0.0.0+develop

flytekitplugins.kfmpi.task

This Plugin adds the capability of running distributed MPI training to Flyte using backend plugins, natively on Kubernetes. It leverages MPI Job <https://github.com/kubeflow/mpi-operator>_ Plugin from kubeflow.

Class	Description
`HorovodFunctionTask`	For more info, check out https://github.
`HorovodJob`	Configuration for an executable `Horovod Job using MPI operator<https://github.
`Launcher`	Launcher replica configuration.
`MPIFunctionTask`	Plugin that submits a MPIJob (see https://github.
`MPIJob`	Configuration for an executable `MPI Job <https://github.
`RunPolicy`	RunPolicy describes some policy to apply to the execution of a kubeflow job.
`Worker`	Worker replica configuration.

flytekitplugins.kfmpi.task.HorovodFunctionTask

For more info, check out https://github.com/horovod/horovod

        
    
class HorovodFunctionTask(
    task_config: flytekitplugins.kfmpi.task.HorovodJob,
    task_function: typing.Callable,
    kwargs,
)

Parameter	Type
`task_config`	`flytekitplugins.kfmpi.task.HorovodJob`
`task_function`	`typing.Callable`
`kwargs`	`**kwargs`

Methods

Method	Description
`compile()`	Generates a node that encapsulates this task in a workflow definition.
`compile_into_workflow()`	In the case of dynamic workflows, this function will produce a workflow definition at execution time which will.
`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.
`dynamic_execute()`	By the time this function is invoked, the local_execute function should have unwrapped the Promises and Flyte.
`execute()`	This method will be invoked to execute the task.
`find_lhs()`
`get_command()`	Returns the command which should be used in the container definition for the serialized version of this task.
`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_default_command()`	Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.
`get_extended_resources()`	Returns the extended resources to allocate to the task on hosted Flyte.
`get_image()`	Update image spec based on fast registration usage, and return string representing the 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()`	Post execute is called after the execution has completed, with the user_params and can be used to clean-up,.
`pre_execute()`	This is the method that will be invoked directly before executing the task method and before all the inputs.
`reset_command_fn()`	Resets the command which should be used in the container definition of this task to the default arguments.
`sandbox_execute()`	Call dispatch_execute, in the context of a local sandbox execution.
`set_command_fn()`	By default, the task will run on the Flyte platform using the pyflyte-execute command.
`set_resolver()`	By default, flytekit uses the DefaultTaskResolver to resolve the task.

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
`ctx`	`flytekit.core.context_manager.FlyteContext`
`args`	`*args`
`kwargs`	`**kwargs`

compile_into_workflow()

        
    
def compile_into_workflow(
    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.

Parameter	Type
`ctx`	`FlyteContext`
`task_function`	`Callable`
`kwargs`	`**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: 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
`ctx`	`flytekit.core.context_manager.FlyteContext`
`input_literal_map`	`flytekit.models.literals.LiteralMap`

dynamic_execute()

        
    
def dynamic_execute(
    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.

Parameter	Type
`task_function`	`Callable`
`kwargs`	`**kwargs`

execute()

        
    
def execute(
    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.

Parameter	Type
`kwargs`	`**kwargs`

find_lhs()

def find_lhs()

get_command()

        
    
def get_command(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.List[str]

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

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

get_config()

        
    
def get_config(
    settings: SerializationSettings,
) -> Optional[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
`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
`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
`settings`	`flytekit.configuration.SerializationSettings`

get_default_command()

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

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

Parameter	Type
`settings`	`SerializationSettings`

get_extended_resources()

        
    
def get_extended_resources(
    settings: SerializationSettings,
) -> Optional[tasks_pb2.ExtendedResources]

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

Parameter	Type
`settings`	`SerializationSettings`

get_image()

        
    
def get_image(
    settings: SerializationSettings,
) -> str

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

Parameter	Type
`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: SerializationSettings,
) -> _task_model.K8sPod

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

Parameter	Type
`settings`	`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
`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
`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
`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
`ctx`	`flytekit.core.context_manager.FlyteContext`
`kwargs`	`**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
`user_params`	`typing.Optional[flytekit.core.context_manager.ExecutionParameters]`
`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
`user_params`	`typing.Optional[flytekit.core.context_manager.ExecutionParameters]`

reset_command_fn()

def reset_command_fn()

Resets the command which should be used in the container definition of this task to the default arguments. This is useful when the command line is overridden at serialization time.

sandbox_execute()

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

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

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

set_command_fn()

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

By default, the task will run on the Flyte platform using the pyflyte-execute command. However, it can be useful to update the command with which the task is serialized for specific cases like running map tasks (“pyflyte-map-execute”) or for fast-executed tasks.

Parameter	Type
`get_command_fn`	`Optional[Callable[[SerializationSettings], List[str]]]`

set_resolver()

        
    
def set_resolver(
    resolver: TaskResolverMixin,
)

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

Parameter	Type
`resolver`	`TaskResolverMixin`

Properties

Property	Type	Description
`container_image`
`deck_fields`		If not empty, this task will output deck html file for the specified decks
`disable_deck`		If true, this task will not output deck html file
`docs`
`enable_deck`		If true, this task will output deck html file
`environment`		Any environment variables that supplied during the execution of the task.
`execution_mode`
`instantiated_in`
`interface`
`lhs`
`location`
`metadata`
`name`		Returns the name of the task.
`node_dependency_hints`
`python_interface`		Returns this task’s python interface.
`resources`
`security_context`
`task_config`		Returns the user-specified task config which is used for plugin-specific handling of the task.
`task_function`
`task_resolver`
`task_type`
`task_type_version`

flytekitplugins.kfmpi.task.HorovodJob

Configuration for an executable Horovod Job using MPI operator<https://github.com/kubeflow/mpi-operator>. Use this to run distributed training on k8s with MPI. For more info, check out Running Horovodhttps://horovod.readthedocs.io/en/stable/summary_include.html#running-horovod`.

        
    
class HorovodJob(
    worker: flytekitplugins.kfmpi.task.Worker,
    launcher: flytekitplugins.kfmpi.task.Launcher,
    run_policy: typing.Optional[flytekitplugins.kfmpi.task.RunPolicy],
    slots: int,
    verbose: typing.Optional[bool],
    log_level: typing.Optional[str],
    discovery_script_path: typing.Optional[str],
    elastic_timeout: typing.Optional[int],
    num_launcher_replicas: typing.Optional[int],
    num_workers: typing.Optional[int],
)

Parameter	Type
`worker`	`flytekitplugins.kfmpi.task.Worker`
`launcher`	`flytekitplugins.kfmpi.task.Launcher`
`run_policy`	`typing.Optional[flytekitplugins.kfmpi.task.RunPolicy]`
`slots`	`int`
`verbose`	`typing.Optional[bool]`
`log_level`	`typing.Optional[str]`
`discovery_script_path`	`typing.Optional[str]`
`elastic_timeout`	`typing.Optional[int]`
`num_launcher_replicas`	`typing.Optional[int]`
`num_workers`	`typing.Optional[int]`

flytekitplugins.kfmpi.task.Launcher

Launcher replica configuration. Launcher command can be customized. If not specified, the launcher will use the command specified in the task signature.

        
    
class Launcher(
    command: typing.Optional[typing.List[str]],
    image: typing.Optional[str],
    requests: typing.Optional[flytekit.core.resources.Resources],
    limits: typing.Optional[flytekit.core.resources.Resources],
    replicas: typing.Optional[int],
    restart_policy: typing.Optional[flytekitplugins.kfmpi.task.RestartPolicy],
)

Parameter	Type
`command`	`typing.Optional[typing.List[str]]`
`image`	`typing.Optional[str]`
`requests`	`typing.Optional[flytekit.core.resources.Resources]`
`limits`	`typing.Optional[flytekit.core.resources.Resources]`
`replicas`	`typing.Optional[int]`
`restart_policy`	`typing.Optional[flytekitplugins.kfmpi.task.RestartPolicy]`

flytekitplugins.kfmpi.task.MPIFunctionTask

Plugin that submits a MPIJob (see https://github.com/kubeflow/mpi-operator) defined by the code within the _task_function to k8s cluster.

        
    
class MPIFunctionTask(
    task_config: flytekitplugins.kfmpi.task.MPIJob,
    task_function: typing.Callable,
    kwargs,
)

Parameter	Type
`task_config`	`flytekitplugins.kfmpi.task.MPIJob`
`task_function`	`typing.Callable`
`kwargs`	`**kwargs`

Methods

Method	Description
`compile()`	Generates a node that encapsulates this task in a workflow definition.
`compile_into_workflow()`	In the case of dynamic workflows, this function will produce a workflow definition at execution time which will.
`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.
`dynamic_execute()`	By the time this function is invoked, the local_execute function should have unwrapped the Promises and Flyte.
`execute()`	This method will be invoked to execute the task.
`find_lhs()`
`get_command()`	Returns the command which should be used in the container definition for the serialized version of this task.
`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_default_command()`	Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.
`get_extended_resources()`	Returns the extended resources to allocate to the task on hosted Flyte.
`get_image()`	Update image spec based on fast registration usage, and return string representing the 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()`	Post execute is called after the execution has completed, with the user_params and can be used to clean-up,.
`pre_execute()`	This is the method that will be invoked directly before executing the task method and before all the inputs.
`reset_command_fn()`	Resets the command which should be used in the container definition of this task to the default arguments.
`sandbox_execute()`	Call dispatch_execute, in the context of a local sandbox execution.
`set_command_fn()`	By default, the task will run on the Flyte platform using the pyflyte-execute command.
`set_resolver()`	By default, flytekit uses the DefaultTaskResolver to resolve the task.

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
`ctx`	`flytekit.core.context_manager.FlyteContext`
`args`	`*args`
`kwargs`	`**kwargs`

compile_into_workflow()

        
    
def compile_into_workflow(
    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.

Parameter	Type
`ctx`	`FlyteContext`
`task_function`	`Callable`
`kwargs`	`**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: 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
`ctx`	`flytekit.core.context_manager.FlyteContext`
`input_literal_map`	`flytekit.models.literals.LiteralMap`

dynamic_execute()

        
    
def dynamic_execute(
    task_function: Callable,
    kwargs,
) -> Any

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.

Parameter	Type
`task_function`	`Callable`
`kwargs`	`**kwargs`

execute()

        
    
def execute(
    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.

Parameter	Type
`kwargs`	`**kwargs`

find_lhs()

def find_lhs()

get_command()

        
    
def get_command(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.List[str]

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

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

get_config()

        
    
def get_config(
    settings: SerializationSettings,
) -> Optional[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
`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
`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
`settings`	`flytekit.configuration.SerializationSettings`

get_default_command()

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

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

Parameter	Type
`settings`	`SerializationSettings`

get_extended_resources()

        
    
def get_extended_resources(
    settings: SerializationSettings,
) -> Optional[tasks_pb2.ExtendedResources]

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

Parameter	Type
`settings`	`SerializationSettings`

get_image()

        
    
def get_image(
    settings: SerializationSettings,
) -> str

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

Parameter	Type
`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: SerializationSettings,
) -> _task_model.K8sPod

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

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

Parameter	Type
`user_params`	`typing.Optional[flytekit.core.context_manager.ExecutionParameters]`
`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 should return either the same context of the mutated context

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

reset_command_fn()

def reset_command_fn()

Resets the command which should be used in the container definition of this task to the default arguments. This is useful when the command line is overridden at serialization time.

sandbox_execute()

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

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

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

set_command_fn()

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

Parameter	Type
`get_command_fn`	`Optional[Callable[[SerializationSettings], List[str]]]`

set_resolver()

        
    
def set_resolver(
    resolver: TaskResolverMixin,
)

Parameter	Type
`resolver`	`TaskResolverMixin`

Properties

Property	Type	Description
`container_image`
`deck_fields`		If not empty, this task will output deck html file for the specified decks
`disable_deck`		If true, this task will not output deck html file
`docs`
`enable_deck`		If true, this task will output deck html file
`environment`		Any environment variables that supplied during the execution of the task.
`execution_mode`
`instantiated_in`
`interface`
`lhs`
`location`
`metadata`
`name`		Returns the name of the task.
`node_dependency_hints`
`python_interface`		Returns this task’s python interface.
`resources`
`security_context`
`task_config`		Returns the user-specified task config which is used for plugin-specific handling of the task.
`task_function`
`task_resolver`
`task_type`
`task_type_version`

flytekitplugins.kfmpi.task.MPIJob

Configuration for an executable MPI Job <https://github.com/kubeflow/mpi-operator>_. Use this to run distributed training on k8s with MPI

        
    
class MPIJob(
    launcher: flytekitplugins.kfmpi.task.Launcher,
    worker: flytekitplugins.kfmpi.task.Worker,
    run_policy: typing.Optional[flytekitplugins.kfmpi.task.RunPolicy],
    slots: int,
    num_launcher_replicas: typing.Optional[int],
    num_workers: typing.Optional[int],
)

Parameter	Type
`launcher`	`flytekitplugins.kfmpi.task.Launcher`
`worker`	`flytekitplugins.kfmpi.task.Worker`
`run_policy`	`typing.Optional[flytekitplugins.kfmpi.task.RunPolicy]`
`slots`	`int`
`num_launcher_replicas`	`typing.Optional[int]`
`num_workers`	`typing.Optional[int]`

flytekitplugins.kfmpi.task.RunPolicy

RunPolicy describes some policy to apply to the execution of a kubeflow job.

        
    
class RunPolicy(
    clean_pod_policy: <enum 'CleanPodPolicy'>,
    ttl_seconds_after_finished: typing.Optional[int],
    active_deadline_seconds: typing.Optional[int],
    backoff_limit: typing.Optional[int],
)

Parameter	Type
`clean_pod_policy`	`<enum 'CleanPodPolicy'>`
`ttl_seconds_after_finished`	`typing.Optional[int]`
`active_deadline_seconds`	`typing.Optional[int]`
`backoff_limit`	`typing.Optional[int]`

flytekitplugins.kfmpi.task.Worker

Worker replica configuration. Worker command can be customized. If not specified, the worker will use default command generated by the mpi operator.

        
    
class Worker(
    command: typing.Optional[typing.List[str]],
    image: typing.Optional[str],
    requests: typing.Optional[flytekit.core.resources.Resources],
    limits: typing.Optional[flytekit.core.resources.Resources],
    replicas: typing.Optional[int],
    restart_policy: typing.Optional[flytekitplugins.kfmpi.task.RestartPolicy],
)

Parameter	Type
`command`	`typing.Optional[typing.List[str]]`
`image`	`typing.Optional[str]`
`requests`	`typing.Optional[flytekit.core.resources.Resources]`
`limits`	`typing.Optional[flytekit.core.resources.Resources]`
`replicas`	`typing.Optional[int]`
`restart_policy`	`typing.Optional[flytekitplugins.kfmpi.task.RestartPolicy]`

On this page

flytekitplugins.kfmpi.task

Directory

Classes

flytekitplugins.kfmpi.task.HorovodFunctionTask

Methods

compile()

compile_into_workflow()

construct_node_metadata()

dispatch_execute()

dynamic_execute()

execute()

find_lhs()

get_command()

get_config()

get_container()

get_custom()

get_default_command()

get_extended_resources()

get_image()

get_input_types()

get_k8s_pod()

get_sql()

get_type_for_input_var()

get_type_for_output_var()

local_execute()

local_execution_mode()

post_execute()

pre_execute()

reset_command_fn()

sandbox_execute()

set_command_fn()

set_resolver()

Properties

flytekitplugins.kfmpi.task.HorovodJob

flytekitplugins.kfmpi.task.Launcher

flytekitplugins.kfmpi.task.MPIFunctionTask

Methods

compile()

compile_into_workflow()

construct_node_metadata()

dispatch_execute()

dynamic_execute()

execute()

find_lhs()

get_command()

get_config()

get_container()

get_custom()

get_default_command()

get_extended_resources()

get_image()

get_input_types()

get_k8s_pod()

get_sql()

get_type_for_input_var()

get_type_for_output_var()

local_execute()

local_execution_mode()

post_execute()

pre_execute()

reset_command_fn()

sandbox_execute()

set_command_fn()

set_resolver()

Properties

flytekitplugins.kfmpi.task.MPIJob

flytekitplugins.kfmpi.task.RunPolicy

flytekitplugins.kfmpi.task.Worker