标签：TrWebOCR 10.1 -- conda cuda https GPU local 搭建
查看显卡信息
查看系统是否受支持
uname -m
http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html
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验证是否有编译环境
验证系统是否安装了正确的内核头文件和开发包
yum install kernel-devel-$(uname -r) kernel-headers-$(uname -r)
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禁用nouveau方法
vim /etc/modprobe.d/blacklist-nouveau.conf
blacklist nouveau
options nouveau modeset=0
:wq
sudo mv /boot/initramfs-$(uname -r).img /boot/initramfs-$(uname -r).img.bak
sudo dracut /boot/initramfs-$(uname -r).img $(uname -r)
sudo reboot
lsmod | grep nouveau
没有任何信息输出，则表示已经禁用nouveau
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安装 CUDA 10.1
wget https://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.243_418.87.00_linux.run
chmod 755 cuda_10.1.243_418.87.00_linux.run
./cuda_10.1.243_418.87.00_linux.run
nvidia-smi
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安装 cuDNN 7.6.5
wget https://developer.nvidia.com/compute/machine-learning/cudnn/secure/7.6.5.32/Production/10.1_20191031/cudnn-10.1-linux-x64-v7.6.5.32.tgz
tar zxvf cudnn-10.1-linux-x64-v7.6.5.32.tgz
cp cuda/include/cudnn.h /usr/local/cuda/include
cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
vim ~/.bashrc
export CUDA_HOME=/usr/local/cuda
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_ROOT=/usr/local/cuda
:wq
source ~/.bashrc
nvcc -V
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创建python3.7运行环境
安装miniconda
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod 755 Miniconda3-latest-Linux-x86_64.sh
./Miniconda3-latest-Linux-x86_64.sh
vim /root/.bashrc
__conda_setup="$('/root/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; then
eval "$__conda_setup"
else
if [ -f "/root/miniconda3/etc/profile.d/conda.sh" ]; then
. "/root/miniconda3/etc/profile.d/conda.sh"
else
export PATH="/root/miniconda3/bin:$PATH"
fi
fi
unset __conda_setup
source /root/.bashrc
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
conda config --set show_channel_urls yes
conda -V
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创建 tr-ocr python3.7运行环境
conda create --name tr-ocr python=3.7
conda activate tr-ocr
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安装 TrWebOCR 环境依赖
conda activate tr-ocr
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
libtorch==1.2.0.1
numpy==1.14.6
opencv-python==3.4.4.19
Pillow==7.1.0
tornado==6.0.4
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pip install cudatoolkit==10.1 -i https://pypi.tuna.tsinghua.edu.cn/simple
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使用GPU运行程序
启动TrWebOCR程序并验证
https://github.com/alisen39/TrWebOCR
cd TrWebOCR-master/
python backend/main.py --open_gpu=1 --port=8089
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启动Tr程序并验证
https://github.com/myhub/tr
cd tr-master/
python test.py
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参考：
https://www.mlzhilu.com/archives/ubuntu2004%E5%AE%89%E8%A3%85nvidia%E6%98%BE%E5%8D%A1%E9%A9%B1%E5%8A%A8
标签：TrWebOCR,
10.1,
--,
conda,
cuda,
https,
GPU,
local,
搭建
From： https://www.cnblogs.com/libin2015/p/17956087