interlink

README

Welcome to the interLink Project

interLink is an abstraction layer for executing Kubernetes pods on remote resources capable of managing container execution lifecycles.

It facilitates the development of provider-specific plugins for the Kubernetes Virtual Kubelet interface, enabling resource providers to leverage Virtual Kubelet capabilities without deep Kubernetes internals knowledge.

Architecture

The project consists of two main components:

• Virtual Kubelet (Virtual Node): Translates Kubernetes pod execution requests into remote calls to the interLink API server
• interLink API Server: A modular, pluggable REST server with provider-specific plugins (sidecars) for different execution environments

Key Features

• Plugin-based Architecture: Extensible sidecar system for different remote providers
• Multiple Deployment Patterns: Edge-node, in-cluster, and tunneled configurations
• Built-in Observability: OpenTelemetry integration with distributed tracing and metrics
• Secure Communication: TLS/mTLS encryption and authentication between components
• Authentication: OAuth2 integration and bearer token support
• Standard Kubernetes API: Maintains full compatibility with existing K8s tooling

interLink is hosted by the Cloud Native Computing Foundation (CNCF).

Getting Started

For usage and development guides please refer to our site

Development

# Build all components
make all

# Run tests (uses Dagger for containerized testing)
make test

# Generate OpenAPI specifications
make openapi

# Clean build artifacts
make clean

Adopters

Please find all our current adopters here

Contributing

Our project welcomes contributions from any member of our community. To get started contributing, please see our Contributor Guide.

Supported Providers

interLink supports a wide range of remote execution environments through its plugin architecture:

• HPC Batch Systems: SLURM, HTCondor with Apptainer, Enroot, or Singularity containers
• Virtual Machines: On-demand VMs with Docker, Podman, or other container runtimes
• Remote Kubernetes: Offload workloads to external Kubernetes clusters
• Serverless: Lambda-like services for event-driven container execution
• Custom Providers: Extensible plugin system for any container-capable remote resource

Use Cases

In Scope

• HPC Workloads: AI training, ML inference, scientific simulations requiring specialized hardware
• GPU-intensive Tasks: Remote execution on powerful GPU resources for ML training, data analysis, rendering
• Batch Processing: On-demand container execution with specific compute requirements
• Hybrid Cloud: Workload distribution across multiple infrastructure providers

Out of Scope

• Long-running Services: Persistent services with continuous availability requirements
• Kubernetes Federation: Multi-cluster resource management and federation

Communications

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Resources

License

This project is licensed under Apache2

Conduct

We follow the CNCF Code of Conduct