import{s as At,n as Gt,o as Bt}from"../chunks/scheduler.56725da7.js";import{S as Ft,i as Lt,e as s,s as a,c as p,q as Vt,h as Qt,a as r,d as l,b as i,f as nt,g as c,j as o,r as xt,k as Et,l as g,m as n,n as u,t as m,o as d,p as y}from"../chunks/index.18a26576.js";import{C as Zt}from"../chunks/CopyLLMTxtMenu.f4001e5e.js";import{C as T}from"../chunks/CodeBlock.e59f1853.js";import{H as ce}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.d7e7b6b3.js";function Xt(ot){let h,ye,ue,Me,U,Te,J,he,b,pt='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.',ge,C,ct="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.",fe,w,ut="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.",Ue,j,mt="You will learn how to:",Je,v,dt='<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>',be,$,Ce,R,yt="Before starting this tutorial, you need to set up your AWS environment:",we,M,se,Mt='Create an AWS SageMaker Hyperpod cluster with at least one <code>trn1.32xlarge</code> instance. You can follow the <a href="https://catalog.workshops.aws/sagemaker-hyperpod-eks/en-US/00-setup/own-account" rel="nofollow">Hyperpod EKS workshop</a> to set up the cluster.',at,re,Tt='Since Llama 3.2 is a gated model users have to register in Hugging Face and obtain an <a href="https://huggingface.co/docs/hub/en/security-tokens" rel="nofollow">access token</a> before running this example. You will also need to review and accept the license agreement on the <a href="https://huggingface.co/meta-llama/Llama-3.2-1B" rel="nofollow">meta-llama/Llama-3.2-1B</a> model page.',it,f,st,oe,ht="aws configure",rt,S,je,I,ve,_,gt="Set up your training environment with the necessary dependencies:",$e,N,Re,W,ft="Login to ECR and pull the <code>huggingface-pytorch-training-neuronx</code> image:",Se,k,Ie,V,Ut="Build and push the Docker image to your ECR registry:",_e,x,Ne,E,Jt="Push the image to your private registry:",We,A,ke,G,Ve,B,bt=`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:`,xe,F,Ee,L,Ct="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.",Ae,Q,Ge,Z,Be,X,wt="Deploy the training job to your Kubernetes cluster:",Fe,H,Le,Y,jt='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>.',Qe,z,vt="You will use the following distributed training techniques in this script:",Ze,P,$t="<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>",Xe,O,Rt="The manifest runs the following command on the cluster. The environment variables are set when creating the manifest in <code>generate-jobspec.sh</code>.",He,q,Ye,D,St="The training job will now start running on the SageMaker Hyperpod cluster.",ze,K,It="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.",Pe,ee,_t="Here’s an overview of the main components in the script:",Oe,te,Nt="<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>",qe,le,De,ne,Wt="You can monitor the progress through Kubernetes logs:",Ke,ae,et,ie,kt="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!",tt,me,lt;return U=new Zt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),J=new ce({props:{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",headingTag:"h1"}}),$=new ce({props:{title:"1. Setup AWS Environment",local:"1-setup-aws-environment",headingTag:"h2"}}),S=new T({props:{code:"YXdzJTIwY29uZmlndXJlJTBBQVdTJTIwQWNjZXNzJTIwS2V5JTIwSUQlMjAlNUJOb25lJTVEJTNBJTIwWU9VUl9BQ0NFU1NfS0VZJTBBQVdTJTIwU2VjcmV0JTIwQWNjZXNzJTIwS2V5JTIwJTVCTm9uZSU1RCUzQSUyMFlPVVJfU0VDUkVUX0tFWSUwQURlZmF1bHQlMjByZWdpb24lMjBuYW1lJTIwJTVCTm9uZSU1RCUzQSUyMFlPVVJfUkVHSU9OJTBBRGVmYXVsdCUyMG91dHB1dCUyMGZvcm1hdCUyMCU1Qk5vbmUlNUQlM0ElMjBqc29u",highlighted:`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`,wrap:!1}}),I=new ce({props:{title:"2. Prepare the Training Environment",local:"2-prepare-the-training-environment",headingTag:"h2"}}),N=new T({props:{code:"Z2l0JTIwY2xvbmUlMjBodHRwcyUzQSUyRiUyRmdpdGh1Yi5jb20lMkZodWdnaW5nZmFjZSUyRm9wdGltdW0tbmV1cm9uLmdpdCUwQW1rZGlyJTIwfiUyRnByZS10cmFpbmluZyUwQWNkJTIwcHJlLXRyYWluaW5nJTBBJTBBY3AlMjAtciUyMC4uJTJGb3B0aW11bS1uZXVyb24lMkZkb2NzJTJGc291cmNlJTJGdHJhaW5pbmdfdHV0b3JpYWxzJTJGYW1hem9uX2VrcyUyMC4lMEFjZCUyMGFtYXpvbl9la3M=",highlighted:`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`,wrap:!1}}),k=new T({props:{code:"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",highlighted:`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`,wrap:!1}}),x=new T({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> AWS_REGION=$(aws ec2 describe-availability-zones --output text --query <span class="hljs-string">&#x27;AvailabilityZones[0].[RegionName]&#x27;</span>)
<span class="hljs-built_in">export</span> ACCOUNT=$(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> .`,wrap:!1}}),A=new T({props:{code:"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",highlighted:`<span class="hljs-comment"># Create registry if needed</span>
<span class="hljs-built_in">export</span> REGISTRY_COUNT=$(aws ecr describe-repositories | grep \\&quot;<span class="hljs-variable">\${IMAGE}</span>\\&quot; | <span class="hljs-built_in">wc</span> -l)
<span class="hljs-keyword">if</span> [ <span class="hljs-string">&quot;<span class="hljs-variable">\${REGISTRY_COUNT//[!0-9]/}</span>&quot;</span> == <span class="hljs-string">&quot;0&quot;</span> ]; <span class="hljs-keyword">then</span>
   <span class="hljs-built_in">echo</span> <span class="hljs-string">&quot;Creating repository <span class="hljs-variable">\${REGISTRY}</span><span class="hljs-variable">\${IMAGE}</span> ...&quot;</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">&quot;Repository <span class="hljs-variable">\${REGISTRY}</span><span class="hljs-variable">\${IMAGE}</span> already exists&quot;</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">&quot;Logging in to <span class="hljs-variable">$REGISTRY</span> ...&quot;</span>
aws ecr get-login-password | docker login --username AWS --password-stdin <span class="hljs-variable">$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>`,wrap:!1}}),G=new ce({props:{title:"3. Configure the Training Job",local:"3-configure-the-training-job",headingTag:"h2"}}),F=new T({props:{code:"ZXhwb3J0JTIwSEZfQUNDRVNTX1RPS0VOJTNEJTIyJTNDeW91cl9IRl90b2tlbl9oZXJlJTNFJTIy",highlighted:'<span class="hljs-built_in">export</span> HF_ACCESS_TOKEN=<span class="hljs-string">&quot;&lt;your_HF_token_here&gt;&quot;</span>',wrap:!1}}),Q=new T({props:{code:"LiUyRmdlbmVyYXRlLWpvYnNwZWMuc2g=",highlighted:"./generate-jobspec.sh",wrap:!1}}),Z=new ce({props:{title:"4. Launch Training on SageMaker Hyperpod",local:"4-launch-training-on-sagemaker-hyperpod",headingTag:"h2"}}),H=new T({props:{code:"a3ViZWN0bCUyMGFwcGx5JTIwLWYlMjBsbGFtYV90cmFpbi55YW1s",highlighted:"kubectl apply -f llama_train.yaml",wrap:!1}}),q=new T({props:{code:"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",highlighted:`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`,wrap:!1}}),le=new ce({props:{title:"5. Monitor and Validate Training",local:"5-monitor-and-validate-training",headingTag:"h2"}}),ae=new T({props:{code:"JTIzJTIwTW9uaXRvciUyMHRyYWluaW5nJTIwbG9ncyUwQWt1YmVjdGwlMjBsb2dzJTIwLWYlMjAtbiUyMGt1YmVmbG93JTIwbGxhbWEtdHJhaW5pbmctZWtzLXdvcmtlci0wJTBBJTBBJTIzJTIwVmFsaWRhdGUlMjBzYXZlZCUyMGNoZWNrcG9pbnRzJTBBa3ViZWN0bCUyMGV4ZWMlMjAtaXQlMjBsbGFtYS10cmFpbmluZy1la3Mtd29ya2VyLTAlMjAtLSUyMGxzJTIwLWwlMjAlMkZmc3glMkZvdXRwdXQ=",highlighted:`<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`,wrap:!1}}),{c(){h=s("meta"),ye=a(),ue=s("p"),Me=a(),p(U.$$.fragment),Te=a(),p(J.$$.fragment),he=a(),b=s("p"),b.innerHTML=pt,ge=a(),C=s("p"),C.textContent=ct,fe=a(),w=s("p"),w.textContent=ut,Ue=a(),j=s("p"),j.textContent=mt,Je=a(),v=s("ul"),v.innerHTML=dt,be=a(),p($.$$.fragment),Ce=a(),R=s("p"),R.textContent=yt,we=a(),M=s("ol"),se=s("li"),se.innerHTML=Mt,at=a(),re=s("li"),re.innerHTML=Tt,it=a(),f=s("li"),st=Vt("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 "),oe=s("code"),oe.textContent=ht,rt=Vt(`. You’ll need to enter your AWS Access Key ID, Secret Access Key, default region, and output format.

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