通过 PyTorch torch.distributed 接口测试昇腾 NPU 通信算子性能。支持指定任意 tensor shape、dtype，使用 torchrun 启动，贴近真实训练场景的通信算子测试与性能分析。Use for testing collective communication operators (AllReduce, AllGather, ReduceScatter, etc.) with specific tensor shapes via torch.distributed on Ascend NPU.
Plugin torch-npu-comm-test from marketplace awesome-ascend-skills in ascend-ai-coding/awesome-ascend-skills. Commands read from the repo's manifest — 30 days ago.
torch-npu-comm-test通过 PyTorch torch.distributed 接口测试昇腾 NPU 通信算子性能。支持指定任意 tensor shape、dtype，使用 torchrun 启动，贴近真实训练场景的通信算子测试与性能分析。Use for testing collective communication operators (AllReduce, AllGather, ReduceScatter, etc.) with specific tensor shapes via torch.distributed on Ascend NPU.
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.
191 skills
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。
191 skills
官方推荐的昇腾基础安装包。适合新同学和通用项目起步，包含设备检查、容器环境与 PyTorch NPU 基础能力。建议作为所有场景的第一步安装入口。
191 skills
官方推荐的昇腾推理安装包。适用于模型转换、量化、推理部署与评测场景，包含 ATC、vLLM-Ascend、ais-bench、msmodelslim、Diffusers 与 Wan 适配相关 skills。
191 skills
官方推荐的昇腾算子开发与迁移安装包。适用于 AscendC 算子开发、torch_npu 自定义算子接入、Triton-Ascend 迁移与算子基准测试场景。
191 skills
官方推荐的昇腾 Profiling 与性能分析安装包。适用于 Profiling 数据采集、瓶颈分析、训练性能诊断与 MFU 分析场景。
191 skills
Build, train, evaluate, and use open source AI models, datasets, and spaces.
26 skills · 1 MCP server
Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.
10 skills · 2 commands · 3 agents
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and external agent monitoring.
19 skills
Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.
1 skill · 3 commands · 1 agent
GPU cloud and compute orchestration including Modal, Lambda Labs, and SkyPilot. Use when deploying training jobs or managing GPU resources.
98 skills
SkyPilot agent skill for launching cloud VMs, Kubernetes pods, and Slurm jobs across 25+ clouds
1 skill
This page updates itself from your repo. Add the badge to your README, or get it Featured to pin it above the organic results with a followed link to your site.