On this tutorial, we discover Hydra, a complicated configuration administration framework initially developed and open-sourced by Meta Analysis. We start by defining structured configurations utilizing Python dataclasses, which permits us to handle experiment parameters in a clear, modular, and reproducible method. As we transfer via the tutorial, we compose configurations, apply runtime overrides, and simulate multirun experiments for hyperparameter sweeps. Try the FULL CODES right here.
import subprocess
import sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "hydra-core"])
import hydra
from hydra import compose, initialize_config_dir
from omegaconf import OmegaConf, DictConfig
from dataclasses import dataclass, discipline
from typing import Listing, Optionally available
import os
from pathlib import PathWe start by putting in Hydra and importing all of the important modules required for structured configurations, dynamic composition, and file dealing with. This setup ensures our surroundings is able to execute the complete tutorial seamlessly on Google Colab. Try the FULL CODES right here.
@dataclass
class OptimizerConfig:
_target_: str = "torch.optim.SGD"
lr: float = 0.01
@dataclass
class AdamConfig(OptimizerConfig):
_target_: str = "torch.optim.Adam"
lr: float = 0.001
betas: tuple = (0.9, 0.999)
weight_decay: float = 0.0
@dataclass
class SGDConfig(OptimizerConfig):
_target_: str = "torch.optim.SGD"
lr: float = 0.01
momentum: float = 0.9
nesterov: bool = True
@dataclass
class ModelConfig:
title: str = "resnet"
num_layers: int = 50
hidden_dim: int = 512
dropout: float = 0.1
@dataclass
class DataConfig:
dataset: str = "cifar10"
batch_size: int = 32
num_workers: int = 4
augmentation: bool = True
@dataclass
class TrainingConfig:
mannequin: ModelConfig = discipline(default_factory=ModelConfig)
information: DataConfig = discipline(default_factory=DataConfig)
optimizer: OptimizerConfig = discipline(default_factory=AdamConfig)
epochs: int = 100
seed: int = 42
system: str = "cuda"
experiment_name: str = "exp_001"We outline clear, type-safe configurations utilizing Python dataclasses for the mannequin, information, and optimizer settings. This construction permits us to handle complicated experiment parameters in a modular and readable method whereas guaranteeing consistency throughout runs. Try the FULL CODES right here.
def setup_config_dir():
config_dir = Path("./hydra_configs")
config_dir.mkdir(exist_ok=True)
main_config = """
defaults:
- mannequin: resnet
- information: cifar10
- optimizer: adam
- _self_
epochs: 100
seed: 42
system: cuda
experiment_name: exp_001
"""
(config_dir / "config.yaml").write_text(main_config)
model_dir = config_dir / "mannequin"
model_dir.mkdir(exist_ok=True)
(model_dir / "resnet.yaml").write_text("""
title: resnet
num_layers: 50
hidden_dim: 512
dropout: 0.1
""")
(model_dir / "vit.yaml").write_text("""
title: vision_transformer
num_layers: 12
hidden_dim: 768
dropout: 0.1
patch_size: 16
""")
data_dir = config_dir / "information"
data_dir.mkdir(exist_ok=True)
(data_dir / "cifar10.yaml").write_text("""
dataset: cifar10
batch_size: 32
num_workers: 4
augmentation: true
""")
(data_dir / "imagenet.yaml").write_text("""
dataset: imagenet
batch_size: 128
num_workers: 8
augmentation: true
""")
opt_dir = config_dir / "optimizer"
opt_dir.mkdir(exist_ok=True)
(opt_dir / "adam.yaml").write_text("""
_target_: torch.optim.Adam
lr: 0.001
betas: [0.9, 0.999]
weight_decay: 0.0
""")
(opt_dir / "sgd.yaml").write_text("""
_target_: torch.optim.SGD
lr: 0.01
momentum: 0.9
nesterov: true
""")
return str(config_dir.absolute())We programmatically create a listing containing YAML configuration information for fashions, datasets, and optimizers. This strategy permits us to reveal how Hydra robotically composes configurations from totally different information, thereby sustaining flexibility and readability in experiments. Try the FULL CODES right here.
@hydra.essential(version_base=None, config_path="hydra_configs", config_name="config")
def practice(cfg: DictConfig) -> float:
print("=" * 80)
print("CONFIGURATION")
print("=" * 80)
print(OmegaConf.to_yaml(cfg))
print("n" + "=" * 80)
print("ACCESSING CONFIGURATION VALUES")
print("=" * 80)
print(f"Mannequin: {cfg.mannequin.title}")
print(f"Dataset: {cfg.information.dataset}")
print(f"Batch Measurement: {cfg.information.batch_size}")
print(f"Optimizer LR: {cfg.optimizer.lr}")
print(f"Epochs: {cfg.epochs}")
best_acc = 0.0
for epoch in vary(min(cfg.epochs, 3)):
acc = 0.5 + (epoch * 0.1) + (cfg.optimizer.lr * 10)
best_acc = max(best_acc, acc)
print(f"Epoch {epoch+1}/{cfg.epochs}: Accuracy = {acc:.4f}")
return best_accWe implement a coaching perform that leverages Hydra’s configuration system to print, entry, and use nested config values. By simulating a easy coaching loop, we showcase how Hydra cleanly integrates experiment management into actual workflows. Try the FULL CODES right here.
def demo_basic_usage():
print("n" + "🚀 DEMO 1: Fundamental Configurationn")
config_dir = setup_config_dir()
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name="config")
print(OmegaConf.to_yaml(cfg))
def demo_config_override():
print("n" + "🚀 DEMO 2: Configuration Overridesn")
config_dir = setup_config_dir()
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(
config_name="config",
overrides=[
"model=vit",
"data=imagenet",
"optimizer=sgd",
"optimizer.lr=0.1",
"epochs=50"
]
)
print(OmegaConf.to_yaml(cfg))
def demo_structured_config():
print("n" + "🚀 DEMO 3: Structured Config Validationn")
from hydra.core.config_store import ConfigStore
cs = ConfigStore.occasion()
cs.retailer(title="training_config", node=TrainingConfig)
with initialize_config_dir(version_base=None, config_dir=setup_config_dir()):
cfg = compose(config_name="config")
print(f"Config sort: {sort(cfg)}")
print(f"Epochs (validated as int): {cfg.epochs}")
def demo_multirun_simulation():
print("n" + "🚀 DEMO 4: Multirun Simulationn")
config_dir = setup_config_dir()
experiments = [
["model=resnet", "optimizer=adam", "optimizer.lr=0.001"],
["model=resnet", "optimizer=sgd", "optimizer.lr=0.01"],
["model=vit", "optimizer=adam", "optimizer.lr=0.0001"],
]
outcomes = {}
for i, overrides in enumerate(experiments):
print(f"n--- Experiment {i+1} ---")
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name="config", overrides=overrides)
print(f"Mannequin: {cfg.mannequin.title}, Optimizer: {cfg.optimizer._target_}")
print(f"Studying Charge: {cfg.optimizer.lr}")
outcomes[f"exp_{i+1}"] = cfg
return outcomes
def demo_interpolation():
print("n" + "🚀 DEMO 5: Variable Interpolationn")
cfg = OmegaConf.create({
"mannequin": {"title": "resnet", "layers": 50},
"experiment": "${mannequin.title}_${mannequin.layers}",
"output_dir": "/outputs/${experiment}",
"checkpoint": "${output_dir}/finest.ckpt"
})
print(OmegaConf.to_yaml(cfg))
print(f"nResolved experiment title: {cfg.experiment}")
print(f"Resolved checkpoint path: {cfg.checkpoint}")We reveal Hydra’s superior capabilities, together with config overrides, structured config validation, multi-run simulations, and variable interpolation. Every demo showcases how Hydra accelerates experimentation velocity, streamlines handbook setup, and fosters reproducibility in analysis. Try the FULL CODES right here.
if __name__ == "__main__":
demo_basic_usage()
demo_config_override()
demo_structured_config()
demo_multirun_simulation()
demo_interpolation()
print("n" + "=" * 80)
print("Tutorial full! Key takeaways:")
print("✓ Config composition with defaults")
print("✓ Runtime overrides by way of command line")
print("✓ Structured configs with sort security")
print("✓ Multirun for hyperparameter sweeps")
print("✓ Variable interpolation")
print("=" * 80)We execute all demonstrations in sequence to look at Hydra in motion, from loading configs to performing multiruns. By the top, we summarize key takeaways, reinforcing how Hydra permits scalable and chic experiment administration.
In conclusion, we grasp how Hydra, pioneered by Meta Analysis, simplifies and enhances experiment administration via its highly effective composition system. We discover structured configs, interpolation, and multirun capabilities that make large-scale machine studying workflows extra versatile and maintainable. With this data, you are actually outfitted to combine Hydra into your personal analysis or growth pipelines, guaranteeing reproducibility, effectivity, and readability in each experiment you run.
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