.. _deploy_docker-compose:

Deploy with Docker Compose
-------------------------------------------

The FindFace Server 12.240830.2 distribution package now includes example ``docker-compose`` files (for CPU and GPU acceleration) that can be used to quickly set up a simple single-server single-shard installation that may be useful for development environments or small installations that are not expected to receive heavy load and do not require high availability. For production applications we recommend using Kubernetes, Docker Swarm or other container orchestration platforms.


Do the following:

#. Download the FindFace Server package.

    .. code::

        wget http://path/to/ffserver-12.240830.2.zip

#. Put the ``.zip`` archive into some directory on the designated host (for example, ``/home/username/``).

#. Create a ``ffserver-12.240830.2`` directory and unzip the archive.

    .. code::

        mkdir ffserver-12.240830.2
        unzip ffserver-12.240830.2.zip -d ffserver-12.240830.2

    .. note::

        To install unzip in Ubuntu, use the following command:

        .. code::

          sudo apt install unzip

#. List files to check the content.

    .. code::

       ls ffserver-12.240830.2

        configs
        docker-compose.cpu.yml
        docker-compose.gpu.yml
        images
        load-images.sh
        models
        push-images.sh
        SHA1SUMS

#. The default configuration files of all services are located in the directory ``/path/to/ffserver-12.240830.2/configs``. Please configure them to your needs before deploying. See :ref:`step-by-step`.

    .. code::

        ls

        counter.yaml
        deduplicator.yaml
        extraction-api.yaml
        liveness-api.yaml
        ntls.yaml
        sf-api.yaml
        tnt-FindFace.lua
        tnt-scheme.lua
        video-manager.yaml
        video-storage.yaml
        video-streamer.yaml
        video-worker.yaml

#. Put recognition models, subject to your needs, into the directory ``/path/to/ffserver-12.240830.2/models``.

    .. code::

      sudo cp -rT /path/to/your-dir/models /path/to/ffserver-12.240830.2/models

#. Enable recognition models in the ``/path/to/ffserver-12.240830.2/configs/extraction-api.yaml`` file. See :ref:`steps 3-6 in the Deploy extraction-api <deploy_extraction-api>` section.

#. Open the ``/path/to/configs/video-worker.yaml`` configuration file and complete all required parameters. See the :ref:`example in step #3 <deploy_video-worker>` that shows the typical structure of the ``models`` section. The specifics will depend on your selected recognition objects and models. Please configure ``video-manager.yaml`` to your needs. See :ref:`deploy_video-objects-detection`.

#. From the ``/path/to/ffserver-12.240830.2`` directory, make ``load-images.sh`` and ``push-images.sh`` scripts executable.

    .. code::

        chmod +x load-images.sh push-images.sh

#. Run the script to load images.

    .. code::

        ./load-images.sh

#. If you plan to deploy FindFace Server in a distributed manner, either on a container orchestration platform (i.e. Kubernetes), or by manually running different containers on different machines, you may find it useful to store all container images in a centralized registry.

    .. Once the images have been downloaded, the ``push-images.sh`` script can be used to push them to a custom registry accessible at ``"MY_URL_REGiSTRY"``.

    #. If you don't yet have a registry, you can set up a simple one as follows:

       .. code::

          docker run -d -p 5000:5000 --restart always --name registry docker.io/library/registry:2

       The ``-d`` flag will run the container in detached mode. The ``-p`` flag publishes port 5000 on your local machine’s network.

    #. FindFace Server images loaded into your local docker daemon can be then uploaded to the registry you set up using ``push-images.sh`` script:

       .. To tag and push images to the local registry, execute the script. The registry may be defined as ``"localhost:5000"``.

       .. code::

          ./push-images.sh "localhost:5000"

#. From the ``/path/to/ffserver-12.240830.2`` directory, run ``docker-compose.cpu.yml`` or ``docker-compose.gpu.yml`` on CPU- and GPU-acceleration accordingly.

    .. code::

       docker-compose -f docker-compose.cpu.yml up -d

    .. code::

        docker-compose -f docker-compose.gpu.yml up -d

#. Upload the license file via the ``ntls`` web interface or directly put the license file into the license directory ``/path/to/ffserver-12.240830.2/data/licenses``. See :ref:`steps #3-5 in the Provide Licensing section <provide_licensing>`.

#. Following any modifications to the configuration files, restart the service containers.

   .. code::

      docker-compose -f docker-compose.cpu.yml restart

   .. code::

      docker-compose -f docker-compose.gpu.yml restart