Flyte enables production-grade orchestration for machine learning workflows and data processing created to accelerate local workflows to production.

Flyte

Hi

I am going though the Flyte-2 code and have a suggestion. This is a only and opinion/suggestion,
```# Current Implementation
run = flyte.run(main, x_list=list(range(10)))```
```# Suggestion
run = flyte.run(main, x_list=list(range(10),logger,log_level))```
Does it sound fair if we could pass a logger to the function, so we can get a detailed trace and place the logger file on a location of our choice.

We would also need a log_level [INFO,DEBUG,ERROR] for the level of verbosity that is required

Flyte CLI `flyte --help` has `flyte -vvv get logs <run-name>`

I don’t see anything like that for python code.

You can set log level in Flyte.withruncontext or in Flyte init set log level 

<https://www.union.ai/docs/v2/byoc/api-reference/flyte-sdk/packages/flyte/#with_runcontext>

or init <https://www.union.ai/docs/v2/byoc/api-reference/flyte-sdk/packages/flyte/#init>

I see there is only logging of type `StreamHandler` / `RichHandler` and no `FileHandler` . What if someone needs store/keep the logs of the run for audit and compliance.

I would suggest that for logging we should have arguments like log_stream=True (for streaming the logs to console) and log_file_loc=“<some location>, if not absolute put it in current directory”. That way it will give a way to get the files into a location if required or if people want they can stream it/pipe it to a file `python main.py > log.txt`

Moreover i think we should `RichHandler(console=sys.stdout)` currently it outputs to std.err so ``python main.py > log.txt`   will not work until the pipe both std.err and std.out to a file

Is PR accepted for flyte2-sdk , i can raise it as PR. Not sure since it’s in Beta