"""
=====================
Configuration
=====================
.. currentmodule:: flytekit.configuration
Flytekit Configuration Ecosystem
--------------------------------
Where can configuration come from?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- Command line arguments. This is the ideal location for settings to go. (See ``pyflyte package --help`` for example.)
- 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.
- A config file - an INI style configuration file. By default, flytekit will look for a file in two places
1. First, a file named ``flytekit.config`` in the Python interpreter's starting directory
2. A file in ``~/.flyte/config`` in the home directory as detected by Python.
How is configuration used?
^^^^^^^^^^^^^^^^^^^^^^^^^^
Configuration usage can roughly be bucketed into the following areas,
- Compile-time settings - things like the default image, where to look for Flyte code, etc.
- Platform settings - Where to find the Flyte backend (Admin DNS, whether to use SSL)
- Run time (registration) 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:
Compilation (Serialization) Time Settings
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:template: custom.rst
:toctree: generated/
:nosignatures:
~Image
~ImageConfig
~SerializationSettings
~FastSerializationSettings
.. _configuration-execution-time-settings:
Execution Time Settings
^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:template: custom.rst
:toctree: generated/
:nosignatures:
~PlatformConfig
~StatsConfig
~SecretsConfig
~S3Config
~GCSConfig
~DataConfig
~Config
"""
from __future__ import annotations
import base64
import datetime
import enum
import gzip
import os
import pathlib
import re
import tempfile
import typing
from dataclasses import dataclass, field
from io import BytesIO
from typing import Dict, List, Optional
from dataclasses_json import dataclass_json
from docker_image import reference
from flytekit.configuration import internal as _internal
from flytekit.configuration.default_images import DefaultImages
from flytekit.configuration.file import ConfigEntry, ConfigFile, get_config_file, read_file_if_exists, set_if_exists
from flytekit.loggers import logger
PROJECT_PLACEHOLDER = "{{ registration.project }}"
DOMAIN_PLACEHOLDER = "{{ registration.domain }}"
VERSION_PLACEHOLDER = "{{ registration.version }}"
DEFAULT_RUNTIME_PYTHON_INTERPRETER = "/opt/venv/bin/python3"
DEFAULT_FLYTEKIT_ENTRYPOINT_FILELOC = "bin/entrypoint.py"
DEFAULT_IMAGE_NAME = "default"
DEFAULT_IN_CONTAINER_SRC_PATH = "/root"
_IMAGE_FQN_TAG_REGEX = re.compile(r"([^:]+)(?=:.+)?")
SERIALIZED_CONTEXT_ENV_VAR = "_F_SS_C"
[docs]@dataclass_json
@dataclass(init=True, repr=True, eq=True, frozen=True)
class Image(object):
"""
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
"""
name: str
fqn: str
tag: str
@property
def full(self) -> str:
""" "
Return the full image name with tag.
"""
return f"{self.fqn}:{self.tag}"
[docs] @staticmethod
def look_up_image_info(name: str, tag: str, optional_tag: bool = False) -> Image:
"""
Looks up the image tag from environment variable (should be set from the Dockerfile).
FLYTE_INTERNAL_IMAGE should be the environment variable.
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 sha of the latest commit.
:param optional_tag:
:param name:
:param Text tag: e.g. somedocker.com/myimage:someversion123
:rtype: Text
"""
ref = reference.Reference.parse(tag)
if not optional_tag and ref["tag"] is None:
raise AssertionError(f"Incorrectly formatted image {tag}, missing tag value")
else:
return Image(name=name, fqn=ref["name"], tag=ref["tag"])
[docs]@dataclass_json
@dataclass(init=True, repr=True, eq=True, frozen=True)
class ImageConfig(object):
"""
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 (str): 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.
"""
default_image: Optional[Image] = None
images: Optional[List[Image]] = None
[docs] def find_image(self, name) -> Optional[Image]:
"""
Return an image, by name, if it exists.
"""
lookup_images = [self.default_image] if self.default_image else []
if self.images:
lookup_images.extend(self.images)
for i in lookup_images:
if i.name == name:
return i
return None
[docs] @staticmethod
def validate_image(_: typing.Any, param: str, values: tuple) -> ImageConfig:
"""
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=img or just img. All other images should be provided with a name, in the format
name=img
This method can be used with the CLI
:param _: click argument, ignored here.
:param param: the click argument, here should be "image"
:param values: user-supplied images
:return:
"""
default_image = None
images = []
for v in values:
if "=" in v:
splits = v.split("=", maxsplit=1)
img = Image.look_up_image_info(name=splits[0], tag=splits[1], optional_tag=False)
else:
img = Image.look_up_image_info(DEFAULT_IMAGE_NAME, v, False)
if default_image and img.name == DEFAULT_IMAGE_NAME:
raise ValueError(
f"Only one default image can be specified. Received multiple {default_image} & {img} for {param}"
)
if img.name == DEFAULT_IMAGE_NAME:
default_image = img
else:
images.append(img)
return ImageConfig.create_from(default_image=default_image, other_images=images)
[docs] @classmethod
def create_from(
cls, default_image: Optional[Image], other_images: typing.Optional[typing.List[Image]] = None
) -> ImageConfig:
if default_image and not isinstance(default_image, Image):
raise ValueError(f"Default image should be of type Image or None not {type(default_image)}")
all_images = [default_image] if default_image else []
if other_images:
all_images.extend(other_images)
return ImageConfig(default_image=default_image, images=all_images)
[docs] @classmethod
def auto(
cls, config_file: typing.Union[str, ConfigFile, None] = None, img_name: Optional[str] = None
) -> ImageConfig:
"""
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..
:param config_file:
:param img_name:
:return:
"""
default_img = Image.look_up_image_info("default", img_name) if img_name else None
config_file = get_config_file(config_file)
other_images = [
Image.look_up_image_info(k, tag=v, optional_tag=True)
for k, v in _internal.Images.get_specified_images(config_file).items()
]
return cls.create_from(default_img, other_images)
[docs] @classmethod
def from_images(cls, default_image: str, m: typing.Optional[typing.Dict[str, str]] = None):
"""
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": "...",
})
:return:
"""
m = m or {}
def_img = Image.look_up_image_info("default", default_image) if default_image else None
other_images = [Image.look_up_image_info(k, tag=v, optional_tag=True) for k, v in m.items()]
return cls.create_from(def_img, other_images)
[docs] @classmethod
def auto_default_image(cls) -> ImageConfig:
return cls.auto(img_name=DefaultImages.default_image())
class AuthType(enum.Enum):
STANDARD = "standard"
BASIC = "basic"
CLIENT_CREDENTIALS = "client_credentials"
EXTERNAL_PROCESS = "external_process"
# The following values are copied from flyteidl's admin client to align the two code bases on the same enum values.
# The enum values above will continue to work.
CLIENTSECRET = "ClientSecret"
PKCE = "Pkce"
EXTERNALCOMMAND = "ExternalCommand"
[docs]@dataclass(init=True, repr=True, eq=True, frozen=True)
class StatsConfig(object):
"""
Configuration for sending statsd.
:param host: The statsd host
:param port: statsd port
:param disabled: Whether or not to send
:param disabled_tags: Turn on to reduce cardinality.
"""
host: str = "localhost"
port: int = 8125
disabled: bool = False
disabled_tags: bool = False
[docs] @classmethod
def auto(cls, config_file: typing.Union[str, ConfigFile] = None) -> StatsConfig:
"""
Reads from environment variable, followed by ConfigFile provided
:param config_file:
:return:
"""
config_file = get_config_file(config_file)
kwargs = {}
kwargs = set_if_exists(kwargs, "host", _internal.StatsD.HOST.read(config_file))
kwargs = set_if_exists(kwargs, "port", _internal.StatsD.PORT.read(config_file))
kwargs = set_if_exists(kwargs, "disabled", _internal.StatsD.DISABLED.read(config_file))
kwargs = set_if_exists(kwargs, "disabled_tags", _internal.StatsD.DISABLE_TAGS.read(config_file))
return StatsConfig(**kwargs)
[docs]@dataclass(init=True, repr=True, eq=True, frozen=True)
class SecretsConfig(object):
"""
Configuration for secrets.
:param env_prefix: This is the prefix that will be used to lookup for injected secrets at runtime.
:param default_dir: This is the default directory that will be used to find secrets as individual files under.
:param file_prefix: This is the prefix for the file in the default dir.
"""
env_prefix: str = "_FSEC_"
default_dir: str = os.path.join(os.sep, "etc", "secrets")
file_prefix: str = ""
[docs] @classmethod
def auto(cls, config_file: typing.Union[str, ConfigFile] = None) -> SecretsConfig:
"""
Reads from environment variable or from config file
:param config_file:
:return:
"""
config_file = get_config_file(config_file)
kwargs = {}
kwargs = set_if_exists(kwargs, "env_prefix", _internal.Secrets.ENV_PREFIX.read(config_file))
kwargs = set_if_exists(kwargs, "default_dir", _internal.Secrets.DEFAULT_DIR.read(config_file))
kwargs = set_if_exists(kwargs, "file_prefix", _internal.Secrets.FILE_PREFIX.read(config_file))
return SecretsConfig(**kwargs)
[docs]@dataclass
class S3Config(object):
"""
S3 specific configuration
"""
enable_debug: bool = False
endpoint: typing.Optional[str] = None
retries: int = 3
backoff: datetime.timedelta = datetime.timedelta(seconds=5)
access_key_id: typing.Optional[str] = None
secret_access_key: typing.Optional[str] = None
[docs] @classmethod
def auto(cls, config_file: typing.Union[str, ConfigFile] = None) -> S3Config:
"""
Automatically configure
:param config_file:
:return: Config
"""
config_file = get_config_file(config_file)
kwargs = {}
kwargs = set_if_exists(kwargs, "enable_debug", _internal.AWS.ENABLE_DEBUG.read(config_file))
kwargs = set_if_exists(kwargs, "endpoint", _internal.AWS.S3_ENDPOINT.read(config_file))
kwargs = set_if_exists(kwargs, "retries", _internal.AWS.RETRIES.read(config_file))
kwargs = set_if_exists(kwargs, "backoff", _internal.AWS.BACKOFF_SECONDS.read(config_file))
kwargs = set_if_exists(kwargs, "access_key_id", _internal.AWS.S3_ACCESS_KEY_ID.read(config_file))
kwargs = set_if_exists(kwargs, "secret_access_key", _internal.AWS.S3_SECRET_ACCESS_KEY.read(config_file))
return S3Config(**kwargs)
[docs]@dataclass
class GCSConfig(object):
"""
Any GCS specific configuration.
"""
gsutil_parallelism: bool = False
[docs] @classmethod
def auto(cls, config_file: typing.Union[str, ConfigFile] = None) -> GCSConfig:
config_file = get_config_file(config_file)
kwargs = {}
kwargs = set_if_exists(kwargs, "gsutil_parallelism", _internal.GCP.GSUTIL_PARALLELISM.read(config_file))
return GCSConfig(**kwargs)
[docs]@dataclass(init=True, repr=True, eq=True, frozen=True)
class DataConfig(object):
"""
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
"""
s3: S3Config = S3Config()
gcs: GCSConfig = GCSConfig()
[docs] @classmethod
def auto(cls, config_file: typing.Union[str, ConfigFile] = None) -> DataConfig:
config_file = get_config_file(config_file)
return DataConfig(
s3=S3Config.auto(config_file),
gcs=GCSConfig.auto(config_file),
)
[docs]@dataclass(init=True, repr=True, eq=True, frozen=True)
class Config(object):
"""
This is 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
Args:
entrypoint_settings: EntrypointSettings object for use with Spark tasks. If supplied, this will be
used when serializing Spark tasks, which need to know the path to the flytekit entrypoint.py file,
inside the container.
"""
platform: PlatformConfig = PlatformConfig()
secrets: SecretsConfig = SecretsConfig()
stats: StatsConfig = StatsConfig()
data_config: DataConfig = DataConfig()
local_sandbox_path: str = tempfile.mkdtemp(prefix="flyte")
[docs] def with_params(
self,
platform: PlatformConfig = None,
secrets: SecretsConfig = None,
stats: StatsConfig = None,
data_config: DataConfig = None,
local_sandbox_path: str = None,
) -> Config:
return Config(
platform=platform or self.platform,
secrets=secrets or self.secrets,
stats=stats or self.stats,
data_config=data_config or self.data_config,
local_sandbox_path=local_sandbox_path or self.local_sandbox_path,
)
[docs] @classmethod
def auto(cls, config_file: typing.Union[str, ConfigFile, None] = None) -> 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.
:param config_file: file path to read the config from, if not specified default locations are searched
:return: Config
"""
config_file = get_config_file(config_file)
kwargs = {}
set_if_exists(kwargs, "local_sandbox_path", _internal.LocalSDK.LOCAL_SANDBOX.read(cfg=config_file))
return Config(
platform=PlatformConfig.auto(config_file),
secrets=SecretsConfig.auto(config_file),
stats=StatsConfig.auto(config_file),
data_config=DataConfig.auto(config_file),
**kwargs,
)
[docs] @classmethod
def for_sandbox(cls) -> Config:
"""
Constructs a new Config object specifically to connect to :std:ref:`deploy-sandbox-local`.
If you are using a hosted Sandbox like environment, then you may need to use port-forward or ingress urls
:return: Config
"""
return Config(
platform=PlatformConfig(endpoint="localhost:30080", auth_mode="Pkce", insecure=True),
data_config=DataConfig(
s3=S3Config(endpoint="http://localhost:30002", access_key_id="minio", secret_access_key="miniostorage")
),
)
[docs] @classmethod
def for_endpoint(
cls,
endpoint: str,
insecure: bool = False,
data_config: typing.Optional[DataConfig] = None,
config_file: typing.Union[str, ConfigFile] = None,
) -> 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
:param endpoint: -> Endpoint where Flyte admin is available
:param insecure: -> if the connection should be insecure, default is secure (SSL ON)
:param data_config: -> Data config, if using specialized connection params like minio etc
:param config_file: -> Optional config file in the flytekit config format.
:return: Config
"""
c = cls.auto(config_file)
return c.with_params(platform=PlatformConfig.for_endpoint(endpoint, insecure), data_config=data_config)
@dataclass_json
@dataclass
class EntrypointSettings(object):
"""
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.
"""
path: Optional[str] = None
[docs]@dataclass_json
@dataclass
class FastSerializationSettings(object):
"""
This object hold information about settings necessary to serialize an object so that it can be fast-registered.
"""
enabled: bool = False
# This is the location that the code should be copied into.
destination_dir: Optional[str] = None
# This is the zip file where the new code was uploaded to.
distribution_location: Optional[str] = None
[docs]@dataclass_json
@dataclass()
class SerializationSettings(object):
"""
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.
TODO: ImageConfig, python_interpreter, venv_root, fast_serialization_settings.destination_dir should be combined.
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.
"""
image_config: ImageConfig
project: typing.Optional[str] = None
domain: typing.Optional[str] = None
version: typing.Optional[str] = None
env: Optional[Dict[str, str]] = None
git_repo: Optional[str] = None
python_interpreter: str = DEFAULT_RUNTIME_PYTHON_INTERPRETER
flytekit_virtualenv_root: Optional[str] = None
fast_serialization_settings: Optional[FastSerializationSettings] = None
def __post_init__(self):
if self.flytekit_virtualenv_root is None:
self.flytekit_virtualenv_root = self.venv_root_from_interpreter(self.python_interpreter)
@property
def entrypoint_settings(self) -> EntrypointSettings:
return EntrypointSettings(
path=os.path.join(
SerializationSettings.venv_root_from_interpreter(self.python_interpreter),
DEFAULT_FLYTEKIT_ENTRYPOINT_FILELOC,
)
)
[docs] @classmethod
def from_transport(cls, s: str) -> SerializationSettings:
compressed_val = base64.b64decode(s.encode("utf-8"))
json_str = gzip.decompress(compressed_val).decode("utf-8")
return cls.from_json(json_str)
[docs] @classmethod
def for_image(
cls,
image: str,
version: str,
project: str = "",
domain: str = "",
python_interpreter_path: str = DEFAULT_RUNTIME_PYTHON_INTERPRETER,
) -> SerializationSettings:
img = ImageConfig(default_image=Image.look_up_image_info(DEFAULT_IMAGE_NAME, tag=image))
return SerializationSettings(
image_config=img,
project=project,
domain=domain,
version=version,
python_interpreter=python_interpreter_path,
flytekit_virtualenv_root=cls.venv_root_from_interpreter(python_interpreter_path),
)
[docs] @staticmethod
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
"""
return os.path.dirname(os.path.dirname(interpreter_path))
[docs] @staticmethod
def default_entrypoint_settings(interpreter_path: str) -> EntrypointSettings:
"""
Assumes the entrypoint is installed in a virtual-environment where the interpreter is
"""
return EntrypointSettings(
path=os.path.join(
SerializationSettings.venv_root_from_interpreter(interpreter_path), DEFAULT_FLYTEKIT_ENTRYPOINT_FILELOC
)
)
[docs] def new_builder(self) -> Builder:
"""
Creates a ``SerializationSettings.Builder`` that copies the existing serialization settings parameters and
allows for customization.
"""
return SerializationSettings.Builder(
project=self.project,
domain=self.domain,
version=self.version,
image_config=self.image_config,
env=self.env.copy() if self.env else None,
git_repo=self.git_repo,
flytekit_virtualenv_root=self.flytekit_virtualenv_root,
python_interpreter=self.python_interpreter,
fast_serialization_settings=self.fast_serialization_settings,
)
[docs] def should_fast_serialize(self) -> bool:
"""
Whether or not the serialization settings specify that entities should be serialized for fast registration.
"""
return self.fast_serialization_settings is not None and self.fast_serialization_settings.enabled
def _has_serialized_context(self) -> bool:
return self.env and SERIALIZED_CONTEXT_ENV_VAR in self.env
@property
def serialized_context(self) -> str:
"""
:return: returns the serialization context as a base64encoded, gzip compressed, json strinnn
"""
if self._has_serialized_context():
return self.env[SERIALIZED_CONTEXT_ENV_VAR]
json_str = self.to_json()
buf = BytesIO()
with gzip.GzipFile(mode="wb", fileobj=buf, mtime=0) as f:
f.write(json_str.encode("utf-8"))
return base64.b64encode(buf.getvalue()).decode("utf-8")
[docs] def with_serialized_context(self) -> SerializationSettings:
"""
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 availabe in the `env` field with the key `SERIALIZED_CONTEXT_ENV_VAR`
:return: A newly constructed SerializationSettings, or self, if it already has the serializationSettings
"""
if self._has_serialized_context():
return self
b = self.new_builder()
if not b.env:
b.env = {}
b.env[SERIALIZED_CONTEXT_ENV_VAR] = self.serialized_context
return b.build()
@dataclass
class Builder(object):
project: str
domain: str
version: str
image_config: ImageConfig
env: Optional[Dict[str, str]] = None
git_repo: Optional[str] = None
flytekit_virtualenv_root: Optional[str] = None
python_interpreter: Optional[str] = None
fast_serialization_settings: Optional[FastSerializationSettings] = None
def with_fast_serialization_settings(self, fss: fast_serialization_settings) -> SerializationSettings.Builder:
self.fast_serialization_settings = fss
return self
def build(self) -> SerializationSettings:
return SerializationSettings(
project=self.project,
domain=self.domain,
version=self.version,
image_config=self.image_config,
env=self.env,
git_repo=self.git_repo,
flytekit_virtualenv_root=self.flytekit_virtualenv_root,
python_interpreter=self.python_interpreter,
fast_serialization_settings=self.fast_serialization_settings,
)
