无法加载动态库"cudnn64_8.dll";DL错误：未找到cudnn64_8.dll

使用tensorflow 2.4.1

当我运行程序时，我会遇到这个错误，无法使用我的gpu。

我正在使用CUDA 11.0、cudnn 8.0

2021-02-07 03:36:18.132005: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
WARNING:tensorflow:From D:/PycharmProjects/pythonProject/models/kpş,i.py:5: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.list_physical_devices('GPU')` instead.
2021-02-07 03:36:19.735127: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-02-07 03:36:19.739052: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-02-07 03:36:20.715634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: GeForce GTX 1650 computeCapability: 7.5
coreClock: 1.56GHz coreCount: 16 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 119.24GiB/s
2021-02-07 03:36:20.716281: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-02-07 03:36:20.723519: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-02-07 03:36:20.724040: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-02-07 03:36:20.729436: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-02-07 03:36:20.731800: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-02-07 03:36:20.741580: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-02-07 03:36:20.745576: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-02-07 03:36:20.746657: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found
2021-02-07 03:36:20.746971: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2021-02-07 03:36:20.836861: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-02-07 03:36:20.837144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 
2021-02-07 03:36:20.837314: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N 
2021-02-07 03:36:20.837493: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set

我想我可以帮助您提供cudnn64_8.dll文件(这是下载链接：https://www.dll-files.com/cudnn64_8.dll.html)。当您获得该文件时，您可以将其放入bin目录中。例如，通常在windows平台中，您可以将其放入C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.3bin中。

丢失的dll文件位于cuDNN文件夹中。我能够通过将cudnn64_8.dll文件复制到CUDA文件夹(即C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.6bin)来解决此问题。

cuDNN被列为tensorflow工作的要求，您可以在这里下载。不过，您需要先注册一个开发人员帐户。

观看此视频以解决此问题，
由于CUDA文件夹中缺少Microsoft visual studio C++可复制文件，因此出现此文件未找到错误
附加
对于PyTorch in conda environment，没有额外的CUDA和Cudnn安装，因为在键入conda install pytorch之后，conda会将CUDA和Cudnn安装到该conda环境中。

在遵循了CuDNN的所有安装说明后，我也遇到了这个问题。问题的根本原因很简单。在安装说明中，它告诉您将<root>NVIDIACUDNNv8.x添加到PATH中。至少对于Tensorflow来说，这是错误的。您需要将<root>NVIDIACUDNNv8.xbin添加到您的PATH中。这应该能解决问题。对我来说的确如此。

我看到一些关于将cudnn64_8.dll文件移动到C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.6bin的答案。这之所以有效，是因为CUDA在安装PATH时会自动将该bin目录添加到CCD_18中。因此，将cudnn64_8.dll移动到那里可以有效地将其添加到CCD20中。

我宁愿把东西放在合适的位置，所以我更喜欢这种方式。

我想分享让我能够解决等所有问题的步骤

Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found
Could not load library cudnn_cnn_infer64_8.dll.
Could not load library cudnn_ops_infer64_8.dll.

首先确保已安装NVIDIA GPU的驱动程序。你可以在这里下载驱动程序
安装可以在此处下载的CUDA工具包
从这里下载cudnn-zip文件并解压缩
从解压缩的cudnn中，将bin、include和lib子目录中的文件复制粘贴到位于...Program FilesNVIDIA GPU Computing ToolkitCUDAv11.x的相应bin、include和lib子目录中
从这里下载Zlib档案并解压缩
将粘贴zlibwapi.dll从Zlib存档复制到...Program FilesNVIDIA GPU Computing ToolkitCUDAv11.x的bin子目录中

我也遇到了这个错误，我认为cudnn需要不同的安装。这是安装指南和所需的软件包https://developer.nvidia.com/cudnn.
*编辑->在上面的链接中，您可以下载tensorflow所需的cuddn版本，然后将它们放在所需的文件夹中，这将解决您的问题。

这将解决您的问题：

conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0

我也有同样的错误。这个链接中提出的解决方案对我有效。他们建议安装cuDNN，并在这里解释如何安装

就我个人而言，在上面的解释中，缺少的只是我通过Anaconda安装的ZLIB(或者更具体地说是zlibwapi)。

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