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Signed-off-by: Anastassios Nanos <ananos@nubificus.co.uk>
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README.md

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# MLSysOps Framework
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The *MLSysOps Framework* is the open‑source outcome of the EU‑funded MLSysOps
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Horizon Europe project (Grant ID 101092912), running from Jan 2023 to Dec 2025.
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Its aim is to deliver an AI‑enabled, agent‑based platform for autonomic,
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cross‑layer management of compute, storage, and network resources across cloud,
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edge, and IoT environments.
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## Key Objectives
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- Provide an *open, AI‑ready framework* for scalable, trustworthy,
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explainable system operation across heterogeneous infrastructures.
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- Enable *continual ML learning* and retraining during runtime via
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hierarchical agents.
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- Support *portable, efficient execution* using container innovation and
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modular, FaaS-inspired offloading.
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- Promote *green, resource‑efficient, and secure operations* while
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maintaining `QoS`/`QoE` targets.
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- Facilitate realistic evaluation using real-world deployments in smart‑city
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and precision‑agriculture scenarios.
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## Core Components
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- Hierarchical Agent Architecture: Interfaces with orchestration/control
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systems and exposes an ML‑model API for plug‑and‑play explainable/re-trainable
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models.
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- Telemetry & Control Knobs: Collects metrics across the continuum and adjusts
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configuration (e.g., compute, network, storage, accelerator usage)
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dynamically.
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- Distributed FaaS‑style Executor: Enables function offloading across tiers to
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optimize latency, energy, and performance.
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- Explainable ML & Reinforcement Learning Module: Offers transparent decisions,
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highlighting input factors influencing agent actions.
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- Use-cases: Includes real applications focusing on smart cities and agriculture.
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## Repository Contents
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| Directory | Description |
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|----------|-------------|
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| `agents/` | Core autonomic agents with policy-based plugins and ML/analytics |
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| `orchestrators/` | Scripts to facilitate testbed setup |
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| `mlsysops_cli/` | Tool to manage MLSysOps-related descriptors (agents, applications, etc.)|
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| `northbound-api/` | Glue API from the CLI to the core Agent framework|
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| `docs/` | Internal and public-facing documentation |
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## Getting Started
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### Prerequisites
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- Kubernetes v1.26+
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- `kubectl`, `karmada`
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- Python 3.10+
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- Access to a 4-node testbed environment
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### Quick Start
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Install the CLI tool:
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```bash
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pip install mlsysops-cli
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```
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Given an `ansible` inventory to setup 4 nodes in `inv.yml`, you can deploy the framework:
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```bash
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mls framework deploy-all --inventory inv.yml
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```
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Create and deploy an example application:
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```bash
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mls framework create-app-test-description
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mls apps deploy-all --path mlsysops-app-test-description.yaml
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```
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See docs/ for detailed component setup guides.
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## Documentation
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Check the full documentation at [docs.mlsysops.eu](https://docs.mlsysops.eu)
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## Contributing
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We welcome contributions from the community!
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Browse [good first issues](https://github.com/mlsysops-eu/mlsysops-framework/issues?q=is%3Aissue%20state%3Aopen%20label%3Agood-first-issue)
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Review our [CONTRIBUTING.md](https://docs.mlsysops.eu/developer-guide/contribute/)
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Follow our [CODE_OF_CONDUCT.md](https://github.com/mlsysops-eu/mlsysops-framework/blob/main/docs/developer-guide/Code-of-Conduct.md)
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## License
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This project is licensed under the Apache 2.0 License.
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## Acknowledgements
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This framework is developed as part of the Horizon Europe MLSysOps Project
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(Grant ID 101092912), coordinated by the University of Thessaly, with
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contributions from 12 European partners across academia and industry.
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Learn more at [mlsysops.eu](https://mlsysops.eu)

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