ubuntu python3.6升级3.8

sudo apt install python3.8

sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 1
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 2

sudo update-alternatives --install /usr/bin/python python /usr/bin/python2 2
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 3

apt install python3-pip -y

先ln -s 相关的so(apt_pkg.so、_gi.so)

root@xxxxx:/usr/lib/python3/dist-packages# ll | grep apt_pkg
lrwxrwxrwx 1 root root    39 Dec 15 02:06 apt_pkg.so -> apt_pkg.cpython-36m-x86_64-linux-gnu.so

root@xxxxx:/usr/lib/python3/dist-packages/gi# ll | grep _gi
lrwxrwxrwx 1 root root    35 Dec 15 02:11 _gi.so -> _gi.cpython-36m-x86_64-linux-gnu.so

参考这里https://blog.csdn.net/qq_30065853/article/details/122414615

和这里https://stackoverflow.com/questions/70508775/error-could-not-build-wheels-for-pycairo-which-is-required-to-install-pyprojec

apt install python3.8-dev python3-dev python-dev -y
apt-get install sox ffmpeg libgirepository1.0-dev python-cairo libcairo2  libcairo2-dev -y
apt-get install texlive-full -y # may be not necessary
python3.8 -m pip install -U manimlib
python3.8 -m pip install -U manimce
python3.8 -m pip install -U pycairo
# 如遇故障，请把-U改成-I
python3.8 -m pip install --ignore-installed PyGObject

bash: add-apt-repository: command not found

sudo apt update && sudo apt upgrade
sudo apt install software-properties-common

升级G++-11 参考这里https://stackoverflow.com/questions/67298443/when-gcc-11-will-appear-in-ubuntu-repositories

apt install gcc g++ make bison binutils gcc-multilib build-essential manpages-dev software-properties-common
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt update && sudo apt install gcc-11 g++-11
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 110 --slave /usr/bin/g++ g++ /usr/bin/g++-11 --slave /usr/bin/gcov gcov /usr/bin/gcov-11 --slave /usr/bin/gcc-ar gcc-ar /usr/bin/gcc-ar-11 --slave /usr/bin/gcc-ranlib gcc-ranlib /usr/bin/gcc-ranlib-11  --slave /usr/bin/cpp cpp /usr/bin/cpp-11;

升级G++-11(方法2)：

# 镜像地址这里选https://gcc.gnu.org/mirrors.html
wget https://bigsearcher.com/mirrors/gcc/releases/gcc-11.3.0/gcc-11.3.0.tar.gz
tar -zxvf gcc-11.3.0.tar.gz 
cd gcc-11.3.0
mkdir build
cd build
../configure --enable-multilib && make && sudo make install

安装clang-12

wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
sudo apt-add-repository "deb http://apt.llvm.org/bionic/ llvm-toolchain-bionic-12 main"

sudo apt install -y \
   software-properties-common \
   cmake \
   libboost-all-dev

# And you will need to install llvm for the AOT runtime
sudo apt install -y \
   llvm-12-dev \
   liblld-12-dev

# If you prefer GCC, then:
sudo apt install -y gcc g++
# Or if you prefer clang, then:
sudo apt install -y clang-12

安装GPU驱动

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install nvidia-440 
sudo reboot

安装CUDA https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

apt -y install cuda-10-2 cuda-toolkit-10-2 cuda-libraries-10-2 [--no-install-recommends]
apt-get install nvidia-cuda-toolkit

OS=ubuntu1804
cudnn_version=8.7.0.*
cuda_version=cuda10.2

wget https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/cuda-${OS}.pin 

sudo mv cuda-${OS}.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/ /"
sudo apt-get update

sudo apt-get install libcudnn8=${cudnn_version}-1+${cuda_version}
sudo apt-get install libcudnn8-dev=${cudnn_version}-1+${cuda_version}

tar 解压cuda后安装至全局

sudo -s
sudo echo '/home/waynexzhou/cuda-11.1/lib64/' >> /etc/ld.so.conf
sudo ldconfig
sudo cp -r -a /home/waynexzhou/cuda-11.1/include/* /usr/include
sudo cp -r -a /home/waynexzhou/cuda-11.1/lib64/* /usr/lib

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/cuda-11.1/lib64

测试OpenCL

// gcc test-opencl.c -lOpenCL -o main -I /usr/local/cuda-10.2/include/ -L /usr/local/cuda-10.2/lib64/
#include <CL/cl.h>
#include <stdio.h>

int main()
{
    // Get the number of available platforms
    cl_uint num_platforms;
    clGetPlatformIDs(0, NULL, &num_platforms);

    // Get the list of all available platforms
    cl_platform_id platforms[num_platforms];
    clGetPlatformIDs(num_platforms, platforms, NULL);
    printf("Number of platforms: %d\n", num_platforms);
    // Print the name and vendor of each platform
    for (int i = 0; i < num_platforms; i++)
    {
        char name[256];
        char vendor[256];
        clGetPlatformInfo(platforms[i], CL_PLATFORM_NAME, sizeof(name), name, NULL);
        clGetPlatformInfo(platforms[i], CL_PLATFORM_VENDOR, sizeof(vendor), vendor, NULL);
        printf("Platform %d: %s - %s\n", i+1, name, vendor);
    }

    return 0;
}

测试nvcc

```

安装NCCL

# https://docs.nvidia.com/deeplearning/nccl/install-guide/index.html
apt install libnccl2 libnccl-dev
# https://developer.nvidia.com/nccl/nccl-legacy-downloads
apt install libnccl2=2.15.5-1+cuda10.2 libnccl-dev=2.15.5-1+cuda10.2

安装bazel https://bazel.build/install/ubuntu

sudo apt install apt-transport-https curl gnupg
curl -fsSL https://bazel.build/bazel-release.pub.gpg | gpg --dearmor >bazel-archive-keyring.gpg
sudo mv bazel-archive-keyring.gpg /usr/share/keyrings
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/bazel-archive-keyring.gpg] https://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list

sudo apt update && sudo apt install bazel-5.3.0

https://github.com/second-state/WasmEdge-tensorflow-tools

docker pull wasmedge/wasmedge:ubuntu-build-gcc
# gcc 9.4.0
docker run -it --rm \
    -v /home/waynexzhou/WasmEdge-tensorflow-tools:/root/WasmEdge-tensorflow-tools \
    wasmedge/wasmedge:ubuntu-build-gcc

# In docker
cd /root/WasmEdge-tensorflow-tools
mkdir -p build && cd build
cmake -DCMAKE_BUILD_TYPE=Release .. && make

tflite

mkdir tflite_build
cd tflite_build
cmake ../tensorflow/lite/c -DTFLITE_ENABLE_GPU=ON
cmake --build . -j -DTFLITE_ENABLE_GPU=ON

# curl -sLO https://github.com/bazelbuild/bazelisk/releases/download/v1.11.0/bazelisk-linux-amd64
# chmod u+x bazelisk-linux-amd64
# mv bazelisk-linux-amd64 /usr/local/bin/bazel
mkdir build
cd build
export CC=gcc
export CXX=g++
# PYTHON_BIN_PATH=/usr/bin/python3.8 USE_DEFAULT_PYTHON_LIB_PATH=1 TF_NEED_CUDA=1 TF_NEED_ROCM=0 TF_DOWNLOAD_CLANG=0 TF_NEED_MPI=0 CC_OPT_FLAGS="-march=native -Wno-sign-compare" TF_SET_ANDROID_WORKSPACE=0 TF_CONFIGURE_IOS=0 ./configure
# BAZEL_LINKLIBS=-l%:libstdc++.a bazel build -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" //tensorflow:libtensorflow.so
# BAZEL_LINKLIBS=-l%:libstdc++.a bazel build -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" //tensorflow/lite/c:libtensorflowlite_c.so
BAZEL_LINKLIBS=-l%:libstdc++.a bazel build -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --config=cuda  --copt -DTFLITE_ENABLE_GPU=ON //tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so --verbose_failures
# --copt -DTFLITE_GPU_BINARY_RELEASE

cmake \
-DCMAKE_CXX_FLAGS=-std=c++14 \
-DCMAKE_BUILD_TYPE=Release \
-DTFLITE_ENABLE_GPU=ON \
../tensorflow_src/tensorflow/lite/c \
&& make -j

# cmake --build . -j

curl -sLO https://github.com/bazelbuild/bazel/releases/download/5.3.0/bazel-5.3.0-installer-linux-x86_64.sh
chmod u+x bazel-5.3.0-installer-linux-x86_64.sh
./bazel-5.3.0-installer-linux-x86_64.sh
  
docker run -it -d --gpus all --ipc=host -p 10036:22 -v  $(pwd):/root/$(basename $(pwd)) --name "paddle2.1" paddlepaddle/paddle:2.1.2-gpu-cuda11.2-cudnn8 bash -c "/etc/rc.local; /bin/bash"
sudo apt install -y tar bzip2 make automake gcc gcc-c++ vim pciutils elfutils-libelf-devel libglvnd-devel iptables

apt-get- yinstall freeglut3-dev libgles2-mesa-dev libegl1-mesa-dev
bazel build //tensorflow/lite:libtensorflowlite.so -c opt
bazel build //tensorflow/lite/c:libtensorflowlite_c.so -c opt
bazel build -c opt tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so --copt -DEGL_NO_X11=1

bazel build -c opt --copt="-DMESA_EGL_NO_X11_HEADERS" --copt="-DEGL_NO_X11" tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_gl.so
# tensorflow/lite/delegates/gpu
bazel build -c opt --copt -Os --copt -DTFLITE_GPU_BINARY_RELEASE --copt -fvisibility=hidden --linkopts -s --strip always :libtensorflowlite_gpu_gl.so
bazel build -c opt --copt="-DMESA_EGL_NO_X11_HEADERS" --copt="-DEGL_NO_X11" tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_gl.so

https://blog.csdn.net/hhaowang/article/details/111866024

conda快速安装cuda11+cudnn8并将其加载系统变量以及快速安装tensorflow

https://www.tensorflow.org/install/pip#step-by-step_instructions

1、https://github.com/jiangxinyang227/nlp_tflite

2、https://github.com/ValYouW/tflite-dist/blob/master/build-android.sh

3、https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/guide/build_cmake.md

sudo apt-get install linux-headers-$(uname -r)
sudo apt-get install nvidia-opencl-icd-<version>

# OpenCL
curl -fsSL https://mirrors.aliyun.com/nvidia-cuda/ubuntu1804/x86_64/7fa2af80.pub | apt-key add -
echo "deb https://mirrors.aliyun.com/nvidia-cuda/ubuntu1804/x86_64/ /" > /etc/apt/sources.list.d/cuda.list
apt update
apt-get install nvidia-opencl-dev
echo "/usr/lib/x86_64-linux-gnu/libnvidia-opencl.so.1" >> /etc/OpenCL/vendors/nvidia.icd

sudo apt -y install nvidia-opencl-dev [ocl-icd-libopencl1]

sudo apt-get update -y
sudo apt-get install -y libhugs-opengl-bundled

# OpenGL
sudo apt-get update
sudo apt-get install cmake pkg-config
sudo apt-get install mesa-utils libglu1-mesa-dev freeglut3-dev mesa-common-dev
sudo apt-get install libglew-dev libglfw3-dev libglm-dev
sudo apt-get install libao-dev libmpg123-dev
# !!!!
cd /usr/local/lib/
git clone https://github.com/glfw/glfw.git
cd glfw
cmake .
make
sudo make install

sudo apt-get -y install build-essential libgl1-mesa-dev libglu1-mesa-dev freeglut3-dev libglew1.8 libglew-dev libgl1-mesa-glx  libxmu-dev install libglm-dev \
--reinstall 
export DISPLAY=$(awk '/nameserver / {print $2; exit}' /etc/resolv.conf 2>/dev/null):0
export LIBGL_ALWAYS_INDIRECT=0

root@iotsc-waynexzhou:~/code/build# ./minimal ~/code/linear.tflite
Activating GPU...
INFO: Created TensorFlow Lite delegate for GPU.
INFO: Initialized OpenCL-based API.
INFO: Created 1 GPU delegate kernels.
=== Pre-invoke Interpreter State ===
Interpreter has 1 subgraphs.

-----------Subgraph-0 has 10 tensors and 2 nodes------------
1 Inputs: [0] -> 4B (0.00MB)
1 Outputs: [3] -> 4B (0.00MB)

Tensor  ID Name                      Type            AllocType          Size (Bytes/MB)    Shape      MemAddr-Offset  
Tensor   0 serving_default_dense_... kTfLiteFloat32  kTfLiteArenaRw     4        / 0.00 [1,1] [0, 4)
Tensor   1 sequential/dense/BiasA... kTfLiteFloat32  kTfLiteMmapRo      4        / 0.00 [1] [24, 28)
Tensor   2 sequential/dense/MatMul   kTfLiteFloat32  kTfLiteMmapRo      4        / 0.00 [1,1] [0, 4)
Tensor   3 StatefulPartitionedCall:0 kTfLiteFloat32  kTfLiteArenaRw     4        / 0.00 [1,1] [64, 68)
Tensor   4 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)
Tensor   5 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)
Tensor   6 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)
Tensor   7 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)
Tensor   8 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)
Tensor   9 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)

kTfLiteArenaRw Info: 
Tensor 0 has the max size 4 bytes (0.000 MB).
This memory arena is estimated as[0x562a383a8644, 0x562a383a8600), taking 68 bytes (0.000 MB).
One possible set of tensors that have non-overlapping memory spaces with each other, and they take up the whole arena:
Tensor 0 -> 3.

kTfLiteArenaRwPersistent Info: not holding any allocation.

kTfLiteMmapRo Info: 
Tensor 1 has the max size 4 bytes (0.000 MB).
This memory arena is estimated as[0x7f14352b91dc, 0x7f14352b91c0), taking 28 bytes (0.000 MB).
One possible set of tensors that have non-overlapping memory spaces with each other, and they take up the whole arena:
Tensor 2 -> 1.

kTfLiteDynamic Info: not holding any allocation.

Node   0 Operator Builtin Code   9 FULLY_CONNECTED (delegated by node 1)
  3 Input Tensors:[0,2,1] -> 0B (0.00MB)
  1 Output Tensors:[3] -> 0B (0.00MB)
Node   1 Operator Custom Name TfLiteGpuDelegateV2 
  3 Input Tensors:[0-2] -> 12B (0.00MB)
  1 Output Tensors:[3] -> 4B (0.00MB)

Execution plan as the list of 1 nodes invoked in-order: [1]
Among these nodes in the execution plan:
  Node 1 is a TfLiteGpuDelegateV2 node (0x562a37a54f70), which has delegated 1 nodes: [0]
--------------Subgraph-0 dump has completed--------------

--------------Memory Arena Status Start--------------
Total memory usage: 324 bytes (0.000 MB)
- Total arena memory usage: 324 bytes (0.000 MB)
- Total dynamic memory usage: 0 bytes (0.000 MB)

Subgraph#0   Arena (Normal)            196 (60.49%)
Subgraph#0   Arena (Persistent)        128 (39.51%)
--------------Memory Arena Status End--------------



=== Post-invoke Interpreter State ===
Interpreter has 1 subgraphs.

-----------Subgraph-0 has 10 tensors and 2 nodes------------
1 Inputs: [0] -> 4B (0.00MB)
1 Outputs: [3] -> 4B (0.00MB)

Tensor  ID Name                      Type            AllocType          Size (Bytes/MB)    Shape      MemAddr-Offset  
Tensor   0 serving_default_dense_... kTfLiteFloat32  kTfLiteArenaRw     4        / 0.00 [1,1] [0, 4)
Tensor   1 sequential/dense/BiasA... kTfLiteFloat32  kTfLiteMmapRo      4        / 0.00 [1] [24, 28)
Tensor   2 sequential/dense/MatMul   kTfLiteFloat32  kTfLiteMmapRo      4        / 0.00 [1,1] [0, 4)
Tensor   3 StatefulPartitionedCall:0 kTfLiteFloat32  kTfLiteArenaRw     4        / 0.00 [1,1] [64, 68)
Tensor   4 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)
Tensor   5 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)
Tensor   6 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)
Tensor   7 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)
Tensor   8 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)
Tensor   9 (nil)                     kTfLiteNoType   kTfLiteMemNone     0        / 0.00 (null) [-1, -1)

kTfLiteArenaRw Info: 
Tensor 0 has the max size 4 bytes (0.000 MB).
This memory arena is estimated as[0x562a383a8644, 0x562a383a8600), taking 68 bytes (0.000 MB).
One possible set of tensors that have non-overlapping memory spaces with each other, and they take up the whole arena:
Tensor 0 -> 3.

kTfLiteArenaRwPersistent Info: not holding any allocation.

kTfLiteMmapRo Info: 
Tensor 1 has the max size 4 bytes (0.000 MB).
This memory arena is estimated as[0x7f14352b91dc, 0x7f14352b91c0), taking 28 bytes (0.000 MB).
One possible set of tensors that have non-overlapping memory spaces with each other, and they take up the whole arena:
Tensor 2 -> 1.

kTfLiteDynamic Info: not holding any allocation.

Node   0 Operator Builtin Code   9 FULLY_CONNECTED (delegated by node 1)
  3 Input Tensors:[0,2,1] -> 0B (0.00MB)
  1 Output Tensors:[3] -> 0B (0.00MB)
Node   1 Operator Custom Name TfLiteGpuDelegateV2 
  3 Input Tensors:[0-2] -> 12B (0.00MB)
  1 Output Tensors:[3] -> 4B (0.00MB)

Execution plan as the list of 1 nodes invoked in-order: [1]
Among these nodes in the execution plan:
  Node 1 is a TfLiteGpuDelegateV2 node (0x562a37a54f70), which has delegated 1 nodes: [0]
--------------Subgraph-0 dump has completed--------------

--------------Memory Arena Status Start--------------
Total memory usage: 324 bytes (0.000 MB)
- Total arena memory usage: 324 bytes (0.000 MB)
- Total dynamic memory usage: 0 bytes (0.000 MB)

Subgraph#0   Arena (Normal)            196 (60.49%)
Subgraph#0   Arena (Persistent)        128 (39.51%)
--------------Memory Arena Status End--------------

npm 重装（`GLIBC_2.28' not found） https://stackoverflow.com/questions/72921215/getting-glibc-2-28-not-found

$ sudo apt-get remove nodejs
$ sudo apt-get remove npm

$ curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.2/install.sh | bash
$ chmod +x ~/.nvm/nvm.sh
$ source ~/.bashrc 

$ nvm -v
0.39.2

$ nvm install 14

$ node -v
v14.20.1
$ npm -v
6.14.17

$ nvm install 16

$ node -v
v16.18.0
$ npm -v
8.19.2

install vtune-gui dependencies

sudo apt-get install -y libgbm1 libasound2
https://www.intel.com/content/www/us/en/docs/vtune-profiler/installation-guide/2023-0/package-managers.html

OpenWhisk GPU

https://medium.com/openwhisk/using-gpus-with-apache-openwhisk-c6773efcccfb

1|0gem install rails ：ERROR

https://blog.csdn.net/henmj/article/details/125015847

网络端口错误

https://blog.csdn.net/qq_29274865/article/details/116016449

https://blog.csdn.net/bowenlaw/article/details/105358102

https://stackoverflow.com/questions/35220654/how-to-verify-if-nginx-is-running-or-not

Temporary failure resolving 'archive.ubuntu.com' https://askubuntu.com/questions/91543/apt-get-update-fails-to-fetch-files-temporary-failure-resolving-error

2|0SSH 免密登录（设置后仍需输入密码的原因及解决方法）

https://blog.csdn.net/sculpta/article/details/108130992

https://blog.csdn.net/qq_19922839/article/details/117488663

PERF权限不足

https://blog.eastonman.com/blog/2021/02/use-perf/

3|0docker: Error response from daemon: Unknown runtime specified nvidia

https://blog.csdn.net/zaf0516/article/details/122617623

4|0完整下载带有引用其他项目的github项目代码及其他

https://coleflowers.github.io/2015/06/28/github-note.html

https://github.com/tensorflow/tensorflow/issues/48407
https://www.tensorflow.org/lite/android/delegates/gpu#c++
https://stackoverflow.com/questions/56837288/tensorflow-lite-c-api-example-for-inference
https://medium.com/analytics-vidhya/inference-tensorflow2-model-in-c-aa73a6af41cf

https://github.com/kashimAstro/tf_inference
https://github.com/google-coral/tflite
https://github.com/jiangxinyang227/nlp_tflite
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/delegates/gpu/README.md

[Has 3 dynamic libs:](https://github.com/ValYouW/tflite-dist)
libtensorflowlite.so - C++ library
libtensorflowlite_c.so - C library
libtensorflowlite_gpu_delegate.so - The C++ GPU delegate library

https://www.tensorflow.org/lite/performance/gpu#c（支持到-2.3.0-版）
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/guide/build_cmake.md
https://zhuanlan.zhihu.com/p/53393267

https://stackoverflow.com/questions/71875103/compile-tflite-script-with-cmake-cmakelists-for-gpu
https://github.com/tensorflow/tensorflow/issues/38746
https://github.com/tensorflow/tensorflow/issues/48407
https://github.com/tensorflow/tensorflow/issues/58497 [!!!]
bazel -c opt --copt -Os --copt -DTFLITE_GPU_BINARY_RELEASE tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so --verbose_failures
-DTFLITE_ENABLE_GPU=ON

Unfortunately, TFLite GPU delegate in official Tensorflow repo does not support Ubuntu.

https://blog.csdn.net/u013701860/article/details/125009412

安装opencl
https://matpool.medium.com/%E7%9F%A9%E6%B1%A0%E4%BA%91%E4%B8%8Anvidia-opencl%E5%AE%89%E8%A3%85%E5%8F%8A%E6%B5%8B%E8%AF%95%E6%95%99%E7%A8%8B-6cbb616cc30d
sudo apt -y install nvidia-opencl-dev [ocl-icd-libopencl1]
https://www.cnblogs.com/vactor/p/9286425.html !!!
clinfo测试

openGL
https://zoomadmin.com/HowToInstall/UbuntuPackage/libhugs-opengl-bundled
https://medium.com/geekculture/a-beginners-guide-to-setup-opengl-in-linux-debian-2bfe02ccd1e
https://www.cntofu.com/book/46/opengl/ubuntuxia_an_zhuang_opengl_tu_xing_ku.md
https://gist.github.com/Mluckydwyer/8df7782b1a6a040e5d01305222149f3c

https://gist.github.com/ksopyla/bf74e8ce2683460d8de6e0dc389fc7f5

__EOF__

本文作者：shenben
本文链接：https://www.cnblogs.com/shenben/p/16984457.html
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