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Docker

Last modified: 19 January 2023

Docker is a tool for deploying and running executables in isolated and reproducible environments. This may be useful, for example, to test code in an environment identical to production.

PyCharm integrates the Docker functionality and provides assistance for creating Docker images, running Docker containers, managing Docker Compose applications, using public and private Docker registries, and much more directly from the IDE.

You can run and debug your Python code in various environments deployed in Docker containers. For more information, see Configure an interpreter using Docker.

Getting started

Enable the Docker plugin

This functionality relies on the Docker plugin, which is bundled and enabled in PyCharm by default. If the relevant features are not available, make sure that you did not disable the plugin.

note

The Docker plugin is available by default only in PyCharm Professional. For PyCharm Community Edition, you need to install the Docker plugin as described in Install plugins.

  1. Press Ctrl+Alt+S to open the IDE settings and select Plugins.

  2. Open the Installed tab, find the Docker plugin, and select the checkbox next to the plugin name.

Install and run Docker

For information about installing and running Docker, see the Docker documentation.

tip

PyCharm supports alternative Docker daemons: Colima and Rancher Desktop (with the dockerd engine).

    Connect to the Docker daemon

    1. Press Ctrl+Alt+S to open the IDE settings and select Build, Execution, Deployment | Docker.

    2. Click The Add button to add a Docker configuration and specify how to connect to the Docker daemon.

      The connection settings depend on your Docker version and operating system. For more information, see Docker connection settings.

      The Connection successful message should appear at the bottom of the dialog.

      Docker connection settings

      note

      The Path mappings table is used to map local folders to corresponding directories in the Docker virtual machine's file system. Only specified folders will be available for volume binding.

      This table is not available on Linux, because when running Docker on Linux, any folder is available for volume binding.

    3. Open the Services tool window (View | Tool Windows | Services or Alt+8), select the configured Docker connection node Docker node and click The Connect button, or select Connect from the context menu.

      The Services tool window, connected to Docker

      To edit the Docker connection settings, select the Docker node and click The Edit Configuration button on the toolbar, or select Edit Configuration from the context menu.

      You can also click the Add Service menu and select Docker Connection to add a Docker connection directly from the Services tool window. If you have Docker contexts configured, you can select Docker Connections from Docker Contexts to add the corresponding connections.

    In the Services tool window (View | Tool Windows | Services or Alt+8), you can pull and push images, create and run containers, manage Docker Compose services, and so on. As with other tool windows, you can start typing the name of an image or container to highlight the matching items.

    Search for a docker image in the Services tool window

    For more information, see the article about the Services tool window.

    Managing images

    Docker images are executable packages for running containers. Depending on your development needs, you can use Docker for the following:

    • Pull pre-built images from a Docker registry

      For example, you can pull an image that runs a Django server container to test how your application will interact with your production environment.

    • Build images locally from a Dockerfile

      For example, you can build an image that runs a container with some specific version of Python to execute your application inside it.

    • Push your images to a Docker registry

      For example, if you want to demonstrate to someone how your application runs in some specific version of the JRE instead of setting up the proper environment, they can run a container from your image.

    Images are distributed via the Docker registry. Docker Hub is the default public registry with all of the most common images: various Linux flavors, database management systems, web servers, runtime environments, and so on. There are other public and private Docker registries, and you can also deploy your own registry server.

    PyCharm stores images that you pull or build locally and lists them in the Services tool window under the Images node. When you select an image, you can view its name, ID, date of last changes, size, tags, and any containers using this image, if any. You can also create a new container from the selected image, push the image, or view the layers used by the image. Click the More button to see more actions: copy the image ID to the clipboard, run the docker image inspect command, or show labels applied to the image.

    Docker image properties

    Images with no tags <none>:<none> can be one of the following:

    • Intermediate images that serve as layers for other images and do not take up any space.

    • Dangling images that remain when you rebuild an image based on a newer version of another image. You should regularly prune dangling images to preserve disk space.

    To hide untagged images from the list, click The Filter menu on the Docker toolbar, and remove the checkmark from Untagged Images.

    To delete one or several images, select them in the list and clickThe Delete Image button.

    Running containers

    Docker containers are runtime instances of the corresponding images. PyCharm uses run configurations to execute the commands that build Docker images and run containers. There are three types of Docker run configurations:

    Docker run configurations

    Create a Docker run configuration

    1. From the main menu, select Run | Edit Configurations.

    2. In the Run/Debug Configurations dialog, clickThe Add New Configuration button, point to Docker, and then click the desired type of run configuration.

    Use this type of configuration to run a Docker container from a locally existing image that you either pulled or built previously.

    Docker uses the docker run command with the following syntax:

    docker run [OPTIONS] IMAGE [COMMAND] [ARG...]

    You can set all the arguments for this command using the options of the Docker Image run configuration.

    By default, the Docker Image configuration has the following options:

    Name

    Specify a name for the run configuration to quickly identify it among others when editing or running.

    Allow multiple instances

    Allow running multiple instances of this run configuration in parallel.

    By default, it is disabled, and when you start this configuration while another instance is still running, PyCharm suggests stopping the running instance and starting another one. This is helpful when a run configuration consumes a lot of resources and there is no good reason to run multiple instances.

    Store as project file

    Save the file with the run configuration settings to share it with other team members. The default location is .idea/runConfigurations. However, if you do not want to share the .idea directory, you can save the configuration to any other directory within the project.

    By default, it is disabled, and PyCharm stores run configuration settings in .idea/workspace.xml.

    Server

    Select the Docker daemon connection to use for the run configuration.

    Image ID or name

    Specify the identifier or the name of the Docker image from which to create the container.

    Container name

    Specify an optional name for the container. If empty, Docker will generate a random name for the container.

    This is similar to using the --name option with the docker run command.

    Before launch

    Specify a list of tasks to perform before starting the run configuration. For example, run another configuration, build the necessary artifacts, run some external tool or a web browser, and so on.

    Click the Add button or press Alt+Insert to add one of the available tasks.

    Move tasks in the list using the Up button and the Down button to change the order in which to perform the tasks. Select a task and click the Edit button to edit the task. Click the Remove button to remove the selected task from the list.

    Show this page

    Show the run configuration settings before actually starting it.

    Activate tool window

    Depending on the type of configuration, open the Run, Debug, or Services tool window when you start this run configuration. If this option is disabled, you can open the tool window manually:

    • View | Tool Windows | Run or Alt+4

    • View | Tool Windows | Debug or Alt+5

    • View | Tool Windows | Services or Alt+8

    Use the Modify options menu to add advanced options to the run configuration:

    Randomly publish all exposed ports

    Publish all exposed container ports to random free ports on the host.

    This is similar to using the -P or --publish-all option on the command line.

    Bind ports

    Map specific container ports to specific ports on the host.

    This is similar to using the -p or --publish option on the command line.

    Click Browse in the Bind ports field and specify which ports on the host should be mapped to which ports in the container. You can also provide a specific host IP from which the port should be accessible (for example, you can set it to 127.0.0.1 to make it accessible only locally, or set it to 0.0.0.0 to open it for all computers in your network).

    Lets say you already have Django running on the host port 5432, and you want to run another instance of Django in a container and access it from the host via port 5433. Binding the host port 5433 to port 5432 in the container is similar to setting the following command-line option:

    -p 5433:5432

    You can set this option explicitly in the Run options field instead of configuring the Bind ports field.

    Entrypoint

    Override the default ENTRYPOINT of the image.

    This is similar to using the --entrypoint option on the command line.

    Command

    Override the default CMD of the image.

    This is similar to adding the command as an argument for docker run.

    Bind mounts

    Mount files and directories on the host to a specific location in the container.

    This is similar to using the -v or --volume option on the command line.

    note

    Make sure that the necessary local paths are mapped to the virtual machine in the Docker connection settings (the Path mappings table).

    Click Browse in the Bind mounts field and specify the host directory and the corresponding path in the container where it should be mounted. Select Read only if you want to disable writing to the container volume.

    For example, you can mount a local Django directory on the host (Users/Shared/django-data) to some directory inside the container (/var/lib/django-data). Mounting volumes in this manner is similar to setting the following command-line option:

    -v Users/Shared/django-data:/var/lib/django-data

    You can set this option explicitly in the Run options field instead of configuring the Bind mounts field.

    Environment variables

    Specify environment variables. There are environment variables associated with the base image that you are using as defined by the ENV instruction in the Dockerfile. There are also environment variables that Docker sets automatically for each new container. Use this field to override any of the variables or specify additional ones.

    This is similar to using the -e or --env option on the command line.

    Click Browse in the Environment variables field to add names and values for variables.

    For example, if you want to connect to Django with a specific username by default (instead of the operating system name of the user running the application), you can set the DJANGO_USER variable to the necessary value. This is similar to setting the following command-line option:

    --env DJANGO_USER=%env-var-value

    You can set this option explicitly in the Run options field instead of configuring the Environment variables field.

    Run options

    Set any other supported docker run options.

    For example, to connect the container to the my-net network and set the my-app alias for it, specify the following:

    --network my-net --network-alias my-app

    note

    Not all docker run options are supported. If you would like to request support for some option, leave a comment in IDEA-181088.

    Attach to container

    Attach to the container's standard input, output, and error streams.

    This is similar to using the -a or --attach option on the command line.

    Show command preview

    Preview the resulting command that will be used to execute the run configuration.

    Interacting with containers

    Created containers are listed in the Services tool window. By default, the Services tool window displays all containers, including those that are not running. To hide stopped containers from the list, click The Filter button in the toolbar, select Docker, and then click Stopped Containers to remove the checkbox.

    When you select a container, you can view the Build Log tab that shows the deployment log produced by the corresponding Docker run configuration while creating and starting the container.

    The Build Log tab of a container selected in the Services tool window

    The Dashboard tab provides important information about the container. Besides its name and hash ID, it also lists the environment variables, ports, and volume bindings. You can add, edit, and remove the environment variables, ports, and volume bindings. However, these changes require you to recreate the container and do not change in the Docker run configuration that is used to create this container. This means that the changes will not persist when you run the configuration next time.

    The Dashboard tab of a container selected in the Services tool window

    For more information, see Container dashboard.

    Docker Compose

    Docker Compose is used to run multi-container applications. For example, you can run a web server, a backend database, and your application code as separate services. Each service can be scaled by adding more containers if necessary. This enables you to perform efficient development and testing in a dynamic environment, similar to production.

    Run a multi-container Docker application

    1. Define necessary services in one or several Docker Compose files.

    2. From the main menu, select Run | Edit Configurations.

    3. ClickThe Add icon, point to Docker and then click Docker-compose.

      The Docker-compose configuration

    4. Specify the Docker Compose files that define services which you want to run in containers. If necessary, you can restrict the services that this configuration will start, specify environment variables, and force building of images before starting corresponding containers (that is, add the --build option for the docker-compose up command).

      For more information about the available options, see Docker-compose.

    5. Click OK to save the Docker Compose run configuration, select it in the main toolbar and click the Run button or press Shift+F10 to start the configuration.

    note

    To quickly create a Docker-compose run configuration and run it with default settings, right-click a Docker Compose file in the Project tool window and click Run in the context menu. You can also use gutter icons and the context menu in the Docker Compose file to control services.

    When Docker Compose runs your multi-container application, you can use the Services tool window to control specific services and interact with containers. The containers are listed under the dedicated Compose nodes, not under the Containers node (which is only for standalone containers).

    The Docker-compose run configuration will identify environment files with the .env suffix if they are located in the same directory as the Docker Compose file.

    Troubleshooting

    If you encounter one of the following problems, try the corresponding suggested solution.

    Docker

    Reported Problem

    Description

    Solution

    Unable to connect to Docker

    Docker is not running, or your Docker connection settings are incorrect.

    In the Settings dialog (Ctrl+Alt+S), select Build, Execution, Deployment | Docker, and select Docker for <your operating system> under Connect to Docker daemon with. For example, if you're on macOS, select Docker for Mac. See more detail in Docker settings.

    If you are using Docker Toolbox, make sure that Docker Machine is running and its executable is specified correctly in the Settings dialog Ctrl+Alt+S under Build, Execution, Deployment | Docker | Tools.

    Docker-composer does not work on Ubuntu using unix socket settings.

    Docker-composer reports the following error: 

    docker.errors.TLSParameterError:
Path to a certificate and key
files must be provided through
the client_config param.
TLS configurations should map the
Docker CLI client configurations.

    1. Open the project Settings (Ctrl+Alt+S).

    2. Go to Build, Execution, Deployment | Docker.

    3. Select TCP socket.

    4. Enter unix:///var/run/docker.sock in the Engine API URL field.

    When you try to pull an image, the following message is displayed:

    Failed to parse dockerCfgFile: <your_home_dir>/.docker/config.json, caused by: ... {"credsStore":"wincred"}

    Invalid Auth config file when using credsStore: http://github.com/docker-java/docker-java/issues/806

    Go to <your_home_dir>/.docker directory and delete the config.json file.

    Unable to use Docker Compose

    Docker Compose executable is specified incorrectly.

    Specify Docker Compose executable in the Settings dialog Ctrl+Alt+S under Build, Execution, Deployment | Docker | Tools

    Unable to use port bindings

    Container ports are not exposed.

    Use the EXPOSE command in your Dockerfile

    High CPU usage while connecting to Docker via services.

    When Hyper-V is selected as the backend for the Docker service on Windows, Hyper-V virtual disk files (.vhdx) are constantly scanned by the anti-virus software. This behavior leads to excessive consumption of CPU, even no container is running.

    Exclude Hyper-V virtual disk files from the anti-virus scan.

    Limitations

    The Docker plugin has certain limitations and bugs, however JetBrains is constantly working on fixes and improvements for it. You can find the list of Docker issues in our bug-tracking system and vote for the ones that affect you the most. You can also file your own bugs and feature requests.