&引用；CUDA错误无效设备：无效设备序号；启动TensorFlow会话时

启动TensorFlow会话时，未检测到GPU（

CUDA\u错误\u无效\u设备：无效设备序号

）：

CuDNN（

CuDNN-10.0-linux-x64-v7.4.2.24.tgz

）也是：

TensorFlow（

pip3安装TensorFlow gpu

）：

Nvidia驱动程序（

Nvidia-Linux-x86_64-410.73.运行

）还包括：

我使用的是LinuxMint18.2

---

有什么想法吗？

解决了。我卸载了synaptic软件包管理器中显示的所有Nvidia驱动程序版本，安装于

Nvidia-Linux-x86_64-410.73。运行
，现在一切正常

请注意：使用命令行卸载可能如下所示：

sudo-nvidia卸载
sudo-apt-get-remove-清除nvidia-*

$python3-c'导入tensorflow作为tf；sess=tf.Session（config=tf.ConfigProto（log\u device\u placement=True））'
2019-07-18 10:57:07.020764:I tensorflow/core/platform/cpu\u feature\u guard.cc:142]您的cpu支持该tensorflow二进制文件未编译为使用的指令：AVX2 FMA
2019-07-18 10:57:07.059271:I tensorflow/core/platform/profile_utils/cpu_utils.cc:94]cpu频率：3312000000 Hz
2019-0718:10:57：7.060038：I TysFrace/编译器/ XLA/Service / Service .CC：168）XLA服务0x53AEC90在平台主机上执行计算。设备：
2019-07-18 10:57:07.060060:I tensorflow/compiler/xla/service/service.cc:175]流执行器设备（0）：，
2019-07-18 10:57:07.069543:I tensorflow/stream_executor/platform/default/dso_loader.cc:42]已成功打开动态库libcuda.so.1
2019-07-18 10:57:07.216124:I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005]从SysFS读取的成功NUMA节点的值为负值（-1），但必须至少有一个NUMA节点，因此返回NUMA节点零
2019-0718:10:57：7.216596：I TysFult/Cys/XLA/Service / Service .CC：168）XLA服务0x54 792A0在CUDA平台上执行计算。设备：
2019-07-18 10:57:07.216612:I tensorflow/compiler/xla/service/service.cc:175]StreamExecutor设备（0）：GeForce GTX 1070，计算能力6.1
2019-07-18 10:57:07.216803:I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005]从SysFS读取的成功NUMA节点的值为负值（-1），但必须至少有一个NUMA节点，因此返回NUMA节点零
2019-07-18 10:57:07.217224:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640]找到了具有以下属性的设备0：
名称：GeForce GTX 1070大调：6小调：1内存锁定速率（GHz）：1.7715
pciBusID:0000:01:00.0
2019-07-18 10:57:07.218763:I tensorflow/stream_executor/platform/default/dso_loader.cc:42]成功打开动态库libcudart.so.10.0
2019-07-18 10:57:07.243155:I tensorflow/stream_executor/platform/default/dso_loader.cc:42]成功打开动态库libcublas.so.10.0
2019-07-18 10:57:07.257961:I tensorflow/stream_executor/platform/default/dso_loader.cc:42]已成功打开动态库libcuft.so.10.0
2019-07-18 10:57:07.263297:I tensorflow/stream_executor/platform/default/dso_loader.cc:42]成功打开动态库libcurand.so.10.0
2019-07-18 10:57:07.298517:I tensorflow/stream_executor/platform/default/dso_loader.cc:42]成功打开了动态库libcusolver.so.10.0
2019-07-18 10:57:07.321558:I tensorflow/stream_executor/platform/default/dso_loader.cc:42]已成功打开动态库libcusparse.so.10.0
2019-07-18 10:57:07.394510:I tensorflow/stream_executor/platform/default/dso_loader.cc:42]成功打开动态库libcudnn.so.7
2019-07-18 10:57:07.394806:I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005]从SysFS读取的成功NUMA节点的值为负值（-1），但必须至少有一个NUMA节点，因此返回NUMA节点零
2019-07-18 10:57:07.396131:I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005]从SysFS读取的成功NUMA节点的值为负值（-1），但必须至少有一个NUMA节点，因此返回NUMA节点零
2019-07-18 10:57:07.397206:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763]添加可见gpu设备：0
2019-07-18 10:57:07.397798:I tensorflow/stream_executor/platform/default/dso_loader.cc:42]已成功打开动态库libcudart.so.10.0
2019-07-18 10:57:07.400997:I tensorflow/core/common_runtime/gpu/gpu_设备。cc:1181]设备互连拖缆执行器与强度1边缘矩阵：
2019-07-18 10:57:07.401041:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]0
2019-07-18 10:57:07.401059:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200]0:N
2019-07-18 10:57:07.401572:I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005]从SysFS读取的成功NUMA节点的值为负值（-1），但必须至少有一个NUMA节点，因此返回NUMA节点零
2019-07-18 10:57:07.402874:I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005]从SysFS读取的成功NUMA节点的值为负值（-1），但必须至少有一个NUMA节点，因此返回NUMA节点零
2019-07-18 10:57:07.404129:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326]创建tensorflow设备（/job:localhost/replica:0/task:0/device:gpu:0，带7060MB内存）->物理gpu（设备：0，名称：GeForce GTX 1070，pci总线id:0000:01:00.0，计算能力：6.1）
设备映射：
/作业：本地主机/副本：0/任务：0/设备：XLA\U CPU:0->设备：XLA\U CPU设备
/作业：本地主机/副本：0/任务：0/设备：XLA\U GPU:0->设备：XLA\U GPU设备
/作业：本地主机/副本：0/任务：0/设备：GPU:0->设备：0，名称：GeForce GTX 1070，pci总线id:0000:01:00.0，计算能力：6.1
2019-07-18 10:57:07.405492:I tensorflow/core/common_runtime/direct_session.cc:296]设备映射：
/作业：本地主机/副本：0/任务：0/设备：XLA\U CPU:0->设备：XLA\U CPU设备
/作业：本地主机/副本：0/任务：0/设备：XLA\U GPU:0->设备：XLA\U GPU设备
/作业：本地主机/副本：0/任务：0/设备：GPU:0->设备：0，名称：GeForce GTX 1070，pci总线id:0
$ CUDA_VISIBLE_DEVICES='0' python3 -c 'import tensorflow as tf; sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))'
2019-07-18 09:36:55.661519: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-07-18 09:36:55.684438: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3312000000 Hz
2019-07-18 09:36:55.684721: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x41adbb0 executing computations on platform Host. Devices:
2019-07-18 09:36:55.684750: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2019-07-18 09:36:55.686513: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-07-18 09:36:55.696958: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal
2019-07-18 09:36:55.697001: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: tobias-Z170-HD3P
2019-07-18 09:36:55.697006: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: tobias-Z170-HD3P
2019-07-18 09:36:55.697084: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 410.73.0
2019-07-18 09:36:55.697108: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 410.73.0
2019-07-18 09:36:55.697113: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 410.73.0
Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
2019-07-18 09:36:55.697380: I tensorflow/core/common_runtime/direct_session.cc:296] Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device

$ cat /usr/local/cuda/version.txt
CUDA Version 10.0.130

$ cat /usr/include/x86_64-linux-gnu/cudnn_v*.h | grep CUDNN_MAJOR -A 2 | head -n 3
#define CUDNN_MAJOR      6
#define CUDNN_MINOR      0
#define CUDNN_PATCHLEVEL 21

$ python3 -c 'import tensorflow as tf; print(tf.__version__)'
1.14.0

$ nvidia-smi 
Thu Jul 18 09:35:03 2019       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.73       Driver Version: 410.73       CUDA Version: 10.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 1070    Off  | 00000000:01:00.0  On |                  N/A |
|  0%   45C    P8    17W / 230W |    569MiB /  8111MiB |     19%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      2270      G   /usr/lib/xorg/Xorg                           301MiB |
|    0      3021      G   /opt/zoom/zoom                                14MiB |
|    0      3503      G   ...-token=CB875E52FAB2279C6A34C6519188AD9C    71MiB |
|    0      3534      G   ...uest-channel-token=16121978823314344450    56MiB |
|    0      3618      G   ...uest-channel-token=12369473663213430887    52MiB |
|    0      4249      G   ...uest-channel-token=13759302641460814281    62MiB |
|    0      4499      G   ...uest-channel-token=10576172133955227583     7MiB |
+-----------------------------------------------------------------------------+

$ python3 -c 'import tensorflow as tf; sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))'

2019-07-18 10:57:07.020764: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-07-18 10:57:07.059271: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3312000000 Hz
2019-07-18 10:57:07.060038: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x53aec90 executing computations on platform Host. Devices:
2019-07-18 10:57:07.060060: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2019-07-18 10:57:07.069543: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-07-18 10:57:07.216124: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-18 10:57:07.216596: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x54792a0 executing computations on platform CUDA. Devices:
2019-07-18 10:57:07.216612: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce GTX 1070, Compute Capability 6.1
2019-07-18 10:57:07.216803: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-18 10:57:07.217224: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.7715
pciBusID: 0000:01:00.0
2019-07-18 10:57:07.218763: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-07-18 10:57:07.243155: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-07-18 10:57:07.257961: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-07-18 10:57:07.263297: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-07-18 10:57:07.298517: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-07-18 10:57:07.321558: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-07-18 10:57:07.394510: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-07-18 10:57:07.394806: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-18 10:57:07.396131: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-18 10:57:07.397206: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-07-18 10:57:07.397798: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-07-18 10:57:07.400997: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-07-18 10:57:07.401041: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 
2019-07-18 10:57:07.401059: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N 
2019-07-18 10:57:07.401572: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-18 10:57:07.402874: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-18 10:57:07.404129: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7060 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1
2019-07-18 10:57:07.405492: I tensorflow/core/common_runtime/direct_session.cc:296] Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1