dantegd

import cupy as cp
import cuml 

ary = cp.array([[1.0, 4.0, 4.0], [2.0, 2.0, 2.0], [5.0, 1.0, 1.0]])

kmeams = cuml.KMeans(n_clusters=2, output_type='numpy')
kmeans.fit(ary)

print(type(kmeans.labels_))
# <class 'numpy.ndarray'>

dantegd

import cuml
import cudf

df = cudf.DataFrame()
df[1] = [1.0, 2.0, 5.0]
df[2] = [4.0, 2.0. 1.0]
df[3] = [4.0, 2.0. 1.0]
                         
kmeans = cuml.KMeans(n_clusters=2)
kmeans.fit(df)

dantegd

import cuml
import cupy as cp

ary = [[1.0, 4.0, 4.0], [2.0, 2.0, 2.0], [5.0, 1.0, 1.0]]
ary = cp.asarray(ary)

with cuml.using_output_type('cudf'):
    dbscan = cuml.DBSCAN(eps=1.0, min_samples=1)
    dbscan.fit(ary)
    print(type(dbscan_float.labels_))

dantegd

import cupy as cp
import numpy as np
import cuml 

cuml.set_global_output_type('numpy')

ary = cp.array([[1.0, 4.0, 4.0], [2.0, 2.0, 2.0], [5.0, 1.0, 1.0]])

kmeans = cuml.KMeans(n_clusters=2)
kmeans.fit(ary)

dantegd

import cuml
import numpy as np

ary = np.array([[1.0, 4.0, 4.0], [2.0, 2.0, 2.0], [5.0, 1.0, 1.0]])

kmeans = cuml.KMeans(n_clusters=2)
kmeans.fit(ary)
print(type(kmeans.labels_))
# <class ‘numpy.ndarray’>