A taipy.core.Pipeline is made to model an algorithm. It represents a direct acyclic graph of input, intermediate, and output data nodes linked together by tasks. A pipeline is a set of tasks designed to perform a set of functions.

For instance, in a typical machine learning application, we may have a pipeline dedicated to preprocessing and preparing data, a pipeline for computing a training model, and a pipeline dedicated to scoring.

In the example

We have chosen to model two pipelines.


First, a sales pipeline (boxed in green in the picture) containing training and predict tasks.

Second, a production pipeline (boxed in dark gray in the picture) containing the planning task. In fact, the two pipelines can be customized to represent two different workflows that run independently, under different schedules (for batch execution running on a fixed schedule (e.g. every week)) or by different users (For interactive execution triggered by end-users).

Note that the pipelines are not necessarily disjoint.

The attributes of a pipeline (the set of tasks) are populated based on the pipeline configuration taipy.core.taipy.PipelineConfig that must be provided when instantiating a new pipeline. (Please refer to the configuration details documentation for more details on configuration).

The next section introduces the Scenario concept.