PackageTrack

Go modules Β· #2494

github.com/kubeedge/sedna

v0.7.0kubeedge/sedna β†—

Release timeline

54 releases since 2021
20222023202420252026

Releases

  1. v0.7.06 Mar 2025
    Release notes

    What's Changed

    This version release of Sedna mainly includes the following functional upgrades and optimizations:

    • Joint inference supports the Horizontal Pod Autoscaling (HPA) mode, providing more powerful instance scaling capabilities to ensure the maximization of resource utilization. For details: πŸ‘‰ joint-inference-hpa
    • Sedna Controller Enhancements (Joint Inference & Federal Learning). For details: πŸ‘‰ sedna-controller-enhancement
      • Manage the joint inference instances using the k8s Deployment object
      • Support instance updates for joint inference and federal learning
    • Kubernetes & Go Version Upgrades. For details: πŸ‘‰ version-upgrade
      • Kubernetes version upgraded to v1.30.7
      • Go version upgraded to v1.22.9

    The detailed pull requests are as follows:

    Bug Fixes

    By @tangming1996

    New Contributors

    Full Changelog: v0.6.0...v0.7.0

    Open source β†’
  2. v0.6.1-0.20241128102935-6f0b2a4e8a8928 Nov 2024pre-release

    Nothing published for this version

  3. v0.6.1-0.20241122020829-01351c51aa0922 Nov 2024pre-release

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  4. v0.6.1-0.20241030124808-7cce21963a2b30 Oct 2024pre-release

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  5. v0.6.1-0.20240913093522-33429555215f13 Sept 2024pre-release

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  6. v0.6.1-0.20240813034624-2ecc30d821f713 Aug 2024pre-release

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  7. v0.6.1-0.20231211075653-ac623ab32dc311 Dec 2023pre-release

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  8. v0.6.1-0.20230920122146-b8ec263422e520 Sept 2023pre-release

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  9. v0.6.1-0.20230816011813-23dfbaaae97816 Aug 2023pre-release

    Nothing published for this version

  10. v0.6.013 Jun 2023
    Release notes

    What's New

    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:

    • Supports unstructured lifelong learning: implement closed-loop data of unstructured data, and provides robot delivery example demo vedio and code based on image segmentation.
    • Supports unseen task recognition.
      • Supports early warning of unseen task: For example, robots can stop in time when encountering image samples that cannot be processed by models for manual intervention or other optional measures.
      • Supports High-value data filtering: Only inference samples those cannot be inferenced correctly by models are considered high-value data, and will be collected then used for model training and update.
    • Supports displaying knowledge base: Displays knowledge base information to users, including the number of unseen samples (or high-value samples) and the corresponding number of trained models.

    The detailed pull requests are as follows:

    • Proposal and tutorial of unstructured lifelong learning by @luosiqi in #391
    • Unstructured Sedna Lifelong Learning Architecture by @luosiqi in #392
    • Unstructured lifelong learning with cloud robotics example by @luosiqi in #382
    • Support displaying knowledge base by @jaypume in #406

    By @luosiqi @jaypume

    O&M and Monitoring

    • Supports observability management for displaying logs and metrics of sedna in real time.
    • Supports lifelong learning exporter and Visualization.

    The detailed pull requests are as follows:

    • The proposal for Observability management by @Kanakami in #340
    • The implementation for Observability management by @Kanakami in #366
    • The proposal for Lifelong Learning O&M by @wjf222 in #335
    • The implementation for Lifelong Learning exporter and Visualization by @wjf222 in #369

    By @Kanakami @wjf222

    More Examples Provided：

    • Provides a Mindspore demo by @Lj1ang in #376
    • Provides a high-frequency Sedna-based end-to-end use case in ModelBox by @Ymh13383894400 in #368
    • Provides a tutorial for ATCII Lifelong Learning Job by @qxygxt in #405

    By @Lj1ang @Ymh13383894400 @qxygxt

    Other Notable Changes

    • Supports JSON format Dataset Parse in Python Lib by @yqhok1 in #375
    • Supports TinyMS backend in Python SDK by @Lj1ang in #341

    By @yqhok1 @Lj1ang

    Bug Fixes

    By @RyanZhaoXB @jaypume

    New Contributors

    Full Changelog: v0.5.1...v0.6.0

    Open source β†’
  11. v0.5.2-0.20230613040632-3e8de61074eb13 Jun 2023pre-release

    Nothing published for this version

  12. v0.5.2-0.20221229063421-1cddd17ef2b229 Dec 2022pre-release

    Nothing published for this version

  13. v0.5.115 Aug 2022
    Release notes

    What's Changed

    Full Changelog: v0.5.0...v0.5.1

    Open source β†’
  14. v0.5.1-0.20220621071104-946f92a9d4a921 Jun 2022pre-release

    Nothing published for this version

  15. v0.5.02 Jun 2022
    Release notes

    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

    Open source β†’
  16. v0.4.4-0.20220316114756-527c574a60d016 Mar 2022pre-release

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  17. v0.4.4-0.20220214084550-449a6d84234314 Feb 2022pre-release

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  18. v0.4.4-0.20220118021525-b30b98eecd5818 Jan 2022pre-release

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  19. v0.4.4-0.20211231083908-397b5e51a1e831 Dec 2021pre-release

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  20. v0.4.4-0.20211217012454-f3e0c17a5ee017 Dec 2021pre-release

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  21. v0.4.4-0.20211215025752-5e329dec583015 Dec 2021pre-release

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  22. v0.4.4-0.20211202014038-5b0afad5e8f22 Dec 2021pre-release

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  23. v0.4.4-0.20211201012137-3b9cef09fc0e1 Dec 2021pre-release

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  24. v0.4.4-0.20211129085836-facde69f72d429 Nov 2021pre-release

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  25. v0.4.4-0.20211122062422-710c2a3e7f1e22 Nov 2021pre-release

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  26. v0.4.4-0.20211122024622-5a08bcb7052022 Nov 2021pre-release

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  27. v0.4.4-0.20211118095118-3d7c0417495318 Nov 2021pre-release

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  28. v0.4.4-0.20211112011911-404fed4e237812 Nov 2021pre-release

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  29. v0.4.4-0.20211109070809-1d0f39ba3fe99 Nov 2021pre-release

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  30. v0.4.32 Nov 2021
    Release notes

    What's Changed

    • Fix the display problem in the home page by @jaypume in #216
    • Fix kb image version in the install script by @JimmyYang20 in #209
    • all-in-one: fix master node unschedule taint bug by @llhuii in #210
    • Improve update-codegen.sh to copy generated files into SEDNA_ROOT. by @vcozzolino in #218
    • Improve build script of examples by @vcozzolino in #214
    • Add missing object search/tracking CRD and RBAC yamls by @llhuii in #223
    • Unify all services exposed as ClusterIP by integrating EdgeMesh by @llhuii in #220
    • example: fix fl_model.train in surface_defect_detection_v2 by @XinYao1994 in #226
    • storage initializer: keep dir name in s3 download by @JimmyYang20 in #221
    • all-in-one: add NO_INSTALL_SEDNA variable flag by @llhuii in #227
    • Fix building wrong GM/LC arm64 docker images by @JimmyYang20 in #215

    New Contributors

    Full Changelog: v0.4.2...v0.4.3

    Open source β†’
  31. v0.4.3-0.20211102074301-1ce64fcaf7eb2 Nov 2021pre-release

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  32. v0.4.3-0.20211030025758-a760f17610d330 Oct 2021pre-release

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  33. v0.4.3-0.20211029095758-b1ec13e9881b29 Oct 2021pre-release

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  34. v0.4.3-0.20211029084158-930e0b6b9f1d29 Oct 2021pre-release

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  35. v0.4.3-0.20211028044957-de27dd7ecc9e28 Oct 2021pre-release

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  36. v0.4.3-0.20211026081854-fa6df45c30b126 Oct 2021pre-release

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  37. v0.4.3-0.20211025015852-ed4756c1330825 Oct 2021pre-release

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  38. v0.4.3-0.20211023012522-e5c7c9765c2223 Oct 2021pre-release

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  39. v0.4.3-0.20211020115706-7fe5387e588e20 Oct 2021pre-release

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  40. v0.4.213 Oct 2021
    Release notes

    What's Changed

    Full Changelog: v0.4.1...v0.4.2

    Open source β†’
  41. v0.4.2-0.20211013035500-1c5c53784ebe13 Oct 2021pre-release

    Nothing published for this version

  42. v0.4.2-0.20210928033145-1d7bd48adcdc28 Sept 2021pre-release

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  43. v0.4.2-0.20210924013241-3d88d0d7b73f24 Sept 2021pre-release

    Nothing published for this version

  44. v0.4.118 Sept 2021
    Release notes

    New Features

    Bug Fixes

    • Optimize installation script (#195)
    • Fixes some bugs in the PR of integrating Plato into Sedna to support federated learning (#197)
    • Fixes GM OOM killer by increasing GM memory limit from 128Mi to 256Mi (#200)
    • Remove externalIPs when creating k8s service in GM (#201)
    Open source β†’
  45. v0.4.08 Sept 2021
    Release notes

    Federated Learning

    • MistNet integrated, a representation extraction aggregation algorithm.
      • further reduce the resources requirement for the edge, by extracting the representation data on the edge side, and aggregation training on the cloud side.
      • further protect data privacy, by quantization and noise.
      • provides a sample code based on the yolov5 network.
    • Supports extending edge-cloud transmission method, including S3 and WebSocket protocols, by abstracting Transmitter interface.
    • Supports extending client choose algorithm in federated learning, by abstracting the ClientChoose interface.
    Open source β†’
  46. v0.3.113 Aug 2021
    Release notes

    Notable Changes:

    • In Incremental Learning, Inference/Train/Eval worker now can be deployed by nodename and nodeselector on multiple nodes.

    Bug Fixes

    • Fixed train/eval/infer callback function args not supported of sklearn backend.
    • Fixed model saving problem in Lifelong Learning Estimator.
    • Fixed incorrectly executing aggregation algorithm on the edge in Federated Learning.
    • Fixed that the job cann't be resumed after the LC restarts.

    Improvment

    • Decouple all features into independent package of gm and lc.
    Open source β†’
  47. v0.3.1-0.20210617062913-9a6e03eaa68217 Jun 2021pre-release

    Nothing published for this version

  48. v0.3.1-0.20210609074606-304059ef46e89 Jun 2021pre-release

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  49. v0.3.1-0.20210608122705-92bde06cd9648 Jun 2021pre-release

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  50. v0.3.07 Jun 2021
    Release notes

    Lifelong learning

    • Support edge-cloud synergy lifelong learning feature:
      • leverages the cloud knowledge base which empowers the scheme with memory ability, which helps to continuously learn and accumulate historical knowledge to overcome the catastrophic forgetting challenge.
      • is essentially the combination of another two learning schemes, i.e., multi-task learning and incremental learning, so that it can learn unseen tasks with shared knowledge among various scenarios over time.
    • Add lifelong learning example.

    Lib refactor

    • By using a registration of class-factory functions to emulate virtual constructors, developers can invoke different components by change variables in the Config file.
    • Clean up and redesign the base Config class, each feature maintains it's specific variables, and ensures that developers can be manually updated the variables.
    • Decouple the ML framework from the features of sedna, allows developers to choose their favorite framework.
    • Add a common file operation and a unified log-format in common module, use an abstract base class to standardize the feature modules' interface, and features are invoked by inheriting the base class.

    Published images

    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

    Open source β†’
  51. v0.2.1-0.20210604102102-dd274f39338e4 Jun 2021pre-release

    Nothing published for this version

  52. v0.2.1-0.20210513030238-1e4d750de76513 May 2021pre-release

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  53. v0.2.030 Apr 2021

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  54. v0.1.01 Apr 2021

    Nothing published for this version