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:

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/. (Note that the contents of this Docker image include a CentOS kernel and CentOS packages.)

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

3. 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

4. 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


5. 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

6. 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, 1.4.0):

 ::

    # Set up directory with the version name
    mkdir dai_rel_VERSION

7. 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 1.4.0 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_64-1.4.0-9.0.tar.gz

8. 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

9. At this point, you can copy data into the data directory on the host machine.  The data will be visible inside the Docker container.


10. 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_64:1.4.0-9.0

 Driverless AI will begin running::

  --------------------------------
  Welcome to H2O.ai's Driverless AI
  ---------------------------------
       version: 1.4.0

  - 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

11. Connect to Driverless AI with your browser:

 ::

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


.. _install-on-ubuntu-cpus-only:

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 <https://www.youtube.com/watch?v=ZQRlvLVHQ3s&index=3&list=PLNtMya54qvOE9fs3ylzaR_McnoUsuMV7X>`__. 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

3. 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, 1.4.0): 

 ::

    # Set up directory with the version name
    mkdir dai_rel_VERSION

4. 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 1.4.0 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_64-1.4.0-9.0.tar.gz

5. 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

6. At this point, you can copy data into the data directory on the host machine. The data will be visible inside the Docker container.


7. 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_64:1.4.0-9.0

 Driverless AI will begin running::

  --------------------------------
  Welcome to H2O.ai's Driverless AI
  ---------------------------------
       version: 1.4.0

  - 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

8. Connect to Driverless AI with your browser:

 ::

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

Stopping the Docker Image
~~~~~~~~~~~~~~~~~~~~~~~~~

.. include:: stop-docker.rst

Upgrading the Docker Image
~~~~~~~~~~~~~~~~~~~~~~~~~~

.. include:: upgrade-docker.rst