「data parallel」の検索結果

Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.

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。

Next.js project structure, App Router conventions, file-based routing primitives, layouts, error boundaries, metadata, image and font optimization, configuration. Apply when implementing or modifying Next.js features. Use this skill to: - Pick the correct file convention (page/layout/loading/error/not-found/route/template/default). - Structure a feature within the App Router tree. - Set up metadata for SEO and OpenGraph. - Configure next.config.js for headers, redirects, image domains. - Use built-in fonts and images correctly. Do NOT use this skill for: - Server vs Client component boundaries (see server-component-patterns). - Data fetching patterns (see nextjs-data-fetching). - Complex routing (parallel/intercepting routes — see nextjs-routing). - Testing (see nextjs-testing).

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やKotlin Multiplatformの非同期処理とデータ更新を実装し、並行実行、画面状態の管理、キャンセル、エラー処理、テストまで扱うスキル。

• 複数データを並行して読み込みたいとき
• StateFlowによる画面状態の管理
• 検索入力の間引きや通信の再試行

クラウド上のGPUで機械学習の学習・推論を実行し、モデルをAPIとして公開するスキル。Pythonで実行環境を定義し、並列処理や定期実行も設定できます。

• GPUサーバーを管理せず学習したいとき
• モデルを自動拡張するAPIとして公開
• 大量のデータを並列処理したいとき

Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2

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 this skill whenever the user wants to conduct an event study, create event study plots, test for parallel trends, implement difference-in-differences designs, or work with any panel data estimation that involves pre/post treatment comparisons. Trigger on phrases like "event study", "parallel trends", "pre-trends", "dynamic treatment effects", "leads and lags", "TWFE", "two-way fixed effects", "staggered adoption", "staggered treatment", "difference-in-differences", "DiD", "Sun and Abraham", "Callaway and Sant'Anna", "de Chaisemartin", "Borusyak", "did_multiplegt", "fixest", "did2s", "bacon decomposition", or any reference to plotting coefficients around a treatment event. Also trigger when the user uploads panel data and wants to estimate treatment effects with variation in treatment timing. All code is in R.

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Start (or restart) a local Temps control plane built from this checkout, and the web dev server (`bun dev`) in `<checkout>/web`. Invoke when the user says "start temps", "restart temps", "launch the server", "kill and restart temps", or asks to bring the local server up after backend changes. Ports, database and data dir are allocated PER CHECKOUT (a "slot") so several worktrees/branches run side by side without killing each other or corrupting each other's schema — the first checkout you start in claims slot 0 (the familiar `:8080` / `:3000`); every other worktree gets its own slot and its own `temps_s<N>` database. Uses the `fast` cargo profile (release semantics, no debug symbols, parallel codegen) for quick rebuilds. Pass `split` (e.g. "start temps split", "/start-temps split") to launch the two-process proxy/console topology for testing that feature.

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。

Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.

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。

Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.

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。

Parallel multi-AI cross-validation research workflow (大版本). Dispatch N internal sub-agents + grok + gemini in parallel, automatically cross-validate findings, tier by confidence (strong consensus / partial / conflict / insufficient), generate tiered action items with arbitration. Use when user says "多 AI 调研", "交叉验证", "独立共识", "三脑调研", "multi-ai research", "parallel research", "cross-validate", or needs deep research that benefits from internal data + external 2026 consensus. NOT for quick factual Q&A, pure code reasoning, or tasks needing deep project context.

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。

60-second SEO snapshot powered by the DataForSEO API. Three parallel calls (DataForSEO Labs domain overview, Backlinks summary, ranked keywords) for core metrics on any domain — total keywords, traffic estimate, backlinks, top rankings — without waiting for a full audit.

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。

Next.js App Router routing primitives: file-based routes, dynamic and catch-all segments, route groups, parallel routes, intercepting routes, middleware, programmatic navigation, link patterns. Use this skill to: - Pick the correct dynamic segment syntax for the route shape. - Use route groups to organize without affecting URLs. - Implement parallel/intercepting routes for modal-style navigation. - Build effective middleware for auth, redirects, A/B tests. - Use Link and useRouter correctly. Do NOT use this skill for: - Data fetching per route (see nextjs-data-fetching). - General file conventions (see nextjs-conventions). - RSC vs Client (see server-component-patterns).

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。

Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.

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。

Enhance an existing marketing plan with parallel research. Use when the agent has a draft plan (brand strategy, campaign brief, content calendar, launch plan) that needs strengthening with real data. Triggers on 'deepen this plan', 'strengthen this strategy', 'research gaps in my plan', 'make this plan better', 'this plan is too surface level', 'add research to this', 'validate this plan', 'back this up with data', or when a plan exists but lacks audience data, competitive positioning, or keyword strategy. Make sure to use this whenever someone has an EXISTING plan that feels thin or unresearched — even if they just say 'is this plan good enough?' or 'what's missing here?', they likely need deepening. This skill ENHANCES existing plans — it does not create new ones. If no plan exists, route to /brainstorm or /launch-strategy first.

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。

Agent-browser usage guide. Read this before running any agent-browser commands. Covers the snapshot-and-ref workflow, navigating pages, interacting with elements (click, fill, type, select), extracting text and data, taking screenshots, managing tabs, handling forms and auth, waiting for content, running multiple browser sessions in parallel, and troubleshooting common failures. Use when the user asks to interact with a website, fill a form, click something, extract data, take a screenshot, log into a site, test a web app, or automate any browser task.

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。

Playwright-first strategy for shared DB + shared test accounts. Use when E2E tests collide in QA/staging, need safe parallelism, or require deterministic cleanup without touching baseline data. Covers per-test ownership, namespacing, ID-based teardown, serial shared-state suites, and optional stale-data janitor jobs.

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。

Orientação especializada para treinamento com Fully Sharded Data Parallel do PyTorch (FSDP) - sharding de parâmetros, precisão mista, CPU offloading, FSDP2

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。

Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2

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。

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

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。

Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2

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。

Uses Parallel CLI for web search, URL extraction, deep research, structured data enrichment, entity discovery, and recurring web monitoring. Best for requests that explicitly need current web evidence, academic-source discovery, repeated entity lookups, exhaustive reports, or ongoing change tracking.

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。

High-performance DataFrame library for Python ETL, analytics, and pandas migration. It supports expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.

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。

Core agent-browser usage guide. Read this before running any agent-browser commands. Covers the snapshot-and-ref workflow, navigating pages, interacting with elements (click, fill, type, select), extracting text and data, taking screenshots, managing tabs, handling forms and auth, waiting for content, running multiple browser sessions in parallel, and troubleshooting common failures. Use when the user asks to interact with a website, fill a form, click something, extract data, take a screenshot, log into a site, test a web app, or automate any browser task.

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。

Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe, ByteDance...). Six analyst lenses — business model, financial forensics, competitive landscape, risk & governance, tech & IP, alternative-data signals — run in parallel via run_swarm, then cross-validated for signal consistency before any verdict. Built around the core challenge of private-company work: information is scarce, so every data point carries a confidence label (high / medium / low), inference is shown separately from fact, and 'I don't know' is a valid output. Outputs a fair-value range, exit-path analysis, and an information-gap map. Use for any unlisted company where you need to judge what the business is actually worth.

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。