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I could not find a way yet of setting the runs name after the first start_run for that run (we can pass a name there).
I Know we can use tags but that is not the same thing. I would like to add a run relevant name, but very often we know the name only after run evaluation or while we're running the run interactively in notebook for example.
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run_id – Unique identifier for the run to delete. Delete a tag from a run. This is irreversible. If no run is active, this method will create a new active run.
MLflow runs can be recorded to local files, to a SQLAlchemy compatible database, or remotely to a tracking server. By default, the MLflow Python API logs runs locally to files in an mlruns directory wherever you ran your program.
artifact_path – (For use with run_id ) If specified, a path relative to the MLflow Run's root directory containing the artifacts to download. dst_path – Path of the local filesystem destination directory to which to download the specified artifacts. If the directory does not exist, it is created.
we can get the experiment id from the experiment name and we can use python API to get the best runs.
It is possible to edit run names from the MLflow UI. First, click into the run whose name you'd like to edit.
Then, edit the run name by clicking the dropdown next the run name (i.e. the downward-pointing caret in this image):
There's currently no stable public API for setting run names - however, you can programmatically set/edit run names by setting the tag with key mlflow.runName, which is what the UI (currently) does under the hood.
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