Docker

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.

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

Connect to the Docker daemon

Press Ctrl+Alt+S to open the IDE settings and select Build, Execution, Deployment | Docker.
Click 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.
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.
Open the Services tool window (View | Tool Windows | Services or Alt+8), select the configured Docker connection node and click , or select Connect from the context menu.
To edit the Docker connection settings, select the Docker node and click on the toolbar, or select Edit Configuration from the context menu.
You can also click 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.

Configure a Docker registry

Press Ctrl+Alt+S to open the IDE settings and select Build, Execution, Deployment | Docker | Registry.
Click to add a Docker registry configuration and specify how to connect to the registry. If you specify the credentials, PyCharm will automatically check the connection to the registry. The Connection successful message should appear at the bottom of the dialog.

For more information, see Docker registry settings.

Pull an image from a Docker registry

In the Services tool window (View | Tool Windows | Services or Alt+8), select the Images node.
Select the Docker registry and specify the repository and tag (name and version of the image, for example, tomcat:latest.
Press Ctrl+Enter to run docker pull.

Specify the image as in the Docker CLI, for example: ubuntu:20.04.

Build an image from a Dockerfile

When you are editing a Dockerfile, PyCharm provides completion for images from the configured registries.

Open the Dockerfile from which you want to build the image.
Click in the gutter and select to build the image.

PyCharm creates a Dockerfile run configuration that runs the docker build command.

Push an image to a Docker registry

In the Services tool window, select the image that you want to upload and click or select Push Image from the context menu.
Select the Docker registry and specify the repository and tag (name and version of the image, for example, my-app:v2).
Click OK to run the docker push command.

Copy image to another Docker daemon

If you have several Docker daemon connections, you can copy an image from one Docker daemon to another.

In the Services tool window, select the image that you want to copy and click or select Copy Docker Image from the context menu.
In the Copy Docker Image dialog, select the Docker daemon to which you want to copy the image and click Copy.

This first runs the docker save command to save the image from the current Docker daemon as a file, and then the docker load command to load the image from the file on the target Docker daemon.

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 to see more actions: copy the image ID to the clipboard, run the docker image inspect command, or show labels applied to the image.

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 on the Docker toolbar, and remove the checkmark from Untagged Images.

To delete one or several images, select them in the list and click.

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 Image: Created automatically when you run a container from an existing image. You can run it from a locally existing Docker image that you either pulled or built previously.
Dockerfile: Created automatically when you run a container from a Dockerfile. This configuration builds an image from the Dockerfile, and then derives a container from this image.
Docker-compose: Created automatically when you run a multi-container Docker application from a Docker Compose file.

Run a container from an existing image

In the Services tool window, select an image and click or select Create Container from the context menu.
In the Create Container popup, click Create….
If you already have a Docker run configuration for this image, the Create Container popup will also contain the name of that run configuration as an option.
In the Create Docker Configuration dialog that opens, you can provide a unique name for the configuration and specify a name for the container. If you leave the Container name field empty, Docker will give it a random unique name.
When you are done, click Run to launch the new configuration.

Run a container from a Dockerfile

Open the Dockerfile from which you want to run the container.
Click in the gutter and select to run the container from this Dockerfile.

This creates and starts a run configuration with default settings, which builds an image based on the Dockerfile and then runs a container based on this image.

To create a run configuration with custom settings, click in the gutter and select New Run Configuration…. You can specify a custom tag for the built image, as well as a name for the container, and a context folder from which to read the Dockerfile. The context folder can be useful, for example, if you have some artifacts outside of the scope of your Dockerfile, which you would like to add to the file system of the image.

Docker run configurations

Create a Docker run configuration

From the main menu, select Run | Edit Configurations.
In the Run/Debug Configurations dialog, click, 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 or press `Alt+Insert` to add one of the available tasks. Move tasks in the list using and to change the order in which to perform the tasks. Select a task and click to edit the task. Click 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 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. Make sure that the necessary local paths are mapped to the virtual machine in the Docker connection settings (the Path mappings table). Click 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 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 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.

Use this type of configuration to build an image from a Dockerfile and then derive a container from this image.

Docker uses the docker build command to build an image from a Dockerfile, and then the docker run command to start a container from it.

By default, the Dockerfile 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.
Dockerfile	Specify the name and location of the Dockerfile used to build the image.
Image tag	Specify an optional name and tag for the built image. This can be helpful for referring to the image in the future. If you leave the field blank, the image will have only a random unique identifier.
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 or press `Alt+Insert` to add one of the available tasks. Move tasks in the list using and to change the order in which to perform the tasks. Select a task and click to edit the task. Click 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:


Context folder	Specify a local directory that the daemon will use during the build process. All host paths in the Dockerfile will be processed relative to this directory. By default, if you leave it blank, Docker uses the same directory where the Dockerfile is located.
Build args	Specify values for build-time variable that can be accessed like regular environment variables during the build process but do not persist in the intermediate or final images. This is similar to using the `--build-args` option with the `docker build` command. These variables must be defined in the Dockerfile with the `ARG` instruction. For example, you can define a variable for the version of the base image that you are going to use: ARG PYTAG=latest FROM python:$PYTAG The `PYTAG` variable in this case will default to `latest` and the Dockerfile will produce an image with the latest available version of Python, unless you redefine it as a build-time argument. If you set, `PYTAG=3.7`, Docker will pull `python:3.7` instead, which will run a container with Python version 3.7. Redefining the `PYTAG` argument is similar to setting the following command-line option: --build-arg PYTAG=3.7
Build options	Set any other supported `docker build` options. For example, you can specify metadata for the built image with the `--label` option.
Enable BuildKit	Use the BuildKit backend when building the images. This is similar to setting the `DOCKER_BUILDKIT=1` environment variable when invoking the `docker build` command.
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 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. Make sure that the necessary local paths are mapped to the virtual machine in the Docker connection settings (the Path mappings table). Click 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 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 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.

Use this type of configuration to run multi-container Docker applications.

Docker uses the docker-compose command to define, configure, and run multi-container applications. The main command that builds, creates, starts, and attaches to containers is docker-compose up.

By default, the Docker-compose 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.
Compose files	Specify the compose files that define the necessary services. Docker Compose builds the configuration in the specified order, so any subsequent files override and add to the fields of the same service in previous files. This is similar to using the `-f` option with the `docker-compose` command.
Services	Specify the services to build, create, and start. Click the Browse icon () to select services that are listed in the YML file.
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 or press `Alt+Insert` to add one of the available tasks. Move tasks in the list using and to change the order in which to perform the tasks. Select a task and click to edit the task. Click 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:


Project name	Specify an alternate project name for Docker Compose. By default, it is the name of the current directory. This is similar to using the `-p` option with the `docker-compose` command.
Environment variables	Specify the Docker Compose environment variables. These are used only by the Docker Compose process. They are not passed on to any of the containers.
Environment variables file	Specify the path to a custom environment file that defines the Docker Compose environment variables. By default, Docker Compose looks for a file named .env in the project directory. This is similar to using the `--env-file` option with the `docker-compose` command.
Enable BuildKit	Use the BuildKit backend when building the images. This is similar to setting the `DOCKER_BUILDKIT=1` environment variable when invoking the `docker build` command.
Remove volumes on `down`	When stopping and removing containers, also delete named volumes declared in the Docker Compose file and anonymous volumes attached to containers. This is similar to using the `-v` or `--volumes` option with the `docker-compose down` command.
Enable compatibility mode	Convert v3 service definitions into v2 compatible parameters. This is similar to using the `--compatibility` option with the `docker-compose` command.
Remove images on `down`	Configure which images should be removed when stopping and removing containers. You can choose to remove all images used by any service or only images that don't have a custom tag set in the `image` field. This is similar to using the `--rmi` option with the `docker-compose down` command.
Timeout	Set a timeout in seconds to forcefully terminate containers that won't shutdown gracefully. Docker usually tries to gracefully terminate any container with `SIGTERM`, but it might end up running indefinitely. Set a timeout after which Docker should send `SIGKILL` to force the shutdown. This is similar to using the `-t` or `--timeout` option with the `docker-compose up` command.
Exit code	Return the exit code of the selected service container. Whenever a container in the selected service stops, return its exit code and stop all other containers in the service. This is similar to using the `--exit-code-from` option with the `docker-compose up` command.
Override scale	Set the number of containers to start for each service. This option overrides the `scale` parameter in the Docker Compose file, if it's present. This is similar to using the `--scale` option with the `docker-compose up` command.
Recreate dependencies	Recreate dependent containers when starting a service. This is similar to using the `--always-recreate-deps` option with the `docker-compose up` command.
Recreate anonymous volumes	Recreate anonymous volumes instead of retrieving data from the previous containers. This is similar to using the `-V` or `--renew-anon-volumes` option with the `docker-compose up` command.
Remove orphans	Remove containers for services not defined in the Docker Compose file. This is similar to using the `--remove-orphans` option with the `docker-compose up` command.
Start	Configure which services to start: Selected and dependencies: By default, Docker Compose starts all of the specified services and linked services. None: Don't start any services after creating them. This is similar to using the `--no-start` option with the `docker-compose up` command. Selected services: Don't start any of the linked services. This is similar to using the `--no-deps` option with the `docker-compose up` command.
Attach to	Configure for which containers to show output streams: Selected services: By default, Docker Compose attaches to all started containers of the specified services. None: Don't attach to any containers. This is similar to using the `-d` or `--detach` option with the `docker-compose up` command. Selected and dependencies: Attach to containers of the specified services and linked services. This is similar to using the `--attach-dependencies` option with the `docker-compose up` command.
Recreate containers	Configure which containers to stop and replace by new ones: Changed configuration: By default, Docker Compose recreates containers only if the corresponding configuration or image has changed. All: Recreate all containers in the services, even if the corresponding configuration or image hasn't changed. This is similar to using the `--force-recreate` option with the `docker-compose up` command. None: Don't recreate any containers in the services, even if the corresponding configuration has changed. This is similar to using the `--no-recreate` option with the `docker-compose up` command.
Build	Configure which images to build before starting containers: Only missing images: By default, Docker Compose only builds images that are not available and uses previously built ones when possible. Never: Don't build any images. Always use previously built images or throw an error if some image is not available. This is similar to using the `--no-build` option with the `docker-compose up` command. Always: Always build images before starting containers. This is similar to using the `--build` option with the `docker-compose up` command.
Stop Containers	Configure how to stop containers in a service. By default, Docker Compose doesn't stop other containers in a service. You have to stop them manually. However, you can choose to stop all containers if any container in a service stops. This is similar to using the `--abort-on-container-exit` option with the `docker-compose up` command.

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 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.

Execute a command inside a running container

In the Services tool window, right-click the container name and then click Exec.
In the Run Command in Container popup, click Create and Run… to create and execute a new command.
Alternatively, you can select one of the commands that you ran previously.
In the Exec dialog, type the command and click OK. For example:
ls /tmp
List the contents of the /tmp directory
mkdir /tmp/my-new-dir
Create the my-new-dir directory inside the /tmp directory
/bin/bash
Start a bash session

For more information, see the docker exec command reference.

View detailed information about a running container

In the Services tool window, right-click the container name and then click Inspect.
The output is rendered as a JSON object on the Inspection tab.

For more information, see the docker inspect command reference.

View processes running in a container

In the Services tool window, right-click the container name and then click Show Processes.
The output is rendered as a JSON array on the Processes tab.

For more information, see the docker top command reference.

Attach a console to the container output

In the Services tool window, right-click the container and then click Attach.
The console is attached to the output of the ENTRYPOINT process running inside a container, and is rendered on the Attached Console tab.

For more information, see the docker attach command reference.

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

Define necessary services in one or several Docker Compose files.
From the main menu, select Run | Edit Configurations.
Click, point to Docker and then click Docker-compose.
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.
Click OK to save the Docker Compose run configuration, select it in the main toolbar and click or press Shift+F10 to start the configuration.

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).

Scale a service

In the Services tool window, select the service you want to scale and click or select Scale from the context menu.
Specify how many containers you want for this service and click OK.

Stop a running service

In the Services tool window, select the service and click or select Stop from the context menu.

Stop all running services

In the Services tool window, select the Compose node and click or select Stop from the context menu.

Bring your application down

In the Services tool window, select the Compose node and click or select Down from the context menu.

This stops and removes containers along with all related networks, volumes, and images.

Open the Docker Compose file that was used to run the application

In the Services tool window, right-click the Compose node or a nested service node and then click Jump to Source in the context menu or pressF4.

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.	Open the project Settings (`Ctrl+Alt+S`). Go to Build, Execution, Deployment \| Docker. Select TCP socket. 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.

Last modified: 19 January 2023

`ls /tmp`	List the contents of the /tmp directory
`mkdir /tmp/my-new-dir`	Create the my-new-dir directory inside the /tmp directory
`/bin/bash`	Start a `bash` session