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Removed setting the experiment_id to the ray trial id. Reordered the priority of the settings dict.
Why are these changes needed?
The MLflow and ray ID syntax is not the same. Setting the default id to the ray id just leads to additional crashes. The correct id will be set from ray - air - _internal - mlflow.py. So there is no need for a default fallback!
Adjusted the ordering of the dictionary to get first these defaults and than any fallbacks from within the function. Probably also necessary for the wandb version necessary.
Decision on how to setup credentials, servers, ... necessary. Either trough the tune dict or with additional changes in the actual trainable function itself.
Related issue number
Cleanup for #31295
Checks
git commit -s
) in this PR.scripts/format.sh
to lint the changes in this PR.