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Containerizing your R model

This tutorial shows how to containerize an R model in a Docker image. As a result, you will be able to run simulations in a container and get the results in a folder that you specify.

Containerizing your model using just an R installation is the preferred method for archival purposes, but if you are looking for a way to containerize your model and still be able to use RStudio within the container, please follow this tutorial. This series of tutorials was developed assuming that the user has a finished model and is looking for a straightforward way to containerize it. Another possibility is to develop your model within the Docker image, which is covered in the R Docker tutorial, produced by rOpenSci Labs.

To start this tutorial, you will need to have Docker installed in your computer, as well as the R files of your model. To be able to see the results of your simulation, the code has to write the outputs in a results folder. We will use as an example the implementation of Schelling’s segregation model proposed in this tutorial by simulatingcomplexity. If you want to try the following steps using the same example, you can copy the script we include at the end of this tutorial. Following our suggested directory structure, we created a project folder named MyDocker and it contains the folders data, docs, results, and src. schelling.R is in the src folder and the rest of the folders are empty.

Here is a video walkthrough of the steps described below:

Building the image


1. Determine what folder to use

Docker looks for the files existing in the folder where you are when you call the build command. Therefore, it is important to verify you are able to navigate to the folder that contains your project using command line. If that is the case, you can continue to step 2. Otherwise, we suggest you to follow Step 1.1 to create a MyDocker folder that you can later navigate to.

Step 1.1 on Mac
  1. Use Finder to go to your home directory: go to Macintosh HD, then to Users and then to the folder with your user name (which should have a house icon).
  2. Create a folder named MyDocker in your home directory.
  3. Copy the contents of your project’s folder to MyDocker. In our example, since our project consists of a single R file (example.R), only the src folder will contain any files, but we still need to have at least a results folder to store the results of our experiment.
Step 1.1 on Windows
  1. Open File Explorer, go to My PC, then to the C: drive, then to Users, and finally your username.
  2. Create a new folder named MyDocker.
  3. Copy the contents of your project’s folder to MyDocker. In our example, since our project consists of a single R file (example.R), only the src folder will contain any files, but we still need to have at least a results folder to store the results of our experiment

2. Create a Dockerfile

The Dockerfile is the file that describes how to create the image of your model and what steps to execute when you run a container based on that image. To create one, open a plain text editor (examples are TextEdit for Mac or Notepad++ for Windows) and copy the following code.

FROM rocker/r-ver:your_R_version

# Copy contents of MyDocker folder to home folder in container
COPY . /home/

# Set working directory to home
WORKDIR /home/ 

# Install packages (uncomment next line if needed)
# RUN R -e "install.packages(vector_of_package_names)"

# Run code
CMD Rscript src/your_code_entrypoint

3. Modify the Dockerfile blueprint as needed

In your plain text editor, modify the following:
  • Replace your_R_version with the R version you are using. A quick way to figure this out is to type R.version$version.string in your R console. In our example, the first line of the Dockerfile would be FROM rocker/r-ver:3.6.0 .
  • If you need to install additional packages:
    • Uncomment line 10.
    • Replace vector_of_package_names with a character vector containing the names of the packages needed.
  • Replace your_code_entrypoint with the name of the R file (including the .R extension) that has the role of entry point. In our example, this last line would be CMD Rscript src/schelling.R.

4. Save your Dockerfile

It is important to save the Dockerfile with the correct format and name, or Docker might not be able to use it to build your image.

Saving your Dockerfile in Mac
  1. If using TextEdit, make sure it will be saved as plain text. To do this, you can click on Format and then on Make Plain Text, or just press shift + command + T.
  2. Click on File, then Save… . In the dialog window that opens:
    • Type Dockerfile as the name of the file.
    • Choose the folder you are going to use (Step 1) as the location of the file. In our example, we would save the Dockerfile in MyDocker.
    • Uncheck the box that allows to use the .txt extension if no extension is provided.
    • Make sure that the format of the file (in the drop-down menu below) is plain text (or Unicode UTF-8).
    • If present, uncheck the box that allows to use the .txt extension.
  3. Open Finder and look for your Dockerfile. It should show up in the folder you chose to use in Step 1 (in our example, in MyDocker) and it should not include any extensions in its name (if it has a name like Dockerfile.txt, you should rename it to Dockerfile)
Saving your Dockerfile in Windows
  1. Save your plain text as Dockerfile (do not include any extensions in the file name, e.g. .txt) and make sure it is saved in the folder you are going to use (Step 1). In our example, we saved our Dockerfile in MyDocker.
  2. Use File Explorer to check that the file was saved with the right name (Dockerfile, no extensions) and in the right location, i.e. the folder you chose to use in Step 1 (MyDocker in our example).

5. Double-check your directory structure

The code in the Dockerfile assumes a specific organization within the folder containing your Dockerfile. Either the build or the run command will fail if it cannot find the right files due to an organization or folder naming that are different than expected. Therefore, it is necessary to ensure that the right contents are in the right folders. Your project’s folder (MyDocker in our example) can have any name, but it must at least contain:

  • Your Dockerfile, and this is also a good time to check that its name does not have any extensions,
  • A results folder, which in our case will be empty,
  • And a src folder (also in lower case), that contains your R file.

Your project’s folder can contain other folders, like data if it uses input files.

6. Build the Docker image

Now we are going to create the virtual entity that will download R, contain your model, and be ready to execute your code. To build the image, open a terminal window (Terminal in Mac or Command Prompt in Windows) and navigate to the folder that contains your model and the Dockerfile (if not sure how to do so and followed Step 1.1, you can follow these short instructions). Once you moved to the directory that contains your model and the Dockerfile, type the following build command:

docker build -t imagename .
  • Replace imagename with the name you want to give to your image. It can be any name, as long as all the letters you use are lower case, with no spaces.
  • Make sure to include the . at the end of the command.

In our example, this build command would look like this: docker build -t r.example . .

When you press return, Docker will start showing the progress of the processes you instructed it to perform with the Dockerfile. When it is done, you will see the line Successfully tagged imagename:latest. Now you will be able to run reproducible simulations in your containerized model.

We included some known errors can that arise when building the image in our short Troubleshooting section.

Running an experiment in a container


1. Determine the folder where the experiment results should be stored

The container will open the model, run the specified experiment, and transfer the results to a folder of your preference. It is not necessary to choose a location within your project’s folder, but doing so can help with the organization of your results.

Note that subsequent runs will overwrite the result files if the same folder is used twice to receive the results. Therefore, it is a good idea to create a separate folder for each experiment that is run.

2. Run the container

To run an experiment in the container, open a terminal window and type the run command according to your operating system:

run command in Mac
docker run -v path/to/your/results/folder:/home/results image name

Where:

  • path/to/your/results/folder is replaced by the absolute path to your results folder in your computer. If the folder does not exist, Docker will create it. In our case, we are going to create a folder named TestResults inside MyDocker/results/. Therefore, our path would be ~/MyDocker/results/TestResults. Note that your path has to be absolute, or, in other words, start with ~/.
  • imagename is replaced by the name you gave to the image in the build command (in our case, r.example).

In our example, the run command would look like this: docker run -v ~/MyDocker/results/TestResults:/home/results/ r.example. At the end of the run, we would see the file output.png, as specified in our example file, in the TestResults folder.

run command in Windows
docker run -v path/to/your/results/folder:/home/results imagename

Where:

  • path/to/your/results/folder/ is replaced by the absolute path to your results folder in your computer. If the folder does not exist, Docker will create it. In our case, we are going to create a folder named TestResults inside MyDocker/results/. Therefore, our path would be /c/Users/yourusername/MyDocker/results/TestResults . Note that your path has to be absolute, or, in other words, start with /c/. Also be sure to replace yourusername with your own user name.
  • imagename is replaced by the name you gave to the image in the build command (in our case, r.example).

In our example, the run command would look like this: docker run -v /c/Users/myusername/MyDocker/results/TestResults:/home/results/ r.example. At the end of the run, we would see the file output.png, as specified in our example file, in the TestResults folder.

Navigating to your folder of interest


If you are not familiar with the use of command line to move between folders (directories) and followed Step 1.1, you can follow these instructions:

Navigating in Mac
  1. Open Terminal: press command + space bar, type “Terminal” and open it.
  2. Type cd ~/MyDocker

Now you can continue building the Docker image. You can visit websites like this to continue becoming familiar with the command line.

Navigating in Windows
  1. Open Command Line
  2. Type cd C:\Users\<your username>\MyDocker, where <your username> is replaced by your own username.

Now you can continue building the Docker image.

Schelling's segregation model


Click here to see the script we used as example

(Modified from this tutorial by simulatingcomplexity).

number<-2000
group<-c(rep(0,(51*51)-number),rep(1,number/2),rep(2,number/2))
grid<-matrix(sample(group,2601,replace=F), ncol=51)
alike_preference<-0.60
happiness_tracker<-c()

get_neighbors<-function(coords) {
  n<-c()
  for (i in c(1:8)) {
    
    if (i == 1) {
      x<-coords[1] + 1
      y<-coords[2]
    }
    
    if (i == 2) {
      x<-coords[1] + 1
      y<-coords[2] + 1
    }
    
    if (i == 3) {
      x<-coords[1]
      y<-coords[2] + 1
    }
    
    if (i == 4) {
      x<-coords[1] - 1
      y<-coords[2] + 1
    }
    
    if (i == 5) {
      x<-coords[1] - 1
      y<-coords[2]
    }
    
    if (i == 6) {
      x<-coords[1] - 1
      y<-coords[2] - 1
    }
    
    if (i == 7) {
      x<-coords[1]
      y<-coords[2] - 1
    }
    
    if (i == 8) {
      x<-coords[1] + 1
      y<-coords[2] - 1
    }
    
    if (x < 1) {
      x<-51
    }
    if (x > 51) {
      x<-1
    }
    if (y < 1) {
      y<-51
    }
    if (y > 51) {
      y<-1
    }
    n<-rbind(n,c(x,y))
  }
  n
}
for (t in c(1:100)) {
  happy_cells<-c()
  unhappy_cells<-c()
  for (j in c(1:51)) {
    for (k in c(1:51)) {
      current<-c(j,k)
      value<-grid[j,k] 
      if (value > 0) {
        like_neighbors<-0
        all_neighbors<-0
        neighbors<-get_neighbors(current)
        for (i in c(1:nrow(neighbors))){
          x<-neighbors[i,1]
          y<-neighbors[i,2]
          if (grid[x,y] > 0) {
            all_neighbors<-all_neighbors + 1
          }
          if (grid[x,y] == value) {
            like_neighbors<-like_neighbors + 1
          }
        }
        if (is.nan(like_neighbors / all_neighbors)==FALSE) {
          if ((like_neighbors / all_neighbors) < alike_preference) {
            unhappy_cells<-rbind(unhappy_cells,c(current[1],current[2]))
          }
          else {
            happy_cells<-rbind(happy_cells,c(current[1],current[2]))
          }
        }
        
        else {
          happy_cells<-rbind(happy_cells,c(current[1],current[2]))
        }
      }
    }
  }
  happiness_tracker<-append(happiness_tracker,length(happy_cells)/(length(happy_cells) + length(unhappy_cells)))
  rand<-sample(nrow(unhappy_cells))
  for (i in rand) {
    mover<-unhappy_cells[i,]
    mover_val<-grid[mover[1],mover[2]]
    move_to<-c(sample(1:51,1),sample(1:51,1))
    move_to_val<-grid[move_to[1],move_to[2]]
    while (move_to_val > 0 ){
      move_to<-c(sample(1:51,1),sample(1:51,1))
      move_to_val<-grid[move_to[1],move_to[2]]
    }
    grid[mover[1],mover[2]]<-0
    grid[move_to[1],move_to[2]]<-mover_val
  }
}

png("results/output.png", width=700, height=400)
par(mfrow=c(1,2))
image(grid,col=c("black","red","green"),axes=F)
plot(1:100, happiness_tracker,ylab="percent happy",xlab="time",ylim=c(0,1),oma = c(0, 0, 2, 0),col="red", type="l")
dev.off()

Troubleshooting Guide


Click here to see some known error messages and their probable causes

Your build command is not correct

When trying to build your image, you get an error message like the following:

“docker build” requires exactly 1 argument.

It means that your build command has some typing error. The most likely one is that it is lacking the last ..

Your image name contains upper case letters.

When trying to build your image, you get an error message like the following:

invalid argument “Rexample” for “-t, --tag” flag: invalid reference format: repository name must be lowercase

Use only lower case letters to name the image.

Your Dockerfile is not in plain text format

When trying to build your image, you get an error message like the following:

Error response from daemon: Dockerfile parse error line 1: unknown instruction: {\RTF1\ANSI\ANSICPG1252\COCOARTF1671\COCOASUBRTF600

You will need to convert the format of your file to plain text. If you are using TextEdit, open your Dockerfile, press shift + command + T and save it. Use Finder to make sure that the name of the file does not include any extensions.

The Dockerfile was saved under a different name

When trying to build your image, you get an error message like the following:

unable to prepare context: unable to evaluate symlinks in Dockerfile path: lstat ~/MyDocker/Dockerfile: no such file or directory

Use Finder or File Explorer to check that the name of the file was correctly spelled (Dockerfile), and it does not end with any extensions. If your file’s name looks like Dockerfile.txt, you have to edit its name to erase the .txt termination. If your file’s name ends with a .rtf, go to the previous error to see how to correct it.

If you are not able to erase the extension from your Dockerfile's name, you can also add a -f flag to your build command. With this flag you can tell Docker the name of your Dockerfile. In our example, and assuming we have a file named Dockerfile.txt, our build command would be docker build -f Dockerfile.txt -t r.example .. Note that it is necessary to have a Dockerfile in plain text format to be able to build a Docker image.

The name of your .R file or the output path are mispelled

When trying to run your container, you do not get any output files in the specified folder, but also no error messages.

Double check the last line of your Dockerfile to make sure that the correct R file is being invoked and that its name is spelled correctly. Also review the output’s path in your script. The Dockerfile specifies the project folder as the working directory, therefore your path should start with "results/", if you are following the recommended directory structure mentioned in the introduction of this tutorial. In our example, our code produces a .png output file as follows: png("results/output.png").