查看linux架构:uname -m,我这里显示是x86_64 如果要下载gpu版本,nvcc --version查看cuda版本 / 用nvidia-smi也可以查看,在表格的右上方 下载之后传到服务器上,进入待安装的conda环境,执行pip install dgl-0.9.0-cp37-cp37m-manylinux1_x86_64.whl 安装完成! 一些常用包的源文件下载地址: pytorch 官网查看指定...
How you installed DGL (conda,pip, source): pip Build command you used (if compiling from source):pip install dgl -f https://data.dgl.ai/wheels/repo.html Python version: 3.10 CUDA/cuDNN version (if applicable): none GPU models and configuration (e.g. V100): none ...
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after ...
Install dependencies conda install mamba -n base -c conda-forge mamba env create -f environment.yaml conda activate GPIP Install DGL from here. pip install dgl -f https://data.dgl.ai/wheels/cu116/repo.html pip install dglgo -f https://data.dgl.ai/wheels-test/repo.html sudo, if ...
打开指令中的网址,手动下载对应包的whl安装文件(图中红色) cuda版本查看指令nvcc -V 安装TF1-gpu tf2-csdn教程 tf和python, conda对应版本查看 condacreate-n sxb_tf1 python=3.7conda activate sxb_tf1conda install tensorflow-gpu==1.15.0
Install dependencies conda install mamba -n base -c conda-forge mamba env create -f environment.yaml conda activate GPIP Install DGL fromhere. sudo, if required How to run this code: InGPIP.ipynb, you can see the running time of pre-training stage and fine-tuning stage, as well as the...