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Configuring Remote Interpreter via DockerCompose


Make sure that the following prerequisites are met:

  • Docker is installed. You can install Docker on the various platforms, but here we'll use the Windows installation.

    Note that you might want to repeat this tutorial on different platforms; then use Docker installations for macOS and Linux (Ubuntu, other distributions-related instructions are available as well).

  • Before you start working with Docker, make sure that the Docker Integration plugin is enabled. The plugin is bundled with PyCharm and is activated by default. If the plugin is not activated, enable it on the Plugins settings page of the Settings / Preferences Dialog as described in Managing Plugins.

  • Before you start working with Docker, make sure that the Python Docker plugin is enabled. The plugin is bundled with PyCharm and is activated by default. If the plugin is not activated, enable it on the Plugins settings page of the Settings / Preferences Dialog as described in Managing Plugins.

  • Also, for Windows, right-click the Docker whale icon, choose Settings on the context menu, and in the General page select the Expose daemon... checkbox:

    py docker general settings

Preparing an example

We could have actually repeated the same example as was used for Docker, but for Docker Compose it makes no sense - too simple...

To show a realistic example of a Docker Compose application, we'll use a Django application with a PostgreSQL database running in a separate container. Get the project from GitHub, and open it in PyCharm (File | Open).

For this Django application, we should create two containers: one for a database, and one for the application itself. We'll use the Docker Compose to link the two containers together.

Adding files for Docker and Docker-Compose

In the Project tool window, right-click the project root and choose New | File (Alt+Insert), enter the file name (here Dockerfile) and enter the following code:

FROM python:3.6 WORKDIR /app # By copying over requirements first, we make sure that Docker will cache # our installed requirements rather than reinstall them on every build COPY requirements.txt /app/requirements.txt RUN pip install -r requirements.txt # Now copy in our code, and run it COPY . /app EXPOSE 8000 CMD ["python", "", "runserver", ""]

Next, repeat the same steps for the docker-compose.yml file and enter the following code:

version: '2' services: web: build: . ports: - "8000:8000" volumes: - .:/app links: - db db: image: "postgres:9.6" ports: - "5432:5432" environment: POSTGRES_PASSWORD: hunter2

Let's look at the docker-compose.yml file. This file defines 2 services: web and db, and links them together.

Configuring Docker

Now that we've prepared our example, let's configure Docker. To do that, open Settings dialog (Ctrl+Alt+S or click icons general settings svg on the main toolbar) and click the Docker page under the Build, Execution, Deployment node. Click icons general add svg to create a Docker server.

Accept the suggested default values:

py docker settings

For macOS, select Docker for Mac to connect to the Docker daemon. Next, apply changes.

Configuring Docker Compose as a remote interpreter

Let's now define a remote interpreter based on Docker-Compose.

To do it, open the Settings dialog (press Ctrl+Alt+S or click icons general settings svg on the main toolbar).

Click the Project Interpreter page, on this page click gear icon next to the Project Interpreter field, and choose Add from the drop-down list:

py add interpreter

In the dialog box that opens, select the Docker Compose option, from the drop-down lists select the Docker server, Docker Compose service (here web), configuration file (here docker-compose.yml)and image name (here python).

Why we've chosen web? This choice is explained by the fact, that after configuring a Docker-Compose-based interpreter, we'll be able to create regular run configurations that will alter the behavior of the container we selected. Therefore, if we want to debug the code in a container, that's the one we should select here. All other containers in the compose file will always be started together with this one, but you won't be able to affect their behavior from PyCharm - they'll always behave as if you started them with the command docker-compose up from the command line.

Next, wait while PyCharm starts your Docker-Compose configuration to scan and index:

py docker as remote

Using the Docker tool window

Since we've configured Docker, the Docker tool window button appears at the bottom of PyCharm's main window:

py docker tool window button

Click this button and see your container running:

py docker running container

Configuring database credentials

Modify the DATABASES section of the file in your Django project to add database configuration details:

DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'NAME': 'postgres', 'USER': 'postgres', 'PASSWORD': 'hunter2', 'HOST': 'db' } }

Running your application under Docker-Compose

First, as we are executing a Django application, we must run a migration.

To do that, choose Tools | Run '' task and enter migrate:

py docker run manage py task

(See Running Tasks of Utility for details.)

Next, create an ordinary Django server run/debug configuration. To do that, on the main menu choose Run | Edit Configurations...; in the dialog box that opens click icons general add svg and select Django Server:

py docker run django server

The only thing you should pay attention to, is that Host field must be set to - to make sure that we listen to requests coming from outside the Docker container.

Launch this configuration (Run | Run 'RunDjangoApp'):

py docker compose run

To see output in your web browser, go to http://localhost:8000 (in the address bar, change to localhost):

py docker django app result

Debugging your application under Docker-Compose

Next, let's launch our Django application in the debug mode under Docker Compose. To do that, set a breakpoint (here the breakpoint is set in a template) and on the main menu choose Run | Debug 'RunDjangoApp', or just click icons actions startDebugger svg next to the run/debug configuration drop-down with the RunDjangoApp run/debug configuration selected:

py debug docker django

The result is shown below:

py docker django app debug


Let's summarize what has been done with the help of PyCharm:

  • We downloaded a Django application from GitHub and opened it.

  • We added specific Docker Compose files to our project.

  • We configured a remote interpreter based on Docker Compose.

  • We ran our Django application in the Docker Compose container.

  • We debugged our Django application's template in the Docker Compose container.

Last modified: 21 November 2018

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