Dask is the primary runtime through which cuML provides multi-GPU functionality in Python. As a result, it's historically been a required cuML dependency. This is a non-issue for users who want the multi-GPU capabilities in cuML.
However, for users who only want to use cuML for single GPU use cases, this can cause unnecessary dependency alignment challenges in their environments -- because it's common practice to tightly pin Dask versions to avoid breakages in stable releases and platforms. We don't expect this behavior to change.
We should explore converting Dask into an optional dependency only necessary if someone attempts to use the Dask multi-GPU capabilities in cuML.
Dask is the primary runtime through which cuML provides multi-GPU functionality in Python. As a result, it's historically been a required cuML dependency. This is a non-issue for users who want the multi-GPU capabilities in cuML.
However, for users who only want to use cuML for single GPU use cases, this can cause unnecessary dependency alignment challenges in their environments -- because it's common practice to tightly pin Dask versions to avoid breakages in stable releases and platforms. We don't expect this behavior to change.
We should explore converting Dask into an optional dependency only necessary if someone attempts to use the Dask multi-GPU capabilities in cuML.