Beginner score
30/100
Competition workspace
适合有 Colossal AI 和 PyTorch 分布式大模型训练经验的团队，能够理解各 rank 的本地梯度，以及数据、张量、流水等并行策略。参赛需组队，每队至少 3 人、最多 5 人；还应能完成软件设计文档、用户手册、测试报告和开源代码仓库提交。
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 Colossal AI 和 PyTorch 分布式大模型训练经验的团队，能够理解各 rank 的本地梯度，以及数据、张量、流水等并行策略。参赛需组队，每队至少 3 人、最多 5 人；还应能完成软件设计文档、用户手册、测试报告和开源代码仓库提交。
If the items below still feel unfamiliar, you usually get a better result by preparing first instead of rushing in.
Colossal AI
PyTorch 分布式训练
数据并行与张量/流水并行
梯度统计与张量操作
ZeRO 分布式优化器
AtomGit 与开源许可证合规
The real friction is usually not library usage. It is validation, time allocation, and task framing.
核心难点是在权重切分、ZeRO 或 TP/PP 等并行场景下，分别正确记录每个具名权重在 DP 聚合前后的各 rank 本地梯度统计，同时不保存原始梯度张量。实现还要兼顾接近 msprobe.monitor 的接口与输出，并把训练性能损耗和额外显存占用控制在要求范围内，还需在指定语言模型和扩散模型上验证。
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.
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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.