TensorFlow 2.4.0的精确CUDA/cudNN版本

我正试图在Windows10上使用Anaconda3（2020.11和Python3.8.5 64位）和Tensorflow 2.4.0，但我必须说这项技术似乎仍然非常。。。不稳定

理解每个库都依赖于另一个库的确切版本，而不是更多，而不是更少，真是令人费解

到目前为止，我成功地安装了：

• Anaconda3（2020.11，带Python 3.8.5 64位）
• tensorflow 2.4.0
• CUDA 11.0.2，仅限运行时，使用网络安装程序
• cudnn-11.0-windows-x64-v8.0.4.30
• GeForce驱动程序461.09-desktop-win10-64bit-international-dch-whql
• 电路板为Geforce RTX 3070

根据手册，这应该是可以的，但不幸的是，我仍然收到可怕的

“检索驱动程序版本时出错：未实现：内核报告的驱动程序版本未在Windows上实现”

消息

以下是完整的跟踪：

2021-01-20 20:53:25.785203: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-20 20:53:29.173495: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-20 20:53:29.175299: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-01-20 20:53:29.213308: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.755GHz coreCount: 46 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-20 20:53:29.213536: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-20 20:53:29.237764: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-20 20:53:29.237865: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-20 20:53:29.244635: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-20 20:53:29.247913: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-20 20:53:29.262791: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-20 20:53:29.268091: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-20 20:53:29.278049: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-20 20:53:29.278203: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-20 20:53:29.279054: 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.
2021-01-20 20:53:29.281144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.755GHz coreCount: 46 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-20 20:53:29.281321: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-20 20:53:29.281786: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-20 20:53:29.282156: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-20 20:53:29.282961: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-20 20:53:29.283385: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-20 20:53:29.284167: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-20 20:53:29.284635: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-20 20:53:29.286872: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-20 20:53:29.289197: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-20 20:53:29.772262: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-20 20:53:29.772375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-20 20:53:29.773599: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-20 20:53:29.774277: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6589 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3070, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-20 20:53:29.775166: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-20 20:53:30.414473: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-01-20 20:53:31.860756: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-20 20:53:32.450199: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-20 20:53:32.476605: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-20 20:53:33.172408: E tensorflow/stream_executor/cuda/cuda_dnn.cc:336] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED
2021-01-20 20:53:33.172484: E tensorflow/stream_executor/cuda/cuda_dnn.cc:340] Error retrieving driver version: Unimplemented: kernel reported driver version not implemented on Windows

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从文档判断，这可能与使用错误的库组合有关，但我真的没有任何线索：我可能正在进行任何测试来解决此问题？

我认为问题在于GeForce驱动程序461.09-desktop-win10-64bit-international-dch-whql。它包括CUDA 11.2，而不是11。我想你需要找到一个与你的卡兼容的GeForce驱动程序版本，包括CUDA 11。看起来450.36.06+版适合您。

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我建议您卸载计算机上的CUDA 11.2和当前驱动程序，并安装较旧版本。

不确定这是否有帮助，但tf nightly gpu 2.5.0.dev20210120不会出现此问题，而是会终止运行时