我使用GPU在tensorflow中使用CUDNNLSTM训练了一个模型。当我尝试在cpu中使用模型进行推理时，我收到此错误：
Invalid argument: No OpKernel was registered to support Op 'CudnnRNN' with these attrs. Registered devices: [CPU], Registered kernels:
<no registered kernels>
[[Node: cudnn_lstm/CudnnRNN = CudnnRNN[T=DT_FLOAT, direction="bidirectional", dropout=0, input_mode="linear_input", is_training=false, rnn_mode="lstm", seed=87654321, seed2=4567](Reshape_1, cudnn_lstm/zeros, cudnn_lstm/zeros_1, cudnn_lstm/opaque_kernel/read)]]
那么，我们如何在cpu中使用这个模型呢？
请查看CuDNN LSTM层的tensorflow源代码中的注释：https://github.com/tensorflow/tensorflow/blob/r1.6/tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py
从第83行开始，他们已经描述了如何做，你想要什么。基本上，在使用CuDNN层进行训练后，您需要将权重转移到使用CuDNN兼容LSTM单元制作的模型。这样的模型将在CPU和GPU上运行。另外，据我所知，张量流中的CuDNN LSTM层是时间主要的，所以不要忘记转置输入（我在最新的tensorflow版本中不确定，请确认一下）。
对于基于上面的简短完整示例，checkout melgor的要点：
https://gist.github.com/melgor/41e7d9367410b71dfddc33db34cba85f?short_path=29ebfc6
Reason: tensorflow doesn`t see your GPU
Summary:
1. check if tensorflow sees your GPU (optional)
2. check if your videocard can work with tensorflow (optional)
3. find versions of CUDA Toolkit and cuDNN SDK, compatible with your tf version
(https://www.tensorflow.org/install/source#linux)
4. install CUDA Toolkit
(https://developer.nvidia.com/cuda-toolkit-archive)
5. install cuDNN SDK
(https://developer.nvidia.com/rdp/cudnn-archive)
6. pip uninstall tensorflow; pip install tensorflow-gpu
7. check if tensorflow sees your GPU
* source - https://www.tensorflow.org/install/gpu
Detailed instruction:
1. check if tensorflow sees your GPU (optional)
from tensorflow.python.client import device_lib
def get_available_devices():
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos]
print(get_available_devices())
# my output was => ['/device:CPU:0']
# good output must be => ['/device:CPU:0', '/device:GPU:0']
2. check if your card can work with tensorflow (optional)
* my PC: GeForce GTX 1060 notebook (driver version - 419.35), windows 10, jupyter notebook
* tensorflow needs Compute Capability 3.5 or higher. (https://www.tensorflow.org/install/gpu#hardware_requirements)
- https://developer.nvidia.com/cuda-gpus
- select "CUDA-Enabled GeForce Products"
- result - "GeForce GTX 1060 Compute Capability = 6.1"
- my card can work with tf!
3. find versions of CUDA Toolkit and cuDNN SDK, that you need
a) find your tf version
import tensorflow as tf
print(tf.__version__)
# my output was => 1.13.1
b) find right versions of CUDA Toolkit and cuDNN SDK for your tf version
https://www.tensorflow.org/install/source#linux
* it is written for linux, but worked in my case
see, that tensorflow_gpu-1.13.1 needs: CUDA Toolkit v10.0, cuDNN SDK v7.4
4. install CUDA Toolkit
a) install CUDA Toolkit 10.0
https://developer.nvidia.com/cuda-toolkit-archive
select: CUDA Toolkit 10.0 and download base installer (2 GB)
installation settings: select only CUDA
(my installation path was: D:\Programs\x64\Nvidia\Cuda_v_10_0\Development)
b) add environment variables:
system variables / path must have:
D:\Programs\x64\Nvidia\Cuda_v_10_0\Development\bin
D:\Programs\x64\Nvidia\Cuda_v_10_0\Development\libnvvp
D:\Programs\x64\Nvidia\Cuda_v_10_0\Development\extras\CUPTI\libx64
D:\Programs\x64\Nvidia\Cuda_v_10_0\Development\include
5. install cuDNN SDK
a) download cuDNN SDK v7.4
https://developer.nvidia.com/rdp/cudnn-archive (needs registration, but it is simple)
select "Download cuDNN v7.4.2 (Dec 14, 2018), for CUDA 10.0"
b) add path to 'bin' folder into "environment variables / system variables / path":
D:\Programs\x64\Nvidia\cudnn_for_cuda_10_0\bin
6. pip uninstall tensorflow
pip install tensorflow-gpu
7. check if tensorflow sees your GPU
restart your PC
print(get_available_devices())
# now this code should return => ['/device:CPU:0', '/device:GPU:0']