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| <link rel="modulepreload" href="/docs/optimum.neuron/v0.4.4/en/_app/immutable/chunks/CodeBlock.58e3e98b.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"🚀 Continuous Pretraining of Llama 3.2 1B on SageMaker Hyperpod with Pre-built Containers","local":"-continuous-pretraining-of-llama-32-1b-on-sagemaker-hyperpod-with-pre-built-containers","sections":[{"title":"1. Setup AWS Environment","local":"1-setup-aws-environment","sections":[],"depth":2},{"title":"2. Prepare the Training Environment","local":"2-prepare-the-training-environment","sections":[],"depth":2},{"title":"3. Configure the Training Job","local":"3-configure-the-training-job","sections":[],"depth":2},{"title":"4. Launch Training on SageMaker Hyperpod","local":"4-launch-training-on-sagemaker-hyperpod","sections":[],"depth":2},{"title":"5. Monitor and Validate Training","local":"5-monitor-and-validate-training","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 max-sm:gap-0.5 h-6 max-sm:h-5 px-2 max-sm:px-1.5 text-[11px] max-sm:text-[9px] 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"><svg class="w-3 h-3 max-sm:w-2.5 max-sm:h-2.5" 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-6 max-sm:h-5 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 w-3 h-3 max-sm:w-2.5 max-sm:h-2.5 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="-continuous-pretraining-of-llama-32-1b-on-sagemaker-hyperpod-with-pre-built-containers" 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="#-continuous-pretraining-of-llama-32-1b-on-sagemaker-hyperpod-with-pre-built-containers"><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>🚀 Continuous Pretraining of Llama 3.2 1B on SageMaker Hyperpod with Pre-built Containers</span></h1> <p data-svelte-h="svelte-umkoc6">This tutorial demonstrates how to continuously pre-train the <a href="https://huggingface.co/meta-llama/Llama-3.2-1B" rel="nofollow">Llama 3.2 1B</a> model using the Hugging Face <a href="https://huggingface.co/docs/optimum-neuron/index" rel="nofollow">Optimum Neuron</a> library on <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod.html" rel="nofollow">Amazon SageMaker Hyperpod</a>. We leverage several performance optimizations such as tensor parallelism, sequence parallelism, and ZeRO-1 to efficiently train large language models on Trainium-powered instances.</p> <p data-svelte-h="svelte-stv42r">One of the key benefits of using SageMaker Hyperpod is the ability to leverage the pre-built Optimum Neuron containers provided by Hugging Face. These containers come with all the necessary libraries and dependencies pre-installed, making it easy to get started with training on AWS Trainium instances.</p> <p data-svelte-h="svelte-vecog1">By using the SageMaker pre-built containers, you can avoid the hassle of manually setting up the environment and focus on the core training and fine-tuning tasks. The containers are optimized for performance and include various optimization techniques, such as tensor parallelism and selective checkpointing, to efficiently train large language models like Llama 3.2 1B.</p> <p data-svelte-h="svelte-1hahfn0">You will learn how to:</p> <ul data-svelte-h="svelte-1rpf5h0"><li><a href="#continuous-pretraining-of-llama-32-1b-on-sagemaker-hyperpod-with-pre-built-containers">Continuous Pretraining of Llama 3.2 1B on SageMaker Hyperpod with Pre-built Containers</a> <ul><li><a href="#1-setup-aws-environment">1. Setup AWS Environment</a></li> <li><a href="#2-prepare-the-training-environment">2. Prepare the Training Environment</a></li> <li><a href="#3-configure-the-training-job">3. Configure the Training Job</a></li> <li><a href="#4-launch-training-on-sagemaker-hyperpod">4. Launch Training on SageMaker Hyperpod</a></li> <li><a href="#5-monitor-and-validate-training">5. Monitor and Validate Training</a></li></ul></li></ul> <h2 class="relative group"><a id="1-setup-aws-environment" 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="#1-setup-aws-environment"><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>1. Setup AWS Environment</span></h2> <p data-svelte-h="svelte-y2j4y7">Before starting this tutorial, you need to set up your AWS environment:</p> <ol><li data-svelte-h="svelte-1syvdtd">Create an AWS SageMaker Hyperpod cluster with at least one <code>trn1.32xlarge</code> instance. You can follow the <a href="https://catalog.workshops.aws/sagemaker-hyperpod-eks/en-US/00-setup/own-account" rel="nofollow">Hyperpod EKS workshop</a> to set up the cluster.</li> <li data-svelte-h="svelte-11uf0xx">Since Llama 3.2 is a gated model users have to register in Hugging Face and obtain an <a href="https://huggingface.co/docs/hub/en/security-tokens" rel="nofollow">access token</a> before running this example. You will also need to review and accept the license agreement on the <a href="https://huggingface.co/meta-llama/Llama-3.2-1B" rel="nofollow">meta-llama/Llama-3.2-1B</a> model page.</li> <li>Configure your AWS credentials. If you haven’t already set up your AWS credentials, you can do this by installing the AWS CLI and running <code data-svelte-h="svelte-1d1e3tl">aws configure</code>. You’ll need to enter your AWS Access Key ID, Secret Access Key, default region, and output format. | |
| <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 -->aws configure | |
| AWS Access Key ID [None]: YOUR_ACCESS_KEY | |
| AWS Secret Access Key [None]: YOUR_SECRET_KEY | |
| Default region name [None]: YOUR_REGION | |
| Default output format [None]: json<!-- HTML_TAG_END --></pre></div></li></ol> <h2 class="relative group"><a id="2-prepare-the-training-environment" 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="#2-prepare-the-training-environment"><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>2. Prepare the Training Environment</span></h2> <p data-svelte-h="svelte-umwl4">Set up your training environment with the necessary dependencies:</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 -->git <span class="hljs-built_in">clone</span> https://github.com/huggingface/optimum-neuron.git | |
| <span class="hljs-built_in">mkdir</span> ~/pre-training | |
| <span class="hljs-built_in">cd</span> pre-training | |
| <span class="hljs-built_in">cp</span> -r ../optimum-neuron/docs/source/training_tutorials/amazon_eks . | |
| <span class="hljs-built_in">cd</span> amazon_eks<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1dk8o0a">Login to ECR and pull the <code>huggingface-pytorch-training-neuronx</code> image:</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 -->region=us-east-1 | |
| dlc_account_id=************ | |
| aws ecr get-login-password --region <span class="hljs-variable">$region</span> | docker login --username AWS --password-stdin <span class="hljs-variable">$dlc_account_id</span>.dkr.ecr.<span class="hljs-variable">$region</span>.amazonaws.com | |
| docker pull <span class="hljs-variable">${dlc_account_id}</span>.dkr.ecr.<span class="hljs-variable">${region}</span>.amazonaws.com/huggingface-pytorch-training-neuronx:2.1.2-transformers4.43.2-neuronx-py310-sdk2.20.0-ubuntu20.04-v1.0<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1m60kbs">Build and push the Docker image to your ECR registry:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> AWS_REGION=$(aws ec2 describe-availability-zones --output text --query <span class="hljs-string">'AvailabilityZones[0].[RegionName]'</span>) | |
| <span class="hljs-built_in">export</span> ACCOUNT=$(aws sts get-caller-identity --query Account --output text) | |
| <span class="hljs-built_in">export</span> REGISTRY=<span class="hljs-variable">${ACCOUNT}</span>.dkr.ecr.<span class="hljs-variable">${AWS_REGION}</span>.amazonaws.com/ | |
| <span class="hljs-built_in">export</span> IMAGE=optimum-neuron-llama-pretraining | |
| <span class="hljs-built_in">export</span> TAG=:latest | |
| docker build -t <span class="hljs-variable">${REGISTRY}</span><span class="hljs-variable">${IMAGE}</span><span class="hljs-variable">${TAG}</span> .<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-8aefpq">Push the image to your private registry:</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 --><span class="hljs-comment"># Create registry if needed</span> | |
| <span class="hljs-built_in">export</span> REGISTRY_COUNT=$(aws ecr describe-repositories | grep \"<span class="hljs-variable">${IMAGE}</span>\" | <span class="hljs-built_in">wc</span> -l) | |
| <span class="hljs-keyword">if</span> [ <span class="hljs-string">"<span class="hljs-variable">${REGISTRY_COUNT//[!0-9]/}</span>"</span> == <span class="hljs-string">"0"</span> ]; <span class="hljs-keyword">then</span> | |
| <span class="hljs-built_in">echo</span> <span class="hljs-string">"Creating repository <span class="hljs-variable">${REGISTRY}</span><span class="hljs-variable">${IMAGE}</span> ..."</span> | |
| aws ecr create-repository --repository-name <span class="hljs-variable">${IMAGE}</span> | |
| <span class="hljs-keyword">else</span> | |
| <span class="hljs-built_in">echo</span> <span class="hljs-string">"Repository <span class="hljs-variable">${REGISTRY}</span><span class="hljs-variable">${IMAGE}</span> already exists"</span> | |
| <span class="hljs-keyword">fi</span> | |
| <span class="hljs-comment"># Login to registry</span> | |
| <span class="hljs-built_in">echo</span> <span class="hljs-string">"Logging in to <span class="hljs-variable">$REGISTRY</span> ..."</span> | |
| aws ecr get-login-password | docker login --username AWS --password-stdin <span class="hljs-variable">$REGISTRY</span> | |
| <span class="hljs-comment"># Push image to registry</span> | |
| docker image push <span class="hljs-variable">${REGISTRY}</span><span class="hljs-variable">${IMAGE}</span><span class="hljs-variable">${TAG}</span><!-- HTML_TAG_END --></pre></div> <h2 class="relative group"><a id="3-configure-the-training-job" 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="#3-configure-the-training-job"><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>3. Configure the Training Job</span></h2> <p data-svelte-h="svelte-nm6jck">Next, you will generate the script to be used by the pre-training job. Begin by logging into Hugging Face using your access token mentioned in the prerequisite steps. | |
| Modify the <code>generate-jobspec.sh</code> script to include the Hugging Face access token before running it:</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 --><span class="hljs-built_in">export</span> HF_ACCESS_TOKEN=<span class="hljs-string">"<your_HF_token_here>"</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1r8nykb">Generate the Kubernetes job specification by executing <code>generate-jobspec.sh</code>. This will create a deployment manifest called <code>llama_train.yaml</code> for the Amazon SageMaker Hyperpod EKS cluster.</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 -->./generate-jobspec.sh<!-- HTML_TAG_END --></pre></div> <h2 class="relative group"><a id="4-launch-training-on-sagemaker-hyperpod" 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="#4-launch-training-on-sagemaker-hyperpod"><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>4. Launch Training on SageMaker Hyperpod</span></h2> <p data-svelte-h="svelte-1yxf8j9">Deploy the training job to your Kubernetes cluster:</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 -->kubectl apply -f llama_train.yaml<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-15nk6hk">The manifest runs the training script on the cluster using torchrun for distributed training. You can explore the complete training script at <a href="https://github.com/huggingface/optimum-neuron/blob/main/examples/language-modeling/run_clm.py" rel="nofollow">run_clm.py</a>.</p> <p data-svelte-h="svelte-1fh7d8c">You will use the following distributed training techniques in this script:</p> <ul data-svelte-h="svelte-1mkk3sr"><li>Distributed Training: Uses torchrun with 8 processes per node for efficient multi-device training</li> <li>Model Parallelism: Implements both tensor parallelism (TP=8) and pipeline parallelism (PP=1)</li> <li>Mixed Precision: Utilizes BFloat16 for improved training efficiency</li> <li>Gradient Checkpointing: Enables memory-efficient training</li></ul> <p data-svelte-h="svelte-cy8ews">The manifest runs the following command on the cluster. The environment variables are set when creating the manifest in <code>generate-jobspec.sh</code>.</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 -->torchrun --nproc_per_node=8 --nnodes=<span class="hljs-variable">${NUM_NODES}</span> run_clm.py \ | |
| --model_name_or_path=<span class="hljs-variable">${HF_MODEL_NAME}</span> | |
| --token=<span class="hljs-variable">${HF_ACCESS_TOKEN}</span> | |
| --dataset_name=<span class="hljs-variable">${DATASET_NAME}</span> | |
| --dataset_config_name=<span class="hljs-variable">${DATASET_CONFIG_NAME}</span> | |
| --streaming=True | |
| --cache_dir=<span class="hljs-variable">${TOKENIZED_DATA_PATH}</span> | |
| --num_train_epochs=1 | |
| --do_train | |
| --learning_rate=1e-4 | |
| --max_steps=<span class="hljs-variable">${MAX_STEPS}</span> | |
| --per_device_train_batch_size=<span class="hljs-variable">${BATCH_SIZE}</span> | |
| --per_device_eval_batch_size=4 | |
| --gradient_accumulation_steps=1 | |
| --gradient_checkpointing | |
| --block_size=4096 | |
| --bf16 | |
| --max_grad_norm=1.0 | |
| --lr_scheduler_type=linear | |
| --tensor_parallel_size=8 | |
| --pipeline_parallel_size=1 | |
| --logging_steps=1 | |
| --save_total_limit=1 | |
| --output_dir=<span class="hljs-variable">${CHECKPOINT_DIR}</span> | |
| --overwrite_output_dir<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-6gn68c">The training job will now start running on the SageMaker Hyperpod cluster.</p> <p data-svelte-h="svelte-1exj7sy">This uses a pre-built script from Optimum-neuron. The script uses the Trainer class from the Optimum Neuron library, which is a specialized version of the Hugging Face Trainer optimized for training on AWS Trainium instances.</p> <p data-svelte-h="svelte-1h422fc">Here’s an overview of the main components in the script:</p> <ul data-svelte-h="svelte-9cyjwx"><li><p>Model Loading: The model is loaded using <code>AutoModelForCausalLM.from_pretrained()</code> with lazy loading for parallelism.</p></li> <li><p>Data Processing: The dataset is tokenized and processed into chunks suitable for language modeling.</p></li> <li><p>Training Arguments: The script uses <code>NeuronTrainingArguments</code> to configure training hyperparameters, including options for tensor parallelism and pipeline parallelism.</p></li> <li><p>Trainer Setup: A Trainer instance <code>[optimum.neuron.NeuronTrainer]</code> is created with the model, training arguments, datasets, and other necessary components.</p></li> <li><p>Training Loop: The <code>trainer.train()</code> method is called to start the continuous pretraining process.</p></li></ul> <h2 class="relative group"><a id="5-monitor-and-validate-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="#5-monitor-and-validate-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>5. Monitor and Validate Training</span></h2> <p data-svelte-h="svelte-j415kd">You can monitor the progress through Kubernetes logs:</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 --><span class="hljs-comment"># Monitor training logs</span> | |
| kubectl logs -f -n kubeflow llama-training-eks-worker-0 | |
| <span class="hljs-comment"># Validate saved checkpoints</span> | |
| kubectl <span class="hljs-built_in">exec</span> -it llama-training-eks-worker-0 -- <span class="hljs-built_in">ls</span> -l /fsx/output<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-rdd17m">Once the pretraining is complete, you can fine-tune the model for specific tasks using the techniques covered in the previous tutorials. Congrats on pre-training Llama on AWS Trainium!</p> <p></p> | |
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