Skip to main content

Building and Running your Custom R Containers on Bacalhau

stars - badge-generator


This example will walk you through building Time Series Forecasting using Prophet. Prophet is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.


Quick script to run custom R container in Bacalhau

bacalhau docker run -i ipfs://QmY8BAftd48wWRYDf5XnZGkhwqgjpzjyUG3hN1se6SYaFt:/example_wp_log_R.csv -- Rscript Saturating-Forecasts.R "/example_wp_log_R.csv" "/outputs/output0.pdf" "/outputs/output1.pdf"


To get started, you need to install the Bacalhau client, see more information here

Running Prophet in R Locally

Open R studio or R-supported IDE. If you want to run this on a notebook server, then make sure you use an R kernel. Prophet is a CRAN package so you can use install.packages to install the prophet package.

R -e "install.packages('prophet',dependencies=TRUE, repos='')"

After installation is finished, you can download the example data that is stored in IPFS.


The code below instantiates the library and fits a model to the data.

mkdir -p outputs
mkdir -p R
%%writefile Saturating-Forecasts.R

args = commandArgs(trailingOnly=TRUE)

input = args[1]
output = args[2]
output1 = args[3]

I <- paste("", input, sep ="")

O <- paste("", output, sep ="")

O1 <- paste("", output1 ,sep ="")

df <- read.csv(I)

df$cap <- 8.5
m <- prophet(df, growth = 'logistic')

future <- make_future_dataframe(m, periods = 1826)
future$cap <- 8.5
fcst <- predict(m, future)
plot(m, fcst)

df$y <- 10 - df$y
df$cap <- 6
df$floor <- 1.5
future$cap <- 6
future$floor <- 1.5
m <- prophet(df, growth = 'logistic')
fcst <- predict(m, future)
plot(m, fcst)
Rscript Saturating-Forecasts.R "example_wp_log_R.csv" "outputs/output0.pdf" "outputs/output1.pdf"

Running R Prophet on Bacalhau

To use Bacalhau, you need to package your code in an appropriate format. The developers have already pushed a container for you to use, but if you want to build your own, you can follow the steps below. You can view a dedicated container example in the documentation.

Containerize Script with Docker

To build your own docker container, create a Dockerfile, which contains instructions to build your image.

FROM r-base
RUN R -e "install.packages('prophet',dependencies=TRUE, repos='')"
RUN mkdir /R
RUN mkdir /outputs
COPY Saturating-Forecasts.R R

These commands specify how the image will be built, and what extra requirements will be included. We use r-base as the base image and then install the prophet package. We then copy the R script into the container and set the working directory to the R folder.

Build the container

We will run docker build command to build the container;

docker build -t <hub-user>/<repo-name>:<tag> .

Before running the command replace;

  • hub-user with your docker hub username, If you don’t have a docker hub account follow these instructions to create docker account, and use the username of the account you created

  • repo-name with the name of the container, you can name it anything you want

  • tag this is not required but you can use the latest tag

In our case:

docker buildx build --platform linux/amd64 --push -t .

Push the container

Next, upload the image to the registry. This can be done by using the Docker hub username, repo name, or tag.

docker push <hub-user>/<repo-name>:<tag>

In our case:

docker push --platform linux/amd64 --push -t .

Running a Job on Bacalhau

The following command passes a prompt to the model and generates the results in the outputs directory. It takes approximately 2 minutes to run.

%%bash --out job_id
bacalhau docker run \
--wait \
--id-only \
-i ipfs://QmY8BAftd48wWRYDf5XnZGkhwqgjpzjyUG3hN1se6SYaFt:/example_wp_log_R.csv \ \
-- Rscript Saturating-Forecasts.R "/example_wp_log_R.csv" "/outputs/output0.pdf" "/outputs/output1.pdf"

Structure of the command

Let's look closely at the command above:

  • bacalhau docker run: call to bacalhau

  • -i ipfs://QmY8BAftd48wWRYDf5XnZGkhwqgjpzjyUG3hN1se6SYaFt: CIDs to use on the job. Mounts them at '/inputs' in the execution.

  • the name and the tag of the docker image we are using

  • /example_wp_log_R.csv : path to the input dataset

  • /outputs/output0.pdf....: path to the output

  • Rscript Saturating-Forecasts.R: execute the R script

When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.

Checking the State of your Jobs

  • Job status: You can check the status of the job using bacalhau list.
bacalhau list --id-filter ${JOB_ID}

When it says Published or Completed, that means the job is done, and we can get the results.

  • Job information: You can find out more information about your job by using bacalhau describe.
bacalhau describe ${JOB_ID}
  • Job download: You can download your job results directly by using bacalhau get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in that directory.
rm -rf results && mkdir -p results
bacalhau get $JOB_ID --output-dir results

Viewing your Job Output

To view the file, run the following command:

ls results/outputs

You can't natively display PDFs in notebooks, so here are some static images of the PDFs:

  • output0.pdf

  • output1.pdf