claude

pytorch-profiling-collection

使用 torch_npu.profiler 在非 MindSpeed-LLM 与非 MindSpeed-MM 的训练/推理脚本中采集 Ascend NPU Profiling 数据。覆盖 level0/level1/level2 采集级别、训练循环/单次推理/指定代码段三种方式,支持 .py 和 .sh 脚本路径。当用户需要性能采集、性能分析、查看算子耗时、定位训练瓶颈时使用。触发关键词:profiling、性能采集、性能分析、算子耗时、瓶颈定位、torch_npu.profiler。

184 on GitHubupdated 30 days agoprofiling
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Plugin pytorch-profiling-collection from marketplace awesome-ascend-skills in ascend-ai-coding/awesome-ascend-skills. Commands read from the repo's manifest — 30 days ago.

What it installs

Skills (1)

  • pytorch-profiling-collection

    使用 torch_npu.profiler 在非 MindSpeed-LLM 与非 MindSpeed-MM 的训练/推理脚本中采集 Ascend NPU Profiling 数据。覆盖 level0/level1/level2 采集级别、训练循环/单次推理/指定代码段三种方式,支持 .py 和 .sh 脚本路径。当用户需要性能采集、性能分析、查看算子耗时、定位训练瓶颈时使用。触发关键词:profiling、性能采集、性能分析、算子耗时、瓶颈定位、torch_npu.profiler。

View source on GitHub

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AI for Science skill set for Huawei Ascend NPU. Includes a main routing skill for profiling collection, model migration, and TensorFlow route selection, plus specialized leaf skills for Boltz2, BoltzGen, DeepFRI, DiffSBDD, GENERator, OligoFormer, ProteinBERT, TensorFlow Community deployment, TF-to-PyTorch conversion, and torch_npu profiling workflows.

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官方推荐的 AI for Science 安装包。适用于 AI for Science 场景下的 Profiling 采集、模型迁移、TensorFlow/Keras 路线选择与相关子方向能力。

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