flytekit.core.base_task

flytekit.core.base_task

This module provides the core task-related functionality in Flytekit.

Core Components

kwtypes

Utility for creating keyword type annotations for tasks.

PythonTask

Base class for Python-based task implementations.

Task

The base class for all Flyte tasks.

TaskResolverMixin

Mixin class that helps resolve a task implementation.

IgnoreOutputs

Exception that can be raised to ignore task outputs.

Directory

Classes

Errors

Methods

Variables

Methods

kwtypes()

def kwtypes(
    kwargs,
) -> typing.OrderedDict[str, typing.Type]

This is a small helper function to convert the keyword arguments to an OrderedDict of types.

kwtypes(a=int, b=str)

flytekit.core.base_task.IgnoreOutputs

This exception should be used to indicate that the outputs generated by this can be safely ignored. This is useful in case of distributed training or peer-to-peer parallel algorithms.

flytekit.core.base_task.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 flytekit.PythonFunctionTask.

class 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,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, NoneType]

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,
) -> typing.Union[flytekit.models.literals.LiteralMap, flytekit.models.dynamic_job.DynamicJobSpec, typing.Coroutine]

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,
) -> typing.Any

This method will be invoked to execute the task.

find_lhs()

def find_lhs()

get_config()

def get_config(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[typing.Dict[str, str]]

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,
) -> typing.Optional[flytekit.models.task.Container]

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,
) -> typing.Optional[typing.Dict[str, typing.Any]]

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

get_extended_resources()

def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flyteidl.core.tasks_pb2.ExtendedResources]

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,
) -> typing.Optional[flytekit.models.task.K8sPod]

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,
) -> typing.Optional[flytekit.models.task.Sql]

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,
) -> typing.Type[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,
) -> typing.Type[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,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, typing.Coroutine, NoneType]

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,
) -> 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],
) -> 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,
) -> flytekit.models.literals.LiteralMap

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

Properties

flytekit.core.base_task.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.

class 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,
) -> 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,
) -> typing.Any

This method will be invoked to execute the task.

get_config()

def get_config(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[typing.Dict[str, str]]

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,
) -> typing.Optional[flytekit.models.task.Container]

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,
) -> typing.Optional[typing.Dict[str, typing.Any]]

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

get_extended_resources()

def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flyteidl.core.tasks_pb2.ExtendedResources]

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

get_input_types()

def get_input_types()

Returns python native types for inputs. In case this is not a python native task (base class) and hence returns a None. we could deduce the type from literal types, but that is not a required exercise

TODO we could use literal type to determine this

get_k8s_pod()

def get_k8s_pod(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.K8sPod]

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,
) -> typing.Optional[flytekit.models.task.Sql]

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,
) -> type

Returns the python native type for the given input variable

TODO we could use literal type to determine this

get_type_for_output_var()

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

Returns the python native type for the given output variable

TODO we could use literal type to determine this

local_execute()

def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, typing.Coroutine, NoneType]

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()

pre_execute()

def pre_execute(
    user_params: flytekit.core.context_manager.ExecutionParameters,
) -> 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,
) -> flytekit.models.literals.LiteralMap

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

Properties

flytekit.core.base_task.TaskMetadata

Metadata for a Task. Things like retries and whether or not caching is turned on, and cache version are specified here.

See the :std:ref:IDL <idl:protos/docs/core/core:taskmetadata> for the protobuf definition.

Attributes: cache (bool): Indicates if caching should be enabled. See :std:ref:Caching <cookbook:caching>. cache_serialize (bool): Indicates if identical (i.e. same inputs) instances of this task should be executed in serial when caching is enabled. See :std:ref:Caching <cookbook:caching>. cache_version (str): Version to be used for the cached value. cache_ignore_input_vars (Tuple[str, …]): Input variables that should not be included when calculating hash for cache. interruptible (Optional[bool]): Indicates that this task can be interrupted and/or scheduled on nodes with lower QoS guarantees that can include pre-emption. deprecated (str): Can be used to provide a warning message for a deprecated task. An absence or empty string indicates that the task is active and not deprecated. retries (int): for retries=n; n > 0, on failures of this task, the task will be retried at-least n number of times. timeout (Optional[Union[datetime.timedelta, int]]): The maximum duration for which one execution of this task should run. The execution will be terminated if the runtime exceeds this timeout. pod_template_name (Optional[str]): The name of an existing PodTemplate resource in the cluster which will be used for this task. generates_deck (bool): Indicates whether the task will generate a Deck URI. is_eager (bool): Indicates whether the task should be treated as eager.

class TaskMetadata(
    cache: bool,
    cache_serialize: bool,
    cache_version: str,
    cache_ignore_input_vars: typing.Tuple[str, ...],
    interruptible: typing.Optional[bool],
    deprecated: str,
    retries: int,
    timeout: typing.Union[datetime.timedelta, int, NoneType],
    pod_template_name: typing.Optional[str],
    generates_deck: bool,
    is_eager: bool,
)

Methods

to_taskmetadata_model()

def to_taskmetadata_model()

Converts to _task_model.TaskMetadata

Properties

flytekit.core.base_task.TaskResolverMixin

Flytekit tasks interact with the Flyte platform very, very broadly in two steps. They need to be uploaded to Admin, and then they are run by the user upon request (either as a single task execution or as part of a workflow). In any case, at execution time, for most tasks (that is those that generate a container target) the container image containing the task needs to be spun up again at which point the container needs to know which task it’s supposed to run and how to rehydrate the task object.

For example, the serialization of a simple task ::

# in repo_root/workflows/example.py
@task
def t1(...) -> ...: ...

might result in a container with arguments like ::

pyflyte-execute --inputs s3://path/inputs.pb --output-prefix s3://outputs/location         --raw-output-data-prefix /tmp/data         --resolver flytekit.core.python_auto_container.default_task_resolver         --         task-module repo_root.workflows.example task-name t1

At serialization time, the container created for the task will start out automatically with the pyflyte-execute bit, along with the requisite input/output args and the offloaded data prefix. Appended to that will be two things,

#. the location of the task’s task resolver, followed by two dashes, followed by #. the arguments provided by calling the loader_args function below.

The default_task_resolver declared below knows that

• When loader_args is called on a task, to look up the module the task is in, and the name of the task (the key of the task in the module, either the function name, or the variable it was assigned to).
• When load_task is called, it interprets the first part of the command as the module to call importlib.import_module on, and then looks for a key t1.

This is just the default behavior. Users should feel free to implement their own resolvers.

Methods

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],
) -> flytekit.core.base_task.Task

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.base_task.Task,
) -> typing.List[str]

Return a list of strings that can help identify the parameter Task

name()

def name()

task_name()

def task_name(
    t: flytekit.core.base_task.Task,
) -> typing.Optional[str]

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

Properties