I am now using RTX2080ti, so I need install cuda 10. After I built pytorch from source, it pop out a  warning when I train a simple RNN model.
UserWarning: PyTorch was compiled without cuDNN support. To use cuDNN, rebuild PyTorch making sure the library is visible to the build system.
"PyTorch was compiled without cuDNN support. To use cuDNN, rebuild "
So I tried to rebuild pytorch, but I got message from building log as below.
I guest setup.py found the cudnn path, but didn’t build with it.
…
– Found CUDA: /usr/local/cuda (found suitable version “10.0”, minimum required is “7.0”)
– Caffe2: CUDA detected: 10.0
– Caffe2: CUDA nvcc is: /usr/local/cuda/bin/nvcc
– Caffe2: CUDA toolkit directory: /usr/local/cuda
– Caffe2: Header version is: 10.0
– Found cuDNN: v7.3.1  (include: /usr/local/cuda/include, library: /usr/local/cuda/lib64/libcudnn.so.7)
– Autodetected CUDA architecture(s): 7.5
– Added CUDA NVCC flags for: -gencode;arch=compute_75,code=sm_75
– Could NOT find NCCL (missing: NCCL_INCLUDE_DIRS NCCL_LIBRARIES)
– Could NOT find CUB (missing: CUB_INCLUDE_DIR)
– CUDA detected: 10.0
…
–   TORCH_VERSION         : 1.0.0
–   CAFFE2_VERSION        : 1.0.0
–   BUILD_ATEN_MOBILE     : OFF
–   BUILD_ATEN_ONLY       : OFF
–   BUILD_BINARY          : OFF
–   BUILD_CUSTOM_PROTOBUF : ON
–     Link local protobuf : ON
–   BUILD_DOCS            : OFF
–   BUILD_PYTHON          : ON
–     Python version      : 2.7.15
–     Python executable   : /home/k123/env/python2.7.15/bin/python
–     Pythonlibs version  : 2.7.15
–     Python library      : /home/k123/.local/lib/libpython2.7.a
–     Python includes     : /home/k123/.local/include/python2.7
–     Python site-packages: lib/python2.7/site-packages
–   BUILD_CAFFE2_OPS      : ON
–   BUILD_SHARED_LIBS     : ON
–   BUILD_TEST            : ON
–   USE_ASAN              : OFF
–   USE_CUDA              : 1
–     CUDA static link    : 0
–     USE_CUDNN           : OFF
–     CUDA version        : 10.0
–     CUDA root directory : /usr/local/cuda
–     CUDA library        : /usr/lib/x86_64-linux-gnu/libcuda.so
–     cudart library      : /usr/local/cuda/lib64/libcudart_static.a;-pthread;dl;/usr/lib/x86_64-linux-gnu/librt.so
–     cublas library      : /usr/local/cuda/lib64/libcublas.so
–     cufft library       : /usr/local/cuda/lib64/libcufft.so
–     curand library      : /usr/local/cuda/lib64/libcurand.so
–     nvrtc               : /usr/local/cuda/lib64/libnvrtc.so
–     CUDA include path   : /usr/local/cuda/include
–     NVCC executable     : /usr/local/cuda/bin/nvcc
–     CUDA host compiler  : /usr/bin/cc
–     USE_TENSORRT        : OFF
–   USE_ROCM              : OFF
–   USE_EIGEN_FOR_BLAS    :
–   USE_FFMPEG            : OFF
…
any suggestion?