issue_comments

Thanks @dcherian. Feel free to let me know if you or others run into this issue elsewhere. I also think it's an edge case the vast majority of users won't encounter (this is the first occurrence I've seen outside of the dask test suite), but if that turns out to not be the case then we can make adjustments upstream in dask 👍

Just checking in, @dcherian does this look okay to you? Happy to continue iterating if you see a problem with just removing this config option

I've not looked super deeply, so please let me know if I'm missing something, but I think that these lines in get_scheduler are the relevant ones here

https://github.com/dask/dask/blob/db5b2178a79cacc1c882d60a82bf86e2e188eccb/dask/base.py#L1405-L1406

Previously, distributed would set the scheduler config option to point to the default distributed.Client (if one existed). We've since changed that logic and distributed no no longer uses the config option for saying "you've got a distributed.Client, you should use it".

I think the problem here is, with the previous config-based behavior, the scheduler option, which is currently being set to the single-threaded scheduler in the test suite, would be overwritten by distributed to point to the Client. However, now that we're no longer using the scheduler config option in distributed, the test suite is actually using the single-threaded scheduler, which is why get_scheduler() is returning get_sync when using the latest dask / distributed release.

I'd argue the new behavior is actually what we want, but I see what you're saying about it being a change in behavior. I think in this case though it's just a tests-related issue. Does that sounds right? Or was setting the scheduler config option important for real-life user code too?

Hmm I'm not able to reproduce with this example using the latest 2023.1.1 release:

```python In [1]: import dask

In [2]: dask.utils.show_versions() { "Python": "3.10.4", "Platform": "Darwin", "dask": "2023.1.1+3.gdb5b2178a", "distributed": "2023.1.1", "numpy": "1.24.1", "pandas": "2.0.0.dev0+1309.g7f2aa8f46a", "cloudpickle": "2.0.0", "fsspec": "2023.1.0+5.g012816b", "bokeh": "2.4.3", "fastparquet": "2022.12.1.dev6", "pyarrow": "11.0.0.dev316", "zarr": "2.4.1.dev528" } In [3]: from distributed import Client

In [4]: type(dask.base.get_scheduler()) Out[4]: NoneType

In [5]: c = Client()

In [6]: dask.base.get_scheduler() Out[6]: <bound method Client.get of \<Client: 'tcp://127.0.0.1:61659' processes=4 threads=8, memory=16.00 GiB>> ```

cc @dcherian