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Sedna v0.5.0 release
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@jaypume jaypume released this 02 Jun 10:07
· 109 commits to main since this release
v0.5.0
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What's New

Add the Multi-Edge Inference Paradigm

The new Multi Edge Inference feature introduces a new mode of collaboration to manage distributed AI applications to combine computing power of edge nodes and fully utilize resources of edge nodes.

  • Provide feature extraction-based collaborative inference to protect privacy of data on the edge.
  • Filters data to reduce the amount of data transmitted from the edge.
  • Message-oriented middleware is introduced to support asynchronous message communication between multi-edge AI application components.

For details, see https://github.com/kubeedge/sedna/tree/main/examples/multiedgeinference/pedestrian_tracking

By @vcozzolino @soumajm @jaypume.

Incremental learning supports heterogeneous chips

The chips of the training, evaluation, and inference worker nodes in incremental learning are different, and it causes that AI models of the same version cannot be used in a unified manner. Therefore, models need to be converted based on the special chip version.
When this feature is imported to Incremental Learning, users do not need to manually convert different models. Instead, users can configure the chip version corresponding to the model when creating an application. In this way, models can be adaptively converted on different nodes.

By @JimmyYang20 in #315

Lifelong learning supports multi-node deployment

Sedna 0.4 supports the lifelong learning application of the single-node version. It need that the dataset and the node name of the training, evaluation, and inference worker must be the same.
However, this method has certain limitations in the following scenarios:

  • Training workers require more resources than evaluation workers and inference workers. In some cases, computing resources cannot meet requirements, and they need to be scheduled on different nodes.
  • In some scenarios, you need to manually specify that training, evaluation, and inference work on a specific node. For example, both of them work on an edge node.

Therefore, the new feature enables the training, evaluation, and inference workers of lifelong learning to support the configuration of different nodeNames and nodeSelectors, allowing users to flexibly specify running nodes when creating workers.

By @JimmyYang20 in #287

Sedna supports Helm deployment

Sedna helm charts are introduced, including helm charts of sedna-gm, sedna-lc, and sedna-kb. Users can install required components on demand. You can also customize or modify the sedna helm charts application template based on helm rules. In addition, users can upload sedna helm charts to various cloud-native app markets to deploy the entire sedna environment in a simpler and more convenient manner.
For details, see https://github.com/kubeedge/sedna/tree/main/build/helm/sedna

By @Poorunga in #297

Other Notable Changes

Bug Fixes

New Contributors

Full Changelog: v0.4.3...v0.5.0

Contributors

llhuii, jaypume, and 8 other contributors
Assets 2
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