1 从用 PyTorch 运行一个模型训练开始
标签
AI
开发/Python/PyTorch
开发/Python
AI/教程
操作系统/macOS
操作系统/Linux
操作系统/Windows
软件/Visual-Studio-Code/vscode
开发/Git
命令行/git
字数
830 字
阅读时间
5 分钟
想要跑这样的训练其实非常简单。
在 macOS，Linux 和 Windows 上都可以操作。
下载 PyTorch 的官方示例
创建一个 pytorch 目录用来存放 pytorch 官方的源代码
shell
mkdir pytorch下载 pytorch/examples 仓库到本地
shell
git clone [email protected]:pytorch/examples.git下载完成之后进入这个仓库目录
shell
cd examples准备编辑器
在 Visual Studio Code 中安装 Python 插件包
创建项目
用 Poetry
创建项目
shell
❯ poetry init
This command will guide you through creating your pyproject.toml config.
Package name [mnist]:
Version [0.1.0]:
Description []:
Author [Neko Ayaka <[email protected]e>, n to skip]:
License []:
Compatible Python versions [^3.11]:
Would you like to define your main dependencies interactively? (yes/no) [yes]
You can specify a package in the following forms:
- A single name (requests): this will search for matches on PyPI
- A name and a constraint (requests@^2.23.0)
- A git url (git+https://github.com/python-poetry/poetry.git)
- A git url with a revision (git+https://github.com/python-poetry/poetry.git#develop)
- A file path (../my-package/my-package.whl)
- A directory (../my-package/)
- A url (https://example.com/packages/my-package-0.1.0.tar.gz)
Package to add or search for (leave blank to skip): torch
Found 20 packages matching torch
Showing the first 10 matches
Enter package # to add, or the complete package name if it is not listed []:
[ 0] torch
[ 1] fast-torch
[ 2] welford-torch
[ 3] torch-soft
[ 4] torch-yolo3
[ 5] sphericart-torch
[ 6] gato-torch
[ 7] metatensor-torch
[ 8] torch-metrics
[ 9] agent-torch
[ 10]
> 0
Enter the version constraint to require (or leave blank to use the latest version):
Using version ^2.1.2 for torch
Add a package (leave blank to skip): torchvision
Found 20 packages matching torchvision
Showing the first 10 matches
Enter package # to add, or the complete package name if it is not listed []:
[ 0] torchvision
[ 1] torchvision4ad
[ 2] torchvision-yolov3
[ 3] torchvision-raspi
[ 4] nvidia-torchvision
[ 5] torchvision-extra
[ 6] rpi-torchvision
[ 7] torchvision-detection
[ 8] torchvision-enhance
[ 9] cjm-torchvision-tfms
[ 10]
> 0
Enter the version constraint to require (or leave blank to use the latest version):
Using version ^0.16.2 for torchvision
Add a package (leave blank to skip):
Would you like to define your development dependencies interactively? (yes/no) [yes] no
Generated file
[tool.poetry]
name = "mnist"
version = "0.1.0"
description = ""
authors = ["Neko Ayaka <[email protected]>"]
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.11"
torch = "^2.1.2"
torchvision = "^0.16.2"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
Do you confirm generation? (yes/no) [yes]安装需要的依赖
shell
❯ poetry install
Creating virtualenv mnist-CoEc1jnQ-py3.11 in /Users/neko/Library/Caches/pypoetry/virtualenvs
Updating dependencies
Resolving dependencies... Downloading
Writing lock file
Package operations: 17 installs, 0 updates, 0 removals
• Installing markupsafe (2.1.3)
• Installing mpmath (1.3.0)
• Installing certifi (2023.11.17)
• Installing charset-normalizer (3.3.2)
• Installing filelock (3.13.1)
• Installing fsspec (2023.12.2)
• Installing idna (3.6)
• Installing jinja2 (3.1.2)
• Installing networkx (3.2.1)
• Installing sympy (1.12)
• Installing typing-extensions (4.9.0)
• Installing urllib3 (2.1.0)
• Installing numpy (1.26.3)
• Installing pillow (10.2.0)
• Installing requests (2.31.0)
• Installing torch (2.1.2)
• Installing torchvision (0.16.2)安装完成之后可能会有这样的提示，这是因为 Python 环境不正确
可以通过 command + Shift + P 打开 VSCode 的命令面板然后输入「Select Interpreter」来选择正确的 Python 环境中的 Python 程序来解决
准备代码
训练
通过
shell
poetry run python main.py --epochs 2 --save-model命令就可以训练了。其中：
--epochs 2参数表示训练完整的两个周期；--save-model参数表示训练之后保存模型。
shell
❯ poetry run python main.py --epochs 2 --save-model
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz
100.0%
Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz
100.0%
Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz
100.0%
Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz
100.0%
Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw
Train Epoch: 1 [0/60000 (0%)] Loss: 2.300024
# ...
Train Epoch: 1 [59520/60000 (99%)] Loss: 0.016723
Test set: Average loss: 0.0513, Accuracy: 9830/10000 (98%)
Train Epoch: 2 [0/60000 (0%)] Loss: 0.060729
# ...
Train Epoch: 2 [59520/60000 (99%)] Loss: 0.000367
Test set: Average loss: 0.0381, Accuracy: 9869/10000 (99%)训练完成之后就能看到 mnist_cnn.pt 的模型文件了：
絢香猫