时间戳

首页 > 解决方案 > CUDA 11.0 和 cuDNN 8.0.2 的 GPU 问题

tensorflow - CUDA 11.0 和 cuDNN 8.0.2 的 GPU 问题

问题描述

我有 CUDA 11.0 和 cuDNN 8.0.2，这是推荐的设置

我有 tensorflow-gpu 2.3 和 keras 2.4

但是没有使用 GPU，我不知道为什么。

通过给出以下命令行

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.

标签： tensorflowkeras

解决方案

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

见链接

您的 python 版本必须介于 3.5 - 3.8 之间。

除此之外，您还需要适用于 Visual Studio 2015、2017 和 2019 的 Microsoft Visual C++ Redistributable。

你可以在这里下载。关联

在此处查看完整的系统要求链接

不要忘记在系统路径中添加 cuda 和 cudnn。见链接

推荐阅读