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uv 工具快速上手

Python 套件管理生態存在多種工具，如 pip pip-tools poetry conda 等，各自具備一定功能。

而今天介紹的 uv 是 Astral 公司推出的一款基於 Rust 編寫的 Python 套件管理工具，旨在成為 Python 的 Cargo。

它提供了快速、可靠且易用的套件管理體驗，在效能、相容性和功能上都有出色表現，為 Python 專案的開發和管理帶來了新的選擇。

為什麼用 uv

與其他 Python 中的套件管理工具相比，uv 更像是全能選手，它的優勢在於:

  • 速度快：得益於 Rust，uv 工具的速度讓人驚艷，例如安裝依賴，速度比其他工具快很多
  • 功能全面：uv 是「一站式服務」的工具，從安裝 Python、管理虛擬環境，到安裝和管理包，再到管理專案依賴，它統統都能處理得很好
  • 前景光明：背後有創投公司 Astral 支持，並採用了 MIT 許可，即使未來出現問題，社區也有應對的辦法

使用 uv，也可以像 NodeJS 或 Rust 專案那樣方便的管理依賴。

如何安裝

使用 curl 下載腳本並執行 sh：

curl -LsSf https://astral.sh/uv/install.sh | sh

如果您的系統沒有curl，您可以使用 wget：

wget -qO- https://astral.sh/uv/install.sh | sh

透過在 URL 中包含特定版本來請求該版本：

curl -LsSf https://astral.sh/uv/0.6.9/install.sh | sh

安裝之後，可以透過 uv help 指令檢查是否安裝成功：

如何使用

下面示範如何使用 uv 來管理 Python 專案。

使用 uv 之前，創建一個 Python 專案對我來說就是創建一個資料夾而已。

使用 uv 之後，終於有了一些項目的感覺，對於 uv，我使用時間也不長，疏漏或錯誤的地方歡迎指正！

接下來，從建立一個專案開始，示範我使用 uv 時常用的一些功能。

首先，介紹 uv 工具主要使用的兩個檔案：

  • pyproject.toml：定義專案的主要依賴，包括專案名稱、版本、描述、支援的 Python 版本等信息
  • uv.lock：記錄項目的所有依賴，包括依賴的依賴，且跨平台，確保在不同環境下安裝的一致性。這個文件由 uv 自動管理，不要手動編輯

建立專案

接下來，建立一個專案，使用 uv init <project dir> 命令。

$  uv init myproject
Initialized project `myproject` at `/home/dxlab/ws/test/myproject`

$ cd myproject/

$ ls
main.py  pyproject.toml  README.md

$ tree
.
├── main.py
├── pyproject.toml
└── README.md

1 directory, 3 files

透過 init 創建專案之後，uv 工具貼心地幫助我們產生了一些預設檔。

其中 main.py 只是一段示範用的程式碼，

隨後我們可以根據實際的項目需要刪除這個程式碼文件，換成自己的實際程式碼。

main.py
def main():
    print("Hello from myproject!")


if __name__ == "__main__":
    main()
pyproject.toml
[project]
name = "myproject"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = []

Info

注意，uv init 建立專案之後，會自動使用專案 Git 來管理。

操作環境

創建專案之後，我們進入專案根資料夾的第一件事就是同步專案依賴。

$ uv sync
Using CPython 3.13.2
Creating virtual environment at: .venv
Resolved 1 package in 13ms
Audited in 0.21ms

同步之後，會自動尋找或下載適當的 Python 版本，建立並設定專案的虛擬環境，建立完整的依賴清單並寫入 uv.lock 文件，最後將依賴同步到虛擬環境。

我們這個是新創建的專案，沒有什麼依賴，所以 uv.lock 文件中的內容也比較簡單。

uv.lock
version = 1
revision = 1
requires-python = ">=3.13"

[[package]]
name = "myproject"
version = "0.1.0"
source = { virtual = "." }

uv sync 同步之後，就可以運行專案的程式碼了。

既然使用 uv 管理專案的話，我們就使用的 uv 命令來運行程式碼，不要像以前那樣使用 python xxx.py 來運行。

我們可以試著運行專案創建時自動產生的程式碼。

$ uv run main.py 
Hello from myproject!

管理依賴

管理依賴是我使用 uv 工具的主要目的，使用 uv 添加依賴非常簡單，和 npm 和 cargo 差不多。

$ uv add pandas
Resolved 7 packages in 731ms
Prepared 6 packages in 2.36s
Installed 6 packages in 20ms
 + numpy==2.2.4
 + pandas==2.2.3
 + python-dateutil==2.9.0.post0
 + pytz==2025.1
 + six==1.17.0
 + tzdata==2025.1

嘗試安裝了一個 pandas 依賴（pandas 依賴的包也自動安裝了），從上面日誌可以看出速度非常快。

這時再看看 uv.lock 文件的變化。

uv.lock
version = 1
revision = 1
requires-python = ">=3.13"

[[package]]
name = "myproject"
version = "0.1.0"
source = { virtual = "." }
dependencies = [
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]

[package.metadata]
requires-dist = [{ name = "pandas", specifier = ">=2.2.3" }]

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sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031 }
wheels = [
    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 },
]

[[package]]
name = "tzdata"
version = "2025.1"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/43/0f/fa4723f22942480be4ca9527bbde8d43f6c3f2fe8412f00e7f5f6746bc8b/tzdata-2025.1.tar.gz", hash = "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694", size = 194950 }
wheels = [
    { url = "https://files.pythonhosted.org/packages/0f/dd/84f10e23edd882c6f968c21c2434fe67bd4a528967067515feca9e611e5e/tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639", size = 346762 },
]

上面的日誌中我刪除了很多內容，因為整體內容太多，詳細記錄了每個套件以及它依賴的套件的情況。

uv.lock 這個檔案我們不要手動去編輯它，使用 uv 工具去管理它。

在引入了 pandas 之後，我們看看是否可以在 main.py 中使用。

main.py
import pandas as pd


def main():
    print("Hello from myproject!")
    df = pd.DataFrame(
        {
            "A": [1, 2, 3],
            "B": [4, 5, 6],
        }
    )
    print(df)


if __name__ == "__main__":
    main()

執行:

$ uv run main.py 
Hello from myproject!
   A  B
0  1  4
1  2  5
2  3  6

可以正常使用安裝的包 pandas，下面在試試刪除依賴會怎麼樣。

$ uv remove pandas
Resolved 1 package in 1ms
Uninstalled 6 packages in 32ms
 - numpy==2.2.4
 - pandas==2.2.3
 - python-dateutil==2.9.0.post0
 - pytz==2025.1
 - six==1.17.0
 - tzdata==2025.1

使用 uv remove 指令刪除 pandas 包之後，也會自動刪除 pandas 依賴的其他包，

我們看到 uv.lock 文件也恢復到最初的內容。

再試試運行 main.py 看看。

$ uv run main.py 
Traceback (most recent call last):
  File "/home/dxlab/ws/test/myproject/main.py", line 1, in <module>
    import pandas as pd
ModuleNotFoundError: No module named 'pandas'

果然，無法運行了。

區分開發與生產環境

還有一個比較常用的功能是區分開發環境和生產環境的依賴，這個功能在 NodeJS 和 Rust 中很常見。

例如，我們想把 pandas 安裝到開發環境中，而把 requests 安裝到生產環境。

$ uv add --group dev pandas
Resolved 7 packages in 320ms
Installed 6 packages in 18ms
 + numpy==2.2.4
 + pandas==2.2.3
 + python-dateutil==2.9.0.post0
 + pytz==2025.1
 + six==1.17.0
 + tzdata==2025.1

$ uv add --group production requests
Resolved 12 packages in 548ms
Prepared 5 packages in 313ms
Installed 5 packages in 2ms
 + certifi==2025.1.31
 + charset-normalizer==3.4.1
 + idna==3.10
 + requests==2.32.3
 + urllib3==2.3.0

安裝之後，uv.lock 檔案自動添加了各個套件及其依賴，這裡不再贅述。

從專案的 pyproject.toml 中可以看出不同環境的包依賴。

pyproject.toml
[project]
name = "myproject"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = []

[dependency-groups]
dev = [
    "pandas>=2.2.3",
]
production = [
    "requests>=2.32.3",
]