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-1.13.9.tar.gz (12.1 kB view details)

Uploaded Source

Built Distribution

flytekitplugins_omegaconf-1.13.9-py3-none-any.whl (10.6 kB view details)

Uploaded Python 3

File details

Details for the file flytekitplugins_omegaconf-1.13.9.tar.gz.

File metadata

File hashes

Hashes for flytekitplugins_omegaconf-1.13.9.tar.gz
Algorithm Hash digest
SHA256 522341f54ebb8c03a300540d725fc4a602b464ff2ec8bc764bdbba2d9abf9734
MD5 a70f3194691fe8968e5985df2b53d5dc
BLAKE2b-256 b0a9479de160a329f7282a4abf373702cd8a34af29efaf0dfe73661173485712

See more details on using hashes here.

File details

Details for the file flytekitplugins_omegaconf-1.13.9-py3-none-any.whl.

File metadata

File hashes

Hashes for flytekitplugins_omegaconf-1.13.9-py3-none-any.whl
Algorithm Hash digest
SHA256 a6429fa04b13bb735fda333c7cdfcb8963824de10df2b1c416441fe074b722ec
MD5 a0174b379d9219e601ff35230f5ea9c8
BLAKE2b-256 0c68d0e6c530e4f3559ab6f5dbb9c2107b7683bae3f75d6ba444169a3de776d2

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