL is required in order to run LightGBM on GPUs. Run the following for Centos7/RH7 based systems using yum and x86.
yum -y clean all yum -y makecache yum -y update wget http://dl.fedoraproject.org/pub/epel/7/x86_64/Packages/c/clinfo-2.1.17.02.09-1.el7.x86_64.rpm wget http://dl.fedoraproject.org/pub/epel/7/x86_64/Packages/o/ocl-icd-2.2.12-1.el7.x86_64.rpm rpm -if clinfo-2.1.17.02.09-1.el7.x86_64.rpm rpm -if ocl-icd-2.2.12-1.el7.x86_64.rpm clinfo mkdir -p /etc/OpenCL/vendors && \ echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd
Looking at Driverless AI log files¶
less log/dai.out less log/h2o.out less log/procsy.out
Stopping Driverless AI¶
# Stop Driverless AI. ./kill-dai.sh
Uninstalling Driverless AI¶
To uninstall Driverless AI, just remove the directory created by the unpacking process. By default, all files for Driverless AI are contained within this directory.
Upgrading Driverless AI¶
WARNING: Experiments, MLIs, and MOJOs reside in the Driverless AI tmp directory and are not automatically upgraded when Driverless AI is upgraded.
- Build MLI models before upgrading.
- Build MOJO pipelines before upgrading.
- Back up 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.
- Stop your previous version of Driverless AI.
- Run the self-extracting archive for the new version of Driverless AI.
- Port any previous changes you made to your config.toml file to the newly unpacked directory.
- Copy the tmp directory (which contains all the Driverless AI working state) from your previous Driverless AI installation into the newly upacked directory.
- Start your newly extracted version of Driverless AI.