Install on Ubuntu

This section describes how to install the Driverless AI Docker image on Ubuntu. The installation steps vary depending on whether your system has GPUs or if it is CPU only.

Environment

Operating System GPUs? Min Mem
Ubuntu with GPUs Yes 64 GB
Ubuntu with CPUs No 64 GB

Install on Ubuntu with GPUs

Note: Driverless AI is supported on Ubuntu 16.04 or later.

Open a Terminal and ssh to the machine that will run Driverless AI. Once you are logged in, perform the following steps.

  1. Retrieve the Driverless AI Docker image from https://www.h2o.ai/download/.
  2. Install and run Docker on Ubuntu (if not already installed):
# Install and run Docker on Ubuntu
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \
 "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt-get update
sudo apt-get install docker-ce
sudo systemctl start docker-ce
  1. Install nvidia-docker2 (if not already installed). More information is available at https://github.com/NVIDIA/nvidia-docker/blob/master/README.md.
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update

# Install nvidia-docker2 and reload the Docker daemon configuration
sudo apt-get install -y nvidia-docker2
  1. Verify that the NVIDIA driver is up and running. If the driver is not up and running, log on to http://www.nvidia.com/Download/index.aspx?lang=en-us to get the latest NVIDIA Tesla V/P/K series driver:
nvidia-smi
  1. 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. 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, 1.3.1):
# Set up directory with the version name
mkdir dai_rel_VERSION
  1. Change directories to the new folder, then 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. Replace this with your image.
# cd into the new directory
cd dai_rel_VERSION

# Load the Driverless AI docker image
docker load < dai-docker-centos7-x86_64-1.3.1-9.0.tar.gz
  1. Set up the data, log, and license directories on the host machine:
# Set up the data, log, license, and tmp directories on the host machine (within the new directory)
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. Start the Driverless AI Docker image with nvidia-docker:
# Start the Driverless AI Docker image
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 \
    h2oai/dai-centos7-x86_64:1.3.1-9.0

Driverless AI will begin running:

--------------------------------
Welcome to H2O.ai's Driverless AI
---------------------------------
     version: 1.3.1

- 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
  1. Connect to Driverless AI with your browser:
http://Your-Driverless-AI-Host-Machine:12345

Install on Ubuntu with CPUs

Note: Driverless AI is supported on Ubuntu 16.04 or later.

This section describes how to install and start the Driverless AI Docker image on Ubuntu. Note that this uses Docker EE and not NVIDIA Docker. GPU support will not be available.

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.

Open a Terminal and ssh to the machine that will run Driverless AI. Once you are logged in, perform the following steps.

  1. Retrieve the Driverless AI Docker image from https://www.h2o.ai/download/.
  2. Install and run Docker on Ubuntu (if not already installed):
# Install and run Docker on Ubuntu
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \
 "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt-get update
sudo apt-get install docker-ce
sudo systemctl start docker-ce
  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, 1.3.1):
# Set up directory with the version name
mkdir dai_rel_VERSION
  1. Change directories to the new folder, then 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. Replace this with your image.
# cd into the new directory
cd dai_rel_VERSION

# Load the Driverless AI docker image
docker load < dai-docker-centos7-x86_64-1.3.1-9.0.tar.gz
  1. Set up the data, log, license, and tmp directories on the host machine (within the new directory):
# Set up the data, log, license, and tmp directories
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. Start the Driverless AI Docker image:
# Start the Driverless AI Docker image
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

Driverless AI will begin running:

--------------------------------
Welcome to H2O.ai's Driverless AI
---------------------------------
     version: 1.3.1

- 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
  1. Connect to Driverless AI with your browser:
http://Your-Driverless-AI-Host-Machine: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.

  1. SSH into the IP address of the machine that is running Driverless AI.
  2. 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
  1. Retrieve the Driverless AI package from https://www.h2o.ai/download/ and add it to the new directory.
  2. 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_64-1.3.1-9.0 with your image.
# Load the Driverless AI docker image
docker load < dai-docker-centos7-x86_64-1.3.1-9.0.tar.gz
  1. 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.

  1. Start the Driverless AI Docker image.
  2. Connect to Driverless AI with your browser at http://Your-Driverless-AI-Host-Machine:12345.