完成--完成获取模型列表方法--时间仍然是初始时区的时间
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example_experiment_list.py
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34
example_experiment_list.py
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@ -0,0 +1,34 @@
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from function.model_manager import ModelManager
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# 创建模型管理器实例
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manager = ModelManager()
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# 获取实验列表
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result = manager.get_experiments(
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page=2,
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page_size=10,
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include_deleted=False
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)
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# 打印结果
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print("\nMLFlow实验列表:")
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print(f"状态: {result['status']}")
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if result['status'] == 'success':
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print(f"\n总数: {result['total_count']}")
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print(f"当前页: {result['page']}")
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print(f"每页数量: {result['page_size']}")
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print("\n实验列表:")
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for exp in result['experiments']:
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print(f"\n实验ID: {exp['experiment_id']}")
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print(f"名称: {exp['name']}")
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print(f"存储位置: {exp['artifact_location']}")
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print(f"状态: {exp['lifecycle_stage']}")
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print(f"创建时间: {exp['creation_time']}")
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print(f"最后更新: {exp['last_update_time']}")
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print(f"运行次数: {exp['runs_count']}")
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if exp['tags']:
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print("标签:")
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for tag_name, tag_value in exp['tags'].items():
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print(f" {tag_name}: {tag_value}")
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else:
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print(f"错误信息: {result['message']}")
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@ -94,6 +94,10 @@ class ModelManager:
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elif key.startswith('metrics.'):
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metrics[key.replace('metrics.', '')] = value
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# 转换时间为本地时间
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start_time = pd.to_datetime(run['start_time']).tz_convert('Asia/Shanghai')
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end_time = pd.to_datetime(run['end_time']).tz_convert('Asia/Shanghai')
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# 构建模型信息
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model_info = {
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'run_id': run['run_id'],
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@ -101,8 +105,8 @@ class ModelManager:
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'algorithm': params['algorithm'], # 从配置或其他地方获取
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'task_type': params['task_type'], # 从配置或其他地方获取
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'dataset': params['dataset'], # 从配置或其他地方获取
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'training_start_time': run['start_time'],
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'training_end_time': run['end_time'],
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'training_start_time': start_time.strftime('%Y-%m-%d %H:%M:%S'), # 格式化为本地时间字符串
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'training_end_time': end_time.strftime('%Y-%m-%d %H:%M:%S'),
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'metrics': metrics,
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'parameters': {
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k: v for k, v in params.items()
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@ -133,3 +137,80 @@ class ModelManager:
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'status': 'error',
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'message': error_msg
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}
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def get_experiments(
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self,
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page: int = 1,
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page_size: int = 10,
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include_deleted: bool = False
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) -> Dict:
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"""
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获取MLflow中保存的实验列表
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Args:
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page: 页码
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page_size: 每页数量
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include_deleted: 是否包含已删除的实验
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Returns:
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实验列表信息
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"""
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try:
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# 获取所有实验
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experiments = mlflow.search_experiments()
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# 过滤已删除的实验
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if not include_deleted:
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experiments = [exp for exp in experiments if exp.lifecycle_stage == 'active']
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# 计算分页
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total_count = len(experiments)
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start_idx = (page - 1) * page_size
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end_idx = start_idx + page_size
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page_experiments = experiments[start_idx:end_idx]
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# 格式化实验信息
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experiment_list = []
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for exp in page_experiments:
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# 获取实验的运行次数
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runs = mlflow.search_runs(
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experiment_ids=[exp.experiment_id],
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filter_string="status = 'FINISHED'"
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)
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# 获取最后更新时间并转换为本地时间
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if len(runs) > 0:
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last_update_time = pd.to_datetime(runs['end_time'].max())
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else:
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last_update_time = pd.to_datetime(exp.creation_time)
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# 创建时间转换为本地时间
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creation_time = pd.to_datetime(exp.creation_time)
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experiment_info = {
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'experiment_id': exp.experiment_id,
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'name': exp.name,
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'artifact_location': exp.artifact_location,
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'lifecycle_stage': exp.lifecycle_stage,
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'creation_time': creation_time.strftime('%Y-%m-%d %H:%M:%S'),
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'last_update_time': last_update_time.strftime('%Y-%m-%d %H:%M:%S'),
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'tags': exp.tags,
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'runs_count': len(runs)
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}
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experiment_list.append(experiment_info)
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return {
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'status': 'success',
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'experiments': experiment_list,
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'total_count': total_count,
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'page': page,
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'page_size': page_size
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}
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except Exception as e:
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error_msg = f"Error getting experiments: {str(e)}"
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self.logger.error(error_msg)
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return {
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'status': 'error',
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'message': error_msg
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}
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