https://fupcode.com/blog/tags/conda/南冥鹏影Hugo 0.168.0 & FixIt v1.0.0-beta.4zh-CNSat, 15 Nov 2025 00:29:34 +0800https://fupcode.com/blog/posts/environment/dl-build/Sat, 15 Nov 2025 00:29:34 +0800https://fupcode.com/blog/posts/environment/dl-build/环境配置<h2 class="heading-element" id="深度学习环境搭建anacondacuda130cudnn"><span>深度学习环境搭建（Anaconda、CUDA13.0、cuDNN）</span> <a href="#%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e7%8e%af%e5%a2%83%e6%90%ad%e5%bb%baanacondacuda130cudnn" class="heading-mark"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a> </h2><p>如果不是从源码构建，那么Pytorch和OpenMM安装不需要CUDA13.0和cuDNN。</p>https://fupcode.com/blog/posts/environment/conda-dependence-upgrade/Fri, 14 Nov 2025 22:51:34 +0800https://fupcode.com/blog/posts/environment/conda-dependence-upgrade/环境配置<h2 class="heading-element" id="问题背景"><span>问题背景</span> <a href="#%e9%97%ae%e9%a2%98%e8%83%8c%e6%99%af" class="heading-mark"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a> </h2><p>Conda、Docker 等技术的确非常强大，它们可以实现“一次开发，到处部署”，在操作系统层面保证兼容性。然而，当遇到 <strong>硬件不兼容</strong> 时，这类工具就无能为力，需要手动干预。</p>