snowflake.snowpark.udf.UDFRegistration.register_from_file¶

UDFRegistration.register_from_file(file_path: str, func_name: str, return_type: Optional[DataType] = None, input_types: Optional[List[DataType]] = None, name: Optional[Union[str, Iterable[str]]] = None, is_permanent: bool = False, stage_location: Optional[str] = None, imports: Optional[List[Union[str, Tuple[str, str]]]] = None, packages: Optional[List[Union[str, module]]] = None, replace: bool = False, if_not_exists: bool = False, parallel: int = 4, strict: bool = False, secure: bool = False, external_access_integrations: Optional[List[str]] = None, secrets: Optional[Dict[str, str]] = None, immutable: bool = False, *, statement_params: Optional[Dict[str, str]] = None, source_code_display: bool = True, skip_upload_on_content_match: bool = False) → UserDefinedFunction[source]¶

Registers a Python function as a Snowflake Python UDF from a Python or zip file, and returns the UDF. Apart from file_path and func_name, the input arguments of this method are the same as register(). See examples in UDFRegistration.

Parameters:

file_path – The path of a local file or a remote file in the stage. See more details on path argument of session.add_import(). Note that unlike path argument of session.add_import(), here the file can only be a Python file or a compressed file (e.g., .zip file) containing Python modules.
func_name – The Python function name in the file that will be created as a UDF.
return_type – A DataType representing the return data type of the UDF. Optional if type hints are provided.
input_types – A list of DataType representing the input data types of the UDF. Optional if type hints are provided.
name – A string or list of strings that specify the name or fully-qualified object identifier (database name, schema name, and function name) for the UDF in Snowflake, which allows you to call this UDF in a SQL command or via call_udf(). If it is not provided, a name will be automatically generated for the UDF. A name must be specified when is_permanent is True.
is_permanent – Whether to create a permanent UDF. The default is False. If it is True, a valid stage_location must be provided.
stage_location – The stage location where the Python file for the UDF and its dependencies should be uploaded. The stage location must be specified when is_permanent is True, and it will be ignored when is_permanent is False. It can be any stage other than temporary stages and external stages.
imports – A list of imports that only apply to this UDF. You can use a string to represent a file path (similar to the path argument in add_import()) in this list, or a tuple of two strings to represent a file path and an import path (similar to the import_path argument in add_import()). These UDF-level imports will override the session-level imports added by add_import(). Note that an empty list means no import for this UDF, and None or not specifying this parameter means using session-level imports.
packages – A list of packages that only apply to this UDF. These UDF-level packages will override the session-level packages added by add_packages() and add_requirements(). Note that an empty list means no package for this UDF, and None or not specifying this parameter means using session-level packages. To use Python packages that are not available in Snowflake, refer to custom_package_usage_config().
replace – Whether to replace a UDF that already was registered. The default is False. If it is False, attempting to register a UDF with a name that already exists results in a SnowparkSQLException exception being thrown. If it is True, an existing UDF with the same name is overwritten.
if_not_exists – Whether to skip creation of a UDF when one with the same signature already exists. The default is False. if_not_exists and replace are mutually exclusive and a ValueError is raised when both are set. If it is True and a UDF with the same signature exists, the UDF creation is skipped.
parallel – The number of threads to use for uploading UDF files with the PUT command. The default value is 4 and supported values are from 1 to 99. Increasing the number of threads can improve performance when uploading large UDF files.
strict – Whether the created UDF is strict. A strict UDF will not invoke the UDF if any input is null. Instead, a null value will always be returned for that row. Note that the UDF might still return null for non-null inputs.
secure – Whether the created UDF is secure. For more information about secure functions, see Secure UDFs.
statement_params – Dictionary of statement level parameters to be set while executing this action.
source_code_display – Display the source code of the UDF func as comments in the generated script. The source code is dynamically generated therefore it may not be identical to how the func is originally defined. The default is True. If it is False, source code will not be generated or displayed.
skip_upload_on_content_match – When set to True and a version of source file already exists on stage, the given source file will be uploaded to stage only if the contents of the current file differ from the remote file on stage. Defaults to False.
external_access_integrations – The names of one or more external access integrations. Each integration you specify allows access to the external network locations and secrets the integration specifies.
secrets – The key-value pairs of string types of secrets used to authenticate the external network location. The secrets can be accessed from handler code. The secrets specified as values must also be specified in the external access integration and the keys are strings used to retrieve the secrets using secret API.
immutable – Whether the UDF result is deterministic or not for the same input.

Note::: The type hints can still be extracted from the local source Python file if they are provided, but currently are not working for a zip file or a remote file. Therefore, you have to provide return_type and input_types when path points to a zip file or a remote file.