「nvidia」の検索結果

Check and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia), Python, and PyTorch. Use when checking component versions, verifying CUDA/driver compatibility, detecting version mismatches across nodes, planning upgrades, documenting cluster configuration, or troubleshooting version-related issues on HyperPod. Triggers on requests about versions, compatibility, component checks, or upgrade planning for HyperPod clusters.

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Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND bindings only. Supports models like LTX-Video, Stable Diffusion, LLaMA, Mistral, and Qwen. Includes integration with HuggingFace Kernels Hub (get_kernel) for loading pre-compiled kernels. Includes benchmarking scripts to compare kernel performance against baseline implementations.

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Use when configuring, repairing, planning, or running MiniMax H3 locally on a Windows NVIDIA computer, especially when installation compatibility, component sets, resolution, generation-time budget, path, low-VRAM risk, or H3 prompt mode must be chosen from hardware and user requirements.

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Use when breaking out of a container or escalating inside Kubernetes — runc/BuildKit CVEs, privileged/capability/cgroup misconfig escapes, NVIDIA GPU toolkit escape, K8s RBAC abuse, kubelet RCE, ingress/admission-controller RCE, node-to-cluster pivot

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Best practices for Docker-based ROS2 development including multi-stage Dockerfiles, docker-compose for multi-container robotic systems, DDS discovery across containers, GPU passthrough for perception, and dev-vs-deploy container patterns. Use this skill when containerizing ROS2 workspaces, setting up docker-compose for robot software stacks, debugging DDS communication between containers, configuring NVIDIA Container Toolkit for GPU workloads, forwarding X11/Wayland for rviz2 and GUI tools, or managing USB device passthrough for cameras and serial devices. Trigger whenever the user mentions Docker with ROS2, docker-compose for robots, Dockerfile for colcon workspaces, container networking for DDS, devcontainer for ROS2, multi-stage builds for ROS2, or deploying ROS2 in containers. Also trigger for CI/CD with Docker-based ROS2 builds, CycloneDDS or FastDDS configuration in containers, or shared memory in Docker. Covers Humble, Jazzy, and Rolling distributions across Ubuntu 22.04 and 24.04 base images.

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Set up and deploy a Boundless prover to a GPU server using Ansible. Handles inventory setup, SSH connectivity, NVIDIA drivers, Docker, and the full bento stack. Use when deploying a new prover, redeploying to an existing server, or troubleshooting prover infrastructure.

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Python backend development standards for FastAPI, SQLModel, pytest, Ruff, and Pyright. Use when writing or modifying backend code in src/merchant, src/payment, src/apps_sdk, agents, tests, API routes, services, models, or protocol logic.

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Discoverability shim for the Retail-Agentic-Commerce backend feature Codex skill. Use when writing or modifying Python backend code, routes, services, models, tests, or protocol logic.

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Discoverability shim for the Retail-Agentic-Commerce pre-commit analysis Codex skill. Use before commits, after significant changes, or for implementation-quality validation.

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Pre-commit implementation analysis for Retail-Agentic-Commerce. Use before commits, after significant changes, or when validating Apps SDK tools, mock data, MCP communication, widget isolation, or implementation quality.

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Dependency vulnerability remediation workflow for this repository. Use when fixing CVE, BDSA, GHSA, OSV, npm, PyPI, Dependabot, Black Duck, Snyk, or other package vulnerability findings, especially when the user asks for minimal package-only updates, validation, commits, branches, or pull requests.

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React and Next.js frontend development standards for Retail-Agentic-Commerce. Use when creating or modifying UI pages, components, hooks, styling, tests, or Apps SDK widget browser behavior in src/ui or related frontend surfaces.

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Discoverability shim for the Retail-Agentic-Commerce CVE remediation Codex skill. Use for CVE, BDSA, GHSA, OSV, Dependabot, Black Duck, Snyk, npm, PyPI, or package advisory remediation.

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Discoverability shim for the Retail-Agentic-Commerce setup Codex skill. Use when the user asks to setup, install, start, launch, or run the project stack.

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Discoverability shim for the Retail-Agentic-Commerce UI Codex skill. Use when creating or modifying frontend pages, components, hooks, styling, tests, or browser behavior.

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Launch all services for the Retail-Agentic-Commerce project. Use when the user types setup, install, or asks to start, launch, or run the project stack.

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Local speech-to-text on Apple Silicon with two cross-checking MLX engines: NVIDIA Parakeet TDT v2 (parakeet-mlx) and Qwen3-ASR 1.7B (mlx-audio, hotword biasing). Batch JSONL with word timestamps, confidence, provenance, and an agreement verdict. No NeMo or PyTorch. Use when transcribing audio files, identifying game or legacy voice clips by content, telling speech from SFX, serving an OpenAI-compatible STT endpoint, or dictating. For push-to-talk into any text field, use the Handy app.

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Contribute to OpenClaw ecosystem projects (OpenClaw, NemoClaw, NanoClaw) through a structured 9-step workflow: target verification, codebase exploration, parallel audit, finding cross-reference, and pull request creation. Emphasizes false positive prevention and project convention adherence.

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Use when writing HIP kernels with hipcc, porting CUDA code through HIPIFY, profiling with rocprofv3, debugging with rocgdb, or optimizing for MI300X. Not for NVIDIA-side kernels: use cuda.

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Use when analyzing warp divergence, memory coalescing, shared memory bank conflicts, cache behavior, atomics, or occupancy tradeoffs on NVIDIA and AMD GPUs. Not for tool commands: use cuda-profiling.

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Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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Geração estruturada rápida e serving para LLMs com RadixAttention para cache de prefixos. Use para saídas JSON/regex, decodificação restrita, workflows com agentes que fazem chamadas de ferramentas, ou quando você precisa de 5× mais rápido que vLLM com compartilhamento de prefixos. Alimenta 300.000+ GPUs na xAI, AMD, NVIDIA e LinkedIn.

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Curação de dados acelerada por GPU para treinamento de LLM. Suporta texto/imagem/vídeo/áudio. Recursos incluem deduplicação fuzzy (16× mais rápida), filtragem de qualidade (30+ heurísticas), deduplicação semântica, redação de PII, detecção NSFW. Escala em GPUs com RAPIDS. Use para preparar datasets de treinamento de alta qualidade, limpar dados web ou deduplicar grandes corpora.

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This skill should be used when the user asks about "Hyprland configuration", "hyprland.lua", "hyprland.conf", "Hyprland Lua config", "hyprctl", "Hyprland keybinds", "Hyprland window rules", "Hyprland monitors", "Hyprland dispatchers", "Hyprland animations", "Hyprland workspaces", "Hyprland IPC", "Hyprland plugins", "hyprlock", "hypridle", "hyprpaper", "hyprcursor", "hyprpicker", "hyprsunset", "xdg-desktop-portal-hyprland", "Hyprland on NixOS", "Hyprland Home Manager", "Hyprland layouts", "Hyprland dwindle", "Hyprland master layout", "Hyprland FAQ", "Hyprland performance", "Hyprland Nvidia", "Hyprland screen sharing", "Hyprland special workspace", "Hyprland smart gaps", "Hyprland multi-monitor", "Hyprland tearing", or any topic related to configuring, troubleshooting, or extending the Hyprland Wayland compositor and its ecosystem tools.

日本語の概要は準備中です。原文の説明を表示しています。