Bacalhau operates by executing jobs within containers. This example shows you how to build and use a custom docker container.
Prerequisite
To get started, you need to install the Bacalhau client, see more information
This example requires Docker. If you don't have Docker installed, you can install it from . Docker commands will not work on hosted notebooks like Google Colab, but the Bacalhau commands will.
1. Running Containers
Docker Command
You're likely familiar with executing Docker commands to start a container:
docker run docker/whalesay cowsay sup old fashioned container run
This command runs a container from the docker/whalesay image. The container executes the cowsay sup old fashioned container run command:
This command also runs a container from the docker/whalesay image, using Bacalhau. We use the bacalhau docker run command to start a job in a Docker container. It contains additional flags such as --wait to wait for job completion and --id-only to return only the job identifier. Inside the container, the bash -c 'cowsay hello web3 uber-run' command is executed.
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.
j-7e41b9b9-a9e2-4866-9fce-17020d8ec9e0
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.
Both commands execute cowsay in the docker/whalesay container, but Bacalhau provides additional features for working with jobs at scale.
Bacalhau Syntax
Bacalhau uses a syntax that is similar to Docker, and you can use the same containers. The main difference is that input and output data is passed to the container via IPFS, to enable planetary scale. In the example above, it doesn't make too much difference except that we need to download the stdout.
The --wait flag tells Bacalhau to wait for the job to finish before returning. This is useful in interactive sessions like this, but you would normally allow jobs to complete in the background and use the bacalhau job list command to check on their status.
Another difference is that by default Bacalhau overwrites the default entry point for the container, so you have to pass all shell commands as arguments to the run command after the -- flag.
2. Building Your Own Custom Container For Bacalhau
To use your own custom container, you must publish the container to a container registry that is accessible from the Bacalhau network. At this time, only public container registries are supported.
To demonstrate this, you will develop and build a simple custom container that comes from an old Docker example. I remember seeing cowsay at a Docker conference about a decade ago. I think it's about time we brought it back to life and distribute it across the Bacalhau network.
docker run --rm ghcr.io/bacalhau-project/examples/codsay:latest codsay I like swimming in data
Once your container is working as expected then you should push it to a public container registry. In this example, I'm pushing to Github's container registry, but we'll skip the step below because you probably don't have permission. Remember that the Bacalhau nodes expect your container to have a linux/amd64 architecture.
Now we're ready to submit a Bacalhau job using your custom container. This code runs a job, downloads the results, and prints the stdout.
The bacalhau docker run command strips the default entry point, so don't forget to run your entry point in the command line arguments.
export JOB_ID=$(bacalhau docker run \
--wait \
--id-only \
ghcr.io/bacalhau-project/examples/codsay:v1.0.0 \
-- bash -c 'codsay Look at all this data')
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.
Download your job results directly by using bacalhau job get command.
