google-cloud/pr_113/en/features.html

<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;🔥 Features &amp; benefits&quot;,&quot;local&quot;:&quot;-features--benefits&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;One command is all you need&quot;,&quot;local&quot;:&quot;one-command-is-all-you-need&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Accelerate machine learning from science to production&quot;,&quot;local&quot;:&quot;accelerate-machine-learning-from-science-to-production&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;High-performance text generation and embedding&quot;,&quot;local&quot;:&quot;high-performance-text-generation-and-embedding&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Built-in performance&quot;,&quot;local&quot;:&quot;built-in-performance&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}">
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		<link rel="modulepreload" href="/docs/google-cloud/pr_113/en/_app/immutable/chunks/EditOnGithub.d1c48e3d.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;🔥 Features &amp; benefits&quot;,&quot;local&quot;:&quot;-features--benefits&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;One command is all you need&quot;,&quot;local&quot;:&quot;one-command-is-all-you-need&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Accelerate machine learning from science to production&quot;,&quot;local&quot;:&quot;accelerate-machine-learning-from-science-to-production&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;High-performance text generation and embedding&quot;,&quot;local&quot;:&quot;high-performance-text-generation-and-embedding&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Built-in performance&quot;,&quot;local&quot;:&quot;built-in-performance&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <h1 class="relative group"><a id="-features--benefits" 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="#-features--benefits"><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>🔥 Features &amp; benefits</span></h1> <p data-svelte-h="svelte-wt21d7">The Hugging Face DLCs provide ready-to-use, tested environments to train and deploy Hugging Face models. They can be used in combination with Google Cloud offerings including Google Kubernetes Engine (GKE) and Vertex AI. GKE is a fully-managed Kubernetes service in Google Cloud that can be used to deploy and operate containerized applications at scale using Google Cloud’s infrastructure. Vertex AI is a Machine Learning (ML) platform that lets you train and deploy ML models and AI applications, and customize Large Language Models (LLMs).</p>  <h2 class="relative group"><a id="one-command-is-all-you-need" 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="#one-command-is-all-you-need"><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>One command is all you need</span></h2> <p data-svelte-h="svelte-17e46z3">With the new Hugging Face DLCs, train cutting-edge Transformers-based NLP models in a single line of code. The Hugging Face PyTorch DLCs for training come with all the libraries installed to run a single command e.g. via TRL CLI to fine-tune LLMs on any setting, either single-GPU, single-node multi-GPU, and more.</p>  <h2 class="relative group"><a id="accelerate-machine-learning-from-science-to-production" 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="#accelerate-machine-learning-from-science-to-production"><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>Accelerate machine learning from science to production</span></h2> <p data-svelte-h="svelte-t3ifdy">In addition to Hugging Face DLCs, we created a first-class Hugging Face library for inference, <a href="https://github.com/huggingface/huggingface-inference-toolkit" rel="nofollow"><code>huggingface-inference-toolkit</code></a>, that comes with the Hugging Face PyTorch DLCs for inference, with full support on serving any PyTorch model on Google Cloud.</p> <p data-svelte-h="svelte-19nk44j">Deploy your trained models for inference with just one more line of code or select <a href="https://huggingface.co/models?library=pytorch,transformers&sort=trending" rel="nofollow">any of the 170,000+ publicly available models from the model Hub</a> and deploy them on either Vertex AI or GKE.</p>  <h2 class="relative group"><a id="high-performance-text-generation-and-embedding" 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="#high-performance-text-generation-and-embedding"><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>High-performance text generation and embedding</span></h2> <p data-svelte-h="svelte-1tu8rbr">Besides the PyTorch-oriented DLCs, Hugging Face also provides high-performance inference for both text generation and embedding models via the Hugging Face DLCs for both <a href="https://github.com/huggingface/text-generation-inference" rel="nofollow">Text Generation Inference (TGI)</a> and <a href="https://github.com/huggingface/text-embeddings-inference" rel="nofollow">Text Embeddings Inference (TEI)</a>, respectively.</p> <p data-svelte-h="svelte-1e1vasz">The Hugging Face DLC for TGI enables you to deploy <a href="https://huggingface.co/models?other=text-generation-inference&sort=trending" rel="nofollow">any of the +140,000 text generation inference supported models from the Hugging Face Hub</a>, or any custom model as long as <a href="https://huggingface.co/docs/text-generation-inference/supported_models" rel="nofollow">its architecture is supported within TGI</a>.</p> <p data-svelte-h="svelte-1c04c20">The Hugging Face DLC for TEI enables you to deploy <a href="https://huggingface.co/models?other=text-embeddings-inference&sort=trending" rel="nofollow">any of the +10,000 embedding, re-ranking or sequence classification supported models from the Hugging Face Hub</a>, or any custom model as long as <a href="https://huggingface.co/docs/text-embeddings-inference/en/supported_models" rel="nofollow">its architecture is supported within TEI</a>.</p> <p data-svelte-h="svelte-a2fp78">Additionally, these DLCs come with full support for Google Cloud meaning that deploying models from Google Cloud Storage (GCS) is also straight forward and requires no configuration.</p>  <h2 class="relative group"><a id="built-in-performance" 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="#built-in-performance"><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>Built-in performance</span></h2> <p data-svelte-h="svelte-jmp6ho">Hugging Face DLCs feature built-in performance optimizations for PyTorch to train models faster. The DLCs also give you the flexibility to choose a training infrastructure that best aligns with the price/performance ratio for your workload.</p> <p data-svelte-h="svelte-19redkc">The Hugging Face Training DLCs are fully integrated with Google Cloud, enabling the use of <a href="https://cloud.google.com/products/compute?hl=en" rel="nofollow">the latest generation of instances available on Google Cloud Compute Engine</a>.</p> <p data-svelte-h="svelte-109zam3">Hugging Face Inference DLCs provide you with production-ready endpoints that scale quickly with your Google Cloud environment, built-in monitoring, and a ton of enterprise features.</p> <hr> <p data-svelte-h="svelte-1hcwah1">Read more about both Vertex AI in <a href="https://cloud.google.com/vertex-ai/docs" rel="nofollow">their official documentation</a> and GKE in <a href="https://cloud.google.com/kubernetes-engine/docs" rel="nofollow">their official documentation</a>.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/Google-Cloud-Containers/blob/main/docs/source/features.mdx" 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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