Buckets:
| <meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"Build your own machine","local":"build-your-own-machine","sections":[{"title":"Power","local":"power","sections":[],"depth":2},{"title":"Cooling","local":"cooling","sections":[],"depth":2},{"title":"Multi-GPU connectivity","local":"multi-gpu-connectivity","sections":[],"depth":2}],"depth":1}"> | |
| <link href="/docs/transformers/pr_36839/en/_app/immutable/assets/0.e3b0c442.css" rel="modulepreload"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/entry/start.6be8d590.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/scheduler.01eeda35.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/singletons.177df05e.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/index.4862150a.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/paths.517376d1.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/entry/app.09748b4b.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/index.6dd51b66.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/nodes/0.8897c14d.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/each.e59479a4.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/nodes/413.39848dbe.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/CodeBlock.864da1b0.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/EditOnGithub.7faefd25.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/HfOption.f7f04550.js"> | |
| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/stores.318eade7.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Build your own machine","local":"build-your-own-machine","sections":[{"title":"Power","local":"power","sections":[],"depth":2},{"title":"Cooling","local":"cooling","sections":[],"depth":2},{"title":"Multi-GPU connectivity","local":"multi-gpu-connectivity","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="build-your-own-machine" 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="#build-your-own-machine"><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>Build your own machine</span></h1> <p data-svelte-h="svelte-18uwh9w">One of the most important consideration when building a machine for deep learning is the GPU choice. GPUs are the standard workhorse for deep learning owing to their tensor cores for performing very efficient matrix multiplication and high memory bandwidth. To train large models, you either need a more powerful GPU, multiple GPUs, or take advantage of techniques that offload some of the load to the CPU or NVMe.</p> <p data-svelte-h="svelte-15osi2d">This guide provides some practical tips for setting up a GPU for deep learning. For a more detailed discussion and comparison of GPUs, take a look at the <a href="https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/" rel="nofollow">Which GPU(s) to Get for Deep Learning</a> blog post.</p> <h2 class="relative group"><a id="power" 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="#power"><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>Power</span></h2> <p data-svelte-h="svelte-1lfwb2r">High-end consumer GPUs may have two or three PCIe 8-pin power sockets, and you should make sure you have the same number of 12V PCIe 8-pin cables connected to each socket. Don’t use a <em>pigtail cable</em>, a single cable with two splits at one end, to connect two sockets or else you won’t get full performance from your GPU.</p> <p data-svelte-h="svelte-1oq9hng">Each PCIe 8-pin power cable should be connected to a 12V rail on the power supply unit (PSU) and can deliver up to 150W. Other GPUs may use a PCIe 12-pin connector which can deliver up to 500-600W. Lower-end GPUs may only use a PCIe 6-pin connector which supplies up to 75W.</p> <p data-svelte-h="svelte-1d8ezz6">It is important the PSU has stable voltage otherwise it may not be able to supply the GPU with enough power to function properly during peak usage.</p> <h2 class="relative group"><a id="cooling" 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="#cooling"><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>Cooling</span></h2> <p data-svelte-h="svelte-oqqm8">An overheated GPU throttles its performance and can even shutdown if it’s too hot to prevent damage. Keeping the GPU temperature low, anywhere between 158 - 167F, is essential for delivering full performance and maintaining its lifespan. Once temperatures reach 183 - 194F, the GPU may begin to throttle performance.</p> <h2 class="relative group"><a id="multi-gpu-connectivity" 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="#multi-gpu-connectivity"><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>Multi-GPU connectivity</span></h2> <p data-svelte-h="svelte-1ggo6vh">When your setup uses multiple GPUs, it is important to consider how they’re connected. <a href="https://www.nvidia.com/en-us/design-visualization/nvlink-bridges/" rel="nofollow">NVLink</a> connections are faster than PCIe bridges, but you should also consider the <a href="./perf_train_gpu_many">parallelism</a> strategy you’re using. For example, in DistributedDataParallel, GPUs communicate less frequently compared to ZeRO-DP. In this case, a slower connection is not as important.</p> <p data-svelte-h="svelte-1htw4vw">Run the command below to check how your GPUs are connected.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->nvidia-smi topo -m<!-- HTML_TAG_END --></pre></div> <div class="flex space-x-2 items-center my-1.5 mr-8 h-7 !pl-0 -mx-3 md:mx-0"><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd border-gray-800 bg-black dark:bg-gray-700 text-white">NVLink </div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">without NVLink </div></div> <div class="language-select"><p data-svelte-h="svelte-1dcwdg9"><a href="https://www.nvidia.com/en-us/design-visualization/nvlink-bridges/" rel="nofollow">NVLink</a> is a high-speed communication system designed by NVIDIA for connecting multiple NVIDIA GPUs. Training <a href="https://huggingface.co/openai-community/gpt2" rel="nofollow">openai-community/gpt2</a> on a small sample of the <a href="https://huggingface.co/datasets/Salesforce/wikitext" rel="nofollow">wikitext</a> dataset is ~23% faster with NVLink.</p> <p data-svelte-h="svelte-2sy7d0">On a machine with two GPUs connected with NVLink, an example output of <code>nvidia-smi topo -m</code> is shown below.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --> GPU0 GPU1 CPU Affinity NUMA Affinity | |
| GPU0 X NV2 0-23 N/A | |
| GPU1 NV2 X 0-23 N/A<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-pz42vg"><code>NV2</code> indicates <code>GPU0</code> and <code>GPU1</code> are connected by 2 NVLinks.</p> </div> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/transformers/blob/main/docs/source/en/perf_hardware.md" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p> | |
| <script> | |
| { | |
| __sveltekit_1bm5psi = { | |
| assets: "/docs/transformers/pr_36839/en", | |
| base: "/docs/transformers/pr_36839/en", | |
| env: {} | |
| }; | |
| const element = document.currentScript.parentElement; | |
| const data = [null,null]; | |
| Promise.all([ | |
| import("/docs/transformers/pr_36839/en/_app/immutable/entry/start.6be8d590.js"), | |
| import("/docs/transformers/pr_36839/en/_app/immutable/entry/app.09748b4b.js") | |
| ]).then(([kit, app]) => { | |
| kit.start(app, element, { | |
| node_ids: [0, 413], | |
| data, | |
| form: null, | |
| error: null | |
| }); | |
| }); | |
| } | |
| </script> | |
Xet Storage Details
- Size:
- 14.7 kB
- Xet hash:
- 23bc9243e460c5e83bf3caae3db87fee5dd2afedb8319ca0eecd68cf49e511d2
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.