flytekit.sensor

Directory

Classes

flytekit.sensor.BaseSensor

Base class for all sensors. Sensors are tasks that are designed to run forever and periodically check for some condition to be met. When the condition is met, the sensor will complete. Sensors are designed to be run by the sensor agent, and not by the Flyte engine.

def BaseSensor(
    name: str,
    timeout: typing.Union[datetime.timedelta, int, NoneType],
    sensor_config: typing.Optional[~T],
    task_type: str,
    kwargs,
):

Methods

agent_signal_handler()

def agent_signal_handler(
    resource_meta: flytekit.extend.backend.base_agent.ResourceMeta,
    signum: int,
    frame: frame,
):

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,
):

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()

poke()

def poke(
    kwargs,
):

This method should be overridden by the user to implement the actual sensor logic. This method should return True if the sensor condition is met, else False.

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.sensor.FileSensor

Base class for all sensors. Sensors are tasks that are designed to run forever and periodically check for some condition to be met. When the condition is met, the sensor will complete. Sensors are designed to be run by the sensor agent, and not by the Flyte engine.

def FileSensor(
    name: str,
    timeout: typing.Union[datetime.timedelta, int, NoneType],
    kwargs,
):

Methods

agent_signal_handler()

def agent_signal_handler(
    resource_meta: flytekit.extend.backend.base_agent.ResourceMeta,
    signum: int,
    frame: frame,
):

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,
):

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()

poke()

def poke(
    path: str,
):

This method should be overridden by the user to implement the actual sensor logic. This method should return True if the sensor condition is met, else False.

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.sensor.SensorEngine

This is the base class for all async agents. It defines the interface that all agents must implement. The agent service is responsible for invoking agents. The propeller will communicate with the agent service to create tasks, get the status of tasks, and delete tasks.

All the agents should be registered in the AgentRegistry. Agent Service will look up the agent based on the task type. Every task type can only have one agent.

def SensorEngine()

Methods

create()

def create(
    task_template: flytekit.models.task.TaskTemplate,
    inputs: typing.Optional[flytekit.models.literals.LiteralMap],
    kwarg,
):

Return a resource meta that can be used to get the status of the task.

delete()

def delete(
    resource_meta: flytekit.sensor.base_sensor.SensorMetadata,
    kwargs,
):

Delete the task. This call should be idempotent. It should raise an error if fails to delete the task.

get()

def get(
    resource_meta: flytekit.sensor.base_sensor.SensorMetadata,
    kwargs,
):

Return the status of the task, and return the outputs in some cases. For example, bigquery job can’t write the structured dataset to the output location, so it returns the output literals to the propeller, and the propeller will write the structured dataset to the blob store.

Properties