flytekit.core.python_auto_container

Directory

Classes

flytekit.core.python_auto_container.ABC

Helper class that provides a standard way to create an ABC using inheritance.

flytekit.core.python_auto_container.BaseAccelerator

Base class for all accelerator types. This class is not meant to be instantiated directly.

Methods

to_flyte_idl()

def to_flyte_idl()

flytekit.core.python_auto_container.CopyFileDetection

Create a collection of name/value pairs.

Example enumeration:

class Color(Enum): … RED = 1 … BLUE = 2 … GREEN = 3

Access them by:

• attribute access:

Color.RED <Color.RED: 1>

• value lookup:

Color(1) <Color.RED: 1>

• name lookup:

Color[‘RED’] <Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

len(Color) 3

list(Color) [<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

flytekit.core.python_auto_container.DefaultNotebookTaskResolver

This resolved is used when the task is defined in a notebook. It is used to load the task from the notebook.

def DefaultNotebookTaskResolver(
    args,
    kwargs,
):

Methods

find_lhs()

def find_lhs()

get_all_tasks()

def get_all_tasks()

Future proof method. Just making it easy to access all tasks (Not required today as we auto register them)

load_task()

def load_task(
    loader_args: List[str],
):

Given the set of identifier keys, should return one Python Task or raise an error if not found

loader_args()

def loader_args(
    settings: SerializationSettings,
    task: PythonAutoContainerTask,
):

Return a list of strings that can help identify the parameter Task

name()

def name()

task_name()

def task_name(
    t: flytekit.core.base_task.Task,
):

Overridable function that can optionally return a custom name for a given task

Properties

flytekit.core.python_auto_container.DefaultTaskResolver

Please see the notes in the TaskResolverMixin as it describes this default behavior.

def DefaultTaskResolver(
    args,
    kwargs,
):

Methods

find_lhs()

def find_lhs()

get_all_tasks()

def get_all_tasks()

Future proof method. Just making it easy to access all tasks (Not required today as we auto register them)

load_task()

def load_task(
    loader_args: List[str],
):

Given the set of identifier keys, should return one Python Task or raise an error if not found

loader_args()

def loader_args(
    settings: SerializationSettings,
    task: PythonAutoContainerTask,
):

Return a list of strings that can help identify the parameter Task

name()

def name()

task_name()

def task_name(
    t: flytekit.core.base_task.Task,
):

Overridable function that can optionally return a custom name for a given task

Properties

flytekit.core.python_auto_container.FlyteContextManager

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

Typical usage is

.. code-block:: python

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

If required - not recommended you can use

FlyteContextManager.push_context()

but correspondingly a pop_context should be called

FlyteContextManager.pop_context()

Methods

add_signal_handler()

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

current_context()

def current_context()

get_origin_stackframe()

def get_origin_stackframe(
    limit,
):

initialize()

def initialize()

Re-initializes the context and erases the entire context

pop_context()

def pop_context()

push_context()

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

size()

def size()

with_context()

def with_context(
    b: FlyteContext.Builder,
):

flytekit.core.python_auto_container.FlyteTrackedABC

This class exists because if you try to inherit from abc.ABC and TrackedInstance by itself, you’ll get the well-known TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases error.

Methods

register()

def register(
    cls,
    subclass,
):

Register a virtual subclass of an ABC.

Returns the subclass, to allow usage as a class decorator.

flytekit.core.python_auto_container.ImageBuildEngine

ImageBuildEngine contains a list of builders that can be used to build an ImageSpec.

Methods

build()

def build(
    image_spec: flytekit.image_spec.image_spec.ImageSpec,
):

get_registry()

def get_registry()

register()

def register(
    builder_type: str,
    image_spec_builder: flytekit.image_spec.image_spec.ImageSpecBuilder,
    priority: int,
):

flytekit.core.python_auto_container.ImageConfig

We recommend you to use ImageConfig.auto(img_name=None) to create an ImageConfig. For example, ImageConfig.auto(img_name=““ghcr.io/flyteorg/flytecookbook:v1.0.0"”) will create an ImageConfig.

ImageConfig holds available images which can be used at registration time. A default image can be specified along with optional additional images. Each image in the config must have a unique name.

Attributes: default_image (Optional[Image]): The default image to be used as a container for task serialization. images (List[Image]): Optional, additional images which can be used in task container definitions.

def ImageConfig(
    default_image: Optional[Image],
    images: Optional[List[Image]],
):

Methods

auto()

def auto(
    config_file: typing.Union[str, ConfigFile, None],
    img_name: Optional[str],
):

Reads from config file or from img_name Note that this function does not take into account the flytekit default images (see the Dockerfiles at the base of this repo). To pick those up, see the auto_default_image function..

auto_default_image()

def auto_default_image()

create_from()

def create_from(
    default_image: Optional[Image],
    other_images: typing.Optional[typing.List[Image]],
):

find_image()

def find_image(
    name,
):

Return an image, by name, if it exists.

from_dict()

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

from_images()

def from_images(
    default_image: str,
    m: typing.Optional[typing.Dict[str, str]],
):

Allows you to programmatically create an ImageConfig. Usually only the default_image is required, unless your workflow uses multiple images

.. code:: python

ImageConfig.from_dict( “ghcr.io/flyteorg/flytecookbook:v1.0.0”, { “spark”: “ghcr.io/flyteorg/myspark:…”, “other”: “…”, } )

urn:

from_json()

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

schema()

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

to_dict()

def to_dict(
    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,
):

validate_image()

def validate_image(
    _: typing.Any,
    param: str,
    values: tuple,
):

Validates the image to match the standard format. Also validates that only one default image is provided. a default image, is one that is specified as default=<image_uri> or just <image_uri>. All other images should be provided with a name, in the format name=<image_uri> This method can be used with the CLI

flytekit.core.python_auto_container.ImageSpec

This class is used to specify the docker image that will be used to run the task.

def ImageSpec(
    name: str,
    python_version: str,
    builder: typing.Optional[str],
    source_root: typing.Optional[str],
    env: typing.Optional[typing.Dict[str, str]],
    registry: typing.Optional[str],
    packages: typing.Optional[typing.List[str]],
    conda_packages: typing.Optional[typing.List[str]],
    conda_channels: typing.Optional[typing.List[str]],
    requirements: typing.Optional[str],
    apt_packages: typing.Optional[typing.List[str]],
    cuda: typing.Optional[str],
    cudnn: typing.Optional[str],
    base_image: typing.Union[str, ForwardRef('ImageSpec'), NoneType],
    platform: str,
    pip_index: typing.Optional[str],
    pip_extra_index_url: typing.Optional[typing.List[str]],
    pip_secret_mounts: typing.Optional[typing.List[typing.Tuple[str, str]]],
    pip_extra_args: typing.Optional[str],
    registry_config: typing.Optional[str],
    entrypoint: typing.Optional[typing.List[str]],
    commands: typing.Optional[typing.List[str]],
    tag_format: typing.Optional[str],
    source_copy_mode: typing.Optional[flytekit.constants.CopyFileDetection],
    copy: typing.Optional[typing.List[str]],
    python_exec: typing.Optional[str],
):

Methods

exist()

def exist()

Check if the image exists in the registry. Return True if the image exists in the registry, False otherwise. Return None if failed to check if the image exists due to the permission issue or other reasons.

force_push()

def force_push()

Builder that returns a new image spec with force push enabled.

from_env()

def from_env(
    pinned_packages: typing.Optional[typing.List[str]],
    kwargs,
):

Create ImageSpec with the environment’s Python version and packages pinned to the ones in the environment.

image_name()

def image_name()

Full image name with tag.

is_container()

def is_container()

Check if the current container image in the pod is built from current image spec. :return: True if the current container image in the pod is built from current image spec, False otherwise.

with_apt_packages()

def with_apt_packages(
    apt_packages: typing.Union[str, typing.List[str]],
):

Builder that returns a new image spec with an additional list of apt packages that will be executed during the building process.

with_commands()

def with_commands(
    commands: typing.Union[str, typing.List[str]],
):

Builder that returns a new image spec with an additional list of commands that will be executed during the building process.

with_copy()

def with_copy(
    src: typing.Union[str, typing.List[str]],
):

Builder that returns a new image spec with the source files copied to the destination directory.

with_packages()

def with_packages(
    packages: typing.Union[str, typing.List[str]],
):

Builder that returns a new image speck with additional python packages that will be installed during the building process.

Properties

flytekit.core.python_auto_container.PickledEntity

Represents the structure of the pickled object stored in the .pkl file for interactive mode.

Attributes: metadata: Metadata about the pickled entities including Python version entities: Dictionary mapping entity names to their PythonAutoContainerTask instances

def PickledEntity(
    metadata: PickledEntityMetadata,
    entities: Dict[str, PythonAutoContainerTask],
):

flytekit.core.python_auto_container.PickledEntityMetadata

Metadata for a pickled entity containing version information.

Attributes: python_version: The Python version string (e.g. “3.12.0”) used to create the pickle

def PickledEntityMetadata(
    python_version: str,
):

flytekit.core.python_auto_container.PodTemplate

Custom PodTemplate specification for a Task.

def PodTemplate(
    pod_spec: typing.Optional[ForwardRef('V1PodSpec')],
    primary_container_name: str,
    labels: typing.Optional[typing.Dict[str, str]],
    annotations: typing.Optional[typing.Dict[str, str]],
):

flytekit.core.python_auto_container.PythonAutoContainerTask

A Python AutoContainer task should be used as the base for all extensions that want the user’s code to be in the container and the container information to be automatically captured. This base will auto configure the image and image version to be used for all its derivatives.

If you are looking to extend, you might prefer to use PythonFunctionTask or PythonInstanceTask

def PythonAutoContainerTask(
    name: str,
    task_config: T,
    task_type,
    container_image: Optional[Union[str, ImageSpec]],
    requests: Optional[Resources],
    limits: Optional[Resources],
    environment: Optional[Dict[str, str]],
    task_resolver: Optional[TaskResolverMixin],
    secret_requests: Optional[List[Secret]],
    pod_template: Optional[PodTemplate],
    pod_template_name: Optional[str],
    accelerator: Optional[BaseAccelerator],
    shared_memory: Optional[Union[L[True], str]],
    resources: Optional[Resources],
    kwargs,
):

Methods

compile()

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

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

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

execute()

def execute(
    kwargs,
):

This method will be invoked to execute the task.

find_lhs()

def find_lhs()

get_command()

def get_command(
    settings: SerializationSettings,
):

Returns the command which should be used in the container definition for the serialized version of this task registered on a hosted Flyte platform.

get_config()

def get_config(
    settings: SerializationSettings,
):

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

get_container()

def get_container(
    settings: SerializationSettings,
):

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

get_custom()

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

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

get_default_command()

def get_default_command(
    settings: SerializationSettings,
):

Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.

get_extended_resources()

def get_extended_resources(
    settings: SerializationSettings,
):

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

get_image()

def get_image(
    settings: SerializationSettings,
):

Update image spec based on fast registration usage, and return string representing the image

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: SerializationSettings,
):

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

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.

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.

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.

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).

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

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

reset_command_fn()

def reset_command_fn()

Resets the command which should be used in the container definition of this task to the default arguments. This is useful when the command line is overridden at serialization time.

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.

set_command_fn()

def set_command_fn(
    get_command_fn: Optional[Callable[[SerializationSettings], List[str]]],
):

By default, the task will run on the Flyte platform using the pyflyte-execute command. However, it can be useful to update the command with which the task is serialized for specific cases like running map tasks (“pyflyte-map-execute”) or for fast-executed tasks.

set_resolver()

def set_resolver(
    resolver: TaskResolverMixin,
):

By default, flytekit uses the DefaultTaskResolver to resolve the task. This method allows the user to set a custom task resolver. It can be useful to override the task resolver for specific cases like running tasks in the jupyter notebook.

Properties

flytekit.core.python_auto_container.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,
):

Methods

compile()

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

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

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

execute()

def execute(
    kwargs,
):

This method will be invoked to execute the task.

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.

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.

get_custom()

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

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

get_extended_resources()

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

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

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.

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.

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.

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.

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).

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

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

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.

Properties

flytekit.core.python_auto_container.ResourceSpec

def ResourceSpec(
    requests: flytekit.core.resources.Resources,
    limits: flytekit.core.resources.Resources,
):

Methods

from_dict()

def from_dict(
    d,
    dialect,
):

from_json()

def from_json(
    data: typing.Union[str, bytes, bytearray],
    decoder: collections.abc.Callable[[typing.Union[str, bytes, bytearray]], dict[typing.Any, typing.Any]],
    from_dict_kwargs: typing.Any,
):

from_multiple_resource()

def from_multiple_resource(
    resource: flytekit.core.resources.Resources,
):

Convert Resources that represent both a requests and limits into a ResourceSpec.

to_dict()

def to_dict()

to_json()

def to_json(
    encoder: collections.abc.Callable[[typing.Any], typing.Union[str, bytes, bytearray]],
    to_dict_kwargs: typing.Any,
):

flytekit.core.python_auto_container.Resources

This class is used to specify both resource requests and resource limits.

.. code-block:: python

Resources(cpu=“1”, mem=“2048”) # This is 1 CPU and 2 KB of memory Resources(cpu=“100m”, mem=“2Gi”) # This is 1/10th of a CPU and 2 gigabytes of memory Resources(cpu=0.5, mem=1024) # This is 500m CPU and 1 KB of memory

For Kubernetes-based tasks, pods use ephemeral local storage for scratch space, caching, and for logs.

This allocates 1Gi of such local storage.

Resources(ephemeral_storage=“1Gi”)

When used together with @task(resources=), you a specific the request and limits with one object. When the value is set to a tuple or list, the first value is the request and the second value is the limit. If the value is a single value, then both the requests and limit is set to that value. For example, the Resource(cpu=("1", "2"), mem=1024) will set the cpu request to 1, cpu limit to 2, mem limit and request to 1024.

.. note::

Persistent storage is not currently supported on the Flyte backend.

Please see the :std:ref:User Guide <cookbook:customizing task resources> for detailed examples. Also refer to the K8s conventions. <https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#resource-units-in-kubernetes>__

def Resources(
    cpu: typing.Union[str, int, float, list, tuple, NoneType],
    mem: typing.Union[str, int, list, tuple, NoneType],
    gpu: typing.Union[str, int, list, tuple, NoneType],
    ephemeral_storage: typing.Union[str, int, NoneType],
):

Methods

from_dict()

def from_dict(
    d,
    dialect,
):

from_json()

def from_json(
    data: typing.Union[str, bytes, bytearray],
    decoder: collections.abc.Callable[[typing.Union[str, bytes, bytearray]], dict[typing.Any, typing.Any]],
    from_dict_kwargs: typing.Any,
):

to_dict()

def to_dict()

to_json()

def to_json(
    encoder: collections.abc.Callable[[typing.Any], typing.Union[str, bytes, bytearray]],
    to_dict_kwargs: typing.Any,
):

flytekit.core.python_auto_container.Secret

See :std:ref:cookbook:secrets for usage examples.

def Secret(
    group: typing.Optional[str],
    key: typing.Optional[str],
    group_version: typing.Optional[str],
    mount_requirement: <enum 'MountType'>,
    env_var: typing.Optional[str],
):

Methods

from_flyte_idl()

def from_flyte_idl(
    pb2_object: flyteidl.core.security_pb2.Secret,
):

serialize_to_string()

def serialize_to_string()

short_string()

def short_string()

to_flyte_idl()

def to_flyte_idl()

verbose_string()

def verbose_string()

Properties

flytekit.core.python_auto_container.SecurityContext

This is a higher level wrapper object that for the most part users shouldn’t have to worry about. You should be able to just use :py:class:flytekit.Secret instead.

def SecurityContext(
    run_as: typing.Optional[flytekit.models.security.Identity],
    secrets: typing.Optional[typing.List[flytekit.models.security.Secret]],
    tokens: typing.Optional[typing.List[flytekit.models.security.OAuth2TokenRequest]],
):

Methods

from_flyte_idl()

def from_flyte_idl(
    pb2_object: flyteidl.core.security_pb2.SecurityContext,
):

serialize_to_string()

def serialize_to_string()

short_string()

def short_string()

to_flyte_idl()

def to_flyte_idl()

verbose_string()

def verbose_string()

Properties

flytekit.core.python_auto_container.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],
):

Methods

default_entrypoint_settings()

def default_entrypoint_settings(
    interpreter_path: str,
):

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

for_image()

def for_image(
    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,
):

from_json()

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

from_transport()

def from_transport(
    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,
):

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,
):

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,
):

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

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

flytekit.core.python_auto_container.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,
):

Methods

to_taskmetadata_model()

def to_taskmetadata_model()

Converts to _task_model.TaskMetadata

Properties

flytekit.core.python_auto_container.TaskResolverMixin

Flytekit tasks interact with the Flyte platform very, very broadly in two steps. They need to be uploaded to Admin, and then they are run by the user upon request (either as a single task execution or as part of a workflow). In any case, at execution time, for most tasks (that is those that generate a container target) the container image containing the task needs to be spun up again at which point the container needs to know which task it’s supposed to run and how to rehydrate the task object.

For example, the serialization of a simple task ::

in repo_root/workflows/example.py

@task def t1(…) -> …: …

might result in a container with arguments like ::

pyflyte-execute –inputs s3://path/inputs.pb –output-prefix s3://outputs/location –raw-output-data-prefix /tmp/data –resolver flytekit.core.python_auto_container.default_task_resolver – task-module repo_root.workflows.example task-name t1

At serialization time, the container created for the task will start out automatically with the pyflyte-execute bit, along with the requisite input/output args and the offloaded data prefix. Appended to that will be two things,

#. the location of the task’s task resolver, followed by two dashes, followed by #. the arguments provided by calling the loader_args function below.

The default_task_resolver declared below knows that

• When loader_args is called on a task, to look up the module the task is in, and the name of the task (the key of the task in the module, either the function name, or the variable it was assigned to).
• When load_task is called, it interprets the first part of the command as the module to call importlib.import_module on, and then looks for a key t1.

This is just the default behavior. Users should feel free to implement their own resolvers.

Methods

get_all_tasks()

def get_all_tasks()

Future proof method. Just making it easy to access all tasks (Not required today as we auto register them)

load_task()

def load_task(
    loader_args: typing.List[str],
):

Given the set of identifier keys, should return one Python Task or raise an error if not found

loader_args()

def loader_args(
    settings: flytekit.configuration.SerializationSettings,
    t: flytekit.core.base_task.Task,
):

Return a list of strings that can help identify the parameter Task

name()

def name()

task_name()

def task_name(
    t: flytekit.core.base_task.Task,
):

Overridable function that can optionally return a custom name for a given task

Properties

flytekit.core.python_auto_container.TrackedInstance

Please see the notes for the metaclass above first.

This functionality has two use-cases currently,

• Keep track of naming for non-function PythonAutoContainerTasks. That is, things like the :py:class:flytekit.extras.sqlite3.task.SQLite3Task task.
• Task resolvers, because task resolvers are instances of :py:class:flytekit.core.python_auto_container.TaskResolverMixin classes, not the classes themselves, which means we need to look on the left hand side of them to see how to find them at task execution time.

def TrackedInstance(
    args,
    kwargs,
):

Methods

find_lhs()

def find_lhs()

Properties

flytekit.core.python_auto_container.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.

flytekit.core.python_auto_container.timeit

A context manager and a decorator that measures the execution time of the wrapped code block or functions. It will append a timing information to TimeLineDeck. For instance:

@timeit(“Function description”) def function()

with timeit(“Wrapped code block description”):

your code

def timeit(
    name: str,
):