union.remote

Class	Description
`HuggingFaceModelInfo`	Captures information about a Hugging Face model.
`UnionRemote`	Main entrypoint for programmatically accessing a Flyte remote backend.

union.remote.HuggingFaceModelInfo

Captures information about a Hugging Face model. Only repo is required, all other fields are optional, and are automatically determined from the model’s config.json file. If not found, the fields are initialized to defaults.

        
    
class HuggingFaceModelInfo(
    repo: str,
    model_type: str | None,
    architecture: str | None,
    task: str,
    modality: typing.List[str] | None,
    serial_format: str,
    short_description: str | None,
)

Parameter	Type
`repo`	`str`
`model_type`	`str \| None`
`architecture`	`str \| None`
`task`	`str`
`modality`	`typing.List[str] \| None`
`serial_format`	`str`
`short_description`	`str \| None`

union.remote.UnionRemote

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.

        
    
class UnionRemote(
    config: typing.Optional[Union[Config, str]],
    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`	`typing.Optional[Union[Config, str]]`
`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()`	.
`async_channel()`
`auto()`
`create_artifact()`	Create an artifact in FlyteAdmin.
`deploy_app()`	Deploy an application.
`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()`
`find_launch_plan_for_node()`
`for_endpoint()`
`for_sandbox()`
`from_api_key()`	Call this if you want to directly instantiate a UnionRemote from an API key.
`generate_console_http_domain()`	This should generate the domain where console is hosted.
`generate_console_url()`	Generate a UnionAI console URL for the given Flyte remote endpoint.
`get()`	General function that works with flyte tiny urls.
`get_artifact()`	Get the specified artifact.
`get_domains()`	Lists registered domains from flyte admin.
`get_execution_metrics()`	Get the metrics for a given execution.
`get_extra_headers_for_protocol()`
`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()`
`raw_register()`	Raw register method, can be used to register control plane entities.
`recent_executions()`
`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.
`search_artifacts()`
`set_input()`	.
`set_signal()`	.
`stop_app()`	Stop an application.
`stream_execution_events()`	Stream execution events from the given tenant.
`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`

async_channel()

def async_channel()

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,
) -> 'FlyteRemote'

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`

create_artifact()

        
    
def create_artifact(
    artifact: Artifact,
) -> n: The artifact as persisted in the service.

Create an artifact in FlyteAdmin.

Parameter	Type
`artifact`	`Artifact`

deploy_app()

        
    
def deploy_app(
    app: App,
    project: Optional[str],
    domain: Optional[str],
) -> n: The App IDL for the deployed application.

Deploy an application.

Parameter	Type
`app`	`App`
`project`	`Optional[str]`
`domain`	`Optional[str]`

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],
) -> FlyteWorkflowExecution

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],
) -> n: FlyteWorkflowExecution object

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],
) -> n: FlyteWorkflowExecution object.

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],
) -> n: FlyteWorkflowExecution object

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],
) -> FlyteWorkflowExecution

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],
) -> FlyteWorkflowExecution

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],
) -> FlyteWorkflowExecution

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],
) -> FlyteWorkflowExecution

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],
) -> FlyteWorkflowExecution

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],
) -> n: md5_bytes, url

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],
) -> n:

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,
) -> typing.Optional[FlyteLaunchPlan]

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,
) -> FlyteWorkflowExecution

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,
) -> FlyteLaunchPlan

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,
) -> FlyteTask

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,
) -> LazyEntity

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,
) -> FlyteWorkflow

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,
) -> LazyEntity[FlyteWorkflow]

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,
) -> 'FlyteRemote'

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,
) -> 'FlyteRemote'

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

from_api_key()

        
    
def from_api_key(
    api_key: str,
    default_project: typing.Optional[str],
    default_domain: typing.Optional[str],
    data_upload_location: str,
    kwargs,
) -> 'UnionRemote'

Call this if you want to directly instantiate a UnionRemote from an API key

Parameter	Type
`api_key`	`str`
`default_project`	`typing.Optional[str]`
`default_domain`	`typing.Optional[str]`
`data_upload_location`	`str`
`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, FlyteLaunchPlan, Artifact],
)

Generate a UnionAI console URL for the given Flyte remote endpoint. It will also handle Union AI specific entities like Artifacts.

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, FlyteLaunchPlan, Artifact]`

get()

        
    
def get(
    uri: typing.Optional[str],
) -> typing.Optional[typing.Union[LiteralsResolver, Literal, bytes]]

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
`uri`	`typing.Optional[str]`

get_artifact()

        
    
def get_artifact(
    uri: typing.Optional[str],
    artifact_key: typing.Optional[art_id.ArtifactKey],
    artifact_id: typing.Optional[art_id.ArtifactID],
    query: typing.Optional[typing.Union[art_id.ArtifactQuery, ArtifactQuery]],
    get_details: bool,
) -> n: The artifact as persisted in the service.

Get the specified artifact.

Parameter	Type
`uri`	`typing.Optional[str]`
`artifact_key`	`typing.Optional[art_id.ArtifactKey]`
`artifact_id`	`typing.Optional[art_id.ArtifactID]`
`query`	`typing.Optional[typing.Union[art_id.ArtifactQuery, ArtifactQuery]]`
`get_details`	`bool`

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,
) -> FlyteExecutionSpan

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],
) -> n: In case of dry-run, return WorkflowBase, else if no_execute return FlyteWorkflow else in the default

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],
) -> typing.List[Project]

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]],
) -> typing.List[Signal]

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],
) -> typing.List[FlyteTask]

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,
) -> n: Identifier of the created entity

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]],
) -> typing.List[FlyteWorkflowExecution]

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],
) -> FlyteLaunchPlan

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],
) -> n:

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],
) -> n:

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],
) -> n:

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.

search_artifacts()

        
    
def search_artifacts(
    project: typing.Optional[str],
    domain: typing.Optional[str],
    name: typing.Optional[str],
    artifact_key: typing.Optional[art_id.ArtifactKey],
    query: typing.Optional[ArtifactQuery],
    partitions: typing.Optional[Union[Partitions, typing.Dict[str, str]]],
    time_partition: typing.Optional[Union[datetime.datetime, TimePartition]],
    group_by_key: bool,
    limit: int,
) -> typing.List[Artifact]

Parameter	Type
`project`	`typing.Optional[str]`
`domain`	`typing.Optional[str]`
`name`	`typing.Optional[str]`
`artifact_key`	`typing.Optional[art_id.ArtifactKey]`
`query`	`typing.Optional[ArtifactQuery]`
`partitions`	`typing.Optional[Union[Partitions, typing.Dict[str, str]]]`
`time_partition`	`typing.Optional[Union[datetime.datetime, TimePartition]]`
`group_by_key`	`bool`
`limit`	`int`

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]`

stop_app()

        
    
def stop_app(
    name: str,
    project: Optional[str],
    domain: Optional[str],
) -> n: The App IDL for the stopped application.

Stop an application.

Parameter	Type
`name`	`str`
`project`	`Optional[str]`
`domain`	`Optional[str]`

stream_execution_events()

        
    
def stream_execution_events(
    event_count: Optional[int],
    include_workflow_executions: bool,
    include_task_executions: bool,
    include_node_executions: bool,
) -> AsyncGenerator[Union[CloudEventWorkflowExecution, CloudEventNodeExecution, CloudEventTaskExecution], None]

Stream execution events from the given tenant. This is a generator that yields events as they are received.

Events are guaranteed to be delivered at least once, and clients must implement handling for potentially out-of-order event processing. Events will be retransmitted until acknowledged, with acknowledgment occurring automatically upon normal return from the caller. Note: if an exception is raised during event processing, the acknowledgment will not occur, and the event will be redelivered in a subsequent transmission.

Parameter	Type
`event_count`	`Optional[int]`
`include_workflow_executions`	`bool`
`include_task_executions`	`bool`
`include_node_executions`	`bool`

sync()

        
    
def sync(
    execution: FlyteWorkflowExecution,
    entity_definition: typing.Union[FlyteWorkflow, FlyteTask],
    sync_nodes: bool,
) -> n: Returns the same execution object, but with additional information pulled in.

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,
) -> FlyteWorkflowExecution

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],
) -> FlyteNodeExecution

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],
) -> FlyteTaskExecution

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],
) -> n: The uploaded location.

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,
) -> FlyteWorkflowExecution

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
`apps_service_client`
`artifacts_client`
`client`		Return a SynchronousFlyteClient for additional operations.
`config`		Image config.
`context`
`default_domain`		Default project to use when fetching or executing flyte entities.
`default_project`		Default project to use when fetching or executing flyte entities.
`file_access`		File access provider to use for offloading non-literal inputs/outputs.
`hooks_async_client`
`hooks_sync_client`
`images_client`
`interactive_mode_enabled`		If set to True, the FlyteRemote will pickle the task/workflow.
`secret_client`
`sync_channel`		Return channel from client. This channel already has the org passed in dynamically by the interceptor.
`users_client`

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