Question

我正在使用nVIDIA 950M图形设备在Ubuntu18.04上工作，试图安装tensorflow-gpu，cuda和cudnn，并一起工作。

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，但是测试运行失败，如下所示：

import tensorflow as tf

/usr/local/lib/python2.7/dist-packages/h5py/ init 。py：36：FutureWarning：issubdtype的第二个参数从float转换为{ {1}}已过时。将来，它将被视为np.floating。从._conv导入register_converters作为_register_converters

np.float64 == np.dtype(float).type

但出现以下错误：

a=tf.constant(10)
b=tf.constant(39)
tf.Session()

然后我尝试导出CUDA_VISIBLE_DEVICES ='0'，但是没有运气。

然后我用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.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nvidia-smi

nvcc --version

deviceQuery

此外，对其中一个CUDA样本进行了测试，没有错误。我该如何解决以上问题？最后但并非最不重要的一点是，当我使用./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启用nVIIDA并重新启动到文本模式时，如何验证nVIDIA卡是控制权而不是集成卡？

那我还能做什么来修复我的环境？

tensorflow-gpu无法创建会话

0 个答案: