「gpu」の検索結果

Web browser game development principles. Framework selection, WebGPU, optimization, PWA.

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。

MATLAB R2026a code generation and deployment workflow for MATLAB Coder, Simulink Coder, Embedded Coder, GPU Coder, HDL Coder, fixed-point conversion, MEX validation, and embedded deployment readiness. Use whenever the user asks to generate C/C++/CUDA/HDL/code, deploy algorithms, or validate generated code.

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-optimized OCR using Surya. Use when extracting text from images/screenshots, recovering image-only content in PDFs or slides, or inspecting multilingual document layouts. Preserve source locations and report recognition gaps separately from verified text.

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。

React Native Skia GPU-accelerated 2D graphics - Canvas, declarative drawing, shaders, image filters, Paragraph text, Atlas batch rendering, Reanimated animations

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。

Use when setting up or troubleshooting Waydroid on Linux - installation and initialization, waydroid CLI, GPU and networking setup, GApps and Play certification, ADB integration, file sharing, or desktop environment integration

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。

Build or iterate on Pulp's native Dawn-backed Three.js workflow using the real three.webgpu.js renderer, focused bridge tests, and native demo capture.

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。

SQL discipline for querying Pulp Perfetto traces (.pftrace) with trace_processor — idempotent CREATE OR REPLACE PERFETTO views, GLOB not LIKE, dur = -1 incomplete-slice handling, EXTRACT_ARG for span args, joining on stable utid/upid, SPAN_JOIN PARTITIONED, and the draft→validate→execute loop. Ships Pulp's CPU/UI trace stdlib plus closed GPU startup, health, and probe views. TRIGGER when writing or debugging SQL over a .pftrace, when `pulp trace query` returns wrong/empty rows, or when the trace-analysis skill needs a query primitive.

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。

Android platform development for Pulp — NDK cross-compilation, Oboe audio, Dawn/Skia GPU rendering, JNI bridge, touch interaction, emulator workflows, and end-to-end smoke validation. Covers build, deploy, debug, and the gotchas discovered during bringup.

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。

Capture faithful PNGs of Pulp view trees / imported UIs headlessly. Covers render_to_png backends (Skia vs CoreGraphics), the image-compositing gotcha, capture_png from a live GPU host, and the --screenshot-backend validate flag. Use whenever you render a UI to a PNG to eyeball or montage it.

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。

Update Pulp's pinned Skia, Dawn, and optional V8 prebuilts as one milestone-matched render-toolchain release. Use for requests such as "update Skia/Dawn", "move to chrome/mNNN", "find the matching V8", "update the GPU toolchain", or "use the Skia/Dawn/V8 tuple that goes together". Distinguishes milestone-matched V8 releases from v8-builder's newer weekly LKGR releases, verifies exact upstream provenance and asset hashes, updates every Pulp mirror, and runs the provider-identity/ODR gates.

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。

Unity 6 graphics and rendering guide. Use when working with render pipelines (URP, HDRP, Built-in), shaders, Shader Graph, materials, textures, cameras, post-processing, or rendering optimization. Covers Render Graph, batching, draw call optimization, and GPU instancing. Based on Unity 6.3 LTS documentation.

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。

用于构建或审查 Canvas 2D、Three.js/WebGL、React Three Fiber、GLSL shader、ShaderToy 到 WebGL 适配、2D/3D 可视化、游戏渲染、动画循环、GPU 资源清理或渲染性能。不要用于标准 DOM UI、已有图表库覆盖的图表或非图形类性能工作；中文触发词包括 Canvas、Three.js、WebGL、GLSL、Shader、3D、数据可视化、游戏。

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。

Use when building or reviewing Canvas 2D, Three.js/WebGL, React Three Fiber, GLSL shaders, ShaderToy-to-WebGL adaptation, 2D/3D visualization, game rendering, animation loops, GPU resource cleanup, or rendering performance. Do not use for standard DOM UI, charts already covered by a chart library, or non-graphical performance work; Chinese triggers include Canvas, Three.js, WebGL, GLSL, Shader, 3D, data visualization, games.

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。

Access ENCODE uniform analysis pipelines, generate user-specific Nextflow/WDL pipelines, manage compute resources, and integrate with cloud platforms. Use when the user wants to understand ENCODE pipelines, run pipelines on their own data, generate custom Nextflow workflows from ENCODE pipeline code, check compute requirements (CPU/GPU/memory), run pipelines in background, or integrate with Google Cloud, AWS, or other cloud platforms. Also use when the user asks about ENCODE pipeline outputs, processing standards, software versions, or wants to replicate ENCODE processing. Covers local execution, HPC, and cloud deployment with resource-aware scheduling. Use this skill for ANY pipeline execution, workflow generation, or compute resource management task involving ENCODE data.

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。

Use when working on SAM 3 video segmentation, vaila_sam.py, checkpoint handling, or GPU memory issues.

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。

Use when working on the SAM3 plus Sapiens2 pose estimation pipeline, batch GPU processing, or VRAM and throughput issues.

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。

Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters. Produces upgrade plans, pre/post-upgrade checklists, maintenance runbooks with gcloud commands, release channel strategy, and troubleshooting guides. Handles node pool upgrade strategies (surge, blue-green), version compatibility, PDB management, and workload-specific concerns (stateful, GPU, operators). Use this skill whenever the user mentions GKE upgrades, Kubernetes version bumps, node pool maintenance, GKE patching, cluster version management, release channel selection, maintenance windows, surge upgrades, stuck upgrades, or any GKE lifecycle management task — even casual mentions like "we need to upgrade our clusters" or "plan our next GKE maintenance" or "our upgrade is stuck." Don't use for GKE cluster creation, application onboarding, general networking/routing setup, or security policy configurations (use gke-basics or relevant GKE skills instead).

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。

Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).

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。

Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto Provisioning configuration or general GKE cluster creation.

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。

Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute). Use when creating GKE clusters, provisioning GKE environments, selecting cluster modes, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead).

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 run local LLMs with Harbor. Use when the user wants to run models locally, install Harbor, start Open WebUI, llama.cpp, Ollama, vLLM, Docker Model Runner, MLX, or oMLX, pull GGUF or HuggingFace models, add SearXNG web search, Speaches TTS/STT, or Open Terminal code execution, launch Codex/Claude/Grok/OpenCode against a Harbor backend, or troubleshoot GPU, VRAM, and service startup.

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 browser game development principles. Framework selection, WebGPU, optimization, PWA.

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。

Work with Runpod GPU infrastructure and documentation. Use when Codex needs to create, deploy, manage, debug, or review Runpod Pods, Serverless workers/endpoints, templates, network volumes, Public Endpoints, Flash apps, runpodctl commands, Runpod Python SDK usage, REST/GraphQL API usage, Runpod MCP setup, Docker images for Runpod, endpoint request payloads, logs, billing/cost checks, or Runpod deployment troubleshooting.

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。

Affordable serverless GPU inference on DeepInfra — broad open-model catalog with straightforward pricing.

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。