Source code for flytekit.core.task

import datetime as _datetime
import inspect
from typing import Any, Callable, Dict, List, Optional, Type, Union

from flytekit.core.base_task import TaskMetadata
from flytekit.core.interface import transform_signature_to_interface
from flytekit.core.python_function_task import PythonFunctionTask
from flytekit.core.reference_entity import ReferenceEntity, TaskReference
from flytekit.core.resources import Resources
from flytekit.models.security import Secret


[docs]class TaskPlugins(object): """ This is the TaskPlugins factory for task types that are derivative of PythonFunctionTask. Every task that the user wishes to use should be available in this factory. Usage .. code-block:: python TaskPlugins.register_pythontask_plugin(config_object_type, plugin_object_type) # config_object_type is any class that will be passed to the plugin_object as task_config # Plugin_object_type is a derivative of ``PythonFunctionTask`` Examples of available task plugins include different query-based plugins such as :py:class:`flytekitplugins.athena.task.AthenaTask` and :py:class:`flytekitplugins.hive.task.HiveTask`, ML tools like :py:class:`plugins.awssagemaker.flytekitplugins.awssagemaker.training.SagemakerBuiltinAlgorithmsTask`, kubeflow operators like :py:class:`plugins.kfpytorch.flytekitplugins.kfpytorch.task.PyTorchFunctionTask` and :py:class:`plugins.kftensorflow.flytekitplugins.kftensorflow.task.TensorflowFunctionTask`, and generic plugins like :py:class:`flytekitplugins.pod.task.PodFunctionTask` which doesn't integrate with third party tools or services. The `task_config` is different for every task plugin type. This is filled out by users when they define a task to specify plugin-specific behavior and features. For example, with a query type task plugin, the config might store information related to which database to query. The `plugin_object_type` can be used to customize execution behavior and task serialization properties in tandem with the `task_config`. """ _PYTHONFUNCTION_TASK_PLUGINS: Dict[type, Type[PythonFunctionTask]] = {} @classmethod def register_pythontask_plugin(cls, plugin_config_type: type, plugin: Type[PythonFunctionTask]): """ Use this method to register a new plugin into Flytekit. Usage :: .. code-block:: python TaskPlugins.register_pythontask_plugin(config_object_type, plugin_object_type) # config_object_type is any class that will be passed to the plugin_object as task_config # Plugin_object_type is a derivative of ``PythonFunctionTask`` """ if plugin_config_type in cls._PYTHONFUNCTION_TASK_PLUGINS: found = cls._PYTHONFUNCTION_TASK_PLUGINS[plugin_config_type] if found == plugin: return raise TypeError( f"Requesting to register plugin {plugin} - collides with existing plugin {found}" f" for type {plugin_config_type}" ) cls._PYTHONFUNCTION_TASK_PLUGINS[plugin_config_type] = plugin @classmethod def find_pythontask_plugin(cls, plugin_config_type: type) -> Type[PythonFunctionTask]: """ Returns a PluginObjectType if found or returns the base PythonFunctionTask """ if plugin_config_type in cls._PYTHONFUNCTION_TASK_PLUGINS: return cls._PYTHONFUNCTION_TASK_PLUGINS[plugin_config_type] # Defaults to returning Base PythonFunctionTask return PythonFunctionTask
[docs]def task( _task_function: Optional[Callable] = None, task_config: Optional[Any] = None, cache: bool = False, cache_version: str = "", retries: int = 0, interruptible: Optional[bool] = None, deprecated: str = "", timeout: Union[_datetime.timedelta, int] = 0, container_image: Optional[str] = None, environment: Optional[Dict[str, str]] = None, requests: Optional[Resources] = None, limits: Optional[Resources] = None, secret_requests: Optional[List[Secret]] = None, execution_mode: Optional[PythonFunctionTask.ExecutionBehavior] = PythonFunctionTask.ExecutionBehavior.DEFAULT, ) -> Union[Callable, PythonFunctionTask]: """ This is the core decorator to use for any task type in flytekit. Tasks are the building blocks of Flyte. They represent users code. Tasks have the following properties * Versioned (usually tied to the git sha) * Strong interfaces (specified inputs and outputs) * Declarative * Independently executable * Unit testable For a simple python task, .. code-block:: python @task def my_task(x: int, y: typing.Dict[str, str]) -> str: ... For specific task types .. code-block:: python @task(task_config=Spark(), retries=3) def my_task(x: int, y: typing.Dict[str, str]) -> str: ... Please see some cookbook :std:ref:`task examples <cookbook:tasks>` for additional information. :param _task_function: This argument is implicitly passed and represents the decorated function :param task_config: This argument provides configuration for a specific task types. Please refer to the plugins documentation for the right object to use. :param cache: Boolean that indicates if caching should be enabled :param cache_version: Cache version to use. Changes to the task signature will automatically trigger a cache miss, but you can always manually update this field as well to force a cache miss. You should also manually bump this version if the function body/business logic has changed, but the signature hasn't. :param retries: Number of times to retry this task during a workflow execution. :param interruptible: [Optional] Boolean that indicates that this task can be interrupted and/or scheduled on nodes with lower QoS guarantees. This will directly reduce the `$`/`execution cost` associated, at the cost of performance penalties due to potential interruptions. Requires additional Flyte platform level configuration. If no value is provided, the task will inherit this attribute from its workflow, as follows: No values set for interruptible at the task or workflow level - task is not interruptible Task has interruptible=True, but workflow has no value set - task is interruptible Workflow has interruptible=True, but task has no value set - task is interruptible Workflow has interruptible=False, but task has interruptible=True - task is interruptible Workflow has interruptible=True, but task has interruptible=False - task is not interruptible :param deprecated: A string that can be used to provide a warning message for deprecated task. Absence / empty str indicates that the task is active and not deprecated :param timeout: the max amount of time for which one execution of this task should be executed for. The execution will be terminated if the runtime exceeds the given timeout (approximately). :param container_image: By default the configured FLYTE_INTERNAL_IMAGE is used for every task. This directive can be used to provide an alternate image for a specific task. This is useful for the cases in which images bloat because of various dependencies and a dependency is only required for this or a set of tasks, and they vary from the default. .. code-block:: python # Use default image name `fqn` and alter the tag to `tag-{{default.tag}}` tag of the default image # with a prefix. In this case, it is assumed that the image like # flytecookbook:tag-gitsha is published alongwith the default of flytecookbook:gitsha @task(container_image='{{.images.default.fqn}}:tag-{{images.default.tag}}') def foo(): ... # Refer to configurations to configure fqns for other images besides default. In this case it will # lookup for an image named xyz @task(container_image='{{.images.xyz.fqn}}:{{images.default.tag}}') def foo2(): ... :param environment: Environment variables that should be added for this tasks execution :param requests: Specify compute resource requests for your task. For Pod-plugin tasks, these values will apply only to the primary container. :param limits: Compute limits. Specify compute resource limits for your task. For Pod-plugin tasks, these values will apply only to the primary container. For more information, please see :py:class:`flytekit.Resources`. :param secret_requests: Keys that can identify the secrets supplied at runtime. Ideally the secret keys should also be semi-descriptive. The key values will be available from runtime, if the backend is configured to provide secrets and if secrets are available in the configured secrets store. Possible options for secret stores are - Vault, Confidant, Kube secrets, AWS KMS etc Refer to :py:class:`Secret` to understand how to specify the request for a secret. It may change based on the backend provider. :param execution_mode: This is mainly for internal use. Please ignore. It is filled in automatically. """ def wrapper(fn) -> PythonFunctionTask: _metadata = TaskMetadata( cache=cache, cache_version=cache_version, retries=retries, interruptible=interruptible, deprecated=deprecated, timeout=timeout, ) task_instance = TaskPlugins.find_pythontask_plugin(type(task_config))( task_config, fn, metadata=_metadata, container_image=container_image, environment=environment, requests=requests, limits=limits, secret_requests=secret_requests, execution_mode=execution_mode, ) return task_instance if _task_function: return wrapper(_task_function) else: return wrapper
class ReferenceTask(ReferenceEntity, PythonFunctionTask): """ This is a reference task, the body of the function passed in through the constructor will never be used, only the signature of the function will be. The signature should also match the signature of the task you're referencing, as stored by Flyte Admin, if not, workflows using this will break upon compilation. """ def __init__( self, project: str, domain: str, name: str, version: str, inputs: Dict[str, Type], outputs: Dict[str, Type] ): super().__init__(TaskReference(project, domain, name, version), inputs, outputs) # Reference tasks shouldn't call the parent constructor, but the parent constructor is what sets the resolver self._task_resolver = None
[docs]def reference_task( project: str, domain: str, name: str, version: str, ) -> Callable[[Callable[..., Any]], ReferenceTask]: """ A reference task is a pointer to a task 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. Example: .. literalinclude:: ../../../tests/flytekit/unit/core/test_references.py :pyobject: ref_t1 """ def wrapper(fn) -> ReferenceTask: interface = transform_signature_to_interface(inspect.signature(fn)) return ReferenceTask(project, domain, name, version, interface.inputs, interface.outputs) return wrapper