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_create_model(sessionId, **kwargs):
try:
from cuml.neighbors.nearest_neighbors_mg import \
NearestNeighborsMG as cumlNN
except ImportError:
raise_mg_import_exception()
handle = worker_state(sessionId)["handle"]
return cumlNN(handle=handle, **kwargs)
def _func_create_model(sessionId, **kwargs):
try:
from cuml.neighbors.kneighbors_classifier_mg import \
KNeighborsClassifierMG as cumlKNN
except ImportError:
raise_mg_import_exception()
handle = worker_state(sessionId)["handle"]
return cumlKNN(handle=handle, **kwargs)
def _func_create_model(sessionId, dfs, **kwargs):
try:
from cuml.decomposition.tsvd_mg import TSVDMG as cumlTSVD
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"]
return cumlTSVD(handle=handle, **kwargs), dfs
def _func_create_model(sessionId, **kwargs):
try:
from cuml.linear_model.ridge_mg import RidgeMG as cumlRidge
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"]
return cumlRidge(handle=handle, **kwargs)
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)
def func_test_recv_any_rank(sessionId, n_trials, r):
handle = worker_state(sessionId)["handle"]
return perform_test_comms_recv_any_rank(handle, n_trials)
def func_test_send_recv(sessionId, n_trials, r):
handle = worker_state(sessionId)["handle"]
return perform_test_comms_send_recv(handle, n_trials)
def func_test_allreduce(sessionId, r):
handle = worker_state(sessionId)["handle"]
return perform_test_comms_allreduce(handle)
def _create_model(sessionId, datatype, **kwargs):
from cuml.solvers.cd_mg import CDMG
handle = worker_state(sessionId)["handle"]
return CDMG(handle=handle, output_type=datatype, **kwargs)
def _create_model(sessionId, datatype, **kwargs):
from cuml.linear_model.ridge_mg import RidgeMG
handle = worker_state(sessionId)["handle"]
return RidgeMG(handle=handle, output_type=datatype, **kwargs)
def _create_model(sessionId, model_func, datatype, **kwargs):
handle = worker_state(sessionId)["handle"]
return model_func(handle, datatype, **kwargs)
def _create_model(sessionId, datatype, **kwargs):
from cuml.linear_model.linear_regression_mg import LinearRegressionMG
handle = worker_state(sessionId)["handle"]
return LinearRegressionMG(handle=handle,
output_type=datatype,
**kwargs)
