Open Issues Need Help
View All on GitHubOME is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs)
AI Summary: Develop a Kubernetes agent within the ome-agent framework to extract metadata from Large Language Model (LLM) configuration files (JSON) mounted via Persistent Volume Claims (PVCs). The agent should update the corresponding BaseModel or ClusterBaseModel Kubernetes Custom Resource (CR) with the extracted metadata, handling various error conditions and using existing utility functions and libraries. Comprehensive unit testing is required.
OME is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs)
AI Summary: Modify the OME model agent to ignore Persistent Volume Claim (PVC) storage types, delegating their handling to the BaseModel controller. This involves adding logic to detect PVC storage, skipping related processing steps (downloads, status updates, ConfigMap updates, node labeling), and ensuring robust logging. The goal is to simplify the model agent and improve the separation of concerns between components.
OME is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs)
AI Summary: Implement comprehensive testing for existing PVC storage URI parsing functionality in the OME Kubernetes operator. This involves adding validation tests for edge cases (empty PVC names, missing subpaths, invalid characters, etc.) and ensuring 100% test coverage. The parsing logic itself is already implemented.
OME is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs)