Support for KMeans with an async Client

[JavierYepez]

Hi,

I'd like to ask if It's possible to add a version of KMeans that works with an async client.

Here is an example that should work:

import distributed
import dask_ml
import dask_ml.datasets
import dask_ml.cluster

client = distributed.Client(asynchronous=True)
await client

X, y = dask_ml.datasets.make_blobs(n_samples=10000000,
                                   chunks=1000000,
                                   random_state=0,
                                   centers=3)
km = dask_ml.cluster.KMeans(n_clusters=3, init_max_iter=2, oversampling_factor=10)
km.fit(X)

Thank you

[TomAugspurger]

Should be doable, as long as nothing in the .fit method is blocking on the main thread.

On Dec 24, 2021, at 10:03 AM, Javier Yepez @.***> wrote:

Hi,

I'd like to ask if It's possible to add a version of KMeans that works with an async client.

Here is an example that should work:

import distributed import dask_ml import dask_ml.datasets import dask_ml.cluster

client = distributed.Client(asynchronous=True) await client

X, y = dask_ml.datasets.make_blobs(n_samples=10000000, chunks=1000000, random_state=0, centers=3) km = dask_ml.cluster.KMeans(n_clusters=3, init_max_iter=2, oversampling_factor=10) km.fit(X) Thank you

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[inesortega]

Hi! any plans on implementing this feature?

[TomAugspurger]

I don’t plan to work on it. A PR would be welcome, but we’ll need to find some help reviewing it: I’m not really sure what the best way to mix sync and async code with an async client.

On Mar 11, 2022 at 2:07:51 AM, Inés Ortega-Fernández < @.***> wrote:

Hi! any plans on implementing this feature?

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