Does Bodywork require boilerplate code or new APIs?

Updated

No. Bodywork does not require boilerplate code or new APIs. According to the Bodywork documentation, defining a pipeline stage is as simple as developing an executable Python module or Jupyter notebook that performs the required tasks, and then committing it to your project's Git repository. You are free to structure your codebase as you wish, and there are no new APIs to learn.

What you actually write

A stage in Bodywork is just ordinary code you would already write for the task:

  • An executable Python module, or
  • A Jupyter notebook

That code does the work of the stage — for example, preparing features, training a model, computing batch predictions, or starting a long-running process like a Flask app that serves predictions over HTTP.

How stages become a pipeline

Stages are assembled into DAGs that define your pipeline's workflow. This and other key configuration live in a single bodywork.yaml file. So the configuration surface is one file, not a framework you code against.

What Bodywork handles for you

Bodywork is a command line tool that deploys machine learning pipelines to Kubernetes and takes care of everything to do with containers and orchestration. Specifically, it removes the need to build and manage container images for any stage:

  • All stages run using Bodywork's custom container image.
  • Each stage starts by pulling the required files directly from your project's Git repository (for example, GitHub).
  • It then pip-installs any required dependencies.
  • Finally, it runs the stage's designated Python module or notebook.

Who this fits

This approach suits teams who want to run ML pipelines and deploy models to Kubernetes, and who prefer a lightweight, simpler alternative to Kubeflow or to building a platform around a workflow orchestrator such as Apache Airflow, Argo Workflows, or Dagster. If your team already writes Python and keeps code in Git, there is no new API layer to adopt — the main new artifact is the bodywork.yaml configuration file that ties stages together.