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一个全新的ubuntu20台式机,在Additional Drivers安装nvidia-470-server(一开始安装450,cunda版本只能到11.0,torch有些库用不了,可以直接切换点击Apply Changes重启就行)
nvidia-smi查看CUDA Version可到11.4,手动安装11.2,以下地址下载并安装,安装完成后加入环境变量,用nvcc -V测试是否安装成功
https://developer.nvidia.com/cuda-11.2.0-download-archive?
sudo sh cuda_11.2.0_460.27.04_linux.run
// 配置cuda环境变量
gedit ~/.bashrc
export PATH=/usr/local/cuda-11.2/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-11.2/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export CUDA_HOME=/usr/local/cuda
source ~/.bashrc
https://pytorch-geometric.com/whl/
下载torch-1.9.0+cu111-cp38-cp38-linux_x86_64.whl手动安装
下载地址https://download.pytorch.org/whl/torch_stable.html
ctrl+f 查找cu111
wget https://download.pytorch.org/whl/cu111/torch-1.9.0%2Bcu111-cp38-cp38-linux_x86_64.whl
pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html
pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html
pip install --no-index torch-cluster -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html
pip install --no-index torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html
pip install torch_geometric==2.0.4 -i https://pypi.doubanio.com/simple
conda list torch
pytorch 1.7.1 py3.8_cuda11.0.221_cudnn8.0.5_0 pytorch
https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
wget https://storage.googleapis.com/jax-releases/cuda11/jaxlib-0.3.18+cuda11.cudnn805-cp38-cp38-manylinux2014_x86_64.whl
pip install jaxlib-0.3.18+cuda11.cudnn805-cp38-cp38-manylinux2014_x86_64.whl
pip install jax==0.3.18
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/miniconda/lib
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