All Configs are derived from rastervision.pipeline.config.Config, which itself is a pydantic Model.

pydantic model ClassificationDataConfig[source]#

Show JSON schema
   "title": "ClassificationDataConfig",
   "description": "Base class that can be extended to provide custom configurations.\n\nThis adds some extra methods to Pydantic BaseModel.\nSee\n\nThe general idea is that configuration schemas can be defined by\nsubclassing this and adding class attributes with types and\ndefault values for each field. Configs can be defined hierarchically,\nie. a Config can have fields which are of type Config.\nValidation, serialization, deserialization, and IDE support is\nprovided automatically based on this schema.",
   "type": "object",
   "properties": {
      "type_hint": {
         "title": "Type Hint",
         "default": "classification_data",
         "enum": [
         "type": "string"
   "additionalProperties": false

  • extra: str = forbid

  • validate_assignment: bool = True

field type_hint: Literal['classification_data'] = 'classification_data'#

Build an instance of the corresponding type of object using this config.

For example, BackendConfig will build a Backend object. The arguments to this method will vary depending on the type of Config.

classmethod deserialize(inp: str | dict | Config) Self#

Deserialize Config from a JSON file or dict, upgrading if possible.

If inp is already a Config, it is returned as is.


inp (str | dict | Config) – a URI to a JSON file or a dict.

Return type


dict(with_rv_metadata: bool = False, **kwargs) dict#

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.


with_rv_metadata (bool) –

Return type


classmethod from_dict(cfg_dict: dict) Self#

Deserialize Config from a dict.


cfg_dict (dict) – Dict to deserialize.

Return type


classmethod from_file(uri: str) Self#

Deserialize Config from a JSON file, upgrading if possible.


uri (str) – URI to load from.

Return type



Recursively validate hierarchies of Configs.

This uses reflection to call validate_config on a hierarchy of Configs using a depth-first pre-order traversal.


Re-validate an instantiated Config.

Runs all Pydantic validators plus self.validate_config().

Adapted from:

to_file(uri: str, with_rv_metadata: bool = True) None#

Save a Config to a JSON file, optionally with RV metadata.

  • uri (str) – URI to save to.

  • with_rv_metadata (bool) – If True, inject Raster Vision metadata such as plugin_versions, so that the config can be upgraded when loaded.

Return type


update(*args, **kwargs)#

Update any fields before validation.

Subclasses should override this to provide complex default behavior, for example, setting default values as a function of the values of other fields. The arguments to this method will vary depending on the type of Config.


Validate fields that should be checked after update is called.

This is to complement the builtin validation that Pydantic performs at the time of object construction.

validate_list(field: str, valid_options: List[str])#

Validate a list field.

  • field (str) – name of field to validate

  • valid_options (List[str]) – values that field is allowed to take


ConfigError – if field is invalid