Introduction to Docker¶
1. Prerequisites¶
There are no specific skills needed for this tutorial beyond a basic comfort with the command line and using a text editor. Prior experience in developing web applications will be helpful but is not required.
2. Docker Installation¶
Getting all the tooling setup on your computer can be a daunting task, but not with Docker. Getting Docker up and running on your favorite OS (Mac/Windows/Linux) is very easy.
The getting started guide on Docker has detailed instructions for setting up Docker on Mac/Windows/Linux.
Note
If you’re using Docker for Windows make sure you have shared your drive.
If you’re using an older version of Windows or MacOS you may need to use Docker Machine instead.
All commands work in either Bash or Powershell on Windows.
Note
Depending on how you’ve installed Docker on your system, you might see a permission denied error after running the above command. If you’re on Linux, you may need to prefix your Docker commands with sudo. Alternatively to run docker command without sudo, you need to add your user (who has root privileges) to docker group.
For this run:
Create the docker group:
$ sudo groupadd docker
Add your user to the docker group:
$ sudo usermod -aG docker $USER
Log out and log back in so that your group membership is re-evaluated
2.1 Testing Docker installation¶
Once you are done installing Docker, test your Docker installation by running the following command to make sure you are using version 1.13 or higher:
$ docker --version
Docker version 18.09.3, build 774a1f4
When run without --version you should see a whole bunch of lines showing the different options available with docker. Alternatively you can test your installation by running the following:
$ docker run hello-world
Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
03f4658f8b78: Pull complete
a3ed95caeb02: Pull complete
Digest: sha256:8be990ef2aeb16dbcb9271ddfe2610fa6658d13f6dfb8bc72074cc1ca36966a7
Status: Downloaded newer image for hello-world:latest
Hello from Docker.
This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
3. The Docker daemon created a new container from that image which runs the
executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it
to your terminal.
.......
3. Running Docker containers from prebuilt images¶
Now that you have everything setup, it’s time to get our hands dirty. In this section, you are going to run a container from Alpine Linux (a lightweight linux distribution) image on your system and get a taste of the docker run command.
But wait, what exactly is a container and image?
Containers - Running instances of Docker images — containers run the actual applications. A container includes an application and all of its dependencies. It shares the kernel with other containers, and runs as an isolated process in user space on the host OS.
Images - The file system and configuration of our application which are used to create containers. To find out more about a Docker image, run docker inspect hello-world. In the demo above, you could have used the docker pull command to download the hello-world image. However when you executed the command docker run hello-world, it also did a docker pull behind the scenes to download the hello-world image with latest tag (we will learn more about tags little later).
Now that we know what a container and image is, let’s run the following command in our terminal:
$ docker run alpine ls -l
total 52
drwxr-xr-x 2 root root 4096 Dec 26 2016 bin
drwxr-xr-x 5 root root 340 Jan 28 09:52 dev
drwxr-xr-x 14 root root 4096 Jan 28 09:52 etc
drwxr-xr-x 2 root root 4096 Dec 26 2016 home
drwxr-xr-x 5 root root 4096 Dec 26 2016 lib
drwxr-xr-x 5 root root 4096 Dec 26 2016 media
........
Similar to docker run hello-world command in the demo above, docker run alpine ls -l command fetches the alpine:latest image from the Docker registry first, saves it in our system and then runs a container from that saved image.
When you run docker run alpine, you provided a command ls -l, so Docker started the command specified and you saw the listing
You can use the docker images command to see a list of all images on your system
$ docker images
REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE
alpine latest c51f86c28340 4 weeks ago 1.109 MB
hello-world latest 690ed74de00f 5 months ago 960 B
Let’s try something more exciting.
$ docker run alpine echo "Hello world"
Hello world
OK, that’s some actual output. In this case, the Docker client dutifully ran the echo command in our alpine container and then exited it. If you’ve noticed, all of that happened pretty quickly. Imagine booting up a virtual machine, running a command and then killing it. Now you know why they say containers are fast!
Try another command.
$ docker run alpine sh
Wait, nothing happened! Is that a bug? Well, no. These interactive shells will exit after running any scripted commands such as sh, unless they are run in an interactive terminal - so for this example to not exit, you need to docker run -it alpine sh. You are now inside the container shell and you can try out a few commands like ls -l, uname -a and others.
Before doing that, now it’s time to see the docker ps command which shows you all containers that are currently running.
$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
Since no containers are running, you see a blank line. Let’s try a more useful variant: docker ps --all
$ docker ps --all
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
36171a5da744 alpine "/bin/sh" 5 minutes ago Exited (0) 2 minutes ago fervent_newton
a6a9d46d0b2f alpine "echo 'hello from alp" 6 minutes ago Exited (0) 6 minutes ago lonely_kilby
ff0a5c3750b9 alpine "ls -l" 8 minutes ago Exited (0) 8 minutes ago elated_ramanujan
c317d0a9e3d2 hello-world "/hello" 34 seconds ago Exited (0) 12 minutes ago stupefied_mcclintock
What you see above is a list of all containers that you ran. Notice that the STATUS column shows that these containers exited a few minutes ago.
If you want to run scripted commands such as sh, they should be run in an interactive terminal. In addition, interactive terminal allows you to run more than one command in a container. Let’s try that now:
$ docker run -it alpine sh
/ # ls
bin dev etc home lib media mnt proc root run sbin srv sys tmp usr var
/ # uname -a
Linux de4bbc3eeaec 4.9.49-moby #1 SMP Wed Sep 27 23:17:17 UTC 2017 x86_64 Linux
Running the run command with the -it flags attaches us to an interactive tty in the container. Now you can run as many commands in the container as you want. Take some time to run your favorite commands.
Exit out of the container by giving the exit command.
/ # exit
Note
If you type exit your container will exit and is no longer active. To check that, try the following:
$ docker ps --latest
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
de4bbc3eeaec alpine "/bin/sh" 3 minutes ago Exited (0) About a minute ago pensive_leavitt
If you want to keep the container active, then you can use keys ctrl +p, ctrl +q. To make sure that it is not exited run the same docker ps --latest command again:
$ docker ps --latest
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
0db38ea51a48 alpine "sh" 3 minutes ago Up 3 minutes elastic_lewin
Now if you want to get back into that container, then you can type docker attach <container id>. This way you can save your container:
$ docker attach 0db38ea51a48
4. Build Docker images which contain your own code¶
Great! so you have now looked at docker run, played with a Docker containers and also got the hang of some terminology. Armed with all this knowledge, you are now ready to get to the real stuff — deploying your own applications with Docker.
4.1 Deploying a command-line app¶
Note
Code for this section is in this repo in the examples/ directory
In this section, let’s dive deeper into what Docker images are. Later on we will build our own image and use that image to run an application locally.
4.1.1 Docker images¶
Docker images are the basis of containers. In the previous example, you pulled the alpine image from the registry and asked the Docker client to run a container based on that image. To see the list of images that are available locally on your system, run the docker images command.
$ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
ubuntu bionic 47b19964fb50 4 weeks ago 88.1MB
alpine latest caf27325b298 4 weeks ago 5.53MB
hello-world latest fce289e99eb9 2 months ago 1.84kB
.........
Above is a list of images that I’ve pulled from the registry and those I’ve created myself (we’ll shortly see how). You will have a different list of images on your machine. The TAG refers to a particular snapshot of the image and the ID is the corresponding unique identifier for that image.
For simplicity, you can think of an image akin to a Git repository - images can be committed with changes and have multiple versions. When you do not provide a specific version number, the client defaults to latest.
For example you could pull a specific version of Ubuntu image as follows:
$ docker pull ubuntu:16.04
If you do not specify the version number of the image, as mentioned, the Docker client will default to a version named latest.
So for example, the docker pull command given below will pull an image named ubuntu:latest
$ docker pull ubuntu
To get a new Docker image you can either get it from a registry (such as the Docker hub) or create your own. There are hundreds of thousands of images available on Docker hub. You can also search for images directly from the command line using docker search.
$ docker search ubuntu
NAME DESCRIPTION STARS OFFICIAL AUTOMATED
ubuntu Ubuntu is a Debian-based Linux operating sys… 7310 [OK]
dorowu/ubuntu-desktop-lxde-vnc Ubuntu with openssh-server and NoVNC 163 [OK]
rastasheep/ubuntu-sshd Dockerized SSH service, built on top of offi… 131 [OK]
ansible/ubuntu14.04-ansible Ubuntu 14.04 LTS with ansible 90 [OK]
ubuntu-upstart Upstart is an event-based replacement for th… 81 [OK]
neurodebian NeuroDebian provides neuroscience research s… 43 [OK]
ubuntu-debootstrap debootstrap --variant=minbase --components=m… 35 [OK]
1and1internet/ubuntu-16-nginx-php-phpmyadmin-mysql-5 ubuntu-16-nginx-php-phpmyadmin-mysql-5 26 [OK]
nuagebec/ubuntu Simple always updated Ubuntu docker images w… 22 [OK]
tutum/ubuntu Simple Ubuntu docker images with SSH access 18
ppc64le/ubuntu Ubuntu is a Debian-based Linux operating sys… 11
i386/ubuntu Ubuntu is a Debian-based Linux operating sys… 9
1and1internet/ubuntu-16-apache-php-7.0 ubuntu-16-apache-php-7.0 7 [OK]
eclipse/ubuntu_jdk8 Ubuntu, JDK8, Maven 3, git, curl, nmap, mc, … 5 [OK]
darksheer/ubuntu Base Ubuntu Image -- Updated hourly 3 [OK]
codenvy/ubuntu_jdk8 Ubuntu, JDK8, Maven 3, git, curl, nmap, mc, … 3 [OK]
1and1internet/ubuntu-16-nginx-php-5.6-wordpress-4 ubuntu-16-nginx-php-5.6-wordpress-4 2 [OK]
1and1internet/ubuntu-16-nginx ubuntu-16-nginx 2 [OK]
pivotaldata/ubuntu A quick freshening-up of the base Ubuntu doc… 1
smartentry/ubuntu ubuntu with smartentry 0 [OK]
pivotaldata/ubuntu-gpdb-dev Ubuntu images for GPDB development 0
1and1internet/ubuntu-16-healthcheck ubuntu-16-healthcheck 0 [OK]
thatsamguy/ubuntu-build-image Docker webapp build images based on Ubuntu 0
ossobv/ubuntu Custom ubuntu image from scratch (based on o… 0
1and1internet/ubuntu-16-sshd ubuntu-16-sshd 0 [OK]
An important distinction with regard to images is between base images and child images and official images and user images (Both of which can be base images or child images.).
Important
Base images are images that have no parent images, usually images with an OS like ubuntu, alpine or debian.
Child images are images that build on base images and add additional functionality.
Official images are Docker sanctioned images. Docker, Inc. sponsors a dedicated team that is responsible for reviewing and publishing all Official Repositories content. This team works in collaboration with upstream software maintainers, security experts, and the broader Docker community. These are not prefixed by an organization or user name. In the list of images above, the python, node, alpine and nginx images are official (base) images. To find out more about them, check out the Official Images Documentation.
User images are images created and shared by users like you. They build on base images and add additional functionality. Typically these are formatted as user/image-name. The user value in the image name is your Dockerhub user or organization name.
4.1.2 Meet our Python app¶
Now that you have a better understanding of images, it’s time to create an image that sandboxes a small Python application. We’ll do this by creating a small Python script which prints a welcome message, then dockerizing it by writing a Dockerfile, and finally we’ll build the image and run it.
- Create a Python script
- Build the image
- Run your image
- Create a Python script which prints a welcome message
Start by creating a directory called simple-script in your home directory where we’ll create the following files:
app.pyDockerfile
$ cd ~ && mkdir simple-script && cd simple-script
1.1 app.py
Create the app.py file with the following content. You can use any of favorite text editor to create this file.
print('hello world!')
print('this is my first attempt')
Note
If you want, you can run this app through your laptop’s native Python installation first just to see what it looks like. Run python app.py.
You should see the message:
hello world!this is my first attempt
This is totally optional - but some people like to see what the app’s supposed to do before they try to Dockerize it.
1.2. Dockerfile
A Dockerfile is a text file that contains a list of commands that the Docker daemon calls while creating an image. The Dockerfile contains all the information that Docker needs to know to run the app — a base Docker image to run from, location of your project code, any dependencies it has, and what commands to run at start-up. It is a simple way to automate the image creation process. The best part is that the commands you write in a Dockerfile are almost identical to their equivalent Linux commands. This means you don’t really have to learn new syntax to create your own Dockerfiles.
We want to create a Docker image with this app. As mentioned above, all user images are based on a base image. Since our application is written in Python, we will build our own Python image based on Alpine. We’ll do that using a Dockerfile.
Create a file called Dockerfile in the simple-script directory, and add content to it as described below.
# our base image# our base image
FROM alpine:3.9
# install python and pip
RUN apk add --update py3-pip
# copy files required for the app to run
COPY app.py /usr/src/app/
# run the application
CMD python3 /usr/src/app/app.py
Now let’s see what each of those lines mean..
1.2.1 We’ll start by specifying our base image, using the FROM keyword:
FROM alpine:3.9
1.2.2. The next step usually is to write the commands of copying the files and installing the dependencies. But first we will install the Python pip package to the alpine linux distribution. This will not just install the pip package but any other dependencies too, which includes the python interpreter. Add the following RUN command next:
RUN apk add --update py3-pip
1.2.3. Copy the file you have created earlier into our image by using COPY command.
COPY app.py /usr/src/app/
1.2.4. The last step is the command for running the application. Use the CMD command to do that:
CMD python3 /usr/src/app/app.py
The primary purpose of CMD is to tell the container which command it should run by default when it is started.
- Build the image
Now that you have your Dockerfile, you can build your image. The docker build command does the heavy-lifting of creating a docker image from a Dockerfile.
The docker build command is quite simple - it takes an optional tag name with the -t flag, and the location of the directory containing the Dockerfile - the . indicates the current directory:
Note
When you run the docker build command given below, make sure to replace <YOUR_DOCKERHUB_USERNAME> with your username. This username should be the same one you created when registering on Docker hub. If you haven’t done that yet, please go ahead and create an account in Dockerhub.
YOUR_DOCKERHUB_USERNAME=<YOUR_DOCKERHUB_USERNAME>
For example this is how I assign my dockerhub username
YOUR_DOCKERHUB_USERNAME=jpistorius
Now build the image using the following command:
$ docker build -t $YOUR_DOCKERHUB_USERNAME/simple-script .
Sending build context to Docker daemon 10.24kB
Step 1/4 : FROM alpine:3.9
---> caf27325b298
Step 2/4 : RUN apk add --update py3-pip
---> Using cache
---> dad2a197fcad
Step 3/4 : COPY app.py /usr/src/app/
---> Using cache
---> a8ebf6cd2735
Step 4/4 : CMD python3 /usr/src/app/app.py
---> Using cache
---> a1fb2906a937
Successfully built a1fb2906a937
Successfully tagged jpistorius/simple-script:latest
If you don’t have the alpine:3.9 image, the client will first pull the image and then create your image. Therefore, your output on running the command will look different from mine. If everything went well, your image should be ready! Run docker images and see if your image $YOUR_DOCKERHUB_USERNAME/simple-script shows.
- Run your image
When Docker can successfully build your Dockerfile, test it by starting a new container from your new image using the docker run command.
$ docker run $YOUR_DOCKERHUB_USERNAME/simple-script
You should see something like this:
hello world!
this is my first attempt
4.2 Deploying a Jupyter Notebook¶
In this section, let’s build a Docker image which can run a Jupyter Notebook
4.2.1 Suitable Docker images for a base¶
Search for images on Docker Hub which contain the string ‘jupyter’
$ docker search jupyter
NAME DESCRIPTION STARS OFFICIAL AUTOMATED
jupyter/datascience-notebook Jupyter Notebook Data Science Stack from htt… 446
jupyter/all-spark-notebook Jupyter Notebook Python, Scala, R, Spark, Me… 223
jupyterhub/jupyterhub JupyterHub: multi-user Jupyter notebook serv… 195 [OK]
jupyter/scipy-notebook Jupyter Notebook Scientific Python Stack fro… 155
jupyter/tensorflow-notebook Jupyter Notebook Scientific Python Stack w/ … 116
jupyter/pyspark-notebook Jupyter Notebook Python, Spark, Mesos Stack … 95
jupyter/minimal-notebook Minimal Jupyter Notebook Stack from https://… 73
ermaker/keras-jupyter Jupyter with Keras (with Theano backend and … 66 [OK]
jupyter/base-notebook Small base image for Jupyter Notebook stacks… 60
xblaster/tensorflow-jupyter Dockerized Jupyter with tensorflow 52 [OK]
jupyter/r-notebook Jupyter Notebook R Stack from https://github… 22
jupyterhub/singleuser single-user docker images for use with Jupyt… 21 [OK]
...
4.2.2 Meet our model¶
Let’s deploy a Python function inside a Docker image along with Jupyter.
- Create a Python file containing a function
Start by creating a directory called mynotebook in your home directory where we’ll create the following files:
- model.py
- Dockerfile
$ cd ~ && mkdir mynotebook && cd mynotebook
1.1 model.py
Create the model.py file with the following content. You can use any of favorite text editor to create this file.
def introduce(name):
return 'Hello ' + name
1.2. Dockerfile
Since we want to use a Jupyter notebook to call our function, we will build an image based on jupyter/minimal-notebook.
Note
This is one of the official Docker images provided by the Jupyter project for you to build your own data science notebooks on:
Create a file called Dockerfile in the mynotebook directory, and add content to it as described below.
# our base image
FROM jupyter/minimal-notebook
# copy files required for the model to work
COPY model.py /home/jovyan/work/
# tell the port number the container should expose
EXPOSE 8888
Now let’s see what each of those lines mean..
1.2.1 We’ll start by specifying our base image, using the FROM keyword:
FROM jupyter/minimal-notebook
1.2.2. Copy the file you have created earlier into our image by using COPY command.
COPY model.py /home/jovyan/work/
1.2.3. Specify the port number which needs to be exposed. Since Jupyter runs on 8888 that’s what we’ll expose.
EXPOSE 8888
1.2.4. What about CMD?
Notice that unlike our previous Dockerfile this one does not end with a CMD command. This is on purpose.
Remember: The primary purpose of CMD is to tell the container which command it should run by default when it is started.
Can you guess what will happen if we don’t specify our own ‘entrypoint’ using CMD?
- Build the image
Note
Remember to replace <YOUR_DOCKERHUB_USERNAME> with your username. This username should be the same one you created when registering on Docker hub.
YOUR_DOCKERHUB_USERNAME=<YOUR_DOCKERHUB_USERNAME>
For example this is how I assign my dockerhub username
YOUR_DOCKERHUB_USERNAME=jpistorius
Now build the image using the following command:
$ docker build -t $YOUR_DOCKERHUB_USERNAME/mynotebook .
Sending build context to Docker daemon 3.072kB
Step 1/3 : FROM jupyter/minimal-notebook
---> 36c8dd0e1d8f
Step 2/3 : COPY model.py /home/jovyan/work/
---> b61aefd7a735
Step 3/3 : EXPOSE 8888
---> Running in 519dcabe4eb3
Removing intermediate container 519dcabe4eb3
---> 7983fe164dc6
Successfully built 7983fe164dc6
Successfully tagged jpistorius/mynotebook:latest
If everything went well, your image should be ready! Run docker images and see if your image $YOUR_DOCKERHUB_USERNAME/mynotebook shows.
- Run your image
When Docker can successfully build your Dockerfile, test it by starting a new container from your new image using the docker run command. Don’t forget to include the port forwarding options you learned about before.
$ docker run -p 8888:8888 $YOUR_DOCKERHUB_USERNAME/mynotebook
You should see something like this:
Executing the command: jupyter notebook
[I 07:21:25.396 NotebookApp] Writing notebook server cookie secret to /home/jovyan/.local/share/jupyter/runtime/notebook_cookie_secret
[I 07:21:25.609 NotebookApp] JupyterLab extension loaded from /opt/conda/lib/python3.7/site-packages/jupyterlab
[I 07:21:25.609 NotebookApp] JupyterLab application directory is /opt/conda/share/jupyter/lab
[I 07:21:25.611 NotebookApp] Serving notebooks from local directory: /home/jovyan
[I 07:21:25.611 NotebookApp] The Jupyter Notebook is running at:
[I 07:21:25.611 NotebookApp] http://(29a022bb5807 or 127.0.0.1):8888/?token=copy-your-own-token-not-this-one
[I 07:21:25.611 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 07:21:25.612 NotebookApp]
Copy/paste this URL into your browser when you connect for the first time,
to login with a token:
http://(29a022bb5807 or 127.0.0.1):8888/?token=copy-your-own-token-not-this-one
Head over to http://localhost:8888 and your Jupyter notebook server should be running.
Note: Copy the token from your own docker run output and paste it into the ‘Password or token’ input box.
5. Dockerfile commands summary¶
Here’s a quick summary of the few basic commands we used in our Dockerfiles.
- FROM starts the Dockerfile. It is a requirement that the Dockerfile must start with the FROM command. Images are created in layers, which means you can use another image as the base image for your own. The FROM command defines your base layer. As arguments, it takes the name of the image. Optionally, you can add the Dockerhub username of the maintainer and image version, in the format username/imagename:version.
- RUN is used to build up the Image you’re creating. For each RUN command, Docker will run the command then create a new layer of the image. This way you can roll back your image to previous states easily. The syntax for a RUN instruction is to place the full text of the shell command after the RUN (e.g., RUN mkdir /user/local/foo). This will automatically run in a /bin/sh shell. You can define a different shell like this: RUN /bin/bash -c ‘mkdir /user/local/foo’
- COPY copies local files into the container.
- CMD defines the commands that will run on the Image at start-up. Unlike a RUN, this does not create a new layer for the Image, but simply runs the command. There can only be one CMD per a Dockerfile/Image. If you need to run multiple commands, the best way to do that is to have the CMD run a script. CMD requires that you tell it where to run the command, unlike RUN. So example CMD commands would be:
CMD ["python", "./app.py"]
CMD ["/bin/bash", "echo", "Hello World"]
- EXPOSE creates a hint for users of an image which ports provide services. It is included in the information which can be retrieved via
$ docker inspect <container-id>.
Note
The EXPOSE command does not actually make any ports accessible to the host! Instead, this requires publishing ports by means of the -p flag when using docker run.
- PUSH pushes your image to Docker Cloud, or alternately to a private registry
Note
If you want to learn more about Dockerfiles, check out Best practices for writing Dockerfiles.
6. Demos¶
6.1 Portainer¶
Portainer is an open-source lightweight managment UI which allows you to easily manage your Docker hosts or Swarm cluster.
- Simple to use: It has never been so easy to manage Docker. Portainer provides a detailed overview of Docker and allows you to manage containers, images, networks and volumes. It is also really easy to deploy, you are just one Docker command away from running Portainer anywhere.
- Made for Docker: Portainer is meant to be plugged on top of the Docker API. It has support for the latest versions of Docker, Docker Swarm and Swarm mode.
6.1.1 Installation¶
Use the following Docker commands to deploy Portainer. Now the second line of command should be familiar to you by now. We will talk about first line of command in the Advanced Docker session.
$ docker volume create portainer_data
$ docker run -d -p 9000:9000 -v /var/run/docker.sock:/var/run/docker.sock -v portainer_data:/data portainer/portainer
- If you are on mac, you’ll just need to access the port 9000 (http://localhost:9000) of the Docker engine where portainer is running using username
adminand passwordtryportainer - If you are running Docker on Atmosphere/Jetstream or on any other cloud, you can open
ipaddress:9000. For my case this ishttp://128.196.142.26:9000
Note
The -v /var/run/docker.sock:/var/run/docker.sock option can be used in Mac/Linux environments only.
6.2 Play-with-docker (PWD)¶
PWD is a Docker playground which allows users to run Docker commands in a matter of seconds. It gives the experience of having a free Alpine Linux Virtual Machine in browser, where you can build and run Docker containers and even create clusters in Docker Swarm Mode. Under the hood, Docker-in-Docker (DinD) is used to give the effect of multiple VMs/PCs. In addition to the playground, PWD also includes a training site composed of a large set of Docker labs and quizzes from beginner to advanced level available at training.play-with-docker.com.
6.2.1 Installation¶
You don’t have to install anything to use PWD. Just open https://labs.play-with-docker.com/ <https://labs.play-with-docker.com/>`_ and start using PWD
Note
You can use your Dockerhub credentials to log-in to PWD
