page_title: Working with Containers page_description: Learn how to manage and operate Docker containers. page_keywords: docker, the docker guide, documentation, docker.io, monitoring containers, docker top, docker inspect, docker port, ports, docker logs, log, Logs # Working with Containers In the [last section of the Docker User Guide](/userguide/dockerizing) we launched our first containers. We launched two containers using the `docker run` command. * Containers we ran interactively in the foreground. * One container we ran daemonized in the background. In the process we learned about several Docker commands: * `docker ps` - Lists containers. * `docker logs` - Shows us the standard output of a container. * `docker stop` - Stops running containers. > **Tip:** > Another way to learn about `docker` commands is our > [interactive tutorial](https://www.docker.io/gettingstarted). The `docker` client is pretty simple. Each action you can take with Docker is a command and each command can take a series of flags and arguments. # Usage: [sudo] docker [flags] [command] [arguments] .. # Example: $ docker run -i -t ubuntu /bin/bash Let's see this in action by using the `docker version` command to return version information on the currently installed Docker client and daemon. $ sudo docker version This command will not only provide you the version of Docker client and daemon you are using, but also the version of Go (the programming language powering Docker). Client version: 0.8.0 Go version (client): go1.2 Git commit (client): cc3a8c8 Server version: 0.8.0 Git commit (server): cc3a8c8 Go version (server): go1.2 Last stable version: 0.8.0 ### Seeing what the Docker client can do We can see all of the commands available to us with the Docker client by running the `docker` binary without any options. $ sudo docker You will see a list of all currently available commands. Commands: attach Attach to a running container build Build an image from a Dockerfile commit Create a new image from a container's changes . . . ### Seeing Docker command usage You can also zoom in and review the usage for specific Docker commands. Try typing Docker followed with a `[command]` to see the usage for that command: $ sudo docker attach Help output . . . Or you can also pass the `--help` flag to the `docker` binary. $ sudo docker images --help This will display the help text and all available flags: Usage: docker attach [OPTIONS] CONTAINER Attach to a running container --no-stdin=false: Do not attach stdin --sig-proxy=true: Proxify all received signal to the process (even in non-tty mode) None of the containers we've run did anything particularly useful though. So let's build on that experience by running an example web application in Docker. > **Note:** > You can see a full list of Docker's commands > [here](/reference/commandline/cli/). ## Running a Web Application in Docker So now we've learnt a bit more about the `docker` client let's move onto the important stuff: running more containers. So far none of the containers we've run did anything particularly useful though. So let's build on that experience by running an example web application in Docker. For our web application we're going to run a Python Flask application. Let's start with a `docker run` command. $ sudo docker run -d -P training/webapp python app.py Let's review what our command did. We've specified two flags: `-d` and `-P`. We've already seen the `-d` flag which tells Docker to run the container in the background. The `-P` flag is new and tells Docker to map any required network ports inside our container to our host. This lets us view our web application. We've specified an image: `training/webapp`. This image is a pre-built image we've created that contains a simple Python Flask web application. Lastly, we've specified a command for our container to run: `python app.py`. This launches our web application. > **Note:** > You can see more detail on the `docker run` command in the [command > reference](/reference/commandline/cli/#run) and the [Docker Run > Reference](/reference/run/). ## Viewing our Web Application Container Now let's see our running container using the `docker ps` command. $ sudo docker ps -l CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES bc533791f3f5 training/webapp:latest python app.py 5 seconds ago Up 2 seconds 0.0.0.0:49155->5000/tcp nostalgic_morse You can see we've specified a new flag, `-l`, for the `docker ps` command. This tells the `docker ps` command to return the details of the *last* container started. > **Note:** > The `docker ps` command only shows running containers. If you want to > see stopped containers too use the `-a` flag. We can see the same details we saw [when we first Dockerized a container](/userguide/dockerizing) with one important addition in the `PORTS` column. PORTS 0.0.0.0:49155->5000/tcp When we passed the `-P` flag to the `docker run` command Docker mapped any ports exposed in our image to our host. > **Note:** > We'll learn more about how to expose ports in Docker images when > [we learn how to build images](/userguide/dockerimages). In this case Docker has exposed port 5000 (the default Python Flask port) on port 49155. Network port bindings are very configurable in Docker. In our last example the `-P` flag is a shortcut for `-p 5000` that makes port 5000 inside the container to a high port (from the range 49000 to 49900) on the local Docker host. We can also bind Docker container's to specific ports using the `-p` flag, for example: $ sudo docker run -d -p 5000:5000 training/webapp python app.py This would map port 5000 inside our container to port 5000 on our local host. You might be asking about now: why wouldn't we just want to always use 1:1 port mappings in Docker containers rather than mapping to high ports? Well 1:1 mappings have the constraint of only being able to map one of each port on your local host. Let's say you want to test two Python applications: both bound to port 5000 inside your container. Without Docker's port mapping you could only access one at a time. So let's now browse to port 49155 in a web browser to see the application. ![Viewing the web application](/userguide/webapp1.png). Our Python application is live! ## A Network Port Shortcut Using the `docker ps` command to return the mapped port is a bit clumsy so Docker has a useful shortcut we can use: `docker port`. To use `docker port` we specify the ID or name of our container and then the port for which we need the corresponding public-facing port. $ sudo docker port nostalgic_morse 5000 0.0.0.0:49155 In this case we've looked up what port is mapped externally to port 5000 inside the container. ## Viewing the Web Application's Logs Let's also find out a bit more about what's happening with our application and use another of the commands we've learnt, `docker logs`. $ sudo docker logs -f nostalgic_morse * Running on http://0.0.0.0:5000/ 10.0.2.2 - - [23/May/2014 20:16:31] "GET / HTTP/1.1" 200 - 10.0.2.2 - - [23/May/2014 20:16:31] "GET /favicon.ico HTTP/1.1" 404 - This time though we've added a new flag, `-f`. This causes the `docker logs` command to act like the `tail -f` command and watch the container's standard out. We can see here the logs from Flask showing the application running on port 5000 and the access log entries for it. ## Looking at our Web Application Container's processes In addition to the container's logs we can also examine the processes running inside it using the `docker top` command. $ sudo docker top nostalgic_morse PID USER COMMAND 854 root python app.py Here we can see our `python app.py` command is the only process running inside the container. ## Inspecting our Web Application Container Lastly, we can take a low-level dive into our Docker container using the `docker inspect` command. It returns a JSON hash of useful configuration and status information about Docker containers. $ sudo docker inspect nostalgic_morse Let's see a sample of that JSON output. [{ "ID": "bc533791f3f500b280a9626688bc79e342e3ea0d528efe3a86a51ecb28ea20", "Created": "2014-05-26T05:52:40.808952951Z", "Path": "python", "Args": [ "app.py" ], "Config": { "Hostname": "bc533791f3f5", "Domainname": "", "User": "", . . . We can also narrow down the information we want to return by requesting a specific element, for example to return the container's IP address we would: $ sudo docker inspect -f '{{ .NetworkSettings.IPAddress }}' nostalgic_morse 172.17.0.5 ## Stopping our Web Application Container Okay we've seen web application working. Now let's stop it using the `docker stop` command and the name of our container: `nostalgic_morse`. $ sudo docker stop nostalgic_morse nostalgic_morse We can now use the `docker ps` command to check if the container has been stopped. $ sudo docker ps -l ## Restarting our Web Application Container Oops! Just after you stopped the container you get a call to say another developer needs the container back. From here you have two choices: you can create a new container or restart the old one. Let's look at starting our previous container back up. $ sudo docker start nostalgic_morse nostalgic_morse Now quickly run `docker ps -l` again to see the running container is back up or browse to the container's URL to see if the application responds. > **Note:** > Also available is the `docker restart` command that runs a stop and > then start on the container. ## Removing our Web Application Container Your colleague has let you know that they've now finished with the container and won't need it again. So let's remove it using the `docker rm` command. $ sudo docker rm nostalgic_morse Error: Impossible to remove a running container, please stop it first or use -f 2014/05/24 08:12:56 Error: failed to remove one or more containers What's happened? We can't actually remove a running container. This protects you from accidentally removing a running container you might need. Let's try this again by stopping the container first. $ sudo docker stop nostalgic_morse nostalgic_morse $ sudo docker rm nostalgic_morse nostalgic_morse And now our container is stopped and deleted. > **Note:** > Always remember that deleting a container is final! # Next steps Until now we've only used images that we've downloaded from [Docker Hub](https://hub.docker.com) now let's get introduced to building and sharing our own images. Go to [Working with Docker Images](/userguide/dockerimages).