def test_lp_documentation():
@task
def t1(a: int) -> typing.NamedTuple("OutputsBC", t1_int_output=int, c=str):
a = a + 2
return a, "world-" + str(a)
@workflow
def wf(a: int, c: str) -> (int, str):
x, y = t1(a=a)
return x, y
# fixed_and_default_start
launch_plan.LaunchPlan.get_or_create(workflow=wf,
name="your_lp_name_1",
default_inputs={"a": 3},
fixed_inputs={"c": "4"})
# fixed_and_default_end
# schedule_start
sched = CronSchedule("* * ? * * *", kickoff_time_input_arg="abc")
email_notif = notification.Email(
phases=[_execution_model.WorkflowExecutionPhase.SUCCEEDED],
recipients_email=["*****@*****.**"])
launch_plan.LaunchPlan.get_or_create(workflow=wf,
name="your_lp_name_2",
schedule=sched,
notifications=[email_notif])
# schedule_end
# auth_role_start
auth_role_model = AuthRole(assumable_iam_role="my:iam:role")
launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="your_lp_name_3",
)
labels_model = Labels({"label": "foo"})
annotations_model = Annotations({"annotate": "bar"})
launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="your_lp_name_4",
auth_role=auth_role_model,
labels=labels_model,
annotations=annotations_model,
)
raw_output_data_config = RawOutputDataConfig("s3://foo/output")
launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="your_lp_name_5",
raw_output_data_config=raw_output_data_config)
def test_lp_all_parameters():
nt = typing.NamedTuple("OutputsBC", t1_int_output=int, c=str)
@task
def t1(a: int) -> nt:
a = a + 2
return nt(a, "world-" + str(a))
@task
def t2(a: str, b: str, c: str) -> str:
return b + a + c
@workflow
def wf(a: int, c: str) -> str:
x, y = t1(a=a)
u = t2(a=x, b=y, c=c)
return u
obj = CronSchedule("* * ? * * *", kickoff_time_input_arg="abc")
obj1 = CronSchedule("10 * ? * * *", kickoff_time_input_arg="abc")
slack_notif = notification.Slack(
phases=[_execution_model.WorkflowExecutionPhase.SUCCEEDED],
recipients_email=["*****@*****.**"])
auth_role_model = AuthRole(assumable_iam_role="my:iam:role")
labels = Labels({"label": "foo"})
annotations = Annotations({"anno": "bar"})
raw_output_data_config = RawOutputDataConfig("s3://foo/output")
lp = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create",
default_inputs={"a": 3},
fixed_inputs={"c": "4"},
schedule=obj,
notifications=[slack_notif],
auth_role=auth_role_model,
labels=labels,
annotations=annotations,
raw_output_data_config=raw_output_data_config,
)
lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create",
default_inputs={"a": 3},
fixed_inputs={"c": "4"},
schedule=obj,
notifications=[slack_notif],
auth_role=auth_role_model,
labels=labels,
annotations=annotations,
raw_output_data_config=raw_output_data_config,
)
assert lp is lp2
with pytest.raises(AssertionError):
launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create",
default_inputs={"a": 3},
fixed_inputs={"c": "4"},
schedule=obj1,
notifications=[slack_notif],
auth_role=auth_role_model,
labels=labels,
annotations=annotations,
raw_output_data_config=raw_output_data_config,
)
from flytekit.common import notifications as _notifications
from flytekit.models.common import Labels, Annotations
from flytekit.models.core import execution as _execution
from workflows import workflows
# This is a launchplan that is created with default values of inputs and added some annotations, labels and
# notification preferences. Default values make it possible to still override when an execution is requested
scale_rotate_default_launchplan = workflows.ScaleAndRotateWorkflow.create_launch_plan(
labels=Labels({
'flyte.org/managed': 'true',
}),
annotations=Annotations({
'flyte.org/secret-inject': 'required',
}),
notifications=[
_notifications.Slack(
[
_execution.WorkflowExecutionPhase.SUCCEEDED,
_execution.WorkflowExecutionPhase.FAILED,
_execution.WorkflowExecutionPhase.TIMED_OUT,
_execution.WorkflowExecutionPhase.ABORTED,
],
['*****@*****.**'],
),
],
default_inputs={})
# This launch plan customizes the input angle to 90 and the scale factor to 4x as `fixed_inputs`. Inputs cannot be
# changed now
scale4x_rotate90degrees_launchplan = workflows.ScaleAndRotateWorkflow.create_launch_plan(
def test_lp_each_parameter():
@task
def t1(a: int) -> typing.NamedTuple("OutputsBC", t1_int_output=int, c=str):
a = a + 2
return a, "world-" + str(a)
@workflow
def wf(a: int, c: str) -> (int, str):
x, y = t1(a=a)
return x, y
# Fixed Inputs Parameter
launch_plan.LaunchPlan.get_or_create(workflow=wf,
name="get_or_create_fixed",
fixed_inputs={
"a": 1,
"c": "4"
})
with pytest.raises(AssertionError):
launch_plan.LaunchPlan.get_or_create(workflow=wf,
name="get_or_create_fixed")
# Schedule Parameter
obj = CronSchedule("* * ? * * *", kickoff_time_input_arg="abc")
schedule_lp = launch_plan.LaunchPlan.get_or_create(
workflow=wf, name="get_or_create_schedule", schedule=obj)
schedule_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf, name="get_or_create_schedule", schedule=obj)
assert schedule_lp is schedule_lp1
# Default Inputs Parameter
launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_schedule_def_inputs",
default_inputs={"a": 9})
with pytest.raises(AssertionError):
launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_schedule_def_inputs",
default_inputs={"a": 19})
# Notifications Parameter
email_notif = notification.Email(
phases=[_execution_model.WorkflowExecutionPhase.SUCCEEDED],
recipients_email=["*****@*****.**"])
notification_lp = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_notification",
notifications=[email_notif])
notification_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_notification",
notifications=[email_notif])
assert notification_lp is notification_lp1
# Auth Parameter
auth_role_model1 = AuthRole(assumable_iam_role="my:iam:role")
auth_role_model2 = _launch_plan_idl.Auth(
kubernetes_service_account="my:service:account")
launch_plan.LaunchPlan.get_or_create(workflow=wf,
name="get_or_create_auth",
auth_role=auth_role_model1)
with pytest.raises(AssertionError):
launch_plan.LaunchPlan.get_or_create(workflow=wf,
name="get_or_create_auth",
auth_role=auth_role_model2)
# Labels parameters
labels_model1 = Labels({"label": "foo"})
labels_model2 = Labels({"label": "foo"})
labels_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf, name="get_or_create_labels", labels=labels_model1)
labels_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf, name="get_or_create_labels", labels=labels_model2)
assert labels_lp1 is labels_lp2
# Annotations parameters
annotations_model1 = Annotations({"anno": "bar"})
annotations_model2 = Annotations({"anno": "bar"})
annotations_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_annotations",
annotations=annotations_model1)
annotations_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_annotations",
annotations=annotations_model2)
assert annotations_lp1 is annotations_lp2
# Raw output prefix parameters
raw_output_data_config1 = RawOutputDataConfig("s3://foo/output")
raw_output_data_config2 = RawOutputDataConfig("s3://foo/output")
raw_output_data_config_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_raw_output_prefix",
raw_output_data_config=raw_output_data_config1)
raw_output_data_config_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_raw_output_prefix",
raw_output_data_config=raw_output_data_config2)
assert raw_output_data_config_lp1 is raw_output_data_config_lp2
# Max parallelism
max_parallelism = 100
max_parallelism_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_max_parallelism",
max_parallelism=max_parallelism,
)
max_parallelism_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_max_parallelism",
max_parallelism=max_parallelism,
)
assert max_parallelism_lp1 is max_parallelism_lp2
# Labels parameters
labels_model1 = Labels({"label": "foo"})
labels_model2 = Labels({"label": "foo"})
labels_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf, name="get_or_create_labels", labels=labels_model1)
labels_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf, name="get_or_create_labels", labels=labels_model2)
assert labels_lp1 is labels_lp2
# Annotations parameters
annotations_model1 = Annotations({"anno": "bar"})
annotations_model2 = Annotations({"anno": "bar"})
annotations_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_annotations",
annotations=annotations_model1)
annotations_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_annotations",
annotations=annotations_model2)
assert annotations_lp1 is annotations_lp2
# Raw output prefix parameters
raw_output_data_config1 = RawOutputDataConfig("s3://foo/output")
raw_output_data_config2 = RawOutputDataConfig("s3://foo/output")
raw_output_data_config_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_raw_output_prefix",
raw_output_data_config=raw_output_data_config1)
raw_output_data_config_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_raw_output_prefix",
raw_output_data_config=raw_output_data_config2)
assert raw_output_data_config_lp1 is raw_output_data_config_lp2
# Max parallelism
max_parallelism = 100
max_parallelism_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_max_parallelism",
max_parallelism=max_parallelism,
)
max_parallelism_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_max_parallelism",
max_parallelism=max_parallelism,
)
assert max_parallelism_lp1 is max_parallelism_lp2
# Labels parameters
labels_model1 = Labels({"label": "foo"})
labels_model2 = Labels({"label": "foo"})
labels_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf, name="get_or_create_labels", labels=labels_model1)
labels_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf, name="get_or_create_labels", labels=labels_model2)
assert labels_lp1 is labels_lp2
# Annotations parameters
annotations_model1 = Annotations({"anno": "bar"})
annotations_model2 = Annotations({"anno": "bar"})
annotations_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_annotations",
annotations=annotations_model1)
annotations_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_annotations",
annotations=annotations_model2)
assert annotations_lp1 is annotations_lp2
# Raw output prefix parameters
raw_output_data_config1 = RawOutputDataConfig("s3://foo/output")
raw_output_data_config2 = RawOutputDataConfig("s3://foo/output")
raw_output_data_config_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_raw_output_prefix",
raw_output_data_config=raw_output_data_config1)
raw_output_data_config_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_raw_output_prefix",
raw_output_data_config=raw_output_data_config2)
assert raw_output_data_config_lp1 is raw_output_data_config_lp2
# Max parallelism
max_parallelism = 100
max_parallelism_lp1 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_max_parallelism",
max_parallelism=max_parallelism,
)
max_parallelism_lp2 = launch_plan.LaunchPlan.get_or_create(
workflow=wf,
name="get_or_create_max_parallelism",
max_parallelism=max_parallelism,
)
assert max_parallelism_lp1 is max_parallelism_lp2
# Default LaunchPlan
name_lp = launch_plan.LaunchPlan.get_or_create(workflow=wf)
name_lp1 = launch_plan.LaunchPlan.get_or_create(workflow=wf)
assert name_lp is name_lp1
def double(a: int) -> int:
return a * 2
@task
def add(a: int, b: int) -> int:
return a + b
@workflow
def my_childwf(a: int = 42) -> int:
b = double(a=a)
return b
child_lp = LaunchPlan.get_or_create(my_childwf,
name="my_fixed_child_lp",
labels=Labels({"l1": "v1"}))
@workflow
def parent_wf(a: int) -> int:
x = double(a=a)
y = child_lp(a=x)
z = add(a=x, b=y)
return z
if __name__ == "__main__":
print(f"Running parent_wf(a=3) {parent_wf(a=3)}")