1.15.4.dev2+g3e3ce2426

flytekit.clis.sdk_in_container.helpers

Class	Description
`CopyFileDetection`	Create a collection of name/value pairs.
`FlyteRemote`	Main entrypoint for programmatically accessing a Flyte remote backend.
`ImageConfig`	We recommend you to use ImageConfig.

flytekit.clis.sdk_in_container.helpers.CopyFileDetection

Create a collection of name/value pairs.

Example enumeration:

class Color(Enum): … RED = 1 … BLUE = 2 … GREEN = 3

Access them by:

attribute access:

Color.RED <Color.RED: 1>

value lookup:

Color(1) <Color.RED: 1>

name lookup:

Color[‘RED’] <Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

len(Color) 3

list(Color) [<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

flytekit.clis.sdk_in_container.helpers.FlyteRemote

Main entrypoint for programmatically accessing a Flyte remote backend.

The term ‘remote’ is synonymous with ‘backend’ or ‘deployment’ and refers to a hosted instance of the Flyte platform, which comes with a Flyte Admin server on some known URI.

        
    
def FlyteRemote(
    config: Config,
    default_project: typing.Optional[str],
    default_domain: typing.Optional[str],
    data_upload_location: str,
    interactive_mode_enabled: typing.Optional[bool],
    kwargs,
):

Initialize a FlyteRemote object.

:type kwargs: All arguments that can be passed to create the SynchronousFlyteClient. These are usually grpc parameters, if you want to customize credentials, ssl handling etc.

Parameter	Type
`config`	`Config`
`default_project`	`typing.Optional[str]`
`default_domain`	`typing.Optional[str]`
`data_upload_location`	`str`
`interactive_mode_enabled`	`typing.Optional[bool]`
`kwargs`	`**kwargs`

Methods

Method	Description
`activate_launchplan()`	Given a launchplan, activate it, all previous versions are deactivated
`approve()`
`auto()`	None
`download()`	Download the data to the specified location
`execute()`	Execute a task, workflow, or launchplan, either something that’s been declared locally, or a fetched entity
`execute_local_launch_plan()`	Execute a locally defined `LaunchPlan`
`execute_local_task()`	Execute a @task-decorated function or TaskTemplate task
`execute_local_workflow()`	Execute an @workflow decorated function
`execute_reference_launch_plan()`	Execute a ReferenceLaunchPlan
`execute_reference_task()`	Execute a ReferenceTask
`execute_reference_workflow()`	Execute a ReferenceWorkflow
`execute_remote_task_lp()`	Execute a FlyteTask, or FlyteLaunchplan
`execute_remote_wf()`	Execute a FlyteWorkflow
`fast_package()`	Packages the given paths into an installable zip and returns the md5_bytes and the URL of the uploaded location
`fast_register_workflow()`	Use this method to register a workflow with zip mode
`fetch_active_launchplan()`	Returns the active version of the launch plan if it exists or returns None
`fetch_execution()`	Fetch a workflow execution entity from flyte admin
`fetch_launch_plan()`	Fetch a launchplan entity from flyte admin
`fetch_task()`	Fetch a task entity from flyte admin
`fetch_task_lazy()`	Similar to fetch_task, just that it returns a LazyEntity, which will fetch the workflow lazily
`fetch_workflow()`	Fetch a workflow entity from flyte admin
`fetch_workflow_lazy()`	Similar to fetch_workflow, just that it returns a LazyEntity, which will fetch the workflow lazily
`find_launch_plan()`	None
`find_launch_plan_for_node()`	None
`for_endpoint()`	None
`for_sandbox()`	None
`generate_console_http_domain()`	This should generate the domain where console is hosted
`generate_console_url()`	Generate a Flyteconsole URL for the given Flyte remote endpoint
`get()`	General function that works with flyte tiny urls
`get_domains()`	Lists registered domains from flyte admin
`get_execution_metrics()`	Get the metrics for a given execution
`get_extra_headers_for_protocol()`	None
`launch_backfill()`	Creates and launches a backfill workflow for the given launchplan
`list_projects()`	Lists registered projects from flyte admin
`list_signals()`
`list_tasks_by_version()`	None
`raw_register()`	Raw register method, can be used to register control plane entities
`recent_executions()`	None
`register_launch_plan()`	Register a given launchplan, possibly applying overrides from the provided options
`register_script()`	Use this method to register a workflow via script mode
`register_task()`	Register a qualified task (PythonTask) with Remote
`register_workflow()`	Use this method to register a workflow
`reject()`
`remote_context()`	Context manager with remote-specific configuration
`set_input()`
`set_signal()`
`sync()`	This function was previously a singledispatchmethod
`sync_execution()`	Sync a FlyteWorkflowExecution object with its corresponding remote state
`sync_node_execution()`	Get data backing a node execution
`sync_task_execution()`	Sync a FlyteTaskExecution object with its corresponding remote state
`terminate()`	Terminate a workflow execution
`upload_file()`	Function will use remote’s client to hash and then upload the file using Admin’s data proxy service
`wait()`	Wait for an execution to finish

activate_launchplan()

        
    
def activate_launchplan(
    ident: Identifier,
):

Given a launchplan, activate it, all previous versions are deactivated.

Parameter	Type
`ident`	`Identifier`

approve()

        
    
def approve(
    signal_id: str,
    execution_name: str,
    project: str,
    domain: str,
):

Parameter	Type
`signal_id`	`str`
`execution_name`	`str`
`project`	`str`
`domain`	`str`

auto()

        
    
def auto(
    config_file: typing.Union[str, ConfigFile],
    default_project: typing.Optional[str],
    default_domain: typing.Optional[str],
    data_upload_location: str,
    interactive_mode_enabled: bool,
    kwargs,
):

Parameter	Type
`config_file`	`typing.Union[str, ConfigFile]`
`default_project`	`typing.Optional[str]`
`default_domain`	`typing.Optional[str]`
`data_upload_location`	`str`
`interactive_mode_enabled`	`bool`
`kwargs`	`**kwargs`

download()

        
    
def download(
    data: typing.Union[LiteralsResolver, Literal, LiteralMap],
    download_to: str,
    recursive: bool,
):

Download the data to the specified location. If the data is a LiteralsResolver, LiteralMap and if recursive is specified, then all file like objects will be recursively downloaded (e.g. FlyteFile/Dir (blob), StructuredDataset etc).

Note: That it will use your sessions credentials to access the remote location. For sandbox, this should be automatically configured, assuming you are running sandbox locally. For other environments, you will need to configure your credentials appropriately.

Parameter	Type
`data`	`typing.Union[LiteralsResolver, Literal, LiteralMap]`
`download_to`	`str`
`recursive`	`bool`

execute()

        
    
def execute(
    entity: typing.Union[FlyteTask, FlyteLaunchPlan, FlyteWorkflow, PythonTask, WorkflowBase, LaunchPlan, ReferenceEntity],
    inputs: typing.Dict[str, typing.Any],
    project: str,
    domain: str,
    name: str,
    version: str,
    execution_name: typing.Optional[str],
    execution_name_prefix: typing.Optional[str],
    image_config: typing.Optional[ImageConfig],
    options: typing.Optional[Options],
    wait: bool,
    type_hints: typing.Optional[typing.Dict[str, typing.Type]],
    overwrite_cache: typing.Optional[bool],
    interruptible: typing.Optional[bool],
    envs: typing.Optional[typing.Dict[str, str]],
    tags: typing.Optional[typing.List[str]],
    cluster_pool: typing.Optional[str],
    execution_cluster_label: typing.Optional[str],
    serialization_settings: typing.Optional[SerializationSettings],
):

Execute a task, workflow, or launchplan, either something that’s been declared locally, or a fetched entity.

This method supports:

Flyte{Task, Workflow, LaunchPlan} remote module objects.
@task-decorated functions and TaskTemplate tasks.
@workflow-decorated functions.
LaunchPlan objects.

For local entities, this code will attempt to find the entity first, and if missing, will compile and register the object.

Not all arguments are relevant in all circumstances. For example, there’s no reason to use the serialization settings for entities that have already been registered on Admin.

Parameter	Type
`entity`	`typing.Union[FlyteTask, FlyteLaunchPlan, FlyteWorkflow, PythonTask, WorkflowBase, LaunchPlan, ReferenceEntity]`
`inputs`	`typing.Dict[str, typing.Any]`
`project`	`str`
`domain`	`str`
`name`	`str`
`version`	`str`
`execution_name`	`typing.Optional[str]`
`execution_name_prefix`	`typing.Optional[str]`
`image_config`	`typing.Optional[ImageConfig]`
`options`	`typing.Optional[Options]`
`wait`	`bool`
`type_hints`	`typing.Optional[typing.Dict[str, typing.Type]]`
`overwrite_cache`	`typing.Optional[bool]`
`interruptible`	`typing.Optional[bool]`
`envs`	`typing.Optional[typing.Dict[str, str]]`
`tags`	`typing.Optional[typing.List[str]]`
`cluster_pool`	`typing.Optional[str]`
`execution_cluster_label`	`typing.Optional[str]`
`serialization_settings`	`typing.Optional[SerializationSettings]`

execute_local_launch_plan()

        
    
def execute_local_launch_plan(
    entity: LaunchPlan,
    inputs: typing.Dict[str, typing.Any],
    version: str,
    project: typing.Optional[str],
    domain: typing.Optional[str],
    name: typing.Optional[str],
    execution_name: typing.Optional[str],
    execution_name_prefix: typing.Optional[str],
    options: typing.Optional[Options],
    wait: bool,
    overwrite_cache: typing.Optional[bool],
    interruptible: typing.Optional[bool],
    envs: typing.Optional[typing.Dict[str, str]],
    tags: typing.Optional[typing.List[str]],
    cluster_pool: typing.Optional[str],
    execution_cluster_label: typing.Optional[str],
    serialization_settings: typing.Optional[SerializationSettings],
):

Execute a locally defined LaunchPlan.

Parameter	Type
`entity`	`LaunchPlan`
`inputs`	`typing.Dict[str, typing.Any]`
`version`	`str`
`project`	`typing.Optional[str]`
`domain`	`typing.Optional[str]`
`name`	`typing.Optional[str]`
`execution_name`	`typing.Optional[str]`
`execution_name_prefix`	`typing.Optional[str]`
`options`	`typing.Optional[Options]`
`wait`	`bool`
`overwrite_cache`	`typing.Optional[bool]`
`interruptible`	`typing.Optional[bool]`
`envs`	`typing.Optional[typing.Dict[str, str]]`
`tags`	`typing.Optional[typing.List[str]]`
`cluster_pool`	`typing.Optional[str]`
`execution_cluster_label`	`typing.Optional[str]`
`serialization_settings`	`typing.Optional[SerializationSettings]`

execute_local_task()

        
    
def execute_local_task(
    entity: PythonTask,
    inputs: typing.Dict[str, typing.Any],
    project: str,
    domain: str,
    name: str,
    version: str,
    execution_name: typing.Optional[str],
    execution_name_prefix: typing.Optional[str],
    image_config: typing.Optional[ImageConfig],
    wait: bool,
    overwrite_cache: typing.Optional[bool],
    interruptible: typing.Optional[bool],
    envs: typing.Optional[typing.Dict[str, str]],
    tags: typing.Optional[typing.List[str]],
    cluster_pool: typing.Optional[str],
    execution_cluster_label: typing.Optional[str],
    options: typing.Optional[Options],
    serialization_settings: typing.Optional[SerializationSettings],
):

Execute a @task-decorated function or TaskTemplate task.

Parameter	Type
`entity`	`PythonTask`
`inputs`	`typing.Dict[str, typing.Any]`
`project`	`str`
`domain`	`str`
`name`	`str`
`version`	`str`
`execution_name`	`typing.Optional[str]`
`execution_name_prefix`	`typing.Optional[str]`
`image_config`	`typing.Optional[ImageConfig]`
`wait`	`bool`
`overwrite_cache`	`typing.Optional[bool]`
`interruptible`	`typing.Optional[bool]`
`envs`	`typing.Optional[typing.Dict[str, str]]`
`tags`	`typing.Optional[typing.List[str]]`
`cluster_pool`	`typing.Optional[str]`
`execution_cluster_label`	`typing.Optional[str]`
`options`	`typing.Optional[Options]`
`serialization_settings`	`typing.Optional[SerializationSettings]`

execute_local_workflow()

        
    
def execute_local_workflow(
    entity: WorkflowBase,
    inputs: typing.Dict[str, typing.Any],
    project: str,
    domain: str,
    name: str,
    version: str,
    execution_name: typing.Optional[str],
    execution_name_prefix: typing.Optional[str],
    image_config: typing.Optional[ImageConfig],
    options: typing.Optional[Options],
    wait: bool,
    overwrite_cache: typing.Optional[bool],
    interruptible: typing.Optional[bool],
    envs: typing.Optional[typing.Dict[str, str]],
    tags: typing.Optional[typing.List[str]],
    cluster_pool: typing.Optional[str],
    execution_cluster_label: typing.Optional[str],
    serialization_settings: typing.Optional[SerializationSettings],
):

Execute an @workflow decorated function.

Parameter	Type
`entity`	`WorkflowBase`
`inputs`	`typing.Dict[str, typing.Any]`
`project`	`str`
`domain`	`str`
`name`	`str`
`version`	`str`
`execution_name`	`typing.Optional[str]`
`execution_name_prefix`	`typing.Optional[str]`
`image_config`	`typing.Optional[ImageConfig]`
`options`	`typing.Optional[Options]`
`wait`	`bool`
`overwrite_cache`	`typing.Optional[bool]`
`interruptible`	`typing.Optional[bool]`
`envs`	`typing.Optional[typing.Dict[str, str]]`
`tags`	`typing.Optional[typing.List[str]]`
`cluster_pool`	`typing.Optional[str]`
`execution_cluster_label`	`typing.Optional[str]`
`serialization_settings`	`typing.Optional[SerializationSettings]`

execute_reference_launch_plan()

        
    
def execute_reference_launch_plan(
    entity: ReferenceLaunchPlan,
    inputs: typing.Dict[str, typing.Any],
    execution_name: typing.Optional[str],
    execution_name_prefix: typing.Optional[str],
    options: typing.Optional[Options],
    wait: bool,
    type_hints: typing.Optional[typing.Dict[str, typing.Type]],
    overwrite_cache: typing.Optional[bool],
    interruptible: typing.Optional[bool],
    envs: typing.Optional[typing.Dict[str, str]],
    tags: typing.Optional[typing.List[str]],
    cluster_pool: typing.Optional[str],
    execution_cluster_label: typing.Optional[str],
):

Execute a ReferenceLaunchPlan.

Parameter	Type
`entity`	`ReferenceLaunchPlan`
`inputs`	`typing.Dict[str, typing.Any]`
`execution_name`	`typing.Optional[str]`
`execution_name_prefix`	`typing.Optional[str]`
`options`	`typing.Optional[Options]`
`wait`	`bool`
`type_hints`	`typing.Optional[typing.Dict[str, typing.Type]]`
`overwrite_cache`	`typing.Optional[bool]`
`interruptible`	`typing.Optional[bool]`
`envs`	`typing.Optional[typing.Dict[str, str]]`
`tags`	`typing.Optional[typing.List[str]]`
`cluster_pool`	`typing.Optional[str]`
`execution_cluster_label`	`typing.Optional[str]`

execute_reference_task()

        
    
def execute_reference_task(
    entity: ReferenceTask,
    inputs: typing.Dict[str, typing.Any],
    execution_name: typing.Optional[str],
    execution_name_prefix: typing.Optional[str],
    options: typing.Optional[Options],
    wait: bool,
    type_hints: typing.Optional[typing.Dict[str, typing.Type]],
    overwrite_cache: typing.Optional[bool],
    interruptible: typing.Optional[bool],
    envs: typing.Optional[typing.Dict[str, str]],
    tags: typing.Optional[typing.List[str]],
    cluster_pool: typing.Optional[str],
    execution_cluster_label: typing.Optional[str],
):

Execute a ReferenceTask.

Parameter	Type
`entity`	`ReferenceTask`
`inputs`	`typing.Dict[str, typing.Any]`
`execution_name`	`typing.Optional[str]`
`execution_name_prefix`	`typing.Optional[str]`
`options`	`typing.Optional[Options]`
`wait`	`bool`
`type_hints`	`typing.Optional[typing.Dict[str, typing.Type]]`
`overwrite_cache`	`typing.Optional[bool]`
`interruptible`	`typing.Optional[bool]`
`envs`	`typing.Optional[typing.Dict[str, str]]`
`tags`	`typing.Optional[typing.List[str]]`
`cluster_pool`	`typing.Optional[str]`
`execution_cluster_label`	`typing.Optional[str]`

execute_reference_workflow()

        
    
def execute_reference_workflow(
    entity: ReferenceWorkflow,
    inputs: typing.Dict[str, typing.Any],
    execution_name: typing.Optional[str],
    execution_name_prefix: typing.Optional[str],
    options: typing.Optional[Options],
    wait: bool,
    type_hints: typing.Optional[typing.Dict[str, typing.Type]],
    overwrite_cache: typing.Optional[bool],
    interruptible: typing.Optional[bool],
    envs: typing.Optional[typing.Dict[str, str]],
    tags: typing.Optional[typing.List[str]],
    cluster_pool: typing.Optional[str],
    execution_cluster_label: typing.Optional[str],
):

Execute a ReferenceWorkflow.

Parameter	Type
`entity`	`ReferenceWorkflow`
`inputs`	`typing.Dict[str, typing.Any]`
`execution_name`	`typing.Optional[str]`
`execution_name_prefix`	`typing.Optional[str]`
`options`	`typing.Optional[Options]`
`wait`	`bool`
`type_hints`	`typing.Optional[typing.Dict[str, typing.Type]]`
`overwrite_cache`	`typing.Optional[bool]`
`interruptible`	`typing.Optional[bool]`
`envs`	`typing.Optional[typing.Dict[str, str]]`
`tags`	`typing.Optional[typing.List[str]]`
`cluster_pool`	`typing.Optional[str]`
`execution_cluster_label`	`typing.Optional[str]`

execute_remote_task_lp()

        
    
def execute_remote_task_lp(
    entity: typing.Union[FlyteTask, FlyteLaunchPlan],
    inputs: typing.Dict[str, typing.Any],
    project: str,
    domain: str,
    execution_name: typing.Optional[str],
    execution_name_prefix: typing.Optional[str],
    options: typing.Optional[Options],
    wait: bool,
    type_hints: typing.Optional[typing.Dict[str, typing.Type]],
    overwrite_cache: typing.Optional[bool],
    interruptible: typing.Optional[bool],
    envs: typing.Optional[typing.Dict[str, str]],
    tags: typing.Optional[typing.List[str]],
    cluster_pool: typing.Optional[str],
    execution_cluster_label: typing.Optional[str],
):

Execute a FlyteTask, or FlyteLaunchplan.

NOTE: the name and version arguments are currently not used and only there consistency in the function signature

Parameter	Type
`entity`	`typing.Union[FlyteTask, FlyteLaunchPlan]`
`inputs`	`typing.Dict[str, typing.Any]`
`project`	`str`
`domain`	`str`
`execution_name`	`typing.Optional[str]`
`execution_name_prefix`	`typing.Optional[str]`
`options`	`typing.Optional[Options]`
`wait`	`bool`
`type_hints`	`typing.Optional[typing.Dict[str, typing.Type]]`
`overwrite_cache`	`typing.Optional[bool]`
`interruptible`	`typing.Optional[bool]`
`envs`	`typing.Optional[typing.Dict[str, str]]`
`tags`	`typing.Optional[typing.List[str]]`
`cluster_pool`	`typing.Optional[str]`
`execution_cluster_label`	`typing.Optional[str]`

execute_remote_wf()

        
    
def execute_remote_wf(
    entity: FlyteWorkflow,
    inputs: typing.Dict[str, typing.Any],
    project: str,
    domain: str,
    execution_name: typing.Optional[str],
    execution_name_prefix: typing.Optional[str],
    options: typing.Optional[Options],
    wait: bool,
    type_hints: typing.Optional[typing.Dict[str, typing.Type]],
    overwrite_cache: typing.Optional[bool],
    interruptible: typing.Optional[bool],
    envs: typing.Optional[typing.Dict[str, str]],
    tags: typing.Optional[typing.List[str]],
    cluster_pool: typing.Optional[str],
    execution_cluster_label: typing.Optional[str],
):

Execute a FlyteWorkflow.

NOTE: the name and version arguments are currently not used and only there consistency in the function signature

Parameter	Type
`entity`	`FlyteWorkflow`
`inputs`	`typing.Dict[str, typing.Any]`
`project`	`str`
`domain`	`str`
`execution_name`	`typing.Optional[str]`
`execution_name_prefix`	`typing.Optional[str]`
`options`	`typing.Optional[Options]`
`wait`	`bool`
`type_hints`	`typing.Optional[typing.Dict[str, typing.Type]]`
`overwrite_cache`	`typing.Optional[bool]`
`interruptible`	`typing.Optional[bool]`
`envs`	`typing.Optional[typing.Dict[str, str]]`
`tags`	`typing.Optional[typing.List[str]]`
`cluster_pool`	`typing.Optional[str]`
`execution_cluster_label`	`typing.Optional[str]`

fast_package()

        
    
def fast_package(
    root: os.PathLike,
    deref_symlinks: bool,
    output: str,
    options: typing.Optional[FastPackageOptions],
):

Packages the given paths into an installable zip and returns the md5_bytes and the URL of the uploaded location

Parameter	Type
`root`	`os.PathLike`
`deref_symlinks`	`bool`
`output`	`str`
`options`	`typing.Optional[FastPackageOptions]`

fast_register_workflow()

        
    
def fast_register_workflow(
    entity: WorkflowBase,
    serialization_settings: typing.Optional[SerializationSettings],
    version: typing.Optional[str],
    default_launch_plan: typing.Optional[bool],
    options: typing.Optional[Options],
    fast_package_options: typing.Optional[FastPackageOptions],
):

Use this method to register a workflow with zip mode.

Parameter	Type
`entity`	`WorkflowBase`
`serialization_settings`	`typing.Optional[SerializationSettings]`
`version`	`typing.Optional[str]`
`default_launch_plan`	`typing.Optional[bool]`
`options`	`typing.Optional[Options]`
`fast_package_options`	`typing.Optional[FastPackageOptions]`

fetch_active_launchplan()

        
    
def fetch_active_launchplan(
    project: str,
    domain: str,
    name: str,
):

Returns the active version of the launch plan if it exists or returns None

Parameter	Type
`project`	`str`
`domain`	`str`
`name`	`str`

fetch_execution()

        
    
def fetch_execution(
    project: str,
    domain: str,
    name: str,
):

Fetch a workflow execution entity from flyte admin.

Parameter	Type
`project`	`str`
`domain`	`str`
`name`	`str`

fetch_launch_plan()

        
    
def fetch_launch_plan(
    project: str,
    domain: str,
    name: str,
    version: str,
):

Fetch a launchplan entity from flyte admin.

Parameter	Type
`project`	`str`
`domain`	`str`
`name`	`str`
`version`	`str`

fetch_task()

        
    
def fetch_task(
    project: str,
    domain: str,
    name: str,
    version: str,
):

Fetch a task entity from flyte admin.

Parameter	Type
`project`	`str`
`domain`	`str`
`name`	`str`
`version`	`str`

fetch_task_lazy()

        
    
def fetch_task_lazy(
    project: str,
    domain: str,
    name: str,
    version: str,
):

Similar to fetch_task, just that it returns a LazyEntity, which will fetch the workflow lazily.

Parameter	Type
`project`	`str`
`domain`	`str`
`name`	`str`
`version`	`str`

fetch_workflow()

        
    
def fetch_workflow(
    project: str,
    domain: str,
    name: str,
    version: str,
):

Fetch a workflow entity from flyte admin.

Parameter	Type
`project`	`str`
`domain`	`str`
`name`	`str`
`version`	`str`

fetch_workflow_lazy()

        
    
def fetch_workflow_lazy(
    project: str,
    domain: str,
    name: str,
    version: str,
):

Similar to fetch_workflow, just that it returns a LazyEntity, which will fetch the workflow lazily.

Parameter	Type
`project`	`str`
`domain`	`str`
`name`	`str`
`version`	`str`

find_launch_plan()

        
    
def find_launch_plan(
    lp_ref: id_models,
    node_launch_plans: Dict[id_models, launch_plan_models.LaunchPlanSpec],
):

Parameter	Type
`lp_ref`	`id_models`
`node_launch_plans`	`Dict[id_models, launch_plan_models.LaunchPlanSpec]`

find_launch_plan_for_node()

        
    
def find_launch_plan_for_node(
    node: Node,
    node_launch_plans: Dict[id_models, launch_plan_models.LaunchPlanSpec],
):

Parameter	Type
`node`	`Node`
`node_launch_plans`	`Dict[id_models, launch_plan_models.LaunchPlanSpec]`

for_endpoint()

        
    
def for_endpoint(
    endpoint: str,
    insecure: bool,
    data_config: typing.Optional[DataConfig],
    config_file: typing.Union[str, ConfigFile],
    default_project: typing.Optional[str],
    default_domain: typing.Optional[str],
    data_upload_location: str,
    interactive_mode_enabled: bool,
    kwargs,
):

Parameter	Type
`endpoint`	`str`
`insecure`	`bool`
`data_config`	`typing.Optional[DataConfig]`
`config_file`	`typing.Union[str, ConfigFile]`
`default_project`	`typing.Optional[str]`
`default_domain`	`typing.Optional[str]`
`data_upload_location`	`str`
`interactive_mode_enabled`	`bool`
`kwargs`	`**kwargs`

for_sandbox()

        
    
def for_sandbox(
    default_project: typing.Optional[str],
    default_domain: typing.Optional[str],
    data_upload_location: str,
    interactive_mode_enabled: bool,
    kwargs,
):

Parameter	Type
`default_project`	`typing.Optional[str]`
`default_domain`	`typing.Optional[str]`
`data_upload_location`	`str`
`interactive_mode_enabled`	`bool`
`kwargs`	`**kwargs`

generate_console_http_domain()

def generate_console_http_domain()

This should generate the domain where console is hosted.

:return:

generate_console_url()

        
    
def generate_console_url(
    entity: typing.Union[FlyteWorkflowExecution, FlyteNodeExecution, FlyteTaskExecution, FlyteWorkflow, FlyteTask, WorkflowExecutionIdentifier, Identifier, FlyteLaunchPlan],
):

Generate a Flyteconsole URL for the given Flyte remote endpoint. This will automatically determine if this is an execution or an entity and change the type automatically

Parameter	Type
`entity`	`typing.Union[FlyteWorkflowExecution, FlyteNodeExecution, FlyteTaskExecution, FlyteWorkflow, FlyteTask, WorkflowExecutionIdentifier, Identifier, FlyteLaunchPlan]`

get()

        
    
def get(
    flyte_uri: typing.Optional[str],
):

General function that works with flyte tiny urls. This can return outputs (in the form of LiteralsResolver, or individual Literals for singular requests), or HTML if passed a deck link, or bytes containing HTML, if ipython is not available locally.

Parameter	Type
`flyte_uri`	`typing.Optional[str]`

get_domains()

def get_domains()

Lists registered domains from flyte admin.

:returns: typing.List[flytekit.models.domain.Domain]

get_execution_metrics()

        
    
def get_execution_metrics(
    id: WorkflowExecutionIdentifier,
    depth: int,
):

Get the metrics for a given execution.

Parameter	Type
`id`	`WorkflowExecutionIdentifier`
`depth`	`int`

get_extra_headers_for_protocol()

        
    
def get_extra_headers_for_protocol(
    native_url,
):

Parameter	Type
`native_url`

launch_backfill()

        
    
def launch_backfill(
    project: str,
    domain: str,
    from_date: datetime,
    to_date: datetime,
    launchplan: str,
    launchplan_version: str,
    execution_name: str,
    version: str,
    dry_run: bool,
    execute: bool,
    parallel: bool,
    failure_policy: typing.Optional[WorkflowFailurePolicy],
    overwrite_cache: typing.Optional[bool],
):

Creates and launches a backfill workflow for the given launchplan. If launchplan version is not specified, then the latest launchplan is retrieved. The from_date is exclusive and end_date is inclusive and backfill run for all instances in between. :: -> (start_date - exclusive, end_date inclusive)

If dry_run is specified, the workflow is created and returned. If execute==False is specified then the workflow is created and registered. In the last case, the workflow is created, registered and executed.

The parallel flag can be used to generate a workflow where all launchplans can be run in parallel. Default is that execute backfill is run sequentially

Parameter	Type
`project`	`str`
`domain`	`str`
`from_date`	`datetime`
`to_date`	`datetime`
`launchplan`	`str`
`launchplan_version`	`str`
`execution_name`	`str`
`version`	`str`
`dry_run`	`bool`
`execute`	`bool`
`parallel`	`bool`
`failure_policy`	`typing.Optional[WorkflowFailurePolicy]`
`overwrite_cache`	`typing.Optional[bool]`

list_projects()

        
    
def list_projects(
    limit: typing.Optional[int],
    filters: typing.Optional[typing.List[filter_models.Filter]],
    sort_by: typing.Optional[admin_common_models.Sort],
):

Lists registered projects from flyte admin.

Parameter	Type
`limit`	`typing.Optional[int]`
`filters`	`typing.Optional[typing.List[filter_models.Filter]]`
`sort_by`	`typing.Optional[admin_common_models.Sort]`

list_signals()

        
    
def list_signals(
    execution_name: str,
    project: typing.Optional[str],
    domain: typing.Optional[str],
    limit: int,
    filters: typing.Optional[typing.List[filter_models.Filter]],
):

Parameter	Type
`execution_name`	`str`
`project`	`typing.Optional[str]`
`domain`	`typing.Optional[str]`
`limit`	`int`
`filters`	`typing.Optional[typing.List[filter_models.Filter]]`

list_tasks_by_version()

        
    
def list_tasks_by_version(
    version: str,
    project: typing.Optional[str],
    domain: typing.Optional[str],
    limit: typing.Optional[int],
):

Parameter	Type
`version`	`str`
`project`	`typing.Optional[str]`
`domain`	`typing.Optional[str]`
`limit`	`typing.Optional[int]`

raw_register()

        
    
def raw_register(
    cp_entity: FlyteControlPlaneEntity,
    settings: SerializationSettings,
    version: str,
    create_default_launchplan: bool,
    options: Options,
    og_entity: FlyteLocalEntity,
):

Raw register method, can be used to register control plane entities. Usually if you have a Flyte Entity like a WorkflowBase, Task, LaunchPlan then use other methods. This should be used only if you have already serialized entities

Parameter	Type
`cp_entity`	`FlyteControlPlaneEntity`
`settings`	`SerializationSettings`
`version`	`str`
`create_default_launchplan`	`bool`
`options`	`Options`
`og_entity`	`FlyteLocalEntity`

recent_executions()

        
    
def recent_executions(
    project: typing.Optional[str],
    domain: typing.Optional[str],
    limit: typing.Optional[int],
    filters: typing.Optional[typing.List[filter_models.Filter]],
):

Parameter	Type
`project`	`typing.Optional[str]`
`domain`	`typing.Optional[str]`
`limit`	`typing.Optional[int]`
`filters`	`typing.Optional[typing.List[filter_models.Filter]]`

register_launch_plan()

        
    
def register_launch_plan(
    entity: LaunchPlan,
    version: typing.Optional[str],
    project: typing.Optional[str],
    domain: typing.Optional[str],
    options: typing.Optional[Options],
    serialization_settings: typing.Optional[SerializationSettings],
):

Register a given launchplan, possibly applying overrides from the provided options. If the underlying workflow is not already registered, it, along with any underlying entities, will also be registered. If the underlying workflow does exist (with the given project/domain/version), then only the launchplan will be registered.

Parameter	Type
`entity`	`LaunchPlan`
`version`	`typing.Optional[str]`
`project`	`typing.Optional[str]`
`domain`	`typing.Optional[str]`
`options`	`typing.Optional[Options]`
`serialization_settings`	`typing.Optional[SerializationSettings]`

register_script()

        
    
def register_script(
    entity: typing.Union[WorkflowBase, PythonTask, LaunchPlan],
    image_config: typing.Optional[ImageConfig],
    version: typing.Optional[str],
    project: typing.Optional[str],
    domain: typing.Optional[str],
    destination_dir: str,
    copy_all: bool,
    default_launch_plan: bool,
    options: typing.Optional[Options],
    source_path: typing.Optional[str],
    module_name: typing.Optional[str],
    envs: typing.Optional[typing.Dict[str, str]],
    fast_package_options: typing.Optional[FastPackageOptions],
):

Use this method to register a workflow via script mode.

Parameter	Type
`entity`	`typing.Union[WorkflowBase, PythonTask, LaunchPlan]`
`image_config`	`typing.Optional[ImageConfig]`
`version`	`typing.Optional[str]`
`project`	`typing.Optional[str]`
`domain`	`typing.Optional[str]`
`destination_dir`	`str`
`copy_all`	`bool`
`default_launch_plan`	`bool`
`options`	`typing.Optional[Options]`
`source_path`	`typing.Optional[str]`
`module_name`	`typing.Optional[str]`
`envs`	`typing.Optional[typing.Dict[str, str]]`
`fast_package_options`	`typing.Optional[FastPackageOptions]`

register_task()

        
    
def register_task(
    entity: PythonTask,
    serialization_settings: typing.Optional[SerializationSettings],
    version: typing.Optional[str],
):

Register a qualified task (PythonTask) with Remote For any conflicting parameters method arguments are regarded as overrides

Parameter	Type
`entity`	`PythonTask`
`serialization_settings`	`typing.Optional[SerializationSettings]`
`version`	`typing.Optional[str]`

register_workflow()

        
    
def register_workflow(
    entity: WorkflowBase,
    serialization_settings: typing.Optional[SerializationSettings],
    version: typing.Optional[str],
    default_launch_plan: typing.Optional[bool],
    options: typing.Optional[Options],
):

Use this method to register a workflow.

Parameter	Type
`entity`	`WorkflowBase`
`serialization_settings`	`typing.Optional[SerializationSettings]`
`version`	`typing.Optional[str]`
`default_launch_plan`	`typing.Optional[bool]`
`options`	`typing.Optional[Options]`

reject()

        
    
def reject(
    signal_id: str,
    execution_name: str,
    project: str,
    domain: str,
):

Parameter	Type
`signal_id`	`str`
`execution_name`	`str`
`project`	`str`
`domain`	`str`

remote_context()

def remote_context()

Context manager with remote-specific configuration.

set_input()

        
    
def set_input(
    signal_id: str,
    execution_name: str,
    value: typing.Union[literal_models.Literal, typing.Any],
    project,
    domain,
    python_type,
    literal_type,
):

Parameter	Type
`signal_id`	`str`
`execution_name`	`str`
`value`	`typing.Union[literal_models.Literal, typing.Any]`
`project`
`domain`
`python_type`
`literal_type`

set_signal()

        
    
def set_signal(
    signal_id: str,
    execution_name: str,
    value: typing.Union[literal_models.Literal, typing.Any],
    project: typing.Optional[str],
    domain: typing.Optional[str],
    python_type: typing.Optional[typing.Type],
    literal_type: typing.Optional[type_models.LiteralType],
):

Parameter	Type
`signal_id`	`str`
`execution_name`	`str`
`value`	`typing.Union[literal_models.Literal, typing.Any]`
`project`	`typing.Optional[str]`
`domain`	`typing.Optional[str]`
`python_type`	`typing.Optional[typing.Type]`
`literal_type`	`typing.Optional[type_models.LiteralType]`

sync()

        
    
def sync(
    execution: FlyteWorkflowExecution,
    entity_definition: typing.Union[FlyteWorkflow, FlyteTask],
    sync_nodes: bool,
):

This function was previously a singledispatchmethod. We’ve removed that but this function remains so that we don’t break people.

Parameter	Type
`execution`	`FlyteWorkflowExecution`
`entity_definition`	`typing.Union[FlyteWorkflow, FlyteTask]`
`sync_nodes`	`bool`

sync_execution()

        
    
def sync_execution(
    execution: FlyteWorkflowExecution,
    entity_definition: typing.Union[FlyteWorkflow, FlyteTask],
    sync_nodes: bool,
):

Sync a FlyteWorkflowExecution object with its corresponding remote state.

Parameter	Type
`execution`	`FlyteWorkflowExecution`
`entity_definition`	`typing.Union[FlyteWorkflow, FlyteTask]`
`sync_nodes`	`bool`

sync_node_execution()

        
    
def sync_node_execution(
    execution: FlyteNodeExecution,
    node_mapping: typing.Dict[str, FlyteNode],
):

Get data backing a node execution. These FlyteNodeExecution objects should’ve come from Admin with the model fields already populated correctly. For purposes of the remote experience, we’d like to supplement the object with some additional fields:

inputs/outputs
task/workflow executions, and/or underlying node executions in the case of parent nodes
TypedInterface (remote wrapper type)

A node can have several different types of executions behind it. That is, the node could’ve run (perhaps multiple times because of retries):

A task
A static subworkflow
A dynamic subworkflow (which in turn may have run additional tasks, subwfs, and/or launch plans)
A launch plan

The data model is complicated, so ascertaining which of these happened is a bit tricky. That logic is encapsulated in this function.

Parameter	Type
`execution`	`FlyteNodeExecution`
`node_mapping`	`typing.Dict[str, FlyteNode]`

sync_task_execution()

        
    
def sync_task_execution(
    execution: FlyteTaskExecution,
    entity_interface: typing.Optional[TypedInterface],
):

Sync a FlyteTaskExecution object with its corresponding remote state.

Parameter	Type
`execution`	`FlyteTaskExecution`
`entity_interface`	`typing.Optional[TypedInterface]`

terminate()

        
    
def terminate(
    execution: FlyteWorkflowExecution,
    cause: str,
):

Terminate a workflow execution.

Parameter	Type
`execution`	`FlyteWorkflowExecution`
`cause`	`str`

upload_file()

        
    
def upload_file(
    to_upload: pathlib.Path,
    project: typing.Optional[str],
    domain: typing.Optional[str],
    filename_root: typing.Optional[str],
):

Function will use remote’s client to hash and then upload the file using Admin’s data proxy service.

Parameter	Type
`to_upload`	`pathlib.Path`
`project`	`typing.Optional[str]`
`domain`	`typing.Optional[str]`
`filename_root`	`typing.Optional[str]`

wait()

        
    
def wait(
    execution: FlyteWorkflowExecution,
    timeout: typing.Optional[typing.Union[timedelta, int]],
    poll_interval: typing.Optional[typing.Union[timedelta, int]],
    sync_nodes: bool,
):

Wait for an execution to finish.

Parameter	Type
`execution`	`FlyteWorkflowExecution`
`timeout`	`typing.Optional[typing.Union[timedelta, int]]`
`poll_interval`	`typing.Optional[typing.Union[timedelta, int]]`
`sync_nodes`	`bool`

Properties

Property	Type	Description
client
config
context
default_domain
default_project
file_access
interactive_mode_enabled

flytekit.clis.sdk_in_container.helpers.ImageConfig

We recommend you to use ImageConfig.auto(img_name=None) to create an ImageConfig. For example, ImageConfig.auto(img_name=““ghcr.io/flyteorg/flytecookbook:v1.0.0"”) will create an ImageConfig.

ImageConfig holds available images which can be used at registration time. A default image can be specified along with optional additional images. Each image in the config must have a unique name.

Attributes: default_image (Optional[Image]): The default image to be used as a container for task serialization. images (List[Image]): Optional, additional images which can be used in task container definitions.

        
    
def ImageConfig(
    default_image: Optional[Image],
    images: Optional[List[Image]],
):

Parameter	Type
`default_image`	`Optional[Image]`
`images`	`Optional[List[Image]]`

Methods

Method	Description
`auto()`	Reads from config file or from img_name
`auto_default_image()`	None
`create_from()`	None
`find_image()`	Return an image, by name, if it exists
`from_dict()`	None
`from_images()`	Allows you to programmatically create an ImageConfig
`from_json()`	None
`schema()`	None
`to_dict()`	None
`to_json()`	None
`validate_image()`	Validates the image to match the standard format

auto()

        
    
def auto(
    config_file: typing.Union[str, ConfigFile, None],
    img_name: Optional[str],
):

Reads from config file or from img_name Note that this function does not take into account the flytekit default images (see the Dockerfiles at the base of this repo). To pick those up, see the auto_default_image function..

Parameter	Type
`config_file`	`typing.Union[str, ConfigFile, None]`
`img_name`	`Optional[str]`

auto_default_image()

def auto_default_image()

create_from()

        
    
def create_from(
    default_image: Optional[Image],
    other_images: typing.Optional[typing.List[Image]],
):

Parameter	Type
`default_image`	`Optional[Image]`
`other_images`	`typing.Optional[typing.List[Image]]`

find_image()

        
    
def find_image(
    name,
):

Return an image, by name, if it exists.

Parameter	Type
`name`

from_dict()

        
    
def from_dict(
    kvs: typing.Union[dict, list, str, int, float, bool, NoneType],
    infer_missing,
):

Parameter	Type
`kvs`	`typing.Union[dict, list, str, int, float, bool, NoneType]`
`infer_missing`

from_images()

        
    
def from_images(
    default_image: str,
    m: typing.Optional[typing.Dict[str, str]],
):

Allows you to programmatically create an ImageConfig. Usually only the default_image is required, unless your workflow uses multiple images

.. code:: python

ImageConfig.from_dict( “ghcr.io/flyteorg/flytecookbook:v1.0.0”, { “spark”: “ghcr.io/flyteorg/myspark:…”, “other”: “…”, } )

urn:

Parameter	Type
`default_image`	`str`
`m`	`typing.Optional[typing.Dict[str, str]]`

from_json()

        
    
def from_json(
    s: typing.Union[str, bytes, bytearray],
    parse_float,
    parse_int,
    parse_constant,
    infer_missing,
    kw,
):

Parameter	Type
`s`	`typing.Union[str, bytes, bytearray]`
`parse_float`
`parse_int`
`parse_constant`
`infer_missing`
`kw`

schema()

        
    
def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
):

Parameter	Type
`infer_missing`	`bool`
`only`
`exclude`
`many`	`bool`
`context`
`load_only`
`dump_only`
`partial`	`bool`
`unknown`

to_dict()

        
    
def to_dict(
    encode_json,
):

Parameter	Type
`encode_json`

to_json()

        
    
def to_json(
    skipkeys: bool,
    ensure_ascii: bool,
    check_circular: bool,
    allow_nan: bool,
    indent: typing.Union[int, str, NoneType],
    separators: typing.Tuple[str, str],
    default: typing.Callable,
    sort_keys: bool,
    kw,
):

Parameter	Type
`skipkeys`	`bool`
`ensure_ascii`	`bool`
`check_circular`	`bool`
`allow_nan`	`bool`
`indent`	`typing.Union[int, str, NoneType]`
`separators`	`typing.Tuple[str, str]`
`default`	`typing.Callable`
`sort_keys`	`bool`
`kw`

validate_image()

        
    
def validate_image(
    _: typing.Any,
    param: str,
    values: tuple,
):

Validates the image to match the standard format. Also validates that only one default image is provided. a default image, is one that is specified as default=<image_uri> or just <image_uri>. All other images should be provided with a name, in the format name=<image_uri> This method can be used with the CLI

Parameter	Type
`_`	`typing.Any`
`param`	`str`
`values`	`tuple`

On this page

flytekit.clis.sdk_in_container.helpers

Directory

Classes

flytekit.clis.sdk_in_container.helpers.CopyFileDetection

flytekit.clis.sdk_in_container.helpers.FlyteRemote

Methods

activate_launchplan()

approve()

auto()

download()

execute()

execute_local_launch_plan()

execute_local_task()

execute_local_workflow()

execute_reference_launch_plan()

execute_reference_task()

execute_reference_workflow()

execute_remote_task_lp()

execute_remote_wf()

fast_package()

fast_register_workflow()

fetch_active_launchplan()

fetch_execution()

fetch_launch_plan()

fetch_task()

fetch_task_lazy()

fetch_workflow()

fetch_workflow_lazy()

find_launch_plan()

find_launch_plan_for_node()

for_endpoint()

for_sandbox()

generate_console_http_domain()

generate_console_url()

get()

get_domains()

get_execution_metrics()

get_extra_headers_for_protocol()

launch_backfill()

list_projects()

list_signals()

list_tasks_by_version()

raw_register()

recent_executions()

register_launch_plan()

register_script()

register_task()

register_workflow()

reject()

remote_context()

set_input()

set_signal()

sync()

sync_execution()

sync_node_execution()

sync_task_execution()

terminate()

upload_file()

wait()

Properties

flytekit.clis.sdk_in_container.helpers.ImageConfig

Methods

auto()

auto_default_image()

create_from()

find_image()

from_dict()

from_images()

from_json()

schema()

to_dict()

to_json()

validate_image()