PyCharm 2019.2 Help

Configure a remote interpreter using Docker

Introduction

PyCharm integration with Docker allows you to run your applications in the variously configured development environments deployed in Docker containers.

Prerequisites

Make sure that the following prerequisites are met:

  • Docker is installed, as described on the page Docker Docs. 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).

  • You have a stable Internet connection.

    To operate with Docker you need the busybox image be available on your machine. Ensure that you have a stable Internet connection, so that PyCharm can download and run busybox:latest. Once you have successfully configured Docker, you can go offline.

  • Before you start working with Docker, make sure that the Docker Integration and Python Docker plugins are enabled. The plugins are bundled with PyCharm and activated by default. If the plugins are not activated, enable them on the Plugins page of the Settings/Preferences dialog Ctrl+Alt+S as described in Managing plugins.

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

    Docker settings

Preparing an example

Create a Python project QuadraticEquation, add the Solver.py file and enter the following code:

import math class Solver: def demo(self, a, b, c): d = b ** 2 - 4 * a * c if d > 0: disc = math.sqrt(d) root1 = (-b + disc) / (2 * a) root2 = (-b - disc) / (2 * a) return root1, root2 elif d == 0: return -b / (2 * a) else: return "This equation has no roots" if __name__ == '__main__': solver = Solver() while True: a = int(input("a: ")) b = int(input("b: ")) c = int(input("c: ")) result = solver.demo(a, b, c) print(result)

Configuring Docker as a remote interpreter

Now that we've prepared our example, let's define a Docker-based remote interpreter.

Open the Add Python Interpreter dialog by either way:

  • When you're in the Editor, the most convenient way is to use the Python Interpreter widget in the Overview of the user interface. Click the widget and select Add Interpreter ...

  • If you are in the Settings/Preferences dialog Ctrl+Alt+S, select Project <project name> | Project Interpreter. Click the The Configure project interpreter icon and select Add.

In the dialog that opens, select the Docker option, from the drop-down lists select the Docker server (if the server is missing, click New...), and specify the image name.

Python interpreter path should have the default value:

Choose a docker

As a result, in the Settings dialog, you should see something like this:

Set up an interpreter

Click OK to apply changes and close the dialog.

Running your application in a Docker container

In the left gutter, next to the main clause, click the Run button, and choose Run 'Solver.py' command. You see that your script runs in the Docker container:

Running in a Docker container

As you can see, the prefix in the Run tool window shows the container ID.

Debugging your application in a Docker container

Next, let's debug our application. For that, let's put a breakpoint on the line that calculates d, then click Run and choose Debug 'Solver' .

As you see in the Console tab of the Debug tool window, the debugger runs also in the Docker container:

Debugging in a Docker container

But now this container has a different id, and hence - different name. You can see it in the Terminal: type the docker ps command and see the container id and name:

Preview in the Terminal

Docker tool window

But is it possible to see all the containers without the Terminal? PyCharm says - yes. You can use the Docker tab in the Services tool window as the UI for the Docker command-line client.

If you have configured Docker as a remote interpreter, you will see the Services tool window button at the bottom side of the main PyCharm window. Click this button and see the docker containers:

Docker tool window

Let's look at this tool window more attentively. What do we see here?

  • First, we are connected to a Docker daemon:

    The Docker tool window, connected to Docker

  • Second, if we open the Run tool window, we'll see that the Docker prefix corresponds to the container ID in the Properties tab of the Docker tool window:

    The Properties tab and the Docker tool window

  • Third, if we open the Debug tool window, we'll see that the Docker prefix (another one!) corresponds to the another container ID in the Properties tab of the Docker tool window:

    The Docker tool window and the Debug tool window

  • And finally, we see the strange names of the containers - they are human-readable and generated by Docker itself.

Summary

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

  • We created a project and added a Python script.

  • We configured the remote interpreter.

  • We ran and debugged our script in the Docker containers.

  • Finally, we launched the Docker tool window and saw all the details visible in the Terminal.

Last modified: 14 August 2019

See Also

Reference: