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prefect.context

Async and thread safe models for passing runtime context data. These contexts should never be directly mutated by the user. For more user-accessible information about the current run, see prefect.runtime.

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Functions

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serialize_context 

serialize_context(asset_ctx_kwargs: Union[dict[str, Any], None] = None) -> dict[str, Any]
Serialize the current context for use in a remote execution environment. Optionally provide asset_ctx_kwargs to create new AssetContext, that will be used in the remote execution environment. This is useful for TaskRunners, who rely on creating the task run in the remote environment.

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hydrated_context 

hydrated_context(serialized_context: Optional[dict[str, Any]] = None, client: Union[PrefectClient, SyncPrefectClient, None] = None) -> Generator[None, Any, None]

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get_run_context 

get_run_context() -> Union[FlowRunContext, TaskRunContext]
Get the current run context from within a task or flow function. Returns:
  • A FlowRunContext or TaskRunContext depending on the function type.
Raises:
  • RuntimeError: If called outside of a flow or task run.

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get_settings_context 

get_settings_context() -> SettingsContext
Get the current settings context which contains profile information and the settings that are being used. Generally, the settings that are being used are a combination of values from the profile and environment. See prefect.context.use_profile for more details.

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tags 

tags(*new_tags: str) -> Generator[set[str], None, None]
Context manager to add tags to flow and task run calls. Tags are always combined with any existing tags. Examples: 
from prefect import tags, task, flow
@task
def my_task():
    pass
Run a task with tags 
@flow
def my_flow():
    with tags("a", "b"):
        my_task()  # has tags: a, b
Run a flow with tags 
@flow
def my_flow():
    pass
with tags("a", "b"):
    my_flow()  # has tags: a, b
Run a task with nested tag contexts 
@flow
def my_flow():
    with tags("a", "b"):
        with tags("c", "d"):
            my_task()  # has tags: a, b, c, d
        my_task()  # has tags: a, b
Inspect the current tags 
@flow
def my_flow():
    with tags("c", "d"):
        with tags("e", "f") as current_tags:
             print(current_tags)
with tags("a", "b"):
    my_flow()
# {"a", "b", "c", "d", "e", "f"}

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use_profile 

use_profile(profile: Union[Profile, str], override_environment_variables: bool = False, include_current_context: bool = True) -> Generator[SettingsContext, Any, None]
Switch to a profile for the duration of this context. Profile contexts are confined to an async context in a single thread. Args:
  • profile: The name of the profile to load or an instance of a Profile.
  • override_environment_variable: If set, variables in the profile will take precedence over current environment variables. By default, environment variables will override profile settings.
  • include_current_context: If set, the new settings will be constructed with the current settings context as a base. If not set, the use_base settings will be loaded from the environment and defaults.

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root_settings_context 

root_settings_context() -> SettingsContext
Return the settings context that will exist as the root context for the module. The profile to use is determined with the following precedence
  • Command line via ‘prefect —profile <name>’
  • Environment variable via ‘PREFECT_PROFILE’
  • Profiles file via the ‘active’ key

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Classes

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ContextModel

A base model for context data that forbids mutation and extra data while providing a context manager Methods:

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get 

get(cls: type[Self]) -> Optional[Self]
Get the current context instance

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model_copy 

model_copy(self: Self) -> Self
Duplicate the context model, optionally choosing which fields to include, exclude, or change. Attributes:
  • include: Fields to include in new model.
  • exclude: Fields to exclude from new model, as with values this takes precedence over include.
  • update: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data.
  • deep: Set to True to make a deep copy of the model.
Returns:
  • A new model instance.

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serialize 

serialize(self, include_secrets: bool = True) -> dict[str, Any]
Serialize the context model to a dictionary that can be pickled with cloudpickle.

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SyncClientContext

A context for managing the sync Prefect client instances. Clients were formerly tracked on the TaskRunContext and FlowRunContext, but having two separate places and the addition of both sync and async clients made it difficult to manage. This context is intended to be the single source for sync clients. The client creates a sync client, which can either be read directly from the context object OR loaded with get_client, inject_client, or other Prefect utilities. with SyncClientContext.get_or_create() as ctx: c1 = get_client(sync_client=True) c2 = get_client(sync_client=True) assert c1 is c2 assert c1 is ctx.client Methods:

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get 

get(cls: type[Self]) -> Optional[Self]
Get the current context instance

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get_or_create 

get_or_create(cls) -> Generator[Self, None, None]

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model_copy 

model_copy(self: Self) -> Self
Duplicate the context model, optionally choosing which fields to include, exclude, or change. Attributes:
  • include: Fields to include in new model.
  • exclude: Fields to exclude from new model, as with values this takes precedence over include.
  • update: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data.
  • deep: Set to True to make a deep copy of the model.
Returns:
  • A new model instance.

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serialize 

serialize(self, include_secrets: bool = True) -> dict[str, Any]
Serialize the context model to a dictionary that can be pickled with cloudpickle.

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AsyncClientContext

A context for managing the async Prefect client instances. Clients were formerly tracked on the TaskRunContext and FlowRunContext, but having two separate places and the addition of both sync and async clients made it difficult to manage. This context is intended to be the single source for async clients. The client creates an async client, which can either be read directly from the context object OR loaded with get_client, inject_client, or other Prefect utilities. with AsyncClientContext.get_or_create() as ctx: c1 = get_client(sync_client=False) c2 = get_client(sync_client=False) assert c1 is c2 assert c1 is ctx.client Methods:

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get 

get(cls: type[Self]) -> Optional[Self]
Get the current context instance

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get_or_create 

get_or_create(cls) -> AsyncGenerator[Self, None]

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model_copy 

model_copy(self: Self) -> Self
Duplicate the context model, optionally choosing which fields to include, exclude, or change. Attributes:
  • include: Fields to include in new model.
  • exclude: Fields to exclude from new model, as with values this takes precedence over include.
  • update: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data.
  • deep: Set to True to make a deep copy of the model.
Returns:
  • A new model instance.

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serialize 

serialize(self, include_secrets: bool = True) -> dict[str, Any]
Serialize the context model to a dictionary that can be pickled with cloudpickle.

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RunContext

The base context for a flow or task run. Data in this context will always be available when get_run_context is called. Attributes:
  • start_time: The time the run context was entered
  • client: The Prefect client instance being used for API communication
Methods:

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get 

get(cls: type[Self]) -> Optional[Self]
Get the current context instance

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model_copy 

model_copy(self: Self) -> Self
Duplicate the context model, optionally choosing which fields to include, exclude, or change. Attributes:
  • include: Fields to include in new model.
  • exclude: Fields to exclude from new model, as with values this takes precedence over include.
  • update: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data.
  • deep: Set to True to make a deep copy of the model.
Returns:
  • A new model instance.

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serialize 

serialize(self: Self, include_secrets: bool = True) -> dict[str, Any]

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serialize 

serialize(self, include_secrets: bool = True) -> dict[str, Any]
Serialize the context model to a dictionary that can be pickled with cloudpickle.

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EngineContext

The context for a flow run. Data in this context is only available from within a flow run function. Attributes:
  • flow: The flow instance associated with the run
  • flow_run: The API metadata for the flow run
  • task_runner: The task runner instance being used for the flow run
  • run_results: A mapping of result ids to run states for this flow run
  • log_prints: Whether to log print statements from the flow run
  • parameters: The parameters passed to the flow run
  • detached: Flag indicating if context has been serialized and sent to remote infrastructure
  • result_store: The result store used to persist results
  • persist_result: Whether to persist the flow run result
  • task_run_dynamic_keys: Counter for task calls allowing unique keys
  • observed_flow_pauses: Counter for flow pauses
  • events: Events worker to emit events
Methods:

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serialize 

serialize(self: Self, include_secrets: bool = True) -> dict[str, Any]

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serialize 

serialize(self: Self, include_secrets: bool = True) -> dict[str, Any]

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TaskRunContext

The context for a task run. Data in this context is only available from within a task run function. Attributes:
  • task: The task instance associated with the task run
  • task_run: The API metadata for this task run
Methods:

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serialize 

serialize(self: Self, include_secrets: bool = True) -> dict[str, Any]

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serialize 

serialize(self: Self, include_secrets: bool = True) -> dict[str, Any]

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AssetContext

The asset context for a materializing task run. Contains all asset-related information needed for asset event emission and downstream asset dependency propagation. Attributes:
  • direct_asset_dependencies: Assets that this task directly depends on (from task.asset_deps)
  • downstream_assets: Assets that this task will create/materialize (from MaterializingTask.assets)
  • upstream_assets: Assets from upstream task dependencies
  • materialized_by: Tool that materialized the assets (from MaterializingTask.materialized_by)
  • task_run_id: ID of the associated task run
  • materialization_metadata: Metadata for materialized assets
Methods:

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add_asset_metadata 

add_asset_metadata(self, asset_key: str, metadata: dict[str, Any]) -> None
Add metadata for a materialized asset. Args:
  • asset_key: The asset key
  • metadata: Metadata dictionary to add
Raises:
  • ValueError: If asset_key is not in downstream_assets
asset_as_related(asset: Asset) -> dict[str, str]
Convert Asset to event related format.

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asset_as_resource 

asset_as_resource(asset: Asset) -> dict[str, str]
Convert Asset to event resource format.

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emit_events 

emit_events(self, state: State) -> None
Emit asset events

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from_task_and_inputs 

from_task_and_inputs(cls, task: 'Task[Any, Any]', task_run_id: UUID, task_inputs: Optional[dict[str, set[Any]]] = None, copy_to_child_ctx: bool = False) -> 'AssetContext'
Create an AssetContext from a task and its resolved inputs. Args:
  • task: The task instance
  • task_run_id: The task run ID
  • task_inputs: The resolved task inputs (TaskRunResult objects)
  • copy_to_child_ctx: Whether this context should be copied on a child AssetContext
Returns:
  • Configured AssetContext

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get 

get(cls: type[Self]) -> Optional[Self]
Get the current context instance

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model_copy 

model_copy(self: Self) -> Self
Duplicate the context model, optionally choosing which fields to include, exclude, or change. Attributes:
  • include: Fields to include in new model.
  • exclude: Fields to exclude from new model, as with values this takes precedence over include.
  • update: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data.
  • deep: Set to True to make a deep copy of the model.
Returns:
  • A new model instance.
related_materialized_by(by: str) -> dict[str, str]
Create a related resource for the tool that performed the materialization

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serialize 

serialize(self: Self, include_secrets: bool = True) -> dict[str, Any]
Serialize the AssetContext for distributed execution.

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serialize 

serialize(self, include_secrets: bool = True) -> dict[str, Any]
Serialize the context model to a dictionary that can be pickled with cloudpickle.

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update_tracked_assets 

update_tracked_assets(self) -> None
Update the flow run context with assets that should be propagated downstream.

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TagsContext

The context for prefect.tags management. Attributes:
  • current_tags: A set of current tags in the context
Methods:

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get 

get(cls) -> Self

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get 

get(cls: type[Self]) -> Optional[Self]
Get the current context instance

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model_copy 

model_copy(self: Self) -> Self
Duplicate the context model, optionally choosing which fields to include, exclude, or change. Attributes:
  • include: Fields to include in new model.
  • exclude: Fields to exclude from new model, as with values this takes precedence over include.
  • update: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data.
  • deep: Set to True to make a deep copy of the model.
Returns:
  • A new model instance.

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serialize 

serialize(self, include_secrets: bool = True) -> dict[str, Any]
Serialize the context model to a dictionary that can be pickled with cloudpickle.

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SettingsContext

The context for a Prefect settings. This allows for safe concurrent access and modification of settings. Attributes:
  • profile: The profile that is in use.
  • settings: The complete settings model.
Methods:

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get 

get(cls) -> Optional['SettingsContext']

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get 

get(cls: type[Self]) -> Optional[Self]
Get the current context instance

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model_copy 

model_copy(self: Self) -> Self
Duplicate the context model, optionally choosing which fields to include, exclude, or change. Attributes:
  • include: Fields to include in new model.
  • exclude: Fields to exclude from new model, as with values this takes precedence over include.
  • update: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data.
  • deep: Set to True to make a deep copy of the model.
Returns:
  • A new model instance.

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serialize 

serialize(self, include_secrets: bool = True) -> dict[str, Any]
Serialize the context model to a dictionary that can be pickled with cloudpickle.