写在前面
试试 m3 的 metal 加速效果如何
- Mac computers with Apple silicon or AMD GPUs
- macOS 12.3 or later
- Python 3.7 or later
- Xcode command-line tools:
xcode-select --install
安装 Python: conda-forge
brew install miniforge
镜像
channels:
- defaults
show_channel_urls: true
auto_activate_base: false
ssl-verify: false
default_channels:
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
bioconda: 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
pytorch-lts: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
deepmodeling: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/
安装
开一个新的虚拟环境, 这个是重点, 如果不开的话, 原有的环境会污染 C 库的链接, 所以这一步是必须的
这个方案不彻底, 直接卸载 numpy 然后重装不能解决问题…
conda create -n py3xi python=3.11
conda activate py3xi
# conda update --all -c conda-forge # optional
# 重点:
conda install pytorch torchvision torchaudio -c pytorch-nightly
然后测试
Accelerated PyTorch training on Mac - Metal - Apple Developer;
import torch
if torch.backends.mps.is_available():
mps_device = torch.device("mps")
x = torch.ones(1, device=mps_device)
print (x)
else:
print ("MPS device not found.")
'''
tensor([1.], device='mps:0')
'''
可以在 MacOS 上跑深度学习了.