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Getting Started With Docker

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Getting Started With Docker

If you know what Docker is about and are ready to get started, this tutorial shows you the steps for getting set up with Docker Machine, Images, and Containers.

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If you are here, then I assume you'd already know what Docker is all about and requires no further explanation. In case you have no clue about it, I'd highly recommend you search it and come back to this post for real hands-on and getting started. In this post, I'll try to give you an individual programmer's perspective on Docker.

Below is the Docker workflow: 

Docker Workflow

There are basically three things you need on your machine to get started with Docker Containers:

  1. Docker Machine: This is simply a Linux Virtual Machine on which Docker Containers are going to run.

  2. Docker Images: These are similar to ISO images that you run over VM, but it's a highly stripped down version. All the redundant packages/libraries that are already there in Docker Machine have already been removed.

  3. Docker Containers: These are snapshots of a Docker Image that you can start, stop, modify, or publish as another image.

Docker Machine

After you install Docker from its official website, you should have Docker Machine called default installed and ready to run on you PC.  In case it's missing you can always create one using the command docker-machine create default .

Note that you can specify disk size and memory size of your Docker Machine in the above command by specifying special switches. I highly recommend it if you plan to do any data science stuff. You may also install multiple Docker machines with different configurations and purposes. You can check the list of machines using  docker-machine ls .

Here is the sample output:

NAME      ACTIVE   DRIVER       STATE     URL                         SWARM   DOCKER        ERRORS
default   *        virtualbox   Running   tcp://           v17.04.0-ce   

You may start the machine using the  docker-machine start  command. Note that I have skipped machine name default because you can skip machine name if it's default; in other cases, you'll have to specify.

You may check the status of your Docker machine by using the  docker-machine status command. This will simply say "running" or "stopped." 

Another thing you need to be careful about is to make sure your host OS understands all the env variables of Docker Machine. The docker-machine env command is the convenient way to get the script to run that would set up all the env variables.

SET DOCKER_CERT_PATH=C:\Users\kushukla\.docker\machine\machines\default
REM Run this command to configure your shell:
REM @FOR /f "tokens=*" %i IN ('docker-machine env') DO @%i

Just copy paste the output to the terminal or command prompt or follow the remark to set the env variables.

Docker Images

Ok, that's a lot about Docker Machine. Now let's pull some images and run on the machine. If you already know a repository, then you can issue the docker pull rocker/rstudio  command to download the image, but if you don't know the image, you can go to Docker Hub (similar to GitHub); it's a repository of Docker images and you can pick one to start with.

Let's install another one this time using run command. Run is similar to pull, it checks if the image is already available locally, if not it pulls it from Docker Hub, and start a new container using this image:

 docker run -p 8888:8888 --name tensorflow -it gcr.io/tensorflow/udacity-assignments:1.0.0

The parameters above are basically used to customize the container. It says which port to map, what will be the name of the container and it should have an interactive tty. So, don't be surprised if this command blocks your terminal since it's an interactive tty, which means your terminal stdin, stdout, and stderr are now linked to the running container.

You may check the different images downloaded on you PC using the docker images command.

REPOSITORY                              TAG                 IMAGE ID            CREATED             SIZE
rocker/rstudio                          latest              7a807646f0be        11 days ago         993MB
gcr.io/tensorflow/udacity-assignments   1.0.0               4e01459e7150        2 months ago        1.03GB

Docker images can be identified using Image ID or Repository in the above table. So, to delete an Image, you can run this command: docker rmi 7a807646f0be

Docker Container

While Docker Images are static in nature, Containers are something that can start/stop and do the actual job. Containers are created using another Docker Image. You may run the below command to check what all containers are there on your PC.

 docker ps -a 

CONTAINER ID        IMAGE                                         COMMAND             CREATED             STATUS                      PORTS                              NAMES
21d0cdc5051d        gcr.io/tensorflow/udacity-assignments:1.0.0   "/run_jupyter.sh"   15 minutes ago      Up 18 minutes                                                  tensorflow

If you remove the -a switch, you should see only the containers that are currently running. In our case, that's only the rstudio container.

You may stop a container using docker stop rstudio 

To start a container, you can guess: docker start rstudio 

I'd rather start it as below:

 docker run -d -p 8787:8787 -v /c/Users/kushukla:/home/rstudio/kushukla --name rstudio rocker/rstudio This is because I want to open up 8787 and also link my local directory to container's file system so that my R code is accessible in that container. Now if you wonder what happens next. I can go to a browser and open up RStudio. IP is specified in env variable and Port in the Docker run.

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That is all. I hope you found it interesting.

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