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	<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"Available DLCs on AWS","local":"available-dlcs-on-aws","sections":[{"title":"Training","local":"training","sections":[],"depth":2},{"title":"Inference","local":"inference","sections":[{"title":"Pytorch Inference DLC","local":"pytorch-inference-dlc","sections":[],"depth":3},{"title":"LLM DLC","local":"llm-dlc","sections":[],"depth":3},{"title":"Text Embedding Inference","local":"text-embedding-inference","sections":[],"depth":3}],"depth":2},{"title":"FAQ","local":"faq","sections":[],"depth":2}],"depth":1}">
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	<link rel="modulepreload" href="/docs/sagemaker/pr_2188/en/_app/immutable/chunks/CodeBlock.543f5448.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Available DLCs on AWS","local":"available-dlcs-on-aws","sections":[{"title":"Training","local":"training","sections":[],"depth":2},{"title":"Inference","local":"inference","sections":[{"title":"Pytorch Inference DLC","local":"pytorch-inference-dlc","sections":[],"depth":3},{"title":"LLM DLC","local":"llm-dlc","sections":[],"depth":3},{"title":"Text Embedding Inference","local":"text-embedding-inference","sections":[],"depth":3}],"depth":2},{"title":"FAQ","local":"faq","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" 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></span> <span>Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></button></div> </div> <h1 class="relative group"><a id="available-dlcs-on-aws" 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="#available-dlcs-on-aws"><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>Available DLCs on AWS</span></h1> <p data-svelte-h="svelte-1eptnyh">Below you can find a listing of all the Deep Learning Containers (DLCs) available on AWS.</p> <p data-svelte-h="svelte-nsjrcm">For each supported combination of use-case (training, inference), accelerator type (CPU, GPU, Neuron), and framework (PyTorch, TGI, TEI) containers are created.</p> <p data-svelte-h="svelte-1mi0ysm">Neuron DLCs for training and inference on AWS Trainium and AWS Inferentia instances can be found in the <a href="https://huggingface.co/docs/optimum-neuron/en/containers" rel="nofollow">Optimum Neuron documentation</a>.</p> <h2 class="relative group"><a id="training" 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="#training"><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>Training</span></h2> <p data-svelte-h="svelte-kuh6r">For training, the DLCs are available for PyTorch via Transformers. They include GPUs and AWS AI chips support, with libraries such as TRL, Sentence Transformers, or Diffusers.</p> <p data-svelte-h="svelte-4leueb">You can also keep track of the latest Pytorch Training DLC releases <a href="https://github.com/aws/deep-learning-containers/releases?q=huggingface-training+AND+NOT+neuronx&expanded=true" rel="nofollow">here</a>.</p> <table data-svelte-h="svelte-1c8lwj3"><thead><tr><th>Container URI</th> <th>Accelerator</th></tr></thead> <tbody><tr><td>763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:2.8.0-transformers4.56.2-gpu-py312-cu129-ubuntu22.04</td> <td>GPU</td></tr> <tr><td>763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training-neuronx:2.8.0-transformers4.55.4-neuronx-py310-sdk2.26.0-ubuntu22.04</td> <td>Neuron</td></tr></tbody></table> <h2 class="relative group"><a id="inference" 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="#inference"><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>Inference</span></h2> <h3 class="relative group"><a id="pytorch-inference-dlc" 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="#pytorch-inference-dlc"><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>Pytorch Inference DLC</span></h3> <p data-svelte-h="svelte-dfyqlg">For inference, there is a general-purpose PyTorch inference DLC, for serving models trained with any of those frameworks mentioned before on CPU, GPU, and AWS AI chips.</p> <table data-svelte-h="svelte-1yo3llp"><thead><tr><th>Container URI</th> <th>Accelerator</th></tr></thead> <tbody><tr><td>763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-inference:2.6.0-transformers4.51.3-cpu-py312-ubuntu22.04-</td> <td>CPU</td></tr> <tr><td>763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-inference:2.6.0-transformers4.51.3-gpu-py312-cu124-ubuntu22.04</td> <td>GPU</td></tr> <tr><td>763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-inference-neuronx:2.8.0-transformers4.55.4-neuronx-py310-sdk2.26.0-ubuntu22.04</td> <td>Neuron</td></tr></tbody></table> <h3 class="relative group"><a id="llm-dlc" 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="#llm-dlc"><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>LLM DLC</span></h3> <p data-svelte-h="svelte-1ews8t2">For high-performance serving of text generation models, there is the LLM DLC, available on GPU and AWS AI chips.</p> <table data-svelte-h="svelte-2bhuz0"><thead><tr><th>Container URI</th> <th>Accelerator</th></tr></thead> <tbody><tr><td>763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference:2.7.0-tgi3.3.6-gpu-py311-cu124-ubuntu22.04</td> <td>GPU</td></tr> <tr><td>763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-vllm-inference-neuronx:0.11.0-optimum0.4.4-neuronx-py310-sdk2.26.1-ubuntu22.04</td> <td>Neuron</td></tr></tbody></table> <h3 class="relative group"><a id="text-embedding-inference" 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="#text-embedding-inference"><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>Text Embedding Inference</span></h3> <p data-svelte-h="svelte-dig27s">Finally, there is the Text Embeddings Inference (TEI) DLC for high-performance serving of embedding models on CPU and GPU.</p> <table data-svelte-h="svelte-yh0mt5"><thead><tr><th>Container URI</th> <th>Accelerator</th></tr></thead> <tbody><tr><td>683313688378.dkr.ecr.us-east-1.amazonaws.com/2.0.1-tei1.8.2-cpu-py310-ubuntu22.04</td> <td>CPU</td></tr> <tr><td>683313688378.dkr.ecr.us-east-1.amazonaws.com/2.0.1-tei1.8.2-gpu-py310-cu122-ubuntu22.04</td> <td>GPU</td></tr></tbody></table> <h2 class="relative group"><a id="faq" 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="#faq"><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>FAQ</span></h2> <p data-svelte-h="svelte-qjgyy3"><strong>How to choose the right inference container for my use case?</strong></p> <p data-svelte-h="svelte-86ji6y"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sagemaker/inference-dlc-decision-tree.png" alt="inference-dlc-decision-tree"></p> <p data-svelte-h="svelte-1w4dc1f"><em>Note:</em> See <a href="https://huggingface.co/docs/sagemaker/main/en/reference/inference-toolkit" rel="nofollow">here</a> for the list of supported task in the inference toolkit.</p> <p data-svelte-h="svelte-8p8z10"><em>Note:</em> Browse through the Hub to see if you model is tagged <a href="https://huggingface.co/models?other=text-generation-inference" rel="nofollow">“text-generation-inference”</a> or <a href="https://huggingface.co/models?other=text-embeddings-inference" rel="nofollow">“text-embeddings-inference”</a></p> <p data-svelte-h="svelte-1he7r2b"><strong>How to find the URI of my container?</strong></p> <p data-svelte-h="svelte-1rzekti">The URI is built with an AWS account ID and an AWS region. Those two values need to be replaced depending on your use case.
	Let’s say you want to use the training DLC for GPUs in</p> <ul data-svelte-h="svelte-tq759t"><li><code>dlc-aws-account-id</code>: The AWS account ID of the account that owns the ECR repository. You can find them in the <a href="https://github.com/aws/sagemaker-python-sdk/blob/e0b9d38e1e3b48647a02af23c4be54980e53dc61/src/sagemaker/image_uri_config/huggingface.json#L21" rel="nofollow">here</a></li> <li><code>region</code>: The AWS region where you want to use it.</li></ul> <p data-svelte-h="svelte-1kgkd98"><strong>How to find the URI of my container but simpler?</strong></p> <p data-svelte-h="svelte-l6c2ju">The Python SagemMaker SDK util functions are not always up to date but it is much simpler than reconstructing the image URI yourself.</p> <blockquote data-svelte-h="svelte-p7runb"><p>[!WARNING][SageMaker Python SDK v3 has been recently released](<a href="https://github.com/aws/sagemaker-python-sdk" rel="nofollow">https://github.com/aws/sagemaker-python-sdk</a>), so unless specified otherwise, all the documentation and tutorials are still using the <a href="https://github.com/aws/sagemaker-python-sdk/tree/master-v2" rel="nofollow">SageMaker Python SDK v2</a>. We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as <code>pip install "sagemaker<3.0.0"</code>.</p></blockquote> <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 --><span class="hljs-keyword">from</span> sagemaker.huggingface <span class="hljs-keyword">import</span> HuggingFaceModel, get_huggingface_llm_image_uri

	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"TGI GPU: <span class="hljs-subst">{get_huggingface_llm_image_uri(<span class="hljs-string">'huggingface'</span>)}</span>"</span>)
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"TEI GPU: <span class="hljs-subst">{get_huggingface_llm_image_uri(<span class="hljs-string">'huggingface-tei'</span>)}</span>"</span>)
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"TEI CPU: <span class="hljs-subst">{get_huggingface_llm_image_uri(<span class="hljs-string">'huggingface-tei-cpu'</span>)}</span>"</span>)
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"TGI Neuron: <span class="hljs-subst">{get_huggingface_llm_image_uri(<span class="hljs-string">'huggingface-neuronx'</span>)}</span>"</span>)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1om219l">For Pytorch Training and Pytorch Inference DLCs, there is no such utility.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/hub-docs/blob/main/docs/sagemaker/source/dlcs/available.md" target="_blank"><svg class="mr-1" 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="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a> <p></p>

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