def func_fit(sessionId, n_clusters, max_iter, tol, verbose, random_state,
precompute_distances, init, n_init, algorithm, dfs, r):
"""
Runs on each worker to call fit on local KMeans instance.
Extracts centroids
:param model: Local KMeans instance
:param dfs: List of cudf.Dataframes to use
:param r: Stops memoizatiion caching
:return: The fit model
"""
try:
from cuml.cluster.kmeans_mg import KMeansMG as cumlKMeans
except ImportError:
raise Exception("cuML has not been built with multiGPU support "
"enabled. Build with the --multigpu flag to"
" enable multiGPU support.")
handle = worker_state(sessionId)["handle"]
df = concat(dfs)
return cumlKMeans(handle=handle,
init=init,
max_iter=max_iter,
tol=tol,
random_state=random_state,
n_init=n_init,
algorithm=algorithm,
precompute_distances=precompute_distances,
n_clusters=n_clusters,
verbose=verbose).fit(df)
def _func_fit(sessionId, objs, datatype, **kwargs):
from cuml.cluster.kmeans_mg import KMeansMG as cumlKMeans
handle = worker_state(sessionId)["handle"]
inp_data = concatenate(objs)
return cumlKMeans(handle=handle, output_type=datatype,
**kwargs).fit(inp_data)
def _func_fit(sessionId, objs, datatype, has_weights, **kwargs):
from cuml.cluster.kmeans_mg import KMeansMG as cumlKMeans
handle = get_raft_comm_state(sessionId)["handle"]
if not has_weights:
inp_data = concatenate(objs)
inp_weights = None
else:
inp_data = concatenate([X for X, weights in objs])
inp_weights = concatenate([weights for X, weights in objs])
return cumlKMeans(handle=handle, output_type=datatype,
**kwargs).fit(inp_data, sample_weight=inp_weights)
def _func_fit(sessionId, dfs, **kwargs):
"""
Runs on each worker to call fit on local KMeans instance.
Extracts centroids
:param model: Local KMeans instance
:param dfs: List of cudf.Dataframes to use
:param r: Stops memoization caching
:return: The fit model
"""
try:
from cuml.cluster.kmeans_mg import KMeansMG as cumlKMeans
except ImportError:
raise_mg_import_exception()
handle = worker_state(sessionId)["handle"]
df = concat(dfs)
return cumlKMeans(handle=handle, **kwargs).fit(df)
def func_fit(sessionId, dfs, **kwargs):
"""
Runs on each worker to call fit on local KMeans instance.
Extracts centroids
:param model: Local KMeans instance
:param dfs: List of cudf.Dataframes to use
:param r: Stops memoizatiion caching
:return: The fit model
"""
try:
from cuml.cluster.kmeans_mg import KMeansMG as cumlKMeans
except ImportError:
raise Exception("cuML has not been built with multiGPU support "
"enabled. Build with the --multigpu flag to"
" enable multiGPU support.")
handle = worker_state(sessionId)["handle"]
df = concat(dfs)
return cumlKMeans(handle=handle, **kwargs).fit(df)