我正在使用nVIDIA 950M图形设备在Ubuntu18.04上工作，尝试安装tensorflow-gpu，cuda和cudnn，一起工作 .
我首先尝试安装nVIDIA驱动程序，cuda 9.0，cudnn 7.1和tensorflow-gpu 1.8 . 按照install tensorflow完成安装步骤，但测试运行失败，如下所示：
import tensorflow as tf
/usr/local/lib/python2.7/dist-packages/h5py/ init .py:36:FutureWarning：不推荐将issubdtype的第二个参数从 float 转换为 np.floating . 将来，它将被视为 np.float64 == np.dtype(float).type . 从._conv导入register_converters作为_register_converters
a=tf.constant(10)
b=tf.constant(39)
tf.Session()
但是我收到以下错误：
2018-06-22 22:42:44.102246: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-22 22:42:44.373094: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-06-22 22:42:44.373508: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: GeForce GTX 950M major: 5 minor: 0 memoryClockRate(GHz): 0.928
pciBusID: 0000:01:00.0
totalMemory: 3.95GiB freeMemory: 3.91GiB
2018-06-22 22:42:44.373526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-22 22:42:44.438843: E tensorflow/core/common_runtime/direct_session.cc:154] Internal: CUDA runtime implicit initialization on GPU:0 failed. Status: unknown error
Traceback (most recent call last):
File `<stdin>`, line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1560, in __init__
super(Session, self).__init__(target, graph, config=config)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 633, in __init__
self._session = tf_session.TF_NewSession(self._graph._c_graph, opts)
tensorflow.python.framework.errors_impl.InternalError: Failed to create session.
然后我尝试导出CUDA_VISIBLE_DEVICES ='0'，但没有运气 .
然后我用 nvidia-smi 检查nVIDIA驱动程序
它正确显示驱动程序：NVIDIA-SMI 390.67驱动程序版本：390.67
然后检查 nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176
另请查看 deviceQuery
./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX 950M"
CUDA Driver Version / Runtime Version 9.1 / 9.0
CUDA Capability Major/Minor version number: 5.0
Total amount of global memory: 4046 MBytes (4242604032 bytes)
( 5) Multiprocessors, (128) CUDA Cores/MP: 640 CUDA Cores
GPU Max Clock rate: 928 MHz (0.93 GHz)
Memory Clock rate: 2505 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 2097152 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Supports Cooperative Kernel Launch: No
Supports MultiDevice Co-op Kernel Launch: No
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.1, CUDA Runtime
Version = 9.0, NumDevs = 1
Result = PASS
此外，其中一个CUDA样本经过测试，没有错误 . 我该如何解决上述问题？最后但并非最不重要的是，当我使用 prime-select nvidia 启用nVIIDA并重新启动到文本模式时，如何验证nVIDIA卡是否正在控制而不是集成卡？
那么我还能做些什么来修复我的环境呢？