ModelData

pydantic model openapi_client.models.model_data.ModelData

ModelData

Fields:

• algorithm_used (str)

• artifacts_location (str | None)

• description (str)

• existing_model_resource (str | None)

• input_schema (openapi_client.models.model_data_input_schema.ModelDataInputSchema | None)

• keywords (List[str] | None)

• model_name (str)

• output_schema (openapi_client.models.model_data_output_schema.ModelDataOutputSchema | None)

• output_type (str)

• post_processed_glue_jobs (str)

• pre_processed_glue_jobs (str)

• supported_file_formats (List[str])

field artifacts_location: Optional[StrictStr] = None (alias 'ArtifactsLocation')

S3 URI of the uploaded artifact from POST /models/getPresignedUrl (uploadPath). Required unless ExistingModelResource is set. Cannot be sent together with ExistingModelResource.

field existing_model_resource: Optional[StrictStr] = None (alias 'ExistingModelResource')

SageMaker ModelName of an existing model to tag and register. Required unless ArtifactsLocation is set. Cannot be sent together with ArtifactsLocation.

field description: StrictStr [Required] (alias 'Description')

Human-readable description of the model.

Constraints:

• strict = True

field model_name: StrictStr [Required] (alias 'ModelName')

Unique display name. Must match ^[a-zA-Z][a-zA-Z0-9-]{2,49}$ (3-50 characters, start with a letter, alphanumeric and hyphen only).

Constraints:

• strict = True

field output_type: StrictStr [Required] (alias 'OutputType')

metadata stores inference as files. datasetdata writes to a dataset and also requires InputSchema and OutputSchema.

Constraints:

• strict = True

field algorithm_used: StrictStr [Required] (alias 'AlgorithmUsed')

SageMaker algorithm key used to resolve the container image (for example BlazingText, XGBoost, DeepARForecasting). Version suffixes after a colon are stripped. Use N/A with ExistingModelResource.

Constraints:

• strict = True

field supported_file_formats: List[StrictStr] [Required] (alias 'SupportedFileFormats')

File formats the model run accepts (for example csv, parquet, jpg, txt).

field pre_processed_glue_jobs: StrictStr [Required] (alias 'PreProcessedGlueJobs')

Glue job name to run before inference.

Constraints:

• strict = True

field post_processed_glue_jobs: StrictStr [Required] (alias 'PostProcessedGlueJobs')

Glue job name to run after inference.

Constraints:

• strict = True

field keywords: Optional[List[StrictStr]] = None (alias 'Keywords')

Free-form tags. Required by the create lambda even though this schema does not mark it required.

field input_schema: Optional[ModelDataInputSchema] = None (alias 'InputSchema')

field output_schema: Optional[ModelDataOutputSchema] = None (alias 'OutputSchema')

to_str()

Returns the string representation of the model using alias

Return type:

str

to_json()

Returns the JSON representation of the model using alias

Return type:

str

classmethod from_json(json_str)

Create an instance of ModelData from a JSON string

Return type:

Optional[Self]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

• None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

classmethod from_dict(obj)

Create an instance of ModelData from a dict

Return type:

Optional[Self]