Beginner score
30/100
Competition workspace
适合有 Linux/openKylin 系统开发经验，并接触过 CPU、GPU 或 NPU 上 AI 推理部署的学生、工程师或个人开发者。参赛者还应能开发库或中间件、设计统一 API，并完成可复现的开源项目交付；可 1—3 人组队。
Suggested next step
Decide whether this contest fits your current stage before you sink time into the leaderboard.
Beginner score
30/100
Learning value
60/100
Estimated effort
24-80 hours
Metric
Official scoring is published on the Op…
适合有 Linux/openKylin 系统开发经验，并接触过 CPU、GPU 或 NPU 上 AI 推理部署的学生、工程师或个人开发者。参赛者还应能开发库或中间件、设计统一 API，并完成可复现的开源项目交付；可 1—3 人组队。
If the items below still feel unfamiliar, you usually get a better result by preparing first instead of rushing in.
openKylin/Linux 系统开发
CPU/GPU/NPU 异构计算
AI 推理运行时
系统资源监控
调度算法设计
Git 与开源许可证
The real friction is usually not library usage. It is validation, time allocation, and task framing.
难点不只是做出调度逻辑，而是要在单机上实际感知并协调 CPU、GPU、NPU，让模型推理的算力分布到多类设备，同时证明相对基线在性能和能效上有改进。作品还需能够持续稳定运行，并按要求提交可运行代码、文档、验证结果和演示材料，最终开放源代码。
This guide is the best pre-read if you want a cleaner start instead of trial-and-error.
How to learn feature engineering, validation, and competition workflow without heavy hardware.
No GPU? Pick Competitions That Still Teach You Good HabitsUse these fields to make a quick decision before you dive deeper.
Registration is unverified; confirm availability on the official page before investing.
Sign in to save competitions and build your own shortlist.
Rules, files, submission details, and the live deadline still come from the official page.
These competitions share a similar domain or difficulty level.
ModelScope
General MLModelScope
General MLKaggle
General MLKaggle
General MLSeparate what is confirmed from what still needs review. Official rules and deadlines win — report anything that looks wrong.
Official metric is not published on the OpenAtom competition listing; verify scoring on the competition page.
reward_value_usd is a rough CNY→USD estimate (×0.14) for ranking only; use reward_summary for the official prize text.