Comment installer CUDA 8.0 dans la dernière version de Tensorflow (1.0) dans l'instance AWS p2.xlarge, pilotes AMI ami-edb11e8d et nvidia à jour (375.39)

Question

J'ai mis à jour Tensorflow version 1.0 et installé CUDA 8.0 avec le version cudnn 5.1 et les pilotes nvidia à jour 375.39. Mon matériel NVIDIA est celui qui est sur Amazon Web Services en utilisant l'instance p2.xlarge, un Tesla K-80. Mon système d'exploitation est Linux 64 bits.

Je reçois le message d'erreur suivant chaque fois que j'utilise la commande: 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 

Je suis complètement désemparés sur la façon de résoudre ce problème. J'ai essayé différentes versions de drivers Nvidia et CUDA mais ça ne marche toujours pas.

Toutes les indications seront appréciées.

Answer 1

Désinstallez les pilotes & cuda, puis suivez les instructions official guide pour les réinstaller.

Exécutez deviceQuery pour vérifier que le périphérique est correctement installé.

Answer 2

Vous devez installer un pilote NVIDIA et exécuter le programme d'installation de CUDA 8.0.

# 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/ 

Answer 3

Vous pouvez également essayer "NVIDIA Volta Deep Learning AMI" avec l'instance p3 (v100 GPU).

Inscrivez-vous sur https://www.nvidia.com/en-us/gpu-cloud/?ncid=van-gpu-cloud et obtenez votre «clé API» pour utiliser l'AMI gratuitement.

EC2/informations de configuration GPU: https://aws.amazon.com/blogs/aws/new-amazon-ec2-instances-with-up-to-8-nvidia-tesla-v100-gpus-p3/