flytekit.core.python_customized_container_task

Directory

Classes

Variables

flytekit.core.python_customized_container_task.PythonCustomizedContainerTask

Please take a look at the comments for flytekit.extend.ExecutableTemplateShimTask as well. This class should be subclassed and a custom Executor provided as a default to this parent class constructor when building a new external-container flytekit-only plugin.

This class provides authors of new task types the basic scaffolding to create task-template based tasks. In order to write such a task, authors need to

• subclass the ShimTaskExecutor class and override the execute_from_model function. This function is where all the business logic should go. Keep in mind though that you, the plugin author, will not have access to anything that’s not serialized within the TaskTemplate which is why you’ll also need to
• subclass this class, and override the get_custom function to include all the information the executor will need to run.
• Also pass the executor you created as the executor_type argument of this class’s constructor.

Keep in mind that the total size of the TaskTemplate still needs to be small, since these will be accessed frequently by the Flyte engine.

class PythonCustomizedContainerTask(
    name: str,
    task_config: TC,
    container_image: str,
    executor_type: Type[ShimTaskExecutor],
    task_resolver: Optional[TaskTemplateResolver],
    task_type,
    requests: Optional[Resources],
    limits: Optional[Resources],
    environment: Optional[Dict[str, str]],
    secret_requests: Optional[List[Secret]],
    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: FlyteContext,
    input_literal_map: _literal_models.LiteralMap,
) -> Union[_literal_models.LiteralMap, _dynamic_job.DynamicJobSpec]

This function is largely similar to the base PythonTask, with the exception that we have to infer the Python interface before executing. Also, we refer to self.task_template rather than just self similar to task classes that derive from the base PythonTask.

execute()

def execute(
    kwargs,
) -> Any

Rather than running here, send everything to the executor.

find_lhs()

def find_lhs()

get_command()

def get_command(
    settings: SerializationSettings,
) -> List[str]

get_config()

def get_config(
    settings: SerializationSettings,
) -> 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: SerializationSettings,
) -> _task_model.Container

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

get_custom()

def get_custom(
    settings: SerializationSettings,
) -> Dict[str, 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_image()

def get_image(
    settings: SerializationSettings,
) -> str

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(
    _: Optional[ExecutionParameters],
    rval: Any,
) -> Any

This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.

pre_execute()

def pre_execute(
    user_params: Optional[ExecutionParameters],
) -> Optional[ExecutionParameters]

This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.

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.

serialize_to_model()

def serialize_to_model(
    settings: SerializationSettings,
) -> _task_model.TaskTemplate

Properties

flytekit.core.python_customized_container_task.TaskTemplateResolver

This is a special resolver that resolves the task above at execution time, using only the TaskTemplate, meaning it should only be used for tasks that contain all pertinent information within the template itself.

This class differs from some TaskResolverMixin pattern a bit. Most of the other resolvers you’ll find,

• restores the same task when load_task is called as the object that loader_args was called on. That is, even though at run time it’s in a container on a cluster and is obviously a different Python process, the Python object in memory should look the same.
• offers a one-to-one mapping between the list of strings returned by the loader_args function, an the task, at least within the container.

This resolver differs in that,

• when loading a task, the task that is a loaded is always an ExecutableTemplateShimTask, regardless of what kind of task it was originally. It will only ever have what’s available to it from the TaskTemplate. No information that wasn’t serialized into the template will be available.
• all tasks will result in the same list of strings for a given subclass of the ShimTaskExecutor executor. The strings will be ["{{.taskTemplatePath}}", "path.to.your.executor"]

Also, get_all_tasks will always return an empty list, at least for now.

def TaskTemplateResolver()

Methods

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: List[str],
) -> ExecutableTemplateShimTask

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

loader_args()

def loader_args(
    settings: SerializationSettings,
    t: PythonCustomizedContainerTask,
) -> 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