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In Kubeflow Pipelines (KFP), the kfp.dsl package is used to define and interact with pipelines and components.

Kubeflow Pipelines is an open-source platform for deploying, orchestrating, and managing scalable and portable machine learning workflows on Kubernetes. The kfp.dsl (Domain Specific Language) package provides a set of Python classes and functions that allow you to define and describe the structure of your machine learning pipelines.

Here's a brief overview of how it works:

1. Pipeline Definition: The kfp.dsl package allows you to define a pipeline by creating Python functions that represent the different steps or components of your workflow. These functions are typically decorated with the @kfp.dsl.pipeline decorator.

2. Components: Within each function, you can use the kfp.dsl.ComponentStore to define reusable components. Components represent individual steps or tasks in your pipeline and encapsulate the logic and dependencies of that step.

3. Pipeline Composition: The functions and components can be composed together to create a complete pipeline. You can define the order of execution, dependencies between steps, and any other configuration necessary for your workflow.

4. Interaction and Configuration: The kfp.dsl package also provides facilities for interacting with and configuring your pipeline. This includes setting parameters, specifying input and output data, and defining execution environments.

5. Execution: Once the pipeline is defined, it can be compiled into a YAML or JSON format, which can be used to deploy and execute the pipeline on a Kubernetes cluster.

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