「gpu」の検索結果

Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). Covers WebGPU renderer setup, TSL syntax and node materials, compute shaders, post-processing effects, and WGSL integration. Use this skill when working with Three.js WebGPU, TSL shaders, node materials, or GPU compute in Three.js.

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。

Web 线上展览开发 (虚拟展厅) (Web 线上展览 / 虚拟展厅开发 (从业者/工程视角) — 用 web 技术开发线上展览、虚拟展厅、云展会、数字展馆，覆盖 3D 空间、全景漫游、WebXR、文博数字化、营销云展厅、3D 商品展示等形态。覆盖: (a) 技术栈 — 3D web(Three.js / React Three Fiber / Babylon.js / Google model-viewer / WebGL / WebGPU)、全景(Pannellum / Marzipano / Photo Sphere Viewer / krpano / 720云)、WebXR(WebXR Device API / A-Frame)、资产管线(glTF·GLB / Draco / Meshopt / KTX2 纹理压缩 / gltf-transform / LOD); (b) 自研 vs SaaS 平台选型(国内: 众趣 / 酷雷曼 / 比目鱼 / 会鸽 / 3DVista; 海外: Matterport / Kuula / Artsteps / Spatial); (c) 性能优化(大场景加载 / 模型轻量化 / draw call / 移动端 / 首屏 / 显存)——web 3D 第一性难题; (d) 策展与导览(动线设计 / 热点 hotspot / 语音讲解 / 交互); (e) 文博数字化(IIIF / 数字孪生 / 高清文物) 与 会展·电商商业化(营销云展厅 / 数据埋点 / 线索转化); (f) 部署(CDN / 流式加载 / 跨端兼容 / WebGL 降级)。学派分歧: 自研 Three.js vs SaaS 平台、真 3D 重场景 vs 720 全景轻量、WebGL vs WebGPU、文博考据派 vs 营销转化派、沉浸优先 vs 加载优先。不含: 线下展陈 / 展台搭建、原生 VR App(非 web)、通用 3D 游戏开发、纯 3D 建模教程。) Master OS — automated mastery of Web 线上展览 / 虚拟展厅开发 (从业者/工程视角) — 用 web 技术开发线上展览、虚拟展厅、云展会、数字展馆，覆盖 3D 空间、全景漫游、WebXR、文博数字化、营销云展厅、3D 商品展示等形态。覆盖: (a) 技术栈 — 3D web(Three.js / React Three Fiber / Babylon.js / Google model-viewer / WebGL / WebGPU)、全景(Pannellum / Marzipano / Photo Sphere Viewer / krpano / 720云)、WebXR(WebXR Device API / A-Frame)、资产管线(glTF·GLB / Draco / Meshopt / KTX2 纹理压缩 / gltf-transform / LOD); (b) 自研 vs SaaS 平台选型(国内: 众趣 / 酷雷曼 / 比目鱼 / 会鸽 / 3DVista; 海外: Matterport / Kuula / Artsteps / Spatial); (c) 性能优化(大场景加载 / 模型轻量化 / draw call / 移动端 / 首屏 / 显存)——web 3D 第一性难题; (d) 策展与导览(动线设计 / 热点 hotspot / 语音讲解 / 交互); (e) 文博数字化(IIIF / 数字孪生 / 高清文物) 与 会展·电商商业化(营销云展厅 / 数据埋点 / 线索转化); (f) 部署(CDN / 流式加载 / 跨端兼容 / WebGL 降级)。学派分歧: 自研 Three.js vs SaaS 平台、真 3D 重场景 vs 720 全景轻量、WebGL vs WebGPU、文博考据派 vs 营销转化派、沉浸优先 vs 加载优先。不含: 线下展陈 / 展台搭建、原生 VR App(非 web)、通用 3D 游戏开发、纯 3D 建模教程。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on Web 线上展览 / 虚拟展厅开发 (从业者/工程视角) — 用 web 技术开发线上展览、虚拟展厅、云展会、数字展馆，覆盖 3D 空间、全景漫游、WebXR、文博数字化、营销云展厅、3D 商品展示等形态。覆盖: (a) 技术栈 — 3D web(Three.js / React Three Fiber / Babylon.js / Google model-viewer / WebGL / WebGPU)、全景(Pannellum / Marzipano / Photo Sphere Viewer / krpano / 720云)、WebXR(WebXR Device API / A-Frame)、资产管线(glTF·GLB / Draco / Meshopt / KTX2 纹理压缩 / gltf-transform / LOD); (b) 自研 vs SaaS 平台选型(国内: 众趣 / 酷雷曼 / 比目鱼 / 会鸽 / 3DVista; 海外: Matterport / Kuula / Artsteps / Spatial); (c) 性能优化(大场景加载 / 模型轻量化 / draw call / 移动端 / 首屏 / 显存)——web 3D 第一性难题; (d) 策展与导览(动线设计 / 热点 hotspot / 语音讲解 / 交互); (e) 文博数字化(IIIF / 数字孪生 / 高清文物) 与 会展·电商商业化(营销云展厅 / 数据埋点 / 线索转化); (f) 部署(CDN / 流式加载 / 跨端兼容 / WebGL 降级)。学派分歧: 自研 Three.js vs SaaS 平台、真 3D 重场景 vs 720 全景轻量、WebGL vs WebGPU、文博考据派 vs 营销转化派、沉浸优先 vs 加载优先。不含: 线下展陈 / 展台搭建、原生 VR App(非 web)、通用 3D 游戏开发、纯 3D 建模教程。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「线上展览」「虚拟展厅」「web 线上展览」「云展厅」「数字展馆」

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。

Enable a Skia + Dawn GPU build of Pulp (MacGpuWindowHost, Skia Graphite). Covers the prebuilt skia-builder binaries, the headers-only fresh-worktree trap, reusing another checkout's cached libs via SKIA_DIR, FindSkia layouts, verifying PULP_HAS_SKIA / MacGpuWindowHost, and the raster-fallback + GPU-wedge gotchas. Use whenever GPU rendering "doesn't work" or a build silently came up CPU-only.

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。

GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.

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。

Rent, manage, and destroy GPU instances on vast.ai. Use when user says "rent gpu", "vast.ai", "rent a server", "cloud gpu", or needs on-demand GPU without owning hardware.

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。

Architecture rules for GPU rendering with TypeGPU/WebGPU — games, data visualisation, canvases, any app that draws with the GPU. Use when adding or changing passes, shaders, buffers, textures, pipelines, frame orchestration, GPU resource lifetimes, or checks that judge rendered output.

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。

Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). Covers WebGPU renderer setup, TSL syntax and node materials, compute shaders, post-processing effects, and WGSL integration. Use this skill when working with Three.js WebGPU, TSL shaders, node materials, or GPU compute in Three.js.

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。

Reference WebGPU APIs, TSL syntax, node materials, WGSL integration, compute shaders, and post-processing in Three.js. For complete particle canvases use threejs-particle-canvas; for GLSL shaders use rocaille-shader. Triggers on TSL shader, WebGPU renderer, node material, WGSL, GPU compute, WebGPU setup.

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。

Pulp in the browser — the WAM v2 and WebCLAP adapters, the wasm runtime, the Skia/WebGL2 browser window host, and the WebGPU (emdawnwebgpu) GPU-audio lane. Covers what does and does not compile to wasm, the worklet-thread constraints (no std::thread, no fetch, no navigator.gpu), the non-realtime tick, memory growth, how to PROVE a GPU lane actually ran rather than silently falling back to CPU, and the silent-failure traps that make a wasm build "work" while producing no audio or no pixels.

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。

Execute model inference on GPU cloud providers. Handles code generation, deployment, execution, and result collection across HF Inference API/Endpoints, Colab, Modal, beam.cloud, Vast.ai, and RunPod. Use when running models on GPU, deploying to cloud, executing notebooks, or troubleshooting GPU execution failures. Triggers on "run on GPU", "execute model", "deploy to modal", "colab notebook", "beam deploy", "HF inference", "HF endpoints", "vast", "runpod".

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。

Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). Covers WebGPU renderer setup, node materials, compute shaders, post-processing, and WGSL integration. Use when working with Three.js WebGPU, TSL shaders, or GPU compute pipelines.

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。

Plan or audit the cross-layer Harbor cooperative editor: Loro replicas for humans and agents, governed edit claims, salvage authority, and transport across shared, LAN, and remote harbors. Use when a change spans editor collaboration, identity, recovery, or transport contracts. For a single GPUI pane use gpui-rust-console; for motion use rust-gpui-motion; for a shader use gpui-shaders. NOT for non-collaborative screens, web editors, or generic Rust development.

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。

Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.

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。

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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。

Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.

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。

Run GPU workloads on Modal — training, fine-tuning, inference, batch processing. Zero-config serverless: no SSH, no Docker, auto scale-to-zero. Use when user says "modal run", "modal training", "modal inference", "deploy to modal", "need a GPU", "run on modal", "serverless GPU", or needs remote GPU compute.

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。

Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.

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。

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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。

Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.

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。

Audit, prepare, and deploy PAIDF Orchestration on a Kubernetes GPU cluster - single-GPU H100/L40S hosts, managed Kubernetes, kubeadm, and similar. Select for requests to set up, install, deploy, configure, or check a PAIDF Orchestration environment; run a workflow on a new or unverified GPU host; connect via kubeconfig; validate GPU compute; deploy the Airflow controller; or choose external versus in-cluster model services. A plain SSH host is not a supported backend.

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。

Run Python code on cloud GPUs using Modal serverless platform. Use when you need A100/T4/A10G GPU access for training ML models. Covers Modal app setup, GPU selection, data downloading inside functions, and result handling.

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。

Set up and manage NVIDIA GPU servers for AI workloads — driver installation, CUDA toolkit, container toolkit, MIG partitioning, GPU health monitoring, and multi-GPU configuration for LLM inference and training.

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。

Implement or debug Hugging Face Spaces ZeroGPU code using `@spaces.GPU`, including isolation, state, duration, sizing, and CUDA-wheel constraints. Use for ZeroGPU-specific behavior; use huggingface-spaces for general hosting.

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。

The investigation harness for "why is this slow?" over a Pulp Perfetto trace (.pftrace). Runs the hypothesis→query→drill-down chain-of-evidence loop autonomously and returns a plain-English root cause + evidence + a concrete fix. TRIGGER on "why is my plugin slow to open", "find the slowest frames", "why is the UI stuttering", "why is my plugin using so much CPU", "which DSP node is expensive", "the load meter looks calm but CPU is pinned", or a named `pulp trace gpu-startup|gpu-health|gpu-probe` analysis. Ships Pulp-specific hints for dsp, frame, js, gpu, and cross-platform symptoms.

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。