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.
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:caption: GUIDE
index/guide
index/quickstart
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:caption: DEPLOY
Cluster Installation (for production) <setup/install>
AllinOne Installation (for development) <setup/all-in-one>
Standalone Installation (for hello world) <setup/local-up>
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:caption: INTRODUCTION
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proposals/architecture
proposals/dataset-and-model
proposals/federated-learning
proposals/incremental-learning
proposals/joint-inference
proposals/lifelong-learning
proposals/object-search
proposals/object-tracking
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:caption: EXAMPLES
examples/joint_inference/helmet_detection_inference/README
examples/incremental_learning/helmet_detection/README
examples/federated_learning/surface_defect_detection/README
examples/federated_learning/yolov5_coco128_mistnet/README
examples/lifelong_learning/atcii/README
examples/storage/s3/*
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:caption: API REFERENCE
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api/lib/*
Python API <autoapi/lib/sedna/index>
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:caption: Contributing
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Control Plane <contributing/prepare-environment>
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:caption: ROADMAP
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index/roadmap
.. mdinclude:: index/related_link.md