flytekit.core.workflow

Directory

Classes

Errors

• FlyteFailureNodeInputMismatchException
• FlyteValidationException
• FlyteValueException

flytekit.core.workflow.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.workflow.ClassStorageTaskResolver

Stores tasks inside a class variable. The class must be inherited from at the point of usage because the task loading process basically relies on the same sequence of things happening.

def ClassStorageTaskResolver(
    args,
    kwargs,
):

Methods

add()

def add(
    t: flytekit.core.python_auto_container.PythonAutoContainerTask,
):

find_lhs()

def find_lhs()

get_all_tasks()

def get_all_tasks()

Future proof method. Just making it easy to access all tasks (Not required today as we auto register them)

load_task()

def load_task(
    loader_args: typing.List[str],
):

Given the set of identifier keys, should return one Python Task or raise an error if not found

loader_args()

def loader_args(
    settings: flytekit.configuration.SerializationSettings,
    t: flytekit.core.python_auto_container.PythonAutoContainerTask,
):

This is responsible for turning an instance of a task into args that the load_task function can reconstitute.

name()

def name()

task_name()

def task_name(
    t: flytekit.core.base_task.Task,
):

Overridable function that can optionally return a custom name for a given task

Properties

flytekit.core.workflow.CompilationState

Compilation state is used during the compilation of a workflow or task. It stores the nodes that were created when walking through the workflow graph.

Attributes: prefix (str): This is because we may one day want to be able to have subworkflows inside other workflows. If users choose to not specify their node names, then we can end up with multiple “n0"s. This prefix allows us to give those nested nodes a distinct name, as well as properly identify them in the workflow. mode (int): refer to :py:class:flytekit.extend.ExecutionState.Mode task_resolver (Optional[TaskResolverMixin]): Please see :py:class:flytekit.extend.TaskResolverMixin nodes (Optional[List]): Stores currently compiled nodes so far.

def CompilationState(
    prefix: str,
    mode: int,
    task_resolver: Optional[TaskResolverMixin],
    nodes: List,
):

Methods

add_node()

def add_node(
    n: Node,
):

with_params()

def with_params(
    prefix: str,
    mode: Optional[int],
    resolver: Optional[TaskResolverMixin],
    nodes: Optional[List],
):

Create a new CompilationState where the mode and task resolver are defaulted to the current object, but they and all other args are taken if explicitly provided as an argument.

Usage: s.with_params(“p”, nodes=[])

flytekit.core.workflow.ConditionalSection

ConditionalSection is used to denote a condition within a Workflow. This default conditional section only works for Compilation mode. It is advised to derive the class and re-implement the start_branch and end_branch methods to override the compilation behavior

.. note::

Conditions can only be used within a workflow context.

Usage:

.. code-block:: python

v = conditional(“fractions”).if_((my_input > 0.1) & (my_input < 1.0)).then(…)…

def ConditionalSection(
    name: str,
):

Methods

compute_output_vars()

def compute_output_vars()

Computes and returns the minimum set of outputs for this conditional block, based on all the cases that have been registered

end_branch()

def end_branch()

This should be invoked after every branch has been visited. In case this is not local workflow execution then, we should check if this is the last case. If so then return the promise, else return the condition

if_()

def if_(
    expr: Union[ComparisonExpression, ConjunctionExpression],
):

start_branch()

def start_branch(
    c: Case,
    last_case: bool,
):

At the start of an execution of every branch this method should be called.

Properties

flytekit.core.workflow.Description

Full user description with formatting preserved. This can be rendered by clients, such as the console or command line tools with in-tact formatting.

def Description(
    value: typing.Optional[str],
    uri: typing.Optional[str],
    icon_link: typing.Optional[str],
    format: <enum 'DescriptionFormat'>,
):

Methods

from_flyte_idl()

def from_flyte_idl(
    pb2_object: flyteidl.admin.description_entity_pb2.Description,
):

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.workflow.Docstring

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

Properties

flytekit.core.workflow.Documentation

DescriptionEntity contains detailed description for the task/workflow/launch plan. Documentation could provide insight into the algorithms, business use case, etc.

def Documentation(
    short_description: typing.Optional[str],
    long_description: typing.Optional[flytekit.models.documentation.Description],
    source_code: typing.Optional[flytekit.models.documentation.SourceCode],
):

Methods

from_flyte_idl()

def from_flyte_idl(
    pb2_object: flyteidl.admin.description_entity_pb2.DescriptionEntity,
):

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.workflow.Enum

Create a collection of name/value pairs.

Example enumeration:

class Color(Enum): … RED = 1 … BLUE = 2 … GREEN = 3

Access them by:

• attribute access:

Color.RED <Color.RED: 1>

• value lookup:

Color(1) <Color.RED: 1>

• name lookup:

Color[‘RED’] <Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

len(Color) 3

list(Color) [<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

flytekit.core.workflow.ExecutionState

This is the context that is active when executing a task or a local workflow. This carries the necessary state to execute. Some required things during execution deal with temporary directories, ExecutionParameters that are passed to the user etc.

Attributes: mode (ExecutionState.Mode): Defines the context in which the task is executed (local, hosted, etc). working_dir (os.PathLike): Specifies the remote, external directory where inputs, outputs and other protobufs are uploaded engine_dir (os.PathLike): branch_eval_mode Optional[BranchEvalMode]: Used to determine whether a branch node should execute. user_space_params Optional[ExecutionParameters]: Provides run-time, user-centric context such as a statsd handler, a logging handler, the current execution id and a working directory.

def ExecutionState(
    working_dir: Union[os.PathLike, str],
    mode: Optional[ExecutionState.Mode],
    engine_dir: Optional[Union[os.PathLike, str]],
    branch_eval_mode: Optional[BranchEvalMode],
    user_space_params: Optional[ExecutionParameters],
):

Methods

branch_complete()

def branch_complete()

Indicates that we are within a conditional / ifelse block and the active branch is not done. Default to SKIPPED

is_local_execution()

def is_local_execution()

take_branch()

def take_branch()

Indicates that we are within an if-else block and the current branch has evaluated to true. Useful only in local execution mode

with_params()

def with_params(
    working_dir: Optional[os.PathLike],
    mode: Optional[Mode],
    engine_dir: Optional[os.PathLike],
    branch_eval_mode: Optional[BranchEvalMode],
    user_space_params: Optional[ExecutionParameters],
):

Produces a copy of the current execution state and overrides the copy’s parameters with passed parameter values.

flytekit.core.workflow.FlyteContext

This is an internal-facing context object, that most users will not have to deal with. It’s essentially a globally available grab bag of settings and objects that allows flytekit to do things like convert complex types, run and compile workflows, serialize Flyte entities, etc.

Even though this object as a current_context function on it, it should not be called directly. Please use the :py:class:flytekit.FlyteContextManager object instead.

Please do not confuse this object with the :py:class:flytekit.ExecutionParameters object.

def FlyteContext(
    file_access: FileAccessProvider,
    level: int,
    flyte_client: Optional['friendly_client.SynchronousFlyteClient'],
    compilation_state: Optional[CompilationState],
    execution_state: Optional[ExecutionState],
    serialization_settings: Optional[SerializationSettings],
    in_a_condition: bool,
    origin_stackframe: Optional[traceback.FrameSummary],
    output_metadata_tracker: Optional[OutputMetadataTracker],
    worker_queue: Optional[Controller],
):

Methods

current_context()

def current_context()

This method exists only to maintain backwards compatibility. Please use FlyteContextManager.current_context() instead.

Users of flytekit should be wary not to confuse the object returned from this function with :py:func:flytekit.current_context

enter_conditional_section()

def enter_conditional_section()

get_deck()

def get_deck()

Returns the deck that was created as part of the last execution.

The return value depends on the execution environment. In a notebook, the return value is compatible with IPython.display and should be rendered in the notebook.

.. code-block:: python

with flytekit.new_context() as ctx: my_task(…) ctx.get_deck()

OR if you wish to explicitly display

.. code-block:: python

from IPython import display display(ctx.get_deck())

get_origin_stackframe_repr()

def get_origin_stackframe_repr()

new_builder()

def new_builder()

new_compilation_state()

def new_compilation_state(
    prefix: str,
):

Creates and returns a default compilation state. For most of the code this should be the entrypoint of compilation, otherwise the code should always uses - with_compilation_state

new_execution_state()

def new_execution_state(
    working_dir: Optional[os.PathLike],
):

Creates and returns a new default execution state. This should be used at the entrypoint of execution, in all other cases it is preferable to use with_execution_state

set_stackframe()

def set_stackframe(
    s: traceback.FrameSummary,
):

with_client()

def with_client(
    c: SynchronousFlyteClient,
):

with_compilation_state()

def with_compilation_state(
    c: CompilationState,
):

with_execution_state()

def with_execution_state(
    es: ExecutionState,
):

with_file_access()

def with_file_access(
    fa: FileAccessProvider,
):

with_new_compilation_state()

def with_new_compilation_state()

with_output_metadata_tracker()

def with_output_metadata_tracker(
    t: OutputMetadataTracker,
):

with_serialization_settings()

def with_serialization_settings(
    ss: SerializationSettings,
):

with_worker_queue()

def with_worker_queue(
    wq: Controller,
):

Properties

flytekit.core.workflow.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.workflow.FlyteEntities

This is a global Object that tracks various tasks and workflows that are declared within a VM during the registration process

flytekit.core.workflow.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.

def 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,
):

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,
):

flytekit.core.workflow.FlyteFailureNodeInputMismatchException

Assertion failed.

def FlyteFailureNodeInputMismatchException(
    failure_node_node: typing.Union[ForwardRef('WorkflowBase'), ForwardRef('Task')],
    workflow: WorkflowBase,
):

Properties

flytekit.core.workflow.FlyteValidationException

Assertion failed.

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

Properties

flytekit.core.workflow.FlyteValueException

Inappropriate argument value (of correct type).

def FlyteValueException(
    received_value,
    error_message,
):

Properties

flytekit.core.workflow.ImperativeWorkflow

An imperative workflow is a programmatic analogue to the typical @workflow function-based workflow and is better suited to programmatic applications.

Assuming you have some tasks like so

.. literalinclude:: ../../../tests/flytekit/unit/core/test_imperative.py :start-after: # docs_tasks_start :end-before: # docs_tasks_end :language: python :dedent: 4

You could create a workflow imperatively like so

.. literalinclude:: ../../../tests/flytekit/unit/core/test_imperative.py :start-after: # docs_start :end-before: # docs_end :language: python :dedent: 4

This workflow would be identical on the back-end to

.. literalinclude:: ../../../tests/flytekit/unit/core/test_imperative.py :start-after: # docs_equivalent_start :end-before: # docs_equivalent_end :language: python :dedent: 4

Note that the only reason we need the NamedTuple is so we can name the output the same thing as in the imperative example. The imperative paradigm makes the naming of workflow outputs easier, but this isn’t a big deal in function-workflows because names tend to not be necessary.

def ImperativeWorkflow(
    name: str,
    failure_policy: Optional[WorkflowFailurePolicy],
    interruptible: bool,
):

Methods

add_entity()

def add_entity(
    entity: Union[PythonTask, _annotated_launch_plan.LaunchPlan, WorkflowBase],
    kwargs,
):

Anytime you add an entity, all the inputs to the entity must be bound.

add_launch_plan()

def add_launch_plan(
    launch_plan: _annotated_launch_plan.LaunchPlan,
    kwargs,
):

add_on_failure_handler()

def add_on_failure_handler(
    entity,
):

This is a special function that mimics the add_entity function, but this is only used to add the failure node. Failure nodes are special because we don’t want them to be part of the main workflow.

add_subwf()

def add_subwf(
    sub_wf: WorkflowBase,
    kwargs,
):

add_task()

def add_task(
    task: PythonTask,
    kwargs,
):

add_workflow_input()

def add_workflow_input(
    input_name: str,
    python_type: Type,
):

Adds an input to the workflow.

add_workflow_output()

def add_workflow_output(
    output_name: str,
    p: Union[Promise, List[Promise], Dict[str, Promise]],
    python_type: Optional[Type],
):

Add an output with the given name from the given node output.

compile()

def compile(
    kwargs,
):

construct_node_metadata()

def construct_node_metadata()

create_conditional()

def create_conditional(
    name: str,
):

execute()

def execute(
    kwargs,
):

Called by local_execute. This function is how local execution for imperative workflows runs. Because when an entity is added using the add_entity function, all inputs to that entity should’ve been already declared, we can just iterate through the nodes in order and we shouldn’t run into any dependency issues. That is, we force the user to declare entities already in a topological sort. To keep track of outputs, we create a map to start things off, filled in only with the workflow inputs (if any). As things are run, their outputs are stored in this map. After all nodes are run, we fill in workflow level outputs the same way as any other previous node.

local_execute()

def local_execute(
    ctx: FlyteContext,
    kwargs,
):

local_execution_mode()

def local_execution_mode()

ready()

def ready()

This function returns whether or not the workflow is in a ready state, which means

• Has at least one node
• All workflow inputs are bound

These conditions assume that all nodes and workflow i/o changes were done with the functions above, which do additional checking.

Properties

flytekit.core.workflow.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.workflow.Node

This class will hold all the things necessary to make an SdkNode but we won’t make one until we know things like ID, which from the registration step

def Node(
    id: str,
    metadata: _workflow_model.NodeMetadata,
    bindings: List[_literal_models.Binding],
    upstream_nodes: List[Node],
    flyte_entity: Any,
):

Methods

runs_before()

def runs_before(
    other: Node,
):

This is typically something we shouldn’t do. This modifies an attribute of the other instance rather than self. But it’s done so only because we wanted this English function to be the same as the shift function. That is, calling node_1.runs_before(node_2) and node_1 » node_2 are the same. The shift operator going the other direction is not implemented to further avoid confusion. Right shift was picked rather than left shift because that’s what most users are familiar with.

with_overrides()

def with_overrides(
    node_name: Optional[str],
    aliases: Optional[Dict[str, str]],
    requests: Optional[Resources],
    limits: Optional[Resources],
    timeout: Optional[Union[int, datetime.timedelta, object]],
    retries: Optional[int],
    interruptible: Optional[bool],
    name: Optional[str],
    task_config: Optional[Any],
    container_image: Optional[str],
    accelerator: Optional[BaseAccelerator],
    cache: Optional[bool],
    cache_version: Optional[str],
    cache_serialize: Optional[bool],
    shared_memory: Optional[Union[L[True], str]],
    pod_template: Optional[PodTemplate],
    resources: Optional[Resources],
    args,
    kwargs,
):

Properties

flytekit.core.workflow.NodeOutput

def NodeOutput(
    node: Node,
    var: str,
    attr_path: Optional[List[Union[str, int]]],
):

Methods

deepcopy()

def deepcopy()

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()

with_attr()

def with_attr(
    key,
):

Properties

flytekit.core.workflow.Options

These are options that can be configured for a launchplan during registration or overridden during an execution. For instance two people may want to run the same workflow but have the offloaded data stored in two different buckets. Or you may want labels or annotations to be different. This object is used when launching an execution in a Flyte backend, and also when registering launch plans.

def Options(
    labels: typing.Optional[flytekit.models.common.Labels],
    annotations: typing.Optional[flytekit.models.common.Annotations],
    raw_output_data_config: typing.Optional[flytekit.models.common.RawOutputDataConfig],
    security_context: typing.Optional[flytekit.models.security.SecurityContext],
    max_parallelism: typing.Optional[int],
    notifications: typing.Optional[typing.List[flytekit.models.common.Notification]],
    disable_notifications: typing.Optional[bool],
    overwrite_cache: typing.Optional[bool],
):

Methods

default_from()

def default_from(
    k8s_service_account: typing.Optional[str],
    raw_data_prefix: typing.Optional[str],
):

flytekit.core.workflow.ParamSpec

Parameter specification.

flytekit.core.workflow.Promise

This object is a wrapper and exists for three main reasons. Let’s assume we’re dealing with a task like ::

@task def t1() -> (int, str): …

#. Handling the duality between compilation and local execution - when the task function is run in a local execution mode inside a workflow function, a Python integer and string are produced. When the task is being compiled as part of the workflow, the task call creates a Node instead, and the task returns two Promise objects that point to that Node. #. One needs to be able to call ::

x = t1().with_overrides(…)

If the task returns an integer or a (int, str) tuple like t1 above, calling with_overrides on the result would throw an error. This Promise object adds that. #. Assorted handling for conditionals.

def Promise(
    var: str,
    val: Union[NodeOutput, _literals_models.Literal],
    type: typing.Optional[_type_models.LiteralType],
):

Methods

deepcopy()

def deepcopy()

eval()

def eval()

is_()

def is_(
    v: bool,
):

is_false()

def is_false()

is_none()

def is_none()

is_true()

def is_true()

with_overrides()

def with_overrides(
    node_name: Optional[str],
    aliases: Optional[Dict[str, str]],
    requests: Optional[Resources],
    limits: Optional[Resources],
    timeout: Optional[Union[int, datetime.timedelta, object]],
    retries: Optional[int],
    interruptible: Optional[bool],
    name: Optional[str],
    task_config: Optional[Any],
    container_image: Optional[str],
    accelerator: Optional[BaseAccelerator],
    cache: Optional[bool],
    cache_version: Optional[str],
    cache_serialize: Optional[bool],
    args,
    kwargs,
):

with_var()

def with_var(
    new_var: str,
):

Properties

flytekit.core.workflow.PythonAutoContainerTask

A Python AutoContainer task should be used as the base for all extensions that want the user’s code to be in the container and the container information to be automatically captured. This base will auto configure the image and image version to be used for all its derivatives.

If you are looking to extend, you might prefer to use PythonFunctionTask or PythonInstanceTask

def PythonAutoContainerTask(
    name: str,
    task_config: T,
    task_type,
    container_image: Optional[Union[str, ImageSpec]],
    requests: Optional[Resources],
    limits: Optional[Resources],
    environment: Optional[Dict[str, str]],
    task_resolver: Optional[TaskResolverMixin],
    secret_requests: Optional[List[Secret]],
    pod_template: Optional[PodTemplate],
    pod_template_name: Optional[str],
    accelerator: Optional[BaseAccelerator],
    shared_memory: Optional[Union[L[True], str]],
    resources: Optional[Resources],
    kwargs,
):

Methods

compile()

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
):

Generates a node that encapsulates this task in a workflow definition.

construct_node_metadata()

def construct_node_metadata()

Used when constructing the node that encapsulates this task as part of a broader workflow definition.

dispatch_execute()

def dispatch_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

This method translates Flyte’s Type system based input values and invokes the actual call to the executor This method is also invoked during runtime.

• VoidPromise is returned in the case when the task itself declares no outputs.
• Literal Map is returned when the task returns either one more outputs in the declaration. Individual outputs may be none
• DynamicJobSpec is returned when a dynamic workflow is executed

execute()

def execute(
    kwargs,
):

This method will be invoked to execute the task.

find_lhs()

def find_lhs()

get_command()

def get_command(
    settings: SerializationSettings,
):

Returns the command which should be used in the container definition for the serialized version of this task registered on a hosted Flyte platform.

get_config()

def get_config(
    settings: SerializationSettings,
):

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

get_container()

def get_container(
    settings: SerializationSettings,
):

Returns the container definition (if any) that is used to run the task on hosted Flyte.

get_custom()

def get_custom(
    settings: flytekit.configuration.SerializationSettings,
):

Return additional plugin-specific custom data (if any) as a serializable dictionary.

get_default_command()

def get_default_command(
    settings: SerializationSettings,
):

Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.

get_extended_resources()

def get_extended_resources(
    settings: SerializationSettings,
):

Returns the extended resources to allocate to the task on hosted Flyte.

get_image()

def get_image(
    settings: SerializationSettings,
):

Update image spec based on fast registration usage, and return string representing the image

get_input_types()

def get_input_types()

Returns the names and python types as a dictionary for the inputs of this task.

get_k8s_pod()

def get_k8s_pod(
    settings: SerializationSettings,
):

Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

get_sql()

def get_sql(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

get_type_for_input_var()

def get_type_for_input_var(
    k: str,
    v: typing.Any,
):

Returns the python type for an input variable by name.

get_type_for_output_var()

def get_type_for_output_var(
    k: str,
    v: typing.Any,
):

Returns the python type for the specified output variable by name.

local_execute()

def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
):

This function is used only in the local execution path and is responsible for calling dispatch execute. Use this function when calling a task with native values (or Promises containing Flyte literals derived from Python native values).

local_execution_mode()

def local_execution_mode()

post_execute()

def post_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
    rval: typing.Any,
):

Post execute is called after the execution has completed, with the user_params and can be used to clean-up, or alter the outputs to match the intended tasks outputs. If not overridden, then this function is a No-op

pre_execute()

def pre_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
):

This is the method that will be invoked directly before executing the task method and before all the inputs are converted. One particular case where this is useful is if the context is to be modified for the user process to get some user space parameters. This also ensures that things like SparkSession are already correctly setup before the type transformers are called

This should return either the same context of the mutated context

reset_command_fn()

def reset_command_fn()

Resets the command which should be used in the container definition of this task to the default arguments. This is useful when the command line is overridden at serialization time.

sandbox_execute()

def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

set_command_fn()

def set_command_fn(
    get_command_fn: Optional[Callable[[SerializationSettings], List[str]]],
):

By default, the task will run on the Flyte platform using the pyflyte-execute command. However, it can be useful to update the command with which the task is serialized for specific cases like running map tasks (“pyflyte-map-execute”) or for fast-executed tasks.

set_resolver()

def set_resolver(
    resolver: TaskResolverMixin,
):

By default, flytekit uses the DefaultTaskResolver to resolve the task. This method allows the user to set a custom task resolver. It can be useful to override the task resolver for specific cases like running tasks in the jupyter notebook.

Properties

flytekit.core.workflow.PythonFunctionWorkflow

Please read :std:ref:flyte:divedeep-workflows first for a high-level understanding of what workflows are in Flyte. This Python object represents a workflow defined by a function and decorated with the :py:func:@workflow <flytekit.workflow> decorator. Please see notes on that object for additional information.

def PythonFunctionWorkflow(
    workflow_function: Callable,
    metadata: WorkflowMetadata,
    default_metadata: WorkflowMetadataDefaults,
    docstring: Optional[Docstring],
    on_failure: Optional[Union[WorkflowBase, Task]],
    docs: Optional[Documentation],
    pickle_untyped: bool,
    default_options: Optional[Options],
):

Methods

add()

def add(
    t: flytekit.core.python_auto_container.PythonAutoContainerTask,
):

compile()

def compile(
    kwargs,
):

Supply static Python native values in the kwargs if you want them to be used in the compilation. This mimics a ‘closure’ in the traditional sense of the word.

construct_node_metadata()

def construct_node_metadata()

execute()

def execute(
    kwargs,
):

This function is here only to try to streamline the pattern between workflows and tasks. Since tasks call execute from dispatch_execute which is in local_execute, workflows should also call an execute inside local_execute. This makes mocking cleaner.

find_lhs()

def find_lhs()

get_all_tasks()

def get_all_tasks()

Future proof method. Just making it easy to access all tasks (Not required today as we auto register them)

load_task()

def load_task(
    loader_args: typing.List[str],
):

Given the set of identifier keys, should return one Python Task or raise an error if not found

loader_args()

def loader_args(
    settings: flytekit.configuration.SerializationSettings,
    t: flytekit.core.python_auto_container.PythonAutoContainerTask,
):

This is responsible for turning an instance of a task into args that the load_task function can reconstitute.

local_execute()

def local_execute(
    ctx: FlyteContext,
    kwargs,
):

local_execution_mode()

def local_execution_mode()

task_name()

def task_name(
    t: PythonAutoContainerTask,
):

Overridable function that can optionally return a custom name for a given task

Properties

flytekit.core.workflow.PythonTask

Base Class for all Tasks with a Python native Interface. This should be directly used for task types, that do not have a python function to be executed. Otherwise refer to :py:class:flytekit.PythonFunctionTask.

def PythonTask(
    task_type: str,
    name: str,
    task_config: typing.Optional[~T],
    interface: typing.Optional[flytekit.core.interface.Interface],
    environment: typing.Optional[typing.Dict[str, str]],
    disable_deck: typing.Optional[bool],
    enable_deck: typing.Optional[bool],
    deck_fields: typing.Optional[typing.Tuple[flytekit.deck.deck.DeckField, ...]],
    kwargs,
):

Methods

compile()

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
):

Generates a node that encapsulates this task in a workflow definition.

construct_node_metadata()

def construct_node_metadata()

Used when constructing the node that encapsulates this task as part of a broader workflow definition.

dispatch_execute()

def dispatch_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

This method translates Flyte’s Type system based input values and invokes the actual call to the executor This method is also invoked during runtime.

• VoidPromise is returned in the case when the task itself declares no outputs.
• Literal Map is returned when the task returns either one more outputs in the declaration. Individual outputs may be none
• DynamicJobSpec is returned when a dynamic workflow is executed

execute()

def execute(
    kwargs,
):

This method will be invoked to execute the task.

find_lhs()

def find_lhs()

get_config()

def get_config(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

get_container()

def get_container(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the container definition (if any) that is used to run the task on hosted Flyte.

get_custom()

def get_custom(
    settings: flytekit.configuration.SerializationSettings,
):

Return additional plugin-specific custom data (if any) as a serializable dictionary.

get_extended_resources()

def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the extended resources to allocate to the task on hosted Flyte.

get_input_types()

def get_input_types()

Returns the names and python types as a dictionary for the inputs of this task.

get_k8s_pod()

def get_k8s_pod(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

get_sql()

def get_sql(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

get_type_for_input_var()

def get_type_for_input_var(
    k: str,
    v: typing.Any,
):

Returns the python type for an input variable by name.

get_type_for_output_var()

def get_type_for_output_var(
    k: str,
    v: typing.Any,
):

Returns the python type for the specified output variable by name.

local_execute()

def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
):

This function is used only in the local execution path and is responsible for calling dispatch execute. Use this function when calling a task with native values (or Promises containing Flyte literals derived from Python native values).

local_execution_mode()

def local_execution_mode()

post_execute()

def post_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
    rval: typing.Any,
):

Post execute is called after the execution has completed, with the user_params and can be used to clean-up, or alter the outputs to match the intended tasks outputs. If not overridden, then this function is a No-op

pre_execute()

def pre_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
):

This is the method that will be invoked directly before executing the task method and before all the inputs are converted. One particular case where this is useful is if the context is to be modified for the user process to get some user space parameters. This also ensures that things like SparkSession are already correctly setup before the type transformers are called

This should return either the same context of the mutated context

sandbox_execute()

def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

Properties

flytekit.core.workflow.ReferenceEntity

def ReferenceEntity(
    reference: typing.Union[flytekit.core.reference_entity.WorkflowReference, flytekit.core.reference_entity.TaskReference, flytekit.core.reference_entity.LaunchPlanReference],
    inputs: typing.Dict[str, typing.Type],
    outputs: typing.Dict[str, typing.Type],
):

Methods

compile()

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
):

construct_node_metadata()

def construct_node_metadata()

execute()

def execute(
    kwargs,
):

local_execute()

def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
):

Please see the local_execute comments in the main task.

local_execution_mode()

def local_execution_mode()

unwrap_literal_map_and_execute()

def unwrap_literal_map_and_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

Please see the implementation of the dispatch_execute function in the real task.

Properties

flytekit.core.workflow.ReferenceWorkflow

A reference workflow is a pointer to a workflow that already exists on your Flyte installation. This object will not initiate a network call to Admin, which is why the user is asked to provide the expected interface. If at registration time the interface provided causes an issue with compilation, an error will be returned.

def ReferenceWorkflow(
    project: str,
    domain: str,
    name: str,
    version: str,
    inputs: Dict[str, Type],
    outputs: Dict[str, Type],
):

Methods

add()

def add(
    t: flytekit.core.python_auto_container.PythonAutoContainerTask,
):

compile()

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
):

construct_node_metadata()

def construct_node_metadata()

execute()

def execute(
    kwargs,
):

find_lhs()

def find_lhs()

get_all_tasks()

def get_all_tasks()

Future proof method. Just making it easy to access all tasks (Not required today as we auto register them)

load_task()

def load_task(
    loader_args: typing.List[str],
):

Given the set of identifier keys, should return one Python Task or raise an error if not found

loader_args()

def loader_args(
    settings: flytekit.configuration.SerializationSettings,
    t: flytekit.core.python_auto_container.PythonAutoContainerTask,
):

This is responsible for turning an instance of a task into args that the load_task function can reconstitute.

local_execute()

def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
):

Please see the local_execute comments in the main task.

local_execution_mode()

def local_execution_mode()

task_name()

def task_name(
    t: PythonAutoContainerTask,
):

Overridable function that can optionally return a custom name for a given task

unwrap_literal_map_and_execute()

def unwrap_literal_map_and_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

Please see the implementation of the dispatch_execute function in the real task.

Properties

flytekit.core.workflow.Task

The base of all Tasks in flytekit. This task is closest to the FlyteIDL TaskTemplate and captures information in FlyteIDL specification and does not have python native interfaces associated. Refer to the derived classes for examples of how to extend this class.

def Task(
    task_type: str,
    name: str,
    interface: flytekit.models.interface.TypedInterface,
    metadata: typing.Optional[flytekit.core.base_task.TaskMetadata],
    task_type_version,
    security_ctx: typing.Optional[flytekit.models.security.SecurityContext],
    docs: typing.Optional[flytekit.models.documentation.Documentation],
    kwargs,
):

Methods

compile()

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
):

dispatch_execute()

def dispatch_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

This method translates Flyte’s Type system based input values and invokes the actual call to the executor This method is also invoked during runtime.

execute()

def execute(
    kwargs,
):

This method will be invoked to execute the task.

get_config()

def get_config(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

get_container()

def get_container(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the container definition (if any) that is used to run the task on hosted Flyte.

get_custom()

def get_custom(
    settings: flytekit.configuration.SerializationSettings,
):