Python Tensorflow:Cuda计算能力3.0。所需的最小Cuda能力为3.5_Python_Tensorflow_Gpu_Bazel

Python Tensorflow:Cuda计算能力3.0。所需的最小Cuda能力为3.5

python tensorflow bazel

Python Tensorflow:Cuda计算能力3.0。所需的最小Cuda能力为3.5,python,tensorflow,gpu,bazel,Python,Tensorflow,Gpu,Bazel,我正在从源代码安装tensorflow Cuda驱动程序版本： nvcc: NVIDIA (R) Cuda compiler driver Cuda compilation tools, release 7.5, V7.5.17 当我运行以下命令时： bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu 它给了我以下错误： I tensorflow/stream_executor/dso_loader.cc:108] succ

我正在从源代码安装tensorflow

Cuda驱动程序版本：

nvcc: NVIDIA (R) Cuda compiler driver
Cuda compilation tools, release 7.5, V7.5.17

当我运行以下命令时：

bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu

它给了我以下错误：

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)

我需要一个不同的gpu来运行这个吗？

我已经安装了Tensorflow 1.8版。它推荐CUDA 9.0。我正在使用GTX650M卡，该卡具有CUDA计算能力3.0，现在可以像一个魔咒一样工作。操作系统是ubuntu 18.04。以下是详细步骤：

安装依赖项我已经为我的opencv 3.4编译提供了ffmpeg和一些相关软件包，如果不需要，请不要安装运行以下命令：

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

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-390 -y

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++

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

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

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

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"

安装英伟达驱动程序运行以下命令：

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

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-390 -y

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++

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

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

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

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"

重新启动并运行以下命令，它将提供下图所述的详细信息：

gcc-6和g++-6检查。 CUDA 9.0需要gcc-6和g++-6，请运行以下命令：

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

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-390 -y

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++

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

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

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

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"

安装CUDA 9.0 去。选择选项：Linux->x86\u64->Ubuntu->17.04->deb（本地）。下载主文件和两个补丁。运行以下命令：

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

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-390 -y

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++

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

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

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

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"

导航到电脑上的第一个补丁并双击它，它将自动执行，第二个补丁也会执行相同的操作

将以下行添加到~/.bashrc文件中，并重新启动该文件：

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}}

为CUDA 9.0安装cudnn 7.1.4 从下载tar文件并将其解压缩到下载文件夹下载需要一个nvidia开发的登录，免费注册运行以下命令：

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

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-390 -y

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++

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

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

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

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"

为CUDA 9.0安装NCCL 2.2.12 从下载tar文件并将其解压缩到下载文件夹下载需要一个nvidia开发的登录，免费注册运行以下命令：

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

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-390 -y

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++

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

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

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

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"

安装Bazel（建议手动安装Bazel，供参考：）从下载“bazel-0.13.1-installer-darwin-x86_64.sh” 运行以下命令：

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

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-390 -y

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++

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

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

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

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"

编译张量流 <>我们将用CUDA编译，XLA JIT（OH）和JEMALOC作为MALOC支持。因此，对于这些事情，我们输入yes。运行下面的命令并回答查询，如运行配置所述

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

编译完成后，您将看到如下图所示的内容，确认编译成功

构建控制盘文件，在下面运行：

bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

使用pip安装生成的控制盘文件

sudo pip install /tmp/tensorflow_pkg/tensorflow*.whl

要在设备上进行探索，现在您可以运行tensorflow，下图是ipython终端上的展示

@Taako，很抱歉这么晚才回复。我没有保存上面显示的编译的控制盘文件。然而，这里是tensorflow 1.9的一个新版本。希望这对你有足够的帮助。请确保以下详细信息用于构建

张量流：1.9 CUDA工具包：9.2 CUDNN:7.1.4 NCCL:2.2.13

下面是指向控制盘文件的链接：

在anaconda中，tensorflow gpu=1.12和cudatoolkit=9.0与具有3.0计算能力的gpu兼容。以下是创建新环境和安装3.0 GPU所需库的命令

conda create -n tf-gpu
conda activate tf-gpu
conda install tensorflow-gpu=1.12
conda install cudatoolkit=9.0

然后你可以通过以下方法来尝试

>python
import tensorflow as tf
tf.Session()

这是我的输出

名称：GeForce GT 650M大调：3小调：0记忆锁定率（GHz）：0.95 pciBusID:0000:01:00.0 总内存：3.94GiB自由内存：3.26GiB 2019-12-09 13:26:11.753591:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511]添加可见gpu设备：0 2019-12-09 13:26:12.050152:I tensorflow/core/common_runtime/gpu/gpu_device.cc:982]设备互连拖缆执行器与强度1边缘矩阵： 2019-12-09 13:26:12.050199:I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]0 2019-12-09 13:26:12.050222:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001]0:N 2019-12-09 13:26:12.050481:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115]创建tensorflow设备（/job:localhost/replica:0/task:0/device:gpu:0，内存2989MB）->物理gpu（设备：0，名称：GeForce GT 650M，pci总线id:0000:01:00.0，计算能力：3.0）

享受吧

感谢您提供您的WHL！我现在终于能够使用TF了，因为我的笔记本电脑只支持Compute3.0，所以我花了好几天的时间来编译它（没有成功）。我无法在新安装的Ubuntu 18.04上按照您的说明进行编译，我想指出以下几点：

在您的“依赖项”部分中，libjasper不再独立提供，您列出的存储库中不再提供ffmpeg，libtiff5-dev也不再可用（我认为有一个新版本）。我知道这主要是为了OpenCV的东西，我也使用它。您还重复了几个包，比如git和unzip
在“Nvidia驱动程序”部分，我认为存储库中没有该驱动程序。至少我拉不动它。有了你的WHL文件，我使用了英伟达网站的418驱动程序，这看起来很好。
在“为CUDA 9.0安装cudnn 7.1.4”一节中，您可以使用“cd/usr/lib/x86_64-linux-gnu”，但文件位于/usr/local/CUDA中。这是正确的吗？我猜这些链接至少应该被告知指向cuda文件夹
在“为CUDA 9.0安装NCCL 2.2.12”一节中，您使用的是2.2.12，但您的命令行都参考了2.1.15
在您的Bazel安装部分中，您说使用Bazel Darwin安装程序，但我认为这是针对Mac的。我认为您需要Bazel Linux安装程序

再次感谢您为此所做的一切工作

另一方面，我能够通过按照这些说明对Tensorflow 1.12进行git检查，并通过pip安装keras_应用程序和keras_预处理，使用CUDA 9.2、CUDNN 7.1.4和NCCL 2,2,13，使用Bazel 0.15.0来实现这一点。有人指出CUDA9.0不能用gcc6/g++6编译。显然，对于Tensorflow 2.1.0，9.2可以。

通过编译TF2.1.0的源代码，我能够在Windows上管理它。由于XLA的原因，TF2.2.0生成失败，即使所有XLA标志DIS都失败