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log environment automatically

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Young
2022-08-09 11:48:47 +08:00
parent 597359f98f
commit 949d96d768
1 changed files with 5 additions and 1 deletions
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6
qlib/workflow/recorder.py
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@@ -253,7 +253,8 @@ class MLflowRecorder(Recorder):
Though it takes extra efforts, but it brings users benefits due to following reasons. Though it takes extra efforts, but it brings users benefits due to following reasons.
- It will be more convenient to change the experiment logging backend without changing any code in upper level - It will be more convenient to change the experiment logging backend without changing any code in upper level
- We can provide more convenience to automatically do some extra things and make interface easier. For examples: - We can provide more convenience to automatically do some extra things and make interface easier. For examples:
- It can automatically log the uncommitted code - Automatically logging the uncommitted code
- Automatically logging part of environment variables
- User can control several different runs by just creating different Recorder (in mlflow, you always have to switch artifact_uri and pass in run ids frequently) - User can control several different runs by just creating different Recorder (in mlflow, you always have to switch artifact_uri and pass in run ids frequently)
""" """
@@ -347,6 +348,9 @@ class MLflowRecorder(Recorder):
self._log_uncommitted_code() self._log_uncommitted_code()
self.log_params(**{"cmd-sys.argv": " ".join(sys.argv)}) # log the command to produce current experiment self.log_params(**{"cmd-sys.argv": " ".join(sys.argv)}) # log the command to produce current experiment
self.log_params(
**{k: v for k, v in os.environ.items() if k.startswith("_QLIB_")}
) # Log necessary environment variables
return run return run
def _log_uncommitted_code(self): def _log_uncommitted_code(self):