first add

examples/01_basic/conf/config.yaml

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+# 基础配置示例
+learning_rate: 0.001
+batch_size: 32
+epochs: 100
+model_name: "ResNet50"
+device: "cuda"

examples/01_basic/train.py

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+"""基础Hydra配置管理示例"""
+
+import hydra
+from omegaconf import DictConfig
+from rich.console import Console
+from rich.table import Table
+
+console = Console()
+
+@hydra.main(version_base=None, config_path="conf", config_name="config")
+def train(cfg: DictConfig) -> None:
+    console.print("\n[bold green]🚀 基础Hydra配置示例[/bold green]\n")
+    
+    table = Table(title="当前配置")
+    table.add_column("参数", style="cyan")
+    table.add_column("值", style="yellow")
+    
+    table.add_row("学习率", str(cfg.learning_rate))
+    table.add_row("批次大小", str(cfg.batch_size))
+    table.add_row("训练轮数", str(cfg.epochs))
+    table.add_row("模型名称", cfg.model_name)
+    
+    console.print(table)
+    console.print("\n[bold green]✅ 训练完成！[/bold green]\n")
+
+if __name__ == "__main__":
+    train()

examples/02_ml_training/conf/config.yaml

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+defaults:
+  - model: resnet
+  - optimizer: adam
+  - dataset: cifar10
+  - _self_
+
+learning_rate: 0.001
+batch_size: 32
+epochs: 100
+device: cuda

examples/02_ml_training/conf/dataset/cifar10.yaml

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+name: CIFAR-10
+num_classes: 10
+image_size: 32
+train_samples: 50000
+val_samples: 10000

examples/02_ml_training/conf/dataset/imagenet.yaml

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+name: ImageNet
+num_classes: 1000
+image_size: 224
+train_samples: 1281167
+val_samples: 50000

examples/02_ml_training/conf/model/resnet.yaml

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+name: ResNet50
+layers: 50
+pretrained: true
+num_classes: 1000
+dropout: 0.5

examples/02_ml_training/conf/model/transformer.yaml

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+name: ViT-B/16
+layers: 12
+pretrained: true
+num_classes: 1000
+hidden_dim: 768
+num_heads: 12
+patch_size: 16

examples/02_ml_training/conf/optimizer/adam.yaml

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+name: Adam
+lr: ${learning_rate}
+betas: [0.9, 0.999]
+weight_decay: 0.0001

examples/02_ml_training/conf/optimizer/sgd.yaml

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+name: SGD
+lr: ${learning_rate}
+momentum: 0.9
+weight_decay: 0.0005
+nesterov: true

examples/02_ml_training/train.py

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+"""机器学习训练配置示例"""
+
+import hydra
+from omegaconf import DictConfig
+from rich.console import Console
+from rich.tree import Tree
+
+console = Console()
+
+@hydra.main(version_base=None, config_path="conf", config_name="config")
+def train(cfg: DictConfig) -> None:
+    console.print("\n[bold green]🤖 机器学习训练配置示例[/bold green]\n")
+    
+    tree = Tree("🎯 Training Configuration")
+    
+    model_tree = tree.add("[yellow]Model")
+    model_tree.add(f"Name: {cfg.model.name}")
+    model_tree.add(f"Layers: {cfg.model.layers}")
+    
+    optimizer_tree = tree.add("[cyan]Optimizer")
+    optimizer_tree.add(f"Name: {cfg.optimizer.name}")
+    optimizer_tree.add(f"Learning Rate: {cfg.optimizer.lr}")
+    
+    dataset_tree = tree.add("[magenta]Dataset")
+    dataset_tree.add(f"Name: {cfg.dataset.name}")
+    dataset_tree.add(f"Classes: {cfg.dataset.num_classes}")
+    
+    console.print(tree)
+    console.print("\n[bold green]✅ 配置加载完成！[/bold green]\n")
+
+if __name__ == "__main__":
+    train()

examples/04_multi_env/app.py

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+"""多环境配置管理示例"""
+
+import hydra
+from omegaconf import DictConfig
+from rich.console import Console
+from rich.table import Table
+
+console = Console()
+
+@hydra.main(version_base=None, config_path="conf", config_name="config")
+def run_app(cfg: DictConfig) -> None:
+    env_colors = {"dev": "yellow", "test": "cyan", "prod": "red"}
+    color = env_colors.get(cfg.env.name, "white")
+    
+    console.print(f"\n[bold {color}]🌍 {cfg.env.name.upper()} 环境配置[/bold {color}]\n")
+    
+    table = Table()
+    table.add_column("配置项", style="cyan")
+    table.add_column("值", style="yellow")
+    
+    table.add_row("环境名称", cfg.env.name)
+    table.add_row("调试模式", "✅" if cfg.env.debug else "❌")
+    table.add_row("数据库主机", cfg.database.host)
+    table.add_row("服务器端口", str(cfg.server.port))
+    
+    console.print(table)
+    console.print()
+
+if __name__ == "__main__":
+    run_app()

examples/04_multi_env/conf/config.yaml

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+defaults:
+  - env: dev
+  - _self_
+
+database:
+  host: localhost
+  port: 5432
+
+server:
+  host: 0.0.0.0
+  port: 8000

examples/04_multi_env/conf/env/dev.yaml

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+name: dev
+debug: true
+
+database:
+  host: localhost
+  port: 5432
+
+server:
+  port: 8000

examples/04_multi_env/conf/env/prod.yaml

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+name: prod
+debug: false
+
+database:
+  host: prod-db.example.com
+  port: 5432
+
+server:
+  port: 8000

examples/04_multi_env/conf/env/test.yaml

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+name: test
+debug: false
+
+database:
+  host: test-db.example.com
+  port: 5432
+
+server:
+  port: 8080