Tensorflow: capacité de calcul Cuda 3.0. La capacité minimale requise de Cuda est de 3,5.
J'installe tensorflow à partir de la source (documentation) .
Version du pilote Cuda:
nvcc: NVIDIA (R) Cuda compiler driver
Cuda compilation tools, release 7.5, V7.5.17
Quand j'ai exécuté la commande suivante:
bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu
cela m'a donné l'erreur suivante:
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:118] Found device 0 with properties:
name: GeForce GT 640
major: 3 minor: 0 memoryClockRate (GHz) 0.9015
pciBusID 0000:05:00.0
Total memory: 2.00GiB
Free memory: 1.98GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:138] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:148] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:843] Ignoring gpu device (device: 0, name: GeForce GT 640, pci bus id: 0000:05:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:843] Ignoring gpu device (device: 0, name: GeForce GT 640, pci bus id: 0000:05:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:843] Ignoring gpu device (device: 0, name: GeForce GT 640, pci bus id: 0000:05:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:843] Ignoring gpu device (device: 0, name: GeForce GT 640, pci bus id: 0000:05:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:843] Ignoring gpu device (device: 0, name: GeForce GT 640, pci bus id: 0000:05:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:843] Ignoring gpu device (device: 0, name: GeForce GT 640, pci bus id: 0000:05:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:843] Ignoring gpu device (device: 0, name: GeForce GT 640, pci bus id: 0000:05:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:843] Ignoring gpu device (device: 0, name: GeForce GT 640, pci bus id: 0000:05:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:843] Ignoring gpu device (device: 0, name: GeForce GT 640, pci bus id: 0000:05:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:843] Ignoring gpu device (device: 0, name: GeForce GT 640, pci bus id: 0000:05:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
F tensorflow/cc/tutorials/example_trainer.cc:128] Check failed: ::tensorflow::Status::OK() == (session->Run({{"x", x}}, {"y:0", "y_normalized:0"}, {}, &outputs)) (OK vs. Invalid argument: Cannot assign a device to node 'Cast': Could not satisfy explicit device specification '/gpu:0' because no devices matching that specification are registered in this process; available devices: /job:localhost/replica:0/task:0/cpu:0
[[Node: Cast = Cast[DstT=DT_FLOAT, SrcT=DT_INT32, _device="/gpu:0"](Const)]])
F tensorflow/cc/tutorials/example_trainer.cc:128] Check failed: ::tensorflow::Status::OK() == (session->Run({{"x", x}}, {"y:0", "y_normalized:0"}, {}, &outputs)) (OK vs. Invalid argument: Cannot assign a device to node 'Cast': Could not satisfy explicit device specification '/gpu:0' because no devices matching that specification are registered in this process; available devices: /job:localhost/replica:0/task:0/cpu:0
[[Node: Cast = Cast[DstT=DT_FLOAT, SrcT=DT_INT32, _device="/gpu:0"](Const)]])
F tensorflow/cc/tutorials/example_trainer.cc:128] Check failed: ::tensorflow::Status::OK() == (session->Run({{"x", x}}, {"y:0", "y_normalized:0"}, {}, &outputs)) (OK vs. Invalid argument: Cannot assign a device to node 'Cast': Could not satisfy explicit device specification '/gpu:0' because no devices matching that specification are registered in this process; available devices: /job:localhost/replica:0/task:0/cpu:0
[[Node: Cast = Cast[DstT=DT_FLOAT, SrcT=DT_INT32, _device="/gpu:0"](Const)]])
F tensorflow/cc/tutorials/example_trainer.cc:128] Check failed: ::tensorflow::Status::OK() == (session->Run({{"x", x}}, {"y:0", "y_normalized:0"}, {}, &outputs)) (OK vs. Invalid argument: Cannot assign a device to node 'Cast': Could not satisfy explicit device specification '/gpu:0' because no devices matching that specification are registered in this process; available devices: /job:localhost/replica:0/task:0/cpu:0
[[Node: Cast = Cast[DstT=DT_FLOAT, SrcT=DT_INT32, _device="/gpu:0"](Const)]])
Aborted (core dumped)
Aurai-je besoin d'un gpu différent pour exécuter ceci?
J'ai installé la version 1.8 de Tensorflow. Il recommande CUDA 9.0. J'utilise une carte GTX 650M qui a la capacité de calcul CUDA 3.0 et fonctionne maintenant à merveille. OS est Ubuntu 18.04. Voici les étapes détaillées:
Installation de dépendances
J'ai inclus ffmpeg et certains packages associés pour ma compilation opencv 3.4, si non requis, ne l'installez pas Exécutez les commandes ci-dessous:
Sudo apt-get update
Sudo apt-get dist-upgrade -y
Sudo apt-get autoremove -y
Sudo apt-get upgrade
Sudo add-apt-repository ppa:jonathonf/ffmpeg-3 -y
Sudo apt-get update
Sudo apt-get install build-essential -y
Sudo apt-get install ffmpeg -y
Sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev -y
Sudo apt-get install python-dev libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev -y
Sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev -y
Sudo apt-get install libxvidcore-dev libx264-dev -y
Sudo apt-get install unzip qtbase5-dev python-dev python3-dev python-numpy python3-numpy -y
Sudo apt-get install libopencv-dev libgtk-3-dev libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff5-dev >libjasper-dev -y
Sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev -y
Sudo apt-get install libv4l-dev libtbb-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev -y
Sudo apt-get install libvorbis-dev libxvidcore-dev v4l-utils vtk6 -y
Sudo apt-get install liblapacke-dev libopenblas-dev libgdal-dev checkinstall -y
Sudo apt-get install libgtk-3-dev -y
Sudo apt-get install libatlas-base-dev gfortran -y
Sudo apt-get install qt-sdk -y
Sudo apt-get install python2.7-dev python3.5-dev python-tk -y
Sudo apt-get install cython libgflags-dev -y
Sudo apt-get install tesseract-ocr -y
Sudo apt-get install tesseract-ocr-eng -y
Sudo apt-get install tesseract-ocr-ell -y
Sudo apt-get install gstreamer1.0-python3-plugin-loader -y
Sudo apt-get install libdc1394-22-dev -y
Sudo apt-get install openjdk-8-jdk
Sudo apt-get install pkg-config Zip g++-6 gcc-6 zlib1g-dev unzip git
Sudo wget https://bootstrap.pypa.io/get-pip.py
Sudo python get-pip.py
Sudo pip install -U pip
Sudo pip install -U numpy
Sudo pip install -U pandas
Sudo pip install -U wheel
Sudo pip install -U six
Installation du pilote nvidia
Exécutez les commandes ci-dessous:
Sudo add-apt-repository ppa:graphics-drivers/ppa
Sudo apt-get update
Sudo apt-get install nvidia-390 -y
Redémarrez et exécutez la commande ci-dessous, qui devrait vous donner les détails décrits dans l'image ci-dessous: 
gcc-6 et g ++ - 6 vérifications.
gcc-6 et g ++ - 6 sont requis pour CUDA 9.0, exécutez les commandes ci-dessous:
cd /usr/bin
Sudo rm -rf gcc gcc-ar gcc-nm gcc-ranlib g++
Sudo ln -s gcc-6 gcc
Sudo ln -s gcc-ar-6 gcc-ar
Sudo ln -s gcc-nm-6 gcc-nm
Sudo ln -s gcc-ranlib-6 gcc-ranlib
Sudo ln -s g++-6 g++
Installer CUDA 9.0
Allez sur https://developer.nvidia.com/cuda-90-download-archive . Sélectionnez les options: Linux-> x86_64-> Ubuntu-> 17.04-> deb (local) . Téléchargez le fichier principal et les deux correctifs . Les commandes suivantes:
Sudo dpkg -i cuda-repo-ubuntu1704-9-0-local_9.0.176-1_AMD64.deb
Sudo apt-key add /var/cuda-repo-9-0-local/7fa2af80.pub
Sudo apt-get update
Sudo apt-get install cuda
Naviguez jusqu'au premier patch sur votre PC et double-cliquez dessus, il s'exécutera automatiquement. Suivez-le de la même manière pour le second patch.
Ajoutez ci-dessous les lignes de votre fichier ~/.bashrc et redémarrez-le:
export PATH=/usr/local/cuda-9.0/bin${PATH:+:$PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
Installation de Cudnn 7.1.4 pour CUDA 9.0
Téléchargez le fichier tar à partir de https://developer.nvidia.com/cudnn et extrayez-le dans votre dossier Téléchargements. Le téléchargement nécessite un identifiant de connexion développé par nvidia, inscription gratuite.
cd ~/Downloads/cudnn-9.0-linux-x64-v7.1/cuda
Sudo cp include/* /usr/local/cuda/include/
Sudo cp lib64/libcudnn.so.7.1.4 lib64/libcudnn_static.a /usr/local/cuda/lib64/
cd /usr/lib/x86_64-linux-gnu
Sudo ln -s libcudnn.so.7.1.4 libcudnn.so.7
Sudo ln -s libcudnn.so.7 libcudnn.so
Installation de NCCL 2.2.12 pour CUDA 9.0
Téléchargez le fichier tar à partir de https://developer.nvidia.com/nccl et extrayez-le dans votre dossier de téléchargements.
Sudo mkdir -p /usr/local/cuda/nccl/lib /usr/local/cuda/nccl/include
cd ~/Downloads/nccl-repo-ubuntu1604-2.2.12-ga-cuda9.0_1-1_AMD64/
Sudo cp *.txt /usr/local/cuda/nccl
Sudo cp include/*.h /usr/include/
Sudo cp lib/libnccl.so.2.1.15 lib/libnccl_static.a /usr/lib/x86_64-linux-gnu/
Sudo ln -s /usr/include/nccl.h /usr/local/cuda/nccl/include/nccl.h
cd /usr/lib/x86_64-linux-gnu
Sudo ln -s libnccl.so.2.1.15 libnccl.so.2
Sudo ln -s libnccl.so.2 libnccl.so
for i in libnccl*; do Sudo ln -s /usr/lib/x86_64-linux-gnu/$i /usr/local/cuda/nccl/lib/$i; done
Installez Bazel (l'installation manuelle recommandée de bazel a fonctionné, pour référence: https://docs.bazel.build/versions/master/install-ubuntu.html#install-with-installer-ubuntu )
Téléchargez "bazel-0.13.1-installer-darwin-x86_64.sh" depuis https://github.com/bazelbuild/bazel/releases Exécutez les commandes ci-dessous:
chmod +x bazel-0.13.1-installer-darwin-x86_64.sh
./bazel-0.13.1-installer-darwin-x86_64.sh --user
export PATH="$PATH:$HOME/bin"
Compilation de Tensorflow
Nous allons compiler avec CUDA, avec XLA JIT (oh yeah) et jemalloc comme support malloc. Donc nous entrons oui pour ces choses . Exécutez la commande ci-dessous et répondez aux requêtes comme décrit pour l'exécution de la configuration
git clone https://github.com/tensorflow/tensorflow
git checkout r1.8
./configure
You have bazel 0.13.0 installed.
Please specify the location of python. [Default is /usr/bin/python]:
Please input the desired Python library path to use. Default is [/usr/local/lib/python2.7/dist-packages]
Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: y
jemalloc as malloc support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
No Google Cloud Platform support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: n
No Amazon S3 File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: n
No Apache Kafka Platform support will be enabled for TensorFlow.
Do you wish to build TensorFlow with XLA JIT support? [y/N]: y
XLA JIT support will be enabled for TensorFlow.
Do you wish to build TensorFlow with GDR support? [y/N]: n
No GDR support will be enabled for TensorFlow.
Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.
Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.
Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]:
Please specify the location where CUDA 9.1 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7.1.4
Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Do you wish to build TensorFlow with TensorRT support? [y/N]: n
No TensorRT support will be enabled for TensorFlow.
Please specify the NCCL version you want to use. [Leave empty to default to NCCL 1.3]: 2.2.12
Please specify the location where NCCL 2 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:/usr/local/cuda/nccl
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 3.0]
Do you want to use clang as CUDA compiler? [y/N]: n
nvcc will be used as CUDA compiler.
Please specify which gcc should be used by nvcc as the Host compiler. [Default is /usr/bin/x86_64-linux-gnu-gcc-7]: /usr/bin/gcc-6
Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
--config=mkl # Build with MKL support.
--config=monolithic # Config for mostly static monolithic build.
Configuration finished
Maintenant, pour compiler tensorflow, exécutez la commande ci-dessous, c’est une consommation très RAM et cela prendra du temps. Vous pouvez supprimer "--local_resources 2048, .5,1.0" de la ligne inférieure si vous avez beaucoup de RAM ou cela fonctionnera avec 2 Go de RAM
bazel build --config=opt --config=cuda --local_resources 2048,.5,1.0 //tensorflow/tools/pip_package:build_pip_package
Une fois la compilation terminée, vous verrez apparaître l’image ci-dessous, confirmant que c’est un succès 
Construisez le fichier de roue, courez ci-dessous:
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
Installer le fichier de roue généré à l'aide de pip
Sudo pip install /tmp/tensorflow_pkg/tensorflow*.whl
Pour explorer maintenant sur les appareils, vous pouvez exécuter tensorflow. L'image ci-dessous représente la vitrine sur le terminal ipython.
Taako, désolé pour cette réponse tardive. Je n'ai pas sauvegardé le fichier wheel de la compilation affichée ci-dessus. Cependant, voici un nouveau pour tensorflow 1.9. J'espère que cela vous aide assez. Veuillez vous assurer des détails ci-dessous utilisés pour la construction.
Tensorflow: 1.9 CUDA Toolkit: 9.2 CUDNN: 7.1.4 NCCL: 2.2.13
Ci-dessous le lien vers le fichier de roue: fichier de roue