PyTorch

Installation

根据OS和cuda选择适配的torch版本

https://pytorch.org/get-started/previous-versions/

pip

安装最新版

pip install torch torchvision torchaudio -i https://pypi.tuna.tsinghua.edu.cn/simple

指定版本(preffered)

创建名为pytorch310的虚拟环境

conda create -n pytorch310 python==3.10

激活环境

conda activate pytorch310

设置清华源，加速安装

pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple

指定版本安装

pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 torchaudio==2.0.1+cu118 -f https://download.pytorch.org/whl/torch_stable.html

conda

添加清华镜像源

# 若不含有`~/.condarc`，生成它
ls ~/.condarc || conda config --set show_channel_urls yes

# 之后配置镜像源（南科大提供的额外软件包镜像可以加速安装CUDA版Pytorch）
tee ~/.condarc > /dev/null << EOF
channels:
  - defaults
show_channel_urls: true
default_channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
  msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch-lts: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  deepmodeling: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  nvidia: https://mirrors.sustech.edu.cn/anaconda-extra/cloud
  
envs_dirs:
  - your-path/anaconda3/envs # 替换为您的路径（另外，若安装的是miniconda3，请自行替换），不设置此项有可能安装在`~/.conda/envs`中
EOF

安装最新版

conda install pytorch torchvision torchaudio cudatoolkit=10.2

指定版本

conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4 cudatoolkit=10.2

Tutorial

pytorch examples
pytorch tutorials
pytorch模型性能分析和优化: weixin
pytorch handbook: [Github:zh-cn]
datawhale/thorough-pytorch: [Github:zh-cn]
杂七杂八的收集DL相关的东西： [Github:en]

Framework

pytorch-lightning: Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.

Visualization

wandb: 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
PyTorch tensorboard: How to use tensorboard in PyTorch.
SwanLab: ⚡️SwanLab: track and visualize all the pieces of your machine learning pipeline. 跟踪与可视化你的机器学习全流程

CSBasicKnowledge