torch.cuda.is_available

pytorch gpu

torch.cuda.is_available()
cuda是否可用；

torch.cuda.device_count()
返回gpu数量；

torch.cuda.get_device_name(0)
返回gpu名字，设备索引默认从0开始；

torch.cuda.current_device()

cuda是nvidia gpu的编程接口，opencl是amd gpu的编程接口

is_available 返回false

torch.cuda.get_device_name(0)
AssertionError: Torch not compiled with CUDA enabled

查看安装版本，支持gpu

解决办法

• 重新编译 pytorch 使得编译时CUDA能够与运行时CUDA保持一致

pip uninstall pytorch #  conda uninstall pytorch, if you use conda
nvcc -V # 查看 nvcc 版本

sudo rm -f /usr/local/cuda # optional, only if you already have this symlink
sudo ln -s /usr/local/cuda-10.0 /usr/local/cuda

# 将如下路径加入环境变量，如~/.bashrc
export CUDA_HOME=/usr/local/cuda
export PATH="/usr/local/cuda/bin:$PATH"
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
export LIBRARY_PATH="$LIBRARY_PATH:/usr/local/cuda/lib64"

source ~/.bashrc # 确保路径被加载

# 编译并安装 pytorch
conda install numpy pyyaml mkl=2019.3 mkl-include setuptools cmake cffi typing
conda install -c pytorch magma-cuda100 # optional step
# clone the pytorch source code
git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
make clean # make clean is needed in my case
export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
sudo python setup.py install # sudo is needed in my case.

• 重新安装CUDA使得其与pytorch编译的版本一致。

torch.__version__   #查看pytorch版本
torch.version.cuda    #查看pytorch版本     查询cuda版本none，需要重新编译cuda

cuda版本为none，原因是下载的时候版本选错误了