Windows 10 Pro¶
This section describes how to install, start, stop, and upgrade Driverless AI on a Windows 10 Pro machine. 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/driverless-ai/. Once obtained, you will be promted to paste the license key into the Driverless AI UI when you first log in, or you can save it as a .sig file and place it in the license folder that you will create during the installation process.
Overview of Installation on Windows¶
The recommended way of installing Driverless AI on Windows is via WSL Ubuntu. Running a Driverless AI Docker image on Windows is also possible but not preferred.
Note: GPU support is not available on Windows.
Caution: This should be used only for experimental purposes and only on small data. For serious use, please use Linux.
|Operating System||GPU Support?||Min Mem||Suitable for|
|Windows 10 Pro||No||16 GB||Experimentation|
This section describes how to install the Driverless AI DEB on Windows 10 using Windows Subsystem for Linux (WSL).
(Note that systemd is not supported for Linux on Windows.)
Run the following commands to install and run the Driverless AI DEB. Replace <VERSION> with your specific version.
# Install Driverless AI. Expect installation of the .deb file to take several minutes on WSL. sudo dpkg -i dai_VERSION.deb # Run Driverless AI. sudo -H -u dai /opt/h2oai/dai/run-dai.sh
Upgrading the DEB¶
The Driverless AI Windows DEB cannot be upgraded. In order to run to a newer version, you must first uninstall the prior version and then install the newer one.
WARNING: Perform the following before uninstalling and then reinstalling Driverless AI.
- Build MLI models.
- Build MOJO pipelines.
- Back up your Driverless AI tmp directory.
If you did not build MLI on a model before installing a newer Driverless AI version, then you will not be able to view MLI on that model after. Before installing a newer version, 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 installing the newer version.
If you did not build a MOJO pipeline on a model before installing a newer Driverless AI version then you will not be able to build a MOJO pipeline on that model after. Before installing a newer version, be sure to build MOJO pipelines on all desired models and then back up your Driverless AI tmp directory.
Run the following commands to uninstall a prior version.
# Stop Driverless AI. sudo pkill -U dai # The processes should now be stopped. Verify. sudo ps -u dai # Back up your /opt/h2oai/dai/tmp directory at this time. # Uninstall Driverless AI. sudo dpkg -r dai # If the above uninstall command results in a message # "failed to lookup unit file state: invalid argument," # then try the below command to force uninstall. sudo dpkg --purge --force-all dai
At this point, follow the previous installation procedure to install a newer version of Driverless AI.
Docker Image Installs¶
- Installing the Driverless AI Docker image on Windows is not the recommended method for running Driverless AI. RPM and DEB installs are preferred.
- Be aware that there are known issues with Docker for Windows. More information is available here: https://github.com/docker/for-win/issues/188.
- Consult with your Windows System Admin if
- Your corporate environment does not allow third-part software installs
- You are running Windows Defender
- You your machine is not running with
Enable-WindowsOptionalFeature -Online -FeatureName Microsoft-Windows-Subsystem-Linux.
Watch the installation video here. Note that some of the images in this video may change between releases, but the installation steps remain the same.
- Windows 10 Pro
- Retrieve the Driverless AI Docker image from https://www.h2o.ai/download/.
- Download, install, and run Docker for Windows from https://docs.docker.com/docker-for-windows/install/. You can verify that Docker is running by typing
docker versionin a terminal (such as Windows PowerShell). Note that you may have to reboot after installation.
- Before running Driverless AI, you must:
- Enable shared access to the C drive. Driverless AI will not be able to see your local data if this is not set.
- Adjust the amount of memory given to Docker to be at least 10 GB. Driverless AI won’t run at all with less than 10 GB of memory.
- Optionally adjust the number of CPUs given to Docker.
You can adjust these settings by clicking on the Docker whale in your taskbar (look for hidden tasks, if necessary), then selecting Settings > Shared Drive and Settings > Advanced as shown in the following screenshots. Don’t forget to Apply the changes after setting the desired memory value. (Docker will restart.) Note that if you cannot make changes, stop Docker and then start Docker again by right clicking on the Docker icon on your desktop and selecting Run as Administrator.
- Open a PowerShell terminal and set up a directory for the version of Driverless AI on the host machine, replacing VERSION below with your Driverless AI Docker image version:
- With Docker running, navigate to the location of your downloaded Driverless AI image. Move the downloaded Driverless AI image to your new directory.
- Change directories to the new directory, then load the image using the following command. This example shows how to load Driverless AI. Replace VERSION with your image.
cd dai_rel_VERSION docker load -i .\dai-docker-centos7-x86_64-VERSION.tar.gz
- Set up the data, log, license, and tmp directories (within the new directory).
md data md log md license md tmp
- Copy data into the /data directory. The data will be visible inside the Docker container at /data.
docker imagesto find the image tag.
- Start the Driverless AI Docker image. Be sure to replace
path_to_below with the entire path to the location of the folders that you created (for example, “c:/Users/user-name/driverlessai_folder/data”), and replace TAG with the Docker image tag. Note that this is regular Docker, not NVIDIA Docker. GPU support will not be available.
docker run --pid=host --init --rm --shm-size=256m -p 12345:12345 -v c:/path_to_data:/data -v c:/path_to_log:/log -v c:/path_to_license:/license -v c:/path_to_tmp:/tmp h2oai/dai-centos7-x86_64:TAG
- Connect to Driverless AI with your browser at http://localhost:12345.
Stopping the Docker Image¶
To stop the Driverless AI Docker image, type Ctrl + C in the Terminal (Mac OS X) or PowerShell (Windows 10) window that is running the Driverless AI Docker image.
Upgrading the Docker Image¶
This section provides instructions for upgrading Driverless AI versions that were installed in a Docker container. These steps ensure that existing experiments are saved.
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.
Note: Stop Driverless AI if it is still running.
- SSH into the IP address of the machine that is running Driverless AI.
- Set up a directory for the version of Driverless AI on the host machine:
# Set up directory with the version name mkdir dai_rel_VERSION # cd into the new directory cd dai_rel_VERSION
- Retrieve the Driverless AI package from https://www.h2o.ai/download/ and add it to the new directory.
- Load the Driverless AI Docker image inside the new directory. This example shows how to load Driverless AI version. If necessary, replace
VERSIONwith your image.
# Load the Driverless AI docker image docker load < dai-docker-centos7-x86_64-VERSION.tar.gz
- Copy the data, log, license, and tmp directories from the previous Driverless AI directory to the new Driverless AI directory:
# Copy the data, log, license, and tmp directories on the host machine cp -a dai_rel_1.4.2/data dai_rel_VERSION/data cp -a dai_rel_1.4.2/log dai_rel_VERSION/log cp -a dai_rel_1.4.2/license dai_rel_VERSION/license cp -a dai_rel_1.4.2/tmp dai_rel_VERSION/tmp
At this point, your experiments from the previous versions will be visible inside the Docker container.
docker imagesto find the new image tag.
- Start the Driverless AI Docker image.
- Connect to Driverless AI with your browser at http://Your-Driverless-AI-Host-Machine:12345.