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Building and Running your Custom R Containers on Bacalhau

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Introduction

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.

{% hint style="info" %} Quick script to run custom R container on Bacalhau:

bacalhau docker run \
    -i ipfs://QmY8BAftd48wWRYDf5XnZGkhwqgjpzjyUG3hN1se6SYaFt:/example_wp_log_R.csv \
    ghcr.io/bacalhau-project/examples/r-prophet:0.0.2 \
    -- Rscript Saturating-Forecasts.R "/example_wp_log_R.csv" "/outputs/output0.pdf" "/outputs/output1.pdf"

{% endhint %}

Prerequisites

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

1. 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='http://cran.rstudio.com/')"

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

wget https://w3s.link/ipfs/QmZiwZz7fXAvQANKYnt7ya838VPpj4agJt5EDvRYp3Deeo/example_wp_log_R.csv

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

mkdir -p outputs
mkdir -p R

Create a new file called Saturating-Forecasts.R and in it paste the following script:

# content of the Saturating-Forecasts.R

# Library Inclusion
library('prophet')


# Command Line Arguments:
args = commandArgs(trailingOnly=TRUE)
args

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


# File Path Processing:
I <- paste("", input, sep ="")

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

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


# Read CSV Data:
df <- read.csv(I)


# Forecasting 1:
df$cap <- 8.5
m <- prophet(df, growth = 'logistic')

future <- make_future_dataframe(m, periods = 1826)
future$cap <- 8.5
fcst <- predict(m, future)
pdf(O)
plot(m, fcst)
dev.off()

# Forecasting 2:
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)
pdf(O1)
plot(m, fcst)
dev.off()

This script performs time series forecasting using the Prophet library in R, taking input data from a CSV file, applying the forecasting model, and generating plots for analysis.

Let's have a look at the command below:

Rscript Saturating-Forecasts.R "example_wp_log_R.csv" "outputs/output0.pdf" "outputs/output1.pdf"

This command uses Rscript to execute the script that was created and written to the Saturating-Forecasts.R file.

The input parameters provided in this case are the names of input and output files:

example_wp_log_R.csv - the example data that was previously downloaded.

outputs/output0.pdf - the name of the file to save the first forecast plot.

outputs/output1.pdf - the name of the file to save the second forecast plot.

2. 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.

3. 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='http://cran.rstudio.com/')"
RUN mkdir /R
RUN mkdir /outputs
COPY Saturating-Forecasts.R R
WORKDIR /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 Saturating-Forecasts.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 -t ghcr.io/bacalhau-project/examples/r-prophet:0.0.1 .

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 ghcr.io/bacalhau-project/examples/r-prophet:0.0.1

4. 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.

export JOB_ID=$(bacalhau docker run \
    --wait \
    --id-only \
    -i ipfs://QmY8BAftd48wWRYDf5XnZGkhwqgjpzjyUG3hN1se6SYaFt:/example_wp_log_R.csv \
    ghcr.io/bacalhau-project/examples/r-prophet:0.0.2 \
    -- Rscript Saturating-Forecasts.R "/example_wp_log_R.csv" "/outputs/output0.pdf" "/outputs/output1.pdf")

Structure of the command

  1. bacalhau docker run: call to Bacalhau
  2. -i ipfs://QmY8BAftd48wWRYDf5XnZGkhwqgjpzjyUG3hN1se6SYaFt:/example_wp_log_R.csv: Mounting the uploaded dataset at /inputs in the execution. It takes two arguments, the first is the IPFS CID (QmY8BAftd48wWRYDf5XnZGkhwqgjpzjyUG3hN1se6SYaFtz) and the second is file path within IPFS (/example_wp_log_R.csv)
  3. ghcr.io/bacalhau-project/examples/r-prophet:0.0.2: the name and the tag of the docker image we are using
  4. /example_wp_log_R.csv : path to the input dataset
  5. /outputs/output0.pdf, /outputs/output1.pdf: paths to the output
  6. 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:

5. Checking the State of your Jobs

Job status: You can check the status of the job using bacalhau job list.

bacalhau job 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 job describe.

bacalhau job describe ${JOB_ID}

Job download: You can download your job results directly by using bacalhau job get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory (results) and downloaded our job output to be stored in that directory.

rm -rf results && mkdir -p results
bacalhau job get ${JOB_ID} --output-dir results

6. 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

Support

If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).

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