def to_gpu_array(arr):
mat = arr.to_gpu_array()
dev = device_of_devicendarray(mat)
# Return canonical device id as string
return mat, dev
def as_gpu_matrix(arr):
blap = arr.compute()
mat = blap.as_gpu_matrix(order="F")
dev = device_of_devicendarray(mat)
# Return canonical device id as string
return mat, dev
def input_to_device_arrays(X, params):
"""
Create output arrays and return them w/ the input array(s)
:param arr:
A tuple in the form of (X, y)
:return:
"""
if len(X[0]) == 0:
return None
start_idx = X[0].index[0]
stop_idx = X[0].index[-1]
X_mat = numba_utils.row_matrix(X[0])
dev = device_of_devicendarray(X_mat)
shape = X_mat.shape[0]*params["k"]
# Create output numba arrays.
I_ndarr = rmm.to_device(np.zeros(shape, dtype=np.int64, order="C"))
D_ndarr = rmm.to_device(np.zeros(shape, dtype=np.float32,
order="C"))
# Return canonical device id as string
return [(X_mat, I_ndarr, D_ndarr)], dev, (start_idx, stop_idx)
def fit_to_device_arrays(arr):
"""
:param arr:
A tuple in the form of (X, y, coef)
:return:
"""
mats = [(X.compute().as_gpu_matrix(order='F'), y.to_gpu_array(), coef)
for X, y, coef in arr]
dev = device_of_devicendarray(mats[0][0])
# Return canonical device id as string
return mats, dev
def to_gpu_matrix(df):
"""
Turn input cudf into a Numba array. Returns device
of current worker and the start/stop indexes
of the cudf.
:param df:
:return:
"""
try:
start_idx = df.index[0]
stop_idx = df.index[-1]
gpu_matrix = numba_utils.row_matrix(df)
dev = device_of_devicendarray(gpu_matrix)
return dev, gpu_matrix, (start_idx, stop_idx)
except Exception as e:
import traceback
logging.error("Error in to_gpu_matrix(" + str(e))
traceback.print_exc()
pass
def predict_to_device_arrays(arr, worker, loc_dict, nparts, dtype):
"""
:param arr:
A tuple in the form of (X, y, coef)
:return:
"""
part_number = list(loc_dict.keys())[list(loc_dict.values()).index(worker)]
mats = []
for X, coef in arr:
nrows = len(X)
part_size = ceil(nrows / nparts)
up_limit = min((part_number + 1) * part_size, nrows)
mat = X.compute().as_gpu_matrix(order='F')
pred = cuda.to_device(
np.zeros(up_limit - (part_number * part_size), dtype=dtype))
mats.append([mat, coef.to_gpu_array(), pred])
dev = device_of_devicendarray(mats[0][0])
# Return canonical device id as string
return mats, dev
