4. Tutorial for developers of WC models and WC modeling tools

Developers should follow these steps to build and use WC modeling computing environments (Docker images and containers) to test, debug, and run WC models and WC modeling tools.

  1. Use wc_env_manager to pull existing WC modeling Docker images
  2. Use wc_env_manager to create Docker containers with volumes mounted from the host and installations of software packages contained on the house
  3. Run models and tools inside the Docker containers created by wc_env_manager

4.1. Pulling existing Docker images

First, use the following command to pull existing WC modeling Docker images. This will pull both the base image with third part dependencies, wc_env_dependencies, and the image with WC models and modeling tools, wc_env.:

wc_env_manager pull

The following commands can also be used to pull the individual images.:

wc_env_manager base-image pull
wc_env_manager image pull

4.2. Building containers for WC modeling

Second, set the configuration for the containers created by wc_env_manager by creating a configuration file ./wc_env_manager.cfg following the schema outlined in /path/to/wc_env_manager/wc_env_manager/config/core.schema.cfg and the defaults in /path/to/wc_env_manager/wc_env_manager/config/core.default.cfg.

  • Configure additional Docker containers that should be run and linked to the main container. For example, the configuration below will generate a second container based on the postgres:10.5-alpine image with the host name postgres_hostname on the wc_network Docker network and the environment variable POSTGRES_USER set to postgres_user. The main Docker image will also be added to the same wc_network Docker network, which will make the second image accessible to the main image with the host name postgres_hostname. In this example, it will then be possible to login to the Postgres service from the main container with the command psql -h postgres_hostname -U postgres_user <DB>.

    [wc_env_manager]
    [[network]]

    name = wc_network [[[containers]]]

    [[[[postgres_hostname]]]]

    image = postgres:10.5-alpine [[[[[environment]]]]]

    POSTGRES_USER = postgres_user

  • Configure environment variables that should be set in the Docker container. The following example illustrates how to set the PYTHONPATH environment variable to the paths to wc_lang and wc_sim. Note, we recommend using pip to manipulate the Python path rather than directly manipulating the PYTHONPATH environment variable. We only recommend manipulating the PYTHONPATH environment variable for packages that don’t have setup.py scripts or for packages that setup.py scripts that you temporarily don’t want to run.:

    [wc_env_manager]
        [[container]]
            [[[environment]]]
                PYTHONPATH = '/root/host/Documents/wc_lang:/root/host/Documents/wc_utils'
    
  • Configure the host paths that should be mounted into the containers. Typically, this should including mounting the parent directory of your Git repositories into the container. For example, this configuration will map (a) the Documents directory of your host (${HOME}/Documents) to the /root/host/Documents directory of the container and (b) your the WC modeling configuration directory of your host (${HOME}/.wc) to the WC modeling configuration directory of the container (/root/.wc). ${HOME} will be substituted for the path to your home directory on your host.:

    [wc_env_manager]
        [[container]]
            [[[paths_to_mount]]]
                [[[[${HOME}/Documents]]]]
                    bind = /root/host/Documents
                    mode = rw
                [[[[${Home}/.wc]]]]
                    bind = /root/.wc
                    mode = rw
    
  • Configure the WC modeling packages that should be installed into wc_env. This should be specified in the pip requirements.txt format and should be specified in terms of paths within the container. The following example illustrates how to create editable installations of clones of wc_lang and wc_utils mounted from the host into the container.:

    [wc_env_manager]
        [[container]]
            python_package = '''
                -e /root/host/Documents/wc_lang
                -e /root/host/Documents/wc_utils
                '''
    
  • Configure additional command(s) that should be run when the main Docker container is created. These commands will be run within a bash shell. For example, this configuration will restore the datanator database when the container is created.:

    [wc_env_manager]
        [[container]]
            setup_script = '''
                if [ -x "$$(command -v datanator)" ]; then
                    datanator db create
                    datanator db migrate
                    datanator db restore --restore-schema --do-not-exit-on-error
                fi
                '''
    
  • Configure the ports that should be exposed by the container. The following example illustrates how to expose port 8888 as 8888.:

    [wc_env_manager]
        [[container]]
            [[[ports]]]
                8888 = 8888
    

Third, use the following command to use wc_env to construct a network of Docker containers.:

wc_env_manager container build

This will print out the id of the WC container that was built. This is the main container that you should use to run WC models and WC modeling tools.

4.3. Using containers to run WC models and WC modeling tools

Fourth, use the following command to execute the container. This launches the container and runs an interactive bash shell inside the container.:

docker exec --interactive --tty <container_id> bash

Fifth, use the integrated WC modeling command line program, *wc*, to run WC models and WC modeling tools. For example, the following command illustrates how to get help for the wc program. See the *wc* documentation for more information.:

container >> wc --help

4.4. Using containers to develop WC models and WC modeling tools

Sixth, use command line programs inside the container, such as python, coverage or pytest, to run WC models and tools. Note, only mounted host paths will be accessible in the container.

4.5. Using WC modeling computing environments with an external IDE such as PyCharm

The Docker images created with wc_env_manager can be used with external integrated development environments (IDEs) such as PyCharm. See the links below for instructions on how to use these tools with Docker images created with wc_env_manager.

4.6. Exiting and removing containers

Next, exit the container by executing exit or typing control-d. The container can be restarted using the following commands:

docker restart <container_id>
docker exec --interactive --tty <container_id> bash

Finally, remove the container by executing the following command:

wc_env_manager container remove