Skip to main content

OmegaConf plugin for Flytekit

Project description

Flytekit OmegaConf Plugin

Flytekit python natively supports serialization of many data types for exchanging information between tasks. The Flytekit OmegaConf Plugin extends these by the DictConfig type from the OmegaConf package as well as related types that are being used by the hydra package for configuration management.

Task example

from dataclasses import dataclass
import flytekitplugins.omegaconf  # noqa F401
from flytekit import task, workflow
from omegaconf import DictConfig

@dataclass
class MySimpleConf:
    _target_: str = "lightning_module.MyEncoderModule"
    learning_rate: float = 0.0001

@task
def my_task(cfg: DictConfig) -> None:
    print(f"Doing things with {cfg.learning_rate=}")


@workflow
def pipeline(cfg: DictConfig) -> None:
    my_task(cfg=cfg)


if __name__ == "__main__":
    from omegaconf import OmegaConf

    cfg = OmegaConf.structured(MySimpleConf)
    pipeline(cfg=cfg)

Transformer configuration

The transformer can be set to one of three modes:

Dataclass - This mode should be used with a StructuredConfig and will reconstruct the config from the matching dataclass during deserialisation in order to make typing information from the dataclass and continued validation thereof available. This requires the dataclass definition to be available via python import in the Flyte execution environment in which objects are (de-)serialised.

DictConfig - This mode will deserialize the config into a DictConfig object. In particular, dataclasses are translated into DictConfig objects and only primitive types are being checked. The definition of underlying dataclasses for structured configs is only required during the initial serialization for this mode.

Auto - This mode will try to deserialize according to the Dataclass mode and fall back to the DictConfig mode if the dataclass definition is not available. This is the default mode.

You can set the transformer mode globally or for the current context only the following ways:

from flytekitplugins.omegaconf import set_transformer_mode, set_local_transformer_mode, OmegaConfTransformerMode

# Set the global transformer mode using the new function
set_transformer_mode(OmegaConfTransformerMode.DictConfig)

# You can also the mode for the current context only
with set_local_transformer_mode(OmegaConfTransformerMode.Dataclass):
    # This will use the Dataclass mode
    pass
Since the DictConfig is flattened and keys transformed into dot notation, the keys of the DictConfig must not contain
dots.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

flytekitplugins_omegaconf-1.14.0b2.tar.gz (12.1 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file flytekitplugins_omegaconf-1.14.0b2.tar.gz.

File metadata

File hashes

Hashes for flytekitplugins_omegaconf-1.14.0b2.tar.gz
Algorithm Hash digest
SHA256 cfa9419e03700355955d890ea925f5412453b7ad7efd8dbe9a4bf1835c7edfad
MD5 eab5d080724dfb2615ab77ab5a70f0c9
BLAKE2b-256 260000713aecb84affb1d9945e9d66470ddb63ba8505c0a893d68d9123799ccd

See more details on using hashes here.

File details

Details for the file flytekitplugins_omegaconf-1.14.0b2-py3-none-any.whl.

File metadata

File hashes

Hashes for flytekitplugins_omegaconf-1.14.0b2-py3-none-any.whl
Algorithm Hash digest
SHA256 aacc8a2777b60b711930808e4a6343a6f8ee772486095e8bf7db8ea92d0c0445
MD5 eea77ea408c742337beca81090b03604
BLAKE2b-256 04278482b3d9063e1357f122ba649d6fd5ae1df70444d8b15a4779556ba53d78

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page