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-1.13.12.tar.gz (12.1 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file flytekitplugins_omegaconf-1.13.12.tar.gz.

File metadata

File hashes

Hashes for flytekitplugins_omegaconf-1.13.12.tar.gz
Algorithm Hash digest
SHA256 520ae0de22054cebe11b07602db583592226d6cd71ff183048b7a2d9514e5a0e
MD5 9a6a4118271e3b65a7c08c6e2f027e60
BLAKE2b-256 987d85672a7fadc8ec19c1c609e7357363cc7789ac91b4a5698b6b96ee8e237e

See more details on using hashes here.

File details

Details for the file flytekitplugins_omegaconf-1.13.12-py3-none-any.whl.

File metadata

File hashes

Hashes for flytekitplugins_omegaconf-1.13.12-py3-none-any.whl
Algorithm Hash digest
SHA256 f94f851528044c37dc6a041dd98ab09468c344aff6f096fae3cbee273e8be1a1
MD5 7c9c769f987bdfdc54a01f271a18aeb4
BLAKE2b-256 9b52bea233e1c573819b0bcc2969dde555780b3cfff10b6a157723ed78bd2def

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