def compile_into_workflow(
self, ctx: FlyteContext, task_function: Callable, **kwargs
) -> Union[_dynamic_job.DynamicJobSpec, _literal_models.LiteralMap]:
with ctx.new_compilation_context(prefix="dynamic"):
# TODO: Resolve circular import
from flytekit.common.translator import get_serializable
workflow_metadata = WorkflowMetadata(on_failure=WorkflowFailurePolicy.FAIL_IMMEDIATELY)
defaults = WorkflowMetadataDefaults(interruptible=False)
self._wf = PythonFunctionWorkflow(task_function, metadata=workflow_metadata, default_metadata=defaults)
self._wf.compile(**kwargs)
wf = self._wf
sdk_workflow = get_serializable(OrderedDict(), ctx.serialization_settings, wf)
# If no nodes were produced, let's just return the strict outputs
if len(sdk_workflow.nodes) == 0:
return _literal_models.LiteralMap(
literals={binding.var: binding.binding.to_literal_model() for binding in sdk_workflow._outputs}
)
# Gather underlying tasks/workflows that get referenced. Launch plans are handled by propeller.
tasks = set()
sub_workflows = set()
for n in sdk_workflow.nodes:
self.aggregate(tasks, sub_workflows, n)
dj_spec = _dynamic_job.DynamicJobSpec(
min_successes=len(sdk_workflow.nodes),
tasks=list(tasks),
nodes=sdk_workflow.nodes,
outputs=sdk_workflow._outputs,
subworkflows=list(sub_workflows),
)
return dj_spec
class PythonFunctionTask(PythonAutoContainerTask[T]):
"""
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 also, create the write 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.
"""
class ExecutionBehavior(Enum):
DEFAULT = 1
DYNAMIC = 2
def __init__(
self,
task_config: T,
task_function: Callable,
task_type="python-task",
ignore_input_vars: Optional[List[str]] = None,
execution_mode: Optional[ExecutionBehavior] = ExecutionBehavior.
DEFAULT,
task_resolver: Optional[TaskResolverMixin] = None,
**kwargs,
):
"""
:param task_config: Configuration object for Task. Should be a unique type for that specific Task
:param task_function: Python function that has type annotations and works for the task
:param ignore_input_vars: When supplied, these input variables will be removed from the interface. This
can be used to inject some client side variables only. Prefer using ExecutionParams
:param task_type: String task type to be associated with this Task
"""
if task_function is None:
raise ValueError(
"TaskFunction is a required parameter for PythonFunctionTask")
self._native_interface = transform_signature_to_interface(
inspect.signature(task_function))
mutated_interface = self._native_interface.remove_inputs(
ignore_input_vars)
super().__init__(
task_type=task_type,
name=f"{task_function.__module__}.{task_function.__name__}",
interface=mutated_interface,
task_config=task_config,
task_resolver=task_resolver,
**kwargs,
)
if self._task_resolver is default_task_resolver:
# The default task resolver can't handle nested functions
# TODO: Consider moving this to a can_handle function or something inside the resolver itself.
if not istestfunction(func=task_function) and isnested(
func=task_function):
raise ValueError(
"TaskFunction cannot be a nested/inner or local function. "
"It should be accessible at a module level for Flyte to execute it. Test modules with "
"names begining with `test_` are allowed to have nested tasks. If you want to create your own tasks"
"use the TaskResolverMixin")
self._task_function = task_function
self._execution_mode = execution_mode
@property
def execution_mode(self) -> ExecutionBehavior:
return self._execution_mode
@property
def task_function(self):
return self._task_function
def execute(self, **kwargs) -> Any:
"""
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.
"""
if self.execution_mode == self.ExecutionBehavior.DEFAULT:
return self._task_function(**kwargs)
elif self.execution_mode == self.ExecutionBehavior.DYNAMIC:
return self.dynamic_execute(self._task_function, **kwargs)
def compile_into_workflow(
self, ctx: FlyteContext, task_function: Callable, **kwargs
) -> Union[_dynamic_job.DynamicJobSpec, _literal_models.LiteralMap]:
if not ctx.compilation_state:
cs = ctx.new_compilation_state("dynamic")
else:
cs = ctx.compilation_state.with_params(prefix="dynamic")
with FlyteContextManager.with_context(ctx.with_compilation_state(cs)):
# TODO: Resolve circular import
from flytekit.common.translator import get_serializable
workflow_metadata = WorkflowMetadata(
on_failure=WorkflowFailurePolicy.FAIL_IMMEDIATELY)
defaults = WorkflowMetadataDefaults(
interruptible=self.metadata.interruptible if self.metadata.
interruptible is not None else False)
self._wf = PythonFunctionWorkflow(task_function,
metadata=workflow_metadata,
default_metadata=defaults)
self._wf.compile(**kwargs)
wf = self._wf
model_entities = OrderedDict()
# See comment on reference entity checking a bit down below in this function.
# This is the only circular dependency between the translator.py module and the rest of the flytekit
# authoring experience.
workflow_spec: admin_workflow_models.WorkflowSpec = get_serializable(
model_entities, ctx.serialization_settings, wf)
# If no nodes were produced, let's just return the strict outputs
if len(workflow_spec.template.nodes) == 0:
return _literal_models.LiteralMap(
literals={
binding.var: binding.binding.to_literal_model()
for binding in workflow_spec.template.outputs
})
# This is not great. The translator.py module is relied on here (see comment above) to get the tasks and
# subworkflow definitions. However we want to ensure that reference tasks and reference sub workflows are
# not used.
# TODO: Replace None with a class.
for value in model_entities.values():
if value is None:
raise Exception(
"Reference tasks are not allowed in the dynamic - a network call is necessary "
"in order to retrieve the structure of the reference task."
)
# Gather underlying TaskTemplates that get referenced. Launch plans are handled by propeller. Subworkflows
# should already be in the workflow spec.
tts = [
v.template for v in model_entities.values()
if isinstance(v, task_models.TaskSpec)
]
if ctx.serialization_settings.should_fast_serialize():
if (not ctx.execution_state
or not ctx.execution_state.additional_context
or not ctx.execution_state.additional_context.get(
"dynamic_addl_distro")):
raise AssertionError(
"Compilation for a dynamic workflow called in fast execution mode but no additional code "
"distribution could be retrieved")
logger.warn(
f"ctx.execution_state.additional_context {ctx.execution_state.additional_context}"
)
for task_template in tts:
sanitized_args = []
for arg in task_template.container.args:
if arg == "{{ .remote_package_path }}":
sanitized_args.append(
ctx.execution_state.additional_context.get(
"dynamic_addl_distro"))
elif arg == "{{ .dest_dir }}":
sanitized_args.append(
ctx.execution_state.additional_context.get(
"dynamic_dest_dir", "."))
else:
sanitized_args.append(arg)
del task_template.container.args[:]
task_template.container.args.extend(sanitized_args)
dj_spec = _dynamic_job.DynamicJobSpec(
min_successes=len(workflow_spec.template.nodes),
tasks=tts,
nodes=workflow_spec.template.nodes,
outputs=workflow_spec.template.outputs,
subworkflows=workflow_spec.sub_workflows,
)
return dj_spec
def dynamic_execute(self, task_function: Callable, **kwargs) -> Any:
"""
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.
"""
ctx = FlyteContextManager.current_context()
if ctx.execution_state and ctx.execution_state.mode == ExecutionState.Mode.LOCAL_WORKFLOW_EXECUTION:
updated_exec_state = ctx.execution_state.with_params(
mode=ExecutionState.Mode.TASK_EXECUTION)
with FlyteContextManager.with_context(
ctx.with_execution_state(updated_exec_state)):
logger.info("Executing Dynamic workflow, using raw inputs")
return task_function(**kwargs)
if ctx.execution_state and ctx.execution_state.mode == ExecutionState.Mode.TASK_EXECUTION:
is_fast_execution = bool(
ctx.execution_state and ctx.execution_state.additional_context
and ctx.execution_state.additional_context.get(
"dynamic_addl_distro"))
if is_fast_execution:
ctx = ctx.with_serialization_settings(
SerializationSettings.new_builder(
).with_fast_serialization_settings(
FastSerializationSettings(enabled=True)).build())
return self.compile_into_workflow(ctx, task_function, **kwargs)
def compile_into_workflow(
self, ctx: FlyteContext, task_function: Callable, **kwargs
) -> Union[_dynamic_job.DynamicJobSpec, _literal_models.LiteralMap]:
if not ctx.compilation_state:
cs = ctx.new_compilation_state("dynamic")
else:
cs = ctx.compilation_state.with_params(prefix="dynamic")
with FlyteContextManager.with_context(ctx.with_compilation_state(cs)):
# TODO: Resolve circular import
from flytekit.common.translator import get_serializable
workflow_metadata = WorkflowMetadata(
on_failure=WorkflowFailurePolicy.FAIL_IMMEDIATELY)
defaults = WorkflowMetadataDefaults(
interruptible=self.metadata.interruptible if self.metadata.
interruptible is not None else False)
self._wf = PythonFunctionWorkflow(task_function,
metadata=workflow_metadata,
default_metadata=defaults)
self._wf.compile(**kwargs)
wf = self._wf
model_entities = OrderedDict()
# See comment on reference entity checking a bit down below in this function.
# This is the only circular dependency between the translator.py module and the rest of the flytekit
# authoring experience.
workflow_spec: admin_workflow_models.WorkflowSpec = get_serializable(
model_entities, ctx.serialization_settings, wf)
# If no nodes were produced, let's just return the strict outputs
if len(workflow_spec.template.nodes) == 0:
return _literal_models.LiteralMap(
literals={
binding.var: binding.binding.to_literal_model()
for binding in workflow_spec.template.outputs
})
# This is not great. The translator.py module is relied on here (see comment above) to get the tasks and
# subworkflow definitions. However we want to ensure that reference tasks and reference sub workflows are
# not used.
# TODO: Replace None with a class.
for value in model_entities.values():
if value is None:
raise Exception(
"Reference tasks are not allowed in the dynamic - a network call is necessary "
"in order to retrieve the structure of the reference task."
)
# Gather underlying TaskTemplates that get referenced. Launch plans are handled by propeller. Subworkflows
# should already be in the workflow spec.
tts = [
v.template for v in model_entities.values()
if isinstance(v, task_models.TaskSpec)
]
if ctx.serialization_settings.should_fast_serialize():
if (not ctx.execution_state
or not ctx.execution_state.additional_context
or not ctx.execution_state.additional_context.get(
"dynamic_addl_distro")):
raise AssertionError(
"Compilation for a dynamic workflow called in fast execution mode but no additional code "
"distribution could be retrieved")
logger.warn(
f"ctx.execution_state.additional_context {ctx.execution_state.additional_context}"
)
for task_template in tts:
sanitized_args = []
for arg in task_template.container.args:
if arg == "{{ .remote_package_path }}":
sanitized_args.append(
ctx.execution_state.additional_context.get(
"dynamic_addl_distro"))
elif arg == "{{ .dest_dir }}":
sanitized_args.append(
ctx.execution_state.additional_context.get(
"dynamic_dest_dir", "."))
else:
sanitized_args.append(arg)
del task_template.container.args[:]
task_template.container.args.extend(sanitized_args)
dj_spec = _dynamic_job.DynamicJobSpec(
min_successes=len(workflow_spec.template.nodes),
tasks=tts,
nodes=workflow_spec.template.nodes,
outputs=workflow_spec.template.outputs,
subworkflows=workflow_spec.sub_workflows,
)
return dj_spec
def compile_into_workflow(
self, ctx: FlyteContext, is_fast_execution: bool,
task_function: Callable, **kwargs
) -> Union[_dynamic_job.DynamicJobSpec, _literal_models.LiteralMap]:
if not ctx.compilation_state:
cs = ctx.new_compilation_state("dynamic")
else:
cs = ctx.compilation_state.with_params(prefix="dynamic")
with FlyteContextManager.with_context(ctx.with_compilation_state(cs)):
# TODO: Resolve circular import
from flytekit.common.translator import get_serializable
workflow_metadata = WorkflowMetadata(
on_failure=WorkflowFailurePolicy.FAIL_IMMEDIATELY)
defaults = WorkflowMetadataDefaults(interruptible=False)
self._wf = PythonFunctionWorkflow(task_function,
metadata=workflow_metadata,
default_metadata=defaults)
self._wf.compile(**kwargs)
wf = self._wf
sdk_workflow = get_serializable(OrderedDict(),
ctx.serialization_settings, wf,
is_fast_execution)
# If no nodes were produced, let's just return the strict outputs
if len(sdk_workflow.nodes) == 0:
return _literal_models.LiteralMap(
literals={
binding.var: binding.binding.to_literal_model()
for binding in sdk_workflow._outputs
})
# Gather underlying tasks/workflows that get referenced. Launch plans are handled by propeller.
tasks = set()
sub_workflows = set()
for n in sdk_workflow.nodes:
self.aggregate(tasks, sub_workflows, n)
if is_fast_execution:
if (not ctx.execution_state
or not ctx.execution_state.additional_context
or not ctx.execution_state.additional_context.get(
"dynamic_addl_distro")):
raise AssertionError(
"Compilation for a dynamic workflow called in fast execution mode but no additional code "
"distribution could be retrieved")
logger.warn(
f"ctx.execution_state.additional_context {ctx.execution_state.additional_context}"
)
sanitized_tasks = set()
for task in tasks:
sanitized_args = []
for arg in task.container.args:
if arg == "{{ .remote_package_path }}":
sanitized_args.append(
ctx.execution_state.additional_context.get(
"dynamic_addl_distro"))
elif arg == "{{ .dest_dir }}":
sanitized_args.append(
ctx.execution_state.additional_context.get(
"dynamic_dest_dir", "."))
else:
sanitized_args.append(arg)
del task.container.args[:]
task.container.args.extend(sanitized_args)
sanitized_tasks.add(task)
tasks = sanitized_tasks
dj_spec = _dynamic_job.DynamicJobSpec(
min_successes=len(sdk_workflow.nodes),
tasks=list(tasks),
nodes=sdk_workflow.nodes,
outputs=sdk_workflow._outputs,
subworkflows=list(sub_workflows),
)
return dj_spec
class PythonFunctionTask(PythonAutoContainerTask[T]):
"""
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 also, create the write 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.
"""
class ExecutionBehavior(Enum):
DEFAULT = 1
DYNAMIC = 2
def __init__(
self,
task_config: T,
task_function: Callable,
task_type="python-task",
ignore_input_vars: Optional[List[str]] = None,
execution_mode: Optional[ExecutionBehavior] = ExecutionBehavior.DEFAULT,
task_resolver: Optional[TaskResolverMixin] = None,
**kwargs,
):
"""
:param task_config: Configuration object for Task. Should be a unique type for that specific Task
:param task_function: Python function that has type annotations and works for the task
:param ignore_input_vars: When supplied, these input variables will be removed from the interface. This
can be used to inject some client side variables only. Prefer using ExecutionParams
:param task_type: String task type to be associated with this Task
"""
if task_function is None:
raise ValueError("TaskFunction is a required parameter for PythonFunctionTask")
self._native_interface = transform_signature_to_interface(inspect.signature(task_function))
mutated_interface = self._native_interface.remove_inputs(ignore_input_vars)
super().__init__(
task_type=task_type,
name=f"{task_function.__module__}.{task_function.__name__}",
interface=mutated_interface,
task_config=task_config,
task_resolver=task_resolver,
**kwargs,
)
if self._task_resolver is default_task_resolver:
# The default task resolver can't handle nested functions
# TODO: Consider moving this to a can_handle function or something inside the resolver itself.
if not istestfunction(func=task_function) and isnested(func=task_function):
raise ValueError(
"TaskFunction cannot be a nested/inner or local function. "
"It should be accessible at a module level for Flyte to execute it. Test modules with "
"names begining with `test_` are allowed to have nested tasks. If you want to create your own tasks"
"use the TaskResolverMixin"
)
self._task_function = task_function
self._execution_mode = execution_mode
@property
def execution_mode(self) -> ExecutionBehavior:
return self._execution_mode
@property
def task_function(self):
return self._task_function
def execute(self, **kwargs) -> Any:
"""
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.
"""
if self.execution_mode == self.ExecutionBehavior.DEFAULT:
return self._task_function(**kwargs)
elif self.execution_mode == self.ExecutionBehavior.DYNAMIC:
return self.dynamic_execute(self._task_function, **kwargs)
def get_command(self, settings: SerializationSettings) -> List[str]:
container_args = [
"pyflyte-execute",
"--inputs",
"{{.input}}",
"--output-prefix",
"{{.outputPrefix}}",
"--raw-output-data-prefix",
"{{.rawOutputDataPrefix}}",
"--resolver",
self.task_resolver.location,
"--",
*self.task_resolver.loader_args(settings, self),
]
return container_args
def compile_into_workflow(
self, ctx: FlyteContext, task_function: Callable, **kwargs
) -> Union[_dynamic_job.DynamicJobSpec, _literal_models.LiteralMap]:
with ctx.new_compilation_context(prefix="dynamic"):
# TODO: Resolve circular import
from flytekit.common.translator import get_serializable
workflow_metadata = WorkflowMetadata(on_failure=WorkflowFailurePolicy.FAIL_IMMEDIATELY)
defaults = WorkflowMetadataDefaults(interruptible=False)
self._wf = PythonFunctionWorkflow(task_function, metadata=workflow_metadata, default_metadata=defaults)
self._wf.compile(**kwargs)
wf = self._wf
sdk_workflow = get_serializable(OrderedDict(), ctx.serialization_settings, wf)
# If no nodes were produced, let's just return the strict outputs
if len(sdk_workflow.nodes) == 0:
return _literal_models.LiteralMap(
literals={binding.var: binding.binding.to_literal_model() for binding in sdk_workflow._outputs}
)
# Gather underlying tasks/workflows that get referenced. Launch plans are handled by propeller.
tasks = set()
sub_workflows = set()
for n in sdk_workflow.nodes:
self.aggregate(tasks, sub_workflows, n)
dj_spec = _dynamic_job.DynamicJobSpec(
min_successes=len(sdk_workflow.nodes),
tasks=list(tasks),
nodes=sdk_workflow.nodes,
outputs=sdk_workflow._outputs,
subworkflows=list(sub_workflows),
)
return dj_spec
@staticmethod
def aggregate(tasks, workflows, node) -> None:
if node.task_node is not None:
tasks.add(node.task_node.sdk_task)
if node.workflow_node is not None:
if node.workflow_node.sdk_workflow is not None:
workflows.add(node.workflow_node.sdk_workflow)
for sub_node in node.workflow_node.sdk_workflow.nodes:
PythonFunctionTask.aggregate(tasks, workflows, sub_node)
if node.branch_node is not None:
if node.branch_node.if_else.case.then_node is not None:
PythonFunctionTask.aggregate(tasks, workflows, node.branch_node.if_else.case.then_node)
if node.branch_node.if_else.other:
for oth in node.branch_node.if_else.other:
if oth.then_node:
PythonFunctionTask.aggregate(tasks, workflows, oth.then_node)
if node.branch_node.if_else.else_node is not None:
PythonFunctionTask.aggregate(tasks, workflows, node.branch_node.if_else.else_node)
def dynamic_execute(self, task_function: Callable, **kwargs) -> Any:
"""
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.
"""
ctx = FlyteContext.current_context()
if ctx.execution_state and ctx.execution_state.mode == ExecutionState.Mode.LOCAL_WORKFLOW_EXECUTION:
with ctx.new_execution_context(ExecutionState.Mode.TASK_EXECUTION):
logger.info("Executing Dynamic workflow, using raw inputs")
return task_function(**kwargs)
if ctx.execution_state and ctx.execution_state.mode == ExecutionState.Mode.TASK_EXECUTION:
return self.compile_into_workflow(ctx, task_function, **kwargs)
class PythonFunctionTask(PythonAutoContainerTask[T]):
"""
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.
"""
class ExecutionBehavior(Enum):
DEFAULT = 1
DYNAMIC = 2
def __init__(
self,
task_config: T,
task_function: Callable,
task_type="python-task",
ignore_input_vars: Optional[List[str]] = None,
execution_mode: Optional[ExecutionBehavior] = ExecutionBehavior.
DEFAULT,
task_resolver: Optional[TaskResolverMixin] = None,
**kwargs,
):
"""
:param T task_config: Configuration object for Task. Should be a unique type for that specific Task
:param Callable task_function: Python function that has type annotations and works for the task
:param Optional[List[str]] ignore_input_vars: When supplied, these input variables will be removed from the interface. This
can be used to inject some client side variables only. Prefer using ExecutionParams
:param Optional[ExecutionBehavior] execution_mode: Defines how the execution should behave, for example
executing normally or specially handling a dynamic case.
:param Optional[TaskResolverMixin] task_type: String task type to be associated with this Task
"""
if task_function is None:
raise ValueError(
"TaskFunction is a required parameter for PythonFunctionTask")
self._native_interface = transform_function_to_interface(
task_function, Docstring(callable_=task_function))
mutated_interface = self._native_interface.remove_inputs(
ignore_input_vars)
name, _, _, _ = extract_task_module(task_function)
super().__init__(
task_type=task_type,
name=name,
interface=mutated_interface,
task_config=task_config,
task_resolver=task_resolver,
**kwargs,
)
if self._task_resolver is default_task_resolver:
# The default task resolver can't handle nested functions
# TODO: Consider moving this to a can_handle function or something inside the resolver itself.
if (not istestfunction(func=task_function)
and isnested(func=task_function)
and not is_functools_wrapped_module_level(task_function)):
raise ValueError(
"TaskFunction cannot be a nested/inner or local function. "
"It should be accessible at a module level for Flyte to execute it. Test modules with "
"names beginning with `test_` are allowed to have nested tasks. "
"If you're decorating your task function with custom decorators, use functools.wraps "
"or functools.update_wrapper on the function wrapper. "
"Alternatively if you want to create your own tasks with custom behavior use the TaskResolverMixin"
)
self._task_function = task_function
self._execution_mode = execution_mode
@property
def execution_mode(self) -> ExecutionBehavior:
return self._execution_mode
@property
def task_function(self):
return self._task_function
def execute(self, **kwargs) -> Any:
"""
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.
"""
if self.execution_mode == self.ExecutionBehavior.DEFAULT:
return exception_scopes.user_entry_point(
self._task_function)(**kwargs)
elif self.execution_mode == self.ExecutionBehavior.DYNAMIC:
return self.dynamic_execute(self._task_function, **kwargs)
def compile_into_workflow(
self, ctx: FlyteContext, task_function: Callable, **kwargs
) -> Union[_dynamic_job.DynamicJobSpec, _literal_models.LiteralMap]:
"""
In the case of dynamic workflows, this function will produce a workflow definition at execution time which will
then proceed to be executed.
"""
# TODO: circular import
from flytekit.core.task import ReferenceTask
if not ctx.compilation_state:
cs = ctx.new_compilation_state(prefix="d")
else:
cs = ctx.compilation_state.with_params(prefix="d")
with FlyteContextManager.with_context(ctx.with_compilation_state(cs)):
# TODO: Resolve circular import
from flytekit.tools.translator import get_serializable
workflow_metadata = WorkflowMetadata(
on_failure=WorkflowFailurePolicy.FAIL_IMMEDIATELY)
defaults = WorkflowMetadataDefaults(
interruptible=self.metadata.interruptible if self.metadata.
interruptible is not None else False)
self._wf = PythonFunctionWorkflow(task_function,
metadata=workflow_metadata,
default_metadata=defaults)
self._wf.compile(**kwargs)
wf = self._wf
model_entities = OrderedDict()
# See comment on reference entity checking a bit down below in this function.
# This is the only circular dependency between the translator.py module and the rest of the flytekit
# authoring experience.
workflow_spec: admin_workflow_models.WorkflowSpec = get_serializable(
model_entities, ctx.serialization_settings, wf)
# If no nodes were produced, let's just return the strict outputs
if len(workflow_spec.template.nodes) == 0:
return _literal_models.LiteralMap(
literals={
binding.var: binding.binding.to_literal_model()
for binding in workflow_spec.template.outputs
})
# Gather underlying TaskTemplates that get referenced.
tts = []
for entity, model in model_entities.items():
# We only care about gathering tasks here. Launch plans are handled by
# propeller. Subworkflows should already be in the workflow spec.
if not isinstance(entity, Task) and not isinstance(
entity, task_models.TaskTemplate):
continue
# Handle FlyteTask
if isinstance(entity, task_models.TaskTemplate):
tts.append(entity)
continue
# We are currently not supporting reference tasks since these will
# require a network call to flyteadmin to populate the TaskTemplate
# model
if isinstance(entity, ReferenceTask):
raise Exception(
"Reference tasks are currently unsupported within dynamic tasks"
)
if not isinstance(model, task_models.TaskSpec):
raise TypeError(
f"Unexpected type for serialized form of task. Expected {task_models.TaskSpec}, but got {type(model)}"
)
# Store the valid task template so that we can pass it to the
# DynamicJobSpec later
tts.append(model.template)
dj_spec = _dynamic_job.DynamicJobSpec(
min_successes=len(workflow_spec.template.nodes),
tasks=tts,
nodes=workflow_spec.template.nodes,
outputs=workflow_spec.template.outputs,
subworkflows=workflow_spec.sub_workflows,
)
return dj_spec
def dynamic_execute(self, task_function: Callable, **kwargs) -> Any:
"""
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.
"""
ctx = FlyteContextManager.current_context()
if ctx.execution_state and ctx.execution_state.mode == ExecutionState.Mode.LOCAL_WORKFLOW_EXECUTION:
updated_exec_state = ctx.execution_state.with_params(
mode=ExecutionState.Mode.TASK_EXECUTION)
with FlyteContextManager.with_context(
ctx.with_execution_state(updated_exec_state)):
logger.info("Executing Dynamic workflow, using raw inputs")
return exception_scopes.user_entry_point(task_function)(
**kwargs)
if ctx.execution_state and ctx.execution_state.mode == ExecutionState.Mode.TASK_EXECUTION:
return self.compile_into_workflow(ctx, task_function, **kwargs)
if ctx.execution_state and ctx.execution_state.mode == ExecutionState.Mode.LOCAL_TASK_EXECUTION:
return exception_scopes.user_entry_point(task_function)(**kwargs)
raise ValueError(
f"Invalid execution provided, execution state: {ctx.execution_state}"
)
def compile_into_workflow(
self, ctx: FlyteContext, task_function: Callable, **kwargs
) -> Union[_dynamic_job.DynamicJobSpec, _literal_models.LiteralMap]:
"""
In the case of dynamic workflows, this function will produce a workflow definition at execution time which will
then proceed to be executed.
"""
# TODO: circular import
from flytekit.core.task import ReferenceTask
if not ctx.compilation_state:
cs = ctx.new_compilation_state(prefix="d")
else:
cs = ctx.compilation_state.with_params(prefix="d")
with FlyteContextManager.with_context(ctx.with_compilation_state(cs)):
# TODO: Resolve circular import
from flytekit.tools.translator import get_serializable
workflow_metadata = WorkflowMetadata(
on_failure=WorkflowFailurePolicy.FAIL_IMMEDIATELY)
defaults = WorkflowMetadataDefaults(
interruptible=self.metadata.interruptible if self.metadata.
interruptible is not None else False)
self._wf = PythonFunctionWorkflow(task_function,
metadata=workflow_metadata,
default_metadata=defaults)
self._wf.compile(**kwargs)
wf = self._wf
model_entities = OrderedDict()
# See comment on reference entity checking a bit down below in this function.
# This is the only circular dependency between the translator.py module and the rest of the flytekit
# authoring experience.
workflow_spec: admin_workflow_models.WorkflowSpec = get_serializable(
model_entities, ctx.serialization_settings, wf)
# If no nodes were produced, let's just return the strict outputs
if len(workflow_spec.template.nodes) == 0:
return _literal_models.LiteralMap(
literals={
binding.var: binding.binding.to_literal_model()
for binding in workflow_spec.template.outputs
})
# Gather underlying TaskTemplates that get referenced.
tts = []
for entity, model in model_entities.items():
# We only care about gathering tasks here. Launch plans are handled by
# propeller. Subworkflows should already be in the workflow spec.
if not isinstance(entity, Task) and not isinstance(
entity, task_models.TaskTemplate):
continue
# Handle FlyteTask
if isinstance(entity, task_models.TaskTemplate):
tts.append(entity)
continue
# We are currently not supporting reference tasks since these will
# require a network call to flyteadmin to populate the TaskTemplate
# model
if isinstance(entity, ReferenceTask):
raise Exception(
"Reference tasks are currently unsupported within dynamic tasks"
)
if not isinstance(model, task_models.TaskSpec):
raise TypeError(
f"Unexpected type for serialized form of task. Expected {task_models.TaskSpec}, but got {type(model)}"
)
# Store the valid task template so that we can pass it to the
# DynamicJobSpec later
tts.append(model.template)
dj_spec = _dynamic_job.DynamicJobSpec(
min_successes=len(workflow_spec.template.nodes),
tasks=tts,
nodes=workflow_spec.template.nodes,
outputs=workflow_spec.template.outputs,
subworkflows=workflow_spec.sub_workflows,
)
return dj_spec