flytekit.core.interface

Directory

Classes

Errors

• FlyteMissingReturnValueException
• FlyteMissingTypeException
• FlyteValidationException

flytekit.core.interface.Any

Special type indicating an unconstrained type.

• Any is compatible with every type.
• Any assumed to have all methods.
• All values assumed to be instances of Any.

Note that all the above statements are true from the point of view of static type checkers. At runtime, Any should not be used with instance checks.

flytekit.core.interface.Artifact

An Artifact is effectively just a metadata layer on top of data that exists in Flyte. Most data of interest will be the output of tasks and workflows. The other category is user uploads.

This Python class has limited purpose, as a way for users to specify that tasks/workflows create Artifacts and the manner (i.e. name, partitions) in which they are created.

Control creation parameters at task/workflow execution time ::

@task def t1() -> Annotated[nn.Module, Artifact(name=“my.artifact.name”)]: …

def Artifact(
    project: Optional[str],
    domain: Optional[str],
    name: Optional[str],
    version: Optional[str],
    time_partitioned: bool,
    time_partition: Optional[TimePartition],
    time_partition_granularity: Optional[Granularity],
    partition_keys: Optional[typing.List[str]],
    partitions: Optional[Union[Partitions, typing.Dict[str, str]]],
):

Methods

create_from()

def create_from(
    o: O,
    card: Optional[SerializableToString],
    args: `*args`,
    kwargs,
):

This function allows users to declare partition values dynamically from the body of a task. Note that you’ll still need to annotate your task function output with the relevant Artifact object. Below, one of the partition values is bound to an input, and the other is set at runtime. Note that since tasks are not run at compile time, flytekit cannot check that you’ve bound all the partition values. It’s up to you to ensure that you’ve done so.

Pricing = Artifact(name=“pricing”, partition_keys=[“region”]) EstError = Artifact(name=“estimation_error”, partition_keys=[“dataset”], time_partitioned=True)

@task def t1() -> Annotated[pd.DataFrame, Pricing], Annotated[float, EstError]: df = get_pricing_results() dt = get_time() return Pricing.create_from(df, region=“dubai”), EstError.create_from(msq_error, dataset=“train”, time_partition=dt)

You can mix and match with the input syntax as well.

@task def my_task() -> Annotated[pd.DataFrame, RideCountData(region=Inputs.region)]: … return RideCountData.create_from(df, time_partition=datetime.datetime.now())

embed_as_query()

def embed_as_query(
    partition: Optional[str],
    bind_to_time_partition: Optional[bool],
    expr: Optional[str],
    op: Optional[Op],
):

This should only be called in the context of a Trigger. The type of query this returns is different from the query() function. This type of query is used to reference the triggering artifact, rather than running a query.

query()

def query(
    project: Optional[str],
    domain: Optional[str],
    time_partition: Optional[Union[datetime.datetime, TimePartition, art_id.InputBindingData]],
    partitions: Optional[Union[typing.Dict[str, str], Partitions]],
    kwargs,
):

to_id_idl()

def to_id_idl()

Converts this object to the IDL representation. This is here instead of translator because it’s in the interface, a relatively simple proto object that’s exposed to the user.

Properties

flytekit.core.interface.ArtifactIDSpecification

This is a special object that helps specify how Artifacts are to be created. See the comment in the call function of the main Artifact class. Also see the handling code in transform_variable_map for more information. There’s a limited set of information that we ultimately need in a TypedInterface, so it doesn’t make sense to carry the full Artifact object around. This object should be sufficient, despite having a pointer to the main artifact.

def ArtifactIDSpecification(
    a: Artifact,
):

Methods

bind_partitions()

def bind_partitions(
    args,
    kwargs,
):

to_partial_artifact_id()

def to_partial_artifact_id()

flytekit.core.interface.ArtifactQuery

def ArtifactQuery(
    artifact: Artifact,
    name: str,
    project: Optional[str],
    domain: Optional[str],
    time_partition: Optional[TimePartition],
    partitions: Optional[Partitions],
    tag: Optional[str],
):

Methods

get_partition_str()

def get_partition_str(
    kwargs,
):

get_str()

def get_str(
    kwargs,
):

get_time_partition_str()

def get_time_partition_str(
    kwargs,
):

to_flyte_idl()

def to_flyte_idl(
    kwargs,
):

Properties

flytekit.core.interface.Docstring

def Docstring(
    docstring: typing.Optional[str],
    callable_: typing.Optional[typing.Callable],
):

Properties

flytekit.core.interface.FlyteContextManager

FlyteContextManager manages the execution context within Flytekit. It holds global state of either compilation or Execution. It is not thread-safe and can only be run as a single threaded application currently. Context’s within Flytekit is useful to manage compilation state and execution state. Refer to CompilationState and ExecutionState for more information. FlyteContextManager provides a singleton stack to manage these contexts.

Typical usage is

.. code-block:: python

FlyteContextManager.initialize() with FlyteContextManager.with_context(o) as ctx: pass

If required - not recommended you can use

FlyteContextManager.push_context()

but correspondingly a pop_context should be called

FlyteContextManager.pop_context()

Methods

add_signal_handler()

def add_signal_handler(
    handler: typing.Callable[[int, FrameType], typing.Any],
):

current_context()

def current_context()

get_origin_stackframe()

def get_origin_stackframe(
    limit,
):

initialize()

def initialize()

Re-initializes the context and erases the entire context

pop_context()

def pop_context()

push_context()

def push_context(
    ctx: FlyteContext,
    f: Optional[traceback.FrameSummary],
):

size()

def size()

with_context()

def with_context(
    b: FlyteContext.Builder,
):

flytekit.core.interface.FlyteMissingReturnValueException

Common base class for all non-exit exceptions.

def FlyteMissingReturnValueException(
    fn: typing.Callable,
    param_name: typing.Optional[str],
):

Properties

flytekit.core.interface.FlyteMissingTypeException

Common base class for all non-exit exceptions.

def FlyteMissingTypeException(
    fn: typing.Callable,
    param_name: typing.Optional[str],
):

Properties

flytekit.core.interface.FlyteValidationException

Assertion failed.

def FlyteValidationException(
    args,
    timestamp: typing.Optional[float],
):

Properties

flytekit.core.interface.Interface

A Python native interface object, like inspect.signature but simpler.

def Interface(
    inputs: Union[Optional[Dict[str, Type]], Optional[Dict[str, Tuple[Type, Any]]]],
    outputs: Union[Optional[Dict[str, Type]], Optional[Dict[str, Optional[Type]]]],
    output_tuple_name: Optional[str],
    docstring: Optional[Docstring],
):

Methods

remove_inputs()

def remove_inputs(
    vars: Optional[List[str]],
):

This method is useful in removing some variables from the Flyte backend inputs specification, as these are implicit local only inputs or will be supplied by the library at runtime. For example, spark-session etc It creates a new instance of interface with the requested variables removed

with_inputs()

def with_inputs(
    extra_inputs: Dict[str, Type],
):

Use this to add additional inputs to the interface. This is useful for adding additional implicit inputs that are added without the user requesting for them

with_outputs()

def with_outputs(
    extra_outputs: Dict[str, Type],
):

This method allows addition of extra outputs are expected from a task specification

Properties

flytekit.core.interface.Literal

def Literal(
    scalar: typing.Optional[flytekit.models.literals.Scalar],
    collection: typing.Optional[flytekit.models.literals.LiteralCollection],
    map: typing.Optional[flytekit.models.literals.LiteralMap],
    hash: typing.Optional[str],
    metadata: typing.Optional[typing.Dict[str, str]],
    offloaded_metadata: typing.Optional[flytekit.models.literals.LiteralOffloadedMetadata],
):

This IDL message represents a literal value in the Flyte ecosystem.

Methods

from_flyte_idl()

def from_flyte_idl(
    pb2_object: flyteidl.core.literals_pb2.Literal,
):

serialize_to_string()

def serialize_to_string()

set_metadata()

def set_metadata(
    metadata: typing.Dict[str, str],
):

Note: This is a mutation on the literal

short_string()

def short_string()

to_flyte_idl()

def to_flyte_idl()

verbose_string()

def verbose_string()

Properties

flytekit.core.interface.OrderedDict

Dictionary that remembers insertion order

flytekit.core.interface.Scalar

def Scalar(
    primitive: typing.Optional[flytekit.models.literals.Primitive],
    blob: typing.Optional[flytekit.models.literals.Blob],
    binary: typing.Optional[flytekit.models.literals.Binary],
    schema: typing.Optional[flytekit.models.literals.Schema],
    union: typing.Optional[flytekit.models.literals.Union],
    none_type: typing.Optional[flytekit.models.literals.Void],
    error: typing.Optional[flytekit.models.types.Error],
    generic: typing.Optional[google.protobuf.struct_pb2.Struct],
    structured_dataset: typing.Optional[flytekit.models.literals.StructuredDataset],
):

Scalar wrapper around Flyte types. Only one can be specified.

Methods

from_flyte_idl()

def from_flyte_idl(
    pb2_object,
):

serialize_to_string()

def serialize_to_string()

short_string()

def short_string()

to_flyte_idl()

def to_flyte_idl()

verbose_string()

def verbose_string()

Properties

flytekit.core.interface.TypeEngine

Core Extensible TypeEngine of Flytekit. This should be used to extend the capabilities of FlyteKits type system. Users can implement their own TypeTransformers and register them with the TypeEngine. This will allow special handling of user objects

Methods

async_to_literal()

def async_to_literal(
    ctx: FlyteContext,
    python_val: typing.Any,
    python_type: Type[T],
    expected: LiteralType,
):

Converts a python value of a given type and expected LiteralType into a resolved Literal value.

async_to_python_value()

def async_to_python_value(
    ctx: FlyteContext,
    lv: Literal,
    expected_python_type: Type,
):

calculate_hash()

def calculate_hash(
    python_val: typing.Any,
    python_type: Type[T],
):

dict_to_literal_map()

def dict_to_literal_map(
    ctx: FlyteContext,
    d: typing.Dict[str, typing.Any],
    type_hints: Optional[typing.Dict[str, type]],
):

dict_to_literal_map_pb()

def dict_to_literal_map_pb(
    ctx: FlyteContext,
    d: typing.Dict[str, typing.Any],
    type_hints: Optional[typing.Dict[str, type]],
):

get_available_transformers()

def get_available_transformers()

Returns all python types for which transformers are available

get_transformer()

def get_transformer(
    python_type: Type,
):

Implements a recursive search for the transformer.

guess_python_type()

def guess_python_type(
    flyte_type: LiteralType,
):

Transforms a flyte-specific LiteralType to a regular python value.

guess_python_types()

def guess_python_types(
    flyte_variable_dict: typing.Dict[str, _interface_models.Variable],
):

Transforms a dictionary of flyte-specific Variable objects to a dictionary of regular python values.

lazy_import_transformers()

def lazy_import_transformers()

Only load the transformers if needed.

literal_map_to_kwargs()

def literal_map_to_kwargs(
    ctx: FlyteContext,
    lm: LiteralMap,
    python_types: typing.Optional[typing.Dict[str, type]],
    literal_types: typing.Optional[typing.Dict[str, _interface_models.Variable]],
):

named_tuple_to_variable_map()

def named_tuple_to_variable_map(
    t: typing.NamedTuple,
):

Converts a python-native NamedTuple to a flyte-specific VariableMap of named literals.

register()

def register(
    transformer: TypeTransformer,
    additional_types: Optional[typing.List[Type]],
):

This should be used for all types that respond with the right type annotation when you use type(…) function

register_additional_type()

def register_additional_type(
    transformer: TypeTransformer[T],
    additional_type: Type[T],
    override,
):

register_restricted_type()

def register_restricted_type(
    name: str,
    type: Type[T],
):

to_html()

def to_html(
    ctx: FlyteContext,
    python_val: typing.Any,
    expected_python_type: Type[typing.Any],
):

to_literal()

def to_literal(
    ctx: FlyteContext,
    python_val: typing.Any,
    python_type: Type[T],
    expected: LiteralType,
):

The current dance is because we are allowing users to call from an async function, this synchronous to_literal function, and allowing this to_literal function, to then invoke yet another async function, namely an async transformer.

to_literal_checks()

def to_literal_checks(
    python_val: typing.Any,
    python_type: Type[T],
    expected: LiteralType,
):

to_literal_type()

def to_literal_type(
    python_type: Type[T],
):

Converts a python type into a flyte specific LiteralType

to_python_value()

def to_python_value(
    ctx: FlyteContext,
    lv: Literal,
    expected_python_type: Type,
):

Converts a Literal value with an expected python type into a python value.

unwrap_offloaded_literal()

def unwrap_offloaded_literal(
    ctx: FlyteContext,
    lv: Literal,
):

flytekit.core.interface.TypeVar

Type variable.

The preferred way to construct a type variable is via the dedicated syntax for generic functions, classes, and type aliases::

class Sequence[T]: # T is a TypeVar …

This syntax can also be used to create bound and constrained type variables::

S is a TypeVar bound to str

class StrSequence[S: str]: …

A is a TypeVar constrained to str or bytes

class StrOrBytesSequence[A: (str, bytes)]: …

However, if desired, reusable type variables can also be constructed manually, like so::

T = TypeVar(‘T’) # Can be anything S = TypeVar(‘S’, bound=str) # Can be any subtype of str A = TypeVar(‘A’, str, bytes) # Must be exactly str or bytes

Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function and type alias definitions.

The variance of type variables is inferred by type checkers when they are created through the type parameter syntax and when infer_variance=True is passed. Manually created type variables may be explicitly marked covariant or contravariant by passing covariant=True or contravariant=True. By default, manually created type variables are invariant. See PEP 484 and PEP 695 for more details.

flytekit.core.interface.UnionTransformer

Transformer that handles a typing.Union[T1, T2, …]

def UnionTransformer()

Methods

assert_type()

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

async_to_literal()

def async_to_literal(
    ctx: FlyteContext,
    python_val: T,
    python_type: Type[T],
    expected: LiteralType,
):

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

async_to_python_value()

def async_to_python_value(
    ctx: FlyteContext,
    lv: Literal,
    expected_python_type: Type[T],
):

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

from_binary_idl()

def from_binary_idl(
    binary_idl_object: Binary,
    expected_python_type: Type[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],
):

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

Note:

• 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: Type[T],
):

Converts the python type to a Flyte LiteralType

get_sub_type_in_optional()

def get_sub_type_in_optional(
    t: Type[T],
):

Return the generic Type T of the Optional type

guess_python_type()

def guess_python_type(
    literal_type: LiteralType,
):

Converts the Flyte LiteralType to a python object type.

is_optional_type()

def is_optional_type(
    t: Type,
):

isinstance_generic()

def isinstance_generic(
    obj,
    generic_alias,
):

to_html()

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

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: FlyteContext,
    python_val: typing.Any,
    python_type: Type[T],
    expected: LiteralType,
):

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: FlyteContext,
    lv: Literal,
    expected_python_type: Type[T],
):

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

Properties

flytekit.core.interface.Void

Methods

from_flyte_idl()

def from_flyte_idl(
    proto,
):

serialize_to_string()

def serialize_to_string()

short_string()

def short_string()

to_flyte_idl()

def to_flyte_idl()

verbose_string()

def verbose_string()

Properties