flytekitplugins.openai.batch.task

Class	Description
`BatchEndpointTask`
`BatchResult`
`DownloadJSONFilesExecutor`
`DownloadJSONFilesTask`
`OpenAIFileConfig`
`OpenAIFileDefaultImages`	Default images for the openai batch plugin.
`UploadJSONLFileExecutor`
`UploadJSONLFileTask`

flytekitplugins.openai.batch.task.BatchEndpointTask

Parameters

        
    
class BatchEndpointTask(
    name: str,
    config: typing.Dict[str, typing.Any],
    openai_organization: typing.Optional[str] = None,
    **kwargs,
)

Parameter	Type	Description
`name`	`str`
`config`	`typing.Dict[str, typing.Any]`
`openai_organization`	`typing.Optional[str]`
`**kwargs`

Properties

Property	Type	Description
`deck_fields`	`typing.List[flytekit.deck.deck.DeckField]`	If not empty, this task will output deck html file for the specified decks
`disable_deck`	`bool`	If true, this task will not output deck html file
`docs`	`flytekit.models.documentation.Documentation`
`enable_deck`	`bool`	If true, this task will output deck html file
`environment`	`typing.Dict[str, str]`	Any environment variables that supplied during the execution of the task.
`instantiated_in`	`str`
`interface`	`flytekit.models.interface.TypedInterface`
`lhs`	`None`
`location`	`str`
`metadata`	`flytekit.core.base_task.TaskMetadata`
`name`	`str`
`python_interface`	`flytekit.core.interface.Interface`	Returns this task’s python interface.
`security_context`	`flytekit.models.security.SecurityContext`
`task_config`	`typing.Optional[~T]`	Returns the user-specified task config which is used for plugin-specific handling of the task.
`task_type`	`str`
`task_type_version`	`int`

Methods

Method	Description
`compile()`	Generates a node that encapsulates this task in a workflow definition.
`connector_signal_handler()`
`construct_node_metadata()`	Used when constructing the node that encapsulates this task as part of a broader workflow definition.
`dispatch_execute()`	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()`
`find_lhs()`
`get_config()`	Returns the task config as a serializable dictionary.
`get_container()`	Returns the container definition (if any) that is used to run the task on hosted Flyte.
`get_custom()`	Return additional plugin-specific custom data (if any) as a serializable dictionary.
`get_extended_resources()`	Returns the extended resources to allocate to the task on hosted Flyte.
`get_input_types()`	Returns the names and python types as a dictionary for the inputs of this task.
`get_k8s_pod()`	Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.
`get_sql()`	Returns the Sql definition (if any) that is used to run the task on hosted Flyte.
`get_type_for_input_var()`	Returns the python type for an input variable by name.
`get_type_for_output_var()`	Returns the python type for the specified output variable by name.
`local_execute()`	This function is used only in the local execution path and is responsible for calling dispatch execute.
`local_execution_mode()`
`post_execute()`	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.
`pre_execute()`	This is the method that will be invoked directly before executing the task method and before all the inputs are converted.
`sandbox_execute()`	Call dispatch_execute, in the context of a local sandbox execution.

compile()

        
    
def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    *args,
    **kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, NoneType]

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

Parameter	Type	Description
`ctx`	`flytekit.core.context_manager.FlyteContext`
`*args`
`**kwargs`

connector_signal_handler()

        
    
def connector_signal_handler(
    resource_meta: flytekit.extend.backend.base_connector.ResourceMeta,
    signum: int,
    frame: frame,
) -> typing.Any

Parameter	Type	Description
`resource_meta`	`flytekit.extend.backend.base_connector.ResourceMeta`
`signum`	`int`
`frame`	`frame`

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,
) -> typing.Union[flytekit.models.literals.LiteralMap, flytekit.models.dynamic_job.DynamicJobSpec, typing.Coroutine]

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

Parameter	Type	Description
`ctx`	`flytekit.core.context_manager.FlyteContext`
`input_literal_map`	`flytekit.models.literals.LiteralMap`

execute()

        
    
def execute(
    **kwargs,
) -> flytekit.models.literals.LiteralMap

Parameter	Type	Description
`**kwargs`

find_lhs()

        
    
def find_lhs()

get_config()

        
    
def get_config(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[typing.Dict[str, str]]

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_container()

        
    
def get_container(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.Container]

Returns the container definition (if any) that is used to run the task on hosted Flyte.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_custom()

        
    
def get_custom(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Dict[str, typing.Any]

Return additional plugin-specific custom data (if any) as a serializable dictionary.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_extended_resources()

        
    
def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flyteidl.core.tasks_pb2.ExtendedResources]

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

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

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,
) -> typing.Optional[flytekit.models.task.K8sPod]

Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_sql()

        
    
def get_sql(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.Sql]

Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_type_for_input_var()

        
    
def get_type_for_input_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]

Returns the python type for an input variable by name.

Parameter	Type	Description
`k`	`str`
`v`	`typing.Any`

get_type_for_output_var()

        
    
def get_type_for_output_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]

Returns the python type for the specified output variable by name.

Parameter	Type	Description
`k`	`str`
`v`	`typing.Any`

local_execute()

        
    
def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    **kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, typing.Coroutine, NoneType]

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).

Parameter	Type	Description
`ctx`	`flytekit.core.context_manager.FlyteContext`
`**kwargs`

local_execution_mode()

        
    
def local_execution_mode()

post_execute()

        
    
def post_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
    rval: typing.Any,
) -> 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

Parameter	Type	Description
`user_params`	`typing.Optional[flytekit.core.context_manager.ExecutionParameters]`	are the modified user params as created during the pre_execute step
`rval`	`typing.Any`

pre_execute()

        
    
def pre_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
) -> 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

Parameter	Type	Description
`user_params`	`typing.Optional[flytekit.core.context_manager.ExecutionParameters]`

sandbox_execute()

        
    
def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> flytekit.models.literals.LiteralMap

Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

Parameter	Type	Description
`ctx`	`flytekit.core.context_manager.FlyteContext`
`input_literal_map`	`flytekit.models.literals.LiteralMap`

flytekitplugins.openai.batch.task.BatchResult

Parameters

        
    
class BatchResult(
    output_file: typing.Optional[flytekit.types.file.file.FlyteFile.__class_getitem__.<locals>._SpecificFormatClass] = None,
    error_file: typing.Optional[flytekit.types.file.file.FlyteFile.__class_getitem__.<locals>._SpecificFormatClass] = None,
)

Parameter	Type	Description
`output_file`	`typing.Optional[flytekit.types.file.file.FlyteFile.__class_getitem__.<locals>._SpecificFormatClass]`
`error_file`	`typing.Optional[flytekit.types.file.file.FlyteFile.__class_getitem__.<locals>._SpecificFormatClass]`

Methods

Method	Description
`from_dict()`
`to_dict()`

from_dict()

        
    
def from_dict(
    d,
    dialect = None,
)

Parameter	Type	Description
`d`
`dialect`

to_dict()

        
    
def to_dict()

flytekitplugins.openai.batch.task.DownloadJSONFilesExecutor

Parameters

        
    
class DownloadJSONFilesExecutor(
    *args,
    **kwargs,
)

Parameter	Type	Description
`*args`
`**kwargs`

Properties

Property	Type	Description
`instantiated_in`	`str`
`lhs`	`None`
`location`	`str`

Methods

Method	Description
`execute_from_model()`	This function must be overridden and is where all the business logic for running a task should live.
`find_lhs()`

execute_from_model()

        
    
def execute_from_model(
    tt: flytekit.models.task.TaskTemplate,
    **kwargs,
) -> typing.Any

This function must be overridden and is where all the business logic for running a task should live. Keep in mind that you’re only working with the TaskTemplate. You won’t have access to any information in the task that wasn’t serialized into the template.

Parameter	Type	Description
`tt`	`flytekit.models.task.TaskTemplate`	This is the template, the serialized form of the task.
`**kwargs`		These are the Python native input values to the task.

Returns: Python native output values from the task.

find_lhs()

        
    
def find_lhs()

flytekitplugins.openai.batch.task.DownloadJSONFilesTask

Parameters

        
    
class DownloadJSONFilesTask(
    name: str,
    task_config: flytekitplugins.openai.batch.task.OpenAIFileConfig,
    container_image: str = 'cr.flyte.org/flyteorg/flytekit:py3.12-openai-batch-1.16.32',
    **kwargs,
)

Parameter	Type	Description
`name`	`str`	unique name for the task, usually the function’s module and name.
`task_config`	`flytekitplugins.openai.batch.task.OpenAIFileConfig`	Configuration object for Task. Should be a unique type for that specific Task
`container_image`	`str`	This is the external container image the task should run at platform-run-time.
`**kwargs`

Properties

Property	Type	Description
`container_image`	`str`
`deck_fields`	`typing.List[flytekit.deck.deck.DeckField]`	If not empty, this task will output deck html file for the specified decks
`disable_deck`	`bool`	If true, this task will not output deck html file
`docs`	`flytekit.models.documentation.Documentation`
`enable_deck`	`bool`	If true, this task will output deck html file
`environment`	`typing.Dict[str, str]`	Any environment variables that supplied during the execution of the task.
`executor`	`ShimTaskExecutor`
`executor_type`	`Type[ShimTaskExecutor]`
`instantiated_in`	`str`
`interface`	`flytekit.models.interface.TypedInterface`
`lhs`	`None`
`location`	`str`
`metadata`	`flytekit.core.base_task.TaskMetadata`
`name`	`str`	Return the name of the underlying task.
`python_interface`	`flytekit.core.interface.Interface`	Returns this task’s python interface.
`resources`	`ResourceSpec`
`security_context`	`flytekit.models.security.SecurityContext`
`task_config`	`typing.Optional[~T]`	Returns the user-specified task config which is used for plugin-specific handling of the task.
`task_resolver`	`TaskTemplateResolver`
`task_template`	`Optional[_task_model.TaskTemplate]`	Override the base class implementation to serialize on first call.
`task_type`	`str`
`task_type_version`	`int`

Methods

Method	Description
`compile()`	Generates a node that encapsulates this task in a workflow definition.
`construct_node_metadata()`	Used when constructing the node that encapsulates this task as part of a broader workflow definition.
`dispatch_execute()`	This function is largely similar to the base PythonTask, with the exception that we have to infer the Python interface before executing.
`execute()`	Rather than running here, send everything to the executor.
`find_lhs()`
`get_command()`
`get_config()`	Returns the task config as a serializable dictionary.
`get_container()`	Returns the container definition (if any) that is used to run the task on hosted Flyte.
`get_custom()`	Return additional plugin-specific custom data (if any) as a serializable dictionary.
`get_extended_resources()`	Returns the extended resources to allocate to the task on hosted Flyte.
`get_image()`
`get_input_types()`	Returns the names and python types as a dictionary for the inputs of this task.
`get_k8s_pod()`	Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.
`get_sql()`	Returns the Sql definition (if any) that is used to run the task on hosted Flyte.
`get_type_for_input_var()`	Returns the python type for an input variable by name.
`get_type_for_output_var()`	Returns the python type for the specified output variable by name.
`local_execute()`	This function is used only in the local execution path and is responsible for calling dispatch execute.
`local_execution_mode()`
`post_execute()`	This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.
`pre_execute()`	This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.
`sandbox_execute()`	Call dispatch_execute, in the context of a local sandbox execution.
`serialize_to_model()`

compile()

        
    
def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    *args,
    **kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, NoneType]

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

Parameter	Type	Description
`ctx`	`flytekit.core.context_manager.FlyteContext`
`*args`
`**kwargs`

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: FlyteContext,
    input_literal_map: _literal_models.LiteralMap,
) -> Union[_literal_models.LiteralMap, _dynamic_job.DynamicJobSpec]

This function is largely similar to the base PythonTask, with the exception that we have to infer the Python interface before executing. Also, we refer to self.task_template rather than just self similar to task classes that derive from the base PythonTask.

Parameter	Type	Description
`ctx`	`FlyteContext`
`input_literal_map`	`_literal_models.LiteralMap`

execute()

        
    
def execute(
    **kwargs,
) -> Any

Rather than running here, send everything to the executor.

Parameter	Type	Description
`**kwargs`

find_lhs()

        
    
def find_lhs()

get_command()

        
    
def get_command(
    settings: SerializationSettings,
) -> List[str]

Parameter	Type	Description
`settings`	`SerializationSettings`

get_config()

        
    
def get_config(
    settings: SerializationSettings,
) -> Dict[str, str]

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

Parameter	Type	Description
`settings`	`SerializationSettings`

get_container()

        
    
def get_container(
    settings: SerializationSettings,
) -> _task_model.Container

Returns the container definition (if any) that is used to run the task on hosted Flyte.

Parameter	Type	Description
`settings`	`SerializationSettings`

get_custom()

        
    
def get_custom(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Dict[str, typing.Any]

Return additional plugin-specific custom data (if any) as a serializable dictionary.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_extended_resources()

        
    
def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flyteidl.core.tasks_pb2.ExtendedResources]

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

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_image()

        
    
def get_image(
    settings: SerializationSettings,
) -> str

Parameter	Type	Description
`settings`	`SerializationSettings`

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,
) -> typing.Optional[flytekit.models.task.K8sPod]

Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_sql()

        
    
def get_sql(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.Sql]

Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_type_for_input_var()

        
    
def get_type_for_input_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]

Returns the python type for an input variable by name.

Parameter	Type	Description
`k`	`str`
`v`	`typing.Any`

get_type_for_output_var()

        
    
def get_type_for_output_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]

Returns the python type for the specified output variable by name.

Parameter	Type	Description
`k`	`str`
`v`	`typing.Any`

local_execute()

        
    
def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    **kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, typing.Coroutine, NoneType]

Parameter	Type	Description
`ctx`	`flytekit.core.context_manager.FlyteContext`
`**kwargs`

local_execution_mode()

        
    
def local_execution_mode()

post_execute()

        
    
def post_execute(
    rval: Any,
) -> Any

This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.

Parameter	Type	Description
`rval`	`Any`

pre_execute()

        
    
def pre_execute(
    user_params: Optional[ExecutionParameters],
) -> Optional[ExecutionParameters]

This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.

Parameter	Type	Description
`user_params`	`Optional[ExecutionParameters]`

sandbox_execute()

        
    
def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> flytekit.models.literals.LiteralMap

Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

Parameter	Type	Description
`ctx`	`flytekit.core.context_manager.FlyteContext`
`input_literal_map`	`flytekit.models.literals.LiteralMap`

serialize_to_model()

        
    
def serialize_to_model(
    settings: SerializationSettings,
) -> _task_model.TaskTemplate

Parameter	Type	Description
`settings`	`SerializationSettings`

flytekitplugins.openai.batch.task.OpenAIFileConfig

Parameters

        
    
class OpenAIFileConfig(
    secret: flytekit.models.security.Secret,
    openai_organization: typing.Optional[str] = None,
)

Parameter	Type	Description
`secret`	`flytekit.models.security.Secret`
`openai_organization`	`typing.Optional[str]`

flytekitplugins.openai.batch.task.OpenAIFileDefaultImages

Default images for the openai batch plugin.

Methods

Method	Description
`default_image()`
`find_image_for()`
`get_version_suffix()`

default_image()

        
    
def default_image()

find_image_for()

        
    
def find_image_for(
    python_version: typing.Optional[flytekit.configuration.default_images.PythonVersion] = None,
    flytekit_version: typing.Optional[str] = None,
) -> str

Parameter	Type	Description
`python_version`	`typing.Optional[flytekit.configuration.default_images.PythonVersion]`
`flytekit_version`	`typing.Optional[str]`

get_version_suffix()

        
    
def get_version_suffix()

flytekitplugins.openai.batch.task.UploadJSONLFileExecutor

Parameters

        
    
class UploadJSONLFileExecutor(
    *args,
    **kwargs,
)

Parameter	Type	Description
`*args`
`**kwargs`

Properties

Property	Type	Description
`instantiated_in`	`str`
`lhs`	`None`
`location`	`str`

Methods

Method	Description
`execute_from_model()`	This function must be overridden and is where all the business logic for running a task should live.
`find_lhs()`

execute_from_model()

        
    
def execute_from_model(
    tt: flytekit.models.task.TaskTemplate,
    **kwargs,
) -> typing.Any

Parameter	Type	Description
`tt`	`flytekit.models.task.TaskTemplate`	This is the template, the serialized form of the task.
`**kwargs`		These are the Python native input values to the task.

Returns: Python native output values from the task.

find_lhs()

        
    
def find_lhs()

flytekitplugins.openai.batch.task.UploadJSONLFileTask

Parameters

        
    
class UploadJSONLFileTask(
    name: str,
    task_config: flytekitplugins.openai.batch.task.OpenAIFileConfig,
    container_image: str = 'cr.flyte.org/flyteorg/flytekit:py3.12-openai-batch-1.16.32',
    **kwargs,
)

Parameter	Type	Description
`name`	`str`	unique name for the task, usually the function’s module and name.
`task_config`	`flytekitplugins.openai.batch.task.OpenAIFileConfig`	Configuration object for Task. Should be a unique type for that specific Task
`container_image`	`str`	This is the external container image the task should run at platform-run-time.
`**kwargs`

Properties

Property	Type	Description
`container_image`	`str`
`deck_fields`	`typing.List[flytekit.deck.deck.DeckField]`	If not empty, this task will output deck html file for the specified decks
`disable_deck`	`bool`	If true, this task will not output deck html file
`docs`	`flytekit.models.documentation.Documentation`
`enable_deck`	`bool`	If true, this task will output deck html file
`environment`	`typing.Dict[str, str]`	Any environment variables that supplied during the execution of the task.
`executor`	`ShimTaskExecutor`
`executor_type`	`Type[ShimTaskExecutor]`
`instantiated_in`	`str`
`interface`	`flytekit.models.interface.TypedInterface`
`lhs`	`None`
`location`	`str`
`metadata`	`flytekit.core.base_task.TaskMetadata`
`name`	`str`	Return the name of the underlying task.
`python_interface`	`flytekit.core.interface.Interface`	Returns this task’s python interface.
`resources`	`ResourceSpec`
`security_context`	`flytekit.models.security.SecurityContext`
`task_config`	`typing.Optional[~T]`	Returns the user-specified task config which is used for plugin-specific handling of the task.
`task_resolver`	`TaskTemplateResolver`
`task_template`	`Optional[_task_model.TaskTemplate]`	Override the base class implementation to serialize on first call.
`task_type`	`str`
`task_type_version`	`int`

Methods

Method	Description
`compile()`	Generates a node that encapsulates this task in a workflow definition.
`construct_node_metadata()`	Used when constructing the node that encapsulates this task as part of a broader workflow definition.
`dispatch_execute()`	This function is largely similar to the base PythonTask, with the exception that we have to infer the Python interface before executing.
`execute()`	Rather than running here, send everything to the executor.
`find_lhs()`
`get_command()`
`get_config()`	Returns the task config as a serializable dictionary.
`get_container()`	Returns the container definition (if any) that is used to run the task on hosted Flyte.
`get_custom()`	Return additional plugin-specific custom data (if any) as a serializable dictionary.
`get_extended_resources()`	Returns the extended resources to allocate to the task on hosted Flyte.
`get_image()`
`get_input_types()`	Returns the names and python types as a dictionary for the inputs of this task.
`get_k8s_pod()`	Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.
`get_sql()`	Returns the Sql definition (if any) that is used to run the task on hosted Flyte.
`get_type_for_input_var()`	Returns the python type for an input variable by name.
`get_type_for_output_var()`	Returns the python type for the specified output variable by name.
`local_execute()`	This function is used only in the local execution path and is responsible for calling dispatch execute.
`local_execution_mode()`
`post_execute()`	This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.
`pre_execute()`	This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.
`sandbox_execute()`	Call dispatch_execute, in the context of a local sandbox execution.
`serialize_to_model()`

compile()

        
    
def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    *args,
    **kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, NoneType]

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

Parameter	Type	Description
`ctx`	`flytekit.core.context_manager.FlyteContext`
`*args`
`**kwargs`

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: FlyteContext,
    input_literal_map: _literal_models.LiteralMap,
) -> Union[_literal_models.LiteralMap, _dynamic_job.DynamicJobSpec]

Parameter	Type	Description
`ctx`	`FlyteContext`
`input_literal_map`	`_literal_models.LiteralMap`

execute()

        
    
def execute(
    **kwargs,
) -> Any

Rather than running here, send everything to the executor.

Parameter	Type	Description
`**kwargs`

find_lhs()

        
    
def find_lhs()

get_command()

        
    
def get_command(
    settings: SerializationSettings,
) -> List[str]

Parameter	Type	Description
`settings`	`SerializationSettings`

get_config()

        
    
def get_config(
    settings: SerializationSettings,
) -> Dict[str, str]

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

Parameter	Type	Description
`settings`	`SerializationSettings`

get_container()

        
    
def get_container(
    settings: SerializationSettings,
) -> _task_model.Container

Returns the container definition (if any) that is used to run the task on hosted Flyte.

Parameter	Type	Description
`settings`	`SerializationSettings`

get_custom()

        
    
def get_custom(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Dict[str, typing.Any]

Return additional plugin-specific custom data (if any) as a serializable dictionary.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_extended_resources()

        
    
def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flyteidl.core.tasks_pb2.ExtendedResources]

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

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_image()

        
    
def get_image(
    settings: SerializationSettings,
) -> str

Parameter	Type	Description
`settings`	`SerializationSettings`

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,
) -> typing.Optional[flytekit.models.task.K8sPod]

Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_sql()

        
    
def get_sql(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.Sql]

Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

Parameter	Type	Description
`settings`	`flytekit.configuration.SerializationSettings`

get_type_for_input_var()

        
    
def get_type_for_input_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]

Returns the python type for an input variable by name.

Parameter	Type	Description
`k`	`str`
`v`	`typing.Any`

get_type_for_output_var()

        
    
def get_type_for_output_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]

Returns the python type for the specified output variable by name.

Parameter	Type	Description
`k`	`str`
`v`	`typing.Any`

local_execute()

        
    
def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    **kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, typing.Coroutine, NoneType]

Parameter	Type	Description
`ctx`	`flytekit.core.context_manager.FlyteContext`
`**kwargs`

local_execution_mode()

        
    
def local_execution_mode()

post_execute()

        
    
def post_execute(
    rval: Any,
) -> Any

This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.

Parameter	Type	Description
`rval`	`Any`

pre_execute()

        
    
def pre_execute(
    user_params: Optional[ExecutionParameters],
) -> Optional[ExecutionParameters]

This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.

Parameter	Type	Description
`user_params`	`Optional[ExecutionParameters]`

sandbox_execute()

        
    
def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> flytekit.models.literals.LiteralMap

Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

Parameter	Type	Description
`ctx`	`flytekit.core.context_manager.FlyteContext`
`input_literal_map`	`flytekit.models.literals.LiteralMap`

serialize_to_model()

        
    
def serialize_to_model(
    settings: SerializationSettings,
) -> _task_model.TaskTemplate

Parameter	Type	Description
`settings`	`SerializationSettings`