Building and Running your Custom R Containers on Bacalhau
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.
:::info Quick script to run custom R container on Bacalhau:
:::
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:
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
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.
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:
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:
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
bacalhau docker run
: call to Bacalhau
-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
)
ghcr.io/bacalhau-project/examples/r-prophet:0.0.2
: 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
, /outputs/output1.pdf
: paths 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:
5. Checking the State of your Jobs
Job status: You can check the status of the job using bacalhau 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 describe
.
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 (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
![](https://docs.bacalhau.org/~gitbook/image?url=https%3A%2F%2F3858638047-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FlgnAvZb4aea9rkAZiCE3%252Fuploads%252Fgit-blob-12c319b3b37632fdf53647cf1e725413a91151eb%252Foutput0.png%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=b066dd01&sv=1)
output1.pdf
![](https://docs.bacalhau.org/~gitbook/image?url=https%3A%2F%2F3858638047-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FlgnAvZb4aea9rkAZiCE3%252Fuploads%252Fgit-blob-2cf500fe168f7af31b54299a759cb416efb2fc5a%252Foutput1.png%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=52b4ea1f&sv=1)
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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