Install on NVIDIA GPU Cloud/NGC Registry¶

Driverless AI is supported on the following NVIDIA DGX products, and the installation steps for each platform are the same.

Environment¶

Provider	GPUs	Min Memory	Suitable for
NVIDIA GPU Cloud	Yes		Serious use
NVIDIA DGX-1/DGX-2	Yes	128 GB	Serious use
NVIDIA DGX Station	Yes	64 GB	Serious Use

Installing the NVIDIA NGC Registry¶

Note: These installation instructions assume that you are running on an NVIDIA DGX machine. Driverless AI is only available in the NGC registry for DGX machines.

Log in to your NVIDIA GPU Cloud account at https://ngc.nvidia.com/registry. (Note that NVIDIA Compute is no longer supported by NVIDIA.)
In the Registry > Partners menu, select h2oai-driverless.

At the bottom of the screen, select one of the H2O Driverless AI tags to retrieve the pull command.

On your NVIDIA DGX machine, open a command prompt and use the specified pull command to retrieve the Driverless AI image. For example:

docker pull nvcr.io/nvidia_partners/h2o-driverless-ai:latest

Set up a directory for the version of Driverless AI on the host machine, replacing VERSION below with your Driverless AI Docker image version:

# Set up directory with the version name
mkdir dai_rel_VERSION

Set up the data, log, license, and tmp directories on the host machine:

# cd into the directory associated with the selected version of Driverless AI
cd dai_rel_VERSION

# Set up the data, log, license, and tmp directories on the host machine
mkdir data
mkdir log
mkdir license
mkdir tmp

At this point, you can copy data into the data directory on the host machine. The data will be visible inside the Docker container.
Enable persistence of the GPU. Note that this only needs to be run once. Refer to the following for more information: http://docs.nvidia.com/deploy/driver-persistence/index.html.

sudo nvidia-persistenced --persistence-mode

Run docker images to find the new image tag.
Start the Driverless AI Docker image with nvidia-docker and replace TAG below with the image tag:

nvidia-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 \
   nvcr.io/h2oai/h2oai-driverless-ai:TAG

Driverless AI will begin running:

--------------------------------
Welcome to H2O.ai's Driverless AI
---------------------------------

- Put data in the volume mounted at /data
- Logs are written to the volume mounted at /log/20180606-044258
- Connect to Driverless AI on port 12345 inside the container
- Connect to Jupyter notebook on port 8888 inside the container

Connect to Driverless AI with your browser:

http://Your-Driverless-AI-Host-Machine:12345

Stopping Driverless AI¶

Use Ctrl+C to stop Driverless AI.

Upgrading Driverless AI¶

The steps for upgrading Driverless AI on an NVIDIA DGX system are similar to the installation steps.

WARNINGS:

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 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.

Note: Use Ctrl+C to stop Driverless AI if it is still running.

Requirements¶

As of 1.7.0, CUDA 9 is no longer supported. Your host environment must have CUDA 10.0 or later with NVIDIA drivers >= 410 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.

Upgrade Steps¶

On your NVIDIA DGX machine, create a directory for the new Driverless AI version.
Copy the data, log, license, and tmp directories from the previous Driverless AI directory into the new Driverless AI directory.
Run docker pull nvcr.io/h2oai/h2oai-driverless-ai:latest to retrieve the latest Driverless AI version.
Start the Driverless AI Docker image.
Connect to Driverless AI with your browser at http://Your-Driverless-AI-Host-Machine:12345.