Taipy's Core configuration

In the previous chapter, the few Taipy Core concepts are defined.

Taipy Core is an application builder designed to help Python developer turn efficiently their algorithms into an interactive production-ready data-driven application.

To build such an application, the first step consists in configuring the characteristics and the desired behavior of your application and its entities.

Reminder: Config vs Entities

The data nodes, tasks, pipelines, and scenarios concepts have two types of Taipy objects related to them: configuration objects and runtime objects.

To differentiate the configuration objects from their runtime counterparts, they are named configs (DataNodeConfig, TaskConfig, PipelineConfig, and ScenarioConfig) while the runtime objects (DataNode, Task, Pipeline, and Scenario) are called entities.

One thing to wrap your head around (it may not be very intuitive for everyone at first) is that the configs are really just configuration objects specifying the characteristics and the behaviors of the concepts they relate to. Configs can be seen as generators. Indeed, each entity is created from a config. Note also that the same config can be used to instantiate multiple entities.

More details on the entities are available in the Entities chapter.

This chapter is dedicated to the configuration and focuses on the various configs objects. Its sections are organized as follows:

• Config section introduces the Config singleton class which is the single entrypoint for Taipy Core configuration.
• Data node configs section provides documentation on the data nodes' configurations using the python DataNodeConfig class.
• Task configs section provides documentation on the tasks' configurations using the python TaskConfig class.
• Pipeline configs section provides documentation on the pipelines' configurations using the python PipelineConfig class.
• Scenario configs section provides documentation on the scenarios' configurations using the python ScenarioConfig class.
• Global config section documents the global configuration fields and the python GlobalAppConfig class.
• Job scheduling config section documents the configuration of the job scheduling using the python JobConfig class.
• Config checker section presents the configuration checkers.
• Advanced configuration section provides details on advanced configuration features, and in particular the capacity to overwrite configuration fields with TOML files.

The next section introduces the Config singleton.