Add integration test for gke inference blueprints for a4 and a4x

[Neelabh94]

This PR adds integration tests for the newly added a4 and a4x inference blueprints.

[gemini-code-assist]

Summary of Changes

Hello @Neelabh94, 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 significantly enhances the Google Kubernetes Engine (GKE) offerings by introducing dedicated blueprints for inference gateways on A4 and A4x GPU instances. It streamlines the deployment of AI/ML inference workloads by pre-configuring essential services like workload managers and optimized storage solutions. Furthermore, it establishes a new, comprehensive integration test suite to ensure the reliability and correct functionality of these inference gateway deployments, covering resource provisioning and actual inference request validation.

Highlights

• New GKE Inference Gateway Blueprints: Introduced new GKE Inference Gateway blueprints for both A4 and A4x GPU instances, providing optimized configurations for deploying large language model inference workloads.
• Workload Management Integration: Integrated key workload management tools including Kueue, Jobset, and NCCL gIB plugin across both A4 and A4x blueprints. The A4x blueprint additionally includes the NVIDIA DRA driver.
• Comprehensive Integration Testing: Added a robust integration testing framework for GKE Inference Gateway deployments. This includes an Ansible playbook to validate Kubernetes resources (vLLM Deployment, Service, InferencePool, InferenceObjective, Gateway, HTTPRoute) and perform a functional inference request.
• Storage Benchmarking Example (A4x): The GKE A4x inference gateway blueprint now includes an example FIO benchmark job template to test storage performance against scratch, training, and checkpoint data buckets.

Changelog

• examples/gke-a4/gke-a4-inference-gateway.yaml
  • Added a new blueprint for GKE A4 inference gateways, configuring network, service accounts, GCS buckets, GKE cluster with inference gateway enabled, A4 node pool, and installation of Kueue, Jobset, and NCCL gIB.
• examples/gke-a4x/gke-a4x-inference-gateway.yaml
  • Added a new blueprint for GKE A4x inference gateways, including A4x specific configurations, NVIDIA DRA driver installation, and an example FIO benchmark job for storage performance testing.
• tools/cloud-build/daily-tests/ansible_playbooks/test-validation/test-gke-inference-gateway.yml
  • Added a new Ansible playbook to validate GKE Inference Gateway deployments by checking Kubernetes resources and performing an inference request.
• tools/cloud-build/daily-tests/builds/gke-a4-inference-gateway-test.yaml
  • Added Cloud Build configuration for the GKE A4 inference gateway integration test.
• tools/cloud-build/daily-tests/builds/gke-a4x-inference-gateway-test.yaml
  • Added Cloud Build configuration for the GKE A4x inference gateway integration test.
• tools/cloud-build/daily-tests/tests/gke-a4-inference-gateway-test.yml
  • Added test variables for the GKE A4 inference gateway integration test, specifying nvidia-a100-80gb accelerator and 1 GPU.
• tools/cloud-build/daily-tests/tests/gke-a4x-inference-gateway-test.yml
  • Added test variables for the GKE A4x inference gateway integration test, specifying nvidia-gb200 accelerator and 4 GPUs.
• tools/cloud-build/daily-tests/validate_tests_metadata.py
  • Performed a minor formatting adjustment by removing trailing whitespace in the CATEGORICAL_TAGS definition.

Activity

• The pull request was created with a standard submission checklist, indicating the initial contribution of these new blueprints and tests.

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[gemini-code-assist]

Code Review

This pull request introduces integration tests for new GKE inference blueprints for A4 and A4x GPUs. This is a valuable addition that improves test coverage for GPU-based environments.

My review has identified a few areas for improvement. Most critically, the new examples for gke-a4 and gke-a4x need to be added to the index in examples/README.md to comply with the repository's contribution guidelines (Rule 33). Additionally, there are some configuration issues in the new blueprint files, such as an incorrect default accelerator type and an expired maintenance window, which I've detailed in the review comments.

Please also consider filling out the PR description to provide more context for future reviewers.