1.15.4.dev2+g3e3ce2426

flytekit.sensor.base_sensor

Directory

Classes

Class Description
Any Special type indicating an unconstrained type.
AsyncAgentExecutorMixin This mixin class is used to run the async task locally, and it’s only used for local execution.
BaseSensor Base class for all sensors.
Interface A Python native interface object, like inspect.
Protocol Base class for protocol classes.
PythonTask Base Class for all Tasks with a Python native Interface.
ResourceMeta This is the metadata for the job.
SensorConfig Base class for protocol classes.
SensorMetadata None.
SerializationSettings These settings are provided while serializing a workflow and task, before registration.
TaskMetadata Metadata for a Task.
TypeVar Type variable.

flytekit.sensor.base_sensor.Any

Special type indicating an unconstrained type.

  • Any is compatible with every type.
  • Any assumed to have all methods.
  • All values assumed to be instances of Any.

Note that all the above statements are true from the point of view of static type checkers. At runtime, Any should not be used with instance checks.

flytekit.sensor.base_sensor.AsyncAgentExecutorMixin

This mixin class is used to run the async task locally, and it’s only used for local execution. Task should inherit from this class if the task can be run in the agent.

Asynchronous tasks are tasks that take a long time to complete, such as running a query.

Methods

Method Description
agent_signal_handler() None
execute() None

agent_signal_handler()

def agent_signal_handler(
    resource_meta: flytekit.extend.backend.base_agent.ResourceMeta,
    signum: int,
    frame: frame,
):
Parameter Type
resource_meta flytekit.extend.backend.base_agent.ResourceMeta
signum int
frame frame

execute()

def execute(
    kwargs,
):
Parameter Type
kwargs **kwargs

flytekit.sensor.base_sensor.BaseSensor

Base class for all sensors. Sensors are tasks that are designed to run forever and periodically check for some condition to be met. When the condition is met, the sensor will complete. Sensors are designed to be run by the sensor agent, and not by the Flyte engine.

def BaseSensor(
    name: str,
    timeout: typing.Union[datetime.timedelta, int, NoneType],
    sensor_config: typing.Optional[~T],
    task_type: str,
    kwargs,
):
Parameter Type
name str
timeout typing.Union[datetime.timedelta, int, NoneType]
sensor_config typing.Optional[~T]
task_type str
kwargs **kwargs

Methods

Method Description
agent_signal_handler() None
compile() Generates a node that encapsulates this task in a workflow definition
construct_node_metadata() Used when constructing the node that encapsulates this task as part of a broader workflow definition
dispatch_execute() This method translates Flyte’s Type system based input values and invokes the actual call to the executor
execute() None
find_lhs() None
get_config() Returns the task config as a serializable dictionary
get_container() Returns the container definition (if any) that is used to run the task on hosted Flyte
get_custom() Return additional plugin-specific custom data (if any) as a serializable dictionary
get_extended_resources() Returns the extended resources to allocate to the task on hosted Flyte
get_input_types() Returns the names and python types as a dictionary for the inputs of this task
get_k8s_pod() Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte
get_sql() Returns the Sql definition (if any) that is used to run the task on hosted Flyte
get_type_for_input_var() Returns the python type for an input variable by name
get_type_for_output_var() Returns the python type for the specified output variable by name
local_execute() This function is used only in the local execution path and is responsible for calling dispatch execute
local_execution_mode() None
poke() This method should be overridden by the user to implement the actual sensor logic
post_execute() Post execute is called after the execution has completed, with the user_params and can be used to clean-up,
pre_execute() This is the method that will be invoked directly before executing the task method and before all the inputs
sandbox_execute() Call dispatch_execute, in the context of a local sandbox execution

agent_signal_handler()

def agent_signal_handler(
    resource_meta: flytekit.extend.backend.base_agent.ResourceMeta,
    signum: int,
    frame: frame,
):
Parameter Type
resource_meta flytekit.extend.backend.base_agent.ResourceMeta
signum int
frame frame

compile()

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
):

Generates a node that encapsulates this task in a workflow definition.

Parameter Type
ctx flytekit.core.context_manager.FlyteContext
args *args
kwargs **kwargs

construct_node_metadata()

def construct_node_metadata()

Used when constructing the node that encapsulates this task as part of a broader workflow definition.

dispatch_execute()

def dispatch_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

This method translates Flyte’s Type system based input values and invokes the actual call to the executor This method is also invoked during runtime.

  • VoidPromise is returned in the case when the task itself declares no outputs.
  • Literal Map is returned when the task returns either one more outputs in the declaration. Individual outputs may be none
  • DynamicJobSpec is returned when a dynamic workflow is executed
Parameter Type
ctx flytekit.core.context_manager.FlyteContext
input_literal_map flytekit.models.literals.LiteralMap

execute()

def execute(
    kwargs,
):
Parameter Type
kwargs **kwargs

find_lhs()

def find_lhs()

get_config()

def get_config(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_container()

def get_container(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the container definition (if any) that is used to run the task on hosted Flyte.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_custom()

def get_custom(
    settings: flytekit.configuration.SerializationSettings,
):

Return additional plugin-specific custom data (if any) as a serializable dictionary.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_extended_resources()

def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the extended resources to allocate to the task on hosted Flyte.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_input_types()

def get_input_types()

Returns the names and python types as a dictionary for the inputs of this task.

get_k8s_pod()

def get_k8s_pod(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_sql()

def get_sql(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_type_for_input_var()

def get_type_for_input_var(
    k: str,
    v: typing.Any,
):

Returns the python type for an input variable by name.

Parameter Type
k str
v typing.Any

get_type_for_output_var()

def get_type_for_output_var(
    k: str,
    v: typing.Any,
):

Returns the python type for the specified output variable by name.

Parameter Type
k str
v typing.Any

local_execute()

def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
):

This function is used only in the local execution path and is responsible for calling dispatch execute. Use this function when calling a task with native values (or Promises containing Flyte literals derived from Python native values).

Parameter Type
ctx flytekit.core.context_manager.FlyteContext
kwargs **kwargs

local_execution_mode()

def local_execution_mode()

poke()

def poke(
    kwargs,
):

This method should be overridden by the user to implement the actual sensor logic. This method should return True if the sensor condition is met, else False.

Parameter Type
kwargs **kwargs

post_execute()

def post_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
    rval: typing.Any,
):

Post execute is called after the execution has completed, with the user_params and can be used to clean-up, or alter the outputs to match the intended tasks outputs. If not overridden, then this function is a No-op

Parameter Type
user_params typing.Optional[flytekit.core.context_manager.ExecutionParameters]
rval typing.Any

pre_execute()

def pre_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
):

This is the method that will be invoked directly before executing the task method and before all the inputs are converted. One particular case where this is useful is if the context is to be modified for the user process to get some user space parameters. This also ensures that things like SparkSession are already correctly setup before the type transformers are called

This should return either the same context of the mutated context

Parameter Type
user_params typing.Optional[flytekit.core.context_manager.ExecutionParameters]

sandbox_execute()

def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

Parameter Type
ctx flytekit.core.context_manager.FlyteContext
input_literal_map flytekit.models.literals.LiteralMap

Properties

Property Type Description
deck_fields
disable_deck
docs
enable_deck
environment
instantiated_in
interface
lhs
location
metadata
name
python_interface
security_context
task_config
task_type
task_type_version

flytekit.sensor.base_sensor.Interface

A Python native interface object, like inspect.signature but simpler.

def Interface(
    inputs: Union[Optional[Dict[str, Type]], Optional[Dict[str, Tuple[Type, Any]]]],
    outputs: Union[Optional[Dict[str, Type]], Optional[Dict[str, Optional[Type]]]],
    output_tuple_name: Optional[str],
    docstring: Optional[Docstring],
):
Parameter Type
inputs Union[Optional[Dict[str, Type]], Optional[Dict[str, Tuple[Type, Any]]]]
outputs Union[Optional[Dict[str, Type]], Optional[Dict[str, Optional[Type]]]]
output_tuple_name Optional[str]
docstring Optional[Docstring]

Methods

Method Description
remove_inputs() This method is useful in removing some variables from the Flyte backend inputs specification, as these are
with_inputs() Use this to add additional inputs to the interface
with_outputs() This method allows addition of extra outputs are expected from a task specification

remove_inputs()

def remove_inputs(
    vars: Optional[List[str]],
):

This method is useful in removing some variables from the Flyte backend inputs specification, as these are implicit local only inputs or will be supplied by the library at runtime. For example, spark-session etc It creates a new instance of interface with the requested variables removed

Parameter Type
vars Optional[List[str]]

with_inputs()

def with_inputs(
    extra_inputs: Dict[str, Type],
):

Use this to add additional inputs to the interface. This is useful for adding additional implicit inputs that are added without the user requesting for them

Parameter Type
extra_inputs Dict[str, Type]

with_outputs()

def with_outputs(
    extra_outputs: Dict[str, Type],
):

This method allows addition of extra outputs are expected from a task specification

Parameter Type
extra_outputs Dict[str, Type]

Properties

Property Type Description
default_inputs_as_kwargs
docstring
inputs
inputs_with_defaults
output_names
output_tuple
output_tuple_name
outputs

flytekit.sensor.base_sensor.Protocol

Base class for protocol classes.

Protocol classes are defined as::

class Proto(Protocol): def meth(self) -> int: …

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing).

For example::

class C: def meth(self) -> int: return 0

def func(x: Proto) -> int: return x.meth()

func(C()) # Passes static type check

See PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as::

class GenProtoT: def meth(self) -> T: …

flytekit.sensor.base_sensor.PythonTask

Base Class for all Tasks with a Python native Interface. This should be directly used for task types, that do not have a python function to be executed. Otherwise refer to :py:class:flytekit.PythonFunctionTask.

def PythonTask(
    task_type: str,
    name: str,
    task_config: typing.Optional[~T],
    interface: typing.Optional[flytekit.core.interface.Interface],
    environment: typing.Optional[typing.Dict[str, str]],
    disable_deck: typing.Optional[bool],
    enable_deck: typing.Optional[bool],
    deck_fields: typing.Optional[typing.Tuple[flytekit.deck.deck.DeckField, ...]],
    kwargs,
):
Parameter Type
task_type str
name str
task_config typing.Optional[~T]
interface typing.Optional[flytekit.core.interface.Interface]
environment typing.Optional[typing.Dict[str, str]]
disable_deck typing.Optional[bool]
enable_deck typing.Optional[bool]
deck_fields typing.Optional[typing.Tuple[flytekit.deck.deck.DeckField, ...]]
kwargs **kwargs

Methods

Method Description
compile() Generates a node that encapsulates this task in a workflow definition
construct_node_metadata() Used when constructing the node that encapsulates this task as part of a broader workflow definition
dispatch_execute() This method translates Flyte’s Type system based input values and invokes the actual call to the executor
execute() This method will be invoked to execute the task
find_lhs() None
get_config() Returns the task config as a serializable dictionary
get_container() Returns the container definition (if any) that is used to run the task on hosted Flyte
get_custom() Return additional plugin-specific custom data (if any) as a serializable dictionary
get_extended_resources() Returns the extended resources to allocate to the task on hosted Flyte
get_input_types() Returns the names and python types as a dictionary for the inputs of this task
get_k8s_pod() Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte
get_sql() Returns the Sql definition (if any) that is used to run the task on hosted Flyte
get_type_for_input_var() Returns the python type for an input variable by name
get_type_for_output_var() Returns the python type for the specified output variable by name
local_execute() This function is used only in the local execution path and is responsible for calling dispatch execute
local_execution_mode() None
post_execute() Post execute is called after the execution has completed, with the user_params and can be used to clean-up,
pre_execute() This is the method that will be invoked directly before executing the task method and before all the inputs
sandbox_execute() Call dispatch_execute, in the context of a local sandbox execution

compile()

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
):

Generates a node that encapsulates this task in a workflow definition.

Parameter Type
ctx flytekit.core.context_manager.FlyteContext
args *args
kwargs **kwargs

construct_node_metadata()

def construct_node_metadata()

Used when constructing the node that encapsulates this task as part of a broader workflow definition.

dispatch_execute()

def dispatch_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

This method translates Flyte’s Type system based input values and invokes the actual call to the executor This method is also invoked during runtime.

  • VoidPromise is returned in the case when the task itself declares no outputs.
  • Literal Map is returned when the task returns either one more outputs in the declaration. Individual outputs may be none
  • DynamicJobSpec is returned when a dynamic workflow is executed
Parameter Type
ctx flytekit.core.context_manager.FlyteContext
input_literal_map flytekit.models.literals.LiteralMap

execute()

def execute(
    kwargs,
):

This method will be invoked to execute the task.

Parameter Type
kwargs **kwargs

find_lhs()

def find_lhs()

get_config()

def get_config(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_container()

def get_container(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the container definition (if any) that is used to run the task on hosted Flyte.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_custom()

def get_custom(
    settings: flytekit.configuration.SerializationSettings,
):

Return additional plugin-specific custom data (if any) as a serializable dictionary.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_extended_resources()

def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the extended resources to allocate to the task on hosted Flyte.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_input_types()

def get_input_types()

Returns the names and python types as a dictionary for the inputs of this task.

get_k8s_pod()

def get_k8s_pod(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_sql()

def get_sql(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

Parameter Type
settings flytekit.configuration.SerializationSettings

get_type_for_input_var()

def get_type_for_input_var(
    k: str,
    v: typing.Any,
):

Returns the python type for an input variable by name.

Parameter Type
k str
v typing.Any

get_type_for_output_var()

def get_type_for_output_var(
    k: str,
    v: typing.Any,
):

Returns the python type for the specified output variable by name.

Parameter Type
k str
v typing.Any

local_execute()

def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
):

This function is used only in the local execution path and is responsible for calling dispatch execute. Use this function when calling a task with native values (or Promises containing Flyte literals derived from Python native values).

Parameter Type
ctx flytekit.core.context_manager.FlyteContext
kwargs **kwargs

local_execution_mode()

def local_execution_mode()

post_execute()

def post_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
    rval: typing.Any,
):

Post execute is called after the execution has completed, with the user_params and can be used to clean-up, or alter the outputs to match the intended tasks outputs. If not overridden, then this function is a No-op

Parameter Type
user_params typing.Optional[flytekit.core.context_manager.ExecutionParameters]
rval typing.Any

pre_execute()

def pre_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
):

This is the method that will be invoked directly before executing the task method and before all the inputs are converted. One particular case where this is useful is if the context is to be modified for the user process to get some user space parameters. This also ensures that things like SparkSession are already correctly setup before the type transformers are called

This should return either the same context of the mutated context

Parameter Type
user_params typing.Optional[flytekit.core.context_manager.ExecutionParameters]

sandbox_execute()

def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

Parameter Type
ctx flytekit.core.context_manager.FlyteContext
input_literal_map flytekit.models.literals.LiteralMap

Properties

Property Type Description
deck_fields
disable_deck
docs
enable_deck
environment
instantiated_in
interface
lhs
location
metadata
name
python_interface
security_context
task_config
task_type
task_type_version

flytekit.sensor.base_sensor.ResourceMeta

This is the metadata for the job. For example, the id of the job.

def ResourceMeta()

Methods

Method Description
decode() Decode the resource meta from bytes
encode() Encode the resource meta to bytes

decode()

def decode(
    data: bytes,
):

Decode the resource meta from bytes.

Parameter Type
data bytes

encode()

def encode()

Encode the resource meta to bytes.

flytekit.sensor.base_sensor.SensorConfig

Base class for protocol classes.

Protocol classes are defined as::

class Proto(Protocol): def meth(self) -> int: …

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing).

For example::

class C: def meth(self) -> int: return 0

def func(x: Proto) -> int: return x.meth()

func(C()) # Passes static type check

See PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as::

class GenProtoT: def meth(self) -> T: …

def SensorConfig(
    args,
    kwargs,
):
Parameter Type
args *args
kwargs **kwargs

Methods

Method Description
from_dict() Deserialize the sensor config from a dictionary
to_dict() Serialize the sensor config to a dictionary

from_dict()

def from_dict(
    d: typing.Dict[str, typing.Any],
):

Deserialize the sensor config from a dictionary.

Parameter Type
d typing.Dict[str, typing.Any]

to_dict()

def to_dict()

Serialize the sensor config to a dictionary.

flytekit.sensor.base_sensor.SensorMetadata

def SensorMetadata(
    sensor_module: str,
    sensor_name: str,
    sensor_config: typing.Optional[dict],
    inputs: typing.Optional[dict],
):
Parameter Type
sensor_module str
sensor_name str
sensor_config typing.Optional[dict]
inputs typing.Optional[dict]

Methods

Method Description
decode() Decode the resource meta from bytes
encode() Encode the resource meta to bytes

decode()

def decode(
    data: bytes,
):

Decode the resource meta from bytes.

Parameter Type
data bytes

encode()

def encode()

Encode the resource meta to bytes.

flytekit.sensor.base_sensor.SerializationSettings

These settings are provided while serializing a workflow and task, before registration. This is required to get runtime information at serialization time, as well as some defaults.

Attributes: project (str): The project (if any) with which to register entities under. domain (str): The domain (if any) with which to register entities under. version (str): The version (if any) with which to register entities under. image_config (ImageConfig): The image config used to define task container images. env (Optional[Dict[str, str]]): Environment variables injected into task container definitions. flytekit_virtualenv_root (Optional[str]): During out of container serialize the absolute path of the flytekit virtualenv at serialization time won’t match the in-container value at execution time. This optional value is used to provide the in-container virtualenv path python_interpreter (Optional[str]): The python executable to use. This is used for spark tasks in out of container execution. entrypoint_settings (Optional[EntrypointSettings]): Information about the command, path and version of the entrypoint program. fast_serialization_settings (Optional[FastSerializationSettings]): If the code is being serialized so that it can be fast registered (and thus omit building a Docker image) this object contains additional parameters for serialization. source_root (Optional[str]): The root directory of the source code.

def SerializationSettings(
    image_config: ImageConfig,
    project: typing.Optional[str],
    domain: typing.Optional[str],
    version: typing.Optional[str],
    env: Optional[Dict[str, str]],
    git_repo: Optional[str],
    python_interpreter: str,
    flytekit_virtualenv_root: Optional[str],
    fast_serialization_settings: Optional[FastSerializationSettings],
    source_root: Optional[str],
):
Parameter Type
image_config ImageConfig
project typing.Optional[str]
domain typing.Optional[str]
version typing.Optional[str]
env Optional[Dict[str, str]]
git_repo Optional[str]
python_interpreter str
flytekit_virtualenv_root Optional[str]
fast_serialization_settings Optional[FastSerializationSettings]
source_root Optional[str]

Methods

Method Description
default_entrypoint_settings() Assumes the entrypoint is installed in a virtual-environment where the interpreter is
for_image() None
from_dict() None
from_json() None
from_transport() None
new_builder() Creates a ``SerializationSettings
schema() None
should_fast_serialize() Whether or not the serialization settings specify that entities should be serialized for fast registration
to_dict() None
to_json() None
venv_root_from_interpreter() Computes the path of the virtual environment root, based on the passed in python interpreter path
with_serialized_context() Use this method to create a new SerializationSettings that has an environment variable set with the SerializedContext

default_entrypoint_settings()

def default_entrypoint_settings(
    interpreter_path: str,
):

Assumes the entrypoint is installed in a virtual-environment where the interpreter is

Parameter Type
interpreter_path str

for_image()

def for_image(
    image: str,
    version: str,
    project: str,
    domain: str,
    python_interpreter_path: str,
):
Parameter Type
image str
version str
project str
domain str
python_interpreter_path str

from_dict()

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

from_json()

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

from_transport()

def from_transport(
    s: str,
):
Parameter Type
s str

new_builder()

def new_builder()

Creates a SerializationSettings.Builder that copies the existing serialization settings parameters and allows for customization.

schema()

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

should_fast_serialize()

def should_fast_serialize()

Whether or not the serialization settings specify that entities should be serialized for fast registration.

to_dict()

def to_dict(
    encode_json,
):
Parameter Type
encode_json

to_json()

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

venv_root_from_interpreter()

def venv_root_from_interpreter(
    interpreter_path: str,
):

Computes the path of the virtual environment root, based on the passed in python interpreter path for example /opt/venv/bin/python3 -> /opt/venv

Parameter Type
interpreter_path str

with_serialized_context()

def with_serialized_context()

Use this method to create a new SerializationSettings that has an environment variable set with the SerializedContext This is useful in transporting SerializedContext to serialized and registered tasks. The setting will be available in the env field with the key SERIALIZED_CONTEXT_ENV_VAR :return: A newly constructed SerializationSettings, or self, if it already has the serializationSettings

Properties

Property Type Description
entrypoint_settings
serialized_context

flytekit.sensor.base_sensor.TaskMetadata

Metadata for a Task. Things like retries and whether or not caching is turned on, and cache version are specified here.

See the :std:ref:IDL <idl:protos/docs/core/core:taskmetadata> for the protobuf definition.

Attributes: cache (bool): Indicates if caching should be enabled. See :std:ref:Caching <cookbook:caching>. cache_serialize (bool): Indicates if identical (i.e. same inputs) instances of this task should be executed in serial when caching is enabled. See :std:ref:Caching <cookbook:caching>. cache_version (str): Version to be used for the cached value. cache_ignore_input_vars (Tuple[str, …]): Input variables that should not be included when calculating hash for cache. interruptible (Optional[bool]): Indicates that this task can be interrupted and/or scheduled on nodes with lower QoS guarantees that can include pre-emption. deprecated (str): Can be used to provide a warning message for a deprecated task. An absence or empty string indicates that the task is active and not deprecated. retries (int): for retries=n; n > 0, on failures of this task, the task will be retried at-least n number of times. timeout (Optional[Union[datetime.timedelta, int]]): The maximum duration for which one execution of this task should run. The execution will be terminated if the runtime exceeds this timeout. pod_template_name (Optional[str]): The name of an existing PodTemplate resource in the cluster which will be used for this task. generates_deck (bool): Indicates whether the task will generate a Deck URI. is_eager (bool): Indicates whether the task should be treated as eager.

def TaskMetadata(
    cache: bool,
    cache_serialize: bool,
    cache_version: str,
    cache_ignore_input_vars: typing.Tuple[str, ...],
    interruptible: typing.Optional[bool],
    deprecated: str,
    retries: int,
    timeout: typing.Union[datetime.timedelta, int, NoneType],
    pod_template_name: typing.Optional[str],
    generates_deck: bool,
    is_eager: bool,
):
Parameter Type
cache bool
cache_serialize bool
cache_version str
cache_ignore_input_vars typing.Tuple[str, ...]
interruptible typing.Optional[bool]
deprecated str
retries int
timeout typing.Union[datetime.timedelta, int, NoneType]
pod_template_name typing.Optional[str]
generates_deck bool
is_eager bool

Methods

Method Description
to_taskmetadata_model() Converts to _task_model

to_taskmetadata_model()

def to_taskmetadata_model()

Converts to _task_model.TaskMetadata

Properties

Property Type Description
retry_strategy

flytekit.sensor.base_sensor.TypeVar

Type variable.

The preferred way to construct a type variable is via the dedicated syntax for generic functions, classes, and type aliases::

class Sequence[T]: # T is a TypeVar …

This syntax can also be used to create bound and constrained type variables::

S is a TypeVar bound to str

class StrSequence[S: str]: …

A is a TypeVar constrained to str or bytes

class StrOrBytesSequence[A: (str, bytes)]: …

However, if desired, reusable type variables can also be constructed manually, like so::

T = TypeVar(‘T’) # Can be anything S = TypeVar(‘S’, bound=str) # Can be any subtype of str A = TypeVar(‘A’, str, bytes) # Must be exactly str or bytes

Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function and type alias definitions.

The variance of type variables is inferred by type checkers when they are created through the type parameter syntax and when infer_variance=True is passed. Manually created type variables may be explicitly marked covariant or contravariant by passing covariant=True or contravariant=True. By default, manually created type variables are invariant. See PEP 484 and PEP 695 for more details.