Mac OS X¶
This section describes how to install, start, stop, and upgrade the Driverless AI Docker image on Mac OS X. Note that this uses regular Docker and not NVIDIA Docker. GPU support will not be available.
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.
- This is an extremely memory-constrained environment for experimental purposes only. Stick to small datasets! For serious use, please use Linux.
- Be aware that there are known performace issues with Docker for Mac. More information is available here: https://docs.docker.com/docker-for-mac/osxfs/#technology.
|Operating System||GPU Support?||Min Mem||Suitable for|
|Mac OS X||No||16 GB||Experimentation|
Installing Driverless AI¶
- Retrieve the Driverless AI Docker image from https://www.h2o.ai/download/.
- Download and run Docker for Mac from https://docs.docker.com/docker-for-mac/install.
- 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. You can optionally adjust the number of CPUs given to Docker. You will find the controls by clicking on (Docker Whale)->Preferences->Advanced as shown in the following screenshots. (Don’t forget to Apply the changes after setting the desired memory value.)
- Set up a directory for the version of Driverless AI within the Terminal, replacing VERSION below with your Driverless AI Docker image version (for example, 1.3.1):
$ mkdir dai_rel_VERSION
- With Docker running, open a Terminal and move the downloaded Driverless AI to your new directory.
- Change directories to the new directory, then load the image using the following command. Note that this example shows how to load Driverless AI version 1.3.1 for Cuda 9. Replace this with your image.
$ cd dai_rel_VERSION $ docker load < dai-docker-centos7-x86_64-1.3.1-9.0.tar.gz
- Set up the data, log, license, and tmp directories (within the new Driverless AI directory):
$ mkdir data $ mkdir log $ mkdir license $ mkdir tmp
- Copy data into the data directory on the host. The data will be visible inside the Docker container at /data.
- Start the Driverless AI Docker image. GPU support will not be available.
$ docker run \ --pid=host \ --init \ --rm \ --shm-size=256m \ -u `id -u`:`id -g` \ -p 12345:12345 \ -v `pwd`/data:/data \ -v `pwd`/log:/log \ -v `pwd`/license:/license \ -v `pwd`/tmp:/tmp \ h2oai/dai-centos7-x86_64:1.3.1-9.0
- 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 so that existing experiments are saved. The instructions show an example of upgrading Driverless AI from version 1.0.18 to version 1.3.1.
WARNING: This is currently alpha software status. Back up your data (especially the Driverless AI tmp directory) before attempting.
WARNING: Experiments, MLIs, and MOJOs are not automatically upgraded when Driverless AI is upgraded.
- Build MLI models before upgrading.
- Build MOJO pipelines 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.
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_1.3.1 # cd into the new directory cd dai_rel_1.3.1
- 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 1.3.1 for Cuda 9 on Linux. If necessary, replace
centos7-x86_64-1.3.1-9.0with your image.
# Load the Driverless AI docker image docker load < dai-docker-centos7-x86_64-1.3.1-9.0.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 -r dai_rel_1.0.18/data dai_rel_1.3.1/data cp -r dai_rel_1.0.18/log dai_rel_1.3.1/log cp -r dai_rel_1.0.18/license dai_rel_1.3.1/license cp -r dai_rel_1.0.18/tmp dai_rel_1.3.1/tmp
At this point, your experiments from the previous versions will be visible inside the Docker container.
- Start the Driverless AI Docker image.
- Connect to Driverless AI with your browser at http://Your-Driverless-AI-Host-Machine:12345.