TensorFlow-gpu-2.0.0rc2无法找到Cud-10.1库并跳过注册的GPU设备

Question

我在一个系统上使用NVIDIA Tesla V100-SXM2-32GB，而我是一个非管理用户，所以我不能更改Cuda版本。目前安装在系统上的Cuda版本是10.1，我正在尝试让TensorFlow与此版本一起运行。在安装TensorFlow version 2.0.0rc2 (使用cudnn-7.6.4和cudatoolkit-10.1.243)之后，我得到以下报告的错误(在默认启用的急切执行模式内)。Cuda库的路径是正确导出的。

根据正式文件和这个职位的说法，TensorFlow目前支持库达10.0。有人知道一个版本(甚至alpha)可以使用Cuda10.1运行吗？

python -c "import tensorflow as tf; tf.zeros(10)"

返回

2019-11-10 11:55:36.118647: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2019-11-10 11:55:39.393230: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: 
name: Tesla V100-SXM2-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.53
pciBusID: 0000:1a:00.0
2019-11-10 11:55:39.395456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 1 with properties: 
name: Tesla V100-SXM2-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.53
pciBusID: 0000:1c:00.0
2019-11-10 11:55:39.397553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 2 with properties: 
name: Tesla V100-SXM2-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.53
pciBusID: 0000:1d:00.0
2019-11-10 11:55:39.399647: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 3 with properties: 
name: Tesla V100-SXM2-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.53
pciBusID: 0000:1e:00.0
2019-11-10 11:55:39.399986: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.0'; dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda-8.0/lib64
2019-11-10 11:55:39.400135: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcublas.so.10.0'; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda-8.0/lib64
2019-11-10 11:55:39.400274: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcufft.so.10.0'; dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda-8.0/lib64
2019-11-10 11:55:39.400414: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcurand.so.10.0'; dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda-8.0/lib64
2019-11-10 11:55:39.400552: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcusolver.so.10.0'; dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda-8.0/lib64
2019-11-10 11:55:39.400687: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcusparse.so.10.0'; dlerror: libcusparse.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda-8.0/lib64
2019-11-10 11:55:39.405250: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-11-10 11:55:39.405367: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2019-11-10 11:55:39.405848: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2019-11-10 11:55:39.412764: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2100000000 Hz
2019-11-10 11:55:39.412951: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x555c0a4adfd0 executing computations on platform Host. Devices:
2019-11-10 11:55:39.413028: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version
2019-11-10 11:55:40.213011: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x555c0a4b0850 executing computations on platform CUDA. Devices:
2019-11-10 11:55:40.213144: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla V100-SXM2-32GB, Compute Capability 7.0
2019-11-10 11:55:40.213208: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (1): Tesla V100-SXM2-32GB, Compute Capability 7.0
2019-11-10 11:55:40.213262: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (2): Tesla V100-SXM2-32GB, Compute Capability 7.0
2019-11-10 11:55:40.213312: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (3): Tesla V100-SXM2-32GB, Compute Capability 7.0
2019-11-10 11:55:40.213562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-10 11:55:40.213647: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      

Answer 1

如果您在Windows中使用Tensorflow，不再需要自己安装所有的CUDA和Cudnn驱动程序，^_^

只需在conda上使用以下命令，它就会自己处理相应的包：

创造一个新的环境：

conda创建-n name python=3.6

conda激活 name

然后，使用：

conda安装-c conda-tensorflow gpu=1.14

conda环境将根据您的系统需要检查和安装软件包。干杯!