如何解决CUDA 11.0和cuDNN 8.0.2的GPU问题
我有CUDA 11.0和cuDNN 8.0.2,这是推荐设置
我有tensorflow-gpu 2.3和keras 2.4
但是没有使用GPU,我也不知道为什么。
通过提供以下命令行
sess = tf.test.is_gpu_available(cuda_only=False,min_cuda_compute_capability=None)
print("GPU available? ",sess)
built = tf.test.is_built_with_cuda()
print("tf is built with CUDA? ",built)
gpus = tf.config.list_physical_devices('GPU')
cpus = tf.config.list_physical_devices('CPU')
print("Num GPUs used: ",len(gpus))
print("Num CPUs used: ",len(cpus))
print(tf.sysconfig.get_build_info())
输出如下:
GPU available? False
tf is built with CUDA? True
Num GPUs used: 0
Num CPUs used: 1
{'cuda_version': '10.1','cudnn_version': '7','cuda_compute_capabilities': ['sm_35','sm_37','sm_52','sm_60','sm_61','compute_70'],'cpu_compiler': '/usr/bin/gcc-5','is_rocm_build': False,'is_cuda_build': True}
它带有以下错误:
W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory
I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
W tensorflow/core/common_runtime/gpu/gpu_device.cc:1753] 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.
解决方法
就我而言,即使我的系统已使用CUDA 10.2设置,此解决方案也可以彻底解决问题。我猜Tensorflow可能需要CUDA 10.1中的某些内容。
conda install cudatoolkit=10.1
选中https://github.com/tensorflow/tensorflow/issues/38578#issuecomment-710104168
,如Tensorflow文档中所述。软件要求如下。
Nvidia gpu drivers - 418.x or higher
Cuda - 10.1 (TensorFlow >= 2.1.0)
cuDNN - 7.6
请参见Link
您必须使用3.5-3.8之间的python版本。
此外,您还需要针对Visual Studio 2015、2017和2019的Microsoft Visual C ++ Redistributable。
您可以在此处下载。 Link
在Link处查看完整的系统要求
别忘了在系统路径中添加cuda和cudnn。参见Link
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