Sedna
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GUIDE

  • Guide
  • Quick Start

DEPLOY

  • Cluster Installation (for production)
  • AllinOne Installation (for development)
  • Standalone Installation (for hello world)

INTRODUCTION

  • Edge Cloud Collaborative AI Framework
  • Dataset and Model
  • Federated Learning
  • Incremental Learning
  • Joint Inference
  • Lifelong Learning
  • Object Search Service
  • Object Tracking Service

EXAMPLES

  • Using Joint Inference Service in Helmet Detection Scenario
  • Using Incremental Learning Job in Helmet Detection Scenario
  • Using Federated Learning Job in Surface Defect Detection Scenario
  • Collaboratively Train Yolo-v5 Using MistNet on COCO128 Dataset
  • Using Lifelong Learning Job in Thermal Comfort Prediction Scenario

API REFERENCE

  • Python API Use Guide
  • Python API
    • lib.sedna.algorithms
    • lib.sedna.backend
    • lib.sedna.common
    • lib.sedna.core
    • lib.sedna.datasources
    • lib.sedna.service
      • lib.sedna.service.multi_edge_inference
      • lib.sedna.service.server
        • lib.sedna.service.server.knowledgeBase
        • lib.sedna.service.server.aggregation
        • lib.sedna.service.server.base
        • lib.sedna.service.server.inference
      • lib.sedna.service.client
      • lib.sedna.service.run_kb
    • lib.sedna.__version__

Contributing

  • 1. Install Tools
  • 2. Clone the code
  • 3. Set up Kubernetes/KubeEdge(optional)
  • 4. What’s Next?

ROADMAP

  • Roadmap
Sedna
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  • lib.sedna.service.server.knowledgeBase
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lib.sedna.service.server.knowledgeBase¶

Submodules¶

  • lib.sedna.service.server.knowledgeBase.database
  • lib.sedna.service.server.knowledgeBase.model
  • lib.sedna.service.server.knowledgeBase.server
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© Copyright 2021, Kubeedge. Revision ff4c17fa. Last updated on Jun 02, 2022.

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