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的最新版本的Tensorflow（1.0）中安裝CUDA 8.0我已經升級到Tensorflow 1.0版，並安裝了CUDA 8.0 cudnn 5.1版本和nvidia驅動程序最新375.39。我的NVIDIA硬件是Amazon Web Services上使用p2.xlarge實例（一種特斯拉K-80）的硬件。我的操作系統是Linux 64位。如何在AWS p2.xlarge實例，AMI ami-edb11e8d和nvidia驅動程序最新版本（375.39）
每次都遇到我用命令的時間一個錯誤信息：tf.Session（）
[[email protected] CUDA]$ python
Python 2.7.12 (default, Sep 1 2016, 22:14:00)
[GCC 4.8.3 20140911 (Red Hat 4.8.3-9)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
>>> sess = tf.Session()
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
E tensorflow/stream_executor/cuda/cuda_driver.cc:509] failed call to cuInit: CUDA_ERROR_NO_DEVICE
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:158] retrieving CUDA diagnostic information for host: ip-172-31-7-96
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:165] hostname: ip-172-31-7-96
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] libcuda reported version is: Invalid argument: expected %d.%d or %d.%d.%d form for driver version; got "1"
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:363] driver version file contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module 375.39 Tue Jan 31 20:47:00 PST 2017
GCC version: gcc version 4.8.3 20140911 (Red Hat 4.8.3-9) (GCC)
"""
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:193] kernel reported version is: 375.39.0
我完全茫然不知如何解決這個問題。 我已經嘗試過不同版本的Nvidia驅動程序和CUDA，但仍然無法正常工作。
任何提示將不勝感激。
也許是你的GPU驅動程序安裝不正確。運行'nvidia-smi'的結果是什麼？您是否按照[cuda linux安裝指南]（http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#abstract）中所述執行了CUDA安裝的任何驗證？ –
謝謝您的及時答覆。 nvidia-smi工作，我沒有按照網站上描述的「驗證」。 我決定在Redhat 7.3系統上從頭開始。 它起初工作，所以不需要進一步的援助。 – basuam