didactic-settings
didactic-settings composes a validated dx.Model from layered configuration:
a primary file and the config-group fragments its defaults: list selects, a
profile, overlay files, environment variables, dotenv files, command-line
arguments and dotted overrides. Every layer is checked against the schema at
every depth, tagged-union fields are descended into the variant their
discriminator selects, ${...} expressions are resolved against the composed
tree, and the instance records which layer wrote each leaf.
Install
pip install didactic-settings
pip install 'didactic-settings[yaml]'
JSON and TOML are read through the standard library; the yaml extra installs
PyYAML for .yaml and .yml files.
Compose a configuration
from typing import Literal
import didactic.api as dx
from didactic.settings import compose_traced
class OptimizerSpec(dx.TaggedUnion, discriminator="kind", extra="forbid"):
lr: float = 1e-3
class Adam(OptimizerSpec):
kind: Literal["adam"] = "adam"
betas: tuple[float, ...] = (0.9, 0.999)
class Sgd(OptimizerSpec):
kind: Literal["sgd"] = "sgd"
momentum: float = 0.9
class TrainerSpec(dx.Model, extra="forbid"):
epochs: int = 1
out_dir: str = "runs"
log_dir: str = "${.out_dir}/logs"
class RunSpec(dx.Model, extra="forbid"):
optimizer: OptimizerSpec = dx.field(default_factory=Adam)
trainer: TrainerSpec = dx.field(default_factory=TrainerSpec)
run = compose_traced(
"conf/run.yaml",
schema=RunSpec,
profile="dev",
overlays=["conf/big.toml"],
groups={"optimizer": "sgd"},
overrides=["optimizer.momentum=0.5", ("trainer.epochs", 3)],
)
run.value.trainer.log_dir
run.provenance["trainer.epochs"].label # "override:trainer.epochs=3"
run.provenance["optimizer.momentum"].label # "override:optimizer.momentum=0.5"
conf/run.yaml may carry a defaults: list selecting root fragments and
config-group fragments (optimizer: adam loads conf/optimizer/adam.yaml);
groups= replaces the file's choice for a slot; profile="dev" loads
conf/profiles/dev.yaml; an override whose key contains /
(model/type_encoder=lstm) selects a nested group and one without sets a
field, decoded by the leaf's annotation. A key the schema does not declare,
at any depth and in any layer, is refused with its dotted path and the layer
that set it, and so is a value of the wrong type.
Class-based settings
from didactic.settings import EnvSource, FileSource, Settings
class RunSettings(Settings, RunSpec):
__sources__ = (FileSource("local.toml"), EnvSource(prefix="APP_"))
settings = RunSettings.load("conf/run.yaml", profile="dev", trainer__epochs=3)
settings.__provenance__["trainer.epochs"].label
Settings.load() composes the primary file, its groups, the profile, overlays,
the declared sources and the overrides in that precedence. EnvSource reads
APP_TRAINER__EPOCHS for trainer.epochs and decodes the text by the field's
annotation; APP_OPTIMIZER='{"kind": "sgd"}' sets a whole slot.
| Source | Input |
|---|---|
EnvSource(prefix="APP_") |
environment variables |
DotEnvSource(path=".env", prefix="APP_") |
a dotenv file |
FileSource(path="config.toml") |
one JSON, TOML, or YAML document |
CliSource(args=namespace) |
an argparse.Namespace or mapping |
Because Settings extends dx.Model, the composed tree receives the same type
checks, axioms, field validators, and indexed-field checks as any other
Didactic model.
Documentation
See the settings guide for the composition ladder, config groups, union descent, interpolation and the resolver registry, and per-leaf provenance.
License
Released under the MIT License.
1 maintainer
Metadata
1 maintainer
Release files for didactic-settings 0.17.1
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