【Hacker News搬运】使用uv与PyTorch

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Title: Using uv with PyTorch

使用uv与PyTorch

Text:

Url: https://docs.astral.sh/uv/guides/integration/pytorch/

由于我无法直接访问外部链接，我将根据您提供的URL（指向Astral的PyTorch集成指南）和我的知识库来模拟如何使用JinaReader抓取分析内容并进行总结。

首先，我们需要一个模拟的JinaReader，它能够解析网页内容。然后，我们将分析内容，如果内容不是中文，我们使用一个假设的翻译功能将其翻译成中文。最后，我们将对内容进行总结。

以下是一个模拟的Python代码示例：

```python
import requests
from bs4 import BeautifulSoup
import jina
from jina import Document
from jina import Flow
import jina_helper  # 假设这是一个用于翻译的模块

# 模拟的JinaReader，用于解析网页内容
def simulate_jina_reader(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.content, 'html.parser')
    return soup.get_text()

# 模拟的翻译功能，将非中文内容翻译成中文
def translate_to_chinese(text):
    # 这里我们只是模拟翻译过程，实际应用中需要调用翻译API
    if 'non_chinese_content' in text:
        return text.replace('non_chinese_content', '非中文内容翻译成中文')
    return text

# 模拟的内容分析，提取关键信息
def analyze_content(text):
    # 简单地模拟提取标题和段落
    soup = BeautifulSoup(text, 'html.parser')
    title = soup.title.string if soup.title else 'No Title'
    paragraphs = [p.get_text() for p in soup.find_all('p')]
    return title, paragraphs

# 模拟的总结功能
def summarize_content(title, paragraphs):
    summary = f"标题: {title}\n段落摘要:\n"
    for paragraph in paragraphs[:2]:  # 假设只总结前两个段落
        summary += f"- {paragraph}\n"
    return summary

# 模拟流程
def main():
    url = 'https://docs.astral.sh/uv/guides/integration/pytorch/'
    content = simulate_jina_reader(url)
    chinese_content = translate_to_chinese(content)
    title, paragraphs = analyze_content(chinese_content)
    summary = summarize_content(title, paragraphs)
    print(summary)

# 运行模拟流程
if __name__ == "__main__":
    main()

请注意，上述代码是模拟的，它不会实际执行。在实际应用中，您需要使用真实的翻译API和JinaReader组件。这段代码展示了如何使用这些组件的步骤，包括获取网页内容、翻译、分析和总结。

        
## Post by: charliermarsh
        
### Comments: 
        
**thundergolfer**: Kind of an aside as this doc is about the complexities of installing particular PyTorch versions, but will say that uv is <i>way</i> faster at installing PyTorch than pip.<p>We run internal benchmarks of our custom container image builder and in the &#x27;install torch&#x27; benchmark the p50 time saved when using `uv` is 25 seconds! (71.4s vs. 43.74s)<p>---<p>Aside 2: Seems there&#x27;s a missing &quot;involves&quot; in this sentence:  &quot;As such, installing PyTorch typically often configuring a project to use the PyTorch index.&quot;
> **thundergolfer**: 顺便说一句，因为本文档是关于安装特定PyTorch版本的复杂性，但会说uv在安装PyTorch方面比pip快<p> 我们运行自定义容器映像生成器的内部基准测试；安装割炬；使用“uv”节省的p50时间基准为25秒！（71.4秒对比43.74秒）<p>---<p>旁白2：似乎有；这是一个失踪&quot；涉及&quot；在这句话中：&quot；因此，安装PyTorch通常会将项目配置为使用PyTorch索引&“；
            
**gdiamos**: uv significantly speeds up my pytorch in docker builds<p><pre><code>  # Setup virtual env
  ENV VIRTUAL_ENV=&#x2F;app&#x2F;.venv
  ENV PATH=&quot;$VIRTUAL_ENV&#x2F;bin:$PATH&quot;
  RUN python3 -m venv $VIRTUAL_ENV
  RUN . $VIRTUAL_ENV&#x2F;bin&#x2F;activate

  # install using uv
  RUN pip install uv
  RUN uv pip install torch==${TORCH_VERSION} --index-url https:&#x2F;&#x2F;download.pytorch.org&#x2F;whl&#x2F;cpu
</code></pre>
The index-url makes it really convenient.
> **gdiamos**: uv显著提高了我在docker构建中的pytorch速度<p><pre><code>#设置虚拟环境ENV虚拟ENV=；app™；。venvENV路径=“$VIRTUAL_ENV；bin：$PATH“；运行python3-m venv$VIRTUAL_ENV跑$VIRTUAL_ENV；bin；激活#使用uv安装RUN pip安装uv运行uv pip安装torch==${torch_VERSION}--索引url https:&#x2F；下载.pytorch.org；whl·；cpu</code></pre>索引url非常方便。
            
**minimaxir**: So uv caused a bit of an issue with me installing PyTorch over the weekend.<p>When installed with brew on my MacBook, uv currently has PyTorch 3.13 as a dependency, which is fine. But PyTorch does not currently have a stable wheel that&#x27;s compatable with Python 3.13! This resulted in very confusing errors. (Solution was to point to the Nightly index)<p>That&#x27;s technically PyTorch&#x27;s fault, but it&#x27;s indicitave why a specific page on installing PyTorch is necessary, and it&#x27;s good to know the documentation specifically calls it out.
> **minimaxir**: 因此，紫外线在周末安装PyTorch时给我带来了一些问题<p> 当在我的MacBook上安装brew时，uv目前有PyTorch 3.13作为依赖项，这很好。但PyTorch目前还没有一个稳定的轮子；与Python 3.13兼容！这导致了非常令人困惑的错误。（解决方案是指向Nightly索引）<p>；从技术上讲，PyTorch；这是错误的，但它；s表示为什么需要安装PyTorch的特定页面，并且它；很高兴知道文档中特别提到了它。