kernels/pr_150/en/index.html

<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Kernels&quot;,&quot;local&quot;:&quot;kernels&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}">
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		<link rel="modulepreload" href="/docs/kernels/pr_150/en/_app/immutable/chunks/getInferenceSnippets.dcce2733.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Kernels&quot;,&quot;local&quot;:&quot;kernels&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <h1 class="relative group"><a id="kernels" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#kernels"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Kernels</span></h1> <div align="center" data-svelte-h="svelte-1u93tqe"><img src="https://github.com/user-attachments/assets/64a652f3-0cd3-4829-b3c1-df13f7933569" width="450" height="450" alt="kernel-builder logo"></div> <p data-svelte-h="svelte-1i1c9ni">The Kernel Hub allows Python libraries and applications to load compute
kernels directly from the <a href="https://hf.co/" rel="nofollow">Hub</a>. To support this kind
of dynamic loading, Hub kernels differ from traditional Python kernel
packages in that they are made to be:</p> <ul data-svelte-h="svelte-7c4qm4"><li><strong>Portable</strong>: a kernel can be loaded from paths outside <code>PYTHONPATH</code>.</li> <li><strong>Unique</strong>: multiple versions of the same kernel can be loaded in the
same Python process.</li> <li><strong>Compatible</strong>: kernels must support all recent versions of Python and
the different PyTorch build configurations (various CUDA versions
and C++ ABIs). Furthermore, older C library versions must be supported.</li></ul> <p data-svelte-h="svelte-1jl8q1x">You can <a href="https://huggingface.co/models?other=kernel" rel="nofollow">search for kernels</a> on
the Hub.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/kernels/blob/main/docs/source/index.md" target="_blank"><span data-svelte-h="svelte-1kd6by1">&lt;</span> <span data-svelte-h="svelte-x0xyl0">&gt;</span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a>  <p></p> 
			
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