[Doc] Adding tutorial for Gateway Inference Extension support

[YuhanLiu11]

This PR adds a tutorial for Gateway Inference Extension support for Production Stack.

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Thank you for your contribution to production-stack! Before submitting the pull request, please ensure the PR meets the following criteria. This helps us maintain the code quality and improve the efficiency of the review process.

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[gemini-code-assist]

Summary of Changes

Hello @YuhanLiu11, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request primarily focuses on enhancing the documentation by adding a detailed tutorial for integrating and utilizing the Gateway Inference Extension within a production-grade Kubernetes setup, specifically leveraging vLLM for model serving. The changes aim to provide clear, step-by-step instructions for users to deploy, configure, and manage their inference workloads via the Gateway API, alongside introducing utility scripts for installation and cleanup.

Highlights

• New Tutorial Document: I've added a comprehensive new tutorial document, tutorials/21-gateway-inference-extension.md, which guides users through setting up and using the Gateway Inference Extension with vLLM in a Kubernetes production environment. This covers everything from prerequisites and environment setup to deploying models, configuring routing, testing, monitoring, and uninstallation.
• Updated Deployment References: I've updated the src/gateway_inference_extension/README.md and src/gateway_inference_extension/install.sh scripts to reflect a change in the VLLM deployment configuration file name, now referencing configs/vllm/gpu-deployment.yaml instead of vllm-runtime.yaml.
• New Cleanup Script: I've introduced a new shell script, src/gateway_inference_extension/delete.sh, to provide a convenient way to uninstall all Kubernetes resources deployed as part of the Gateway Inference Extension tutorial, ensuring a clean teardown.

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[gemini-code-assist]

Code Review

This PR adds a tutorial for Gateway Inference Extension support for Production Stack. The tutorial covers setting up the environment, deploying vLLM models, configuring inference resources and gateway routing, testing the setup, monitoring, troubleshooting, and uninstalling the resources. The changes include updates to the README and install/delete scripts to use gpu-deployment.yaml instead of vllm-runtime.yaml, and adding a PORT variable in the README. The tutorial provides a comprehensive guide for users to get started with the Gateway Inference Extension.

[gemini-code-assist]

Consider adding a comment explaining why gpu-deployment.yaml is preferred over vllm-runtime.yaml. This will help users understand the rationale behind the change.

Suggested change

	kubectl apply -f configs/vllm/gpu-deployment.yaml
	kubectl apply -f configs/vllm/gpu-deployment.yaml # Use gpu-deployment for better GPU utilization

[gemini-code-assist]

It would be helpful to provide a brief explanation of what the configs/vllm/gpu-deployment.yaml file contains and what aspects users might want to customize.

Suggested change

	The vLLM Runtime is a custom resource that manages model deployments. Please check ``configs/vllm/gpu-deployment.yaml`` for an example config.
	The vLLM Runtime is a custom resource that manages model deployments. Please check ``configs/vllm/gpu-deployment.yaml`` for an example config. This file defines the deployment configuration for vLLM, including the model to use, resource requests, and other settings.

[gemini-code-assist]

Similar to the vLLM Runtime, briefly describe what the configs/inferencepool-resources.yaml file contains and what aspects users might want to customize.

Suggested change

	For routing to multiple model instances, check ``configs/inferencepool-resources.yaml`` for example.
	For routing to multiple model instances, check ``configs/inferencepool-resources.yaml`` for example. This file configures the InferencePool, which manages the routing and load balancing of requests to multiple model instances.

[gemini-code-assist]

The uninstall instructions include deleting resources by applying the same manifests used for installation. It's generally safer and more explicit to use kubectl delete with the --ignore-not-found flag to ensure the script doesn't fail if some resources were not created. Also, consider adding a comment about the order of deletion to avoid dependency issues.

Suggested change

	To uninstall all the resources installed on the cluster, run the following:
	```bash
	# Delete the inference extension
	kubectl delete -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/raw/main/config/manifests/gateway/kgateway/gateway.yaml
	# Delete the inference model and pool resources
	kubectl delete -f configs/inferencemodel.yaml
	kubectl delete -f configs/inferencepool-resources.yaml
	# Delete the VLLM deployment
	kubectl delete -f configs/vllm/gpu-deployment.yaml
	kubectl delete -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/releases/download/v0.3.0/manifests.yaml
	helm uninstall kgateway -n kgateway-system
	helm uninstall kgateway-crds -n kgateway-system
	kubectl delete ns kgateway-system
	# Delete the inference extension
	kubectl delete -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/raw/main/config/manifests/gateway/kgateway/gateway.yaml --ignore-not-found=true
	# Delete the inference model and pool resources
	kubectl delete -f configs/inferencemodel.yaml --ignore-not-found=true
	kubectl delete -f configs/inferencepool-resources.yaml --ignore-not-found=true
	# Delete the VLLM deployment
	kubectl delete -f configs/vllm/gpu-deployment.yaml --ignore-not-found=true
	kubectl delete -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/releases/download/v0.3.0/manifests.yaml --ignore-not-found=true
	# Delete helm releases
	helm uninstall kgateway -n kgateway-system
	helm uninstall kgateway-crds -n kgateway-system
	# Delete the namespace last to ensure all resources are removed
	kubectl delete ns kgateway-system --ignore-not-found=true

[Shaoting-Feng]

LGTM