Hi. There is the following suggestion in the prefect-dask documentation:

Then, register to view the block on Prefect Cloud:

prefect block register -m prefect_dask.credentials

But there is no module

credentials

in

prefect_dask

. And I get the following error (not a surpirse):

```Unable to load prefect_dask.credentials. Please make sure the module is installed in your current

environment.```

I wonder if this module has ever existed? Or maybe there are plans to create it?

@Andrew Huang I believe there is not credentials block for prefect-dask. Is that correct?

there aren't any blocks to register from this collection, we just haven't updated the docs on the PyPI page yet but @Evgeny Ivanov you can find the most recent documentation here in the README

Khuyen, Nate, thank you for the answers. It was clear for me, that there are no blocks in this collection. But after seeing this line in documentation I thought, that there might be plans to add it. It could be useful if I want to have different dask settings in dev and prod environments but don't want to hardcode them. In my case I just created a simple custom block:

class DaskTaskRunnerConfig(Block):
    """Dask Task Runner Config.

    Attributes:
        address (str, optional): Address of a currently running dask scheduler;
            if one is not provided, a temporary cluster will be created in
            `DaskTaskRunner.start()`.  Defaults to `None`.
        cluster_class (str, optional): The cluster class to use when creating
            a temporary dask cluster. Should be the full class name
            (e.g. `"distributed.LocalCluster"`).
        cluster_kwargs (dict, optional): Additional kwargs to pass to the
            `cluster_class` when creating a temporary dask cluster.
        adapt_kwargs (dict, optional): Additional kwargs to pass to
            `cluster.adapt` when creating a temporary dask cluster. Note that
            adaptive scaling is only enabled if `adapt_kwargs` are provided.
        client_kwargs (dict, optional): Additional kwargs to use when creating a
            `dask.distributed.Client`,
            <https://distributed.dask.org/en/latest/api.html#client>.
    """
    _block_type_name = 'Dask Task Runner Config'

    address: str | None = None
    cluster_class: str | None = None
    cluster_kwargs: dict | None = None
    adapt_kwargs: dict | None = None
    client_kwargs: dict | None = None

    def create_task_runner(self) -> DaskTaskRunner:
        return DaskTaskRunner(
            address=self.address,
            cluster_class=self.cluster_class,
            cluster_kwargs=self.cluster_kwargs,
            adapt_kwargs=self.adapt_kwargs,
            client_kwargs=self.client_kwargs,
        )

That sounds interesting! I created an issue here: https://github.com/PrefectHQ/prefect-dask/issues/48