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问题:

I have upgraded to Tensorflow version 1.0 and installed CUDA 8.0 with the cudnn 5.1 version and the nvidia drivers up to date 375.39. My NVIDIA hardware is the one that is on Amazon Web Services using the p2.xlarge instance, a Tesla K-80. My OS is Linux 64-bit.

I get the next error message every time I use the command: tf.Session()

[ec2-user@ip-172-31-7-96 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

I'm completely clueless about how to fix this. I have tried different versions of Nvidia drivers and CUDA but still it does not work.

Any hints will be appreciated.

回答1:

You need to install a NVIDIA Driver and run the CUDA 8.0 installer.

# Requirements # - NVIDIA Driver - NVIDIA-Linux-x86_64-375.39.run - http://www.nvidia.fr/Download/index.aspx # - CUDA runfile (local) - cuda_8.0.61_375.26_linux.run - https://developer.nvidia.com/cuda-downloads # - cudnn-8.0-linux-x64-v5.0-ga.tgz  sudo apt update -y && sudo apt upgrade -y sudo apt install build-essential linux-image-extra-`uname -r` -y  chmod +x NVIDIA-Linux-x86_64-375.39.run sudo ./NVIDIA-Linux-x86_64-375.39.run  chmod +x cuda_8.0.61_375.26_linux.run ./cuda_8.0.61_375.26_linux.run --extract=`pwd`/extracts sudo ./extracts/cuda-linux64-rel-8.0.61-21551265.run  echo -e "export CUDA_HOME=/usr/local/cuda\nexport PATH=\$PATH:\$CUDA_HOME/bin\nexport LD_LIBRARY_PATH=\$LD_LINKER_PATH:\$CUDA_HOME/lib64" >> ~/.bashrc source .bashrc  tar xf cudnn-8.0-linux-x64-v5.0-ga.tgz cd cuda sudo cp lib64/* /usr/local/cuda/lib64/ sudo cp include/cudnn.h /usr/local/cuda/include/