first add

examples/02_ml_training/conf/config.yaml

@@ -0,0 +1,10 @@
+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()