Summary of Changes

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This pull request enhances the framework's robustness and usability by updating core dependencies, improving input validation mechanisms across various modules, and expanding support for advanced GKE TPU configurations. It also refines GCSFuse integration for machine learning workloads and optimizes the continuous integration pipeline with new tests and build process simplifications.

Highlights

• Dockerfile and README Updates: The Dockerfile for the a3mega nemo framework has been updated with revised NCCL environment variables, and the corresponding README.md instructions have been adjusted to reflect these changes, including new data download steps and container mount paths.
• Dependency Updates: Numerous Python package dependencies in community/front-end/ofe/requirements.txt have been updated to their latest versions, and Go dependencies (golang.org/x/oauth2, golang.org/x/sys) have also been bumped.
• New Range Validator for Metadata: A new RangeValidator has been implemented and integrated into the blueprint validation system. This validator allows defining minimum and maximum bounds for numeric input variables or the length of lists in metadata.yaml files, enhancing input validation capabilities across various modules.
• Slurm Cluster Name Regex Expansion: The regular expression for slurm_cluster_name validation in several Slurm-related modules (gke-nodeset, schedmd-slurm-gcp-v6-controller, slurm-gcp/instance_template, slurm-gcp/internal_instance_template) has been updated to allow hyphens and extend the maximum length from 10 to 20 characters, providing more flexibility for cluster naming.
• Shared Reservation Permission Clarification: Documentation and code related to shared reservations in schedmd-slurm-gcp-v6-nodeset have been updated to clarify that while the compute.reservations.get permission is ideal, the system will now gracefully fall back to minimal reservation settings and proceed if this permission is missing.
• GKE TPU Flex Start Examples: New examples and configurations have been added for GKE TPU Flex Start clusters, specifically for TPU 7x and TPU v6e, including deployment YAMLs, Kueue configurations, and detailed READMEs on how to set up and test dynamic scaling for these TPU types.
• GCSFuse Mount Options Refinement: GCSFuse mount options in several machine learning blueprints (a3-ultragpu-8g, a4-highgpu-8g, a4x-highgpu-4g) have been updated to use underscore-separated keys and leverage new profile options (e.g., aiml-checkpointing, aiml-training, aiml-serving) for optimized performance based on workload type.
• Cloud Build Test Infrastructure Improvements: Cloud Build test configurations have been streamlined by removing redundant gcluster-bundle.zip download/unzip steps and associated secretEnv variables, replacing them with a local make command. New daily tests for GKE H4D on-spot and ML H4D on-spot Slurm have also been introduced.