How to PREVENT mlflow's autologging from logging ALL runs?

naveen_marthala
Contributor

‎05-02-2022 09:08 AM

I am logging runs from jupyter notebook. the cells which has `mlflow.sklearn.autlog()` behaves as expected. but, the cells which has .fit() method being called on sklearn's estimators are also being logged as runs without explicitly mentioning `mlflow.sklearn.autlog()` on top. How do I have mlflow log only the ones I call `mlflow.xxxx.autlog()` or by doing `with mlflow.star_run()`?

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Joe_Breath1
New Contributor III

‎08-24-2025 04:39 PM

It looks like MLflow auto-logging is kicking in by default whenever you call .fit(), which is why you’re seeing runs even without explicitly using mlflow.sklearn.autolog(). To fix this, you can disable the global autologging and only trigger it when you explicitly call mlflow.xxx.autolog() or wrap your code with with mlflow.start_run(). For more details, you can also visit website resources on MLflow’s official docs.

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Joe_Breath1
New Contributor III
In response to Joe_Breath1

‎10-28-2025 07:41 AM

Nice tip! I didn’t know about that auto-logging part. I’ll try this in my next bus apk setup.

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alexsheer9003
New Contributor II

‎01-19-2026 02:47 AM

NICE TIP!

 

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Easysjs
New Contributor II

2 weeks ago

Great question! To prevent MLflow's autologging from logging ALL runs, you can disable it entirely or selectively control which libraries or runs get logged.

You can also start a run with mlflow.start_run() and set log_models=False or use mlflow.end_run() to stop logging. For finer control, consider using mlflow.set_tracking_uri() and managing runs manually.

On a side note, logging every single run can feel as chaotic as traffic in Bus Simulator Indonesia you need to steer manually to avoid total chaos

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ericholland009
New Contributor II

2 weeks ago

Good question—mlflow autologging can easily capture more runs than expected if not configured properly. Managing it carefully improves experiment tracking. Similar control and optimization are important in bussid mod workflows, where users fine-tune settings and assets for better performance and results.

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