Skip to content

prefect.settings

Prefect settings management.

Each setting is defined as a Setting type. The name of each setting is stylized in all caps, matching the environment variable that can be used to change the setting.

All settings defined in this file are used to generate a dynamic Pydantic settings class called Settings. When insantiated, this class will load settings from environment variables and pull default values from the setting definitions.

The current instance of Settings being used by the application is stored in a SettingsContext model which allows each instance of the Settings class to be accessed in an async-safe manner.

Aside from environment variables, we allow settings to be changed during the runtime of the process using profiles. Profiles contain setting overrides that the user may persist without setting environment variables. Profiles are also used internally for managing settings during task run execution where differing settings may be used concurrently in the same process and during testing where we need to override settings to ensure their value is respected as intended.

The SettingsContext is set when the prefect module is imported. This context is referred to as the "root" settings context for clarity. Generally, this is the only settings context that will be used. When this context is entered, we will instantiate a Settings object, loading settings from environment variables and defaults, then we will load the active profile and use it to override settings. See enter_root_settings_context for details on determining the active profile.

Another SettingsContext may be entered at any time to change the settings being used by the code within the context. Generally, users should not use this. Settings management should be left to Prefect application internals.

Generally, settings should be accessed with SETTING_VARIABLE.value() which will pull the current Settings instance from the current SettingsContext and retrieve the value of the relevant setting.

Accessing a setting's value will also call the Setting.value_callback which allows settings to be dynamically modified on retrieval. This allows us to make settings dependent on the value of other settings or perform other dynamic effects.

Setting

Setting definition type.

Source code in prefect/settings.py
class Setting(Generic[T]):
    """
    Setting definition type.
    """

    def __init__(
        self,
        type: Type[T],
        *,
        value_callback: Callable[["Settings", T], T] = None,
        **kwargs,
    ) -> None:
        self.field: pydantic.fields.FieldInfo = Field(**kwargs)
        self.type = type
        self.value_callback = value_callback
        self.name = None  # Will be populated after all settings are defined

        self.__doc__ = self.field.description

    def value(self) -> T:
        """
        Get the current value of a setting.

        Example:
        ```python
        from prefect.settings import PREFECT_API_URL
        PREFECT_API_URL.value()
        ```
        """
        return self.value_from(get_current_settings())

    def value_from(self, settings: "Settings") -> T:
        """
        Get the value of a setting from a settings object

        Example:
        ```python
        from prefect.settings import get_default_settings
        PREFECT_API_URL.value_from(get_default_settings())
        ```
        """
        return settings.value_of(self)

    def __repr__(self) -> str:
        return f"<{self.name}: {self.type.__name__}>"

    def __bool__(self) -> bool:
        """
        Returns a truthy check of the current value.
        """
        return bool(self.value())

Setting.value

Get the current value of a setting.

Examples:

from prefect.settings import PREFECT_API_URL
PREFECT_API_URL.value()
Source code in prefect/settings.py
def value(self) -> T:
    """
    Get the current value of a setting.

    Example:
    ```python
    from prefect.settings import PREFECT_API_URL
    PREFECT_API_URL.value()
    ```
    """
    return self.value_from(get_current_settings())

Setting.value_from

Get the value of a setting from a settings object

Examples:

from prefect.settings import get_default_settings
PREFECT_API_URL.value_from(get_default_settings())
Source code in prefect/settings.py
def value_from(self, settings: "Settings") -> T:
    """
    Get the value of a setting from a settings object

    Example:
    ```python
    from prefect.settings import get_default_settings
    PREFECT_API_URL.value_from(get_default_settings())
    ```
    """
    return settings.value_of(self)

Settings pydantic-model

Contains validated Prefect settings.

Settings should be accessed using the relevant Setting object. For example:

from prefect.settings import PREFECT_HOME
PREFECT_HOME.value()

Accessing a setting attribute directly will ignore any value_callback mutations. This is not recommended:

from prefect.settings import Settings
Settings().PREFECT_PROFILES_PATH  # PosixPath('${PREFECT_HOME}/profiles.toml')

Source code in prefect/settings.py
class Settings(SettingsFieldsMixin):
    """
    Contains validated Prefect settings.

    Settings should be accessed using the relevant `Setting` object. For example:
    ```python
    from prefect.settings import PREFECT_HOME
    PREFECT_HOME.value()
    ```

    Accessing a setting attribute directly will ignore any `value_callback` mutations.
    This is not recommended:
    ```python
    from prefect.settings import Settings
    Settings().PREFECT_PROFILES_PATH  # PosixPath('${PREFECT_HOME}/profiles.toml')
    ```
    """

    def value_of(self, setting: Setting[T]) -> T:
        """
        Retrieve a setting's value.
        """
        value = getattr(self, setting.name)
        if setting.value_callback:
            value = setting.value_callback(self, value)
        return value

    @root_validator
    def post_root_validators(cls, values):
        """
        Add root validation functions for settings here.
        """
        # TODO: We could probably register these dynamically but this is the simpler
        #       approach for now. We can explore more interesting validation features
        #       in the future.
        values = max_log_size_smaller_than_batch_size(values)
        return values

    def copy_with_update(
        self,
        updates: Mapping[Setting, Any] = None,
        set_defaults: Mapping[Setting, Any] = None,
        restore_defaults: Iterable[Setting] = None,
    ) -> "Settings":
        """
        Create a new `Settings` object with validation.

        Arguments:
            updates: A mapping of settings to new values. Existing values for the
                given settings will be overridden.
            set_defaults: A mapping of settings to new default values. Existing values for
                the given settings will only be overridden if they were not set.
            restore_defaults: An iterable of settings to restore to their default values.

        Returns:
            A new `Settings` object.
        """
        updates = updates or {}
        set_defaults = set_defaults or {}
        restore_defaults = restore_defaults or set()
        restore_defaults_names = {setting.name for setting in restore_defaults}

        return self.__class__(
            **{
                **{setting.name: value for setting, value in set_defaults.items()},
                **self.dict(exclude_unset=True, exclude=restore_defaults_names),
                **{setting.name: value for setting, value in updates.items()},
            }
        )

    def to_environment_variables(
        self, include: Iterable[Setting] = None, exclude_unset: bool = False
    ) -> Dict[str, str]:
        """
        Convert the settings object to environment variables.

        Note that setting values will not be run through their `value_callback` allowing
        dynamic resolution to occur when loaded from the returned environment.

        Args:
            include_keys: An iterable of settings to include in the return value.
                If not set, all settings are used.
            exclude_unset: Only include settings that have been set (i.e. the value is
                not from the default). If set, unset keys will be dropped even if they
                are set in `include_keys`.

        Returns:
            A dictionary of settings with values cast to strings
        """
        include = set(include or SETTING_VARIABLES.values())

        if exclude_unset:
            set_keys = {
                # Collect all of the "set" keys and cast to `Setting` objects
                SETTING_VARIABLES[key]
                for key in self.dict(exclude_unset=True)
            }
            include.intersection_update(set_keys)

        # Validate the types of items in `include` to prevent exclusion bugs
        for key in include:
            if not isinstance(key, Setting):
                raise TypeError(
                    "Invalid type {type(key).__name__!r} for key in `include`."
                )

        env = {
            # Use `getattr` instead of `value_of` to avoid value callback resolution
            key: getattr(self, key)
            for key, setting in SETTING_VARIABLES.items()
            if setting in include
        }

        # Cast to strings and drop null values
        return {key: str(value) for key, value in env.items() if value is not None}

    class Config:
        frozen = True

    __reduce__ = reduce_settings

PREFECT_HOME pydantic-field

Type: Path

Prefect's home directory. Defaults to ~/.prefect. This directory may be created automatically when required.

PREFECT_DEBUG_MODE pydantic-field

Type: bool

If True, places the API in debug mode. This may modify behavior to facilitate debugging, including extra logs and other verbose assistance. Defaults to False.

PREFECT_TEST_MODE pydantic-field

Type: bool

If True, places the API in test mode. This may modify behavior to faciliate testing. Defaults to False.

PREFECT_TEST_SETTING pydantic-field

Type: Any

This variable only exists to faciliate testing of settings. If accessed when PREFECT_TEST_MODE is not set, None is returned.

PREFECT_API_URL pydantic-field

Type: str

If provided, the url of an externally-hosted Orion API. Defaults to None.

PREFECT_API_KEY pydantic-field

Type: str

API key used to authenticate against Orion API. Defaults to None.

PREFECT_CLOUD_URL pydantic-field

Type: str

API URL for Prefect Cloud

PREFECT_API_REQUEST_TIMEOUT pydantic-field

Type: float

The default timeout for requests to the API

PREFECT_PROFILES_PATH pydantic-field

Type: Path

The path to a profiles configuration files.

PREFECT_LOGGING_LEVEL pydantic-field

Type: str

The default logging level for Prefect loggers. Defaults to "INFO" during normal operation. Is forced to "DEBUG" during debug mode.

PREFECT_LOGGING_SERVER_LEVEL pydantic-field

Type: str

The default logging level for the Orion API.

PREFECT_LOGGING_SETTINGS_PATH pydantic-field

Type: Path

The path to a custom YAML logging configuration file. If no file is found, the default logging.yml is used. Defaults to a logging.yml in the Prefect home directory.

PREFECT_LOGGING_EXTRA_LOGGERS pydantic-field

Type: str

Additional loggers to attach to Prefect logging at runtime. Values should be comma separated. The handlers attached to the 'prefect' logger will be added to these loggers. Additionally, if the level is not set, it will be set to the same level as the 'prefect' logger.

PREFECT_LOGGING_ORION_ENABLED pydantic-field

Type: bool

Should logs be sent to Orion? If False, logs sent to the OrionHandler will not be sent to the API.

PREFECT_LOGGING_ORION_BATCH_INTERVAL pydantic-field

Type: float

The number of seconds between batched writes of logs to Orion.

PREFECT_LOGGING_ORION_BATCH_SIZE pydantic-field

Type: int

The maximum size in bytes for a batch of logs.

PREFECT_LOGGING_ORION_MAX_LOG_SIZE pydantic-field

Type: int

The maximum size in bytes for a single log.

PREFECT_AGENT_QUERY_INTERVAL pydantic-field

Type: float

The agent loop interval, in seconds. Agents will check for new runs this often. Defaults to 5.

PREFECT_AGENT_PREFETCH_SECONDS pydantic-field

Type: int

Agents will look for scheduled runs this many seconds in the future and attempt to run them. This accounts for any additional infrastructure spin-up time or latency in preparing a flow run. Note flow runs will not start before their scheduled time, even if they are prefetched. Defaults to 10.

PREFECT_ORION_DATABASE_CONNECTION_URL pydantic-field

Type: str

A database connection URL in a SQLAlchemy-compatible format. Orion currently supports SQLite and Postgres. Note that all Orion engines must use an async driver - for SQLite, use sqlite+aiosqlite and for Postgres use postgresql+asyncpg.

SQLite in-memory databases can be used by providing the url sqlite+aiosqlite:///file::memory:?cache=shared&uri=true&check_same_thread=false, which will allow the database to be accessed by multiple threads. Note that in-memory databases can not be accessed from multiple processes and should only be used for simple tests.

Defaults to a sqlite database stored in the Prefect home directory.

PREFECT_ORION_DATABASE_ECHO pydantic-field

Type: bool

If True, SQLAlchemy will log all SQL issued to the database. Defaults to False.

PREFECT_ORION_DATABASE_MIGRATE_ON_START pydantic-field

Type: bool

If True, the database will be upgraded on application creation. If False, the database will need to be upgraded manually.

PREFECT_ORION_DATABASE_TIMEOUT pydantic-field

Type: float

A statement timeout, in seconds, applied to all database interactions made by the API. Defaults to 1.

PREFECT_ORION_DATABASE_CONNECTION_TIMEOUT pydantic-field

Type: float

A connection timeout, in seconds, applied to database connections. Defaults to 5.

PREFECT_ORION_SERVICES_SCHEDULER_LOOP_SECONDS pydantic-field

Type: float

The scheduler loop interval, in seconds. This determines how often the scheduler will attempt to schedule new flow runs, but has no impact on how quickly either flow runs or task runs are actually executed. Creating new deployments or schedules will always create new flow runs optimistically, without waiting for the scheduler. Defaults to 60.

PREFECT_ORION_SERVICES_SCHEDULER_DEPLOYMENT_BATCH_SIZE pydantic-field

Type: int

The number of deployments the scheduler will attempt to schedule in a single batch. If there are more deployments than the batch size, the scheduler immediately attempts to schedule the next batch; it does not sleep for scheduler_loop_seconds until it has visited every deployment once. Defaults to 100.

PREFECT_ORION_SERVICES_SCHEDULER_MAX_RUNS pydantic-field

Type: int

The scheduler will attempt to schedule up to this many auto-scheduled runs in the future. Note that runs may have fewer than this many scheduled runs, depending on the value of scheduler_max_scheduled_time. Defaults to 100.

PREFECT_ORION_SERVICES_SCHEDULER_MAX_SCHEDULED_TIME pydantic-field

Type: timedelta

The scheduler will create new runs up to this far in the future. Note that this setting will take precedence over scheduler_max_runs: if a flow runs once a month and scheduled_max_scheduled_time is three months, then only three runs will be scheduled. Defaults to 100 days (8640000 seconds).

PREFECT_ORION_SERVICES_SCHEDULER_INSERT_BATCH_SIZE pydantic-field

Type: int

The number of flow runs the scheduler will attempt to insert in one batch across all deployments. If the number of flow runs to schedule exceeds this amount, the runs will be inserted in batches of this size. Defaults to 500.

PREFECT_ORION_SERVICES_LATE_RUNS_LOOP_SECONDS pydantic-field

Type: float

The late runs service will look for runs to mark as late this often. Defaults to 5.

PREFECT_ORION_SERVICES_LATE_RUNS_AFTER_SECONDS pydantic-field

Type: timedelta

The late runs service will mark runs as late after they have exceeded their scheduled start time by this many seconds. Defaults to 5 seconds.

PREFECT_ORION_API_DEFAULT_LIMIT pydantic-field

Type: int

The default limit applied to queries that can return multiple objects, such as POST /flow_runs/filter.

PREFECT_ORION_API_HOST pydantic-field

Type: str

The API's host address (defaults to 127.0.0.1).

PREFECT_ORION_API_PORT pydantic-field

Type: int

The API's port address (defaults to 4200).

PREFECT_ORION_UI_ENABLED pydantic-field

Type: bool

Whether or not to serve the Orion UI.

PREFECT_ORION_UI_API_URL pydantic-field

Type: str

The connection url for communication from the UI to the API. Defaults to PREFECT_API_URL if set. Otherwise, the default URL is generated from PREFECT_ORION_API_HOST and PREFECT_ORION_API_PORT. If providing a custom value, the aforementioned settings may be templated into the given string.

PREFECT_ORION_ANALYTICS_ENABLED pydantic-field

Type: bool

If True, Orion sends anonymous data (e.g. count of flow runs, package version) to Prefect to help us improve.

PREFECT_ORION_SERVICES_SCHEDULER_ENABLED pydantic-field

Type: bool

Whether or not to start the scheduling service in the Orion application. If disabled, you will need to run this service separately to schedule runs for deployments.

PREFECT_ORION_SERVICES_LATE_RUNS_ENABLED pydantic-field

Type: bool

Whether or not to start the late runs service in the Orion application. If disabled, you will need to run this service separately to have runs past their scheduled start time marked as late.

Settings.value_of

Retrieve a setting's value.

Source code in prefect/settings.py
def value_of(self, setting: Setting[T]) -> T:
    """
    Retrieve a setting's value.
    """
    value = getattr(self, setting.name)
    if setting.value_callback:
        value = setting.value_callback(self, value)
    return value

Settings.post_root_validators classmethod

Add root validation functions for settings here.

Source code in prefect/settings.py
@root_validator
def post_root_validators(cls, values):
    """
    Add root validation functions for settings here.
    """
    # TODO: We could probably register these dynamically but this is the simpler
    #       approach for now. We can explore more interesting validation features
    #       in the future.
    values = max_log_size_smaller_than_batch_size(values)
    return values

Settings.to_environment_variables

Convert the settings object to environment variables.

Note that setting values will not be run through their value_callback allowing dynamic resolution to occur when loaded from the returned environment.

Parameters:

Name Description Default
include_keys

An iterable of settings to include in the return value. If not set, all settings are used.

required
exclude_unset

Only include settings that have been set (i.e. the value is not from the default). If set, unset keys will be dropped even if they are set in include_keys.

bool
False

Returns:

Type Description
Dict[str, str]

A dictionary of settings with values cast to strings

Source code in prefect/settings.py
def to_environment_variables(
    self, include: Iterable[Setting] = None, exclude_unset: bool = False
) -> Dict[str, str]:
    """
    Convert the settings object to environment variables.

    Note that setting values will not be run through their `value_callback` allowing
    dynamic resolution to occur when loaded from the returned environment.

    Args:
        include_keys: An iterable of settings to include in the return value.
            If not set, all settings are used.
        exclude_unset: Only include settings that have been set (i.e. the value is
            not from the default). If set, unset keys will be dropped even if they
            are set in `include_keys`.

    Returns:
        A dictionary of settings with values cast to strings
    """
    include = set(include or SETTING_VARIABLES.values())

    if exclude_unset:
        set_keys = {
            # Collect all of the "set" keys and cast to `Setting` objects
            SETTING_VARIABLES[key]
            for key in self.dict(exclude_unset=True)
        }
        include.intersection_update(set_keys)

    # Validate the types of items in `include` to prevent exclusion bugs
    for key in include:
        if not isinstance(key, Setting):
            raise TypeError(
                "Invalid type {type(key).__name__!r} for key in `include`."
            )

    env = {
        # Use `getattr` instead of `value_of` to avoid value callback resolution
        key: getattr(self, key)
        for key, setting in SETTING_VARIABLES.items()
        if setting in include
    }

    # Cast to strings and drop null values
    return {key: str(value) for key, value in env.items() if value is not None}

Profile pydantic-model

A user profile containing settings.

Source code in prefect/settings.py
class Profile(pydantic.BaseModel):
    """
    A user profile containing settings.
    """

    name: str
    settings: Dict[Setting, Any] = Field(default_factory=dict)
    source: Optional[Path]

    @pydantic.validator("settings", pre=True)
    def map_names_to_settings(cls, value):
        if value is None:
            return value

        # Cast string setting names to variables
        validated = {}
        for setting, val in value.items():
            if isinstance(setting, str) and setting in SETTING_VARIABLES:
                validated[SETTING_VARIABLES[setting]] = val
            elif isinstance(setting, Setting):
                validated[setting] = val
            else:
                raise ValueError(f"Unknown setting {setting!r}.")

        return validated

    class Config:
        arbitrary_types_allowed = True

ProfilesCollection

" A utility class for working with a collection of profiles.

Profiles in the collection must have unique names.

The collection may store the name of the active profile.

Source code in prefect/settings.py
class ProfilesCollection:
    """ "
    A utility class for working with a collection of profiles.

    Profiles in the collection must have unique names.

    The collection may store the name of the active profile.
    """

    def __init__(
        self, profiles: Iterable[Profile], active: Optional[str] = None
    ) -> None:
        self.profiles_by_name = {profile.name: profile for profile in profiles}
        self.active_name = active

    @property
    def names(self) -> Set[str]:
        """
        Return a set of profile names in this collection.
        """
        return set(self.profiles_by_name.keys())

    @property
    def active_profile(self) -> Optional[Profile]:
        """
        Retrieve the active profile in this collection.
        """
        if self.active_name is None:
            return None
        return self[self.active_name]

    def set_active(self, name: Optional[str]):
        """
        Set the active profile name in the collection.

        A null value may be passed to indicate that this collection does not determine
        the active profile.
        """
        if name is not None and name not in self.names:
            raise ValueError(f"Unknown profile name {name!r}.")
        self.active_name = name

    def update_profile(
        self, name: str, settings: Mapping[Union[Dict, str], Any], source: Path = None
    ) -> Profile:
        """
        Add a profile to the collection or update the existing on if the name is already
        present in this collection.

        If updating an existing profile, the settings will be merged. Settings can
        be dropped from the existing profile by setting them to `None` in the new
        profile.

        Returns the new profile object.
        """
        existing = self.profiles_by_name.get(name)

        # Convert the input to a `Profile` to cast settings to the correct type
        profile = Profile(name=name, settings=settings, source=source)

        if existing:
            new_settings = {**existing.settings, **profile.settings}

            # Drop null keys to restore to default
            for key, value in tuple(new_settings.items()):
                if value is None:
                    new_settings.pop(key)

            new_profile = Profile(
                name=profile.name,
                settings=new_settings,
                source=source or profile.source,
            )
        else:
            new_profile = profile

        self.profiles_by_name[new_profile.name] = new_profile

        return new_profile

    def add_profile(self, profile: Profile) -> None:
        """
        Add a profile to the collection.

        If the profile name already exists, an exception will be raised.
        """
        if profile.name in self.profiles_by_name:
            raise ValueError(
                f"Profile name {profile.name!r} already exists in collection."
            )

        self.profiles_by_name[profile.name] = profile

    def remove_profile(self, name: str) -> None:
        """
        Remove a profile from the collection.
        """
        self.profiles_by_name.pop(name)

    def without_profile_source(self, path: Optional[Path]) -> "ProfilesCollection":
        """
        Remove profiles that were loaded from a given path.

        Returns a new collection.
        """
        return ProfilesCollection(
            [
                profile
                for profile in self.profiles_by_name.values()
                if profile.source != path
            ],
            active=self.active_name,
        )

    def to_dict(self):
        """
        Convert to a dictionary suitable for writing to disk.
        """
        return {
            "active": self.active_name,
            "profiles": {
                profile.name: {
                    setting.name: value for setting, value in profile.settings.items()
                }
                for profile in self.profiles_by_name.values()
            },
        }

    def __getitem__(self, name: str) -> Profile:
        return self.profiles_by_name[name]

    def __iter__(self):
        return self.profiles_by_name.__iter__()

    def __eq__(self, __o: object) -> bool:
        if not isinstance(__o, ProfilesCollection):
            return False

        return (
            self.profiles_by_name == __o.profiles_by_name
            and self.active_name == __o.active_name
        )

    def __repr__(self) -> str:
        return f"ProfilesCollection(profiles={list(self.profiles_by_name.values())!r}, active={self.active_name!r})>"

names property readonly

Type: Set[str]

Return a set of profile names in this collection.

active_profile property readonly

Type: Optional[prefect.settings.Profile]

Retrieve the active profile in this collection.

ProfilesCollection.set_active

Set the active profile name in the collection.

A null value may be passed to indicate that this collection does not determine the active profile.

Source code in prefect/settings.py
def set_active(self, name: Optional[str]):
    """
    Set the active profile name in the collection.

    A null value may be passed to indicate that this collection does not determine
    the active profile.
    """
    if name is not None and name not in self.names:
        raise ValueError(f"Unknown profile name {name!r}.")
    self.active_name = name

ProfilesCollection.update_profile

Add a profile to the collection or update the existing on if the name is already present in this collection.

If updating an existing profile, the settings will be merged. Settings can be dropped from the existing profile by setting them to None in the new profile.

Returns the new profile object.

Source code in prefect/settings.py
def update_profile(
    self, name: str, settings: Mapping[Union[Dict, str], Any], source: Path = None
) -> Profile:
    """
    Add a profile to the collection or update the existing on if the name is already
    present in this collection.

    If updating an existing profile, the settings will be merged. Settings can
    be dropped from the existing profile by setting them to `None` in the new
    profile.

    Returns the new profile object.
    """
    existing = self.profiles_by_name.get(name)

    # Convert the input to a `Profile` to cast settings to the correct type
    profile = Profile(name=name, settings=settings, source=source)

    if existing:
        new_settings = {**existing.settings, **profile.settings}

        # Drop null keys to restore to default
        for key, value in tuple(new_settings.items()):
            if value is None:
                new_settings.pop(key)

        new_profile = Profile(
            name=profile.name,
            settings=new_settings,
            source=source or profile.source,
        )
    else:
        new_profile = profile

    self.profiles_by_name[new_profile.name] = new_profile

    return new_profile

ProfilesCollection.add_profile

Add a profile to the collection.

If the profile name already exists, an exception will be raised.

Source code in prefect/settings.py
def add_profile(self, profile: Profile) -> None:
    """
    Add a profile to the collection.

    If the profile name already exists, an exception will be raised.
    """
    if profile.name in self.profiles_by_name:
        raise ValueError(
            f"Profile name {profile.name!r} already exists in collection."
        )

    self.profiles_by_name[profile.name] = profile

ProfilesCollection.remove_profile

Remove a profile from the collection.

Source code in prefect/settings.py
def remove_profile(self, name: str) -> None:
    """
    Remove a profile from the collection.
    """
    self.profiles_by_name.pop(name)

ProfilesCollection.without_profile_source

Remove profiles that were loaded from a given path.

Returns a new collection.

Source code in prefect/settings.py
def without_profile_source(self, path: Optional[Path]) -> "ProfilesCollection":
    """
    Remove profiles that were loaded from a given path.

    Returns a new collection.
    """
    return ProfilesCollection(
        [
            profile
            for profile in self.profiles_by_name.values()
            if profile.source != path
        ],
        active=self.active_name,
    )

get_extra_loggers

value_callback for PREFECT_LOGGING_EXTRA_LOGGERSthat parses the CSV string into a list and trims whitespace from logger names.

Source code in prefect/settings.py
def get_extra_loggers(_: "Settings", value: str) -> List[str]:
    """
    `value_callback` for `PREFECT_LOGGING_EXTRA_LOGGERS`that parses the CSV string into a
    list and trims whitespace from logger names.
    """
    return [name.strip() for name in value.split(",")] if value else []

debug_mode_log_level

value_callback for PREFECT_LOGGING_LEVEL that overrides the log level to DEBUG when debug mode is enabled.

Source code in prefect/settings.py
def debug_mode_log_level(settings, value):
    """
    `value_callback` for `PREFECT_LOGGING_LEVEL` that overrides the log level to DEBUG
    when debug mode is enabled.
    """
    if PREFECT_DEBUG_MODE.value_from(settings):
        return "DEBUG"
    else:
        return value

only_return_value_in_test_mode

value_callback for PREFECT_TEST_SETTING that only allows access during test mode

Source code in prefect/settings.py
def only_return_value_in_test_mode(settings, value):
    """
    `value_callback` for `PREFECT_TEST_SETTING` that only allows access during test mode
    """
    if PREFECT_TEST_MODE.value_from(settings):
        return value
    else:
        return None

default_ui_api_url

value_callback for PREFECT_ORION_UI_API_URL that sets the default value to PREFECT_API_URL if set otherwise it constructs an API URL from the API settings.

Source code in prefect/settings.py
def default_ui_api_url(settings, value):
    """
    `value_callback` for `PREFECT_ORION_UI_API_URL` that sets the default value to
    `PREFECT_API_URL` if set otherwise it constructs an API URL from the API settings.
    """
    if value is None:
        # Set a default value
        if PREFECT_API_URL.value_from(settings):
            value = "${PREFECT_API_URL}"
        else:
            value = "http://${PREFECT_ORION_API_HOST}:${PREFECT_ORION_API_PORT}/api"

    return template_with_settings(
        PREFECT_ORION_API_HOST, PREFECT_ORION_API_PORT, PREFECT_API_URL
    )(settings, value)

template_with_settings

Returns a value_callback that will template the given settings into the runtime value for the setting.

Source code in prefect/settings.py
def template_with_settings(*upstream_settings: Setting) -> Callable[["Settings", T], T]:
    """
    Returns a `value_callback` that will template the given settings into the runtime
    value for the setting.
    """

    def templater(settings, value):
        original_type = type(value)
        template_values = {
            setting.name: setting.value_from(settings) for setting in upstream_settings
        }
        template = string.Template(str(value))
        return original_type(template.substitute(template_values))

    return templater

max_log_size_smaller_than_batch_size

Validator for settings asserting the batch size and match log size are compatible

Source code in prefect/settings.py
def max_log_size_smaller_than_batch_size(values):
    """
    Validator for settings asserting the batch size and match log size are compatible
    """
    if (
        values["PREFECT_LOGGING_ORION_BATCH_SIZE"]
        < values["PREFECT_LOGGING_ORION_MAX_LOG_SIZE"]
    ):
        raise ValueError(
            "`PREFECT_LOGGING_ORION_MAX_LOG_SIZE` cannot be larger than `PREFECT_LOGGING_ORION_BATCH_SIZE`"
        )
    return values

reduce_settings

Workaround for issues with cloudpickle when using cythonized pydantic which throws exceptions when attempting to pickle the class which has "compiled" validator methods dynamically attached to it.

We cannot define this in the model class because the class is the type that contains unserializable methods.

Note that issue is not specific to the Settings model or its implementation. Any model using some features of Pydantic (e.g. Path validation) with a Cython compiled Pydantic installation may encounter pickling issues.

See related issue at https://github.com/cloudpipe/cloudpickle/issues/408

Source code in prefect/settings.py
def reduce_settings(settings):
    """
    Workaround for issues with cloudpickle when using cythonized pydantic which
    throws exceptions when attempting to pickle the class which has "compiled"
    validator methods dynamically attached to it.

    We cannot define this in the model class because the class is the type that
    contains unserializable methods.

    Note that issue is not specific to the `Settings` model or its implementation.
    Any model using some features of Pydantic (e.g. `Path` validation) with a Cython
    compiled Pydantic installation may encounter pickling issues.

    See related issue at https://github.com/cloudpipe/cloudpickle/issues/408
    """
    # TODO: Consider moving this to the cloudpickle serializer and applying it to all
    #       pydantic models
    return (
        unreduce_settings,
        (settings.json(),),
    )

unreduce_settings

Helper for restoring settings

Source code in prefect/settings.py
def unreduce_settings(json):
    """Helper for restoring settings"""
    return Settings.parse_raw(json)

get_current_settings

Returns a settings object populated with values from the current profile or, if no profile is active, the environment.

Source code in prefect/settings.py
def get_current_settings() -> Settings:
    """
    Returns a settings object populated with values from the current profile or, if no
    profile is active, the environment.
    """
    from prefect.context import SettingsContext

    profile = SettingsContext.get()
    if profile is not None:
        return profile.settings

    return get_settings_from_env()

get_settings_from_env

Returns a settings object populated with default values and overrides from environment variables, ignoring any values in profiles.

Calls with the same environment return a cached object instead of reconstructing to avoid validation overhead.

Source code in prefect/settings.py
def get_settings_from_env() -> Settings:
    """
    Returns a settings object populated with default values and overrides from
    environment variables, ignoring any values in profiles.

    Calls with the same environment return a cached object instead of reconstructing
    to avoid validation overhead.
    """
    # Since os.environ is a Dict[str, str] we can safely hash it by contents, but we
    # must be careful to avoid hashing a generator instead of a tuple
    cache_key = hash(tuple((key, value) for key, value in os.environ.items()))

    if cache_key not in _FROM_ENV_CACHE:
        _FROM_ENV_CACHE[cache_key] = Settings()

    return _FROM_ENV_CACHE[cache_key]

get_default_settings

Returns a settings object populated with default values, ignoring any overrides from environment variables or profiles.

This is cached since the defaults should not change during the lifetime of the module.

Source code in prefect/settings.py
def get_default_settings() -> Settings:
    """
    Returns a settings object populated with default values, ignoring any overrides
    from environment variables or profiles.

    This is cached since the defaults should not change during the lifetime of the
    module.
    """
    global _DEFAULTS_CACHE

    if not _DEFAULTS_CACHE:
        old = os.environ
        try:
            os.environ = {}
            settings = get_settings_from_env()
        finally:
            os.environ = old

        _DEFAULTS_CACHE = settings

    return _DEFAULTS_CACHE

temporary_settings

Temporarily override the current settings by entering a new profile.

See Settings.copy_with_update for details on different argument behavior.

Examples:

>>> from prefect.settings import PREFECT_API_URL
>>>
>>> with temporary_settings(updates={PREFECT_API_URL: "foo"}):
>>>    assert PREFECT_API_URL.value() == "foo"
>>>
>>>    with temporary_settings(set_defaults={PREFECT_API_URL: "bar"}):
>>>         assert PREFECT_API_URL.value() == "foo"
>>>
>>>    with temporary_settings(restore_defaults={PREFECT_API_URL}):
>>>         assert PREFECT_API_URL.value() is None
>>>
>>>         with temporary_settings(set_defaults={PREFECT_API_URL: "bar"})
>>>             assert PREFECT_API_URL.value() == "bar"
>>> assert PREFECT_API_URL.value() is None
Source code in prefect/settings.py
@contextmanager
def temporary_settings(
    updates: Mapping[Setting, Any] = None,
    set_defaults: Mapping[Setting, Any] = None,
    restore_defaults: Iterable[Setting] = None,
) -> Settings:
    """
    Temporarily override the current settings by entering a new profile.

    See `Settings.copy_with_update` for details on different argument behavior.

    Example:
        >>> from prefect.settings import PREFECT_API_URL
        >>>
        >>> with temporary_settings(updates={PREFECT_API_URL: "foo"}):
        >>>    assert PREFECT_API_URL.value() == "foo"
        >>>
        >>>    with temporary_settings(set_defaults={PREFECT_API_URL: "bar"}):
        >>>         assert PREFECT_API_URL.value() == "foo"
        >>>
        >>>    with temporary_settings(restore_defaults={PREFECT_API_URL}):
        >>>         assert PREFECT_API_URL.value() is None
        >>>
        >>>         with temporary_settings(set_defaults={PREFECT_API_URL: "bar"})
        >>>             assert PREFECT_API_URL.value() == "bar"
        >>> assert PREFECT_API_URL.value() is None
    """
    import prefect.context

    context = prefect.context.get_settings_context()

    new_settings = context.settings.copy_with_update(
        updates=updates, set_defaults=set_defaults, restore_defaults=restore_defaults
    )

    with prefect.context.SettingsContext(
        profile=context.profile, settings=new_settings
    ):
        yield new_settings

load_profiles

Load all profiles from the default and current profile paths.

Source code in prefect/settings.py
def load_profiles() -> ProfilesCollection:
    """
    Load all profiles from the default and current profile paths.
    """
    profiles = _read_profiles_from(DEFAULT_PROFILES_PATH)

    user_profiles_path = PREFECT_PROFILES_PATH.value()
    if user_profiles_path.exists():
        user_profiles = _read_profiles_from(user_profiles_path)

        # Merge all of the user profiles with the defaults
        for name in user_profiles:
            profiles.update_profile(
                name,
                settings=user_profiles[name].settings,
                source=user_profiles[name].source,
            )

        # Include the active key if set
        if user_profiles.active_name:
            profiles.set_active(user_profiles.active_name)

    return profiles

save_profiles

Writes all non-default profiles to the current profiles path.

Source code in prefect/settings.py
def save_profiles(profiles: ProfilesCollection) -> None:
    """
    Writes all non-default profiles to the current profiles path.
    """
    profiles_path = PREFECT_PROFILES_PATH.value()
    profiles = profiles.without_profile_source(DEFAULT_PROFILES_PATH)
    return _write_profiles_to(profiles_path, profiles)

load_profile

Load a single profile by name.

Source code in prefect/settings.py
def load_profile(name: str) -> Profile:
    """
    Load a single profile by name.
    """
    profiles = load_profiles()
    try:
        return profiles[name]
    except KeyError:
        raise ValueError(f"Profile {name!r} not found.")

update_current_profile

Update the persisted data for the profile currently in-use.

If the profile does not exist in the profiles file, it will be created.

Given settings will be merged with the existing settings as described in ProfilesCollection.update_profile.

Returns:

Type Description
Profile

The new profile.

Source code in prefect/settings.py
def update_current_profile(settings: Dict[Union[str, Setting], Any]) -> Profile:
    """
    Update the persisted data for the profile currently in-use.

    If the profile does not exist in the profiles file, it will be created.

    Given settings will be merged with the existing settings as described in
    `ProfilesCollection.update_profile`.

    Returns:
        The new profile.
    """
    import prefect.context

    current_profile = prefect.context.get_settings_context().profile

    if not current_profile:
        raise MissingProfileError("No profile is currently in use.")

    profiles = load_profiles()

    # Ensure the current profile's settings are present
    profiles.update_profile(current_profile.name, current_profile.settings)
    # Then merge the new settings in
    profiles.update_profile(current_profile.name, settings)

    save_profiles(profiles)

    return profiles[current_profile.name]