This project is a Kubernetes wrapper for the lm-evaluation-harness, aimed at facilitating the deployment and management of language model evaluations within Kubernetes/OpenShift environments. It's currently under active development and resides in this repository as a work-in-progress.

The Kubernetes wrapper for lm-evaluation-harness (hereafter referred to as lm-eval-aas) extends the basic functionality of the original tool by integrating with Kubernetes APIs for state management and deploying as a Custom Resource (CR). This integration allows for more scalable and flexible deployment options suitable for various computational and storage needs.

We have an ADR available for this project, which outlines the rationale behind major architectural decisions. You can view and comment on the ADR here: View ADR.

• Kubernetes API Integration: Unlike previous demonstrations that managed
  state with a NoSQL database or local disk storage, lm-eval-aas uses the Kubernetes API for state management, enhancing the robustness and scalability of the application.

• Deployment as a Custom Resource (CR): The tool is designed to be deployed as a CR within a Kubernetes cluster, allowing for better integration with existing cluster management practices and tools.

• Support for Persistent Volume Claims (PVCs): lm-eval-aas can mount PVCs to access custom data sets. This is particularly useful for evaluations that require large or specific data sets not typically stored within the cluster, and for customers with sentative data handling requirements.

This project is currently in a work-in-progress state. Contributions and feedback are welcome. Please refer to the issue tracker in this repository to report bugs or suggest enhancements.

As the project is still under development, detailed instructions on deploying and using lm-eval-aas will be provided as the features are finalized and the project reaches a stable release.

graph LR;
    A[Client] --> B{Request};
    B -->|GET| C[Kubernetes Deployment];
    C -->|Process Request| D1[Pod 1];
    C -->|Process Request| D2[Flask App];
    F --> |Response| C;
    C -->|Process Request| D3[Pod N];
    D1 -->C;
    D3 -->C;
    C -->|Response| B;
    B -->|Response| A;

    subgraph Pod2
        D2((Flask App))
        D2 --> E1{Parse Arguments};
        E1 -->|Command Line Invocation| E2[lm-eval];
        E2 --> E3[TGIS];
        E2 --> E4[BAM];
        E2 --> E5[RHOAI Inference];
        E2 --> E6[...Other];
        E3 --> |Inference| E2;
        E4 --> |Inference| E2;
        E5 --> |Inference| E2;
        E6 --> |Inference| E2;
        D2 --> F[Calculate Metrics];
        E2 --> F;
        
    end

graph LR;

subgraph "Docker Container" 
    %% style rounded
    A[Install lm-eval and Flask]
    B[Expose REST Interface to lm-eval cli via Flask]
    C[Run lm-eval via cli with transpiled parameters]
end;

subgraph "Kubernetes/OpenShift Deployment" 
    %% style rounded
    D[Ingress]
    E[Load Balancer]
    F[Pod 1]
    G[Pod 2]
    H[...]
end;

subgraph "Log and Output Storage" 
    %% style rounded
    I[Log Stream]
    J[output.json Storage -PVC/COS/etc.]
end;

subgraph "User" 
    %% style rounded
    K[UI]
    L[Client]
end;

A --> B;
B --> C;

D --> E;
E -->|Ticket ID|D;
E --> F;
F -->|Ticket ID|E;
E --> G;
E --> H;

F --> I;
G --> I;
H --> I;

F --> J;
G --> J;
H --> J;

K --> L;

L --> |GET| D;
D --> |Ticket ID| L;
K --> |Ticket ID| I;
K --> |Ticket ID| J;
L --> |Ticket ID| K;
I --> |Logs| K;
J --> |Results| K;