我是一名新手，负责管理conda环境和pip等。当我尝试制作两个Cupy数组矩阵（matrix_V和vector_u）点积时，遇到以下错误消息：
vector_predict = matrix_V.dot(vector_u)
File "cupy/core/core.pyx", line 1791, in cupy.core.core.ndarray.dot
File "cupy/core/core.pyx", line 3809, in cupy.core.core.dot
File "cupy/core/core.pyx", line 4193, in cupy.core.core.tensordot_core
File "cupy/cuda/device.pyx", line 29, in cupy.cuda.device.get_cublas_handle
File "cupy/cuda/device.pyx", line 34, in cupy.cuda.device.get_cublas_handle
File "cupy/cuda/device.pyx", line 159, in cupy.cuda.device.Device.cublas_handle.__get__
File "cupy/cuda/device.pyx", line 160, in cupy.cuda.device.Device.cublas_handle.__get__
File "cupy/cuda/cublas.pyx", line 297, in cupy.cuda.cublas.create
File "cupy/cuda/cublas.pyx", line 286, in cupy.cuda.cublas.check_status
cupy.cuda.cublas.CUBLASError: CUBLAS_STATUS_NOT_INITIALIZED
我认为这可能是由某些软件包版本冲突引起的。但是我不知道如何解决这个问题。我正在使用Cuda 10.0.130和CuDNN 7.3.1。我已经证实他们两个都能工作。我正在使用通过pip安装的cupy-cuda100，并且可以在虚拟环境中成功导入它。我之所以不使用conda中的那个，是因为conda（5.1.0）中cupy的版本可能太低，我的程序对此有所抱怨。希望这些信息对您有所帮助。如果没有，请告诉我哪些信息有帮助。
谢谢。
我尝试按照Kenichi的建议调用cupy.cuda.get_cublas_handle（）。我收到以下错误消息：
cupy.cuda.get_cublas_handle()
File "cupy/cuda/device.pyx", line 29, in cupy.cuda.device.get_cublas_handle
File "cupy/cuda/device.pyx", line 34, in cupy.cuda.device.get_cublas_handle
File "cupy/cuda/device.pyx", line 159, in cupy.cuda.device.Device.cublas_handle.__get__
File "cupy/cuda/device.pyx", line 160, in cupy.cuda.device.Device.cublas_handle.__get__
File "cupy/cuda/cublas.pyx", line 297, in cupy.cuda.cublas.create
File "cupy/cuda/cublas.pyx", line 286, in cupy.cuda.cublas.check_status
cupy.cuda.cublas.CUBLASError: CUBLAS_STATUS_NOT_INITIALIZED
我还注意到pip install cupy也安装了一个numpy，而在我的虚拟环境中已经安装了numpy来进行tensorflow安装。即使两个numpy具有相同的版本，我也想知道这是否是问题。
这是运行batchCUBLAS示例的输出：
batchCUBLAS Starting...
GPU Device 0: "GeForce RTX 2080" with compute capability 7.5
==== Running single kernels ====
Testing sgemm
#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0xbf800000, -1)
beta= (0x40000000, 2)
#### args: lda=128 ldb=128 ldc=128
^^^^ elapsed = 0.00004601 sec GFLOPS=91.1512
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0x0000000000000000, 0) beta= (0x0000000000000000, 0)
#### args: lda=128 ldb=128 ldc=128
^^^^ elapsed = 0.00005293 sec GFLOPS=79.2441
@@@@ dgemm test OK
==== Running N=10 without streams ====
Testing sgemm
#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0xbf800000, -1) beta= (0x00000000, 0)
#### args: lda=128 ldb=128 ldc=128
^^^^ elapsed = 0.00008917 sec GFLOPS=470.379
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0xbff0000000000000, -1) beta= (0x0000000000000000, 0)
#### args: lda=128 ldb=128 ldc=128
^^^^ elapsed = 0.00029612 sec GFLOPS=141.644
@@@@ dgemm test OK
==== Running N=10 with streams ====
Testing sgemm
#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0x40000000, 2) beta= (0x40000000, 2)
#### args: lda=128 ldb=128 ldc=128
^^^^ elapsed = 0.00004601 sec GFLOPS=911.512
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0xbff0000000000000, -1) beta= (0x0000000000000000, 0)
#### args: lda=128 ldb=128 ldc=128
^^^^ elapsed = 0.00018787 sec GFLOPS=223.251
@@@@ dgemm test OK
==== Running N=10 batched ====
Testing sgemm
#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0x3f800000, 1) beta= (0xbf800000, -1)
#### args: lda=128 ldb=128 ldc=128
^^^^ elapsed = 0.00003600 sec GFLOPS=1165.05
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0xbff0000000000000, -1) beta= (0x4000000000000000, 2)
#### args: lda=128 ldb=128 ldc=128
^^^^ elapsed = 0.00030279 sec GFLOPS=138.521
@@@@ dgemm test OK
Test Summary
0 error(s)
cupy.show_config()输出：
CuPy Version : 5.2.0
CUDA Root : /usr/local/cuda-10.0
CUDA Build Version : 10000
CUDA Driver Version : 10000
CUDA Runtime Version : 10000
cuDNN Build Version : 7301
cuDNN Version : 7401
NCCL Build Version : 2307
pip freeze | grep cupy输出：
cupy-cuda100==5.2.0