flytekit.core.legacy_map_task

Flytekit map tasks specify how to run a single task across a list of inputs. Map tasks themselves are constructed with a reference task as well as run-time parameters that limit execution concurrency and failure tolerations.

Directory

Classes

flytekit.core.legacy_map_task.Any

Special type indicating an unconstrained type.

• Any is compatible with every type.
• Any assumed to have all methods.
• All values assumed to be instances of Any.

Note that all the above statements are true from the point of view of static type checkers. At runtime, Any should not be used with instance checks.

flytekit.core.legacy_map_task.ArrayJob

def ArrayJob(
    parallelism,
    size,
    min_successes,
    min_success_ratio,
):

Initializes a new ArrayJob.

Methods

from_dict()

def from_dict(
    idl_dict,
):

from_flyte_idl()

def from_flyte_idl(
    idl_object,
):

serialize_to_string()

def serialize_to_string()

short_string()

def short_string()

to_dict()

def to_dict()

to_flyte_idl()

def to_flyte_idl()

verbose_string()

def verbose_string()

Properties

flytekit.core.legacy_map_task.Container

def Container(
    image,
    command,
    args,
    resources,
    env,
    config,
    data_loading_config,
):

This defines a container target. It will execute the appropriate command line on the appropriate image with the given configurations.

Methods

add_env()

def add_env(
    key: str,
    val: str,
):

from_flyte_idl()

def from_flyte_idl(
    pb2_object,
):

serialize_to_string()

def serialize_to_string()

short_string()

def short_string()

to_flyte_idl()

def to_flyte_idl()

verbose_string()

def verbose_string()

Properties

flytekit.core.legacy_map_task.ExecutionState

This is the context that is active when executing a task or a local workflow. This carries the necessary state to execute. Some required things during execution deal with temporary directories, ExecutionParameters that are passed to the user etc.

Attributes: mode (ExecutionState.Mode): Defines the context in which the task is executed (local, hosted, etc). working_dir (os.PathLike): Specifies the remote, external directory where inputs, outputs and other protobufs are uploaded engine_dir (os.PathLike): branch_eval_mode Optional[BranchEvalMode]: Used to determine whether a branch node should execute. user_space_params Optional[ExecutionParameters]: Provides run-time, user-centric context such as a statsd handler, a logging handler, the current execution id and a working directory.

def ExecutionState(
    working_dir: Union[os.PathLike, str],
    mode: Optional[ExecutionState.Mode],
    engine_dir: Optional[Union[os.PathLike, str]],
    branch_eval_mode: Optional[BranchEvalMode],
    user_space_params: Optional[ExecutionParameters],
):

Methods

branch_complete()

def branch_complete()

Indicates that we are within a conditional / ifelse block and the active branch is not done. Default to SKIPPED

is_local_execution()

def is_local_execution()

take_branch()

def take_branch()

Indicates that we are within an if-else block and the current branch has evaluated to true. Useful only in local execution mode

with_params()

def with_params(
    working_dir: Optional[os.PathLike],
    mode: Optional[Mode],
    engine_dir: Optional[os.PathLike],
    branch_eval_mode: Optional[BranchEvalMode],
    user_space_params: Optional[ExecutionParameters],
):

Produces a copy of the current execution state and overrides the copy’s parameters with passed parameter values.

flytekit.core.legacy_map_task.FlyteContext

This is an internal-facing context object, that most users will not have to deal with. It’s essentially a globally available grab bag of settings and objects that allows flytekit to do things like convert complex types, run and compile workflows, serialize Flyte entities, etc.

Even though this object as a current_context function on it, it should not be called directly. Please use the :py:class:flytekit.FlyteContextManager object instead.

Please do not confuse this object with the :py:class:flytekit.ExecutionParameters object.

def FlyteContext(
    file_access: FileAccessProvider,
    level: int,
    flyte_client: Optional['friendly_client.SynchronousFlyteClient'],
    compilation_state: Optional[CompilationState],
    execution_state: Optional[ExecutionState],
    serialization_settings: Optional[SerializationSettings],
    in_a_condition: bool,
    origin_stackframe: Optional[traceback.FrameSummary],
    output_metadata_tracker: Optional[OutputMetadataTracker],
    worker_queue: Optional[Controller],
):

Methods

current_context()

def current_context()

This method exists only to maintain backwards compatibility. Please use FlyteContextManager.current_context() instead.

Users of flytekit should be wary not to confuse the object returned from this function with :py:func:flytekit.current_context

enter_conditional_section()

def enter_conditional_section()

get_deck()

def get_deck()

Returns the deck that was created as part of the last execution.

The return value depends on the execution environment. In a notebook, the return value is compatible with IPython.display and should be rendered in the notebook.

.. code-block:: python

with flytekit.new_context() as ctx: my_task(…) ctx.get_deck()

OR if you wish to explicitly display

.. code-block:: python

from IPython import display display(ctx.get_deck())

get_origin_stackframe_repr()

def get_origin_stackframe_repr()

new_builder()

def new_builder()

new_compilation_state()

def new_compilation_state(
    prefix: str,
):

Creates and returns a default compilation state. For most of the code this should be the entrypoint of compilation, otherwise the code should always uses - with_compilation_state

new_execution_state()

def new_execution_state(
    working_dir: Optional[os.PathLike],
):

Creates and returns a new default execution state. This should be used at the entrypoint of execution, in all other cases it is preferable to use with_execution_state

set_stackframe()

def set_stackframe(
    s: traceback.FrameSummary,
):

with_client()

def with_client(
    c: SynchronousFlyteClient,
):

with_compilation_state()

def with_compilation_state(
    c: CompilationState,
):

with_execution_state()

def with_execution_state(
    es: ExecutionState,
):

with_file_access()

def with_file_access(
    fa: FileAccessProvider,
):

with_new_compilation_state()

def with_new_compilation_state()

with_output_metadata_tracker()

def with_output_metadata_tracker(
    t: OutputMetadataTracker,
):

with_serialization_settings()

def with_serialization_settings(
    ss: SerializationSettings,
):

with_worker_queue()

def with_worker_queue(
    wq: Controller,
):

Properties

flytekit.core.legacy_map_task.FlyteContextManager

FlyteContextManager manages the execution context within Flytekit. It holds global state of either compilation or Execution. It is not thread-safe and can only be run as a single threaded application currently. Context’s within Flytekit is useful to manage compilation state and execution state. Refer to CompilationState and ExecutionState for more information. FlyteContextManager provides a singleton stack to manage these contexts.

Typical usage is

.. code-block:: python

FlyteContextManager.initialize() with FlyteContextManager.with_context(o) as ctx: pass

If required - not recommended you can use

FlyteContextManager.push_context()

but correspondingly a pop_context should be called

FlyteContextManager.pop_context()

Methods

add_signal_handler()

def add_signal_handler(
    handler: typing.Callable[[int, FrameType], typing.Any],
):

current_context()

def current_context()

get_origin_stackframe()

def get_origin_stackframe(
    limit,
):

initialize()

def initialize()

Re-initializes the context and erases the entire context

pop_context()

def pop_context()

push_context()

def push_context(
    ctx: FlyteContext,
    f: Optional[traceback.FrameSummary],
):

size()

def size()

with_context()

def with_context(
    b: FlyteContext.Builder,
):

flytekit.core.legacy_map_task.K8sPod

def K8sPod(
    metadata: flytekit.models.task.K8sObjectMetadata,
    pod_spec: typing.Dict[str, typing.Any],
    data_config: typing.Optional[flytekit.models.task.DataLoadingConfig],
    primary_container_name: typing.Optional[str],
):

This defines a kubernetes pod target. It will build the pod target during task execution

Methods

from_flyte_idl()

def from_flyte_idl(
    pb2_object: flyteidl.core.tasks_pb2.K8sPod,
):

from_pod_template()

def from_pod_template(
    pod_template: PodTemplate,
):

serialize_to_string()

def serialize_to_string()

short_string()

def short_string()

to_flyte_idl()

def to_flyte_idl()

to_pod_template()

def to_pod_template()

verbose_string()

def verbose_string()

Properties

flytekit.core.legacy_map_task.MapPythonTask

A MapPythonTask defines a :py:class:flytekit.PythonTask which specifies how to run an inner :py:class:flytekit.PythonFunctionTask across a range of inputs in parallel.

def MapPythonTask(
    python_function_task: typing.Union[flytekit.core.python_function_task.PythonFunctionTask, flytekit.core.python_function_task.PythonInstanceTask, functools.partial],
    concurrency: typing.Optional[int],
    min_success_ratio: typing.Optional[float],
    bound_inputs: typing.Optional[typing.Set[str]],
    kwargs,
):

Wrapper that creates a MapPythonTask

Methods

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,
):

This method will be invoked to execute the task.

find_lhs()

def find_lhs()

get_command()

def get_command(
    settings: flytekit.configuration.SerializationSettings,
):

TODO ADD bound variables to the resolver. Maybe we need a different resolver?

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,
):

We override this method from flytekit.core.base_task Task because the dispatch_execute method uses this interface to construct outputs. Each instance of an container_array task will however produce outputs according to the underlying run_task interface and the array plugin handler will actually create a collection from these individual outputs as the final output value.

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()

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

prepare_target()

def prepare_target()

Alters the underlying run_task command to modify it for map task execution and then resets it after.

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.

set_command_prefix()

def set_command_prefix(
    cmd: typing.Optional[typing.List[str]],
):

Properties

flytekit.core.legacy_map_task.MapTaskResolver

Special resolver that is used for MapTasks. This exists because it is possible that MapTasks are created using nested “partial” subtasks. When a maptask is created its interface is interpolated from the interface of the subtask - the interpolation, simply converts every input into a list/collection input.

For example: interface -> (i: int, j: str) -> str => map_task interface -> (i: List[int], j: List[str]) -> List[str]

But in cases in which j is bound to a fixed value by using functools.partial we need a way to ensure that the interface is not simply interpolated, but only the unbound inputs are interpolated.

.. code-block:: python

def foo((i: int, j: str) -> str: …

mt = map_task(functools.partial(foo, j=10))

print(mt.interface)

output:

(i: List[int], j: str) -> List[str]

But, at runtime this information is lost. To reconstruct this, we use MapTaskResolver that records the “bound vars” and then at runtime reconstructs the interface with this knowledge

def MapTaskResolver(
    args,
    kwargs,
):

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: typing.List[str],
    max_concurrency: int,
):

Loader args should be of the form vars “var1,var2,..” resolver “resolver” [resolver_args]

loader_args()

def loader_args(
    settings: flytekit.configuration.SerializationSettings,
    t: flytekit.core.legacy_map_task.MapPythonTask,
):

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,
):

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

Properties

flytekit.core.legacy_map_task.PythonFunctionTask

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

.. code-block: python

@task def my_func(a: int) -> str: …

In the above code, the name of the function, the module, and the interface (inputs = int and outputs = str) will be auto detected.

def PythonFunctionTask(
    task_config: T,
    task_function: Callable,
    task_type,
    ignore_input_vars: Optional[List[str]],
    execution_mode: ExecutionBehavior,
    task_resolver: Optional[TaskResolverMixin],
    node_dependency_hints: Optional[Iterable[Union['PythonFunctionTask', '_annotated_launch_plan.LaunchPlan', WorkflowBase]]],
    pickle_untyped: bool,
    kwargs,
):

Methods

compile()

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
):

Generates a node that encapsulates this task in a workflow definition.

compile_into_workflow()

def compile_into_workflow(
    ctx: FlyteContext,
    task_function: Callable,
    kwargs,
):

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()

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

dynamic_execute()

def dynamic_execute(
    task_function: Callable,
    kwargs,
):

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()

def execute(
    kwargs,
):

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.

find_lhs()

def find_lhs()

get_command()

def get_command(
    settings: SerializationSettings,
):

Returns the command which should be used in the container definition for the serialized version of this task registered on a hosted Flyte platform.

get_config()

def get_config(
    settings: 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: 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_default_command()

def get_default_command(
    settings: SerializationSettings,
):

Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.

get_extended_resources()

def get_extended_resources(
    settings: SerializationSettings,
):

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

get_image()

def get_image(
    settings: SerializationSettings,
):

Update image spec based on fast registration usage, and return string representing the image

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: 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()

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

reset_command_fn()

def 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()

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.

set_command_fn()

def set_command_fn(
    get_command_fn: Optional[Callable[[SerializationSettings], List[str]]],
):

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.

set_resolver()

def set_resolver(
    resolver: TaskResolverMixin,
):

By default, flytekit uses the DefaultTaskResolver to resolve the task. This method allows the user to set a custom task resolver. It can be useful to override the task resolver for specific cases like running tasks in the jupyter notebook.

Properties

flytekit.core.legacy_map_task.PythonInstanceTask

This class should be used as the base class for all Tasks that do not have a user defined function body, but have a platform defined execute method. (Execute needs to be overridden). This base class ensures that the module loader will invoke the right class automatically, by capturing the module name and variable in the module name.

.. code-block: python

x = MyInstanceTask(name=“x”, …..)

this can be invoked as

x(a=5) # depending on the interface of the defined task

def PythonInstanceTask(
    name: str,
    task_config: T,
    task_type: str,
    task_resolver: Optional[TaskResolverMixin],
    kwargs,
):

Please see class level documentation.

Methods

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,
):

This method will be invoked to execute the task.

find_lhs()

def find_lhs()

get_command()

def get_command(
    settings: SerializationSettings,
):

Returns the command which should be used in the container definition for the serialized version of this task registered on a hosted Flyte platform.

get_config()

def get_config(
    settings: 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: 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_default_command()

def get_default_command(
    settings: SerializationSettings,
):

Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.

get_extended_resources()

def get_extended_resources(
    settings: SerializationSettings,
):

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

get_image()

def get_image(
    settings: SerializationSettings,
):

Update image spec based on fast registration usage, and return string representing the image

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: 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()

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

reset_command_fn()

def 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()

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.

set_command_fn()

def set_command_fn(
    get_command_fn: Optional[Callable[[SerializationSettings], List[str]]],
):

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.

set_resolver()

def set_resolver(
    resolver: TaskResolverMixin,
):

By default, flytekit uses the DefaultTaskResolver to resolve the task. This method allows the user to set a custom task resolver. It can be useful to override the task resolver for specific cases like running tasks in the jupyter notebook.

Properties

flytekit.core.legacy_map_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 :py:class:flytekit.PythonFunctionTask.

def 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,
):

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,
):

This method will be invoked to execute the task.

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()

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.core.legacy_map_task.SerializationSettings

These settings are provided while serializing a workflow and task, before registration. This is required to get runtime information at serialization time, as well as some defaults.

Attributes: project (str): The project (if any) with which to register entities under. domain (str): The domain (if any) with which to register entities under. version (str): The version (if any) with which to register entities under. image_config (ImageConfig): The image config used to define task container images. env (Optional[Dict[str, str]]): Environment variables injected into task container definitions. flytekit_virtualenv_root (Optional[str]): During out of container serialize the absolute path of the flytekit virtualenv at serialization time won’t match the in-container value at execution time. This optional value is used to provide the in-container virtualenv path python_interpreter (Optional[str]): The python executable to use. This is used for spark tasks in out of container execution. entrypoint_settings (Optional[EntrypointSettings]): Information about the command, path and version of the entrypoint program. fast_serialization_settings (Optional[FastSerializationSettings]): If the code is being serialized so that it can be fast registered (and thus omit building a Docker image) this object contains additional parameters for serialization. source_root (Optional[str]): The root directory of the source code.

def SerializationSettings(
    image_config: ImageConfig,
    project: typing.Optional[str],
    domain: typing.Optional[str],
    version: typing.Optional[str],
    env: Optional[Dict[str, str]],
    git_repo: Optional[str],
    python_interpreter: str,
    flytekit_virtualenv_root: Optional[str],
    fast_serialization_settings: Optional[FastSerializationSettings],
    source_root: Optional[str],
):

Methods

default_entrypoint_settings()

def default_entrypoint_settings(
    interpreter_path: str,
):

Assumes the entrypoint is installed in a virtual-environment where the interpreter is

for_image()

def for_image(
    image: str,
    version: str,
    project: str,
    domain: str,
    python_interpreter_path: str,
):

from_dict()

def from_dict(
    kvs: typing.Union[dict, list, str, int, float, bool, NoneType],
    infer_missing,
):

from_json()

def from_json(
    s: typing.Union[str, bytes, bytearray],
    parse_float,
    parse_int,
    parse_constant,
    infer_missing,
    kw,
):

from_transport()

def from_transport(
    s: str,
):

new_builder()

def new_builder()

Creates a SerializationSettings.Builder that copies the existing serialization settings parameters and allows for customization.

schema()

def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
):

should_fast_serialize()

def should_fast_serialize()

Whether or not the serialization settings specify that entities should be serialized for fast registration.

to_dict()

def to_dict(
    encode_json,
):

to_json()

def to_json(
    skipkeys: bool,
    ensure_ascii: bool,
    check_circular: bool,
    allow_nan: bool,
    indent: typing.Union[int, str, NoneType],
    separators: typing.Tuple[str, str],
    default: typing.Callable,
    sort_keys: bool,
    kw,
):

venv_root_from_interpreter()

def venv_root_from_interpreter(
    interpreter_path: str,
):

Computes the path of the virtual environment root, based on the passed in python interpreter path for example /opt/venv/bin/python3 -> /opt/venv

with_serialized_context()

def with_serialized_context()

Use this method to create a new SerializationSettings that has an environment variable set with the SerializedContext This is useful in transporting SerializedContext to serialized and registered tasks. The setting will be available in the env field with the key SERIALIZED_CONTEXT_ENV_VAR :return: A newly constructed SerializationSettings, or self, if it already has the serializationSettings

Properties

flytekit.core.legacy_map_task.Sql

def Sql(
    statement: str,
    dialect: int,
):

This defines a kubernetes pod target. It will build the pod target during task execution

Methods

from_flyte_idl()

def from_flyte_idl(
    pb2_object: flyteidl.core.tasks_pb2.Sql,
):

serialize_to_string()

def serialize_to_string()

short_string()

def short_string()

to_flyte_idl()

def to_flyte_idl()

verbose_string()

def verbose_string()

Properties

flytekit.core.legacy_map_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.

def 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,
):

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,
):

This method will be invoked to execute the task.

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 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,
):

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 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,
):

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,
):

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,
):

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.core.legacy_map_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],
):

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,
):

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,
):

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

Properties

flytekit.core.legacy_map_task.TrackedInstance

Please see the notes for the metaclass above first.

This functionality has two use-cases currently,

• Keep track of naming for non-function PythonAutoContainerTasks. That is, things like the :py:class:flytekit.extras.sqlite3.task.SQLite3Task task.
• Task resolvers, because task resolvers are instances of :py:class:flytekit.core.python_auto_container.TaskResolverMixin classes, not the classes themselves, which means we need to look on the left hand side of them to see how to find them at task execution time.

def TrackedInstance(
    args,
    kwargs,
):

Methods

find_lhs()

def find_lhs()

Properties

flytekit.core.legacy_map_task.Variable

def Variable(
    type,
    description,
    artifact_partial_id: typing.Optional[flyteidl.core.artifact_id_pb2.ArtifactID],
    artifact_tag: typing.Optional[flyteidl.core.artifact_id_pb2.ArtifactTag],
):

Methods

from_flyte_idl()

def from_flyte_idl(
    variable_proto,
):

serialize_to_string()

def serialize_to_string()

short_string()

def short_string()

to_flyte_idl()

def to_flyte_idl()

to_flyte_idl_list()

def to_flyte_idl_list()

verbose_string()

def verbose_string()

Properties

flytekit.core.legacy_map_task.timeit