lakhera2023/IdeaWeaver-Kubernetes-LLM-Leaderboard
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IdeaWeaver Kubernetes LLM Leaderboard
Overview
This leaderboard evaluates Large Language Models (LLMs) on their understanding of Kubernetes, container orchestration, and related DevOps practices. The evaluation uses a comprehensive dataset of 1,000 high-quality multiple choice questions covering:
- Kubernetes Fundamentals: Pods, Services, Deployments, Namespaces, ConfigMaps, Secrets
- Container Orchestration: StatefulSets, DaemonSets, Jobs, CronJobs, Persistent Volumes
- Advanced Kubernetes: RBAC, Network Policies, Ingress, Helm, Monitoring, Security
- DevOps Integration: CI/CD, GitOps, Infrastructure as Code, Monitoring, Logging
Dataset Statistics
- Total Questions: 1,000
- Categories: Kubernetes (971), Docker (4), DevOps (25), Git (0)
- Difficulty Levels: Advanced (150), Intermediate (842), Beginner (8)
- Format: Multiple choice with 4 options (A, B, C, D)
Configuration
Most of the variables to change for a default leaderboard are in src/env.py (replace the path for your leaderboard) and src/about.py (for tasks).
Results files should have the following format and be stored as json files:
{
"config": {
"model_dtype": "torch.float16", # or torch.bfloat16 or 8bit or 4bit
"model_name": "path of the model on the hub: org/model",
"model_sha": "revision on the hub",
},
"results": {
"task_name": {
"metric_name": score,
},
"task_name2": {
"metric_name": score,
}
}
}Request files are created automatically by this tool.
If you encounter problem on the space, don't hesitate to restart it to remove the create eval-queue, eval-queue-bk, eval-results and eval-results-bk created folder.
Code logic for more complex edits
You'll find
- the main table' columns names and properties in
src/display/utils.py - the logic to read all results and request files, then convert them in dataframe lines, in
src/leaderboard/read_evals.py, andsrc/populate.py - the logic to allow or filter submissions in
src/submission/submit.pyandsrc/submission/check_validity.py
