Using Docker - jhudsl/OTTR_Template GitHub Wiki
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Particularly for courses that involve running example code, it's highly recommended that you use a Docker image for development to maintain software version consistency across course developers.
If your course doesn't require any additional packages to run, then you do not need to set up Docker locally but this Docker image: jhudsl/course_template will run and re-render all of your changes as you add them.
We advise you use the jhudsl/course_template:main tagged image as opposed to the latest. The main is what is ready for use, while the latest may be under development. All GitHub actions by default use the jhudsl/course_template:main.
If you are new to Docker, you may find it helpful to read this introduction to Docker.
- You will need to sign up with a Docker account if you don't have one.
- If you haven't installed Docker desktop (or need to update it), you can do so here.
If your Docker desktop is running, you should see a Docker whale in your tool bar. On Macs, this will be on the bar on the top of your screen; in Windows, on the bottom right.
A Docker image is similar to a virtual machine - it contains preinstalled software in a preconfigured environment. Docker images can be downloaded from DockerHub, or you can create your own.
We have created the course_template:main image as a starting point; you can download it from jhudsl/course_template:main on DockerHub using the docker pull command we have below.
To pull the docker image associated with this template, you can run this command below in your command line.
This may take a while.
docker pull jhudsl/course_template:main
This pulls the course_template:main image from Docker Hub and copies it to your computer. It will be placed in your local collection of Docker images, managed by Docker (not in your pwd). If you get an error, it may be because you forgot to have your Docker desktop running... see above.
To use the Docker image associated with the course template, first navigate to the the top of this GitHub repository. Now you can start up the Docker container using the command below.
This runs your local copy of the course_template:main image (which you downloaded from DockerHub).
The option -v $PWD:/home/rstudio mounts pwd (this repo) and makes it available at /home/rstudio within the container.
Replace all of <CHOOSE_PASSWORD> (including the < and >) with a password of your choosing.
On a Mac:
docker run -it -v $PWD:/home/rstudio -e PASSWORD=<CHOOSE_PASSWORD> -p 8787:8787 jhudsl/course_template:main
On a Windows:
docker run -it -v %CD%:/home/rstudio -e PASSWORD=<CHOOSE_PASSWORD> -p 8787:8787 jhudsl/course_template:main
Do not close this window, but you can minimize it.
Open up a new command line window and run the command: docker ps, you should see a container is up and running!
Couple other handy Docker commands:
- To stop your Docker container, run
docker psto obtain the docker container ID. Then you can use that ID to rundocker stop <CONTAINER_ID>. - To remove a docker image (which you may need to do from time to time to clear out space), you can run
docker image lsto see all your current images. Then you can run eitherdocker image rm <IMAGE_ID>. - If you really need to clear out space, you can follow this StackOverflow post on how to remove all images and all containers.
For more info on how to use Docker, they have very extensive documentation here.
In a web browser navigate to the following to get to an RStudio that is run from your Docker container.
localhost:8787
To log in, you'll need to use rstudio as the username and whatever password you put for <CHOOSE_PASSWORD> in the above command.
Should you find that your course needs additional packages beyond what's included in the template, you should probably start a new Docker image and you'll need to do these steps to get this going:
- Create a Dockerhub account if you don't have one.
- Create a new Docker image on your Dockerhub account. Follow these instructions if you don't know how to do that.
- Set up Dockerhub secrets in your repository or organization.
- Update the Github actions workflows accordingly.
- Edit the Dockerfile in your repository.
You only need to do this once as an organization (if this course is under jhudsl you don't need to do this step).
To give them permission for all these Docker actions, you need to set a GitHub secret. Go to Settings > Secrets and scroll down to organization secrets or you can set this for each repository by creating a repository secret.
- Click
New repository secretbutton for each of these secrets - The login information in these GitHub secrets must be from an account that has write permissions to the Docker image you wish to manage).
Name: DOCKERHUB_USERNAME:
For value: put your login username for https://hub.docker.com/
Name: DOCKERHUB_TOKEN:
For value: put an access token for Dockerhub.
You can create this by following these instructions.
To make sure that your new Docker image is being used for rendering in the GitHub actions, you need to change the rendering-docker-image: from the default of jhudsl/course_template:main and replace it with your docker image tag.
Then start a new branch so that you can submit a new pull request with your changes.
Now, when you file a pull request, the Dockerfile build for your docker image will be tested automatically by the GitHub actions if docker-test is set to yes. The default is docker-test: no
Read this chapter for instructions on how to modify Docker images
You will probably want to create your Docker image by using the jhudsl/course_template:main as your base -- this means that all the packages that are in our jhudsl/course_template:main image will be included in the Docker image you build.
FROM jhudsl/course_template:main
You can take a look at this Dockerfile template we've set up here (note that the commands would need to be uncommented and real package names put in place of package_name's).
This section gives you the basics on what it looks like to add new packages to your new Docker image.
For R packages installed from CRAN, you can add to the running vector list of R packages.
To add an R package from Bioconductor, you can follow this kind of format:
RUN Rscript -e "options(warn = 2); BiocManager::install( \
c('limma', \
'newpackagename')
To add a Python package, you will need to add pip3 to install Python packages using this format:
RUN pip3 install \
"somepackage==0.1.0"
Read this chapter for instructions on how to modify Docker images
When you've added a package to the Dockerfile, you may want to check that it builds successfully before it's added to your repository. You can include changes to your Dockerfile in a pull request which will trigger an automatic testing of building it.
Read this chapter for more tips on how to modify Docker images
OR
If you prefer to test it locally, then you can follow these steps.
First create a GITHUB token file by making a token and copying a pasting it into a plain text file named docker/git_token.txt. (Make sure you do not push this to github and possibly delete it after testing your docker image build!)
Then you'll need to rebuild the Docker image using this command after you move into the docker directory) (But replace the <TAG_FOR_COURSE> with the tag for your new image including dropping the < and >:
docker build -f Dockerfile . -t <TAG_FOR_COURSE>
If it fails, often the issue is a missing dependency. Take a look at the error messages and see if you can determine the issue with some Googling. Also be sure that all your directories and files are named correctly.
Once it builds successfully, run the above command with the new name for your docker image make sure that the tag does not have upper case characters):
docker build -f Dockerfile . -t jhudsl/<TAG_FOR_COURSE>
For any changes you make to your Docker image to take effect in your repository's github actions and workflows, you must push your updated docker image to Dockerhub. There are two different ways you can push your Docker image.
Locally, you can push your updated image to Dockerhub using (make sure that the tag does not have upper case characters):
docker push jhudsl/<TAG_FOR_COURSE>
OR
You can use GitHub actions to do this by going to your course's GitHub repository, go to Actions and then to Test build of Dockerfile.
Click on run workflow type in true underneath Push to Dockerhub?.
Then click Run. If your Dockerfile builds an image successfully it will automatically be pushed to Dockerhub.