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)