Config checker

Taipy provides a checking mechanism to validate if your configuration is correct.

You can trigger the check by calling:

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from taipy import Config

Config.check()

The Config.check() method returns a collector of issues. Each issue corresponds to an inconsistency in the configuration attached to an issue level (INFO, WARNING, ERROR). Config.check() raises an exception if at least one issue collected has the ERROR level.

Here is the list of the possible issues that could be returned by the checker:

  • An ERROR issue is created if the clean_entities_enabled property is populated in the GlobalAppConfig with a non-Boolean value.
  • An ERROR issue is created if the storage_type and the scope properties of any DataNodeConfig have not been provided with a correct value.
  • Depending on the storage_type value of a DataNodeConfig, an ERROR issue is created if a specific required property is missing.
  • An ERROR issue is created if one of the inputs and outputs parameters of a TaskConfig does not correspond to a DataNodeConfig.
  • A WARNING issue is created if a TaskConfig has no input and no output.
  • An ERROR issue is created if the function parameter of a TaskConfig is not a callable function.
  • An ERROR issue is created if one of the task parameters of a PipelineConfig does not correspond to a TaskConfig.
  • A WARNING issue is created if a PipelineConfig has no task configuration defined.
  • An ERROR issue is created if one of the pipeline parameters of a ScenarioConfig does not correspond to a PipelineConfig.
  • A WARNING issue is created if a ScenarioConfig has no pipeline configuration defined.
  • An ERROR issue is created if the frequency parameter of a ScenarioConfig has an incorrect Frequency value.
  • An INFO issue is created if a ScenarioConfig has no comparator defined.
  • If the JobConfig has been configured with multiple workers, an ERROR issue is created if an "in_memory" DataNodeConfig is defined.

The next section presents advanced configuration.