flytekit.configuration

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 command.

Python Config Object

A Config object can be used directly, e.g. when initializing a FlyteRemote object. See the Control Plane design docs 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 PlatformConfig.endpoint setting, the environment variable would be FLYTE_PLATFORM_URL.

YAML Format Configuration File

A configuration file that contains settings for both flytectl and flytekit. This is the recommended configuration file format. Invoke the flytectl config init command to create a boilerplate ~/.flyte/config.yaml file, and flytectl --help to learn about all of the configuration yaml options.

Example config.yaml file:

# Sample config file

admin:
  # For GRPC endpoints you might want to use dns:///flyte.myexample.com
  endpoint: dns:///localhost:8089
  authType: Pkce
  insecure: true

logger:
  show-source: true
  level: 0

console:
  endpoint: http://localhost:8080
  insecure: true

# This section is used only in the control plane to trigger a remote execution
storage:
  type: minio
  stow:
    kind: s3
    config:
      auth_type: accesskey
      access_key_id: minio
      secret_key: miniostorage
      endpoint: http://localhost:9000
      region: us-east-1
      disable_ssl: true
      addressing_style: "path"


### 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.

Example `flytekit.config` file:
```ini
[sdk]
workflow_packages=my_cool_workflows, other_workflows

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:

Serialization Time Settings

These are serialization/compile-time settings that are used when using commands like pyflyte package or 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 flag, or can be specified in the yaml or ini configuration files (see examples above).

• Image: Represents a container image with optional configuration overrides.
• ImageConfig: Represents an image configuration for a given project/domain combination.
• SerializationSettings: Controls how to serialize Flyte entities when registering with Admin.
• FastSerializationSettings: Configuration for faster serialization 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.

• PlatformConfig: Configuration for how to connect to the Flyte platform.
• StatsConfig: Configuration for how to emit statsd metrics.
• SecretsConfig: Configuration for how to access secrets.
• S3Config: Amazon S3 specific configuration.
• GCSConfig: Google Cloud Storage specific configuration.
• DataConfig: Configuration for data access.

Directory

Classes

Variables

flytekit.configuration.AzureBlobStorageConfig

Any Azure Blob Storage specific configuration.

class 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],
) -> GCSConfig

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

class 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],
) -> n: Config

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],
) -> n: Config

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,
) -> Config

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

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

Methods

auto()

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

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.

class EntrypointSettings(
    path: Optional[str],
)

Methods

from_dict()

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

from_json()

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

schema()

def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
) -> SchemaType[A]

to_dict()

def to_dict(
    encode_json,
) -> typing.Dict[str, typing.Union[dict, list, str, int, float, bool, NoneType]]

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,
) -> str

flytekit.configuration.FastSerializationSettings

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

class 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,
) -> ~A

from_json()

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

schema()

def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
) -> SchemaType[A]

to_dict()

def to_dict(
    encode_json,
) -> typing.Dict[str, typing.Union[dict, list, str, int, float, bool, NoneType]]

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,
) -> str

flytekit.configuration.GCSConfig

Any GCS specific configuration.

class GCSConfig(
    gsutil_parallelism: bool,
)

Methods

auto()

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

flytekit.configuration.GenericPersistenceConfig

Data storage configuration that applies across any provider.

class GenericPersistenceConfig(
    attach_execution_metadata: bool,
)

Methods

auto()

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

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

class 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,
) -> ~A

from_json()

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

look_up_image_info()

def look_up_image_info(
    name: str,
    image_identifier: str,
    allow_no_tag_or_digest: bool,
) -> e: Image

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,
) -> SchemaType[A]

to_dict()

def to_dict(
    encode_json,
) -> typing.Dict[str, typing.Union[dict, list, str, int, float, bool, NoneType]]

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,
) -> str

Properties

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.

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

Methods

auto()

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

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]],
) -> ImageConfig

find_image()

def find_image(
    name,
) -> Optional[Image]

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,
) -> ~A

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

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,
) -> ~A

schema()

def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
) -> SchemaType[A]

to_dict()

def to_dict(
    encode_json,
) -> typing.Dict[str, typing.Union[dict, list, str, int, float, bool, NoneType]]

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,
) -> str

validate_image()

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

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.LocalConfig

Any configuration specific to local runs.

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

Methods

auto()

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

flytekit.configuration.PlatformConfig

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

class 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]],
) -> n:

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

for_endpoint()

def for_endpoint(
    endpoint: str,
    insecure: bool,
) -> PlatformConfig

flytekit.configuration.S3Config

S3 specific configuration

class 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],
) -> n: Config

Automatically configure

flytekit.configuration.SecretsConfig

Configuration for secrets.

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

Methods

auto()

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

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.

class 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,
) -> EntrypointSettings

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,
) -> SerializationSettings

from_dict()

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

from_json()

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

from_transport()

def from_transport(
    s: str,
) -> SerializationSettings

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,
) -> SchemaType[A]

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,
) -> typing.Dict[str, typing.Union[dict, list, str, int, float, bool, NoneType]]

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,
) -> str

venv_root_from_interpreter()

def venv_root_from_interpreter(
    interpreter_path: str,
) -> 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.

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

Methods

auto()

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

Reads from environment variable, followed by ConfigFile provided