Closed ravi-mr closed 1 year ago
There are two fixes:
If you only want a CPU version of kaldifeat, please install a CPU version of PyTorch, which does not need cudnn at all.
Please follow
https://k2-fsa.github.io/k2/installation/cuda-cudnn.html
to install cudnn.
@csukuangfj adapted the same as given at https://k2-fsa.github.io/k2/installation/cuda-cudnn.html#cuda-11-7,
-- /usr/local/cuda-11.7.1/lib64/libnvrtc.so shorthash is 581f1f99
-- USE_CUDNN is set to 0. Compiling without cuDNN support
-- Autodetected CUDA architecture(s): 8.6
-- Added CUDA NVCC flags for: -gencode;arch=compute_86,code=sm_86
CMake Warning at /venv/lib/python3.8/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message):
static library kineto_LIBRARY-NOTFOUND not found.
Call Stack (most recent call first):
/venv/lib/python3.8/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:127 (append_torchlib_if_found)
cmake/torch.cmake:14 (find_package)
CMakeLists.txt:59 (include)
-- Found Torch: /venv/lib/python3.8/site-packages/torch/lib/libtorch.so
-- PyTorch version: 2.0.0+cu117
still USE_CUDNN is not getting set to 1?
That's a warning, not an error.
Is there still anything stopping you from installing kaldifeat?
I could install kaldifeat. Was thinking if USE_CUDNN sets to 1, kaldifeat extraction can be faster in respective contexts. Does linking kaldifeat with cuda_dnn increase any speed?
I could install kaldifeat.
Was thinking if USE_CUDNN sets to 1, kaldifeat extraction can be faster in respective contexts.
Does linking kaldifeat with cuda_dnn increase any speed?
USE_CUDNN exists only in torch 2.0.0
We don't have much experience about it now.
Maybe you could try torch < 2.0.0
True.
@csukuangfj
Despite any flags, compiling kaldifeat could not find CUDNN,
` -- Caffe2: CUDA detected: 11.7
-- Caffe2: CUDA nvcc is: /usr/local/cuda-11.7/bin/nvcc
-- Caffe2: CUDA toolkit directory: /usr/local/cuda-11.7
-- Caffe2: Header version is: 11.7
-- /usr/local/cuda-11.7/lib64/libnvrtc.so shorthash is d833c4f3
-- USE_CUDNN is set to 0. Compiling without cuDNN support ` Following https://github.com/csukuangfj/kaldifeat/issues/12, I tried passing in
export KALDIFEAT_CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release -DCUDNN_LIBRARY_PATH=/usr/local/cuda-11.7/lib64/libcudnn.so -DCUDNN_INCLUDE_PATH=/usr/local/cuda-11.7/include -DUSE_CUDNN=1"
what is the impact of not having kaldifeat compiled with cudnn? any other suggestion for compiling kaldifeat with cudnn?