Install on NVIDIA DGX 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 Yes 128 GB Serious use
NVIDIA DGX Station Yes 64 GB Serious Use

Installing the NVIDIA DGX Registry

Note: The DGX installation instructions assume that you are running on an NVIDIA DGX machine. Driverless AI is only available in the registry for DGX machines. If you are not running on an NVIDIA DGX machine, then follow the installation instructions for Ubuntu.

  1. Log in to your NVIDIA DGX account at https://compute.nvidia.com/registry.
  2. In the Registry menu, select one of the h2oai-driverless-ai options. Note that one registry is for Cuda 8 and the other is for Cuda 9.
../_images/dgx_select_h2odai.png
  1. At the bottom of the screen, select one of the H2O Driverless AI tags to retrieve the pull command.
../_images/dgx_select_tag.png
  1. 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
  1. Set up a directory for the version of Driverless AI on the host machine, replacing VERSION below with your Driverless AI Docker image version (for example, 1.1.3):
# Set up directory with the version name
mkdir dai_rel_VERSION
  1. 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
  1. At this point, you can copy data into the data directory on the host machine. The data will be visible inside the Docker container.
  2. 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.
nvidia-persistenced --user <USER>
nvidia-smi -pm 1
  1. Start the Driverless AI Docker image. The following example starts the Driverless AI image that is tagged with latest. Specify a different tag if you are using a version of Driverless AI other than latest.
nvidia-docker run \
   --pid=host \
   --init \
   --rm \
   -u `id -u`:`id -g` \
   -p 12345:12345 \
   -p 54321:54321 \
   -p 9090:9090 \
   -v `pwd`/data:/data \
   -v `pwd`/log:/log \
   -v `pwd`/license:/license \
   -v `pwd`/tmp:/tmp \
   nvcr.io/h2oai/h2oai-driverless-ai:latest

Driverless AI will begin running:

---------------------------------
Welcome to H2O.ai's Driverless AI
---------------------------------
   version: X.Y.Z

- Put data in the volume mounted at /data
- Logs are written to the volume mounted at /log/YYYYMMDD-HHMMSS
- Connect to Driverless AI on port 12345 inside the container
- Connect to Jupyter notebook on port 8888 inside the container
  1. Connect to Driverless AI with your browser:
http://Your-Driverless-AI-Host-Machine:12345

Upgrading Driverless AI

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

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

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