configgle🤭

Hierarchical experiment configuration using pure Python dataclasses with typed factory methods, covariant protocols, inheritance support, and tooling for pretty printing, autodecorating, updating, and semi-deep copying.

Installation

python -m pip install configgle

Example

from configgle import Fig

class Model:
    class Config(Fig):
        hidden_size: int = 256
        num_layers: int = 4

    def __init__(self, config: Config):
        self.config = config

# Create and modify config
cfg = Model.Config()
cfg.hidden_size = 512

# Instantiate the parent class
model = cfg.make()
print(model.config)
assert isinstance(model, Model)

Configs are plain mutable dataclasses, so experiments are just functions that tweak a baseline:

def exp000() -> Model.Config:
    return Model.Config()

def exp001() -> Model.Config:
    cfg = exp000()
    cfg.hidden_size = 512
    cfg.num_layers = 8
    return cfg

Or use @autofig to auto-generate the Config from __init__:

from configgle import autofig
from torch import nn

@autofig
class Model(nn.Module):
    def __init__(self, hidden_size: int = 256, num_layers: int = 4):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers

# Config is auto-generated from __init__ signature
model = Model.Config(hidden_size=512).make()
print(model.hidden_size)  # 512

Features

Type-safe make()

tl;dr: Both ty and basedpyright are first-class supported. Unfortunately neither is perfect:

Details:

When Config is defined as a nested class, MakerMeta.__get__ uses the descriptor protocol to infer the parent class automatically. The return type of __get__ is Intersection[type[Config], type[Makeable[Parent]]], so make() knows the exact return type with zero annotation effort:

class Model:
    class Config(Fig):
        hidden_size: int = 256

    def __init__(self, config: Config):
        self.hidden_size = config.hidden_size

model = Model.Config(hidden_size=512).make()  # inferred as Model

Type checkers that support Intersection (like ty) resolve this fully -- bare Fig is all you need. For type checkers that don't yet support Intersection (like basedpyright), parameterize with the parent class name to give the checker the same information explicitly:

class Model:
    class Config(Fig["Model"]):  # explicit type parameter only for basedpyright
        hidden_size: int = 256

    def __init__(self, config: Config):
        self.hidden_size = config.hidden_size

model: Model = Model.Config(hidden_size=512).make()  # returns Model, not object

Without ["Model"], non-ty checkers fall back to Any (so attribute access works without typecheck suppressions).

ty gets full inference from Intersection -- bare Fig and inherited configs just work. basedpyright doesn't support Intersection yet, so it needs explicit Fig["Parent"] and Makes["Child"] annotations. ty honors class decorator return types (#143, fixed in 0.0.49), so @autofig-decorated classes resolve .Config with no suppression on both checkers. When Intersection lands in the type spec, Makes becomes unnecessary and both checkers will infer everything from bare Fig.

Inheritance with Makes (only for basedpyright)

When a child class inherits a parent's Config, the make() return type would normally be the parent. Use Makes to re-bind it (again, only needed for basedpyright):

from configgle import Makes

class Animal:
    class Config(Fig["Animal"]):
        name: str = "animal"

    def __init__(self, config: Config):
        self.config = config
        self.name = config.name

class Dog(Animal):
    class Config(Makes["Dog"], Animal.Config):
        breed: str = "mutt"

    def __init__(self, config: Config):
        super().__init__(config)
        self.breed = config.breed

dog: Dog = Dog.Config(name="Rex", breed="labrador").make()  # returns Dog, not Animal

Makes contributes nothing to the MRO at runtime -- it exists purely for the type checker (see the type checker table above). When Intersection lands, Makes becomes unnecessary.

Covariant Makeable protocol

Makeable[T] is a covariant protocol satisfied by any Fig, InlineConfig, or custom class with make(), finalize(), and update(). Because it's covariant, Makeable[Dog] is assignable to Makeable[Animal]:

from configgle import Makeable

def train(config: Makeable[Animal]) -> Animal:
    return config.make()

# All valid:
train(Animal.Config())
train(Dog.Config(breed="poodle"))

This makes it easy to write functions that accept any config for a class hierarchy without losing type information.

Nested config finalization -- pre / super / post

Override finalize() to compute derived fields. super().finalize() cascades into the child configs, so it splits the method into a pre phase (before children finalize -- push values down) and a post phase (after -- derive values up):

from configgle import Configurable  # Just an alias to Makeable.
from dataclasses import field

class Encoder:
    class Config(Fig):
        c_in: int = 256
        mlp: Configurable[nn.Module] = field(default_factory=MLP.Config)

        def finalize(self) -> Self:
            self.mlp.c_in = self.c_in   # pre: push down into the child
            self = super().finalize()   # children finalize here
            self.out = self.mlp.out     # post: derive up from the child
            return self

Inject into a child before super() (so it finalizes with the value); derive from a child after (so you read its finalized result). Pushdown is the common case, so super() is usually last -- but it need not be.

finalize() mutates in place; the copy that protects the original happens once at the make() / pprint boundary (copy_tree().finalize()), so a config is finalized exactly once on a fresh tree (never re-finalized) and the config passed to make() is left untouched.

update() for bulk mutation

Configs support bulk updates from another config or keyword arguments:

cfg = Model.Config(hidden_size=256)
cfg.update(hidden_size=512, num_layers=8)

# Or copy from another config (kwargs take precedence):
cfg.update(other_cfg, num_layers=12)

InlineConfig / PartialConfig

InlineConfig wraps an arbitrary callable and its arguments into a config object with deferred execution. Use it for classes where all constructor arguments are known at config time:

from configgle import InlineConfig
import torch.nn as nn

cfg = InlineConfig(nn.Linear, in_features=256, out_features=128, bias=False)
cfg.out_features = 64     # attribute-style access to kwargs
layer = cfg.make()        # calls nn.Linear(in_features=256, out_features=64, bias=False)
y = layer(x)              # use the constructed module

PartialConfig is shorthand for InlineConfig(functools.partial, fn, ...) -- use it for functions where some arguments aren't known at config time:

from configgle import PartialConfig
import torch.nn.functional as F

cfg = PartialConfig(F.cross_entropy, label_smoothing=0.1)
loss_fn = cfg.make()      # returns functools.partial(F.cross_entropy, label_smoothing=0.1)
loss = loss_fn(logits, targets)  # calls F.cross_entropy(logits, targets, label_smoothing=0.1)

Nested configs in args/kwargs are finalized and make()-d recursively, so both compose naturally with Fig configs.

copy_tree()

copy_tree() is a "semi-deep" copy: nested configs and mutable containers holding configs are duplicated, while leaf values (primitives, tensors, loggers) are aliased. make() and pprint apply it before finalizing so the source config stays pristine. Reach for it directly to finalize a copy without touching the source:

finalized = cfg.copy_tree().finalize()  # cfg unchanged

pprint / pformat

Config-aware pretty printing that hides default values, auto-finalizes before printing, and scrubs memory addresses:

from configgle import Configurable, Fig, pformat

class MLP:
    class Config(Fig):
        c_in: int = 256
        c_out: int = 256
        num_layers: int = 2
        dropout: float = 0.1
        use_bias: bool = True
    def __init__(self, config: Config): ...

class Model:
    class Config(Fig):
        hidden_size: int = 256
        num_layers: int = 4
        mlp: Configurable[nn.Module] = field(default_factory=MLP.Config)
        output_mlp: Configurable[nn.Module] = field(default_factory=MLP.Config)
    def __init__(self, config: Config): ...

def exp001():
    cfg = Model.Config()
    cfg.hidden_size = 512
    cfg.num_layers = 12
    cfg.mlp.c_in = 512
    cfg.mlp.c_out = 1024
    cfg.mlp.num_layers = 4
    cfg.mlp.dropout = 0.2
    cfg.mlp.use_bias = False
    cfg.output_mlp.c_in = 1024
    cfg.output_mlp.c_out = 256
    cfg.output_mlp.dropout = 0.3
    return cfg

print(pformat(exp001(), continuation_pipe=0))
# Model.Config(
#    hidden_size=512,
#    num_layers=12,
#    mlp=MLP.Config(
#    │       c_in=512,
#    │       c_out=1_024,
#    │       num_layers=4,
#    │       dropout=0.2,
#    │       use_bias=False
#    ),
#    output_mlp=MLP.Config(c_in=1_024, dropout=0.3)
# )

Default values are hidden, continuation pipes show where nested blocks belong, large numbers get underscores (1_024), and short sub-configs collapse onto one line. pformat and pprint are also available as methods on any Fig config:

cfg = exp001()
cfg.pprint()         # prints to stdout
s = cfg.pformat()    # returns string

Dataclass base

Dataclass provides the auto-dataclass metaclass (with the same opinionated defaults as Fig: kw_only=True, slots=True, etc.) but without Maker or make(). Use it for plain data objects that don't need the factory pattern.

@autofig for zero-boilerplate configs

When you don't need a hand-written Config, @autofig generates one from __init__ (see Example above).

Pickling and cloudpickle

Configs are fully compatible with pickle and cloudpickle, including the parent class reference. This is important for distributed workflows (e.g., sending configs across processes):

import cloudpickle, pickle

cfg = Model.Config(hidden_size=512)
cfg_ = pickle.loads(cloudpickle.dumps(cfg))
model = cfg_.make()  # parent_class is preserved

Comparison

✅ = yes, 🟡 = partial, ❌ = no. Corrections welcome -- open a PR.

How each library works

Hydra (Meta) -- YAML-centric with optional "structured configs" (Python dataclasses registered in a ConfigStore). Instantiation uses hydra.utils.instantiate(), which resolves a string _target_ field to an import path -- the return type is Any. Config composition is done via YAML defaults lists, not class inheritance. Dataclass inheritance works at the schema level. configen is an experimental code-generation tool (v0.9.0.dev8) that produces structured configs from class signatures. Configs survive pickle trivially since _target_ is a string, not a class reference.

Sacred -- Experiment management framework. Config is defined via @ex.config scopes (local variables become config entries) or loaded from YAML/JSON files. Sacred auto-injects config values into captured functions by parameter name (dependency injection), but does not auto-generate configs from function signatures. No typed factory methods, no config inheritance, no pickle support for the experiment/config machinery.

OmegaConf -- YAML-native configuration with a "structured config" mode that accepts @dataclass schemas. Configs are always wrapped in DictConfig proxy objects at runtime (not actual dataclass instances). Supports dataclass inheritance for schema definition. Good pickle support (__getstate__/__setstate__). No factory method (to_object() returns Any), no auto-generation, no protocols.

Gin (Google) -- Global string-based registry. You decorate functions with @gin.configurable and bind parameters via .gin files or gin.bind_parameter('fn.param', val). There are no config objects -- parameter values live in a global dict keyed by dotted strings. No typed returns, no config inheritance. The docs state "gin-configurable functions are not pickleable," though a 2020 PR added __reduce__ methods that improve support.

ml_collections (Google) -- Dict-like ConfigDict with dot-access, type-checking on mutation, and FieldReference for lazy cross-references between values. Pure Python, no YAML. No factory method or typed instantiation. Pickle works for plain configs, but FieldReference operations that use lambdas internally (.identity(), .to_int()) fail with standard pickle (cloudpickle handles them).

Fiddle (Google) -- Python-first. You build config graphs with fdl.Config[MyClass] objects and call fdl.build() to instantiate them. build(Config[T]) -> T is typed via @overload. Config modification is functional (fdl.copy_with), not inheritance-based -- there are no config subclasses. @auto_config rewrites a factory function's AST to produce a config graph automatically. Full pickle/cloudpickle support.

Confugue -- YAML-based hierarchical configuration. The configure() method instantiates objects from YAML dicts, with the class specified via a !type YAML tag. Returns Any. Partial config inheritance via YAML merge keys (<<: *base). No pickle support, no auto-generation, no protocols.

Citing

If you find our work useful, please consider citing:

@misc{dillon2026configgle,
      title={Configgle - Type-safe hierarchical experiment configuration using pure Python dataclass factories and dependency injection.},
      author={Joshua V. Dillon},
      year={2026},
      howpublished={Github},
      url={https://github.com/rekursiv-ai/configgle},
}

License

Apache License 2.0