qacaller

通过MLflow跟踪纪录程序运行的参数、指标、日志等信息

在mlruns 目录下执行 mlflow ui打开界面

Usage

pip install qacaller
qacaller --run <run_name> --cmd <command> --o True
mlflow ui

Example

# test.py

print("test@artifact@hello")
print("test@param@K1:100")
print("test@param@K1:200")
print('test@params@{"ma":20,"K2":3}')
for i in range(10):
    print(f"thread1@metric@number:{i}")
    print(f'thread1@metrics@{{"age":{i + 2},"total":{i + 3}}}')
print("test@tag@color:red")
print("test@tag@color:Green")
print('test@tags@{"age":"18","性别":"male"}')
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qacaller --run test --cmd "python <absolute path>/test.py"

Protocol

需调用程序遵循协议并打印输出

    
    - <run_name>@artifact@[str]            //日志文件
   
    - <run_name>@param@[key]:[str]         //参数,不可覆盖      
    
    - <run_name>@params@[json]
    
    - <run_name>@metric@[key]:[float/int]  //指标,可累积
    
    - <run_name>@metrics@[json]
    
    - <run_name>@tag@[key]:[str]           //标签,可覆盖

    - <run_name>@tags@[json]

MLflow Track

- Experiment
    - |___ Run
        - param
        - metric
        - tag
        - artifact
        - |___ child_run
            - param
            - metric
            - tag
            - artifact
        - |___ child_run
        - |___ child_run

     - |___ Run
        - |___ child_run
        - |___ child_run
        - |___ child_run