# 用户使用Torch报错Unexpected error from cudaGetDeviceCount
#### 问题现象
在Notebook执行兼容GPU的脚本时报错不兼容，但是通过nvcc --version排查显示是兼容。
```
import torch
import sys
print('A', sys.version)
print('B', torch.__version__)
print('C', torch.cuda.is_available())
print('D', torch.backends.cudnn.enabled)
device = torch.device('cuda')
print('E', torch.cuda.get_device_properties(device))
print('F', torch.tensor([1.0, 2.0]).cuda())
```
报错如下：
```
Traceback (most recent call last):
File "test.py", line 8, in <module>
print('E', torch.cuda.get_device_properties(device))
File "/opt/conda/lib/python3.7/site-packages/torch/cuda/__init__.py", line 356, in get_device_properties
_lazy_init() # will define _get_device_properties
File "/opt/conda/lib/python3.7/site-packages/torch/cuda/__init__.py", line 214, in _lazy_init
torch._C._cuda_init()
RuntimeError: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 803: system has unsupported display driver / cuda driver combination</module>
```
#### 解决方式
1. 先排查CUDA和Torch版本是否兼容。
   ```
   # CUDA版本
   nvcc --version
   # nvidia-smi版本
   nvidia-smi
   # Torch版本（要确定用户用的哪个conda下的python）
   python -c "import torch;print(torch.__version__)"
   ```
   通过[PyTorch官网](https://pytorch.org/get-started/previous-versions/)可查兼容版本。
   
2. 如果环境中装了多版本的CUDA，可以排查LD_LIBRARY_PATH中的cuda优先级，需要手动调整下。 例如，如果CUDA只兼容CUDA-9.1，查询到*LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:/usr/local/cuda-9.1/lib64*
   需要手动调整优先级，执行命令*export LD_LIBRARY_PATH=/usr/local/cuda-9.1/lib64:$LD_LIBRARY_PATH*