Lightning-AI / Lightning-AI/pytorch-lightning
Set experiment tags in MLflow
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- Dominant language
- Python
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Description
### Description & Motivation
Currently, _MLFlowLogger_ accepts a _tags_ parameter. However, contrary to the function docstring this sets tags on the run instead of on the MLflow experiment.
### Pitch
Add an _experiment_tags_ parameter to _MLFlowLogger_ that sets tags on the experiment. Keep the old _tags_ parameter intact.
### Alternatives
_No response_
### Additional context
Related issue: https://github.com/Lightning-AI/pytorch-lightning/issues/297
cc @lantiga
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Locate the MLFlowLogger implementation and inspect how its existing tags parameter is passed to MLflow. Review related issue #297 for context, then verify that a separate experiment_tags parameter sets experiment tags while the existing tags behavior remains unchanged. Add or update focused tests if the logger's test entry point is found.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100