ModelByIdGetResponseBody

pydantic model openapi_client.models.model_by_id_get_response_body.ModelByIdGetResponseBody

Registered ML model record. InputSchema and OutputSchema are N/A when OutputType is metadata.

Fields:
field supported_file_formats: Optional[List[StrictStr]] = None (alias 'SupportedFileFormats')

File formats accepted when running the model (for example csv, parquet, txt).

field input_schema: Optional[ModelByIdGetResponseBodyInputSchema] = None (alias 'InputSchema')
field last_modified_by: Optional[StrictStr] = None (alias 'LastModifiedBy')

User who last updated the model metadata.

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

S3 URI of the model artifact for custom models, or N/A for subscribed/existing SageMaker resources.

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

Free-form tags stored on the model. Defaults to [] when absent.

field description: Optional[StrictStr] = None (alias 'Description')

Human-readable description of the model.

field post_processed_glue_jobs: Optional[StrictStr] = None (alias 'PostProcessedGlueJobs')

Glue job name used after inference.

field is_subscribed_model: Optional[StrictStr] = None (alias 'IsSubscribedModel')

no for custom artifact-created models (SageMaker model is deleted with Amorphic). yes for models registered from an existing SageMaker resource (SageMaker model is not deleted).

field model_name: Optional[StrictStr] = None (alias 'ModelName')

Display name. Must be unique (case-insensitive) and match ^[a-zA-Z][a-zA-Z0-9-]{2,49}$.

field algorithm_used: Optional[StrictStr] = None (alias 'AlgorithmUsed')

Training algorithm key used to select the SageMaker container image (for example BlazingText, XGBoost). N/A for subscribed models.

field last_modified_time: Optional[StrictStr] = None (alias 'LastModifiedTime')

Last metadata update time as YYYY-MM-DD HH:MM:SS.

field output_type: Optional[StrictStr] = None (alias 'OutputType')

metadata writes inference output as files; datasetdata writes to a dataset and requires InputSchema and OutputSchema.

field output_schema: Optional[ModelByIdGetResponseBodyOutputSchema] = None (alias 'OutputSchema')
field model_id: Optional[StrictStr] = None (alias 'ModelId')

Unique identifier. For custom creates this is also the SageMaker ModelName.

field creation_time: Optional[StrictStr] = None (alias 'CreationTime')

Creation time as YYYY-MM-DD HH:MM:SS.

field created_by: Optional[StrictStr] = None (alias 'CreatedBy')

User who registered the model.

field pre_processed_glue_jobs: Optional[StrictStr] = None (alias 'PreProcessedGlueJobs')

Glue job name used before inference.

field access_type: Optional[StrictStr] = None (alias 'AccessType')

Caller’s ACL on the model (owner, editor, or read-only). Defaults to read-only when the stored value is missing.

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 ModelByIdGetResponseBody 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 ModelByIdGetResponseBody from a dict

Return type:

Optional[Self]