flytekit.configuration

===================== Configuration

.. currentmodule:: flytekit.configuration

Flytekit Configuration Sources ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

There are multiple ways to configure flytekit settings:

Command Line Arguments: This is the recommended way of setting configuration values for many cases. For example, see pyflyte package <pyflyte.html#pyflyte-package>_ command.

Python Config Object: A :py:class:~flytekit.configuration.Config object can by used directly, e.g. when initializing a :py:class:~flytefit.remote.remote.FlyteRemote object. See :doc:here <design/control_plane> for examples on how to specify a Config object.

Environment Variables: Users can specify these at compile time, but when your task is run, Flyte Propeller will also set configuration to ensure correct interaction with the platform. The environment variables must be specified with the format FLYTE_{SECTION}_{OPTION}, all in upper case. For example, to specify the :py:class:PlatformConfig.endpoint <flytekit.configuration.PlatformConfig> setting, the environment variable would be FLYTE_PLATFORM_URL.

.. note::

Environment variables won’t work for image configuration, which need to be specified with the pyflyte package --image ... <pyflyte.html#cmdoption-pyflyte-package-i>_ option or in a configuration file.

YAML Format Configuration File: A configuration file that contains settings for both flytectl <https://docs.flyte.org/en/latest/flytectl/api/overview.html>__ and flytekit. This is the recommended configuration file format. Invoke the :ref:flytectl config init <flytectl_config_init> command to create a boilerplate ~/.flyte/config.yaml file, and flytectl --help to learn about all of the configuration yaml options.

.. dropdown:: See example config.yaml file :animate: fade-in-slide-down

.. literalinclude:: ../../tests/flytekit/unit/configuration/configs/sample.yaml :language: yaml :caption: config.yaml

INI Format Configuration File: A configuration file for flytekit. By default, flytekit will look for a file in two places:

1. First, a file named flytekit.config in the Python interpreter’s working directory.
2. A file in ~/.flyte/config in the home directory as detected by Python.

.. dropdown:: See example flytekit.config file :animate: fade-in-slide-down

.. literalinclude:: ../../tests/flytekit/unit/configuration/configs/images.config :language: ini :caption: flytekit.config

.. warning::

The INI format configuration is considered a legacy configuration format. We recommend using the yaml format instead if you’re using a configuration file.

How is configuration used? ^^^^^^^^^^^^^^^^^^^^^^^^^^

Configuration usage can roughly be bucketed into the following areas,

• Compile-time settings: these are settings like the default image and named images, where to look for Flyte code, etc.
• Platform settings: Where to find the Flyte backend (Admin DNS, whether to use SSL)
• Registration Run-time settings: these are things like the K8s service account to use, a specific S3/GCS bucket to write off-loaded data (dataframes and files) to, notifications, labels & annotations, etc.
• Data access settings: Is there a custom S3 endpoint in use? Backoff/retry behavior for accessing S3/GCS, key and password, etc.
• Other settings - Statsd configuration, which is a run-time applicable setting but is not necessarily relevant to the Flyte platform.

Configuration Objects

The following objects are encapsulated in a parent object called Config.

.. autosummary:: :template: custom.rst :toctree: generated/ :nosignatures:

~Config

.. _configuration-compile-time-settings:

Serialization Time Settings ^^^^^^^^^^^^^^^^^^^^^^^^^^^

These are serialization/compile-time settings that are used when using commands like pyflyte package <pyflyte.html#pyflyte-package>_ or pyflyte register <pyflyte.html#pyflyte-register>_. These configuration settings are typically passed in as flags to the above CLI commands.

The image configurations are typically either passed in via an --image <pyflyte.html#cmdoption-pyflyte-package-i>_ flag, or can be specified in the yaml or ini configuration files (see examples above).

.. autosummary:: :template: custom.rst :toctree: generated/ :nosignatures:

~Image ~ImageConfig ~SerializationSettings ~FastSerializationSettings

.. _configuration-execution-time-settings:

Execution Time Settings ^^^^^^^^^^^^^^^^^^^^^^^

Users typically shouldn’t be concerned with these configurations, as they are typically set by FlytePropeller or FlyteAdmin. The configurations below are useful for authenticating to a Flyte backend, configuring data access credentials, secrets, and statsd metrics.

.. autosummary:: :template: custom.rst :toctree: generated/ :nosignatures:

~PlatformConfig ~StatsConfig ~SecretsConfig ~S3Config ~GCSConfig ~DataConfig

Directory

Classes

flytekit.configuration.AuthType

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.configuration.AzureBlobStorageConfig

Any Azure Blob Storage specific configuration.

def AzureBlobStorageConfig(
    account_name: typing.Optional[str],
    account_key: typing.Optional[str],
    tenant_id: typing.Optional[str],
    client_id: typing.Optional[str],
    client_secret: typing.Optional[str],
):

Methods

auto()

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

flytekit.configuration.BytesIO

Buffered I/O implementation using an in-memory bytes buffer.

flytekit.configuration.Config

This the parent configuration object and holds all the underlying configuration object types. An instance of this object holds all the config necessary to

1. Interactive session with Flyte backend
2. Some parts are required for Serialization, for example Platform Config is not required
3. Runtime of a task

def Config(
    platform: PlatformConfig,
    secrets: SecretsConfig,
    stats: StatsConfig,
    data_config: DataConfig,
    local_sandbox_path: str,
):

Methods

auto()

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

Automatically constructs the Config Object. The order of precedence is as follows

1. first try to find any env vars that match the config vars specified in the FLYTE_CONFIG format.
2. If not found in environment then values ar read from the config file
3. If not found in the file, then the default values are used.

for_endpoint()

def for_endpoint(
    endpoint: str,
    insecure: bool,
    data_config: typing.Optional[DataConfig],
    config_file: typing.Union[str, ConfigFile],
):

Creates an automatic config for the given endpoint and uses the config_file or environment variable for default. Refer to Config.auto() to understand the default bootstrap behavior.

data_config can be used to configure how data is downloaded or uploaded to a specific Blob storage like S3 / GCS etc. But, for permissions to a specific backend just use Cloud providers reqcommendation. If using fsspec, then refer to fsspec documentation

for_sandbox()

def for_sandbox()

Constructs a new Config object specifically to connect to :std:ref:deployment-deployment-sandbox. If you are using a hosted Sandbox like environment, then you may need to use port-forward or ingress urls :return: Config

with_params()

def with_params(
    platform: PlatformConfig,
    secrets: SecretsConfig,
    stats: StatsConfig,
    data_config: DataConfig,
    local_sandbox_path: str,
):

flytekit.configuration.ConfigEntry

A top level Config entry holder, that holds multiple different representations of the config. Legacy means the INI style config files. YAML support is for the flytectl config file, which is there by default when flytectl starts a sandbox

def ConfigEntry(
    legacy: LegacyConfigEntry,
    yaml_entry: typing.Optional[YamlConfigEntry],
    transform: typing.Optional[typing.Callable[[str], typing.Any]],
):

Methods

read()

def read(
    cfg: typing.Optional[ConfigFile],
):

Reads the config Entry from the various sources in the following order, #. First try to read from the relevant environment variable, #. If missing, then try to read from the legacy config file, if one was parsed. #. If missing, then try to read from the yaml file.

The constructor for ConfigFile currently does not allow specification of both the ini and yaml style formats.

flytekit.configuration.ConfigFile

def ConfigFile(
    location: str,
):

Load the config from this location

Methods

get()

def get(
    c: typing.Union[LegacyConfigEntry, YamlConfigEntry],
):

Properties

flytekit.configuration.DataClassJsonMixin

DataClassJsonMixin is an ABC that functions as a Mixin.

As with other ABCs, it should not be instantiated directly.

Methods

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,
):

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,
):

flytekit.configuration.DataConfig

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

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

Methods

auto()

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

flytekit.configuration.DefaultImages

We may want to load the default images from remote - maybe s3 location etc?

Methods

default_image()

def default_image()

find_image_for()

def find_image_for(
    python_version: typing.Optional[flytekit.configuration.default_images.PythonVersion],
    flytekit_version: typing.Optional[str],
):

get_version_suffix()

def get_version_suffix()

flytekit.configuration.EntrypointSettings

This object carries information about the path of the entrypoint command that will be invoked at runtime. This is where pyflyte-execute code can be found. This is useful for cases like pyspark execution.

def EntrypointSettings(
    path: Optional[str],
):

Methods

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,
):

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,
):

flytekit.configuration.FastSerializationSettings

This object hold information about settings necessary to serialize an object so that it can be fast-registered.

def FastSerializationSettings(
    enabled: bool,
    destination_dir: Optional[str],
    distribution_location: Optional[str],
):

Methods

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,
):

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,
):

flytekit.configuration.GCSConfig

Any GCS specific configuration.

def GCSConfig(
    gsutil_parallelism: bool,
):

Methods

auto()

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

flytekit.configuration.GenericPersistenceConfig

Data storage configuration that applies across any provider.

def GenericPersistenceConfig(
    attach_execution_metadata: bool,
):

Methods

auto()

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

flytekit.configuration.Image

Image is a structured wrapper for task container images used in object serialization.

Attributes: name (str): A user-provided name to identify this image. fqn (str): Fully qualified image name. This consists of #. a registry location #. a username #. a repository name For example: hostname/username/reponame tag (str): Optional tag used to specify which version of an image to pull digest (str): Optional digest used to specify which version of an image to pull

def Image(
    name: str,
    fqn: str,
    tag: Optional[str],
    digest: Optional[str],
):

Methods

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,
):

look_up_image_info()

def look_up_image_info(
    name: str,
    image_identifier: str,
    allow_no_tag_or_digest: bool,
):

Creates an Image object from an image identifier string or a path to an ImageSpec yaml file.

This function is used when registering tasks/workflows with Admin. When using the canonical Python-based development cycle, the version that is used to register workflows and tasks with Admin should be the version of the image itself, which should ideally be something unique like the git revision SHA1 of the latest commit.

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,
):

Properties

flytekit.configuration.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.configuration.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.configuration.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.configuration.LocalConfig

Any configuration specific to local runs.

def LocalConfig(
    cache_enabled: bool,
    cache_overwrite: bool,
):

Methods

auto()

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

flytekit.configuration.PlatformConfig

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

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

Methods

auto()

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

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

for_endpoint()

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

flytekit.configuration.S3Config

S3 specific configuration

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

Methods

auto()

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

Automatically configure

flytekit.configuration.SecretsConfig

Configuration for secrets.

def SecretsConfig(
    env_prefix: str,
    default_dir: str,
    file_prefix: str,
):

Methods

auto()

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

Reads from environment variable or from config file

flytekit.configuration.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.configuration.StatsConfig

Configuration for sending statsd.

def StatsConfig(
    host: str,
    port: int,
    disabled: bool,
    disabled_tags: bool,
):

Methods

auto()

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

Reads from environment variable, followed by ConfigFile provided