flytekit.PythonFunctionTask#
- class flytekit.PythonFunctionTask(*args, **kwargs)[source]#
A Python Function task should be used as the base for all extensions that have a python function. It will automatically detect interface of the python function and when serialized on the hosted Flyte platform handles the writing execution command to execute the function
It is advised this task is used using the @task decorator as follows
In the above code, the name of the function, the module, and the interface (inputs = int and outputs = str) will be auto detected.
Methods
- compile(ctx, *args, **kwargs)#
Generates a node that encapsulates this task in a workflow definition.
- Parameters
- Return type
Optional[Union[Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise]]
- compile_into_workflow(ctx, task_function, **kwargs)[source]#
In the case of dynamic workflows, this function will produce a workflow definition at execution time which will then proceed to be executed.
- construct_node_metadata()#
Used when constructing the node that encapsulates this task as part of a broader workflow definition.
- Return type
flytekit.models.core.workflow.NodeMetadata
- dispatch_execute(ctx, input_literal_map)#
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.
VoidPromiseis returned in the case when the task itself declares no outputs.Literal Mapis returned when the task returns either one more outputs in the declaration. Individual outputs may be noneDynamicJobSpecis returned when a dynamic workflow is executed
- dynamic_execute(task_function, **kwargs)[source]#
By the time this function is invoked, the local_execute function should have unwrapped the Promises and Flyte literal wrappers so that the kwargs we are working with here are now Python native literal values. This function is also expected to return Python native literal values.
Since the user code within a dynamic task constitute a workflow, we have to first compile the workflow, and then execute that workflow.
When running for real in production, the task would stop after the compilation step, and then create a file representing that newly generated workflow, instead of executing it.
- execute(**kwargs)[source]#
This method will be invoked to execute the task. If you do decide to override this method you must also handle dynamic tasks or you will no longer be able to use the task as a dynamic task generator.
- Return type
- get_command(settings)#
Returns the command which should be used in the container definition for the serialized version of this task registered on a hosted Flyte platform.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
- get_config(settings)#
Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
- get_container(settings)#
Returns the container definition (if any) that is used to run the task on hosted Flyte.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
flytekit.models.task.Container
- get_custom(settings)#
Return additional plugin-specific custom data (if any) as a serializable dictionary.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
- get_default_command(settings)#
Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
- get_extended_resources(settings)#
Returns the extended resources to allocate to the task on hosted Flyte.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
Optional[flyteidl.core.tasks_pb2.ExtendedResources]
- get_input_types()#
Returns the names and python types as a dictionary for the inputs of this task.
- get_k8s_pod(settings)#
Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
flytekit.models.task.K8sPod
- get_sql(settings)#
Returns the Sql definition (if any) that is used to run the task on hosted Flyte.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
Optional[flytekit.models.task.Sql]
- get_type_for_input_var(k, v)#
Returns the python type for an input variable by name.
- get_type_for_output_var(k, v)#
Returns the python type for the specified output variable by name.
- local_execute(ctx, **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).
- Parameters
- Return type
Optional[Union[Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, Coroutine]]
- local_execution_mode()#
- post_execute(user_params, rval)#
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
- Parameters
execute (rval is returned value from call to) –
user_params (Optional[flytekit.core.context_manager.ExecutionParameters]) – are the modified user params as created during the pre_execute step
rval (Any) –
- Return type
- pre_execute(user_params)#
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
- Parameters
user_params (Optional[flytekit.core.context_manager.ExecutionParameters]) –
- Return type
- reset_command_fn()#
Resets the command which should be used in the container definition of this task to the default arguments. This is useful when the command line is overridden at serialization time.
- sandbox_execute(ctx, input_literal_map)#
Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.
- Parameters
input_literal_map (flytekit.models.literals.LiteralMap) –
- Return type
flytekit.models.literals.LiteralMap
- set_command_fn(get_command_fn=None)#
By default, the task will run on the Flyte platform using the pyflyte-execute command. However, it can be useful to update the command with which the task is serialized for specific cases like running map tasks (“pyflyte-map-execute”) or for fast-executed tasks.
- Parameters
get_command_fn (Optional[Callable[[flytekit.configuration.SerializationSettings], List[str]]]) –
Attributes
- container_image
- disable_deck
If true, this task will not output deck html file
- docs
- environment
Any environment variables that supplied during the execution of the task.
- execution_mode
- instantiated_in
- interface
- lhs
- location
- metadata
- name
Returns the name of the task.
- node_dependency_hints
- python_interface
Returns this task’s python interface.
- resources
- security_context
- task_config
Returns the user-specified task config which is used for plugin-specific handling of the task.
- task_function
- task_resolver
- task_type
- task_type_version