You can use official Docker containers for each language, like R or Python. In this example, we will use the official R container and run it on Bacalhau.

In this tutorial example, we will run a "hello world" R script on Bacalhau.

Prerequisites​

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

1. Running an R Script Locally​

To install R follow these instructions A Installing R and RStudio | Hands-On Programming with R. After R and RStudio are installed, create and run a script called hello.R:

# hello.R
print("hello world")

Run the script:

Rscript hello.R

Next, upload the script to your public storage (in our case, IPFS). We've already uploaded the script to IPFS and the CID is: QmVHSWhAL7fNkRiHfoEJGeMYjaYZUsKHvix7L54SptR8ie. You can look at this by browsing to one of the HTTP IPFS proxies like ipfs.io or w3s.link.

2. Running a Job on Bacalhau​

Now it's time to run the script on Bacalhau:

export JOB_ID=$(bacalhau docker run \
    --wait \
    --id-only \
    -i ipfs://QmQRVx3gXVLaRXywgwo8GCTQ63fHqWV88FiwEqCidmUGhk:/hello.R \
    r-base \
    -- Rscript hello.R)

Structure of the command​

1. bacalhau docker run: call to Bacalhau

2. i ipfs://QmQRVx3gXVLaRXywgwo8GCTQ63fHqWV88FiwEqCidmUGhk:/hello.R: Mounting the uploaded dataset at /inputs in the execution. It takes two arguments, the first is the IPFS CID (QmQRVx3gXVLaRXywgwo8GCTQ63fHqWV88FiwEqCidmUGhk) and the second is file path within IPFS (/hello.R)

3. r-base: docker official image we are using

4. Rscript hello.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:

Declarative job description​

The same job can be presented in the declarative format. In this case, the description will look like this:

name: Running a Simple R Script
type: batch
count: 1
tasks:
  - name: My main task
    Engine:
      type: docker
      params:
        Image: r-base:latest
        Entrypoint:
          - /bin/bash
        Parameters:
          - -c        
          - Rscript /hello.R
    InputSources:
      - Target: "/"
        Source:
          Type: urlDownload
          Params:
            URL: https://raw.githubusercontent.com/bacalhau-project/examples/main/scripts/hello.R
            Path: /hello.R

The job description should be saved in .yaml format, e.g. rhello.yaml, and then run with the command:

bacalhau job run rhello.yaml

3. 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 results
bacalhau job get ${JOB_ID} --output-dir results

4. Viewing your Job Output​

To view the file, run the following command:

cat results/stdout

Futureproofing your R Scripts​

You can generate the job request using bacalhau job describe with the --spec flag. This will allow you to re-run that job in the future:

bacalhau job describe ${JOB_ID} --spec > job.yaml

cat job.yaml

Support​

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

Introduction

This example will walk you through building Time Series Forecasting using . 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.

Prerequisites

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

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:

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.

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

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:

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

3. Containerize Script with Docker

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

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:

Before running the command replace:

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:

Push the container

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

In our case:

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.

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.

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.

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.

6. Viewing your Job Output

To view the file, run the following command:

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

output0.pdf

output1.pdf

Support