pytorch skillA
pytorch is agent-read markdown (skill) from yuecao365/offercome: PyTorch 怎么面：自动求导、训练循环、数据管道、分布式训练、混合精度。JD 点名 PyTorch 时读。.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
## 面试官在意什么 PyTorch 是深度学习与大模型训练的默认框架，凡是简历上写"深度学习""模型训练""大模型微调"的候选人几乎都要过这一关。真实面试里 PyTorch 题分两档：一档是"会不会用"——张量操作、autograd、写训练循环、DataLoader；另一档是"能不能把训练跑快跑稳"——GPU 利用率低怎么查、显存 OOM 怎么省、多卡训练怎么配、loss 出 NaN 怎么办。面试官最在意的是候选人是否真正踩过坑：eval 忘了 no_grad、DDP 各卡 loss 不同步、DataLoader 成为瓶颈、混合精度溢出——这些都是能把"跟着教程跑过"和"独立训练过模型"区分开的点。 校招侧重基础机制：计算图怎么建、backward 做了什么、in-place 操作为什么会报错、train/eval 模式区别、手写一个完整训练循环；社招侧重工程效率：性能剖析、分布式训练（DDP/FSDP）、显存优化（checkpointing、混合精度、优化器状态分片）、torch.compile 的收益与坑、模型导出与推理部署。大模型方向额外追 FSDP 分片策略、序列并行、长序列训练的显存账。 怎么问才像这个方向的面试官： - 所有题都从一个异常现象切入（NaN、OOM、hang、慢、结果不一致），考排查顺序与原理，不考 API 拼写。 - 必须问显存账和吞吐数字：候选人说"优化了训练"，追优化前后的具体数值和测量方法。 - 规模要与候选人项目匹配：单卡跑 ResNet 的候选人不问 FSDP 分片策略，训过 7B 以上模型的必须追分布式细节。 - torch.compile、FSDP2 等新特性只考"用过的坑"，不考版本差异。 ## 项目 / 实习怎么深挖 简历上出现下面这类经历时从哪里切、追什么。追到候选人能说出机制、数字的来源与一次真实的故障或取舍才算实；只有框架名与结论、说不出自己那一段的，记为危险信号。通用的追问方法见 project-deep-dive。 - 简历出现"训练 X 模型" → 追多少卡、多久、GPU 利用率多少、遇到的最大工程问题 - 简历出现 DDP / FSDP / DeepSpeed → 追分片策略、通信瓶颈、显存账、出过什么 hang 或不一致 - 简历出现"显存优化 / 大 batch" → 追用了什么手段、速度代价、怎么验证效果不变 - 简历出现混合精度 → 追 FP16 还是 BF16、有没有 NaN、哪些层保留 FP32 - 简历出现"训练加速 X 倍" → 追优化前后 profiler 数据、瓶颈在哪、改了什么 - 简历出现模型部署 / ONNX / TensorRT → 追导出遇到的算子问题、精度对比、延迟数据 …
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How to install
mdr add yuecao365/offercome/pytorch@git:20260920.a4843f5mdr add yuecao365/offercome/pytorch@sha256:da3b0b7cba7c0cc1Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_njff62et4tcd6dov)
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Source
yuecao365/offercome · 23 stars · license MIT · pushed 2026-09-23 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_njff62et4tcd6dov GET https://markdownregistry.com/api/v1/resolve?ref=yuecao365/offercome/pytorch GET https://markdownregistry.com/api/v1/blob/da3b0b7cba7c0cc1779fd638a950ed78e7d8ed799dfe1cffa267d59a78859cf8
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