Command line interface

Once you assemble a pipeline and start developing, you’d want to see how changes to the source code affect the pipeline’s output. Ploomber provides a command line interface to do this and to perform other common tasks. Here are the most common commands.

Build pipeline (skips up-to-date tasks):

ploomber build

Forced build (runs all tasks):

ploomber build --force

Generate pipeline plot:

ploomber plot

By default, all commands assume your pipeline is defined in a pipeline.yaml file in the current directory. The pipeline.yaml file is known as “entry point”, there are other types of entry points, which we explain in the next section.

Entry points

There are three types of entry points (ordered by flexibility):

  1. Directory (a directory with scripts)

  2. Spec (aka pipeline.yaml)

  3. Factory function (a function that returns a DAG object)

For more information on entry points, see Spec API vs Python API.


Ploomber can figure out your pipeline without even having a pipeline.yaml, by just passing a directory. This kind of entry point is the simplest one but also the less flexible, it is a good option for small pipelines where you don’t need to customize settings.

Internally, Ploomber uses the ploomber.spec.DAGSpec.from_directory method. See the documentation for details on how a directory is converted into a pipeline.

All commands that accept the --entry-point/-e parameter, can take a directory as a value. For example, to build a pipeline using the current directory:

ploomber build --entry-point .


This is the default entry point because it allows a greater level of flexibility without requiring you to write Python code to specify your pipeline. It is a good choice for simple to moderately sophisticated pipelines.

It is a YAML file with certain structure, for schema details see: Spec API.

Factory function

This is the most flexible type of entry point. A factory function is a Python function that returns a DAG object. Factory functions are a good choice for pipelines that require a high level of flexibility than a YAML file cannot easily express. However it requires you to write your pipeline using the Python API.

from ploomber import DAG

def make():
    dag = DAG()
    # add tasks to your pipeline...
    return dag

To use it in the command line interface, use the --entry-point/-e and pass a dotted path to the function. Assuming is importable in the current directory, you can build the pipeline with the following command:

ploomber build --entry-point factory.make

Note: If your factory function has a docstring, the first line will be displayed in the CLI help menu (e.g. ploomber build --entry-point factory.make --help). If the docstring is in the numpydoc format (and numpydoc is installed, pip install numpydoc), descriptions for documented parameters will be displayed as well.

Where to go next

The command line interface is a convenient way to quickly iterate pipeline development, just modify your source code and use the CLI to see results.

In some cases, we don’t want our pipeline to be static but to have input parameters determine its behavior. Go to the next guide to see how you can add parameters to your pipeline and use the CLI to pass different parameter values.