「ols」の検索結果

Set up worker environment and per-worker state for parallel pools. Use when code needs paths, environment variables, database connections, loaded libraries, or expensive objects available on workers before parfor/parfeval runs. Teaches parallel.pool.Constant, parfevalOnAll, and parpool name-value pairs. Also use when refactoring existing code that uses spmd for side-effect setup (an anti-pattern). Triggers: worker setup, pool constant, per-worker state, non-serializable, loadlibrary on workers, database connection parfor, addpath workers, spmd before parfor, worker environment, reduce parfor overhead, parfor setup, resource creation in parallel loop, cannot serialize error, undefined function or variable on workers error, load data per worker, reduce data transfer, parallelize setup, improve parallel 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。

Speed up local parfor, parfeval, or spmd by switching to a thread-based parallel pool. Trigger when a user describes slow or disappointing local parallel performance, even if they don't mention threads. Symptoms: parfor on a laptop/workstation is slower than expected or "only slightly faster than for"; parfor scales poorly with the number of workers; ticBytes/tocBytes, the Parallel Pool dashboard, mpiprofile, or system tools show large per-worker data transfer; large broadcast variables or sliced inputs make iterations slow; opening a process pool dominates a short workload; user mentions serialisation or data transfer overhead. Also trigger on any question about whether code or a function works on a thread pool. For non-pool MATLAB performance work (vectorisation, preallocation, profiling), defer to matlab-optimize-performance.

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。

Display 3-D image volumes, medical image volumes, surface meshes, and annotations for 3-D image processing. Use when displaying 3-D images or isosurfaces with volshow, creating volume viewers with viewer3d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming volumetric data, or building apps with volume display.

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。

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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。

Scan container images for vulnerabilities using Trivy, Grype, and cloud-native tools. Identify security issues in base images, packages, and configurations. Use when implementing container security, building secure images, or meeting compliance requirements.

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。

Perform static application security testing with tools like Semgrep, CodeQL, and SonarQube. Identify security vulnerabilities in source code before deployment. Use when implementing secure SDLC, code review automation, or security gates in CI/CD 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。

Generate, sign, and verify SBOMs and provenance attestations to secure the software supply chain. Use when implementing SLSA controls, artifact trust policies, or compliance evidence for releases.

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。

Scan systems and dependencies for CVEs and security vulnerabilities. Use tools like Nessus, OpenVAS, and Qualys to identify and prioritize vulnerabilities. Use when performing security assessments, compliance scanning, or vulnerability management.

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。

Conduct threat modeling using STRIDE methodology. Identify threats, assess risks, and design security controls. Use when designing secure systems or assessing application security.

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。

Harden AI/LLM deployments against prompt injection, data exfiltration, model theft, and supply chain attacks. Covers input validation, output filtering, access control, model API security, and compliance controls for production AI systems.

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。

Secure the AI model supply chain with artifact signing, provenance attestation, SBOM workflows, dependency controls, and trusted model promotion.

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。

Defend AI systems against prompt injection and indirect prompt attacks using input controls, tool permissions, output validation, and isolation boundaries.

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 and remediate CIS benchmark violations. Use automated tools to assess compliance and implement hardening recommendations. Use when meeting compliance requirements or implementing security baselines.

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。

Secure LLM-powered applications with input validation, output controls, tenant isolation, and abuse prevention.

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。

Perform basic penetration testing and security assessments. Use reconnaissance, vulnerability discovery, and exploitation techniques. Use when validating security controls or assessing system security.

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。

Design secure, multi-tenant LLM hosting platforms with tenant isolation, quotas, billing attribution, noisy-neighbor protection, and per-tenant policy controls.

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 structured AI red team exercises for jailbreak resistance, data exfiltration risk, harmful output controls, and agent tool abuse resilience.

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。

Harden OpenClaw self-hosted environments with baseline host controls, auth tightening, secret handling, network segmentation, and safe update/rollback workflows. Use when deploying OpenClaw in home labs, startups, or production-like local AI infrastructure.

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。

Configure object storage with S3, GCS, and MinIO. Implement lifecycle policies and access controls. Use when managing object storage.

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。

Manage users, groups, and permissions on Linux systems. Configure sudo and access controls. Use when managing system access.

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。

Deploy and manage Google Kubernetes Engine clusters. Configure node pools, networking, and workload identity. Use when running Kubernetes on GCP.

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。

Automate versioning and changelog generation using semantic versioning principles. Configure release automation, version bumping, and changelog tools. Use when implementing version management or automating release processes.

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。

Operate GPU-backed Kubernetes clusters for AI inference and training with scheduling, autoscaling, node health, MIG partitioning, and cost controls.

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。

Deploy and manage Azure Kubernetes Service clusters. Configure node pools, networking, and integrations. Use when running Kubernetes workloads on Azure.

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。