flytekit.core.node_creation

Directory

Classes

flytekit.core.node_creation.BranchEvalMode

This is a 3-way class, with the None value meaning that we are not within a conditional context. The other two values are

• Active - This means that the next then should run
• Skipped - The next then should not run

flytekit.core.node_creation.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.node_creation.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.node_creation.LaunchPlan

Launch Plans are one of the core constructs of Flyte. Please take a look at the discussion in the :std:ref:core concepts <flyte:divedeep-launchplans> if you are unfamiliar with them.

Every workflow is registered with a default launch plan, which is just a launch plan with none of the additional attributes set - no default values, fixed values, schedules, etc. Assuming you have the following workflow

.. code-block:: python

@workflow def wf(a: int, c: str) -> str: …

Create the default launch plan with

.. code-block:: python

LaunchPlan.get_or_create(workflow=my_wf)

If you specify additional parameters, you’ll also have to give the launch plan a unique name. Default and fixed inputs can be expressed as Python native values like so:

.. literalinclude:: ../../../tests/flytekit/unit/core/test_launch_plan.py :start-after: # fixed_and_default_start :end-before: # fixed_and_default_end :language: python :dedent: 4

Additionally, a launch plan can be configured to run on a schedule and emit notifications.

Please see the relevant Schedule and Notification objects as well.

To configure the remaining parameters, you’ll need to import the relevant model objects as well.

.. literalinclude:: ../../../tests/flytekit/unit/core/test_launch_plan.py :start-after: # schedule_start :end-before: # schedule_end :language: python :dedent: 4

.. code-block:: python

from flytekit.models.common import Annotations, AuthRole, Labels, RawOutputDataConfig

Then use as follows

.. literalinclude:: ../../../tests/flytekit/unit/core/test_launch_plan.py :start-after: # auth_role_start :end-before: # auth_role_end :language: python :dedent: 4

def LaunchPlan(
    name: str,
    workflow: _annotated_workflow.WorkflowBase,
    parameters: _interface_models.ParameterMap,
    fixed_inputs: _literal_models.LiteralMap,
    schedule: Optional[_schedule_model.Schedule],
    notifications: Optional[List[_common_models.Notification]],
    labels: Optional[_common_models.Labels],
    annotations: Optional[_common_models.Annotations],
    raw_output_data_config: Optional[_common_models.RawOutputDataConfig],
    max_parallelism: Optional[int],
    security_context: Optional[security.SecurityContext],
    trigger: Optional[LaunchPlanTriggerBase],
    overwrite_cache: Optional[bool],
    auto_activate: bool,
):

Methods

clone_with()

def clone_with(
    name: str,
    parameters: Optional[_interface_models.ParameterMap],
    fixed_inputs: Optional[_literal_models.LiteralMap],
    schedule: Optional[_schedule_model.Schedule],
    notifications: Optional[List[_common_models.Notification]],
    labels: Optional[_common_models.Labels],
    annotations: Optional[_common_models.Annotations],
    raw_output_data_config: Optional[_common_models.RawOutputDataConfig],
    max_parallelism: Optional[int],
    security_context: Optional[security.SecurityContext],
    trigger: Optional[LaunchPlanTriggerBase],
    overwrite_cache: Optional[bool],
    auto_activate: bool,
):

construct_node_metadata()

def construct_node_metadata()

create()

def create(
    name: str,
    workflow: _annotated_workflow.WorkflowBase,
    default_inputs: Optional[Dict[str, Any]],
    fixed_inputs: Optional[Dict[str, Any]],
    schedule: Optional[_schedule_model.Schedule],
    notifications: Optional[List[_common_models.Notification]],
    labels: Optional[_common_models.Labels],
    annotations: Optional[_common_models.Annotations],
    raw_output_data_config: Optional[_common_models.RawOutputDataConfig],
    max_parallelism: Optional[int],
    security_context: Optional[security.SecurityContext],
    auth_role: Optional[_common_models.AuthRole],
    trigger: Optional[LaunchPlanTriggerBase],
    overwrite_cache: Optional[bool],
    auto_activate: bool,
):

get_default_launch_plan()

def get_default_launch_plan(
    ctx: FlyteContext,
    workflow: _annotated_workflow.WorkflowBase,
):

Users should probably call the get_or_create function defined below instead. A default launch plan is the one that will just pick up whatever default values are defined in the workflow function signature (if any) and use the default auth information supplied during serialization, with no notifications or schedules.

get_or_create()

def get_or_create(
    workflow: _annotated_workflow.WorkflowBase,
    name: Optional[str],
    default_inputs: Optional[Dict[str, Any]],
    fixed_inputs: Optional[Dict[str, Any]],
    schedule: Optional[_schedule_model.Schedule],
    notifications: Optional[List[_common_models.Notification]],
    labels: Optional[_common_models.Labels],
    annotations: Optional[_common_models.Annotations],
    raw_output_data_config: Optional[_common_models.RawOutputDataConfig],
    max_parallelism: Optional[int],
    security_context: Optional[security.SecurityContext],
    auth_role: Optional[_common_models.AuthRole],
    trigger: Optional[LaunchPlanTriggerBase],
    overwrite_cache: Optional[bool],
    auto_activate: bool,
):

This function offers a friendlier interface for creating launch plans. If the name for the launch plan is not supplied, this assumes you are looking for the default launch plan for the workflow. If it is specified, it will be used. If creating the default launch plan, none of the other arguments may be specified.

The resulting launch plan is also cached and if called again with the same name, the cached version is returned

Properties

flytekit.core.node_creation.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.node_creation.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.node_creation.VoidPromise

This object is returned for tasks that do not return any outputs (declared interface is empty) VoidPromise cannot be interacted with and does not allow comparisons or any operations

def VoidPromise(
    task_name: str,
    ref: Optional[NodeOutput],
):

Methods

runs_before()

def runs_before(
    args,
    kwargs,
):

This is a placeholder and should do nothing. It is only here to enable local execution of workflows where a task returns nothing.

with_overrides()

def with_overrides(
    args,
    kwargs,
):

Properties

flytekit.core.node_creation.WorkflowBase

def WorkflowBase(
    name: str,
    workflow_metadata: WorkflowMetadata,
    workflow_metadata_defaults: WorkflowMetadataDefaults,
    python_interface: Interface,
    on_failure: Optional[Union[WorkflowBase, Task]],
    docs: Optional[Documentation],
    default_options: Optional[Options],
    kwargs,
):

Methods

compile()

def compile(
    kwargs,
):

construct_node_metadata()

def construct_node_metadata()

execute()

def execute(
    kwargs,
):

local_execute()

def local_execute(
    ctx: FlyteContext,
    kwargs,
):

local_execution_mode()

def local_execution_mode()

Properties