「large scale」の検索結果

Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native 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。

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

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。

Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference performance and memory; debugging multi-node/ multi-GPU hangs; and writing/running tests. Use when the user is training or fine-tuning large models, hits low TFLOPS/MFU, OOM, slow dataloading, a loss spike/divergence, a NCCL/InfiniBand or multi-node hang, node/GPU failures, checkpoint or preemption problems, storage/network bottlenecks, or needs to pick GPUs/cloud/file-systems or size inference latency/throughput. Distilled from "Machine Learning Engineering", the latest version of which can be found at https://github.com/stas00/ml-engineering The latest SKILL.md version can be found at https://github.com/stas00/ml-engineering/blob/master/skills/ml-engineering/SKILL.md

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。

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

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 writing, reviewing, or refactoring SwiftUI code for iOS or macOS, including state and `@Observable` data flow, view composition, resizable layouts, safe areas, display scale, performance, lists, environment, localization, animation, Liquid Glass, and API migration. Also use for iPhone Duo, foldable, or large-display layouts (`NavigationSplitView` on large displays, iPhone tab sidebar, two-column reflow, foldable grids, `ArrangementView`, `ReservedRegion`), hinge effects, vertical bars, `@State` initialization or synthesized-property diagnostics, `@ContentBuilder` ambiguity, `reorderable` drag/drop, custom `AsyncImage` `URLSession`, swipe actions outside List, item-bound `alert`/`confirmationDialog`, `ToolbarOverflowMenu`, `AnimatableValues`, Document APIs (`Document`/`DocumentReader`), and Instruments `.trace` capture or analysis.

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。

Golang refactoring — safe, at-scale restructuring of existing Go code: a coverage-adaptive safety net, behavior-preserving transforms (gopls Rename/Extract, `gofmt -r`, `gopatch`), the Fowler catalog mapped to Go, breaking import cycles, and small stacked PRs. Apply when a function or type has grown too large, a code smell blocks a feature, or the user asks to refactor Go code — also for renaming at scale, extracting functions or interfaces, moving code between packages, or planning a multi-step refactor. Target styles owned elsewhere → See `samber/cc-skills-golang@golang-naming` (renames), `samber/cc-skills-golang@golang-project-layout` (splits), `samber/cc-skills-golang@golang-modernize` (idioms), `samber/cc-skills-golang@golang-code-style` (control flow), `samber/cc-skills-golang@golang-design-patterns` (patterns/DI).

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 an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for /figure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.

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。

Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for /figure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.

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。

Ad Creative workflow skill. Use this skill when the user needs Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Ad Creative workflow skill. Use this skill when the user needs Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Applies the operational and scientific frameworks of Stacey Gabriel (genomicist and sequencing platform leader at the Broad Institute). Use this skill whenever the user is designing large-scale biomedical research, operationalizing lab workflows, standardizing data governance, or transitioning from bespoke academic experiments to industrial-scale science. It is highly relevant for queries involving genomics, precision medicine, multiomics, cellular perturbation pipelines, or building massive datasets to fuel AI. Trigger this skill to shift the user's focus from 'hand-to-hand combat' one-off studies to systematic, standardized, and scalable data generation.

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 custom Apify Actors via the Apify API v2 for large-scale web crawling using CrawleeJS. Leverages Apify dataset storage, RequestQueue, and proxy configuration for distributed scraping at scale.

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。

Biblioteca do Facebook para busca eficiente de similaridade e clustering de vetores densos. Suporta bilhões de vetores, aceleração GPU e vários tipos de índice (Flat, IVF, HNSW). Use para busca k-NN rápida, recuperação de vetores em larga escala ou quando você precisa de busca pura de similaridade sem metadados. Melhor para aplicações de alto desempenho.

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。

Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

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 an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for /figure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.

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。

Vector search via embeddings_* (large-scale HNSW) and ruvllm_hnsw_* (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction

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。

Research and plan a large-scale change, then execute it in parallel across 5–30 isolated worktree agents that each open a PR.

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。

Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.

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。

Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization.

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。

Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.

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。

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。

Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention

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。