NewYour coding agent can read the release notes before it upgrades.Set up the MCP server →
Go modules · #2664 by repository stars
Last release 2 years ago
no release in 18 months
Release timing varies
gaps range from 3 weeks to 8 months
Most releases are documented
notes for 10 of 12 stable releases
Nothing withdrawn
no release was ever pulled
6 years old
54 releases · first in 2021
One column per quarter.
This version release of Sedna mainly includes the following functional upgrades and optimizations:
This version release of Sedna mainly includes the following functional upgrades and optimizations:
The detailed pull requests are as follows:
Surport Horizontal Pod Autoscaling (HPA) in joint inference by @ajie65 & @tangming1996 in #457 #465
joint Inference & federal learning controller Enhancements by @SherlockShemol in #437 #438 #445 #446
Kubernetes & Go version upgrades by @WillardHu in #462
Full Changelog: v0.6.0...v0.7.0
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Supports Unstructured Lifelong Learning
The new unstructured lifelong learning feature try to improve the efficiency and reduce cost of high-value data collection, in many scenarios like robot, factory, etc.. Those scenarios usually require a lot of unstructured image data to be processed, and will also face various corner cases. For example, robots need to process image data from cameras for perception, they may encounter different curb on different roads while the class of curb already exists at training phase , or a different new class like flower bed. If these classes are not recognized correctly, the robot will wrestle and even cause environmental damage.
Therefore, this version of unstructured lifelong learning supports the following functions to solve the preceding problems:
The detailed pull requests are as follows:
The detailed pull requests are as follows:
By @Lj1ang @Ymh13383894400 @qxygxt
Full Changelog: v0.5.1...v0.6.0
Nothing published for this version
Nothing published for this version
Add key envs for train worker in LL by @JimmyYang20 in #345
Full Changelog: v0.5.0...v0.5.1
Nothing published for this version
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.
For details, see https://github.com/kubeedge/sedna/tree/main/examples/multiedgeinference/pedestrian_tracking
By @vcozzolino @soumajm @jaypume.
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
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:
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 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
v by @llhuii in #247Full Changelog: v0.4.3...v0.5.0
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Fix the display problem in the home page by @jaypume in #216
Full Changelog: v0.4.2...v0.4.3
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Add object search controller by @EnfangCui in #190
Full Changelog: v0.4.1...v0.4.2
Nothing published for this version
Nothing published for this version
Nothing published for this version
Add all-in-one script and docs, you can try our online katacoda courses
MistNet integrated, a representation extraction aggregation algorithm.
In Incremental Learning, Inference/Train/Eval worker now can be deployed by nodename and nodeselector on multiple nodes.
Notable Changes:
Bug Fixes
Improvment
Nothing published for this version
Nothing published for this version
Nothing published for this version
Support edge-cloud synergy lifelong learning feature:
The published images can be found under docker.io/kubeedge:
kubeedge/sedna-gm:v0.3.0
kubeedge/sedna-lc:v0.3.0
kubeedge/sedna-kb:v0.3.0
kubeedge/sedna-example-joint-inference-helmet-detection-big:v0.3.0
kubeedge/sedna-example-joint-inference-helmet-detection-little:v0.3.0
kubeedge/sedna-example-incremental-learning-helmet-detection:v0.3.0
kubeedge/sedna-example-federated-learning-surface-defect-detection-train:v0.3.0
kubeedge/sedna-example-federated-learning-surface-defect-detection-aggregation:v0.3.0
kubeedge/sedna-example-lifelong-learning-atcii-classifier:v0.3.0
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Your coding agent can read these notes before it upgrades. Set up the MCP server →