Merge branch 'awslabs:main' into main

Author: devfloor9

website/docs/blueprints/gateways/envoy-gateway.md

@@ -1,7 +1,7 @@
 ---
 sidebar_label: Envoy Gateway implementation on EKS
 ---
-import CollapsibleContent from '../../../src/components/CollapsibleContent';
+import CollapsibleContent from '@site/src/components/CollapsibleContent';
 
 # Envoy gateway
 

website/docs/blueprints/inference/framework-guides/Neuron/llama3-inf2.md

@@ -68,7 +68,7 @@ In this section, we will delve into the architecture of our solution, which comb
 
 ## Deploying the Solution
 
-To get started with deploying `Llama-4-8b-instruct` on [Amazon EKS](https://aws.amazon.com/eks/), we will cover the necessary prerequisites and guide you through the deployment process step by step.
+To get started with deploying `Llama-3-8B-Instruct` on [Amazon EKS](https://aws.amazon.com/eks/), we will cover the necessary prerequisites and guide you through the deployment process step by step.
 
 This includes setting up the infrastructure, deploying the **Ray cluster**, and creating the [Gradio](https://www.gradio.app/) WebUI app.
 
@@ -154,7 +154,7 @@ To deploy the llama3-8B-Instruct model, it's essential to configure your Hugging
 
 
 ```bash
-# set the Hugging Face Hub Token as an environment variable. This variable will be substituted when applying the ray-service-mistral.yaml file
+# set the Hugging Face Hub Token as an environment variable. This variable will be substituted when applying the ray-service-llama3.yaml file
 
 export  HUGGING_FACE_HUB_TOKEN=<Your-Hugging-Face-Hub-Token-Value>
 
@@ -231,13 +231,13 @@ The Gradio app interacts with the locally exposed service created solely for the
 First, execute a port forward to the Llama-3 Ray Service using kubectl:
 
 ```bash
-kubectl port-forward svc/llama2-service 8000:8000 -n llama3
+kubectl port-forward svc/llama3 8000:8000 -n llama3
 ```
 
 ## Deploying the Gradio WebUI App
 Discover how to create a user-friendly chat interface using [Gradio](https://www.gradio.app/) that integrates seamlessly with deployed models.
 
-Let's move forward with setting up the Gradio app as a Docker container running on localhost. This setup will enable interaction with the Stable Diffusion XL model, which is deployed using RayServe.
+Let's move forward with setting up the Gradio app as a Docker container running on localhost. This setup will enable interaction with the Llama-3-8B Instruct model, which is deployed using RayServe.
 
 ### Build the Gradio app docker container
 

website/docs/blueprints/training/GPUs/bionemo.md

@@ -2,7 +2,7 @@
 sidebar_position: 1
 sidebar_label: BioNeMo on EKS
 ---
-import CollapsibleContent from '../../../../src/components/CollapsibleContent';
+import CollapsibleContent from '@site/src/components/CollapsibleContent';
 
 # BioNeMo on EKS
 

website/docs/blueprints/training/GPUs/slinky-slurm.md

@@ -2,7 +2,7 @@
 sidebar_label: Slurm on EKS
 ---
 
-import CollapsibleContent from '../../../../src/components/CollapsibleContent';
+import CollapsibleContent from '@site/src/components/CollapsibleContent';
 
 # Slurm on EKS
 

website/docs/blueprints/training/Neuron/Llama-LoRA-Finetuning.md

@@ -1,7 +1,7 @@
 ---
 sidebar_label: Llama 3 Fine-tuning with LoRA
 ---
-import CollapsibleContent from '../../../../src/components/CollapsibleContent';
+import CollapsibleContent from '@site/src/components/CollapsibleContent';
 
 :::warning
 To deploy this example for fine-tuning a LLM on EKS, you need access to AWS Trainium ec2 instance. If deployment fails, check if you have access to this instance type. If nodes aren't starting, check Karpenter or Node group logs.

website/docs/blueprints/training/Neuron/Llama2.md

@@ -3,7 +3,7 @@ title: Llama-2 with Nemo-Megatron on Trn1
 sidebar_position: 2
 description: Training a Llama-2 Model using Trainium, Neuronx-Nemo-Megatron and MPI operator
 ---
-import CollapsibleContent from '../../../../src/components/CollapsibleContent';
+import CollapsibleContent from '@site/src/components/CollapsibleContent';
 
 :::warning
 Deployment of ML models on EKS requires access to GPUs or Neuron instances. If your deployment isn't working, it’s often due to missing access to these resources. Also, some deployment patterns rely on Karpenter autoscaling and static node groups; if nodes aren't initializing, check the logs for Karpenter or Node groups to resolve the issue.

website/docs/blueprints/training/Neuron/RayTrain-Llama2.md

@@ -2,7 +2,7 @@
 sidebar_position: 1
 sidebar_label: Llama-2 with RayTrain on Trn1
 ---
-import CollapsibleContent from '../../../../src/components/CollapsibleContent';
+import CollapsibleContent from '@site/src/components/CollapsibleContent';
 
 :::warning
 Deployment of ML models on EKS requires access to GPUs or Neuron instances. If your deployment isn't working, it’s often due to missing access to these resources. Also, some deployment patterns rely on Karpenter autoscaling and static node groups; if nodes aren't initializing, check the logs for Karpenter or Node groups to resolve the issue.

website/docs/guidance/dynamic-resource-allocation.md

@@ -845,14 +845,14 @@ Standard GPU allocation without sharing - each workload gets exclusive access to
 <TabItem value="template" label="ResourceClaimTemplate">
 
 <CodeBlock language="yaml" title="basic-gpu-claim-template.yaml" showLineNumbers>
-{require('!!raw-loader!../../../infra/jark-stack/examples/k8s-dra/basic/basic-gpu-claim-template.yaml').default}
+{require('!!raw-loader!@site/../infra/jark-stack/examples/k8s-dra/basic/basic-gpu-claim-template.yaml').default}
 </CodeBlock>
 
 </TabItem>
 <TabItem value="pod" label="Basic Pod">
 
 <CodeBlock language="yaml" title="basic-gpu-pod.yaml" showLineNumbers>
-{require('!!raw-loader!../../../infra/jark-stack/examples/k8s-dra/basic/basic-gpu-pod.yaml').default}
+{require('!!raw-loader!@site/../infra/jark-stack/examples/k8s-dra/basic/basic-gpu-pod.yaml').default}
 </CodeBlock>
 
 </TabItem>
@@ -896,14 +896,14 @@ Time-slicing is a GPU sharing mechanism where multiple workloads take turns usin
 <TabItem value="template" label="ResourceClaimTemplate">
 
 <CodeBlock language="yaml" title="timeslicing-claim-template.yaml" showLineNumbers>
-{require('!!raw-loader!../../../infra/jark-stack/examples/k8s-dra/timeslicing/timeslicing-claim-template.yaml').default}
+{require('!!raw-loader!@site/../infra/jark-stack/examples/k8s-dra/timeslicing/timeslicing-claim-template.yaml').default}
 </CodeBlock>
 
 </TabItem>
 <TabItem value="pod" label="Pod Configuration">
 
 <CodeBlock language="yaml" title="timeslicing-pod.yaml" showLineNumbers>
-{require('!!raw-loader!../../../infra/jark-stack/examples/k8s-dra/timeslicing/timeslicing-pod.yaml').default}
+{require('!!raw-loader!@site/../infra/jark-stack/examples/k8s-dra/timeslicing/timeslicing-pod.yaml').default}
 </CodeBlock>
 
 </TabItem>
@@ -952,14 +952,14 @@ NVIDIA Multi-Process Service (MPS) is a GPU sharing technology that allows multi
 <TabItem value="template" label="ResourceClaimTemplate">
 
 <CodeBlock language="yaml" title="mps-claim-template.yaml" showLineNumbers>
-{require('!!raw-loader!../../../infra/jark-stack/examples/k8s-dra/mps/mps-claim-template.yaml').default}
+{require('!!raw-loader!@site/../infra/jark-stack/examples/k8s-dra/mps/mps-claim-template.yaml').default}
 </CodeBlock>
 
 </TabItem>
 <TabItem value="pod" label="Multi-Container Pod">
 
 <CodeBlock language="yaml" title="mps-pod.yaml" showLineNumbers>
-{require('!!raw-loader!../../../infra/jark-stack/examples/k8s-dra/mps/mps-pod.yaml').default}
+{require('!!raw-loader!@site/../infra/jark-stack/examples/k8s-dra/mps/mps-pod.yaml').default}
 </CodeBlock>
 
 </TabItem>
@@ -1008,14 +1008,14 @@ Multi-Instance GPU (MIG) is a hardware-level GPU partitioning technology availab
 <TabItem value="template" label="ResourceClaimTemplate">
 
 <CodeBlock language="yaml" title="mig-claim-template.yaml" showLineNumbers>
-{require('!!raw-loader!../../../infra/jark-stack/examples/k8s-dra/mig/mig-claim-template.yaml').default}
+{require('!!raw-loader!@site/../infra/jark-stack/examples/k8s-dra/mig/mig-claim-template.yaml').default}
 </CodeBlock>
 
 </TabItem>
 <TabItem value="pod" label="MIG Pod">
 
 <CodeBlock language="yaml" title="mig-pod.yaml" showLineNumbers>
-{require('!!raw-loader!../../../infra/jark-stack/examples/k8s-dra/mig/mig-pod.yaml').default}
+{require('!!raw-loader!@site/../infra/jark-stack/examples/k8s-dra/mig/mig-pod.yaml').default}
 </CodeBlock>
 
 </TabItem>

website/docs/infra/index.md

@@ -5,7 +5,7 @@ sidebar_label: Introduction
 
 # Introduction
 
-The AIoEKS foundational infrastructure lives in the `infra/base` directory. This directory contains the base
+The AI on EKS foundational infrastructure lives in the `infra/base` directory. This directory contains the base
 infrastructure and all its modules that allow composing an environment that supports experimentation, AI/ML training,
 LLM inference, model tracking, and more.
 

website/docs/infra/inference/aibrix.md

@@ -1,7 +1,7 @@
 ---
 sidebar_label: AIBrix on EKS
 ---
-import CollapsibleContent from '../../../src/components/CollapsibleContent';
+import CollapsibleContent from '@site/src/components/CollapsibleContent';
 
 # AIBrix on EKS