Ubuntu中配置TensorFlow使用环境的方法

bash Anaconda3-2020.02-Linux-x86_64.sh

$ sudo apt-get install autoconf automake libtool curl make g++ unzip

$ git clone 
$ https://github.com/protocolbuffers/protobuf.git
$ cd protobuf
$ git submodule update --init --recursive
$ ./autogen.sh

$ ./configure
$ make
$ make check
$ sudo make install
$ sudo ldconfig # refresh shared library cache.

$ protoc --version

$ sudo apt-get install pkg-config zip g++ zlib1g-dev unzip 

$ chmod +x bazel-0.4.3-jdk7-installer-linux-x86_64.sh
$ ./bazel-0.4.3-jdk7-installer-linux-x86_64.sh --user

$ sudo apt-get install python3-numpy swig python3-dev python3-wheel

export PATH"$PATH:$HOME/bin"

$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
$ sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
$ wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb
$ sudo dpkg -i cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb
$ sudo apt-key add /var/cuda-repo-10-2-local-10.2.89-440.33.01/7fa2af80.pub
$ sudo apt-get update
$ sudo apt-get -y install cuda

$ cd /usr/local/sample
$ make all

FILTER_OUT := 0_Simple/cudaNvSci/Makefile

CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 1060 6GB"
 CUDA Driver Version / Runtime Version     10.2 / 10.2
 CUDA Capability Major/Minor version number:  6.1
 Total amount of global memory:         6075 MBytes (6370295808 bytes)
 (10) Multiprocessors, (128) CUDA Cores/MP:   1280 CUDA Cores
 GPU Max Clock rate:              1759 MHz (1.76 GHz)
 Memory Clock rate:               4004 Mhz
 Memory Bus Width:               192-bit
 L2 Cache Size:                 1572864 bytes
 Maximum Texture Dimension Size (x,y,z)     1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
 Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
 Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
 Total amount of constant memory:        65536 bytes
 Total amount of shared memory per block:    49152 bytes
 Total number of registers available per block: 65536
 Warp size:                   32
 Maximum number of threads per multiprocessor: 2048
 Maximum number of threads per block:      1024
 Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
 Max dimension size of a grid size  (x,y,z): (2147483647, 65535, 65535)
 Maximum memory pitch:             2147483647 bytes
 Texture alignment:               512 bytes
 Concurrent copy and kernel execution:     Yes with 2 copy engine(s)
 Run time limit on kernels:           Yes
 Integrated GPU sharing Host Memory:      No
 Support host page-locked memory mapping:    Yes
 Alignment requirement for Surfaces:      Yes
 Device has ECC support:            Disabled
 Device supports Unified Addressing (UVA):   Yes
 Device supports Compute Preemption:      Yes
 Supports Cooperative Kernel Launch:      Yes
 Supports MultiDevice Co-op Kernel Launch:   Yes
 Device PCI Domain ID / Bus ID / location ID:  0 / 1 / 0
 Compute Mode:
   

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1, Device0 = GeForce GTX 1060 6GB
Result = PASS

$ tar -zxvf cudnn-10.2-linux-x64-v7.6.5.32.tgz

$ cd cuda

sudo cp lib64/libcudnn* /usr/local/cuda/lib64/
sudo cp include/cudnn.h /usr/local/cuda/include

$ cd /usr/local/cuda/lib64
$ sudo chmod +r libcudnn.so.7.6.5
$ sudo ln -s libcudnn.so.7.6.5 libcudnn.so.7
$ sudo ln -s libcudnn.so.7.6.5 libcudnn.so
$ sudo ldconfig

export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:usr/local/cuda-10.2/extras/CUPTI/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda-10.2
export PATH=/usr/local/cuda-10.2/bin:$PATH

$ sudo apt-get install python3-pip python3-dev

Usage：
	pip  [options]

$ pip install tensorflow

$ pip install tensorflow-cpu

$ python3

>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'