from __future__ import annotations
import typing
from typing import Any, Callable, Dict, List, Optional, Type
from flytekit.core import workflow as _annotated_workflow
from flytekit.core.context_manager import FlyteContext, FlyteContextManager, FlyteEntities
from flytekit.core.interface import Interface, transform_function_to_interface, transform_inputs_to_parameters
from flytekit.core.promise import create_and_link_node, translate_inputs_to_literals
from flytekit.core.reference_entity import LaunchPlanReference, ReferenceEntity
from flytekit.core.schedule import LaunchPlanTriggerBase
from flytekit.models import common as _common_models
from flytekit.models import interface as _interface_models
from flytekit.models import literals as _literal_models
from flytekit.models import schedule as _schedule_model
from flytekit.models import security
from flytekit.models.core import workflow as _workflow_model
[docs]
class LaunchPlan(object):
"""
Launch Plans are one of the core constructs of Flyte. Please take a look at the discussion in the
:std:ref:`core concepts <flyte:divedeep-launchplans>` if you are unfamiliar with them.
Every workflow is registered with a default launch plan, which is just a launch plan with none of the additional
attributes set - no default values, fixed values, schedules, etc. Assuming you have the following workflow
.. code-block:: python
@workflow
def wf(a: int, c: str) -> str:
...
Create the default launch plan with
.. code-block:: python
LaunchPlan.get_or_create(workflow=my_wf)
If you specify additional parameters, you'll also have to give the launch plan a unique name. Default and
fixed inputs can be expressed as Python native values like so:
.. literalinclude:: ../../../tests/flytekit/unit/core/test_launch_plan.py
:start-after: # fixed_and_default_start
:end-before: # fixed_and_default_end
:language: python
:dedent: 4
Additionally, a launch plan can be configured to run on a schedule and emit notifications.
Please see the relevant Schedule and Notification objects as well.
To configure the remaining parameters, you'll need to import the relevant model objects as well.
.. literalinclude:: ../../../tests/flytekit/unit/core/test_launch_plan.py
:start-after: # schedule_start
:end-before: # schedule_end
:language: python
:dedent: 4
.. code-block:: python
from flytekit.models.common import Annotations, AuthRole, Labels, RawOutputDataConfig
Then use as follows
.. literalinclude:: ../../../tests/flytekit/unit/core/test_launch_plan.py
:start-after: # auth_role_start
:end-before: # auth_role_end
:language: python
:dedent: 4
"""
# The reason we cache is simply because users may get the default launch plan twice for a single Workflow. We
# don't want to create two defaults, could be confusing.
CACHE: typing.Dict[str, LaunchPlan] = {}
[docs]
@staticmethod
def get_default_launch_plan(ctx: FlyteContext, workflow: _annotated_workflow.WorkflowBase) -> LaunchPlan:
"""
Users should probably call the get_or_create function defined below instead. A default launch plan is the one
that will just pick up whatever default values are defined in the workflow function signature (if any) and
use the default auth information supplied during serialization, with no notifications or schedules.
:param ctx: This is not flytekit.current_context(). This is an internal context object. Users familiar with
flytekit should feel free to use this however.
:param workflow: The workflow to create a launch plan for.
"""
if workflow.name in LaunchPlan.CACHE:
return LaunchPlan.CACHE[workflow.name]
parameter_map = transform_inputs_to_parameters(ctx, workflow.python_interface)
lp = LaunchPlan(
name=workflow.name,
workflow=workflow,
parameters=parameter_map,
fixed_inputs=_literal_models.LiteralMap(literals={}),
)
# Ensure default parameters are available when using lp.__call__()
default_inputs = {
name: default for name, (type, default) in workflow.python_interface.inputs_with_defaults.items()
}
lp._saved_inputs = default_inputs
LaunchPlan.CACHE[workflow.name] = lp
return lp
[docs]
@classmethod
def create(
cls,
name: str,
workflow: _annotated_workflow.WorkflowBase,
default_inputs: Optional[Dict[str, Any]] = None,
fixed_inputs: Optional[Dict[str, Any]] = None,
schedule: Optional[_schedule_model.Schedule] = None,
notifications: Optional[List[_common_models.Notification]] = None,
labels: Optional[_common_models.Labels] = None,
annotations: Optional[_common_models.Annotations] = None,
raw_output_data_config: Optional[_common_models.RawOutputDataConfig] = None,
max_parallelism: Optional[int] = None,
security_context: Optional[security.SecurityContext] = None,
auth_role: Optional[_common_models.AuthRole] = None,
trigger: Optional[LaunchPlanTriggerBase] = None,
overwrite_cache: Optional[bool] = None,
) -> LaunchPlan:
ctx = FlyteContextManager.current_context()
default_inputs = default_inputs or {}
fixed_inputs = fixed_inputs or {}
# Default inputs come from two places, the original signature of the workflow function, and the default_inputs
# argument to this function. We'll take the latter as having higher precedence.
wf_signature_parameters = transform_inputs_to_parameters(ctx, workflow.python_interface)
# Construct a new Interface object with just the default inputs given to get Parameters, maybe there's an
# easier way to do this, think about it later.
temp_inputs = {}
for k, v in default_inputs.items():
temp_inputs[k] = (workflow.python_interface.inputs[k], v)
temp_interface = Interface(inputs=temp_inputs, outputs={}) # type: ignore
temp_signature = transform_inputs_to_parameters(ctx, temp_interface)
wf_signature_parameters._parameters.update(temp_signature.parameters)
# These are fixed inputs that cannot change at launch time. If the same argument is also in default inputs,
# it'll be taken out from defaults in the LaunchPlan constructor
fixed_literals = translate_inputs_to_literals(
ctx,
incoming_values=fixed_inputs,
flyte_interface_types=workflow.interface.inputs,
native_types=workflow.python_interface.inputs,
)
fixed_lm = _literal_models.LiteralMap(literals=fixed_literals)
if auth_role:
if security_context:
raise ValueError("Use of AuthRole is deprecated. You cannot specify both AuthRole and SecurityContext")
security_context = security.SecurityContext(
run_as=security.Identity(
iam_role=auth_role.assumable_iam_role,
k8s_service_account=auth_role.kubernetes_service_account,
),
)
lp = cls(
name=name,
workflow=workflow,
parameters=wf_signature_parameters,
fixed_inputs=fixed_lm,
schedule=schedule,
notifications=notifications,
labels=labels,
annotations=annotations,
raw_output_data_config=raw_output_data_config,
max_parallelism=max_parallelism,
security_context=security_context,
trigger=trigger,
overwrite_cache=overwrite_cache,
)
# This is just a convenience - we'll need the fixed inputs LiteralMap for when serializing the Launch Plan out
# to protobuf, but for local execution and such, why not save the original Python native values as well so
# we don't have to reverse it back every time.
default_inputs.update(fixed_inputs)
lp._saved_inputs = default_inputs
if name in cls.CACHE:
raise AssertionError(f"Launch plan named {name} was already created! Make sure your names are unique.")
cls.CACHE[name] = lp
return lp
[docs]
@classmethod
def get_or_create(
cls,
workflow: _annotated_workflow.WorkflowBase,
name: Optional[str] = None,
default_inputs: Optional[Dict[str, Any]] = None,
fixed_inputs: Optional[Dict[str, Any]] = None,
schedule: Optional[_schedule_model.Schedule] = None,
notifications: Optional[List[_common_models.Notification]] = None,
labels: Optional[_common_models.Labels] = None,
annotations: Optional[_common_models.Annotations] = None,
raw_output_data_config: Optional[_common_models.RawOutputDataConfig] = None,
max_parallelism: Optional[int] = None,
security_context: Optional[security.SecurityContext] = None,
auth_role: Optional[_common_models.AuthRole] = None,
trigger: Optional[LaunchPlanTriggerBase] = None,
overwrite_cache: Optional[bool] = None,
) -> LaunchPlan:
"""
This function offers a friendlier interface for creating launch plans. If the name for the launch plan is not
supplied, this assumes you are looking for the default launch plan for the workflow. If it is specified, it
will be used. If creating the default launch plan, none of the other arguments may be specified.
The resulting launch plan is also cached and if called again with the same name, the
cached version is returned
:param security_context: Security context for the execution
:param workflow: The Workflow to create a launch plan for.
:param name: If you supply a name, keep it mind it needs to be unique. That is, project, domain, version, and
this name form a primary key. If you do not supply a name, this function will assume you want the default
launch plan for the given workflow.
:param default_inputs: Default inputs, expressed as Python values.
:param fixed_inputs: Fixed inputs, expressed as Python values. At call time, these cannot be changed.
:param schedule: Optional schedule to run on.
:param notifications: Notifications to send.
:param labels: Optional labels to attach to executions created by this launch plan.
:param annotations: Optional annotations to attach to executions created by this launch plan.
:param raw_output_data_config: Optional location of offloaded data for things like S3, etc.
:param auth_role: Add an auth role if necessary.
:param max_parallelism: Controls the maximum number of tasknodes that can be run in parallel for the entire
workflow. This is useful to achieve fairness. Note: MapTasks are regarded as one unit, and
parallelism/concurrency of MapTasks is independent from this.
:param trigger: [alpha] This is a new syntax for specifying schedules.
"""
if name is None and (
default_inputs is not None
or fixed_inputs is not None
or schedule is not None
or notifications is not None
or labels is not None
or annotations is not None
or raw_output_data_config is not None
or auth_role is not None
or max_parallelism is not None
or security_context is not None
or trigger is not None
or overwrite_cache is not None
):
raise ValueError(
"Only named launchplans can be created that have other properties. Drop the name if you want to create a default launchplan. Default launchplans cannot have any other associations"
)
if name is not None and name in LaunchPlan.CACHE:
cached_outputs = vars(LaunchPlan.CACHE[name])
notifications = notifications or []
default_inputs = default_inputs or {}
fixed_inputs = fixed_inputs or {}
default_inputs.update(fixed_inputs)
if auth_role and not security_context:
security_context = security.SecurityContext(
run_as=security.Identity(
iam_role=auth_role.assumable_iam_role,
k8s_service_account=auth_role.kubernetes_service_account,
),
)
if (
workflow != cached_outputs["_workflow"]
or schedule != cached_outputs["_schedule"]
or notifications != cached_outputs["_notifications"]
or default_inputs != cached_outputs["_saved_inputs"]
or labels != cached_outputs["_labels"]
or annotations != cached_outputs["_annotations"]
or raw_output_data_config != cached_outputs["_raw_output_data_config"]
or max_parallelism != cached_outputs["_max_parallelism"]
or security_context != cached_outputs["_security_context"]
or overwrite_cache != cached_outputs["_overwrite_cache"]
):
raise AssertionError("The cached values aren't the same as the current call arguments")
return LaunchPlan.CACHE[name]
elif name is None and workflow.name in LaunchPlan.CACHE:
return LaunchPlan.CACHE[workflow.name]
# Otherwise, handle the default launch plan case
if name is None:
ctx = FlyteContext.current_context()
lp = cls.get_default_launch_plan(ctx, workflow)
else:
lp = cls.create(
name,
workflow,
default_inputs,
fixed_inputs,
schedule,
notifications,
labels,
annotations,
raw_output_data_config,
max_parallelism,
auth_role=auth_role,
security_context=security_context,
trigger=trigger,
overwrite_cache=overwrite_cache,
)
LaunchPlan.CACHE[name or workflow.name] = lp
return lp
def __init__(
self,
name: str,
workflow: _annotated_workflow.WorkflowBase,
parameters: _interface_models.ParameterMap,
fixed_inputs: _literal_models.LiteralMap,
schedule: Optional[_schedule_model.Schedule] = None,
notifications: Optional[List[_common_models.Notification]] = None,
labels: Optional[_common_models.Labels] = None,
annotations: Optional[_common_models.Annotations] = None,
raw_output_data_config: Optional[_common_models.RawOutputDataConfig] = None,
max_parallelism: Optional[int] = None,
security_context: Optional[security.SecurityContext] = None,
trigger: Optional[LaunchPlanTriggerBase] = None,
overwrite_cache: Optional[bool] = None,
):
self._name = name
self._workflow = workflow
# Ensure fixed inputs are not in parameter map
parameters = {k: v for k, v in parameters.parameters.items() if k not in fixed_inputs.literals}
self._parameters = _interface_models.ParameterMap(parameters=parameters)
self._fixed_inputs = fixed_inputs
# See create() for additional information
self._saved_inputs: Dict[str, Any] = {}
self._schedule = schedule
self._notifications = notifications or []
self._labels = labels
self._annotations = annotations
self._raw_output_data_config = raw_output_data_config
self._max_parallelism = max_parallelism
self._security_context = security_context
self._trigger = trigger
self._overwrite_cache = overwrite_cache
FlyteEntities.entities.append(self)
[docs]
def clone_with(
self,
name: str,
parameters: Optional[_interface_models.ParameterMap] = None,
fixed_inputs: Optional[_literal_models.LiteralMap] = None,
schedule: Optional[_schedule_model.Schedule] = None,
notifications: Optional[List[_common_models.Notification]] = None,
labels: Optional[_common_models.Labels] = None,
annotations: Optional[_common_models.Annotations] = None,
raw_output_data_config: Optional[_common_models.RawOutputDataConfig] = None,
max_parallelism: Optional[int] = None,
security_context: Optional[security.SecurityContext] = None,
trigger: Optional[LaunchPlanTriggerBase] = None,
overwrite_cache: Optional[bool] = None,
) -> LaunchPlan:
return LaunchPlan(
name=name,
workflow=self.workflow,
parameters=parameters or self.parameters,
fixed_inputs=fixed_inputs or self.fixed_inputs,
schedule=schedule or self.schedule,
notifications=notifications or self.notifications,
labels=labels or self.labels,
annotations=annotations or self.annotations,
raw_output_data_config=raw_output_data_config or self.raw_output_data_config,
max_parallelism=max_parallelism or self.max_parallelism,
security_context=security_context or self.security_context,
trigger=trigger,
overwrite_cache=overwrite_cache or self.overwrite_cache,
)
@property
def overwrite_cache(self) -> Optional[bool]:
return self._overwrite_cache
@property
def python_interface(self) -> Interface:
return self.workflow.python_interface
@property
def interface(self) -> _interface_models.TypedInterface:
return self.workflow.interface
@property
def name(self) -> str:
return self._name
@property
def parameters(self) -> _interface_models.ParameterMap:
return self._parameters
@property
def fixed_inputs(self) -> _literal_models.LiteralMap:
return self._fixed_inputs
@property
def workflow(self) -> _annotated_workflow.WorkflowBase:
return self._workflow
@property
def saved_inputs(self) -> Dict[str, Any]:
# See note in create()
# Since the call-site will typically update the dict returned, and since update updates in place, let's return
# a copy.
# TODO: What issues will there be when we start introducing custom classes as input types?
return self._saved_inputs.copy()
@property
def schedule(self) -> Optional[_schedule_model.Schedule]:
return self._schedule
@property
def notifications(self) -> List[_common_models.Notification]:
return self._notifications
@property
def labels(self) -> Optional[_common_models.Labels]:
return self._labels
@property
def annotations(self) -> Optional[_common_models.Annotations]:
return self._annotations
@property
def raw_output_data_config(self) -> Optional[_common_models.RawOutputDataConfig]:
return self._raw_output_data_config
@property
def max_parallelism(self) -> Optional[int]:
return self._max_parallelism
@property
def security_context(self) -> Optional[security.SecurityContext]:
return self._security_context
@property
def trigger(self) -> Optional[LaunchPlanTriggerBase]:
return self._trigger
def __call__(self, *args, **kwargs):
if len(args) > 0:
raise AssertionError("Only Keyword Arguments are supported for launch plan executions")
ctx = FlyteContext.current_context()
if ctx.compilation_state is not None:
inputs = self.saved_inputs
inputs.update(kwargs)
return create_and_link_node(ctx, entity=self, **inputs)
else:
# Calling a launch plan should just forward the call to the workflow, nothing more. But let's add in the
# saved inputs.
inputs = self.saved_inputs
inputs.update(kwargs)
return self.workflow(*args, **inputs)
class ReferenceLaunchPlan(ReferenceEntity, LaunchPlan):
"""
A reference launch plan serves as a pointer to a Launch Plan 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.
"""
def __init__(
self, project: str, domain: str, name: str, version: str, inputs: Dict[str, Type], outputs: Dict[str, Type]
):
super().__init__(LaunchPlanReference(project, domain, name, version), inputs, outputs)
[docs]
def reference_launch_plan(
project: str,
domain: str,
name: str,
version: str,
) -> Callable[[Callable[..., Any]], ReferenceLaunchPlan]:
"""
A reference launch plan is a pointer to a launch plan 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
via the function definition.
If at registration time the interface provided causes an issue with compilation, an error will be returned.
:param project: Flyte project name of the launch plan
:param domain: Flyte domain name of the launch plan
:param name: launch plan name
:param version: specific version of the launch plan to use
"""
def wrapper(fn) -> ReferenceLaunchPlan:
interface = transform_function_to_interface(fn)
return ReferenceLaunchPlan(project, domain, name, version, interface.inputs, interface.outputs)
return wrapper