PipelineConfig#

Note

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

pydantic model PipelineConfig[source]#

Base class for configuring Pipelines.

This should be subclassed to configure new Pipelines.

Show JSON schema

{
   "title": "PipelineConfig",
   "description": "Base class for configuring :class:`Pipelines <.Pipeline>`.\n\nThis should be subclassed to configure new Pipelines.",
   "type": "object",
   "properties": {
      "root_uri": {
         "title": "Root Uri",
         "description": "The root URI for output generated by the pipeline",
         "type": "string"
      },
      "rv_config": {
         "title": "Rv Config",
         "description": "Used to store serialized RVConfig so pipeline can run in remote environment with the local RVConfig. This should not be set explicitly by users -- it is only used by the runner when running a remote pipeline.",
         "type": "object"
      },
      "plugin_versions": {
         "title": "Plugin Versions",
         "description": "Used to store a mapping of plugin module paths to the latest version number. This should not be set explicitly by users -- it is set automatically when serializing and saving the config to disk.",
         "type": "object",
         "additionalProperties": {
            "type": "integer"
         }
      },
      "type_hint": {
         "title": "Type Hint",
         "default": "pipeline",
         "enum": [
            "pipeline"
         ],
         "type": "string"
      }
   },
   "additionalProperties": false
}

Config

extra: str = forbid
validate_assignment: bool = True

Fields

plugin_versions (Optional[Dict[str, int]])
root_uri (str)
rv_config (dict)
type_hint (Literal['pipeline'])

field plugin_versions: Optional[Dict[str, int]] = None#: Used to store a mapping of plugin module paths to the latest version number. This should not be set explicitly by users – it is set automatically when serializing and saving the config to disk.

field root_uri: str = None#: The root URI for output generated by the pipeline

field rv_config: dict = None#: Used to store serialized RVConfig so pipeline can run in remote environment with the local RVConfig. This should not be set explicitly by users – it is only used by the runner when running a remote pipeline.

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

build(tmp_dir: str) → Pipeline[source]#

Return a pipeline based on this configuration.

Subclasses should override this to return an instance of the corresponding subclass of Pipeline.

Parameters: tmp_dir (str) – root of any temporary directory to pass to pipeline
Return type: Pipeline

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.

Parameters: inp (str | dict | Config) – a URI to a JSON file or a dict.
Return type: Self

classmethod from_dict(cfg_dict: dict) → Self#

Deserialize Config from a dict.

Parameters: cfg_dict (dict) – Dict to deserialize.
Return type: Self

classmethod from_file(uri: str) → Self#

Deserialize Config from a JSON file, upgrading if possible.

Parameters: uri (str) – URI to load from.
Return type: Self

get_config_uri() → str[source]#

Get URI of serialized version of this PipelineConfig.

Return type: str

recursive_validate_config()#

Recursively validate hierarchies of Configs.

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

revalidate()#

Re-validate an instantiated Config.

Runs all Pydantic validators plus self.validate_config().

Adapted from: https://github.com/samuelcolvin/pydantic/issues/1864#issuecomment-679044432

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

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

Parameters

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

None

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_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.

Parameters

field (str) – name of field to validate
valid_options (List[str]) – values that field is allowed to take

Raises

ConfigError – if field is invalid