我正在尝试使用 mlflow 和 hydra 管理机器学习的结果。所以我尝试使用 hydra 的多运行功能来运行它。我使用以下代码作为测试。
import mlflow
import hydra
from hydra import utils
from pathlib import Path
import time
@hydra.main('config.yaml')
def main(cfg):
print(cfg)
mlflow.set_tracking_uri('file://' + utils.get_original_cwd() + '/mlruns')
mlflow.set_experiment(cfg.experiment_name)
mlflow.log_param('param1',5)
# mlflow.log_param('param1',5)
# mlflow.log_param('param1',5)
with mlflow.start_run() :
mlflow.log_artifact(Path.cwd() / '.hydra/config.yaml')
if __name__ == '__main__':
main()
此代码将不起作用。我收到以下错误
Exception: Run with UUID [RUNID] is already active. To start a new run, first end the current run with mlflow.end_run(). To start a nested run, call start_run with nested=True
所以我修改了代码如下
import mlflow
import hydra
from hydra import utils
from pathlib import Path
import time
@hydra.main('config.yaml')
def main(cfg):
print(cfg)
mlflow.set_tracking_uri('file://' + utils.get_original_cwd() + '/mlruns')
mlflow.set_experiment(cfg.experiment_name)
mlflow.log_param('param1',5)
# mlflow.log_param('param1',5)
# mlflow.log_param('param1',5)
with mlflow.start_run(nested=True) :
mlflow.log_artifact(Path.cwd() / '.hydra/config.yaml')
if __name__ == '__main__':
main()
此代码有效,但未保存工件。进行了以下更正以保存工件。
import mlflow
import hydra
from hydra import utils
from pathlib import Path
import time
@hydra.main('config.yaml')
def main(cfg):
print(cfg)
mlflow.set_tracking_uri('file://' + utils.get_original_cwd() + '/mlruns')
mlflow.set_experiment(cfg.experiment_name)
mlflow.log_param('param1',5)
# mlflow.log_param('param1',5)
# mlflow.log_param('param1',5)
mlflow.log_artifact(Path.cwd() / '.hydra/config.yaml')
if __name__ == '__main__':
main()
结果,工件现在被保存。但是,当我运行以下命令时
python test.py model=A,B hidden=12,212,31 -m
仅保存了最后一个执行条件的工件。
如何利用 hydra 的多运行功能修改 mlflow 以管理实验参数?