安装 homebrew
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
下载 Miniforge3 (Conda 安装工具)
for macOS arm64 chips (M1, M1 Pro, M1 Max, M1 Ultra, M2).
chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh
source ~/miniforge3/bin/activate
我发现他会给 .zshrc 自动加上 source 信息
# >>> conda initialize >>>
# !! Contents within this block are managed by 'conda init' !!
__conda_setup="$('/Users/kelu/miniforge3/bin/conda' 'shell.zsh' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; then
eval "$__conda_setup"
else
if [ -f "/Users/kelu/miniforge3/etc/profile.d/conda.sh" ]; then
. "/Users/kelu/miniforge3/etc/profile.d/conda.sh"
else
export PATH="/Users/kelu/miniforge3/bin:$PATH"
fi
fi
unset __conda_setup
# <<< conda initialize <<<
创建 pytorch 环境的文件夹
mkdir pytorch-test
cd pytorch-test
初始化:conda环境
conda create --prefix ./env python=3.8
conda activate ./env
安装pytorch
pip3 install torch torchvision torchaudio
安装jupyter
conda install jupyter pandas numpy matplotlib scikit-learn tqdm
jupyter notebook
验证:
验证的脚本:
import torch
import numpy as np
import pandas as pd
import sklearn
import matplotlib.pyplot as plt
import math
print(f"PyTorch version: {torch.__version__}")
# Check PyTorch has access to MPS (Metal Performance Shader, Apple's GPU architecture)
print(f"Is MPS (Metal Performance Shader) built? {torch.backends.mps.is_built()}")
print(f"Is MPS available? {torch.backends.mps.is_available()}")
# Set the device
device = "mps" if torch.backends.mps.is_available() else "cpu"
print(f"Using device: {device}")
# Create data and send it to the device
x = torch.rand(size=(3, 4)).to(device)
x
dtype = torch.float
device = torch.device("mps")
# Create random input and output data
x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype)
y = torch.sin(x)
# Randomly initialize weights
a = torch.randn((), device=device, dtype=dtype)
b = torch.randn((), device=device, dtype=dtype)
c = torch.randn((), device=device, dtype=dtype)
d = torch.randn((), device=device, dtype=dtype)
learning_rate = 1e-6
for t in range(2000):
# Forward pass: compute predicted y
y_pred = a + b * x + c * x ** 2 + d * x ** 3
# Compute and print loss
loss = (y_pred - y).pow(2).sum().item()
if t % 100 == 99:
print(t, loss)
# Backprop to compute gradients of a, b, c, d with respect to loss
grad_y_pred = 2.0 * (y_pred - y)
grad_a = grad_y_pred.sum()
grad_b = (grad_y_pred * x).sum()
grad_c = (grad_y_pred * x ** 2).sum()
grad_d = (grad_y_pred * x ** 3).sum()
# Update weights using gradient descent
a -= learning_rate * grad_a
b -= learning_rate * grad_b
c -= learning_rate * grad_c
d -= learning_rate * grad_d
print(f'Result: y = {a.item()} + {b.item()} x + {c.item()} x^2 + {d.item()} x^3')
再次启动环境
再次启动环境,只需要激活虚拟环境然后启动jupyter即可:
conda activate ~/Workspace/pytorch-test/env
cd ~/Workspace/pytorch-test
jupyter notebook