本文整理汇总了Python中quagga.cuda.cudart.check_cuda_status函数的典型用法代码示例。如果您正苦于以下问题：Python check_cuda_status函数的具体用法？Python check_cuda_status怎么用？Python check_cuda_status使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了check_cuda_status函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: assign_sequential_weighted_sum
def assign_sequential_weighted_sum(stream, nrows, ncols, matrices, weights, n, out):
status = gpu_matrix_kernels._assignSequentialWeightedSum(stream, nrows, ncols, matrices, weights, n, out)
cudart.check_cuda_status(status)示例2: transpose_int
def transpose_int(stream, nrows, ncols, in_, out):
status = gpu_matrix_kernels._transposeInt(stream, nrows, ncols, in_, out)
cudart.check_cuda_status(status)示例3: callback
def callback(stream, status, user_data):
cudart.check_cuda_status(status)
args, kwargs = ct.cast(user_data, ct_py_object_p).contents.value
function(*args, **kwargs)
GpuContext._user_data[ct.cast(stream, ct.c_void_p).value].popleft()示例4: add_repeat_along_col_derivative
def add_repeat_along_col_derivative(stream, repeats, a, nrows, ncols, derivative):
status = gpu_matrix_kernels._addRepeatAlongColDerivative(stream, repeats, a, nrows, ncols, derivative)
cudart.check_cuda_status(status)示例5: add_scaled_div_sqrt
def add_scaled_div_sqrt(stream, nelems, alpha, a, b, epsilon, c):
status = gpu_matrix_kernels._addScaledDivSqrt(stream, nelems, alpha, a, b, epsilon, c)
cudart.check_cuda_status(status)示例6: matrix_vector_column_hprod
def matrix_vector_column_hprod(stream, nrows, ncols, matrix, vector, out):
status = gpu_matrix_kernels._matrixVectorColumnHprod(stream, nrows, ncols, matrix, vector, out)
cudart.check_cuda_status(status)示例7: batch_horizontal_split
def batch_horizontal_split(stream, n, nrows, x_ncols, y_ncols, matrices, x_matrices, y_matrices):
status = gpu_matrix_kernels._batchHorizontalSplit(stream, n, nrows, x_ncols, y_ncols, matrices, x_matrices, y_matrices)
cudart.check_cuda_status(status)示例8: sliced_rows_batch_scaled_add
def sliced_rows_batch_scaled_add(stream, embd_rows_indxs, nrows, ncols, alpha, dense_matrices, embd_nrows, embd_ncols, embd_matrix):
status = gpu_matrix_kernels._slicedRowsBatchScaledAdd(stream, embd_rows_indxs, nrows, ncols, alpha, dense_matrices, embd_nrows, embd_ncols, embd_matrix)
cudart.check_cuda_status(status)示例9: assign_scaled_addition
def assign_scaled_addition(stream, nelems, alpha, a, b, out):
status = gpu_matrix_kernels._assignScaledAddition(stream, nelems, alpha, a, b, out)
cudart.check_cuda_status(status)示例10: add_attention_tile
def add_attention_tile(stream, nrows, ncols, derivative, a, dL_dpre_a, u, n, matrices_derivs):
status = gpu_matrix_kernels._addAttentionTile(stream, nrows, ncols, derivative, a, dL_dpre_a, u, n, matrices_derivs)
cudart.check_cuda_status(status)示例11: slice_rows_batch
def slice_rows_batch(stream, embd_rows_indxs, nrows, ncols, embd_matrix, embd_nrows, embd_ncols, dense_matrices):
status = gpu_matrix_kernels._sliceRowsBatch(stream, embd_rows_indxs, nrows, ncols, embd_matrix, embd_nrows, embd_ncols, dense_matrices)
cudart.check_cuda_status(status)示例12: add_attention_derivative
def add_attention_derivative(stream, nrows, ncols, matrices, derivative, n, out):
status = gpu_matrix_kernels._addAttentionDerivative(stream, nrows, ncols, matrices, derivative, n, out)
cudart.check_cuda_status(status)示例13: assign_dL_dpre_a
def assign_dL_dpre_a(stream, nrows, ncols, matrices, derivative, weights, n, out):
status = gpu_matrix_kernels._assignDLDprea(stream, nrows, ncols, matrices, derivative, weights, n, out)
cudart.check_cuda_status(status)示例14: sequentially_tile
def sequentially_tile(stream, nelems, a, matrices, n):
status = gpu_matrix_kernels._sequentiallyTile(stream, nelems, a, matrices, n)
cudart.check_cuda_status(status)示例15: add_hprod_one_minus_mask
def add_hprod_one_minus_mask(stream, nelems, mask, a, out):
status = gpu_matrix_kernels._addHprodOneMinusMask(stream, nelems, mask, a, out)
cudart.check_cuda_status(status)