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 on Bacalhau:
To get started, you need to install the Bacalhau client, see more information here
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.
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.
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.
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:
Next, upload the image to the registry. This can be done by using the Docker hub username, repo name, or tag.
In our case:
The following command passes a prompt to the model and generates the results in the outputs directory. It takes approximately 2 minutes to run.
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:
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.
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
If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).
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.
To get started, you need to install the Bacalhau client, see more information here
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
:
Run the script:
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.
Now it's time to run the script on Bacalhau:
bacalhau docker run
: call to Bacalhau
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
)
r-base
: docker official image we are using
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:
The same job can be presented in the declarative format. In this case, the description will look like this:
The job description should be saved in .yaml
format, e.g. rhello.yaml
, and then run with the command:
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.
To view the file, run the following command:
You can generate the job request using bacalhau describe
with the --spec
flag. This will allow you to re-run that job in the future:
If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).