Enable parallel logging for private logging servers with high latency

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### Description & Motivation

I am using a private MLFlow server, which has a very high latency. This results in my training process to be stuck in logging 99% of the time.

In my mind, this should be easy to fix - simply open a thread in parallel to the trainer which does the logging in the background, while the trainer keeps training.

### Pitch

Add a trainer flag "log_in_parallel" which detaches the logging from the training process, and lets it train before the logging is finished.

### Alternatives

An alternative would be to store all the logging data offline and only synchronize it in predetermined intervals, such as every 10 epochs.

### Additional context

99% is only a slight hyperbole, this is what my gpu usage looks like:

cc @borda

No file, test, or entry point is named in the issue. Start by locating the trainer's logging entry points and the existing private MLFlow-server integration; define how background logging, failures, ordering, shutdown, and the log_in_parallel flag should behave. Done means training continues without waiting for logging and the behavior is covered by tests.

Written by the indexing model from the issue text.