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Adding KubeEdge ideas to LFX-23-Q1 #797

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Jan 31, 2023
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Adding KubeEdge ideas to LFX-23-Q1 #797

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add kubeedge ideas for lfx
fisherxu Jan 31, 2023
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Merge branch 'main' into lfx-231
nate-double-u Jan 31, 2023
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28 changes: 28 additions & 0 deletions lfx-mentorship/2023/01-Mar-May/project_ideas.md
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Expand Up @@ -215,3 +215,31 @@ Note: This item is a work in progress. The selected mentee is expected to contin
- Recommended Skills: Rancher, Grafana stack, Javascript
- Mentor(s): Anurag Kumar (@kranurag7, contact.anurag7@gmail.com), Ankur Kothiwal (@Ankurk99, ankur.kothiwal99@gmail.com), Barun Acharya (@daemon1024, barun1024@gmail.com), Rahul Jadhav (@nyrahul, nyrahul@gmail.com)
- Issue: <https://github.com/kubearmor/KubeArmor/issues/992>

### KubeEdge

#### Design and implement the KubeEdge Dashboard

- Description: Users now can use K8s API or Kubectl to talk to KubeEdge, in this project we will design and implement the KubeEdge dashboard, so users can talk to KubeEdge cluster through UI.
- Expected Outcome: Create the KubeEdge dashboard, users can view and operate the resource through UI.
- Recommended Skills: JS, Kubernetes, KubeEdge, Html
- Mentors: Vincent Lin (@vincentgoat, linguohui1@huawei.com), Fisher Xu (@fisherxu, fisherxu1@gmail.com)
- Issue: <https://github.com/kubeedge/dashboard/issues/1>


#### Re-design and implement the KubeEdge website

- Description: KubeEdge's website has been running for a few years, and now we have more customer cases and more developer courses, so this project will update KubeEdge's website, with more readable documents on the homepage, covering user cases, developer courses, etc.
- Expected Outcome: The website has more readable documentation, covering user cases, developer courses, etc.
- Recommended Skills: JS, KubeEdge, Html
- Mentor(s): Shelley Bao (@Shelley-BaoYue, baoyue2@huawei.com), Fisher Xu (@fisherxu, fisherxu1@gmail.com)
- Issue: <https://github.com/kubeedge/website/issues/292>


#### Cloud-Robotic AI Benchmarking for Edge-cloud Collaborative Lifelong Learning

- Description: Based on real-world datasets provided by industry members of KubeEdge SIG AI, the issue aims to build a lifelong learning benchmarking on KubeEdge-Ianvs. Namely, it aims to help all Edge AI application developers to validate and select the best-matched algorithm of lifelong learning.
- Expected Outcome: The benchmark includes: 1) Work together to release a new dataset to the public! 2) Implement critical algorithm or system metrics, e.g., BWT, FWT and thoughput; 3) (Optional) Develop a baseline algorithm for this benchmark.
- Recommended Skills: TensorFlow/Pytorch, Python, Kubernetes
- Mentor(s): Siqi Luo (@luosiqi, luosiqi2@huawei.com), Fisher Xu (@fisherxu, fisherxu1@gmail.com)
- Issue: <https://github.com/kubeedge/ianvs/issues/48>