kubeedge/ianvs

Track of Runnable Examples

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#194 opened on Apr 19, 2025

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good first issuekind/cleanup

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Description

This issue serves to track the runnable examples in the latest release of Ianvs.

Ianvs includes a wide range of examples showcasing its benchmarking capabilities. So far, I have verified the following examples as runnable and also updated their quick-start documentation guide for easy setup with no errors:

  • MOT17/multiedge_inference_bench
  • aoa/single_task_bench
  • bdd/lifelong_learning_bench
  • cifar100/fci_ssl
  • cifar100/federated_class_incremental_learning
  • cifar100/federated_learning
  • cifar100/sedna_federated_learning
  • cityscapes/lifelong_learning_bench
  • cityscapes/singletask_learning_bench
  • cityscapes-synthia/lifelong_learning_bench
  • cityscapes-synthia/scene-based-unknown-task-recognition
  • cloud-edge-collaborative-inference-for-llm
  • government/singletask_learning_bench
  • imagenet/multiedge_inference_bench
  • llm-agent/singletask_learning_bench
  • llm-edge-benchmark-suite/single_task_bench
  • llm-edge-benchmark-suite/single_task_bench_with_compression
  • llm_simple_qa
  • pcb-aoi/incremental_learning_bench
  • pcb-aoi/singletask_learning_bench
  • robot/lifelong_learning_bench
  • robot-cityscapes-synthia/lifelong_learning_bench
  • smart_coding/smart_coding_learning_bench
  • yaoba/singletask_learning_boost
  • yaoba/singletask_learning_yolox_tta

I'll continue updating this list as I verify that the other examples work correctly and update their quick-start documentation as needed.

Contributor guide

Research direction
Select an unchecked example from the list. Read its existing quick start guide and attempt to run it. Note any errors or missing steps. Update the documentation to make it accurate and reproducible. Then mark the checkbox in the issue and explain what you verified.
Tech stack
python
Domain
documentationtesting
Issue type
Documentation
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
Half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
GitPython
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
80