crypto_predictor
233f0c5d10
- start_system.py: 跨平台进程管理 - Windows 使用 CREATE_NEW_PROCESS_GROUP 和 terminate() - Unix 使用 setsid 和 killpg - 自动检测并启用 Windows ANSI 颜色支持 - 设备选择逻辑优化： - macOS: 优先使用 MPS，回退到 CPU - Windows/Linux: 强制使用 CPU（无 CUDA 依赖） - 影响文件： - api/prediction_service.py - auto_trade_polymarket.py - training/config.py
64 lines
1.5 KiB
Python
64 lines
1.5 KiB
Python
"""Training configuration."""
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from dataclasses import dataclass
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from typing import Literal
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@dataclass
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class TrainingConfig:
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"""Configuration for model training."""
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# Training parameters
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batch_size: int = 64
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max_epochs: int = 100
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learning_rate: float = 0.001
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weight_decay: float = 1e-5
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# Early stopping
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early_stopping_patience: int = 10
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early_stopping_min_delta: float = 1e-4
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# Learning rate scheduler
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lr_scheduler: Literal['plateau', 'cosine', 'none'] = 'plateau'
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lr_patience: int = 5
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lr_factor: float = 0.5
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# Device
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device: str = 'auto' # 'auto', 'mps', 'cuda', or 'cpu'
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# Logging
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log_interval: int = 10 # Log every N batches
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# Checkpointing
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save_best: bool = True
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checkpoint_dir: str = './models/checkpoints'
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def get_device(self) -> str:
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"""Get the device to use for training.
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Returns:
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Device string ('mps', 'cuda', or 'cpu')
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Note:
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macOS 使用 MPS，Windows/Linux 使用 CPU
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"""
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import torch
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import platform
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if self.device != 'auto':
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return self.device
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# 根据操作系统选择设备：macOS 用 MPS，Windows/Linux 用 CPU
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if platform.system() == 'Darwin': # macOS
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if torch.backends.mps.is_available():
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return 'mps'
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else:
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return 'cpu'
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else: # Windows / Linux
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return 'cpu'
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if __name__ == "__main__":
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config = TrainingConfig()
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print(f"Using device: {config.get_device()}")
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