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Sedna
What is Sedna?
Sedna is an edge-cloud synergy AI project incubated in KubeEdge SIG AI. Benefiting from the edge-cloud synergy capabilities provided by KubeEdge, Sedna can implement across edge-cloud collaborative training and collaborative inference capabilities, such as joint inference, incremental learning, federated learning, and lifelong learning. Sedna supports popular AI frameworks, such as TensorFlow, Pytorch, PaddlePaddle, MindSpore.
Sedna can simply enable edge-cloud synergy capabilities to existing training and inference scripts, bringing the benefits of reducing costs, improving model performance, and protecting data privacy.
Features
Sedna has the following features:
Architecture
Sedna's edge-cloud synergy is implemented based on the following capabilities provided by KubeEdge:
- Unified orchestration of across edge-cloud applications.
- Router: across edge-cloud message channel in management plane.
- EdgeMesh: across edge-cloud microservice discovery and traffic governance in data plane.
Component
Sedna consists of the following components:
GlobalManager
- Unified edge-cloud synergy AI task management
- Cross edge-cloud synergy management and collaboration
- Central Configuration Management
LocalController
- Local process control of edge-cloud synergy AI tasks
- Local general management: model, dataset, and status synchronization
Worker
- Do inference or training, based on existing ML framework.
- Launch on demand, imagine they are docker containers.
- Different workers for different features.
- Could run on edge or cloud.
Lib
- Expose the Edge AI features to applications, i.e. training or inference programs.
Guides
Documents
Documentation is located on readthedoc.io. These documents can help you understand Sedna better.
Installation
Follow the Sedna installation document to install Sedna.
Examples
Example1:Using Joint Inference Service in Helmet Detection Scenario.
Example2:Using Incremental Learning Job in Helmet Detection Scenario.
Example3:Using Federated Learning Job in Surface Defect Detection Scenario.
Example4:Using Federated Learning Job in YoLov5-based Object Detection.
Example5:Using Lifelong Learning Job in Thermal Comfort Prediction Scenario.
Example6:Using MultiEdge Inference Service to Track an Infected COVID-19 Carrier in Pandemic Scenarios.
Roadmap
Meeting
Regular Community Meeting:
Resources:
Contact
If you have questions, feel free to reach out to us in the following ways:
Contributing
If you're interested in being a contributor and want to get involved in developing the Sedna code, please see CONTRIBUTING for details on submitting patches and the contribution workflow.
License
Sedna is under the Apache 2.0 license. See the LICENSE file for details.