NativeInterface

A class representing the native interface for a task. This is used to interact with the task and its execution context.

Attributes

Attribute	Type	Description
inputs	Dict[str, Tuple[Type, Any]]	A mapping of input names to tuples containing their Python type and default value, used to define the task's input signature.
outputs	Dict[str, Type]	A mapping of output names to their expected Python types, used to define the task's return signature.
docstring	Optional[Docstring] = None	The parsed documentation object for the task, used to provide metadata and descriptions for the interface components.
has_default	ClassVar[Type[_has_default]] = _has_default	This can be used to indicate if a specific input has a default value or not, in the case when the default value is not known.

---

Constructor

Signature

def NativeInterface(
    inputs: Dict[str, Tuple[Type, Any]],
    outputs: Dict[str, Type],
    docstring: Optional[Docstring] = None,
    _remote_defaults: Optional[Dict[str, literals_pb2.Literal]] = None
) -> null

Parameters

Name	Type	Description
inputs	Dict[str, Tuple[Type, Any]]	A dictionary mapping input names to a tuple containing their Python type and default value.
outputs	Dict[str, Type]	A dictionary mapping output names to their respective Python types.
docstring	Optional[Docstring] = None	An optional Docstring object containing documentation for the task.
_remote_defaults	Optional[Dict[str, literals_pb2.Literal]] = None	An optional dictionary of default values for inputs already in remote literal form.

---

Methods

---

has_outputs()

def has_outputs(self) -> bool: ...

Check if the task has outputs. This is used to determine if the task has outputs or not.

Returns

Type	Description
boolean	True if the interface defines one or more output parameters, False otherwise

---

required_inputs()

def required_inputs(self) -> List[str]: ...

Get the names of the required inputs for the task. This is used to determine which inputs are required for the task execution.

Returns

Type	Description
array	A list of required input names.

---

num_required_inputs()

def num_required_inputs(self) -> int: ...

Get the number of required inputs for the task. This is used to determine how many inputs are required for the task execution.

Returns

Type	Description
integer	The total count of input parameters that do not have a default value

---

from_types()

@classmethod
def from_types(
        cls,
        inputs: Dict[str, Tuple[Type, Type[_has_default] | Type[inspect._empty]]],
        outputs: Dict[str, Type],
        default_inputs: Optional[Dict[str, literals_pb2.Literal]] = None,
    ) -> NativeInterface: ...

Create a new NativeInterface from the given types. This is used to create a native interface for the task.

Parameters

Name	Type	Description
inputs	dict	A dictionary of input names and their types and a value indicating if they have a default value.
outputs	dict	A dictionary of output names and their types.
default_inputs	dict	Optional dictionary of default inputs for remote tasks.

Returns

Type	Description
[NativeInterface](nativeinterface.md?sid=flyte_models_nativeinterface)	A NativeInterface object with the given inputs and outputs.

---

from_callable()

@classmethod
def from_callable(cls, func: Callable) -> NativeInterface: ...

Extract the native interface from the given function. This is used to create a native interface for the task.

Parameters

Name	Type	Description
func	callable	The Python function or callable to inspect for parameter and return type information

Returns

Type	Description
[NativeInterface](nativeinterface.md?sid=flyte_models_nativeinterface)	A new NativeInterface instance derived from the function's signature, type hints, and docstring

---

convert_to_kwargs()

def convert_to_kwargs(self, *args, **kwargs) -> Dict[str, Any]: ...

Convert the given arguments to keyword arguments based on the native interface. This is used to convert the arguments to the correct types for the task execution.

Parameters

Name	Type	Description
args	any	Positional arguments provided by the caller to be mapped to interface input names
kwargs	any	Keyword arguments provided by the caller

Returns

Type	Description
dict	A dictionary mapping input names to their corresponding values provided via positional or keyword arguments

---

get_input_types()

def get_input_types(self) -> Dict[str, Type]: ...

Get the input types for the task. This is used to get the types of the inputs for the task execution.

Returns

Type	Description
dict	A dictionary mapping input names to their respective Python types

---

json_schema()

@property
def json_schema(self) -> Dict[str, Any]: ...

Convert task inputs to a JSON schema dict. Uses the Flyte type engine to produce a LiteralType for each input, then converts to JSON schema.

Returns

Type	Description
dict	A JSON schema representation of the inputs, including property types and a list of required fields

---