omegaconf | The last one | Configuration Management library

import torch
from unittest import mock
from typing import List
import pytorch_lightning.cli as pl_cli
from pytorch_lightning import LightningModule, LightningDataModule, Trainer, Callback


class NoFitTrainer(Trainer):
    def fit(self, *_, **__):
    

from monet_pytorch import Monet

monet = Monet.from_config(model='monet')

from monet_pytorch import Monet

monet = Monet.from_config(model='monet', dataset='tetrominoes')

from monet_pytorch import Monet

monet = Monet.from_config(model='monet', dat  

python my_scripy.py MY_PATHS="[a,b,c]"

conf = OmegaConf.structured(ConfDef())
cli_conf = OmegaConf.from_cli()
conf = OmegaConf.merge(conf, cli_conf)
print("\n\nCONFIGURATION:")
print(OmegaConf.to_yaml(conf), end="\

# config.yaml

arg1: numpy.float32  # note: use "numpy" here, not "np"
arg2: tensorflow.float16

# python code
...
from hydra.utils import get_class
arg1 = get_class(config.arg1)
arg2 = get_class(config.arg2)

with initialize(config_path="conf", job_name="test_app"):
    cfg = compose(config_name="config", overrides=["db=mysql", "db.user=me"])

from typing import List, Optional

import typer
from omegaconf import OmegaCon

# conf.yaml
_target_: app.ClassA
foo: bar
model:
  _target_: app.ClassB
  hello: world
modelconf: "${remove_target: ${.model}}"

# app.py
import hydra
from omegaconf import DictConfig, OmegaConf


class ClassB:
    

# my_app.py
import hydra
from pprint import pprint
from hydra.core.hydra_config import HydraConfig
from omegaconf import OmegaConf

@hydra.main(config_path=".", config_name="config")
def main(config):
    hydra_cfg = HydraConfig.get()
    

# my_app.py
import hydra
import hydra.utils as hu

from omegaconf import OmegaConf

def resolve_tuple(*args):
    return tuple(args)

OmegaConf.register_new_resolver('as_tuple', resolve_tuple)

@hydra.main(config_path='conf', config_name='