Python-first config loader with composition and CLI overrides.
Project description
cfgx
Python-first config loader with parent chaining, lazy computed values, and CLI-style overrides.
Docs: https://kabouzeid.github.io/cfgx/
Install
pip install cfgx
Quick start
Example config file:
# configs/model.py
config = {
"model": {"name": "resnet18"},
"optimizer": {"lr": 3e-4},
}
from cfgx import load
cfg = load("configs/model.py", overrides=["optimizer.lr=1e-3"])
Works well with specbuild when you want to build your model and other classes from config dictionaries.
Advanced example
Base config:
# configs/base.py
from cfgx import Lazy
config = {
"model": {"depth": 8, "width": 512},
"optimizer": {
"lr": 3e-4,
"weight_decay": 0.01,
"schedule": {"type": "linear", "warmup_steps": 1_000},
},
"trainer": {
"max_steps": 50_000,
"hooks": ["progress", "checkpoint"],
"stages": [{"name": "warmup", "max_steps": 5_000}],
"log_every": Lazy("c.trainer.max_steps // 100"),
},
}
Derived config:
# configs/finetune.py
from cfgx import Delete, Lazy, Replace
parents = ["base.py"]
config = {
"model": {"depth": 12},
"optimizer": {
"weight_decay": Delete(),
"schedule": Replace({"type": "cosine", "t_max": 40_000}),
},
"trainer": {"max_steps": 10_000},
"scheduler": {
"warmup_steps": 500,
"decay_steps": Lazy(lambda c: c.trainer.max_steps - c.scheduler.warmup_steps),
},
}
Load, override, and snapshot:
from cfgx import dump, format, load
cfg = load(
"configs/finetune.py",
overrides=[
"optimizer.lr=1e-4",
"trainer.hooks+=wandb",
"trainer.hooks-='checkpoint'",
"trainer.stages[0].max_steps=2_000",
"scheduler.warmup_steps=lazy:c.trainer.max_steps * 0.1",
],
)
print(format(cfg))
dump(cfg, "runs/finetune_config.py")
Assignments create intermediate dicts and extend lists with None as needed.
List indices follow Python semantics in overrides: negative indices are allowed
when the list exists and are in range (otherwise IndexError). Deletes and list
removals are forgiving no-ops when the path is missing or out of range.
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