1.15.4.dev2+g3e3ce2426

flytekit.experimental.eager_function

Class	Description
`DataConfig`	Any data storage specific configuration.
`ExecutionState`	This is the context that is active when executing a task or a local workflow.
`FlyteContext`	This is an internal-facing context object, that most users will not have to deal with.
`FlyteContextManager`	FlyteContextManager manages the execution context within Flytekit.
`FlyteRemote`	Main entrypoint for programmatically accessing a Flyte remote backend.
`PlatformConfig`	This object contains the settings to talk to a Flyte backend (the DNS location of your Admin server basically).
`S3Config`	S3 specific configuration.

flytekit.experimental.eager_function.DataConfig

Any data storage specific configuration. Please do not use this to store secrets, in S3 case, as it is used in Flyte sandbox environment we store the access key id and secret. All DataPersistence plugins are passed all DataConfig and the plugin should correctly use the right config

        
    
def DataConfig(
    s3: S3Config,
    gcs: GCSConfig,
    azure: AzureBlobStorageConfig,
    generic: GenericPersistenceConfig,
):

Parameter	Type
`s3`	`S3Config`
`gcs`	`GCSConfig`
`azure`	`AzureBlobStorageConfig`
`generic`	`GenericPersistenceConfig`

Methods

Method	Description
`auto()`	None

auto()

        
    
def auto(
    config_file: typing.Union[str, ConfigFile],
):

Parameter	Type
`config_file`	`typing.Union[str, ConfigFile]`

flytekit.experimental.eager_function.ExecutionState

This is the context that is active when executing a task or a local workflow. This carries the necessary state to execute. Some required things during execution deal with temporary directories, ExecutionParameters that are passed to the user etc.

Attributes: mode (ExecutionState.Mode): Defines the context in which the task is executed (local, hosted, etc). working_dir (os.PathLike): Specifies the remote, external directory where inputs, outputs and other protobufs are uploaded engine_dir (os.PathLike): branch_eval_mode Optional[BranchEvalMode]: Used to determine whether a branch node should execute. user_space_params Optional[ExecutionParameters]: Provides run-time, user-centric context such as a statsd handler, a logging handler, the current execution id and a working directory.

        
    
def ExecutionState(
    working_dir: Union[os.PathLike, str],
    mode: Optional[ExecutionState.Mode],
    engine_dir: Optional[Union[os.PathLike, str]],
    branch_eval_mode: Optional[BranchEvalMode],
    user_space_params: Optional[ExecutionParameters],
):

Parameter	Type
`working_dir`	`Union[os.PathLike, str]`
`mode`	`Optional[ExecutionState.Mode]`
`engine_dir`	`Optional[Union[os.PathLike, str]]`
`branch_eval_mode`	`Optional[BranchEvalMode]`
`user_space_params`	`Optional[ExecutionParameters]`

Methods

Method	Description
`branch_complete()`	Indicates that we are within a conditional / ifelse block and the active branch is not done
`is_local_execution()`	None
`take_branch()`	Indicates that we are within an if-else block and the current branch has evaluated to true
`with_params()`	Produces a copy of the current execution state and overrides the copy’s parameters with passed parameter values

branch_complete()

def branch_complete()

Indicates that we are within a conditional / ifelse block and the active branch is not done. Default to SKIPPED

is_local_execution()

def is_local_execution()

take_branch()

def take_branch()

Indicates that we are within an if-else block and the current branch has evaluated to true. Useful only in local execution mode

with_params()

        
    
def with_params(
    working_dir: Optional[os.PathLike],
    mode: Optional[Mode],
    engine_dir: Optional[os.PathLike],
    branch_eval_mode: Optional[BranchEvalMode],
    user_space_params: Optional[ExecutionParameters],
):

Produces a copy of the current execution state and overrides the copy’s parameters with passed parameter values.

Parameter	Type
`working_dir`	`Optional[os.PathLike]`
`mode`	`Optional[Mode]`
`engine_dir`	`Optional[os.PathLike]`
`branch_eval_mode`	`Optional[BranchEvalMode]`
`user_space_params`	`Optional[ExecutionParameters]`

flytekit.experimental.eager_function.FlyteContext

This is an internal-facing context object, that most users will not have to deal with. It’s essentially a globally available grab bag of settings and objects that allows flytekit to do things like convert complex types, run and compile workflows, serialize Flyte entities, etc.

Even though this object as a current_context function on it, it should not be called directly. Please use the :py:class:flytekit.FlyteContextManager object instead.

Please do not confuse this object with the :py:class:flytekit.ExecutionParameters object.

        
    
def FlyteContext(
    file_access: FileAccessProvider,
    level: int,
    flyte_client: Optional['friendly_client.SynchronousFlyteClient'],
    compilation_state: Optional[CompilationState],
    execution_state: Optional[ExecutionState],
    serialization_settings: Optional[SerializationSettings],
    in_a_condition: bool,
    origin_stackframe: Optional[traceback.FrameSummary],
    output_metadata_tracker: Optional[OutputMetadataTracker],
    worker_queue: Optional[Controller],
):

Parameter	Type
`file_access`	`FileAccessProvider`
`level`	`int`
`flyte_client`	`Optional['friendly_client.SynchronousFlyteClient']`
`compilation_state`	`Optional[CompilationState]`
`execution_state`	`Optional[ExecutionState]`
`serialization_settings`	`Optional[SerializationSettings]`
`in_a_condition`	`bool`
`origin_stackframe`	`Optional[traceback.FrameSummary]`
`output_metadata_tracker`	`Optional[OutputMetadataTracker]`
`worker_queue`	`Optional[Controller]`

Methods

Method	Description
`current_context()`	This method exists only to maintain backwards compatibility
`enter_conditional_section()`	None
`get_deck()`	Returns the deck that was created as part of the last execution
`get_origin_stackframe_repr()`	None
`new_builder()`	None
`new_compilation_state()`	Creates and returns a default compilation state
`new_execution_state()`	Creates and returns a new default execution state
`set_stackframe()`	None
`with_client()`	None
`with_compilation_state()`	None
`with_execution_state()`	None
`with_file_access()`	None
`with_new_compilation_state()`	None
`with_output_metadata_tracker()`	None
`with_serialization_settings()`	None
`with_worker_queue()`	None

current_context()

def current_context()

This method exists only to maintain backwards compatibility. Please use FlyteContextManager.current_context() instead.

Users of flytekit should be wary not to confuse the object returned from this function with :py:func:flytekit.current_context

enter_conditional_section()

def enter_conditional_section()

get_deck()

def get_deck()

Returns the deck that was created as part of the last execution.

The return value depends on the execution environment. In a notebook, the return value is compatible with IPython.display and should be rendered in the notebook.

.. code-block:: python

with flytekit.new_context() as ctx: my_task(…) ctx.get_deck()

OR if you wish to explicitly display

.. code-block:: python

from IPython import display display(ctx.get_deck())

get_origin_stackframe_repr()

def get_origin_stackframe_repr()

new_builder()

def new_builder()

new_compilation_state()

        
    
def new_compilation_state(
    prefix: str,
):

Creates and returns a default compilation state. For most of the code this should be the entrypoint of compilation, otherwise the code should always uses - with_compilation_state

Parameter	Type
`prefix`	`str`

new_execution_state()

        
    
def new_execution_state(
    working_dir: Optional[os.PathLike],
):

Creates and returns a new default execution state. This should be used at the entrypoint of execution, in all other cases it is preferable to use with_execution_state

Parameter	Type
`working_dir`	`Optional[os.PathLike]`

set_stackframe()

        
    
def set_stackframe(
    s: traceback.FrameSummary,
):

Parameter	Type
`s`	`traceback.FrameSummary`

with_client()

        
    
def with_client(
    c: SynchronousFlyteClient,
):

Parameter	Type
`c`	`SynchronousFlyteClient`

with_compilation_state()

        
    
def with_compilation_state(
    c: CompilationState,
):

Parameter	Type
`c`	`CompilationState`

with_execution_state()

        
    
def with_execution_state(
    es: ExecutionState,
):

Parameter	Type
`es`	`ExecutionState`

with_file_access()

        
    
def with_file_access(
    fa: FileAccessProvider,
):

Parameter	Type
`fa`	`FileAccessProvider`

with_new_compilation_state()

def with_new_compilation_state()

with_output_metadata_tracker()

        
    
def with_output_metadata_tracker(
    t: OutputMetadataTracker,
):

Parameter	Type
`t`	`OutputMetadataTracker`

with_serialization_settings()

        
    
def with_serialization_settings(
    ss: SerializationSettings,
):

Parameter	Type
`ss`	`SerializationSettings`

with_worker_queue()

        
    
def with_worker_queue(
    wq: Controller,
):

Parameter	Type
`wq`	`Controller`

Properties

Property	Type	Description
user_space_params

flytekit.experimental.eager_function.FlyteContextManager

FlyteContextManager manages the execution context within Flytekit. It holds global state of either compilation or Execution. It is not thread-safe and can only be run as a single threaded application currently. Context’s within Flytekit is useful to manage compilation state and execution state. Refer to CompilationState and ExecutionState for more information. FlyteContextManager provides a singleton stack to manage these contexts.

Typical usage is

.. code-block:: python

FlyteContextManager.initialize() with FlyteContextManager.with_context(o) as ctx: pass

If required - not recommended you can use

FlyteContextManager.push_context()

but correspondingly a pop_context should be called

FlyteContextManager.pop_context()

Methods

Method	Description
`add_signal_handler()`	None
`current_context()`	None
`get_origin_stackframe()`	None
`initialize()`	Re-initializes the context and erases the entire context
`pop_context()`	None
`push_context()`	None
`size()`	None
`with_context()`	None

add_signal_handler()

        
    
def add_signal_handler(
    handler: typing.Callable[[int, FrameType], typing.Any],
):

Parameter	Type
`handler`	`typing.Callable[[int, FrameType], typing.Any]`

current_context()

def current_context()

get_origin_stackframe()

        
    
def get_origin_stackframe(
    limit,
):

Parameter	Type
`limit`

initialize()

def initialize()

Re-initializes the context and erases the entire context

pop_context()

def pop_context()

push_context()

        
    
def push_context(
    ctx: FlyteContext,
    f: Optional[traceback.FrameSummary],
):

Parameter	Type
`ctx`	`FlyteContext`
`f`	`Optional[traceback.FrameSummary]`

size()

def size()

with_context()

        
    
def with_context(
    b: FlyteContext.Builder,
):

Parameter	Type
`b`	`FlyteContext.Builder`

flytekit.experimental.eager_function.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.experimental.eager_function.PlatformConfig

This object contains the settings to talk to a Flyte backend (the DNS location of your Admin server basically).

        
    
def PlatformConfig(
    endpoint: str,
    insecure: bool,
    insecure_skip_verify: bool,
    ca_cert_file_path: typing.Optional[str],
    console_endpoint: typing.Optional[str],
    command: typing.Optional[typing.List[str]],
    proxy_command: typing.Optional[typing.List[str]],
    client_id: typing.Optional[str],
    client_credentials_secret: typing.Optional[str],
    scopes: List[str],
    auth_mode: AuthType,
    audience: typing.Optional[str],
    rpc_retries: int,
    http_proxy_url: typing.Optional[str],
):

Parameter	Type
`endpoint`	`str`
`insecure`	`bool`
`insecure_skip_verify`	`bool`
`ca_cert_file_path`	`typing.Optional[str]`
`console_endpoint`	`typing.Optional[str]`
`command`	`typing.Optional[typing.List[str]]`
`proxy_command`	`typing.Optional[typing.List[str]]`
`client_id`	`typing.Optional[str]`
`client_credentials_secret`	`typing.Optional[str]`
`scopes`	`List[str]`
`auth_mode`	`AuthType`
`audience`	`typing.Optional[str]`
`rpc_retries`	`int`
`http_proxy_url`	`typing.Optional[str]`

Methods

Method	Description
`auto()`	Reads from Config file, and overrides from Environment variables
`for_endpoint()`	None

auto()

        
    
def auto(
    config_file: typing.Optional[typing.Union[str, ConfigFile]],
):

Reads from Config file, and overrides from Environment variables. Refer to ConfigEntry for details

Parameter	Type
`config_file`	`typing.Optional[typing.Union[str, ConfigFile]]`

for_endpoint()

        
    
def for_endpoint(
    endpoint: str,
    insecure: bool,
):

Parameter	Type
`endpoint`	`str`
`insecure`	`bool`

flytekit.experimental.eager_function.S3Config

S3 specific configuration

        
    
def S3Config(
    enable_debug: bool,
    endpoint: typing.Optional[str],
    retries: int,
    backoff: datetime.timedelta,
    access_key_id: typing.Optional[str],
    secret_access_key: typing.Optional[str],
):

Parameter	Type
`enable_debug`	`bool`
`endpoint`	`typing.Optional[str]`
`retries`	`int`
`backoff`	`datetime.timedelta`
`access_key_id`	`typing.Optional[str]`
`secret_access_key`	`typing.Optional[str]`

Methods

Method	Description
`auto()`	Automatically configure

auto()

        
    
def auto(
    config_file: typing.Union[str, ConfigFile],
):

Automatically configure

Parameter	Type
`config_file`	`typing.Union[str, ConfigFile]`

On this page

flytekit.experimental.eager_function

Directory

Classes

flytekit.experimental.eager_function.DataConfig

Methods

auto()

flytekit.experimental.eager_function.ExecutionState

Methods

branch_complete()

is_local_execution()

take_branch()

with_params()

flytekit.experimental.eager_function.FlyteContext

Methods

current_context()

enter_conditional_section()

get_deck()

get_origin_stackframe_repr()

new_builder()

new_compilation_state()

new_execution_state()

set_stackframe()

with_client()

with_compilation_state()

with_execution_state()

with_file_access()

with_new_compilation_state()

with_output_metadata_tracker()

with_serialization_settings()

with_worker_queue()

Properties

flytekit.experimental.eager_function.FlyteContextManager

If required - not recommended you can use

but correspondingly a pop_context should be called

Methods

add_signal_handler()

current_context()

get_origin_stackframe()

initialize()

pop_context()

push_context()

size()

with_context()

flytekit.experimental.eager_function.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()