flytekit.types.error.error

Directory

Classes

Variables

flytekit.types.error.error.ErrorTransformer

Enables converting a python type FlyteError to LiteralType.Error

Parameters

def ErrorTransformer()

Properties

Methods

assert_type()

def assert_type(
    t: Type[T],
    v: T,
)

from_binary_idl()

def from_binary_idl(
    binary_idl_object: Binary,
    expected_python_type: Type[T],
) -> Optional[T]

This function primarily handles deserialization for untyped dicts, dataclasses, Pydantic BaseModels, and attribute access.｀

For untyped dict, dataclass, and pydantic basemodel: Life Cycle (Untyped Dict as example): python val -> msgpack bytes -> binary literal scalar -> msgpack bytes -> python val (to_literal) (from_binary_idl)

For attribute access: Life Cycle: python val -> msgpack bytes -> binary literal scalar -> resolved golang value -> binary literal scalar -> msgpack bytes -> python val (to_literal) (propeller attribute access) (from_binary_idl)

from_generic_idl()

def from_generic_idl(
    generic: Struct,
    expected_python_type: Type[T],
) -> Optional[T]

TODO: Support all Flyte Types. This is for dataclass attribute access from input created from the Flyte Console.

• This can be removed in the future when the Flyte Console support generate Binary IDL Scalar as input.

get_literal_type()

def get_literal_type(
    t: typing.Type[~T],
) -> flytekit.models.types.LiteralType

Converts the python type to a Flyte LiteralType

guess_python_type()

def guess_python_type(
    literal_type: flytekit.models.types.LiteralType,
) -> typing.Type[flytekit.types.error.error.FlyteError]

Converts the Flyte LiteralType to a python object type.

isinstance_generic()

def isinstance_generic(
    obj,
    generic_alias,
)

schema_match()

def schema_match(
    schema: dict,
) -> bool

Check if a JSON schema fragment matches this transformer’s python_type.

For BaseModel subclasses, automatically compares the schema’s title, type, and required fields against the type’s own JSON schema. For other types, returns False by default — override if needed.

to_html()

def to_html(
    ctx: FlyteContext,
    python_val: T,
    expected_python_type: Type[T],
) -> str

Converts any python val (dataframe, int, float) to a html string, and it will be wrapped in the HTML div

to_literal()

def to_literal(
    ctx: flytekit.core.context_manager.FlyteContext,
    python_val: flytekit.types.error.error.FlyteError,
    python_type: typing.Type[~T],
    expected: flytekit.models.types.LiteralType,
) -> flytekit.models.literals.Literal

Converts a given python_val to a Flyte Literal, assuming the given python_val matches the declared python_type. Implementers should refrain from using type(python_val) instead rely on the passed in python_type. If these do not match (or are not allowed) the Transformer implementer should raise an AssertionError, clearly stating what was the mismatch

to_python_value()

def to_python_value(
    ctx: flytekit.core.context_manager.FlyteContext,
    lv: flytekit.models.literals.Literal,
    expected_python_type: typing.Type[~T],
) -> ~T

Converts the given Literal to a Python Type. If the conversion cannot be done an AssertionError should be raised

flytekit.types.error.error.FlyteError

Special Task type that will be used in the failure node. Propeller will pass this error to failure task, so users have to add an input with this type to the failure task.

Parameters

class FlyteError(
    message: str,
    failed_node_id: str,
)

Methods

from_dict()

def from_dict(
    d,
    dialect,
)

from_json()

def from_json(
    data: typing.Union[str, bytes, bytearray],
    decoder: collections.abc.Callable[[typing.Union[str, bytes, bytearray]], dict[typing.Any, typing.Any]],
    from_dict_kwargs: typing.Any,
) -> ~T

to_dict()

def to_dict()

to_json()

def to_json(
    encoder: collections.abc.Callable[[typing.Any], typing.Union[str, bytes, bytearray]],
    to_dict_kwargs: typing.Any,
) -> typing.Union[str, bytes, bytearray]