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    <title>CUDA on Wokron&#39;s Blog</title>
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    <description>Recent content in CUDA on Wokron&#39;s Blog</description>
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    <copyright>©2022-2026 Yitang Yang. All rights reserved.</copyright>
    <lastBuildDate>Mon, 20 Jan 2025 21:31:16 +0800</lastBuildDate>
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      <title>Building TensorFlow from Source: Gotchas</title>
      <link>https://wokron.github.io/en/posts/build-tensorflow-from-source/</link>
      <pubDate>Mon, 20 Jan 2025 21:31:16 +0800</pubDate>
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      <description>A while back I found a small bug in TensorFlow. Now that I had some free time, I decided to submit a PR. The bug was fixed quickly, but when I tried to build TensorFlow locally, I ran into quite a few gotchas. Here are my notes.
1. So Many Versions, So Confusing Before we start building, let&amp;rsquo;s go over the relevant Nvidia GPU dependencies.
Nvidia has various GPU architectures. To distinguish between them, Nvidia uses Compute Capability.</description>
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      <title>Installing CUDA in a Conda Environment</title>
      <link>https://wokron.github.io/en/posts/conda-install-cuda/</link>
      <pubDate>Mon, 14 Oct 2024 19:18:09 +0800</pubDate>
      <guid>https://wokron.github.io/en/posts/conda-install-cuda/</guid>
      <description>I&amp;rsquo;ve been wanting to learn CUDA recently. Here are my notes on setting up the environment.
Create an environment. We&amp;rsquo;re just using Conda for environment isolation here — no Python needed. $ conda create -n cuda-dev $ conda activate cuda-dev Check the CUDA version with nvidia-smi. Note that this version is the maximum CUDA version supported by the driver, not the CUDA runtime version we&amp;rsquo;ll install later. When installing, make sure the CUDA runtime version ≤ the driver version.</description>
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